Analyzing SME Digitalization Requirements Through a Technology Radar Framework in Southeast Lower Saxony

Abstract

This study investigates the specific requirements of small and medium-sized enterprises (SMEs) in Southeast Lower Saxony in the context of digital transformation, with a particular focus on aligning these needs with current technological offerings. Utilizing a Technology Radar framework as the methodological approach, the research aims to systematically match identified SME business demands with relevant technological developments, thereby offering a transparent representation of prevailing technology trends. The overarching objective is to support regional SMEs and associated institutions in navigating digitalization challenges by providing recommendations derived from the application of this methodology. To this end, the study outlines the theoretical foundations of digital transformation and explicates the operational principles of the Technology Radar. Subsequently, the digitalization needs of SMEs in key regional industries and contemporary technology trends are analyzed and categorized. These findings are integrated within the Technology Radar framework, facilitating a structured comparison between technological supply and SME organizational demand. The study concludes with a discussion of the results and presents practical implementation strategies to guide regional SME stakeholders in their digital transformation efforts.

1. Introduction

The Southeast Lower Saxony (SON) region, driven by a strong automotive sector and research infrastructure, faces complex challenges in adapting to digital transformation, requiring coordinated, innovation-oriented strategies [1]. This study specifically addresses the needs of small- and medium-sized enterprises (SMEs) in Southeast Lower Saxony, analyzing how these can be effectively aligned with current technological offerings. To achieve this, we employ a Technology Radar framework as a structured methodological tool that systematically matches SME requirements with technological solutions. The goal is to support targeted transformation strategies and provide actionable recommendations for regional SME stakeholders and associated institutions.

1.1. Motivation

The SON region is characterized by a robust economic structure, particularly due to its concentration of original equipment manufacturers (OEMs) and suppliers within the automotive industry. This sector plays a pivotal role in the region’s economic landscape. Complementing this industrial strength is a well-established knowledge and research infrastructure, which is likewise of strategic relevance to the automotive sector [1]. This provides this region with a unique composition of enterprises of different sizes, a research-driven ecosystem, and public structures. The digital transformation poses substantial challenges for regional stakeholders in terms of their technological, ecological, and economic sustainability. Addressing these challenges necessitates the development of regionally tailored instruments, processes, and solutions that are jointly supported by all key actors and institutions [1]. Given the multifaceted impact of digital transformation, a coordinated and holistic approach is essential to fully exploit its opportunities and potential. To this end, it is imperative to leverage and expand the region’s existing strengths—particularly the close integration between the automotive industry and scientific research—to foster collaborative innovation. The established infrastructure, including advanced transport systems and a diverse ecosystem of service providers and suppliers, provides a solid foundation for digital advancement. Digitalization in the automotive sector, specifically, offers numerous avenues for innovation and efficiency gains through the deployment of modern technologies such as the Internet of Things (IoT), Artificial Intelligence (AI), Big Data, and Cloud Computing. These technologies facilitate the emergence of new business models, product innovations, and mobility concepts. In this context, the SON region is well positioned to serve as an innovation hub, where companies, research institutions, and other stakeholders collaboratively develop forward-looking solutions. This study initially focuses on identifying the general needs of SMEs and analyzing available technology offerings in the context of digitalization. The objective is to align relevant technologies with these identified needs, thereby supporting regional companies and institutions in strategic decision-making and providing a structured overview. The findings serve as a foundation for the development of tailored transformation strategies that enable the SON region to effectively address digitalization challenges. Given these conditions, this research focuses on SMEs as the primary actors of regional digital transformation and introduces a Technology Radar approach to provide them with systematic guidance for technology selection and implementation.

1.2. Aims and Scope

This study conducts a structured analysis of the needs of SMEs and current technological offerings in the context of digital transformation, with the objective of aligning these via a technology radar to support strategic decision-making. The essential role of SMEs in the region requires an approach specifically tailored for the special needs of this region, which are based on the unique structure described above. By systematically mapping technologies to organizational needs, the study aims to provide a clear overview of relevant trends and derive actionable recommendations for stakeholders in the SON region. To this end, the study addresses three core research questions:

(1)

What are the key digitalization needs and barriers faced by SMEs in the Southeast Lower Saxony region?

(2)

Which current technologies are most relevant to these SMEs, and how can they be categorized within a Technology Radar framework?

(3)

How can the Technology Radar be used by SMEs and regional stakeholders to effectively allocate technologies to organizational domains?

The methodological framework includes a theoretical foundation of digital transformation—covering key definitions, enabling technologies, and the structure of the technology radar. Following this, the needs of SMEs and current technological developments are analyzed, categorized, and synthesized through the development of the radar. The study concludes with a critical discussion of results, offering a foundation for tailored transformation strategies and future research. It is important to note that the contribution of this study lies in the development of a methodological framework. The recommendations presented are derived from applying this framework, rather than being generated by a software system. The scientific novelty of this study lies in the integration of a systematic SME needs assessment with a Technology Radar framework. While previous studies [2,3,4] have analyzed SME digitalization barriers or maturity levels, these works generally remain descriptive and do not provide a structured tool for aligning SME needs with technological developments. Our approach contributes a methodological framework that combines literature-based needs identification with a structured technology radar, and situates this within the specific regional context of Southeast Lower Saxony. To our knowledge, this combination of needs assessment, technology mapping, and regional application has not been systematically presented in prior research.

2. State of the Art

This section establishes the theoretical framework for understanding digital transformation in the automotive industry and underpins the analyses presented in subsequent sections. It begins by defining the concept of digitalization and its key characteristics, followed by an examination of the implications of digital transformation for the automotive sector, with particular emphasis on the evolution of processes and products. The section concludes with an overview of the trend and technology radar as methodological tools for guiding and supporting transformation processes. These theoretical foundations are essential for contextualizing the study’s objectives and form the basis for the practical analyses carried out in the following sections.

2.1. Digitalization

In the context of rapid technological advancements and increasing socio-economic interconnectivity, digitalization has become a key driver of transformation, profoundly impacting both organizational operations and broader economic structures, particularly within the automotive industry. This section provides a foundational overview by defining digitalization, outlining its core characteristics, and analyzing its economic implications with a focus on the automotive sector. It establishes the conceptual basis for understanding and assessing the digitalization-related needs and challenges of SMEs in subsequent analyses.

2.1.1. Definitions and Characteristics

Digitalization constitutes a comprehensive transformation of manual processes and physical objects into digital formats [5], encompassing the integration of information and communication technologies (ICT) across all areas of work and life [6]. Closely linked to automation, it enables efficiency gains through Information Technology (IT) systems that connect information and generate economic synergies [7]. Becker and Ulrich define digitalization as the ICT-driven transformation of business models to reduce interfaces, enhance cross-functional connectivity, and improve effectiveness and efficiency [8]. Its impacts extend beyond organizational processes to affect individuals, the environment, society, and market dynamics, with potential for disruption through innovative technologies [5,9]. Applications span all major business functions—sales, marketing, HR, customer service—and yield significant advantages, particularly in administrative domains like supply chain and logistics [10]. Drivers include cost pressure, the need for productivity gains, and increasing demands for cross-functional collaboration, prompting changes in communication, control, and organizational structure. Digitalization supports data availability and decision-making, reduces redundancies, and enhances process reproducibility and precision [7]. Key benefits include process acceleration, error minimization, agile collaboration [5,6,11,12], employee relief from routine tasks [10], cost advantages, and customer-oriented product and service innovation. Efficient implementation requires thorough process analysis to prevent transferring inefficiencies, clearly defined digital interfaces, and a collaborative culture to avoid silo mentalities [5,10]. Organizational transformation necessitates digital competencies, training, and sometimes external expertise [7]. Successful deployment depends on stakeholder acceptance and the systematic evaluation of digital initiatives based on economic impact before technology adoption [5].

Small and Medium-sized Enterprises (SMEs) are defined according to the criteria set out by the European Commission Recommendation 2003/361/EC. An SME is an enterprise that employs fewer than 250 persons and has either an annual turnover not exceeding EUR 50 million or an annual balance sheet total not exceeding EUR 43 million. Within this category, small enterprises are defined as those employing fewer than 50 persons and with an annual turnover or balance sheet total not exceeding EUR 10 million. These thresholds serve to differentiate enterprise size classes and are essential for targeting specific policy measures. Furthermore, an enterprise’s classification also depends on its degree of independence, with distinctions made between autonomous, partner, and linked enterprises based on ownership and control relationships [13].

2.1.2. Impact on the Automotive Industry

Digitalization is a disruptive force across industries, fundamentally transforming the automotive sector by integrating digital technologies and data into core business processes. Traditional OEMs, once successful through vehicle development, production, and sales underpinned by quality and expertise, now face growing pressure from electrification and digital innovation [14]. New entrants, often focused on disruptive technologies like autonomous driving, leverage agile digital business models that challenge the rigid structures of incumbents [15]. Digital transformation adds a new value layer through services based on driver and vehicle data, enabling offerings such as predictive maintenance, personalized experiences, and infrastructure communication [14]. As smart features and infotainment gain importance, software and updatability become key differentiators—nearly 40% of buyers would switch brands based on digital services [16]. ICT now plays a central role in vehicle value creation, enabling connectivity and interaction with the environment [14]. While incumbents struggle to adapt, new players exploit their inertia to redefine market structures. For OEMs, digitalization demands strategic and operational transformation in an increasingly data- and software-driven landscape [14].

2.1.3. Digitalization Process

Digitalization constitutes a strategic imperative that must be embedded into long-term corporate planning as a cross-functional element of the business model. Continuous analysis of customer expectations, competition, technology, and disruptive trends provides the foundation for identifying market potential, shaping business strategy, and developing lean, agile models that foster efficiency, adaptability, and innovation. A clearly defined digital vision, aligned with corporate strategy and supported by innovation management, enables the systematic identification of digital fields, evaluation of technological fit, and benchmarking through pilot projects. The iterative nature of this process, supported by the interplay between organizational culture and information technology, ensures responsiveness to dynamic market and customer developments while maintaining competitiveness [15].

2.2. Key Technologies

The concept of key technologies is complex and variably defined across academic and policy contexts, encompassing frameworks such as “Key Enabling Technologies,” “Advanced Technologies for Industry,” and “General Purpose Technologies” [17]. Key technologies are primarily viewed as catalysts for innovation, enabling new products, processes, and services across sectors, with broad societal impact [17]. They are central to present and future value creation, often driving or accelerating advances in other technological domains [18]. For example, high-performance control chips are foundational in modern manufacturing and in advancing energy and mobility systems [18]. Characterized by their disruptive potential and association with long-term economic growth cycles, key technologies foster market success and global competitiveness [17,18]. However, their increasing complexity poses challenges, especially for SMEs, which require access to appropriate infrastructures to exploit their innovation potential [18] outlines three key criteria for identifying such technologies:

(1)

broad applicability across industries,

(2)

strong, non-substitutable complementarity with other technologies, and

(3)

high performance enhancement potential, both for the technology itself and its applications.

Identifying emerging key technologies is empirically uncertain and benefits from expert-driven, dialog-based evaluation [18]. This study focuses on digitalization within enterprise processes and adopts the definition by Stich et al., which considers key technologies as digital systems composed of interconnected application software, hardware, and data-processing components that create value within networked business environments [19].

2.3. Needs Assessment

This study aims to identify and analyze the digitalization needs of SMEs. In this context, “need” refers to a deficiency or requirement, often used synonymously with “demand” in organizational settings [20], and reflects internal or external pressures to adopt digital technologies—arising from customers, competitors, or regulations [21]. A needs assessment, therefore, seeks to determine technological requirements from a strategic and organizational perspective, necessitating a comprehensive evaluation of technological application contexts [22]. The process includes defining objectives, gathering relevant data, and conducting a current-state analysis to identify digitalization needs. According to Work System Theory (Alter 2013), such assessments should examine domains including processes and activities, participants, information, and technologies. The extended framework further covers environment, infrastructure, and organizational strategy. The result is a formal requirements specification that informs the economic evaluation of implementation projects. Such assessments are crucial for aligning digitalization initiatives with business objectives, uncovering process inefficiencies, and developing targeted digital strategies. They also enable realistic estimations of digitalization’s costs and benefits, supporting informed decision-making [22].

2.4. Trend- and Technology-Radars

In the context of digital transformation and technological advancement, the early identification and assessment of trends and technologies are increasingly vital for organizations. Trend and technology radars serve as strategic tools for visually aggregating and evaluating relevant developments, thereby enabling timely recognition of opportunities and risks and supporting innovation [23]. Unlike trend studies, which focus on individual trends, a trend radar presents a broad spectrum of developments, structured according to company-specific relevance and sectoral focus [23]. Technological radars additionally illustrate the maturity of technologies along the path from research to industrial application, supporting strategic planning in both business and research [24,25]. Trends are visually arranged in concentric circles, with proximity to the center indicating importance, and segmented by categories. Criteria such as trend relevance (impact on the organization) and trend maturity (technological development stage) are used for evaluation, based on measurable indicators like project volume, standardization levels, and market activity [23]. These assessments guide prioritization and are often categorized into stages: observe, evaluate, pilot, and implement. A clear definition of criteria is essential to avoid subjectivity and ensure meaningful recommendations [23,24]. For effective use, trend radars are supplemented with detailed profiles—trend portraits—containing statistical data, application examples, and key actors, enhancing interpretability and contextual understanding [23]. Technology briefs may further consolidate technical details, offering a comprehensive, structured overview of innovation fields and strategic action needs [24].

3. Methods I—Analysis of the Needs of SMEs

The German Mittelstand plays a crucial role in national economic performance and faces both opportunities and challenges in the context of digitalization. Survey data from Ernst & Young indicate a growing relevance of digital technologies: between 2016 and 2022, the share of SMEs attributing high importance to digital technologies rose from 21% to 38%, while the share of those seeing little to no relevance declined from 26% to 19% [2]. This trend highlights a growing awareness of digital transformation as essential for competitiveness and business development. The significance of digitalization varies by industry: in mechanical engineering, 58% of firms rate it as highly important, compared to 54% in electrical engineering and only 27% in the transport sector [2]. Company size also plays a role, with 51% of firms generating over €100 million in annual revenue emphasizing digital relevance, versus only 30% among those below €30 million [2]. These findings suggest that smaller firms, especially in the SON region, face greater difficulties in implementing digital solutions and likely show lower levels of digital maturity. This section analyzes the digitalization needs of SMEs, from business process optimization to support across value creation stages. The analysis is based on a systematic literature review of academic sources, industry studies, and theoretical contributions. Findings were critically assessed, thematically organized, and structured in Section 2 to ensure a coherent representation of insights. Given the lack of a uniform definition, this study adopts a general understanding of “Mittelstand” as independent, medium-sized enterprises positioned between small firms and large corporations, typically characterized by limited resources and a strong link between ownership and management [26]. Although based solely on secondary data, the literature provides valuable insights into the digital needs of this enterprise segment.

3.1. Categorization of the Central Needs of Digitalization

This section analyzes and categorizes the digitalization needs of SMEs, particularly those in vehicle and mechanical engineering, mobility, logistics, energy, and the chemical and pharmaceutical industries—core sectors in the SON region. Due to the complexity of digital transformation, various studies offer differing classifications of enterprise needs, often structured by functional areas, industry-specific requirements, or firm size and context. There is no universally applicable typology, reflecting the heterogeneous nature of digital challenges and opportunities. A survey by Ernst & Young highlights that 75% of German SMEs use digital technologies in customer relations, 56% in supply chain integration, and 55% in automated production, while only 6% develop entirely new digital business models [2]. The impact of digital technologies also varies across corporate functions: accounting (49%), sales (41%), production (39%), and procurement (36%) report high relevance, whereas strategy (28%) and HR (18%) remain less affected [2]. These differences suggest that not all firms have fully recognized digital potential across all areas, particularly in strategic and personnel processes. Based on a thorough literature review, this study develops an original categorization of digitalization needs aligned with Porter’s value chain. It includes overarching categories such as business models and strategic management, as well as primary value-creating activities—development, procurement, production, and sales/customer interaction—alongside digital products and services. Cross-cutting dimensions like digital processes, data management, and IT security are also included, as shown in Figure 1. A needs catalog provides a structured overview of these requirements and forms part of the methodological basis for the Technology Radar framework, as shown in Table S1.

3.1.1. Business Model

A business model defines the structure through which a company creates value, delivers customer benefits, and generates profit [27]. Digitalization enables companies to optimize, extend, or reinvent these models using digital technologies. Three primary strategic objectives guide digital business model transformation. First, companies seek to improve and protect their core business through digital technologies, focusing on efficiency, productivity, and profitability—often via short-term measures and supported by lighthouse projects or real-world labs to foster internal awareness [28]. The second objective involves extending existing business models by integrating adjacent digital services or products to enhance customer value and unlock new revenue streams. Five approaches characterize this expansion [27]:

(1)

Adding services to physical products using sensors and connectivity;

(2)

Enhancing machinery with digital monitoring for predictive maintenance;

(3)

Shifting from product sales to usage-based service models;

(4)

Implementing digital customer interfaces for sales and configuration; and

(5)

Enabling mass customization via smart manufacturing.

The third objective is the creation of entirely new digital business models, requiring significant innovation and entrepreneurial initiative. These may be developed internally or through partnerships, investments, or collaborations to access external expertise [28].

3.1.2. Strategic and Operative Management

Digitalization significantly impacts core management domains, including human resources [HR], controlling, finance, and operational coordination. In HR, key needs involve the digital optimization of recruitment, administration, and personnel development processes—particularly in streamlining administrative tasks (e.g., payroll, time tracking) and enhancing training and skill development through digital platforms [29]. The rise in controlling faces is increasing demand for automated data collection and analysis to support reporting and decision-making. Manual data handling, especially in SMEs, impedes value-adding activities due to low standardization and automation [30]. Digital solutions, including Big Data and analytics, enhance reporting accuracy, reduce workload, and improve strategic planning and budgeting precision [30]. In finance and accounting, digitalization aims to automate financial processes, ensure regulatory compliance, and improve transparency in financial planning and operations [31]. Operational management requires enhanced communication, coordination, and knowledge management. Digital tools enable real-time communication, virtual collaboration, and streamlined project coordination, thereby increasing productivity and responsiveness [27]. Knowledge management systems, such as digital repositories and internal wikis, support knowledge retention, sharing, and innovation, strengthening organizational competitiveness [27].

3.1.3. Research and Development

Product development today faces increasing complexity driven by rapid technological advancements and evolving customer demands. Companies aim to shorten development cycles, enhance efficiency, and improve responsiveness, which necessitates the adoption of digital and interconnected development processes [32]. However, many firms still rely on outdated 2D methodologies, resulting in inefficiencies and communication barriers due to heterogeneous systems and data formats [32].

To address these issues, the integration of automation and advanced digital technologies is essential. Central to this transformation is the digital dataset, which supports data-driven development, facilitates process optimization, and enables continuous data enrichment along the value chain [32,33]. These datasets also enable monitoring and control of products, supporting performance optimization and personalization through data analytics [27].

Machine learning, when applied to extensive legacy product data, can reveal patterns that improve the reliability of current product features [33]. This requires the digitization of analog records, with benefits varying by product complexity and the specificity of descriptive data [32]. Overall, implementing digital product development enhances process efficiency, data quality, and cross-functional and external collaboration [32].

3.1.4. Procurement and Service Provision

Digitalization significantly transforms value creation processes in Business-to-Business (B2B) procurement and production. A central development is the digital integration of suppliers and customers into business processes, enabling automated data exchange for forecasting, sales, and inventory, and supporting systems that autonomously trigger and transmit orders—reducing lead times and administrative effort while improving efficiency [27]. Electronic marketplaces and automated procurement processes shift routine tasks from buyers to suppliers, who benefit from stronger customer integration but face increased system demands. Additionally, procurement digitalization extends to supplier search, contract management, and data integration, requiring seamless handling of various data formats and real-time interoperability. In production, digital technologies enable embedded systems in machines and robots to collect, process, and communicate data, fostering efficiency, flexibility, and quality improvements [27]. A survey by TU Chemnitz highlights digital process documentation, integration with quality management, and predictive maintenance as the top digital trends in manufacturing, although remote maintenance and machine data monitoring remain underutilized [34]. The concept of the “Smart Factory” encapsulates this evolution, characterized by decentralized, self-controlling production processes and intelligent workpieces that carry or access data for autonomous navigation through production stages. Smart Factories involve vertical integration—linking machines to enterprise systems such as planning and control—and horizontal integration with supply chain partners for end-to-end data exchange from order to distribution [27]. These transformations demand advanced technical expertise and foster continuous optimization through data-driven collaboration between humans and machines.

3.1.5. Sales and Customer Interaction

A key dimension of digital transformation in the Business-to-Customer (B2C) sector is the digitalization of sales and customer relationships. The adoption of digital technologies—alongside evolving consumer behavior—fundamentally reshapes how companies offer products and engage with customers. Central to this is managing the entire customer journey, encompassing all interaction points from initial contact to post-sale service, thereby fostering trust and long-term customer loyalty [27].

In many industries, customer integration into digital processes during product development, purchasing, consultation, and after-sales support is well advanced. Mobile commerce, for instance, is now standard for many consumers. To effectively manage this end-to-end digital engagement, companies must design seamless customer experiences and ensure continuous interaction across all service stages [27]. Customer centricity—placing individual customer needs at the core of value creation—is increasingly vital. This involves tailoring interactions across the customer journey, including personalized content delivery and channel-specific communication [27]. Effective implementation requires integrated IT systems, shared data platforms, and cross-channel strategies. Digital tools enable the collection and analysis of customer data, supporting personalized experiences and identifying cross-selling opportunities, thereby enhancing customer satisfaction and loyalty.

3.1.6. Products and Services

Over time, traditionally mechanical and electrical products have evolved into complex, connected systems through the integration of sensors, microprocessors, software, and networking technologies. This transformation enables formerly analog products to become digitized, allowing them to collect operational and environmental data and communicate with other products, IT systems, machines, users, or customers. Smart products go a step further by autonomously analyzing data and responding accordingly [27]. A key application is the monitoring and control of product functions, where operational data collected over time supports predictive maintenance planning. Another core feature is connectivity, enabling interaction with external systems—for example, synchronizing agricultural machinery via geolocation to optimize processes. These products leverage cloud services and Internet-based communication to function within integrated networks [27]. The essential functions of digitized or intelligent products include monitoring, control, optimization, autonomy, and networking. These features benefit both users and manufacturers by enabling real-time status tracking, environmental sensing, and behavior-based customization. Additionally, collected data supports performance optimization, fault prediction, and proactive maintenance, while informing future product development through insights into user needs [27]. Furthermore, usage data enables cost reductions in service, field operations, and spare parts management. It also facilitates warranty verification and the detection of misuse based on empirical data, thus enhancing overall efficiency and customer value [27].

3.1.7. Digital Processes, Data Analysis, and IT-Security

This section addresses cross-functional needs related to digital processes and data management, which are increasingly critical in interconnected business environments. Companies must implement end-to-end digital workflows that enhance interdepartmental collaboration and ensure seamless data flow. Efficient processing and utilization of large data volumes are essential for decision-making and competitive advantage. This requires structured process digitalization, enabling automation, transparency, standardization, and flexibility across time and location [27]. Digital transformation should begin with identifying and prioritizing under-optimized, low-digitized processes, particularly those spanning departmental boundaries where media disruptions and inefficiencies are common. Special attention must be given to eliminating “shadow IT” systems—unofficial tools used outside centralized IT oversight—as they create data silos, redundancies, and security risks. Full system integration increases efficiency, minimizes transmission errors, and ensures centralized data governance and security compliance [27]. Automation potential lies primarily in highly standardized and digitized administrative tasks. Although technically complex, automation improves accuracy, reduces workload, and can lower personnel demands. As digitalization, automation, and system integration progress, data exchange intensifies, making high data quality and governance essential. Establishing data governance frameworks, including standardized roles, responsibilities, and master data harmonization, ensures data reliability and consistent quality [27,28]. Furthermore, digitalization inherently raises IT security demands. With increasing data exchange and system interconnectivity, new vulnerabilities emerge. Integrated networks involving products, sensors, cloud platforms, and IT systems introduce potential attack vectors. Therefore, SMEs must implement robust cybersecurity measures—encryption, firewalls, access controls—and maintain comprehensive, regularly updated security strategies. These include infrastructure assessments, employee training, incident response plans, and ongoing adaptation to evolving threats and regulations [27]. IT security must thus be regarded as a strategic, continuous process central to long-term digital resilience.

3.2. Barriers and Challenges of Digitalization

Digital transformation offers significant potential for enterprises but also presents various obstacles. This chapter focuses on four key challenge areas: financial constraints, resource scarcity, uncertainty and internal resistance, and technical barriers that may arise during the digitalization process [27].

3.2.1. Financial Challenges

According to Marbler, 12% of surveyed companies identify limited financial resources as a key barrier to digital investment [2]. This highlights the significant role of financial constraints in hindering digital transformation efforts. Core challenges include securing funds for acquiring and implementing digital technologies, as well as for associated training and IT security enhancements [35,36,37]. The complexity of value chain digitalization makes it particularly difficult for SMEs to estimate the required investment upfront. High investment costs, often perceived as exceeding medium-term returns, further deter companies, especially when potential benefits remain uncertain [38]. Approximately one-third of SMEs surveyed by Commerzbank cite uncertain outcomes and failure risks as major concerns, with 50% viewing the investment burden as a key obstacle to digitalization [3]. For 46% of industrial firms, unclear economic benefits combined with high capital requirements constitute the primary challenge [38]. This uncertainty undermines investment decisions, regardless of company size or sector [37].

3.2.2. Resource Limitations

The availability of qualified personnel is critical to the successful implementation of digitalization strategies. According to Marbler, a shortage of skilled labor is a major barrier to digital investment, affecting approximately 10% of surveyed firms. SMEs, in particular, struggle to attract highly qualified professionals, reducing their capacity to manage both daily operations and innovation activities [2]. Larger firms, while requiring a broader range of digital expertise, generally have a more accurate understanding of the necessary skill profiles due to deeper engagement with digitalization [37]. Beyond staffing shortages, inadequate employee qualifications and limited training also hinder innovation [35,37], with insufficient IT skills cited as a key obstacle [36]. As digital processes become more agile and data-driven, the demand for ICT specialists, data analysts, and IT experts grows across the entire value chain [38]. However, a lack of alignment between training systems and digital skill requirements in Germany exacerbates long-term talent shortages [37], highlighting the urgent need for comprehensive workforce upskilling.

3.2.3. Uncertainty and Internal Resistance

Uncertainty regarding future digital standards poses a significant barrier to digital transformation, particularly due to insufficient knowledge about the benefits, applications, and value of digital technologies [36]. These information gaps hinder strategic planning and delay decision-making. The rapid pace and complexity of technological development further intensify this challenge, often leading firms to perceive digital transformation as risk-laden and unproductive, especially when initiatives fail to meet expectations and instead generate additional burdens [3,28]. Internal resistance—shaped by leadership disengagement, fears of cultural disruption, innovation pressure, and concerns over business model shifts—further complicates implementation [35,37]. Legal uncertainties also contribute, particularly for SMEs, which face difficulties navigating evolving regulations, such as data protection, cloud computing, and liability for autonomous systems [37,39]. Ambiguities in intellectual property enforcement and lengthy approval procedures at national and EU levels hinder innovation, affecting approximately 80% of German SMEs [4]. These challenges underscore the need for clear, harmonized legal frameworks and targeted support to enable effective digital transformation.

3.2.4. Technical Challenges

Four major technical challenges critical to digital transformation are the demands of IT security, the inadequacy of digital infrastructure, the lack of standardized interfaces, and issues of data quality.

IT Security: Ensuring IT security is essential in digitalization, particularly regarding data integrity and reliability [35]. As connectivity between systems, machines, and people increases, so do the risks of cyberattacks, data theft (e.g., customer, employee, or operational data), and manipulation [37]. Securing digital environments requires technical (e.g., firewalls, encryption), organizational (e.g., access management), and personnel-related (e.g., training) measures, with security considerations embedded from the design phase of smart systems. Many SMEs, however, lack the expertise and resources to manage these risks effectively [37].

Digital Infrastructure: Effective digitalization depends on robust infrastructure characterized by high bandwidth, low latency, and symmetric data transmission. Inadequate broadband—particularly in rural areas—remains a key barrier, especially for SMEs [4,37]. While large firms may compensate through private investment, smaller firms are disproportionately affected. According to the DIHK Innovation Report, 57% of companies cite poor broadband as a barrier, rising to 74% among firms with 10–20 employees [4].

Lack of Standards and Interface Challenges: The absence of uniform standards and consistent interfaces significantly hinders digital process integration. Inter- and intra-organizational workflows often suffer from analog-digital transitions that reduce efficiency [35]. With no universal model for digitalization, inconsistent technological maturity across firms exacerbates the problem [37]. Forty-two percent of digitally active firms report the absence of reliable standards [3], leading to uncertainty in technology selection and increased implementation costs. Despite these impacts, many firms underestimate the significance of interface issues, underscoring the need for empirical research to raise awareness [37].

Data Quality: High-quality data is fundamental to digitalization, as it underpins reliable analysis, decision-making, and process efficiency. Inaccurate or incomplete data can disrupt automated, real-time systems, causing inefficiencies and operational failures [27]. Key data quality dimensions include relevance and clarity in the design phase, and accuracy, timeliness, completeness, and consistency during use. Maintaining high data quality across all process phases remains a persistent and complex challenge [27].

4. Methods II—Analysis of Technology Offerings

In the era of digital transformation, the strategic identification and application of technologies are essential for maintaining competitiveness, particularly among SMEs. This chapter presents a condensed analysis and categorization of current technologies and trends, focusing on their relevance and potential impact on SME digitalization. Technologies—defined as concrete, often stable tools and systems—and trends—representing dynamic patterns of technological or societal change—are examined both separately and where they overlap. Based on six key academic sources on digitalization and Industry 4.0, technologies and trends were categorized into Networking, Data Storage and IT Security, Data Processing, Virtualization, Development and Simulation, and Products. Detailed catalogs (Supplementary Materials Tables S2–S5) structure and summarize this classification, while emphasizing technologies with high transformative potential, such as AI, IoT, Blockchain, and 5G–6G. Although categorizations involve some subjectivity, this framework offers SMEs a valuable analytical tool for selecting technologies aligned with their operational needs and strategic goals. The framework is methodological in nature; it provides a structured orientation rather than a software tool. This analysis is carried out as part of the methodological framework developed in this study and does not describe a software implementation.

4.1. Networks

The “Networks” category encompasses technologies and trends that enable seamless communication between devices, systems, and users, forming a foundational element of digital transformation in enterprises. Central to this domain is the Internet of Things (IoT), which connects physical objects through sensors and software, allowing real-time data collection to optimize processes such as predictive maintenance, inventory control, and supply chain management [40,41]. Complementing this is Cloud Computing, which grants SMEs scalable access to computing resources without requiring on-premise infrastructure [19,41]. Edge Computing further enhances this ecosystem by enabling decentralized data processing near the data source, reducing latency and ensuring system reliability, even during cloud connectivity issues [41]. These systems function synergistically, with the cloud offering scalability and centralized control, while edge nodes provide real-time processing capabilities. Trends such as the Internet of Behaviors (IoB) allow firms to leverage behavioral data for personalized services [19], while Cloud Manufacturing enhances collaboration across production and supply chains [42]. Additionally, Web3 promotes decentralized, blockchain-based infrastructures [43]. Core enabling technologies include 5G—characterized by high bandwidth and low latency, vital for real-time IoT and Edge Computing [44]—and versatile cloud platforms. Supplementary technologies such as Bluetooth5 and ZigBee offer short-range wireless communication and energy-efficient IoT networking, respectively [19], illustrating the diversity of networking solutions available for specific enterprise needs.

4.2. Data Storage and IT-Security

The “Data Storage and IT Security” category is critical in digital transformation, as data protection, security, and sovereignty are central to its implementation. Key trends include digital sovereignty—emphasizing individual and organizational control over data [45]—authentication for secure identity verification, and cybersecurity to defend against increasingly complex threats [46]. Authentication technologies such as biometrics, tokens, and two-factor verification enhance access control to sensitive data. In practice, various technologies address these needs. The InterPlanetary File System (IPFS), a decentralized peer-to-peer storage network, enables tamper-proof and traceable data storage [19,47]. Distributed Ledger Technologies (DLTs), particularly blockchain, support transparent, immutable data logging and identity verification, enhancing trust and security in transactions [19,48]. In IT security, artificial intelligence (AI) plays a growing role through AI Security, which uses machine learning to proactively detect system vulnerabilities [19]. Similarly, the Digital Immune System applies advanced software engineering to minimize operational and security risks [49]. Alongside these emerging tools, traditional solutions such as encryption, firewalls, and intrusion detection systems remain essential. An effective IT security strategy thus requires an integrated approach combining innovative and established technologies to ensure comprehensive data protection.

4.3. Data Processing

Data processing encompasses various technologies and methodologies aimed at extracting insights from data, with current trends including Data Analytics, Data Mining, and Process Mining. Data Analytics applies statistical methods and algorithms to uncover patterns and trends, while Data Mining focuses on identifying complex relationships in large datasets. Process Mining visualizes and analyzes event logs to improve operational processes [19]. A key development is data-centric AI, which relies on extensive datasets and advanced machine learning algorithms to derive insights and support automated decision-making [46]. This requires robust cloud infrastructure and is closely tied to Big Data in areas such as production and user behavior. AI’s economic potential lies in automating routine tasks, enabling scalability in service sectors like finance, consulting, and healthcare [50]. Further technologies include Natural Language Processing (NLP), which facilitates the interpretation of human language, and Computer Vision, enabling automated analysis of visual inputs such as images and video [19]. These tools collectively enhance data-driven decision-making and provide competitive advantages.

4.4. Virtualization

The “Virtualization” category encompasses technologies that enable the digital replication and simulation of real-world objects, processes, or environments, offering new avenues for interaction, communication, and optimization. A key trend is the digital shadow, a data-driven virtual representation of a physical object used for real-time monitoring and analysis. More advanced is the digital twin, which remains continuously synchronized with its physical counterpart to support predictive analytics and scenario-based optimization [19,46]. Real-time data visualization aids in comprehending these dynamics by making system behaviors intuitively observable [51]. The Metaverse represents an immersive virtual space connecting digital realities, facilitating virtual product testing, collaborative work environments, and new business models [19,49,52]. Conversational interfaces, through speech and text recognition, enable intuitive interaction with virtual environments, enhancing efficiency and user engagement [19]. Additionally, Augmented Reality (AR) and Virtual Reality (VR) support immersive applications in product development, training, and customer service. System virtualization—the emulation of hardware or software components—allows for parallel, independent use of computing resources, promoting cost efficiency, scalability, and flexible IT infrastructure management [53].

4.5. Development and Simulation

The subchapter “Development and Simulation” addresses current trends and technologies relevant to the digital modeling and simulation of products and processes. Virtual modeling and performance simulation are increasingly used to optimize systems and innovate solutions. Factory management simulations enable companies to digitally model production workflows and evaluate scenarios for efficiency and cost reduction [54]. Applied Observability, combining real-time monitoring, machine learning, and analytics, enhances system understanding and supports proactive decision-making [49]. Technologies such as 3D scanning, generative design, and advanced simulations play a growing role in translating physical objects into accurate digital models and optimizing designs algorithmically [19,54]. Additionally, Low-Code/No-Code platforms and Model-Based Systems Engineering (MBSE) simplify application and system development by using visual interfaces and abstract modeling. Low-code tools allow non-programmers to build software, while MBSE offers a structured approach for analyzing and optimizing complex systems [19]. Collectively, these technologies significantly accelerate development cycles, reduce costs, and support faster innovation delivery.

4.6. Products

In the context of digital transformation, innovative products are essential for enabling firms to adapt to evolving market demands. These products often integrate digital assistance systems and wireless value realization to enhance customer value and unlock new business opportunities. Digital assistants employ AI, voice control, and machine learning to deliver personalized support, improving user experience, productivity, and comfort [55]. Wireless value realization links physical products to digital services and platforms, facilitating new revenue models. App stores and digital marketplaces allow firms to distribute products globally, enhancing reach and accessibility, while super-apps consolidate multiple services within a single interface to create seamless user experiences [19,49]. Furthermore, trends such as human augmentation and multiexperience, powered by AR/VR, extend users’ sensory and cognitive capabilities, enabling immersive interaction with products and services [19]. Underlying these developments are enabling technologies such as cyber-physical systems, system-on-a-chip architectures, silicon photonics, and micro-/nanoelectronics, which deliver the computing power, connectivity, and integration required for next-generation intelligent products [19]. Cryptocurrencies further support secure and efficient digital transactions. Additionally, identification and localization sensors, advanced sensors, and 3D laser scanning technologies capture real-time data on product performance and environment, enabling customized user experiences and continuous product optimization [19,56].

5. Results—Technology Radar Concept Development

Section 3.1 and Section 4 compiled a selection of key digitalization needs and corresponding technologies relevant to SMEs. The present section aims to integrate these findings into a practical tool that helps companies match specific technological solutions to identified needs. For this purpose, a Technology radar was developed to facilitate such alignment. The subsections address the general structure and application of the radar, the function of the Needs-Technology Matrix, the radar’s detailed configuration, and its practical implementation using a representative example. Additionally, concrete recommendations for companies and institutions are provided. Overall, this section offers a comprehensive overview of the Technology radars’ conceptual design and its utility in supporting strategic technology-related decision-making.

5.1. Structure and Applications

Section 3 and Section 4 of this work systematically identified the digitalization-related needs of SMEs and cataloged relevant technologies and trends. A comprehensive needs catalog and corresponding visual map were created to illustrate the challenges organizations face during digital transformation. Concurrently, a technology and trend catalog was compiled, encompassing a wide array of innovations. This section aims to interlink the identified needs with the appropriate technologies and trends, thereby enabling SMEs to select suitable solutions aligned with their transformation objectives. To support this process, four integrated methodological instruments were developed as part of the conceptual framework: (1) the needs catalog (Tables S1–S3), which outline digitalization-relevant domains across business functions; (2) the technology and trend catalog with its visual representation (Table S4), offering a structured overview of emerging technologies; (3) the needs-technology matrix, which aligns specific technologies with corresponding needs; and (4) the technology radar, a framework for independently assessing the suitability of technologies in addressing selected organizational needs. Together, these tools provide companies with a strategic and practical foundation for technology evaluation and targeted implementation within the context of digital transformation.

5.1.1. Setup and Functions of the “Needs–Technology Matrix”

The Needs–Technology Matrix (Table S5) serves as the central link between organizational needs and relevant technologies by aligning each identified need with technologies or trends that exhibit significant potential to address it. This mapping is visually represented using an “X” to indicate relevance, based on causal relationships derived from literature and expert knowledge. While the matrix provides valuable orientation, it must be interpreted in context—technological applicability varies depending on organizational goals and circumstances. It supports two main applications: identifying technologies suited to specific needs and offering a frequency-based overview of widely applicable technologies. For instance, in strategic and operational management, technologies such as data analytics, AI, and virtualization are key to interpreting large datasets and informing decision-making (e.g., Big Data, data mining, and machine learning). In product development and simulation, generative design, advanced simulation, and model-based systems engineering enhance efficiency, reduce time-to-market, and support innovation. In procurement and production, technologies like IoT and Radio-Frequency Identification (RFID) enable real-time data collection and transparency in supply chains, improving automation and decision-making. In IT security, technologies such as Distributed Ledger Technology (DLT), blockchain, cloud platforms, and digital security systems offer scalable, secure, and resilient data management and access protection. Only 10 out of 64 technologies achieve more than 15 categories, indicating strong clustering around data processing (e.g., AI, Big Data, machine learning) and connectivity (e.g., cloud computing, IoT), which are key enablers of digital transformation. However, infrequent mentions do not imply low relevance—technologies may be sector-specific, emerging, or highly innovative, and thus underrepresented despite significant future potential. Ultimately, the matrix should be used as a flexible decision-support tool, enabling organizations to prioritize technologies aligned with their unique digital transformation strategies while remaining open to niche or emerging innovations.

5.1.2. Setup of the Technology Radar

The Technology Radar, illustrated in Figure 2, serves as a framework for the organization-specific assessment of technologies and trends in relation to defined needs. It enables companies to evaluate and prioritize technological implementation based on their individual progress, highlighting current adoption levels and identifying gaps and opportunities.

The radar is structured into four concentric rings representing maturity stages: Exploration, Development, Implementation, and Optimization, as shown in Figure 2. These phases cover the lifecycle of a technology from initial awareness to full integration and refinement. In the outermost Exploration phase, technologies are recognized but not yet integrated; organizations assess relevance through exploratory activities like innovation workshops. The Development phase follows, involving strategic planning, resource allocation, and pilot initiatives. In the Implementation phase, technologies are actively integrated into operations, contributing to business outcomes but with room for further enhancement. The innermost Optimization phase denotes technologies that are fully embedded and continuously improved through feedback and the development of best practices. Using the Needs-Technology Matrix, companies place technologies as points within the radar according to their maturity stage, assessed using criteria such as research activity, product availability, and standardization. This visual tool offers a comprehensive overview of implementation status, aiding strategic alignment and helping prioritize technologies with high transformative potential. Regular updates to the radar support early identification of emerging technologies and informed planning, enabling targeted actions to maximize digital transformation outcomes [19,49].

5.1.3. Application Example

The conceptual application of the Technology Radar framework begins with the identification of an enterprise’s specific digitalization needs, guided by the previously developed needs catalog and visual map. These tools enable companies to pinpoint relevant transformation areas. Once needs are identified, suitable technologies are matched using the Needs-Technology Matrix, incorporating both cataloged and newly considered technologies. These technologies are then evaluated via the Technology Radar, which visually depicts their maturity across four implementation phases: exploration, development, implementation, and optimization, as shown in Figure 3. This enables companies to identify gaps, formulate strategic actions, and support transformation. For instance, pilot or lighthouse projects and living labs help gather practical experience before broader rollout, while staff training ensures internal readiness. Strategic partnerships with technology providers can accelerate implementation and offer access to expert resources. The evaluation is organization-specific, reflecting existing implementation levels and future priorities. Regular updates ensure adaptability to emerging trends. A practical example illustrates this: if a company identifies the need to personalize marketing for improved customer retention and revenue, the Matrix reveals relevant technologies—Data Analytics (to extract insights), AI (for tailored recommendations), IoB (real-time behavioral tracking), and Conversational Interfaces (for personalized communication). In the Radar, Data Analytics may fall in the optimization phase, while AI is still under exploration, prompting investment in training and pilot applications. IoB is in development, with plans for focused testing, and Chatbots, though implemented, are optimized further with NLP and personalized scripts. Figure 4 exemplifies how the Technology Radar helps align technologies with organizational needs, facilitating strategic prioritization and strengthening competitiveness through structured digital transformation.

5.2. Recommendations for Companies and Institutions

In the context of digital transformation, companies face the challenge of effectively implementing suitable measures to digitalize their processes. A structured approach—such as outlined in Section 2.1.3—is recommended to ensure systematic execution and achievement of intended outcomes. Initially, firms must identify their specific needs to prioritize digitalization efforts in areas that promise substantial value. A gradual implementation strategy, focused on select technologies or processes, allows for targeted resource allocation and better progress control. Pilot projects are advisable to test technological feasibility and address risks before full-scale deployment. Prior to implementation, it is crucial to eliminate inefficiencies in existing workflows to avoid digitizing suboptimal processes. Digitalization should serve efficiency and value creation rather than replicating all analog processes. Organizational restructuring may also be necessary to break traditional hierarchies and enhance agility. Equally critical is fostering an innovation-friendly corporate culture and involving employees early in the transformation, supported by training and change management. Policymakers and regional authorities play a pivotal role in addressing barriers identified in Section 3.2 by providing financial incentives (e.g., grants, loans, tax relief) to mitigate investment-related uncertainties (Section 3.2.1). Ensuring broadband access, particularly in rural areas, and supporting the development of digital infrastructure are vital. Enhancing SMEs’ digital literacy through education programs, workshops, and cooperation with academic and technology institutions—such as in the Regional Transformation Network South-East Lower Saxony (ReTraSON) initiative—further strengthens capabilities. Facilitating networks among SMEs, technology providers, and research institutions enables knowledge sharing and innovation. Real-world testbeds (“living labs”) also support digitalization by allowing firms to test and refine new technologies under practical conditions. In ReTraSON, such labs empirically validate transformation strategies and help businesses develop tailored solutions to their digital challenges.

6. Summary

This study aims to examine the challenges of digital transformation within the economically robust and knowledge-intensive region of SON. Within the framework of the ReTraSON project—designed to establish a regional communication platform and foster intelligent mobility strategies—this work seeks to systematically identify and categorize the core needs and obstacles of SMEs regarding digitalization. It also investigates current technology offerings relevant to digital transformation. The outcome is a methodological tool integrating both needs and technologies to support SMEs in selecting suitable technologies for their specific digitalization goals.

Section 2 provides a theoretical foundation by defining digital transformation, outlining its features, and discussing its impacts on the automotive industry. It also introduces the concept of key enabling technologies and real-world laboratories, and explains the ReTraSON project framework alongside the methodology of needs analysis and the design of trend and technology radars.

Section 3 presents an in-depth analysis of SME needs in the digital context, covering domains such as business models, strategic and operational management, development, procurement, sales, products and services, digital processes, and IT security. It further identifies structural barriers and challenges in digital implementation. These insights are grounded in extensive literature analysis and serve as a valuable basis for decision-makers, consultants, and researchers.

Section 4 focuses on the analysis of technology offerings, categorizing them into key domains: connectivity, data storage and IT security, data processing, virtualization, development and simulation, and product innovation. Technologies such as IoT, cloud computing, data analytics, data mining, artificial intelligence (AI), AR, and VR are examined for their relevance and utility to SMEs. The analysis synthesizes scientific literature to clarify the application potential of these technologies in advancing digital transformation.

Section 5 introduces and details the concept of the technology radar. It explains its structure, the integration of the needs-technology matrix, and the application of the radar for self-assessment and strategic planning. Furthermore, it provides actionable recommendations for firms and institutions to address digitalization challenges.

Overall, this work offers a comprehensive synthesis of SME needs, digital technologies, and transformation trends within the automotive sector. The developed technology radar supports SMEs in evaluating their digital readiness and selecting appropriate innovations. However, further validation and empirical studies are required to refine the tool and fully integrate it into the ReTraSON project framework.

These findings are consistent with earlier analyses of SME digitalization needs, such as Marbler et al., who highlight process integration and customer relations, and Müller et al., who stress predictive maintenance and quality management [2,34]. However, our study extends these contributions by embedding such needs within a structured Technology Radar framework. While prior works, such as Fraunhofer IEM or Stich et al., provide trend radars or maturity indices, they do not explicitly connect SME needs to concrete technological domains in a regional context [19,46]. This combination underlines the added value and novelty of the present study.

7. Discussion

The critical reflection of the results enables an evaluation of their validity and relevance, as well as the identification of potential limitations or overlooked dimensions. While the conducted needs analysis offers valuable insights for SMEs in the context of digitalization, it is primarily based on an extensive literature review, limiting its representativeness. The theoretical needs identified may not fully align with the actual, company-specific requirements and should therefore be empirically validated. Selection bias in source materials may have led to overrepresentation of certain perspectives while neglecting others, suggesting that future studies should employ broader and more diverse data sources to achieve greater balance and robustness. Furthermore, the identified needs are of a general nature and can vary substantially across enterprises due to factors such as industry, firm size, available resources, and strategic orientation. These needs are also dynamic and context-dependent, necessitating ongoing review and adjustment to reflect evolving market conditions and organizational developments. Regarding the challenges to digital transformation discussed in Section 3.2, their practical relevance and actual impact require further investigation, as other unaddressed barriers may also exist. Similarly, the technology analysis in this work may exhibit limitations due to the restricted dataset and source selection, potentially leading to a skewed representation of technological developments. As a result, the presented overview reflects only a subset of current and relevant digital technologies and trends. The categorization of technologies into defined groups is a simplification, given the existence of multiple valid classification approaches. Although the current structure facilitates systematic examination, future research should explore alternative categorizations to gain a more nuanced understanding of the technological landscape and its implications for SMEs. Additionally, the implementation of technologies cannot be viewed in isolation. Their deployment demands a holistic perspective that integrates organizational, cultural, and strategic dimensions. The analysis of technologies and trends alone is insufficient to capture the full complexity of digital transformation processes. Thus, the findings should be interpreted as part of a broader digitalization strategy, recognizing the multifaceted nature of transformation efforts in SMEs.

The needs identified in our framework align with prior findings in the literature. For example, refs. [2,3] also emphasize financial constraints and uncertainty as major barriers, while [4] reports inadequate infrastructure as a widespread problem, particularly for smaller firms. Our study confirms these observations and extends them by providing a structured categorization of needs along Porter’s value chain, which allows a clearer mapping of needs to technological solutions. This systematic structuring differentiates our work from earlier surveys that primarily highlight barriers without offering a methodological framework for addressing them.

With respect to the methodological approach, our use of the Technology Radar builds on and complements earlier work such as [46]. While these radars provide valuable overviews of technological maturity, they are not explicitly linked to SME-specific needs. Similarly, Stich et al. [19] propose categorizations of key digital technologies, but without embedding them in a regional context or aligning them systematically with SME requirements. Our framework bridges this gap by directly connecting identified needs with technologies and presenting this alignment in a form that regional stakeholders can apply for decision-making.

In this sense, the scientific contribution of our work lies not only in the cataloging of needs and technologies, but also in their integration within a methodological radar framework that can be adapted to regional SME contexts. This goes beyond existing descriptive studies of SME digitalization by providing a replicable methodology for technology–needs alignment.

8. Conclusions

This study presents a comprehensive analysis of the digitalization needs of SMEs, with a specific focus on the regional context of the ReTraSON project. The research is grounded in an extensive review of scientific literature and aims to systematically identify, categorize, and correlate the key organizational needs and technological opportunities relevant to the digital transformation of SMEs. Central to the study is the development of a structured instrument that integrates a categorized needs catalog, a comprehensive technology and trend compendium, and a needs-technology matrix, culminating in the conceptualization of a technology radar. This radar enables firms to assess their current state of technological implementation and to identify potential deficits and strategic action areas across different digital maturity levels. Key technological domains examined include connectivity, data storage and cybersecurity, data processing, virtualization, development and simulation, and product innovation. The study also critically evaluates major challenges such as financial constraints, skill shortages, organizational resistance, and technical limitations, including infrastructure deficits and a lack of interoperability standards. The findings offer actionable insights and provide a foundation for SMEs to align digital technologies with their specific transformation requirements. Moreover, the work contributes to the broader objectives of the ReTraSON project by informing strategic decision-making, facilitating inter-organizational knowledge transfer, and supporting the empirical validation of digital transformation strategies through mechanisms such as real-world laboratories. While the study provides a robust framework for navigating digital transformation, it also acknowledges methodological limitations and emphasizes the need for empirical validation and iterative refinement. Overall, the study delivers substantial value as a strategic orientation tool within the ReTraSON innovation ecosystem, fostering the targeted advancement of SME digitalization efforts. The Technology Radar presented here is a methodological framework for analysis and strategic orientation. While it could be implemented in the future as a software application, this article focuses on its conceptual development and methodological validation. Thus, the novelty of this study is twofold: (1) it offers a structured methodology for aligning SME digitalization needs with current technology trends, and (2) it applies this methodology in a regional context (Southeast Lower Saxony), thereby extending the scope of prior work that has remained either general or purely descriptive.

9. Outlook

The rapid pace of technological advancement necessitates the continuous updating and refinement of the technology radar. Consequently, this work should ideally be validated and complemented through further research or comparative analysis with existing studies to enable a more comprehensive and robust assessment of the digitalization needs of SMEs. This could include integrating primary data collection, such as surveys, within the SON region to substantiate and enhance the findings obtained thus far. Furthermore, developing a supportive tool to help companies identify and classify their specific needs would be highly beneficial during the transformation phase. Building on this, the technology radar developed in this study could serve as a valuable instrument for evaluating the implementation of relevant technologies and for planning strategic actions regarding digital technologies and trends.

To further increase its practical utility, the instrument should be augmented with defined analytical patterns and a structured evaluation framework for assigning technologies to different maturity stages, thereby enabling more evidence-based decision-making. In addition, as outlined in Section 2.4, the technology catalog should be extended with detailed technology and trend profiles to consolidate essential information. This would allow the technology radar, in combination with these profiles, to provide a structured overview of the examined research domains and a synthesis of the corresponding strategic requirements.

Finally, additional empirical research focused on the implementation and integration of the identified technologies and trends within enterprises is strongly recommended. Such efforts would foster deeper engagement with the subject matter and assist organizations in successfully executing their digital transformation strategies. This process could be accompanied by a dedicated research initiative aimed at analyzing the SON region’s strengths, weaknesses, opportunities, and challenges, thereby enabling a more precise formulation of policy recommendations and regional development strategies.