{"id":1904,"date":"2026-06-02T13:03:06","date_gmt":"2026-06-02T10:03:06","guid":{"rendered":"https:\/\/verkkolehdet.jamk.fi\/finnish-business-review\/?p=1904"},"modified":"2026-06-02T13:03:07","modified_gmt":"2026-06-02T10:03:07","slug":"data-driven-business-ecosystem-development-in-the-circular-economy","status":"publish","type":"post","link":"https:\/\/verkkolehdet.jamk.fi\/finnish-business-review\/2026\/06\/02\/data-driven-business-ecosystem-development-in-the-circular-economy\/","title":{"rendered":"Data-driven business ecosystem development in the circular economy"},"content":{"rendered":"\n<p><strong>Abstract<\/strong>: This study focuses on the Eko\u00e4ly\u00e4 project (<a href=\"https:\/\/www.ekoalya.fi\/\">https:\/\/www.ekoalya.fi\/<\/a>) and, more specifically, on the collaboration opportunities between circular economy actors and universities within this initiative.<!--more--> The theoretical framework is based on the Triple Helix Model. The overall aim of the study is to advance waste fraction identification and contribute to the broader development of the circular economy. The research process can be characterized as action research due to the close collaboration between researchers, developers, and business partners throughout the project. Empirically, the study is based on 12 qualitative one-to-one interviews with members of the waste business ecosystem, complemented by survey data collected at a later stage. In addition, six workshops were organized across Finland to discuss and deepen understanding of the challenges and opportunities within the waste business ecosystem, as well as the possibilities for data sharing among both municipal and private waste sector actors. The findings indicate several potential benefits of data sharing, including improved understanding of the waste business environment, new service development and innovations, and enhanced productivity. At this stage, the role of universities has primarily been catalytic and coordinative. However, there appears to be further potential for collaboration that could generate more advanced benefits. These implications are discussed in the concluding section of the paper.<\/p>\n\n\n\n<p><strong>Keywords:<\/strong> Circular economy, Triple Helix, Data sharing, Waste business ecosystem<\/p>\n\n\n\n<p><strong>Authors:<\/strong><\/p>\n\n\n\n<p>Marko M\u00e4ki, KTL, yliopettaja; Haaga-Helia ammattikorkeakoulu, Ratapihantie 13, 00520 Helsinki, <a href=\"mailto:marko.maki@haaga-helia.fi\">marko.maki@haaga-helia.fi<\/a> (corresponding author)<\/p>\n\n\n\n<p>Tiina Brandt, KTT, yliopettaja; Haaga-Helia ammattikorkeakoulu, Ratapihantie 13, 00520, Helsinki, <a href=\"mailto:tiina.brandt@haaga-helia.fi\">tiina.brandt@haaga-helia.fi<\/a><\/p>\n\n\n\n<p><\/p>\n\n\n\n<h2 class=\"wp-block-heading\"><strong>1. Introduction<\/strong><\/h2>\n\n\n\n<p>Business ecosystems have become increasingly significant for contemporary organizations, particularly in advancing the circular economy and promoting sustainable development. Within these ecosystems, data and technology play a central role in developing new solutions and innovations, particularly in business environments that have undergone profound transformations due to digitalization and data-driven management practices (Arza &amp; L\u00f3pez, 2011; Kovaleski et al., 2022).&nbsp; Data is frequently characterized as the \u201cnew oil\u201d driving business transformation, while digitalization provides companies with substantial opportunities to generate value for ecosystem partners (see Abaidi &amp; Vernette, 2018). For value creation, participants must typically share and integrate resources and data (see Carida et al., 2022); however, the waste business ecosystem currently suffers from a lack of data related to waste fraction identification. This deficiency creates several challenges, including mismanagement of hazardous waste and used batteries, which may ultimately be directed to incineration facilities. Such outcomes can lead to serious operational inefficiencies in waste management processes and contribute to toxic air pollution.<\/p>\n\n\n\n<p>Foundations and funding bodies provide higher education institutions with opportunities to address societal challenges that lack immediate commercial viability or clearly defined customer demand. These challenges are often associated with environmental issues and broader sustainability concerns (e.g., Di Maria et al., 2019). The widely recognized Triple Helix model conceptualizes the collaborative interaction between universities, industry, and government in generating innovations and addressing complex societal problems (Etzkowitz &amp; Leydesdorff, 2000). In this study, the empirical context is the circular economy, the funding body is the European Regional Development Fund (ERDF), the academic institutions are Haaga-Helia University of Applied Sciences, the University of Turku, and the business partners represent the waste management sector. The universities have developed an analytical device that integrates artificial intelligence and sensor fusion technology to analyze waste fractions, with the aim of reducing operational inefficiencies and mitigating air pollution. Furthermore, the project has generated valuable data that could be leveraged within the waste business ecosystem, provided that an open data-sharing ecosystem can be established to support development and innovation. In general, business ecosystems may be conceptualized as platforms, communities, networks, or institutions, and resource flows (Muegge, 2013; Weber &amp; Hine, 2015).&nbsp; In this study, the business ecosystem is approached as an innovation platform, where the term &#8216;innovation ecosystem&#8217; particularly emphasizes the ability to co-create value within ecosystems (see Lee et al., 2020).<\/p>\n\n\n\n<p>This study examines data utilization and data-sharing practices within waste business ecosystems, an area that has received limited research attention. Additionally, we study the role of universities as facilitators of innovative data sharing within the Triple Helix model.&nbsp; The objective is to enhance waste fraction identification and contribute to the advancement of the circular economy from a broader systemic perspective. The research questions are:<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>What kind of benefits and hindrances would data sharing bring to circular economy partners?<\/li>\n\n\n\n<li>What could be the roles of universities in data-sharing ecosystems in the circular economy?<\/li>\n<\/ul>\n\n\n\n<p>The paper is structured as follows: First, in the theoretical base, we introduce the Triple Helix model, which was chosen here as the theoretical base. The Triple Helix model was deemed the most appropriate framework for this study, as the empirical focus was on collaboration between industry, academia, and public authorities. Although the Quadruple and Quintuple Helix models extend this framework by incorporating civil society and sustainability dimensions, citizens were not directly involved as active actors in the research at this stage. Therefore, the Triple Helix model offers a suitable and conceptually aligned basis for analyzing the innovation and data\u2011sharing dynamics observed in the waste business ecosystem. Secondly, we discuss waste business as an innovation ecosystem, which is the focus of this study and offers the ecosystem to Triple Helix analyses. The study is made with mixed methods, including interviews and questionnaires. After the results, the study discusses the implications and study restrictions.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\"><strong>2. Theoretical framework<\/strong><\/h2>\n\n\n\n<h3 class=\"wp-block-heading\">2.1 Cooperation between universities and industries produces innovation ecosystems \u2013 Triple Helix<\/h3>\n\n\n\n<p>Innovation cannot be understood as the isolated action of one individual or company, but rather as a social process, and it is achieved through efforts of cooperation, collaboration, exchange of experiences linked by various actors, information, infrastructure, training, and other resources, e.g., human beings and finances.<\/p>\n\n\n\n<p>The mission of higher education institutes, in addition to teaching and research, is also to have a social impact on society and contribute to the economic growth of the regions where they are located&nbsp; (Arocena &amp; Sutz, 2005; Etzkowitz &amp; Leydesdorff, 2000; Rasmussen &amp; Wright, 2015). Higher education institutes, as knowledge-intensive organizations (KIOs), produce knowledge through research. At present, HEIs important contribution to countries\u2019 economy is widely recognized through the development of new knowledge and technical know-how. Universities are a source of innovation for firms, which ultimately translates into social welfare improvements (Ter\u00e1n-Bustamante et al., 2021). There is a correlation between a country\u2019s competitiveness and its degree of collaboration with universities; it is evident that the most competitive countries have the highest linkage levels. (World Economic Forum, 2016). The studies indicate that governments can stimulate collaboration positively via policy instruments by fostering R&amp;D (Veugelers &amp; Cassiman, 2005).<\/p>\n\n\n\n<p>One of the knowledge services offered by universities is technology transfer. Technology transfer (TT) is conceptualized as passing on a technique or knowledge (Lundquist, 2003), which has been developed in an organization, such as universities, to another organization\/business, where it is adopted, used, and meets appropriate performance indicators in the recipient\u2019s environment. Thus, TT is considered a continuous, frequent, and strategic process based on close collaboration between the parties involved (L\u00f6nnqvist &amp; Laihonen, 2017). The importance of technology transfer in promoting innovation and economic and social development has been stressed by many authors (e.g., Arza &amp; L\u00f3pez, 2011; Kovaleski et al., 2022). For example, Cassiolato and Lastres (2013) view TT as a critical tool for improving the competitiveness of companies and fostering economic and social development. Challenges faced in TT have been found out as lack of collaboration between academia and industry, and the lack of incentives for technology transfer (Hagedoorn, 2002).<\/p>\n\n\n\n<p>Knowledge transfer can assist in rapidly building the technological capabilities required for constructing digital products and services, often through collaborative research and development (<a>Arias-P\u00e9rez et al., 2021<\/a>). Knowledge Transfer Partnerships (KTPs) have gained attention as an instrumental tool for aiding Digital Transformation (DT). They function as a bridge between academic research and industrial practice, allowing for a streamlined sharing of knowledge and technological insights (<a>Ate\u015f et al., 2024<\/a>). The research by Di Maria et al. (2019) focused on the characteristics and performance of university\u2013industry (U-I) collaboration for knowledge transfer in relation to environmental sustainability, considering both parties of the collaborations. Results suggested that U-I collaboration positively impacts the performance of firms, but not professors. Firms\u2019 financial performance was positively associated with U-I collaboration focused on knowledge transfer for environmental innovation; the higher the number of contracts activated, the better the economic performance (Di Maria et al., 2019).<\/p>\n\n\n\n<p>Nowadays, the presence of research-intensive universities is automatically assumed to positively influence the innovative activities of local firms (Uyarra, 2010). Studies have employed Triple Helix as a framework for&nbsp;analyzing&nbsp;policies and&nbsp;programs&nbsp;to address economic development through a university-premised approach (Leydesdorff, 2012;&nbsp;Todeva, 2013). Interesting studies from&nbsp;the Triple&nbsp;Helix model&nbsp;have&nbsp;been found, for example,&nbsp;in Japan, China,&nbsp;Norway,&nbsp;Spain, and Denmark. The&nbsp;model revealed&nbsp;that the cooperation of firms with any Triple Helix agent increased&nbsp;the likelihood of innovation. This increase was happening with all innovation types:&nbsp;product innovation, process&nbsp;innovation,&nbsp;or a combination&nbsp;of those. The study also found that the&nbsp;more Triple&nbsp;Helix model agents who cooperate, the greater the chances of business innovation, confirming the synergic effect between different agents.&nbsp;<\/p>\n\n\n\n<p>There are studies of the impact of university-industry and university-government&nbsp;co-operation in different regions. The study of&nbsp;Zhuang et al. (2021) is one of those, where they study the impact of these co-operations&nbsp;in&nbsp;China. According to them, cooperation&nbsp;between universities and&nbsp;industries&nbsp;is beneficial&nbsp;to improve regional innovation efficiency.&nbsp;Also,&nbsp;cooperation between universities and governments&nbsp;significantly promotes scale efficiency&nbsp;in the long run.&nbsp;&nbsp;By collaborating with universities, firms may access specialized equipment and infrastructures (Callejon et al., 2008) and work with researchers and specialists (Dooley &amp; Kirk, 2007). Basic university research is a source of new knowledge and ideas (Hagedoorn&nbsp;et al., 2000; Isaksen &amp; Karlsen, 2010), while the strength of applied research is that it provides answers to specific problems and demands of companies or ecosystems.<\/p>\n\n\n\n<p>The role of universities in innovation and development within waste business ecosystems can also be examined through the lenses of business ecosystems and value creation. A wide range of conceptual approaches and terminologies have been developed to describe value, including value co-creation, value-in-use, value-in-context, and value co-production (see Heinonen et al., 2013; Vargo &amp; Lusch, 2016; Wu et al., 2022). In essence, value creation can be understood as a process through which customers are, in some respect, made better off (Gr\u00f6nroos &amp; Voima, 2013). It is also important to recognize the dual nature of value creation, encompassing both value generated for the customer and value generated for the firm (Gr\u00f6nroos &amp; Helle, 2010). In this context, \u201cfirms\u201d refer broadly to actors within the waste business ecosystem, including universities. Consequently, the value created by universities constitutes an integral component of the broader innovation process.<\/p>\n\n\n\n<p>Effective knowledge transfer is associated with higher productivity, survivability (Argote et al., 2000), and competitive advantage (Alzubi, 2018; Argote &amp; Ingram, 2000); however, knowledge transfer may fail if participants are reluctant to cooperate and collaborate (Pasaribu et al., 2017).<\/p>\n\n\n\n<p>Trust is important in facilitating university\u2013industry co-operation (Santoro &amp; Saparito, 2003), since parties are often required to share sensitive information and tacit knowledge. Earlier studies indicate that universities that have a background of co-operation with industry are likely to continue in that way (Wen &amp; Kobayashi, 2001). Expert exchange between university and industry has been indicated as a positive driver for collaboration. Also, that enhances building personal relationships, which are more important, for example, to effective technology transfer than formal instruments (Casper, 2013; Siegel et al., 2003). Similarly, Thune (2009) found that prior collaborative experience among researcher teams and firms was positively related to the likelihood and success of collaboration. According to T\u00f6dtling et al. (2009), prior experience is a strong predictor of future collaboration; even if previous collaboration was unsuccessful, the firms were still positively associated with the probability of interacting with universities again (T\u00f6dtling et al., 2009).<\/p>\n\n\n\n<p>According to the analysis of Bruneel et al. (2010) of trust between partners, results show that prior experience of collaborative research lowers orientation-related barriers and further that greater levels of trust reduce orientation-related and transaction-related barriers. The study also found that the breadth of interaction diminishes the orientation-related but increases transaction-related barriers.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\">2.2 Waste business as an innovation ecosystem<\/h3>\n\n\n\n<p>Most widely studied Triple Helix ecosystems relate to innovativeness. Here, the waste business ecosystem is conceptualized as an <em>innovation ecosystem<\/em>, in which data serves as a foundational basis for innovation. According to Adner\u2019s widely cited definition, an \u201cinnovation ecosystem consists of the collaborative arrangements through which firms combine their individual offerings into a coherent, customer-facing solution\u201d (Adner, 2006). Firms within such ecosystems may not only share resources but also engage in co-development activities to achieve superior innovation performance (Hung-Tai et al., 2019). Consequently, the ecosystem can function as a platform for innovation. The concept of an innovation ecosystem further highlights the capacity of its actors to co-create collaborative value (Lee et al., 2020).<\/p>\n\n\n\n<p>The waste business ecosystem comprises a diverse range of actors and organizations from both the private and public sectors. Private sector actors typically operate in logistics, technology provision, or recycling operations, whereas the public sector includes municipal waste management authorities, such as regional waste boards. In addition, recycling facilities and waste incineration plants are often owned and managed by municipal actors. The project consortium focusing on AI- and sensory fusion-based waste fraction identification includes representatives from all the mentioned groups. The objective of the project is to promote recycling by ensuring that waste is directed to appropriate treatment processes, to enhance productivity within the waste management sector, and more broadly, to generate value for all participants within the waste business ecosystem.<\/p>\n\n\n\n<p>Data and technology play key roles when ecosystems include a digital dimension in their operations. As Yadav and Pavlou (2020) state, the driving changes in the marketplace were not just technology, but how technology-enabled interactions between the key marketplace stakeholders were being transformed by technology. In this multi-party, partly digital ecosystem structure, we are facing new challenges like digital interface equivalency or data utilization and handling procedures.<\/p>\n\n\n\n<p>Waste business ecosystem produces many types of data units, like the weight of waste load, the emptying frequency of waste can, chemical composition of the waste load, CO2 emissions (of the incineration plant), for example. The AI-enabled sensory fusion waste fraction analysis device developed in our project creates additional data attributes for waste business ecosystems. Combining necessary data will open new possibilities for waste business ecosystems&#8217; development and renewal.<\/p>\n\n\n\n<p>A central factor influencing the functioning and well-being of ecosystems is their capacity to share and integrate resources (see Mustak &amp; Pl\u00e9, 2020). In the context of our project, the key resource comprises data related to the waste business ecosystem. However, resources and data sharing may encounter challenges when digital services and platforms are embedded within the ecosystem structure. Misalignments between digital interfaces may even lead to the exclusion of potential ecosystem members. More broadly, such interfaces can reflect fundamental challenges within \u201cdata ecosystems\u201d, where companies may be concerned about losing control over their data or the associated business value (see D\u2019Hauwers et al., 2022). These concerns may persist even in situations where organizations share a common interest in establishing data-sharing systems (Hakanen &amp; Rajala, 2018).<\/p>\n\n\n\n<h2 class=\"wp-block-heading\"><strong>3. Methods<\/strong><\/h2>\n\n\n\n<p>To deepen our understanding of data-sharing practices, we conducted 12 qualitative, semi-structured one-to-one interviews with members of the waste business ecosystem. The interview themes and coding structure were informed by the study\u2019s theoretical framework. An initial, flexible coding process was carried out in line with this framework, which provided a solid foundation for subsequent analysis (see Miles &amp; Huberman, 1994). Following the preliminary qualitative insights, a survey was distributed to 274 waste business ecosystem actors across Finland to obtain further evidence regarding the perceived benefits and challenges of data sharing. A total of 31 respondents from top- or middle-management positions completed the survey, corresponding to a response rate of 11.3%. This can be considered satisfactory, particularly given that the majority of respondents represented senior managerial roles. The primary objective of the survey component was exploratory rather than confirmatory; consequently, the analysis is predominantly descriptive in nature for ecosystem members. The interview themes and coding structure were guided by the theoretical framework. Loose coding was conducted according to the theoretical framework, a process which offered good grounding for analysis (see Miles &amp; Huberman, 1994).<\/p>\n\n\n\n<p>The overall research process can be characterized as action research due to the close collaboration between researchers, developers, and business partners. The current research project utilizes the triangulation principle as part of the research process. According to Bergman (2008), the term \u2018triangulation\u2019 referred to checking the validity of an interpretation based on a single source of data by recourse to at least one further source that is of a strategically different type. Flick (2018) furthermore defines that the \u2018concept of triangulation\u2019 means that an issue of research is considered \u2013 or in a constructivist formulation is constituted \u2013 from (at least) two points or perspectives\u201d. While the research process\u2019s data gathering followed the mixed- and multi-method protocol, this strategy meant that data analysis and interpretations were based on the principles of triangulation. In practical terms, this meant that our findings were not solely grounded in survey or qualitative interview data, but several workshops and interactions between waste ecosystem participants deepened our understanding of ecosystem challenges and possibilities.<\/p>\n\n\n\n<p>All together six workshops have been conducted so far in Vaasa, Ylivieska, and Forssa to discuss and gain understanding about the business ecosystem challenges and opportunities, as well as data sharing possibilities among both communal and private waste business actors. The workshop findings were summarized as a business model canvas for illustration of the possibilities for data sharing and business model development in a more general way.<\/p>\n\n\n\n<p>The research project followed Quinton and Smallbone&#8217;s (2006) line of thinking to improve the study\u2019s reliability. Quinton and Smallbone (2006) define ways to improve reliability in research by using different data sources, using different data collection tools, applying established theory from one area to another, and collecting data at different time points, for example. The current study exploits multiple data sources such as documented workshops, meeting memos, qualitative interviews, and surveys. We also focused especially on the pragmatic validity of the study, which means that the results of the study should be evaluated based on their usefulness (see Miles &amp; Huberman, 1994). The current study yielded numerous results that may be exploited in data sharing development, as well as clarifying the university\u2019s role in business ecosystems.<\/p>\n\n\n\n<p>Ethical issues were carefully considered throughout the research process, particularly given the close collaboration with business and public-sector actors within the waste ecosystem. Participation in interviews, surveys, and workshops was voluntary, and all participants were informed about the purpose of the study, the use of the collected data, and their right to withdraw at any stage without consequences. Informed consent was obtained prior to data collection. To ensure confidentiality and anonymity, no personally identifiable information was included in the analysis or reporting, and organizational data were treated with care to avoid reputational or competitive harm. Given the action research approach and potential power asymmetries between researchers and practitioners, particular attention was paid to transparency, reflexivity, and the responsible use of insights generated through ongoing interaction. All data were stored securely, and the study was conducted in accordance with established ethical guidelines for research involving human participants.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\"><strong>4. Findings<\/strong><\/h2>\n\n\n\n<p>The AI-enabled sensory fusion technology developed during our project will produce new waste fraction data. Moreover, the different waste business ecosystem actors have a variety of data sources, which are sometimes underutilized because different actors do not share the data among business ecosystems, although 76% of respondents (n=31) felt that data sharing is useful.<\/p>\n\n\n\n<p>A central question is how companies benefit from sharing data. According to our analysis, the first benefit will be <em>\u00b4better understanding of the waste business environment<\/em>\u00b4. While the waste business ecosystem includes a variety of actors like technology providers, incineration plants producing electricity and heating, recycling units, logistics providers, and communal waste management authorities, for example, it may be difficult to scan all data sources that a single actor is utilizing.<\/p>\n\n\n\n<p>The second benefit of data sharing is \u00b4<em>new service development and innovations\u00b4.<\/em> While data is a key raw material for business development, data sharing may open up new opportunities and innovations for waste businesses.<\/p>\n\n\n\n<p><em>\u201cInnovations are created when customers say, or we notice (from data) that this item or process may be included in our business model.\u201d<\/em><\/p>\n\n\n\n<p><em>\u201cData about process bottlenecks will help us to improve our processes.\u201d<\/em><\/p>\n\n\n\n<p>The benefits of data sharing may be approached from a customer perspective, like the \u201creverse-use-of-customer-data\u201d framework by Saarij\u00e4rvi et al. (2014), where customer data is offered back to customers after analysis that creates value. While \u2018value\u2019 seems to be an ill-defined concept in general (see Gr\u00f6nroos &amp; Voima, 2013), one quite plain definition of value is that value creation entails a process that increases the customer\u2019s well-being, such that the user becomes better off in some respect. Waste business ecosystems may advance value creation in several ways.<\/p>\n\n\n\n<p><em>\u201cIf customers can take care of their waste with speed and with reasonable costs\u2026, that creates value.\u201d<\/em><\/p>\n\n\n\n<p><em>\u201c\u2026., it is like water supply companies, it works well when you do not recognize the service.\u201d<\/em><\/p>\n\n\n\n<p><em>\u201cThe biggest value is when customers do not have to take care of anything\u2026.\u201d<\/em><\/p>\n\n\n\n<p>If we can identify different waste fractions with sensory fusion technology and return the data to customers with an analysis of the level of circulation of an independent waste box, this will open interesting innovation possibilities. Those households (or housing companies) that separate their waste fraction well may be rewarded with lower waste prices. This pricing system may motivate those who are not so interested in waste fraction separation. In sum, this data utilization and data analysis sharing will produce clear value to all ecosystem participants.<\/p>\n\n\n\n<p>The third data sharing benefit was <em>\u00b4productivity gains and utilization of AI\u00b4. <\/em>Companies felt that productivity is a major gain of increased data sharing, and AI exploitation is a key driver for the full benefits of efficiency and productivity. Waste business ecosystem actors still conduct some tasks in an inefficient way, like recording the same data units several times or the utilization of separate Excel sheets for data analysis. While productivity may be generally defined as the ratio between output and input (see Parasuraman, 2010), the AI-enabled data sharing will advance productivity within data operations.<\/p>\n\n\n\n<p><em>\u201cSome AI technologies may help\u2026, now that we have done all the analysis, the situation has already changed\u2026.\u201d<\/em><\/p>\n\n\n\n<p><em>\u201cAutomation (with AI) brings value; customers can reduce planning efforts.\u201d<\/em><\/p>\n\n\n\n<div id=\"table-block_4f0549aa01572ea3d23ab9df6d094281\" class=\"table block-table\">\n  <section class=\"table-section\">\n    <div class=\"table-header\">\n              <h2 class=\"table-title\">Table 1. Benefits of data sharing in the waste business ecosystem.<\/h2>\n            <div class=\"wrapper\">\n\n        <button class=\"button scroll-button left-button\">\n          <i class='icon--left' ><svg aria-hidden=\"true\" viewBox=\"0 0 9 12\" fill=\"none\" xmlns=\"http:\/\/www.w3.org\/2000\/svg\"><path d=\"M1 1v10l7-5.026L1 1z\" stroke=\"#fff\"\/><\/svg><\/i>          Scroll the table to the left        <\/button>\n        <button class=\"button scroll-button right-button\">\n          Scroll the table to the right          <i class='icon--right' ><svg aria-hidden=\"true\" viewBox=\"0 0 9 12\" fill=\"none\" xmlns=\"http:\/\/www.w3.org\/2000\/svg\"><path d=\"M1 1v10l7-5.026L1 1z\" stroke=\"#fff\"\/><\/svg><\/i>        <\/button>\n      <\/div>\n    <\/div>\n    <div class=\"table-white-wrap\">\n      <div class=\"table-scroll-container\">\n        <table class=\"table\">\n                      <thead class=\"table__header\">\n              <tr class=\"table__row\">\n                                  <th class=\"table__cell table__cell--header\">Benefit category<\/th>\n                                  <th class=\"table__cell table__cell--header\">Key elements<\/th>\n                                  <th class=\"table__cell table__cell--header\">Representative insights<\/th>\n                              <\/tr>\n            <\/thead>\n          \n          <tbody class=\"table__body\">\n                          <tr class=\"table__row\">\n                                  <td class=\"table__cell\">\u2022\tBetter understanding of the waste business environment<\/td>\n                                  <td class=\"table__cell\">\u2022\tCross-actor visibility, reduced data silos, improved situational awareness<\/td>\n                                  <td class=\"table__cell\">\u2022\tEcosystem actors gain a holistic view of waste flows and operations<\/td>\n                              <\/tr>\n                          <tr class=\"table__row\">\n                                  <td class=\"table__cell\">\u2022\tNew services and innovations<\/td>\n                                  <td class=\"table__cell\">\u2022\tData-driven business models, incentive-based pricing, reverse use of customer data<\/td>\n                                  <td class=\"table__cell\">\u2022\tOpportunities for new services, rewards for efficient waste separation<\/td>\n                              <\/tr>\n                          <tr class=\"table__row\">\n                                  <td class=\"table__cell\">\u2022\tProductivity gains and AI utilization<\/td>\n                                  <td class=\"table__cell\">\u2022\tAutomation, reduced duplication, faster decision-making<\/td>\n                                  <td class=\"table__cell\">\u2022\tAI improves process efficiency and data handling<\/td>\n                              <\/tr>\n                      <\/tbody>\n        <\/table>\n      <\/div>\n    <\/div>\n  <\/section>\n\n<\/div>\n\n\n<p><\/p>\n\n\n\n<p>Several factors prevent data sharing activities, even though getting access to a major part of waste business ecosystems&#8217; data may benefit companies in several ways. The first factor that prevents data sharing is simply \u00b4<em>unwillingness to do so\u00b4.<\/em> This is understandable when a specific data unit is important from a competitiveness perspective. However, many data types in waste business ecosystems are not critical competitive advantage attributes, but data may benefit business ecosystems.<\/p>\n\n\n\n<p>\u201c<em>We have not shared our data yet&#8230;, even though data sharing is in the planning pipeline.\u201d<\/em><\/p>\n\n\n\n<p><em>\u201cLack of trust prohibits us from sharing data with other ecosystem partners.\u201d<\/em><\/p>\n\n\n\n<p><em>\u201cOur data is at the core of our competitiveness.\u201d<\/em><\/p>\n\n\n\n<p>The second inhibiting factor for data sharing was the concern of <em>\u00b4data privacy\u00b4.<\/em> The finding is evident, while GDPR legislation requires companies to take action in a detailed manner. The other perspective on data privacy is growing concerns of customers to reveal their data (see M\u00e4ki &amp; Alam\u00e4ki, 2019).&nbsp;&nbsp;&nbsp; Privacy concerns arise when customers perceive threats to their personal data and are reinforced by reports of digital service vulnerabilities and data breaches, which also pose business risks for firms (Ackerman et al., 1999; Martin et al., 2017). All data utilization practices should be designed to advance customer experience and, hence, overcome the data privacy concerns of the customer. To achieve this goal, a customer journey framework would be a suitable tool for data utilization management because data privacy concerns seem to have different strengths during different customer journey phases (see M\u00e4ki &amp; Alam\u00e4ki, 2019).<\/p>\n\n\n\n<p>The last factor that affects data sharing is <em>system incompatibility. <\/em>This may exclude some potential business ecosystem partners and is a relatively common phenomenon also in other business sectors, like retail. (see M\u00e4ki &amp; Toivola, 2022).<\/p>\n\n\n\n<p><em>\u201cFirst, we should build a system that is fully compatible with all business ecosystem partners, and secondly, we should ensure free data flow.\u201d<\/em><\/p>\n\n\n\n<p>In sum, data utilization has many possibilities, but there are also several factors that prohibit effective data exploitation for innovations, better customer experience, or new service development. For advancing data sharing, trust is a key component for that. Our findings indicate that some waste business ecosystem partners do not trust other ecosystem partners, especially when data is crucial for companies\u2019 competitiveness.<\/p>\n\n\n\n<p>For managing a data-driven business ecosystem, a data operator is needed.&nbsp; Many business ecosystem partners indicated that \u00b4a neutral party\u00b4 may be suitable for the data operator\u2019s role.<\/p>\n\n\n\n<p><em>\u201cSome neutral party\u201d<\/em><\/p>\n\n\n\n<p><em>\u201cPerhaps LUKE may be a good choice.\u201d&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;<\/em><\/p>\n\n\n\n<p><em>\u201cUniversities or some academy\u201d<\/em><\/p>\n\n\n\n<p>The data operator role is to connect, analyze, and deliver data produced within the waste business ecosystem, including a new AI-enabled sensory fusion waste fraction analysis device. Universities may have a role in data storage and delivery due to their rather neutral position in the named business ecosystem. Moreover, universities can function as coordinating entities within the waste management ecosystem, given their central role in advancing research and development. In general, the implementation of AI-based waste fraction identification systems, together with initiatives that facilitate data sharing, is likely to create new value-generation opportunities for all stakeholders, including both customers and firms. Furthermore, such developments may contribute to the advancement of the circular economy by ensuring that identified waste fractions are directed to appropriate treatment or recycling facilities.<\/p>\n\n\n\n<div id=\"table-block_369436a9c646a4cb2e7179c203fbe7ba\" class=\"table block-table\">\n  <section class=\"table-section\">\n    <div class=\"table-header\">\n              <h2 class=\"table-title\">Table 2. Barriers and enablers of data sharing in the waste ecosystem.<\/h2>\n            <div class=\"wrapper\">\n\n        <button class=\"button scroll-button left-button\">\n          <i class='icon--left' ><svg aria-hidden=\"true\" viewBox=\"0 0 9 12\" fill=\"none\" xmlns=\"http:\/\/www.w3.org\/2000\/svg\"><path d=\"M1 1v10l7-5.026L1 1z\" stroke=\"#fff\"\/><\/svg><\/i>          Scroll the table to the left        <\/button>\n        <button class=\"button scroll-button right-button\">\n          Scroll the table to the right          <i class='icon--right' ><svg aria-hidden=\"true\" viewBox=\"0 0 9 12\" fill=\"none\" xmlns=\"http:\/\/www.w3.org\/2000\/svg\"><path d=\"M1 1v10l7-5.026L1 1z\" stroke=\"#fff\"\/><\/svg><\/i>        <\/button>\n      <\/div>\n    <\/div>\n    <div class=\"table-white-wrap\">\n      <div class=\"table-scroll-container\">\n        <table class=\"table\">\n                      <thead class=\"table__header\">\n              <tr class=\"table__row\">\n                                  <th class=\"table__cell table__cell--header\">Dimension<\/th>\n                                  <th class=\"table__cell table__cell--header\">Description<\/th>\n                                  <th class=\"table__cell table__cell--header\">Examples from findings<\/th>\n                              <\/tr>\n            <\/thead>\n          \n          <tbody class=\"table__body\">\n                          <tr class=\"table__row\">\n                                  <td class=\"table__cell\">\u2022\tInhibiting factor: Unwillingness to share<\/td>\n                                  <td class=\"table__cell\">\u2022\tReluctance due to competitive concerns and lack of trust<\/td>\n                                  <td class=\"table__cell\">\u2022\tData viewed as core of competitiveness<\/td>\n                              <\/tr>\n                          <tr class=\"table__row\">\n                                  <td class=\"table__cell\">\u2022\tInhibiting factor: Data privacy<\/td>\n                                  <td class=\"table__cell\">\u2022\tConcerns related to GDPR compliance and customer data protection<\/td>\n                                  <td class=\"table__cell\">\u2022\tFear of misuse of personal or sensitive information<\/td>\n                              <\/tr>\n                          <tr class=\"table__row\">\n                                  <td class=\"table__cell\">\u2022\tInhibiting factor: System incompatibility<\/td>\n                                  <td class=\"table__cell\">\u2022\tNon-integrated IT systems across actors<\/td>\n                                  <td class=\"table__cell\">\u2022\tExclusion of potential ecosystem partners<\/td>\n                              <\/tr>\n                          <tr class=\"table__row\">\n                                  <td class=\"table__cell\">\u2022\tEnabler: Neutral data operator<\/td>\n                                  <td class=\"table__cell\">\u2022\tTrusted intermediary managing data flows and analytics<\/td>\n                                  <td class=\"table__cell\">\u2022\tUniversities or research institutes suggested<\/td>\n                              <\/tr>\n                          <tr class=\"table__row\">\n                                  <td class=\"table__cell\">\u2022\tOutcome<\/td>\n                                  <td class=\"table__cell\">\u2022\tSecure data sharing, innovation, and circular economy advancement<\/td>\n                                  <td class=\"table__cell\">\u2022\tValue creation for firms, customers, and the ecosystem<\/td>\n                              <\/tr>\n                      <\/tbody>\n        <\/table>\n      <\/div>\n    <\/div>\n  <\/section>\n\n<\/div>\n\n\n<p><\/p>\n\n\n\n<h2 class=\"wp-block-heading\"><strong>5. Discussion<\/strong><\/h2>\n\n\n\n<p>The study highlights the importance of digital connectivity among different business ecosystem partners, which is essential for daily operations and value creation processes that benefit customers and other stakeholders. Companies within the waste ecosystem generally exhibited a positive attitude toward sharing ecosystem-related data with other ecosystem actors. Data sharing was perceived to be linked to improvements in overall productivity, the development of artificial intelligence applications, and service innovations. However, organizations were reluctant to share sensitive data that typically contains competitive elements with other ecosystem partners. Furthermore, a lack of trust in some cases hindered the willingness to engage in data sharing more broadly. This finding aligns with prior research emphasizing the critical role of trust in business ecosystems (e.g., Casper, 2013; Santoro &amp; Saparito, 2003; Siegel et al., 2003).<\/p>\n\n\n\n<p>Universities may play a significant role in Triple Helix-type collaboration precisely because of their trust-based position. As earlier studies have indicated the importance of longitudinal cooperation as a foundation for building trust (Bruneel et al., 2010; T\u00f6dtling et al., 2009), this project provides opportunities for long-term collaboration in the sustainable development of the circular economy and illustrates the potential of universities as data-sharing hubs. Universities are well-positioned to promote data-sharing practices due to their neutral and trustworthy role from the perspective of the waste business ecosystem. However, a short-term role as a data operator, which is often characteristic of project-based R&amp;D activities in universities, does not sufficiently serve the goals of the business ecosystem. Therefore, new types of development roles for universities are needed. The academic contribution of this study is twofold. First, the study proposes a new role for universities in addressing circular economic challenges. This role encompasses data storage and analytical functions, thereby deepening the involvement of universities in waste management business ecosystems and, more broadly, within the Triple Helix framework. Second, the study positions universities within the innovation ecosystem framework (see Lee et al., 2020) as active actors in advancing circular economy objectives.<\/p>\n\n\n\n<p>The practical implications of the study are numerous; the AI-enabled sensory waste fraction identification device may create new opportunities for service development, particularly if data are shared within the business ecosystem. This may occur within the reverse use of the data framework proposed by Saarij\u00e4rvi et al. (2014), whereby collected and analyzed waste-fraction data are provided back to consumers in formats that generate value for them. Improved waste-sorting performance could potentially result in lower waste management fees in the future. Such a scenario may serve as a motivational factor that encourages more effective waste sorting. In general, advancements in waste fraction analysis and data-sharing practices may more broadly support the achievement of circular economy targets.<\/p>\n\n\n\n<p>It may be that universities are important actors in the Triple Helix kind of co-operation because of trust. As earlier studies (Bruneel et al. 2010; T\u00f6dtling et al., 2009) have indicated the importance of longitudinal co-operation as a basis for building trust, this project offers possibilities for long-range cooperation in the sustainable development of the circular economy and points out the universities\u2019 possibilities as data sharing centers.&nbsp; Universities have an excellent position to advance data sharing practices, due to their neutral and trustworthy position from a waste business ecosystem perspective. Short-term role as a data operator does not serve business ecosystem goals, which is typically the case in project-funded operation modes of R&amp;D work of universities, hence new types of development roles would be needed.<\/p>\n\n\n\n<p>An AI-enabled sensory waste fraction identification device may open new service development possibilities, especially if data is shared within the business ecosystem. Moreover, advances in waste friction analysis and data sharing may exploit the circulation target achievement in a more general way. This study has some limitations. The data were collected primarily from participants within the waste management business ecosystem, although implications related to consumers were also discussed. Consequently, future research should incorporate consumer perspectives and insights related to waste-sorting behavior. Ultimately, the key implications of AI-enabled waste-fraction analysis lie in its potential to influence and promote behavioral change among consumers.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\"><strong>References<\/strong><\/h2>\n\n\n\n<p>Abaidi, I., &amp; Vernette, E. (2018). Does digitalization create or reduce perceived global value? <em>The Journal of Consumer Marketing, 35<\/em>(7), 676-687.<\/p>\n\n\n\n<p>Ackerman, M. S., Cranor, L. F., &amp; Reagle, J. (1999). Privacy in e-commerce: Examining user scenarios and privacy preferences. <em>Proceedings of the 1st ACM conference on electronic commerce<\/em>, ACM, 1-8.<\/p>\n\n\n\n<p>Adner, R. (2006). Match your innovation strategy to your innovation ecosystem. <em>Harvard Business Review,<\/em> April 2006. <a href=\"https:\/\/hbr.org\/2006\/04\/match-your-innovation-strategy-to-your-innovation-ecosystem\">https:\/\/hbr.org\/2006\/04\/match-your-innovation-strategy-to-your-innovation-ecosystem<\/a>.<\/p>\n\n\n\n<p>Alzubi, Y. (2018). Knowledge transfer for sustainability: The role of knowledge enablers in the construction industries in Jordan. <em>World Journal of Science, Technology and Sustainable Development<\/em>, 15, 325\u201337.&nbsp;<\/p>\n\n\n\n<p>Argote, L. &amp; P. Ingram. (2000). Knowledge transfer: A basis for competitive advantage in firms.<em> Organizational Behavior and Human Decision Processes<\/em> 82, 150\u201369.<\/p>\n\n\n\n<p>Argote, L., Ingram, P., Levine, J. M., &amp; Moreland, R. L. (2000). Knowledge transfer in organizations: Learning from the experience of others. <em>Organizational Behavior and Human Decision Processes<\/em>,82, 1\u20138.<\/p>\n\n\n\n<p>Arias-P\u00e9rez, J., Velez-Ocampo, J., &amp; Cepeda-Cardona, J. (2021). Strategic orientation toward digitalization to improve innovation capability: why knowledge acquisition and exploitation through external embeddedness matter. <em>Journal of Knowledge Management, 25<\/em>(5), 1319-1335.<\/p>\n\n\n\n<p>Arocena, R. &amp; Sutz, J. (2005). Latin American universities: From an original revolution to an uncertain transition. <em>Higher Education, 50, <\/em>573\u2013592<em>.<\/em><\/p>\n\n\n\n<p>Arza V. &amp; L\u00f3pez A. (2011). Firms\u2019 linkages with public research organizations in Argentina: Drivers, perceptions, and behaviors. <em>Technovation, 31<\/em>(8), 384\u2013400<em>.<\/em><\/p>\n\n\n\n<p>Ate\u015f, A., Rogge, K. S., &amp; Lovell, K. (2024). Governance in multi-system transitions: A new methodological approach for actor involvement in policy making processes. <em>Energy policy<\/em>, <em>195<\/em>, 114313.<\/p>\n\n\n\n<p>Bergman, M. M. (2008). The straw men of the qualitative\u2013quantitative divide and their influence on mixed method research. In M. M. Bergman (ed.), <em>Advances in mixed methods research. <\/em>Sage, pp. 11\u201321.<\/p>\n\n\n\n<p>Bruneel, J., D\u2019Este, P., &amp; Salter, A. (2010). Investigating the factors that diminish the barriers to university\u2013industry collaboration, <em>Research Policy, 39<\/em>(7), 858-868.<\/p>\n\n\n\n<p>Callejon, M., Barge-Gil, A. &amp; Lopez, A. (2008). La cooperacion publica-privada en la innovacion a traves de los centros tecnologicos [Public-private cooperation in innovation through technology centers]. <em>Economy Industrial,<\/em> 366, 123\u2013132.<\/p>\n\n\n\n<p>Carida, A., Colurcio, M., Edvardsson, B. &amp; Pastore, A. (2022). Creating harmony through a plethora of interests, resources and actors: The challenging task of orchestrating the service ecosystem. <em>Journal of Service Theory and Practice, 32<\/em>(4), 477-504.<\/p>\n\n\n\n<p>Casper, S. (2013). The spill-over theory reversed: the impact of regional economies on the commercialization of university <em>science. Research Policy, 42<\/em>(8), 1313\u20131324<em>.<\/em><\/p>\n\n\n\n<p>Cassiolato J. E. &amp; Lastres, H. M. M. (2013). <em>Innovation systems and development: Brazil in the global context.<\/em> Routledge.<\/p>\n\n\n\n<p>D&#8217;Hauwers, R., Walravens, N. &amp; Ballon, P. (2022). Data ecosystem business models: Value and control in data ecosystems. <em>Journal of Business Models, 10<\/em>(2), 1-30.<\/p>\n\n\n\n<p>Di Maria E., De Marchi V. &amp; Spraul, K. (2019). Who benefits from university\u2013industry collaboration for environmental sustainability? <em>International Journal of Sustainability in Higher Education, 20<\/em>(6), 1022\u20131041.<\/p>\n\n\n\n<p>Dooley, L. &amp; Kirk, D. (2007). University-industry collaboration. Grafting the entrepreneurial paradigm onto academic structures. <em>European Journal of Innovation Management<\/em>, <em>10<\/em>(2), 316\u2013332.<\/p>\n\n\n\n<p>Etzkowitz, H. &amp; Leydesdorff, L. (2000). The dynamics of innovation: From National Systems and \u201cMode 2\u201d to a Triple Helix of university\u2013industry\u2013government relations. <em>Research Policy,<\/em> 29, 109\u2013123.<\/p>\n\n\n\n<p>Flick, U. (2018). Triangulation in data collection. In U. Flick (Ed.) <em>The SAGE handbook of qualitative data collection<\/em>. Sage, pp. 527-544.<\/p>\n\n\n\n<p>Gr\u00f6nroos, C. &amp; Helle, P. (2010). Adopting a service logic in manufacturing: Conceptual foundation and metrics for mutual value creation. <em>Journal of Service Management, 21<\/em>(5), 564-590.<\/p>\n\n\n\n<p>Gr\u00f6nroos, C. &amp; Voima, P. (2013). Critical service logic: Making sense of value creation and co-creation. <em>Journal of the Academy of Marketing Science, 41<\/em>(2), 133-150.<\/p>\n\n\n\n<p>Hagedoorn J. (2002). Inter-firm R&amp;D partnerships: An overview of major trends and patterns since 1960. <em>Research Policy, 31<\/em>(4), 477\u2013492.<\/p>\n\n\n\n<p>Hagedoorn, J., Link, A. &amp; Vonortas, N. (2000). Research partnerships. <em>Research Policy<\/em>, 29 (4\u20135), 567\u2013586.<\/p>\n\n\n\n<p>Hakanen, E. &amp; Rajala, R. (2018). Material intelligence as a driver for value creation in IoT-enabled business ecosystems. <em>The Journal of Business &amp; Industrial Marketing, 33<\/em>(6), 857-867.<\/p>\n\n\n\n<p>Heinonen, K., Strandvik, T. &amp; Voima, P. (2013). Customer dominant value formation in service. <em>European Business Review, 25<\/em>(2), 104-123.<\/p>\n\n\n\n<p>Hung-Tai, T., Ja-Shen, C. &amp; Yu, Y. W. (2019). Antecedents of co-development and its effect on innovation performance: A business ecosystem perspective. <em>Management Decision, 57<\/em>(7), 1609-1637.<\/p>\n\n\n\n<p>Isaksen, A. &amp; Karlsen, J. (2010). Different modes of innovation and the challenge of connecting universities and industry: case studies of two regional industries in Norway. <em>European Planned Studies, 18<\/em>(12), 1993\u20132008.<\/p>\n\n\n\n<p>Kovaleski, F., Picinin, C. T. &amp; Kovaleski, J. L. (2022). The challenges of technology transfer in the industry 4.0 era regarding anthropotechnological aspects: A systematic review. Sage Open, <a href=\"https:\/\/doi.org\/10.1177\/21582440221111104\">https:\/\/doi.org\/10.1177\/21582440221111104<\/a>.<\/p>\n\n\n\n<p><a>Lee, Y. W., Hwy-Chang M. &amp; Yin, W<\/a>. (2020). Innovation process in the business ecosystem: The four cooperations practices in the media platform. <em>Business Process Management Journal, 26<\/em>(4), 943-971.<\/p>\n\n\n\n<p>Leydesdorff, L. (2012). The triple helix, quadruple helix\u2026, and an N-tuple of helices: explanatory models for analyzing the knowledge-based economy. <em>Journal of Knowledge Economics, 3<\/em>(1), 25\u201335.<\/p>\n\n\n\n<p>Lundquist, G. (2003). A rich vision of technology transfer technology value management. The <em>Journal of Technology Transfer,<\/em> 28, 265\u2013284.<\/p>\n\n\n\n<p>L\u00f6nnqvist, A. &amp; Laihonen, H. (2017). Management of knowledge-intensive organizations: What do we know after 20 years of research? <em>International Journal of Knowledge-Based Development,<\/em> 8, 154\u2013167.<\/p>\n\n\n\n<p>Martin, K. D., Borah, A. &amp; Palmatier, R. W. (2017). Data privacy: Effects on customer and firm performance. <em>Journal of Marketing<\/em>, <em>81<\/em>(1), 36-58.<\/p>\n\n\n\n<p>Miles, M. B. &amp; Huberman, A. (1994). <em>Qualitative data analysis: An expanded source bo<\/em>ok. Sage.<\/p>\n\n\n\n<p>Muegge, S. (2013). Platforms, communities, and business ecosystems: Lessons learned about technology entrepreneurship in an interconnected world. <em>Technology Innovation Management Review, 3<\/em>(2), 5-15.<\/p>\n\n\n\n<p>Mustak, M. &amp; Pl\u00e9, L. (2020).&nbsp; A critical analysis of service ecosystems research: Rethinking its premises to move forward.&nbsp; <em>The Journal of Services Marketing, 34<\/em>(3), 399-413.<\/p>\n\n\n\n<p>M\u00e4ki, M., &amp; Alam\u00e4ki, A. (2019). Data privacy concerns throughout the customer journey and different service industries. PROVE &#8211; Collaborative Networks and Digital Transformation \u2013 Conference Proceedings<em>, <\/em>Springer.<\/p>\n\n\n\n<p>M\u00e4ki, M., &amp; Toivola, T. (2022). Creating an innovation ecosystem in Urban Helsinki for superior customer experience<em>. <\/em>The International Society for Professional Innovation Management (ISPIM) conference, Copenhagen, Denmark.<\/p>\n\n\n\n<p>Parasuraman, A. (2010). Service productivity, quality and innovation: Implications for service-design practice and research. <em>International Journal of Quality and Service Sciences, 2<\/em>(3), 277-286.<\/p>\n\n\n\n<p>Pasaribu, B. I., Afrianti, A., &nbsp;Gumilar, G. G., Rizanti, H. P. &amp; Rohajawati, S. (2017). Knowledge transfer: A conceptual model and facilitating feature in start-up business. <em>Procedia Computer Science,<\/em> 116, 259\u2013266.<\/p>\n\n\n\n<p>Quinton, S. &amp; Smallbone, T. (2006). Reliability, validity, and generalization. In S. Quinton &amp; T. Smallbone (Eds.)<em> Postgraduate research in business<\/em>, Sage, pp. 125-140.<\/p>\n\n\n\n<p>Rasmussen, E. &amp; Wright, M. (2015). How can universities facilitate academic spin-offs? An entrepreneurial competency perspective. <em>The Journal of Technology Transfe<\/em>r, 40, 782\u2013799.<\/p>\n\n\n\n<p>Saarij\u00e4rvi, H., Gr\u00f6nroos, C. &amp; Kuusela, H. (2014). Reverse use of customer data: Implications for service-based business models. <em>The Journal of Services Marketing, 28<\/em>(7), 529-537.<\/p>\n\n\n\n<p>Santoro, M. &amp; Saparito, P. (2003). The firm&#8217;s trust in its university partner as a key mediator in advancing knowledge and new technologies. <em>IEEE Transactions in Engineering Management<\/em>, 50, 362-373.<\/p>\n\n\n\n<p>Siegel, D. S., Waldman, D. &amp; Link, A. (2003). Assessing the impact of organizational practices on the relative productivity of university technology transfer offices: an exploratory study. <em>Research Policy, 32<\/em>(1), 27\u201348.<\/p>\n\n\n\n<p>Ter\u00e1n-Bustamante, A., Mart\u00ednez-Velasco, A. &amp; L\u00f3pez-Fern\u00e1ndez, A. M. (2021). University\u2013industry collaboration: A sustainable technology transfer model. <em>Administrative Sciences<\/em>, <em>11<\/em>(4), 142.<\/p>\n\n\n\n<p>Thune, T. (2009). Doctoral students on the university\u2013industry interface: a review of the literature. <em>Industry and Higher Education,<\/em> 58, 637\u2013651.<\/p>\n\n\n\n<p>Todeva, E. (2013). Governance of innovation and intermediation in Triple Helix interactions. <em>Industry and Higher Education<\/em>, <em>27<\/em>(4), 263-278.<\/p>\n\n\n\n<p>T\u00f6dtling, F., Lehner, P. &amp; Kaufmann, A. (2009). Do different types of innovation rely on specific kinds of knowledge interactions?&nbsp; <em>Technovation, 29<\/em>(1), 59\u201371.<\/p>\n\n\n\n<p>Uyarra, E. (2010). Conceptualizing the regional roles of universities, implications and contradictions. <em>European Planning Studies, 18<\/em>(8), 1227-1246.<\/p>\n\n\n\n<p>Vargo, S. L. &amp; Lusch, R. F. (2016). Institutions and axioms: An extension and update of service-dominant logic. <em>Journal of the Academy of Marketing Science, 44<\/em>(1), 5-23.<\/p>\n\n\n\n<p>Veugelers, R. &amp; Cassiman, B. (2005). R&amp;D cooperation between firms and universities. Some empirical evidence from Belgian manufacturing. <em>International Journal of Industrial Organization<\/em>, 23(5\u20136), 355\u2013379.<\/p>\n\n\n\n<p>Weber, M. L. &amp; Hine, M. J. (2015). Who inhabits a business ecosystem? The technospecies as a unifying concept. <em>Technology Innovation Management Review, 5<\/em>(5), 31-44.<\/p>\n\n\n\n<p>Wen, J. &amp; Kobayashi, S. (2001). Exploring collaborative R&amp;D network: some new evidence from Japan. <em>Research Policy, 30<\/em>(8), 1309\u20131319.<\/p>\n\n\n\n<p>World Economic Forum (2016). <em>The global competitiveness report<\/em> 2016\u201317. The World Economic Forum.<\/p>\n\n\n\n<p>Wu, L. W., Rouyer, E. &amp; Wang, C. Y. (2022). Value co-creation or value co-destruction: co-production and its double-sided effect. <em>International Journal of Bank Marketing<\/em>, <em>40<\/em>(4), 842-864.<\/p>\n\n\n\n<p>Yadav, M. S. &amp; Pavlou, P. A. (2020). Technology-enabled interactions in digital environments: A conceptual foundation for current and future research. <em>Journal of the Academy of Marketing Science<\/em>, 48, 132-136.<\/p>\n\n\n\n<p>Zhuang, T., Zhou, Z. &amp; Li, Q. (2021). University\u2010industry\u2010government triple helix relationship and regional innovation efficiency in China. <em>Growth and Change<\/em>, <em>52<\/em>(1), 349-370.<\/p>\n<!-- AddThis Advanced Settings generic via filter on the_content --><!-- AddThis Share Buttons generic via filter on the_content -->","protected":false},"excerpt":{"rendered":"<p>Abstract: This study focuses on the Eko\u00e4ly\u00e4 project (https:\/\/www.ekoalya.fi\/) and, more specifically, on the collaboration opportunities between circular economy actors and universities within this initiative.<!-- AddThis Advanced Settings generic via filter on get_the_excerpt --><!-- AddThis Share Buttons generic via filter on get_the_excerpt --><\/p>\n","protected":false},"author":48,"featured_media":1910,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"_acf_changed":false,"_relevanssi_hide_post":"","_relevanssi_hide_content":"","_relevanssi_pin_for_all":"","_relevanssi_pin_keywords":"","_relevanssi_unpin_keywords":"","_relevanssi_related_keywords":"","_relevanssi_related_include_ids":"","_relevanssi_related_exclude_ids":"","_relevanssi_related_no_append":"","_relevanssi_related_not_related":"","_relevanssi_related_posts":"477,1189,831,840,701,1274","_relevanssi_noindex_reason":"","footnotes":""},"categories":[55],"tags":[222,321,320,322],"class_list":["post-1904","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-articles","tag-circular-economy","tag-data-sharing","tag-triple-helix","tag-waste-business-ecosystem"],"acf":false,"_links":{"self":[{"href":"https:\/\/verkkolehdet.jamk.fi\/finnish-business-review\/wp-json\/wp\/v2\/posts\/1904","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/verkkolehdet.jamk.fi\/finnish-business-review\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/verkkolehdet.jamk.fi\/finnish-business-review\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/verkkolehdet.jamk.fi\/finnish-business-review\/wp-json\/wp\/v2\/users\/48"}],"replies":[{"embeddable":true,"href":"https:\/\/verkkolehdet.jamk.fi\/finnish-business-review\/wp-json\/wp\/v2\/comments?post=1904"}],"version-history":[{"count":4,"href":"https:\/\/verkkolehdet.jamk.fi\/finnish-business-review\/wp-json\/wp\/v2\/posts\/1904\/revisions"}],"predecessor-version":[{"id":1909,"href":"https:\/\/verkkolehdet.jamk.fi\/finnish-business-review\/wp-json\/wp\/v2\/posts\/1904\/revisions\/1909"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/verkkolehdet.jamk.fi\/finnish-business-review\/wp-json\/wp\/v2\/media\/1910"}],"wp:attachment":[{"href":"https:\/\/verkkolehdet.jamk.fi\/finnish-business-review\/wp-json\/wp\/v2\/media?parent=1904"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/verkkolehdet.jamk.fi\/finnish-business-review\/wp-json\/wp\/v2\/categories?post=1904"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/verkkolehdet.jamk.fi\/finnish-business-review\/wp-json\/wp\/v2\/tags?post=1904"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}