2.1. New ways of working in engineering

New ways of working have become increasingly relevant in modern engineering contexts, driven by the transformational shifts brought about by the industrial revolution and digitization. These changes have reshaped operational needs and expectations across both individual and organizational levels, challenging traditional workplace management practices and necessitating the exploration of more effective work methodologies. Originating in the 1970s, the term “New Work” was coined to address the changing nature of work in a rapidly evolving society, aiming to create a more human-centered approach to employment. As described by Hofmann et al. Reference Hofmann, Piele and Piele(2019), and further elaborated by Schnell and Schnell Reference Schnell and Schnell(2021), the idea emerged from the visionary insights of Austro-American social philosopher Frithjof Bergmann. Confronted with the automation-driven job losses in industries like General Motors, Bergmann proposed a revolutionary model where employees would retain their jobs but work only six months per year (Reference Schnell and SchnellSchnell and Schnell 2021). His vision expanded to suggest that individuals split their work into three parts: traditional employment, work they are genuinely passionate about, and high-tech self-production. This framework not only promoted self-determination, freedom, and community participation but also aimed to make work not just a means to an end but a fulfilling aspect of life that caters to individual needs and desires. Devivere Reference Devivere and Beate(2018) highlights that new ways of working address the several challenges, including the integration and management of innovative technologies, transformation in goods production, and establishing new work modalities that fulfill individual preferences and facilitate coordination within communal frameworks. The evolution of these practices also coincides with the rise of digital and technological advancements, as noted by Jurian. During the same period that Bergmann developed his ideas, other visionaries like Nilles, Drucker, and Toffler were conceptualizing mobile offices and flexible workspaces, largely in response to shifting workforce demands such as the desire for part-time work and flexible working hours (Reference van Meelvan Meel 2011). These adaptations aim to harness digital tools to achieve greater work flexibility, altering leadership principles to accommodate a more fluid, value-based approach to work. Today, new ways of working are often embraced by dynamic, less structured organizations and pose questions about their applicability in traditional, larger firms. The dual approach of New Work-enhancing productivity through technological empowerment and focusing on personal and professional development, champions a culture that values both efficiency and individual well-being. As these practices continue to evolve, they unite various initiatives aiming to prepare work environments for future challenges, making the continuous exploration and adaptation of these practices a pertinent area of research (Reference Teichert, Pospisil, Brugger and LödigeTeichert et al. 2023). Recent research further explores these challenges in the context of hybrid work and digital collaboration tools. Studies on hybrid work models indicate that flexible work arrangements enhance job execution, teamwork, and employee satisfaction when supported by effective communication strategies (Reference Santillan, Santillan, Doringo, Pigao and MesinaSantillan et al. 2023). Additionally, digital collaboration tools and AI-driven assistance are reshaping teamwork dynamics by improving efficiency, automating repetitive tasks, and influencing decision-making processes (Reference Ulfsnes, Moe, Stray and SkarpenUlfsnes et al. 2024). However, these technologies also introduce new complexities, such as the need for structured knowledge-sharing frameworks to prevent fragmented communication and information silos (Reference Jackson, Hoek and PrikladnickiJackson et al. 2022). Moreover, AI-assisted teamwork in engineering environments presents both opportunities and challenges. AI tools have been shown to enhance team productivity and problem-solving capabilities, but their impact on human collaboration and trust varies depending on implementation strategies (Reference Xu, Hong, Zurita, Gyory, Stump, Nolte, Cagan and McCombXu et al. 2023). In summary, new ways of working not only reflect a shift towards more adaptive, fulfilling, and technologically integrated work environments but also embody a broader movement towards redefining the role of work (Reference KingmaKingma 2019) in society, with ongoing implications for both policy and practice in the face of future industrial transformations.

New ways of working are increasingly being recognized for their potential to enhance productivity across various industries. For instance, BorgWarner Ludwigsburg GmbH implemented digital personnel deployment planning, which significantly improved the efficiency of shift scheduling and provided greater flexibility for employees, leading to higher overall productivity (Reference Hofmann, Piele and PieleHofmann et al. 2019). Similarly, an experiment conducted by the BMW group introduced job rotation, resulting in employees gaining a deeper understanding of company processes and continuous growth in their skill sets (Reference JulichJulich 2000). This strategic approach not only enhanced organizational flexibility but also improved the quality of work and productivity at BMW. Further examples of companies adopting new work practices include the introduction of co-working areas at Deutsche Bahn, life situation-based flexible working at AOK Baden-Württemberg, trust-based working hours at Kärcher SE and Co. KG, consistent process organization at Roto, and cross-functional project initiatives at DKB Berlin (Reference HelmoldHelmold 2021; Reference Hofmann, Piele and PieleHofmann et al. 2019). These initiatives highlight the broad applicability and effectiveness of new work practices in enhancing operational efficiencies and fostering a more adaptable and skilled workforce.

As organizations continue to explore new work methodologies, integrating insights from both historical theories and modern empirical research will be crucial in designing work environments that balance flexibility, productivity, and innovation.

2.2. Engineering Simulator

The Engineering Simulator bridges the gap between theoretical research and their practical realization. Building on the concept of live labs (Reference Albers, Walter, Wilmsen and BursacAlbers et al. 2018) it enables iterative testing of design processes to refine and implement innovative solutions. By offering a controlled yet realistic environment, studies have shown that such an approach allows for the observation of ideation, collaboration, creativity, and team-based problem-solving (Reference Walter, Albers, Haupt and BursacWalter et al. 2016). The Engineering Simulator serves as an efficient platform for design support validation (Reference Hofelich, Mantel, Bursac, Omidvarkarjan, Matthiesen, Meboldt and SchneiderManfred Hofelich et al. 2021), by simulating real world product development scenarios and constraints in a controlled research environment, e.g. shown in the study by Duehr et al. Reference Duehr, Mai, Rapp, Albers, Bursac and Anwer(2023). Drawing from the Engineering Simulator developed for sheet metal design validation developed by Maass et al. Reference Maass, Ritzer, Ammersdorfer, Krause, Inkermann and Bursac(2023), our Engineering Simulator compresses a typical product development cycle into an eight-hour session. The primary purpose of the Engineering Simulator in this research is to assess the impact of new ways of working on the product development process, enabling the controlled evaluation of these methodologies by replicating dynamic conditions typical of a product development cycle but within a compressed timeframe. The Engineering Simulator is divided into distinct phases, each reflecting critical stages of a product development lifecycle, designed to elicit specific behaviors and outcomes relevant to both traditional and new working practices (cf. Figure 1).

Figure 1. Engineering Simulator timeline based on Maass et al. Reference Maass, Ritzer, Ammersdorfer, Krause, Inkermann and Bursac(2023)

• Problem definition: This introductory phase sets the stage for the product development activities by providing a comprehensive briefing on the project scope and objectives, focusing on the task of iterating on an existing product, which in this instance is a barbeque tray in a portable grill machine. The briefing underscores the PGE - Product Generation Engineering concept (Reference Albers, Bursac and RappAlbers et al. 2017), emphasizing iterative design and enhancement based on prior product generations.

• Conceptualization: In this phase, participants are tasked with generating and refining innovative design solutions that prioritize usability, and manufacturability of the grill trays. The methodology here facilitates a divergence from standard ideation processes by allowing participants in experimental groups to autonomously structure their ideation activities and therefore encouraging self-directed project management and creative independence, in contrast to structured and inflexible nature of traditional product development practices.

• Prototyping: Following ideation, participants are challenged with developing low-fidelity prototypes using materials and tools that simulate actual conditions of sheet metal fabrication and considering design constraints which play an important role in manufacturing using sheet metal. These prototypes not only test design manufacturability but also serve as reference models for creating 3D models of the newly iterated trays.

• Digital modeling: Participants use 3D modeling software to translate their physical prototypes into digital formats. The emphasis here is on refining prototypes into workable digital models that can be tested in subsequent simulation activities.

• Simulation testing and final evaluation: The final phase involves simulating the solutions using a virtual bending machine simulator, allowing participants to observe how well their designs translate into physical products and thus, providing valuable insights into the effectiveness and practicality of the proposed solutions.

3.1. Research objective and research question

The overarching goal of this research is to conduct a systematic validation of new ways of working within the context of product development. This study aims to distinguish these modern practices from traditional ones by demonstrating their core components and establishing a clear understanding of their credibility and adaptability in enhancing product development processes. By investigating both the theoretical underpinnings and practical realization of these practices, this research seeks to illuminate the distinctions between contemporary and conventional work approaches, focusing on their impact on employee motivation, technical affinity, productivity, and other key performance indicators within an Engineering Simulator environment. This leads to the following research question:

How do specific new ways of working affect individual and organizational performance in product development, compared to structured, predefined workflows in a simulated product development environment?

In this study, new ways of working refer to the implementation of five key dimensions: flexibility and autonomy, activity-based workspaces, resource allocation with employee involvement, digital dexterity, and job purposing. These are contrasted with ‘traditional practices,’ which involve structured, predefined workflows with limited flexibility in tool selection, decision-making, and collaboration.

3.2. Research methodology

This research is structured in three main stages (cf. Figure 2).

Figure 2. Structure of the research based on three stages

The first stage entailed defining the research context by gaining a broad understanding of the study's focus, accomplished through a comprehensive literature review that sharpens the study's objectives. In the second stage, we collected and analyzed existing data on new ways of working, setting a theoretical foundation that guides the practical work on adapting the Engineering Simulator in a way that a comparison between traditional practices and new ways of working was possible. These adaptations were realized by implementing different new ways of working strategies within a controlled experimental setting, the Engineering Simulator, to observe and record their effects in a real-time, dynamic environment. In the third and final stage, we carried out the experiment an analyzed the data obtained to evaluate the effectiveness of new work practices, synthesizing the findings into actionable insights and recommendations for future application in industry practices.

6.1. Applicability and limitations of the Engineering Simulator

The Engineering Simulator (ES) provided a controlled environment to study workflow flexibility, collaboration, and decision-making in product development. However, its applicability has limitations:

• Domain-specific scope: Designed for mechanical engineering, findings may not directly translate to software, electrical, or civil engineering, where work methodologies and constraints differ.

• Participant demographics: The study was conducted with M.Sc. mechanical engineering and management students, making the results most applicable to early-career engineers. While these groups have comparable technical backgrounds, the extent to which the results are transferable to experienced industry professionals remains unclear.

• Small sample size and team composition: The study was conducted with teams consisting of only three participants per group. In contrast, real-world engineering teams often involve larger and more diverse groups, including specialists from multiple disciplines. So certain complexities of real-world team dynamics were not fully captured in this study.

• Time constraints: The Engineering Simulator compresses a product development cycle into an 8-hour session, meaning it does not capture long-term adaptation or sustainability of new ways of working over extended project timelines.

• Organizational and cultural context: Unlike corporate settings, the ES does not reflect hierarchical decision-making, regulatory influences, or external stakeholder interactions, all of which shape the success of new work practices.

While the ES provides valuable insights into team behavior and workflow adaptability, future research should validate findings in longitudinal industry studies to assess effectiveness across diverse engineering and business environments.

6.2. Future research directions

This research has systematically explored the impact of New Work practices within an Engineering To strengthen the applicability of these findings and address the study's limitations, future research should explore the following areas:

• Longitudinal studies: Future studies should extend beyond short-term experimental settings to assess how new ways of working influence teams over months or years, particularly in relation to long-term efficiency, adaptation, and organizational culture shifts.

• Industry validation across sectors: The findings should be validated in real-world engineering environments, with diverse participant groups, including experienced professionals and interdisciplinary teams from diverse industries such as automotive, aerospace, software development, and manufacturing etc..

• Cross-disciplinary comparisons: Research should explore how new ways of working compare across different engineering domains, particularly in fields that rely on rigid regulatory constraints (e.g., civil engineering, aviation etc.) versus those that encourage agile methodologies (e.g., software development).

• Integration of organizational factors: Future research should consider how corporate structures, leadership styles, and workplace hierarchies influence the success or failure of new ways of working when implemented at large-scale industrial levels.

• Impact of multiple factors: The simultaneous implementation of multiple new work practices in the test groups reflects a realistic approach but complicates the isolation of individual effects. Future research will isolate individual effects using a Design of Experiments (DoE) approach.

• Digital tools and AI integration: Future research will explore how real-time collaboration platforms, AI-driven project management tools, and augmented reality workspaces impact team dynamics, communication efficiency, and resource management.

• Robust data collection method: The current study relied on surveys and direct observations, which may introduce observer bias. Future studies should explore automated tracking systems, AI-driven analytics, and multi-source data validation to improve the reliability and depth of empirical findings.

By addressing these research gaps, future studies can enhance the generalizability and real-world applicability of new ways of working, ensuring that organizations are equipped to navigate the evolving landscape of engineering work environments.

6.3. Practical implications for organizations

As Organizations implementing new work methodologies must address cultural resistance and adapt management structures to foster flexibility while maintaining team cohesion. Shifting to these models may require rethinking project management strategies, redefining collaboration, and integrating digital tools to enhance efficiency. While new work practices offer significant benefits, their success depends on strategic implementation tailored to industry-specific needs. Longitudinal studies, industry collaborations, and adaptive leadership approaches will be crucial in ensuring that organizations not only transition effectively but thrive in an increasingly digital and autonomous work environment.