Measuring Ecosystem Complexity - Decision-Making Based on Complementarity Graphs

Platforms feature increasingly complex architectures with regard to interconnecting with other digital platforms as well as with a variety of devices and services. This development also impacts the structure of digital platform ecosystems and forces providers of these services, devices, and services to incorporate this complexity in their decision-making. To contribute to the existing body of knowledge on measuring ecosystem complexity, the present research proposes two key artefacts based on ecosystem intelligence: On the one hand, complementarity graphs represent ecosystems with an ecosystem's functional modules as vertices and complementarities as edges. The nodes carry information about the category membership of the module. On the other hand, a process is suggested that can collect important information for ecosystem intelligence using proxies and web scraping. Our approach allows replacing data, which today is largely unavailable due to competitive reasons. We demonstrated the use of the artefacts in category-oriented complementarity maps that aggregate the information from complementarity graphs and support decision-making. They show which combination of module categories creates strong and weak complementarities. The paper evaluates complementarity maps and the data collection process by creating category-oriented complementarity graphs on the Alexa skill ecosystem and concludes with a call to pursue more research based on functional ecosystem intelligence

Integrating Data and Service Lifecycle for Smart Service Systems Engineering: Compilation of a Lifecycle Model for the Data Ecosystem of Smart Living

In smart service systems engineering, where actors rely on the mutual exchange of data to create complex and holistic solutions, integration is crucial. Nevertheless, the management of data as a driving resource still lacks organizational structure. There is no holistic lifecycle approach that integrates data and service lifecycle and adopts a cross-actor perspective. Especially in data ecosystems, where sovereign actors depend on the mutual exchange of data to create complex, but transparent service systems, an integration is of crucial importance. This particularly applies to the smart living domain, where different industries, products and services interact in a complex environment. In this paper we address this shortcoming by proposing an integrated model that covers the different relevant lifecycles based on a systematic literature review and supplement it by concrete domain requirements from the smart living ecosystem obtained through semi-structured expert interviews

Token Economy – Towards Building a Sustainable Blockchain Token Ecosystem Framework

Dissertation presented as the partial requirement for obtaining a Master's degree in Information Management, specialization in Information Systems and Technologies ManagementIn the context of the internet’s historical trajectory, blockchain technology represents a significant paradigm shift from Web 2.0 to Web 3.0. Web 2.0, the current world of the interactive and social web, is an internet siloed by centralized organizations that provide services in exchange for personal data. Web 3.0, on the other hand, is based on cryptographic blockchain technology and enables an economic institutional infrastructure that is natively available on the web, hands ownership back to the creators and users and operates without an intermediary. Blockchain tokens enable digital scarcity and a novel internet-native value transfer mechanism. Tokens can have a magnitude of different use cases ranging from serving as unit of account (currency), promoting usage incentive, as tool for governance, representation of ownership or as a funding instrument. The research field of token creation is still in its very infant stage and a lot of blockchain project launches still happen without proper structure and long term strategy – leading to suboptimal and short lasting results. Based on the Design Science Research methodology, this dissertation attempts to design a holistic conceptual framework that can serve as a base for a decision aid for organizations when creating a blockchain token ecosystem. This artifact will finally be evaluated by domain experts to ensure proper correctness

INVESTIGATION OF INDUSTRY 5.0 HURDLES AND THEIR MITIGATION TACTICS IN EMERGING ECONOMIES BY TODIM ARITHMETIC AND GEOMETRIC AGGREGATION OPERATORS IN SINGLE VALUE NEUTROSOPHIC ENVIRONMENT

Industry 5.0 acceptance is accelerating, but research is still in its infancy, and existing research covers a small subset of context-specific obstacles. This study aims to enumerate all potential obstacles, quantitatively rank them, and assess interdependencies at the organizational level for Industry 5.0 adoption. To achieve this, we thoroughly review the literature, identify obstacles, and investigate causal relationships using a multi-criteria decision-making approach called single value Neutrosophic TODIM. Single-valued Neutrosophic sets (SVNS) ensembles are employed in a real-world setting to deal with uncertainty and indeterminacy. The suggested strategy enables the experts to conduct group decision-making by focusing on ranking the smaller collection of criterion values and the comparison with the decision-making trial and evaluation laboratory method (DEMATEL). According to the findings, the most significant hurdles are expenses and the funding system, capacity scalability, upskilling, and reskilling of human labor. As a result, a comfortable atmosphere is produced for decision-making, enabling the experts to handle an acceptable amount of data while still making choices

Building Digital Foundations: A Course of Action Towards a Circular Construction Industry : An Exploratory Case Study

This thesis aims to explore the potential for digital platform ecosystems to support the development of the circular economy in the Norwegian construction industry. While there is a general understanding among scholars and industry professionals that digitalization can enable circularity, the existing literature on the intersection of these two concepts is limited. Existing literature does not adequately address the potential for using digital platforms to promote circularity across industry value chains and achieve the goals of a circular economy. To gain a holistic perspective on this potential, the thesis is based on an exploratory case study involving clients, consultants, architects, and contractors in the construction industry. The study aims to contribute to existing literature by developing a conceptual framework linking the concept of a circular economy to digital platform ecosystems, as well as by exploring why and how such a platform ecosystem can support the transition to circularity in the construction industry. The study's findings are twofold. Firstly, the study suggests the need for an improved organization of the value chain actors on digital platforms to facilitate iterative collaboration on project-level. Particularly, we identified that the implementation of circularity in the industry depend on frequent involvement of contractors and consultants. Moreover, in order to succeed in the transition towards circularity, we argue that the industry needs an industrywide platform to create a market for reused materials. Therefore, our study suggests that the industry requires a multidimensional platform with both project-specific and industry-wide components. Secondly, we identified three fundamental attributes that need to be present on a digital platform ecosystem for circularity: flexibility, data accumulation, and interaction. Based on these findings, we reassess our preliminary framework linking the circular economy to digital platform ecosystems and describe how the fundamental attributes can support this relationship. Overall, our thesis contributes to a better understanding of how industry actors can be organized on digital platform ecosystems to support circularity. In addition, the thesis provides the fundamental attributes necessary to configure a digital platform ecosystem for circularity in the construction industry.nhhma

Strategies Automotive Purchasing Managers Use for Managing Material Costs

AbstractBusiness leaders continually face challenges with the rising cost of materials. Automotive purchasing managers need to develop strategies to manage the cost of materials. Grounded in resource dependency theory, the purpose of this qualitative multiple case study was to explore strategies purchasing leaders in the automotive industry use for managing material costs. Data collection included semistructured interviews and documentation, including purchasing policies and directives. The participants comprised four automotive purchasing managers who formulated and implemented material cost strategies. Five themes were identified using thematic analysis: (a) negotiation, (b) total cost of ownership, (c) reducing design complexity, (d) supplier strategic relationship, and (e) role of information technology. A key recommendation for purchasing managers is to use third-party sources to benchmark materials prices and utilize cost engineers to calculate and analyze the total cost of ownership of critical materials purchased by the firm. The implications for positive social change include the potential to develop sustainable partnerships with local companies to help create jobs for residents of the area and support local social programs

Innovation ecosystems for industry 4.0 : a collaborative perspective for the provision of digital technologies and platforms

Industry 4.0 considers complex interrelated IoT-based technologies for the provision of digital solutions. This complexity demands a vast set of capabilities that are hard to be found in a single technology provider, especially in small and medium-sized enterprises (SMEs). Innovation ecosystems allow SMEs to integrate resources and cocreate Industry 4.0 solutions. This thesis investigates the role of collaboration for the development of technologies and solutions in the Industry 4.0 context. To this end, this thesis was organized into three papers, which objectives are: (i) to verify if collaboration through inbound Open Innovation activities with different actors in the supply chain positively moderates the relationship between Industry 4.0 technologies and their expected benefits; (ii) to identify how the characteristics of an innovation ecosystem focused on solutions for Industry 4.0 change at each evolutionary lifecycle stage using elements from social exchange theory; and (iii) to identify which technologies can be configured as platforms through boundary-spanning activities and how they operate collaboratively to develop solutions for Industry 4.0. As a result, this thesis proposes a model that explains the role of collaboration at different levels (supply chains, ecosystems, and platforms) for the development of solutions in the Industry 4.0 context. This research approach combines both qualitative (i.e., focus group, interviews, and case studies) and quantitative (i.e., survey research with multivariate data analysis) aspects. The main results obtained are: (i) we show how collaboration with different actors in the supply chain through Open Innovation strategy has both positive and negative impacts on three strategies associated with product development (cost reduction, focalization, and innovation); (ii) we define the main characteristics of innovation ecosystems focused on the provision of Industry 4.0 solutions, considering an evolutionary lifecycles perspective and a Social Exchange view (iii) we define which are the different technology platforms of the Industry 4.0 context at different operation levels using Boundary-Spanning view. As remarking conclusions, from an academic perspective, these results help to understand how collaboration for the development of new solutions in Industry 4.0 can be analyzed under different perspectives (Open Innovation, Social Exchange Theory, and Boundary-Spanning) and in different contexts of integration (supply chains, ecosystems, and platforms). From a practical perspective, the results help to enlighten a trending business topic by showing how the collaboration among technology providers for Industry 4.0 should be fostered and developed

Emergence and Dynamics of Circular Economy Ecosystem

The circular economy ecosystem (CEE) offers the potential to effectively manage the pressing issue of residual resources, encompassing waste and by-products that pose a challenge to our planet. Among various organizational forms, the ecosystem approach has emerged as the preferred method, fostering cross-industry collaboration to sustainably address residual resources. While business and innovation ecosystems have been extensively studied to understand their emergence, structure, and value proposition, they only provide a partial understanding of how CEEs come into being and manage these resources. Furthermore, CEEs encounter constraints from linear economic practices and environmental conditions. Given the prominent role of digital technologies within CEEs, this study delves into their influence, aiming to uncover their multifaceted impact beyond technical aspects.This thesis sheds light on the distinctive factors driving the emergence of CEEs and how they differ from business and innovation ecosystems. Additionally, it explores the cohesive role played by digital technologies, extending beyond their conventional functions. This exploration is rooted in two case studies, one CEE in Africa and one in Europe. Both cases were selected due to their effective management of residual resources through sustainable approaches, coupled with their incorporation of digital technologies.The findings of this research indicate that CEEs arise as a response to the need for coordinated collective action in the face of linear constraints and the necessity to access interdisciplinary knowledge. The pursuit of interdisciplinary knowledge takes precedence over economic considerations and competition due to the intrinsic motivation to acquire complementary knowledge. In addition, digital technologies act as a unifying force facilitating knowledge appropriation during experimentation, fostering cooperation among stakeholders, rather than promoting competition.This thesis is positioned at the intersection of sustainable transitioning of strategic management and information systems

Automatic generation of software interfaces for supporting decisionmaking processes. An application of domain engineering & machine learning

[EN] Data analysis is a key process to foster knowledge generation in particular domains or fields of study. With a strong informative foundation derived from the analysis of collected data, decision-makers can make strategic choices with the aim of obtaining valuable benefits in their specific areas of action. However, given the steady growth of data volumes, data analysis needs to rely on powerful tools to enable knowledge extraction. Information dashboards offer a software solution to analyze large volumes of data visually to identify patterns and relations and make decisions according to the presented information. But decision-makers may have different goals and, consequently, different necessities regarding their dashboards. Moreover, the variety of data sources, structures, and domains can hamper the design and implementation of these tools. This Ph.D. Thesis tackles the challenge of improving the development process of information dashboards and data visualizations while enhancing their quality and features in terms of personalization, usability, and flexibility, among others. Several research activities have been carried out to support this thesis. First, a systematic literature mapping and review was performed to analyze different methodologies and solutions related to the automatic generation of tailored information dashboards. The outcomes of the review led to the selection of a modeldriven approach in combination with the software product line paradigm to deal with the automatic generation of information dashboards. In this context, a meta-model was developed following a domain engineering approach. This meta-model represents the skeleton of information dashboards and data visualizations through the abstraction of their components and features and has been the backbone of the subsequent generative pipeline of these tools. The meta-model and generative pipeline have been tested through their integration in different scenarios, both theoretical and practical. Regarding the theoretical dimension of the research, the meta-model has been successfully integrated with other meta-model to support knowledge generation in learning ecosystems, and as a framework to conceptualize and instantiate information dashboards in different domains. In terms of the practical applications, the focus has been put on how to transform the meta-model into an instance adapted to a specific context, and how to finally transform this later model into code, i.e., the final, functional product. These practical scenarios involved the automatic generation of dashboards in the context of a Ph.D. Programme, the application of Artificial Intelligence algorithms in the process, and the development of a graphical instantiation platform that combines the meta-model and the generative pipeline into a visual generation system. Finally, different case studies have been conducted in the employment and employability, health, and education domains. The number of applications of the meta-model in theoretical and practical dimensions and domains is also a result itself. Every outcome associated to this thesis is driven by the dashboard meta-model, which also proves its versatility and flexibility when it comes to conceptualize, generate, and capture knowledge related to dashboards and data visualizations