Tanja Brugger

Municipal smart city programs remain hampered by conceptual fragmentation and the absence of validated, context-specific maturity assessments. We develop the Smart Municipality Maturity Model (SMMM) via a design-science process, synthesizing 183 publications and adapting a practitioner-oriented self-assessment with 99 binary items across ten dimensions. Validation proceeded in four stages: expert review, industry validation, a pilot with 24 municipalities, and a large-scale rollout to 1136 municipalities. The five-level model yields comparable maturity scores and reveals a structural capability gap—governance and strategy outpace foundational technical capacities, especially digital infrastructure and data management. Maturity rises with municipality size, yet leadership, partnerships, and innovation culture act as moderators. The SMMM represents one of the first empirically validated, large-scale maturity assessments tailored to municipal administrations, providing a robust analytical basis for diagnosing capability gaps at scale. Its architecture directly supports municipal policy by translating conceptual smart city ambitions into measurable, comparable operational capacities and by enabling more targeted, evidence-driven interventions. The SMMM provides a low-burden instrument for self-assessment, peer benchmarking, and evidence-based policy design. Closing the identified capability gap requires capability-first investment and more explicit integration of cybersecurity and data privacy in future models and municipal practice.

This paper presents the “AI Sweet Spot” framework for systematically identifying Artificial Intelligence (AI) initiatives that balance technical feasibility, commercial viability, and user desirability, addressing the challenge of turning ambitious AI concepts into sustainable, high-impact solutions. A structured literature review of innovation management and AI deployment studies was combined with analyses of industry best-practice case studies. Insights were synthesized through the Innovation Sweet Spot lens to form a three-dimensional model, with design-thinking methods embedded to keep user desirability central. Critical success factors include robust data governance, rapid prototyping, cross-functional collaboration, and executive sponsorship. Recurring pitfalls involve siloed decision-making, underinvestment in user research, and misaligned performance metrics. Case studies show design-thinking interventions surface user pain points early, improving adoption and reducing wasted effort. A balanced view of feasibility, viability, and desirability is essential for AI project success. The “AI Sweet Spot” framework provides a roadmap for assessing trade-offs, prioritizing high-value opportunities, and mitigating technical, commercial, and human-centered risks. Practitioners can use the framework’s diagnostic questions and prioritization criteria to evaluate use cases, allocate resources strategically, and establish governance processes that enable responsible, scalable, and user-centric AI deployments.

This research paper examines the risks posed by the MIH EV Open platform to German automotive suppliers, in particular, the risk of commoditization and falling into a commodity trap. The term commodity trap describes a situation in which companies dealing with standardized products or services face intense price and margin pressure and struggle to differentiate themselves from competitors. The MIH EV Open platform, established by Foxconn, also known as Hon Hai Precision Industry Co. Ltd., headquartered in Tucheng, Taipei, Taiwan, aims to create a collaborative platform for the comprehensive development of key software, hardware components, and services in the electric vehicle (EV) industry. It unites over 2700 companies from more than 70 countries and fosters collaboration to accelerate the development and market entry of new EV products. This paper analyzes the MIH EV Open business ecosystem model and assesses the strengths and weaknesses of German suppliers in addressing these challenges. This study highlights strategic approaches, including innovation, portfolio adaptation, customer relationships, and sustainability practices, that can enable German suppliers to mitigate commodity trap risks. The findings underscore the importance of proactive, segment-specific strategies amidst the transformation of the automotive industry. Key insights are provided on the potential impact of open platform ecosystems and recommendations for German automotive suppliers to maintain competitiveness. This research fills a gap in the literature by examining the commoditization risks posed by the MIH EV Open platform for German automotive suppliers. Unlike previous studies that focus on traditional market structures, this study explores the novel dynamics introduced by platform ecosystems and provides strategic insights to mitigate these risks.

The transition to battery electric vehicles (BEVs) poses significant challenges for automotive suppliers, particularly in the Northern Black Forest Region, Germany’s largest industrial area. This study examines the risk of falling into the commodity trap and the impact of platform economics on these suppliers. A VUCA (volatility, uncertainty, complexity, ambiguity) analysis is used to assess the consequences of the open platform approach promoted by the Mobility in Harmony (MIH) Consortium. The methodology is based on a comprehensive literature review on commodity traps and platform economies, as well as an analysis of the MIH Consortium’s strategies for standardizing BEV components. The results show that while the MIH Consortium’s modularization and standardization efforts can reduce costs and facilitate mass production, they also intensify competition and limit differentiation, threatening the profit margins of smaller suppliers. The study highlights the importance of strategic positioning and innovation to counter these risks. It concludes with recommendations on how automotive suppliers can adapt to the evolving environment and be agile in responding to new technological standards and market dynamics.

This paper focuses on quantifying the economic and financial viability of NB-IoT and LoRaWAN technologies, two low-power wide-area network (LPWAN) technologies with unique characteristics that make them suitable for IoT applications. The purpose of this study is to propose a “pragmatic” artifact for performing life cycle cost analysis and demonstrate its application to these technologies. The methodology uses pragmatic computational tools to facilitate the analysis and considers all relevant economic and financial factors, such as operating costs, equipment costs, and revenue potential. The main finding of this study is that Narrow Band-Internet of Things (NB-IoT) and Long Range Wide Area Network (LoRaWAN) technologies have different cost structures and revenue potentials, which may affect their economic and financial viability for different IoT applications. Ultimately, the study concludes that a comprehensive life cycle cost analysis is critical to making informed decisions about technology adoption, and that the proposed methodology can be applied to other IoT technologies to gain insight into their economic and financial viability.

This paper focuses on quantifying the economic and financial viability of NB-IoT and LoRaWAN technologies, two low-power wide-area network (LPWAN) technologies with unique characteristics that make them suitable for IoT applications. The purpose of the study is to propose an artefact for performing life cycle cost analysis and demonstrate its application to these technologies. The methodology uses pragmatic computational tools to facilitate the analysis and considers all relevant economic and financial factors, such as operating costs, equipment costs, and revenue potential. The main finding of the study is that NB-IoT and LoRaWAN technologies have different cost structures and revenue potentials, which may affect their economic and financial viability for different IoT applications. Ultimately, the study concludes that a comprehensive life cycle cost analysis is critical to making informed decisions about technology adoption, and that the proposed methodology can be applied to other IoT technologies to gain insight into their economic and financial viability.