{"id":110104,"date":"2025-08-15T12:00:00","date_gmt":"2025-08-15T10:00:00","guid":{"rendered":"https:\/\/industry-science.com\/?post_type=article&p=110104"},"modified":"2025-08-14T12:47:41","modified_gmt":"2025-08-14T10:47:41","slug":"design-thinking","status":"publish","type":"article","link":"https:\/\/industry-science.com\/en\/articles\/design-thinking\/","title":{"rendered":"Design Thinking"},"content":{"rendered":"\n

This article analyzes a central development path of today’s Design Thinking (DT), focusing on the understanding of DT at Stanford University and the resulting d-school at the Hasso Plattner Institute. The development is the outcome of interdisciplinary collaboration between research and industry. A detailed account of all actors and theories is beyond the scope. Instead, this article aims to maintain a practical perspective on the predominant application of DT in Germany.<\/p>\n\n\n\n

Empirical studies reveal that companies using DT often lack a uniform and in-depth understanding of the method. However, when applied correctly, design thinking can serve not only as an effective creativity technique but also as a catalyst for organizational change. [1, 2]\n\n\n\n

The aim of this article is to develop a comprehensive and practical understanding of DT by systematically processing key findings from literature and presenting them in an application-oriented manner.<\/p>\n\n\n\n

Creativity in the learning process<\/h2>\n\n\n\n

“Creative thinking” as a teaching concept in mechanical engineering originated in the Mechanical Engineering Department at Stanford University. It was introduced by John Edward Arnold, professor of mechanical engineering, in the 1950s within his product design courses and later developed further as the Joint Product Design Program (JPD).<\/p>\n\n\n\n

His interdisciplinary work with designers, psychologists, and industrial researchers led to unusual combinations in engineering education. He aimed to break through the conventional mindset of engineers to promote creativity. Arnold’s teaching concept is often cited as the starting point for the development of design thinking. A way of thinking that, in today\u2019s understanding, is often described as an agile method for user-centered problem solving. [3, 4, 5]\n\n\n\n

Quantifying creativity: Guilford’s four dimensions<\/h2>\n\n\n\n

Before studying mechanical engineering, Arnold earned a degree in psychology. There is a strong link between design philosophy and the theories of humanistic psychology, particularly in creativity research.<\/p>\n\n\n\n

The influence of psychologist Joy Paul Guilford’s creativity research is explained in more detail below. Guilford’s research formed the foundation of the methodology, culture, and philosophy of the concept developed at that time and remains recognizable in today’s understanding of design thinking [3, 5]. For this reason, Guilford’s four dimensions of creativity are discussed below, which, according to Arnold, appear in nearly every piece of literature on creative thinking and innovation, albeit under different names. They serve as metrics for creativity.<\/p>\n\n\n\n

1) “Problem sensitivity”<\/strong> describes the ability to recognize problems early on and anticipate their existence. Even when problems are nor obvious, individuals with high problem sensitivity identify opportunities for improvement and strive to address them.<\/p>\n\n\n\n

2) “Fluency”<\/strong> refers to the ability to generate a wide variety of ideas or solutions. Creative individuals produce more ideas per unit of time. A study conducted by Arnold recorded the positive effect of an open problem description on the number of ideas generated.<\/p>\n\n\n\n

3) “Flexibility”<\/strong> describes the ability to change one\u2019s thought process. It is measured by the number of different options considered, that is how many different categories or approaches a person thinks of. “Flexibility” also describes the ability to adopt different perspectives, such as that of an observer or a user. Arnold concluded that trying to take multiple perspectives simultaneously can cause confusion and therefore recommends shifting perspectives sequentially. This insight later influenced the iterative process of modern design thinking.<\/p>\n\n\n\n

4) “Originality” <\/strong>refers to the uniqueness of ideas. Individuals with this ability usually connect seemingly incompatible ideas or elements, thereby creating new and useful synergies. [3, 4]\n\n\n\n

Organized creativity through thinking modes and attitudes<\/h2>\n\n\n\n

Based on Arnold’s work, these four dimensions reflect the core abilities of a designer. In his research, he emphasizes that these abilities can be learned and purposefully applied, describing the process as “organized creativity.” The three thinking modes of “analysis,” “synthesis,” and “evaluation” form the foundation.<\/p>\n\n\n\n

“Analysis” refers to breaking down a problem into its individual parts, identifying connections, and scientifically examining the situation. “Synthesis” describes the combination of elements into new ideas through interdisciplinary approaches. “Evaluation” refers to the critical examination of ideas based on their feasibility using objective criteria. Arnold further proposed a “creative attitude”, which involves continous questioning, observing, associating, and predicting. [4, 5]\n\n\n\n

Overcoming creativity blocks<\/h2>\n\n\n\n

Psychologist Abraham Maslow, known for his hierarchy of needs, identified what Arnold later termed “creativity blocks” (Figure 1), which stem from individuals or their environment. Arnold developed a sequence of steps to overcome these blocks.<\/p>\n\n\n\n

\n
\n
\"Figure
Figure 1: Creative blocks based on [4, 6].<\/em><\/figcaption><\/figure>\n<\/div>\n\n\n\n
\n

First, the problem is defined as broadly as possible to enable the collection of relevant data. This data is then analyzed to identify desires and limitations. In the next step, ideas are gathered and listed. Arnold recommends techniques such as brainstorming or the use of checklists to support this phase.<\/p>\n<\/div>\n<\/div>\n\n\n\n

After generating as many ideas as possible, each idea is evaluated individually. The most promising concepts are then examined in greater detail and the results are documented. This sequence resembles today’s design thinking process. [6]\n\n\n\n

From teaching concept to design philosophy<\/h2>\n\n\n\n

In 1963, Arnold founded the design department within the Mechanical Engineering Department at Stanford University. There, he conducted research on the creative problem-solving process with designer Robert McKim, among others. Beyond the “thinking modes” and “creative attitude” described earlier, their research emphasizes the importance of human values in the creative process. Understanding the needs and emotions of the users of an idea is essential for solving problems. A combination of these techniques has the potential to spark innovations that solve global challenges while also considering human needs [5].<\/p>\n\n\n\n

Following Arnold’s research, his colleagues further explored techniques to overcome “creative blocks.” McKim introduced the term “visual thinking,” emphasizing the importance of careful observation and iterative prototyping to discover users\u2019 real needs. To support this, he provided students with the “Design Loft” at Stanford University, an environment equipped with tools and materials for creative problem solving. For the first time, the design philosophy was applied beyond product design and developed into a general approach for addressing complex problems.<\/p>\n\n\n\n

The business world also recognized the potential of these theories. In the early 1980s, Apple incorporated the Stanford philosophy into its design values, influencing the product development<\/a> of the Apple II, III, Lisa, Macintosh, and the first commercial computer mouse. Steve Jobs worked closely with Stanford University staff during this process.<\/p>\n\n\n\n

IDEO (formerly Hovey-Kelley Design), a design firm co-founded by Stanford professors Bill Moggridge and David Kelley, also played a decisive role in spreading the design philosophy. From its inception, the Stanford professors integrated the design philosophy into the company\u2019s culture. Throughout the 1990s, IDEO consistently won BusinessWeek awards for best product design in its consulting projects [5].<\/p>\n\n\n\n

In 1999, Larry Leifer first used the term “design thinking” in a course at Stanford University. At the same time, IDEO also adopted the term. Design thinking was intended to summarize the central philosophy of the Joint Product Design Program, which had been developed over more than half a century. Professors Bernard Roth, Larry Leifer, and David Kelley (co-founder of IDEO) teamed up with Hasso Plattner (co-founder of SAP) to establish the d.school.<\/p>\n\n\n\n

The d.school focuses on practical research and teaching in the field of design thinking. The first location opened at Stanford University in 2005, followed by a second location in Potsdam in 2007. The d.school at the Hasso Plattner Institute (HPI) in Potsdam is the first university educational institution for design thinking in Germany. [1, 3, 5]\n\n\n\n

Thinking like a designer<\/h2>\n\n\n\n

Starting in product design, DT has evolved into a generally applicable approach to solving complex problems. The development of design thinking is accompanied by two main theoretical discourses: the design discourse and the management discourse. There are fundamental differences in how DT is understood within these two scientific communities. While the trend emerging from design research in the 1950s understands DT as a way of thinking and working specific to designers, the management discourse that emerged in the 2000s sees DT as a method for innovation and value creation. [1, 7]\n\n\n\n

<\/p>\n\n\n\n

\n
\n
\"Figure
Figure 2: Components of the design thinking approach based on [10].<\/figcaption><\/figure>\n<\/div>\n\n\n\n
\n

In a hybrid form that emphasizes the management discourse perspective, the HPI teaches design thinking, significantly influencing the understanding of DT in German-speaking countries.<\/p>\n\n\n\n

Design thinking is described as a discipline that applies designers\u2019 empathy and methods to align people’s needs with technological feasibility and viable business strategies. <\/p>\n\n\n\n

The “sweet spot” for innovation lies at the intersection of the DT components shown in Figure 2. [1, 8, 9]\n<\/div>\n<\/div>\n\n\n\n

DT is a discipline in which creativity techniques and strategies are applied in a targeted manner, based on a few basic principles:<\/p>\n\n\n\n

    \n
  1. Human-centered design<\/li>\n\n\n\n
  2. A creative environment<\/li>\n\n\n\n
  3. A natural, iterative process<\/li>\n\n\n\n
  4. Early implementation of ideas in tangible prototypes to facilitate understanding and communication<\/li>\n\n\n\n
  5. Working in multidisciplinary teams. [1, 10, 11]<\/li>\n<\/ol>\n\n\n\n

    The philosophy of human-centered design focuses on people and their needs. It is rooted in humanistic psychology and emphasizes the importance of perception, imagination, and expressiveness. In design thinking, empathy for the fears and needs of users is considered a central component. [4, 10]\n\n\n\n

    A creative environment, supported by “variable space,” influences the successful application of design thinking. Flexible, adaptable room design fosters interactive work processes, enables spontaneous idea development, and supports both collaborative and individual work. In contrast to traditional meeting rooms, which favor hierarchical communication, DT teams require dynamic workspaces that can be tailoredto the situation. [12, 13]\n\n\n\n

    The iterative process is a core principle of design thinking that enables continuous optimization of solutions through repeated cycles. Although no rigid process structure is prescribed, the DT process is characterized by a systematic approach. Each phase is followed by critical reflection to gain insights and adjust subsequent steps accordingly. Iteration occurs not only at the process level but also within individual methods and work steps. [8, 13]\n\n\n\n

    Unlike monodisciplinary teams, which often operate within existing thought patterns, multidisciplinary teams enable a holistic view of complex challenges. A proven method for team composition is based on the concept of the “T-shaped profile.” The vertical axis of this profile represents in-depth expertise in a specific area, while the horizontal axis represents soft skills such as openness, curiosity, and communication. This combination of expertise and cross-functional thinking promotes a comprehensive approach to innovation processes. [13, 14, 15]\n\n\n\n

    Design thinking as a process<\/h2>\n\n\n\n

    The DT process is an iterative, user-centered approach to developing creative solutions to complex problems. It systematizes the DT philosophy but does not replace the underlying mindset. The HPI model is based on a flexible, multi-phase structure that is iteratively navigated and alternates between divergent and convergent ways of thinking. In practice, these phases are often used in an overlapping or adaptive manner to better reflect real-world creativity processes. [1, 10, 16]\n\n\n\n

    \"Figure
    Figure 3: The design thinking process based on [2, 14].<\/figcaption><\/figure>\n\n\n\n

    The understanding phase forms the basis of the design thinking process. The team develops a common understanding of the problem, focusing on the user perspective. In the observation phase, empirical data is collected using qualitative methods such as interviews, market analyses, or direct observations. The aim is to identify user needs, existing solutions, and relevant contextual factors.<\/p>\n\n\n\n

    The “define perspective” phase serves to prioritize and consolidate the information gathered. The problem is specified, often using personas that represent key user characteristics. This step provides a clear starting point for targeted idea generation.<\/p>\n\n\n\n

    In the idea generation phase, creativity techniques such as brainstorming or mind mapping are used to develop numerous possible solutions, which are then discussed and evaluated. The most promising ideas are translated into physical or digital models in the prototyping phase. The focus is on rapid visualization and iterative adaptation rather than on creating perfect solutions.<\/p>\n\n\n\n

    In the testing phase, potential users evaluate the prototypes. Their feedback reveals strengths and weaknesses and enables targeted optimizations. Thanks to the iterative nature of the process, earlier phases can be revisited if necessary. Only after successful validation can one assess technical, economic, and social feasibility. Many methods used in DT, such as interviews, personas, and brainstorming, are considered well-established but their effectiveness depends heavily on context, user attitudes, and methodological competence. [8, 13, 14]\n\n\n\n

    A key success factor is striking the right balance between structure and autonomy within the DT process. Creativity flourishes when teams are given sufficient freedom to act while maintaining process efficiency. DT coaches play a central role: they structure the process methodically, moderate discussions, and guide the iterative process. They promote interdisciplinary collaboration and challenge established ways of thinking to enable innovative solutions. Coaches must be comfortable with uncertainty, divergent team dynamics, and different perspectives, which require extensive social skills in addition to methodological expertise. [3, 11, 14]\n\n\n\n

    Maximizing potential<\/h2>\n\n\n\n

    Implementing design thinking in organizations presents various challenges, particularly in terms of scalability and acceptance. While DT is considered highly effective in boosting creativity within small teams, its implementation in large companies often proves difficult. Rigid structures, silo thinking, and hierarchical decision-making processes can significantly impede iterative, creativity-driven workflows.
    According to Gehm (2022), many change initiatives fail due to insufficient employee involvement, overly abstract goals, limited resources, and an excessively rigid approach.<\/p>\n\n\n\n

    It is recommended to form a diverse change team with strong recognition inside the organization. The team should act autonomously, make independent decisions, and take full responsibility. At the same time, its user-centered approach serves as a role model. A clear vision, integrated into a transparent communication strategy, provides orientation and reduces resistance by authentically involving employees.<\/p>\n\n\n\n

    Using pilot projects for the initial implementation of DT makes it possible to iteratively develop, test, implement, and, if necessary, scale new organizational forms. Risks remain limited due to the temporary nature of such projects. Achieving early, visible successes ensures motivation among participants and should be communicated purposefully.<\/p>\n\n\n\n

    Furthermore, organizations should create room for experimentation while managing the risk of failure. Ideally, the initial DT implementation should begin in smaller sub-units or within organizational structures that balance efficiency with innovation. In [2], it is made clear that, in practice, DT is rarely implemented according to the textbook. Instead, hybrid, context-adapted forms of implementation dominate. [1, 2, 15]\n\n\n\n

    Despite the popularity of design thinking, its success depends heavily on the organizational context, the mindset of those involved, and the quality of methodological implementation. Empirical studies suggest that outcomes are less dependent on individual tools and more on a profound understanding of the underlying philosophy. A critical examination of both structural prerequisites and actual impact is therefore essential. [1, 2, 8, 15]\n

    Bibliography <\/h2>[1]\tAugsten, A.: Design Thinking und organisationale Ver\u00e4nderungen. Wuppertal 2021.\r
    [2]\tGerken, S.; Uebernickel, F. et al.: Design Thinking: A Global Study on Implementation Practices in Organizations \u2013 Past, Present, Future. Potsdam 2022.\r
    [3] Thienen, J.; Clancey, W. et al.: Theoretical Foundations of Design Thinking. In: Plattner, H.; Meinel C. et al. (eds.): Design Thinking Research. Cham 2018.\r
    [4] Arnold, J.: Creative engineering. In: Clancey, W. (ed.), Creative engineering. Promoting innovation by thinking differently. Stanford 2016. (Original manuscript 1959)\r
    [5] Auernhammer, J.; Roth, B.: The Origin and Evolution of Stanford University\u2019s Design Thinking. From Product Design to Design Thinking in Innovation Management. In: Journal of Product Innovation Management 38 (2021) 6, pp. 623\u2013644. \r
    [6] Maslow, A.: Emotional Blocks to Creativity. In: Clancey, W. (ed.), Creative engineering. Promoting innovation by thinking differently. Stanford 2016. (Original manuscript 1959).\r
    [7]\tHassi, L.; Laakso, M.: Conceptions of Design Thinking in the design and management discourses. In: Proceedings of IASDR2011, the 4th world conference on design research. Delft 2011, pp. 1\u201310.\r
    [8]\tPlattner, H.; Meinel, C. et al.: Design thinking. Innovation lernen. Ideenwelten \u00f6ffnen. Munich 2009.\r
    [9] Brown, T.: Design Thinking. In: Harvard Business Review 86 (2008) 6, pp. 84\u201392.\r
    [10]\tHPI d-school: Wichtige Begriffe im Design Thinking. URL: https:\/\/hpi.de\/d-school\/ressourcen\/glossar\/, accessed 23.02.2025.\r
    [11]\tMeinel, C.; Weinberg, U. et al.: Design Thinking live.Wie man Ideen entwickelt und Probleme l\u00f6st. . Hamburg 2015. \r
    [12]\tSalzmann, E.: Design Thinking und Innovationskultur in der Industrie. Darmstadt, 2020.\r
    [13]\tSchallmo, D.; Lang, K.: Design Thinking erfolgreich anwenden. Wiesbaden 2020.\r
    [14]\tHPI d-school: Design Thinking. URL: https:\/\/hpi.de\/d-school\/themen\/design-thinking\/, accessed 28.03.2025.\r
    [15]\tGehm, J.: Establishing Design Thinking. Wiesbaden 2022.<\/div>
    Tags: change management<\/a><\/span> Design Thinking<\/a><\/span> <\/div>
    <\/i><\/a><\/i><\/a><\/i><\/a><\/i><\/a><\/i><\/a><\/div><\/div><\/div>
    \n

    You might also be interested in<\/h2>\n\n
    \n
    \n \n
    \n \n \n \n \"Building\n <\/picture>\n <\/div>\n
    \n \t \n
    \t\t

    Building the Future Workforce Today<\/h4>\n
    Trendiation as a strategic framework for employee qualification and training<\/div>
    J\u00fcrgen Fritz<\/a>, Sebastian Busse<\/a>, Ingo Dieckmann<\/a>, Torsten Laub<\/a><\/div>\n <\/td>\n <\/tr>\n <\/table>\n
    \n As Industry 4.0 and artificial intelligence reshape organizational capabilities, traditional training systems struggle to keep pace with evolving skill requirements. This paper introduces Trendiation\u2014a structured methodology for translating emerging trends into actionable strategies\u2014as a systematic approach to this challenge. Through a workshop-based application examining Edutainment, Human-Centered Design, and Workforce Transformation, we demonstrate how organizations can move from abstract trend identification to concrete qualification requirements and prioritized training initiatives. The method produces a traceable artifact chain spanning trend framing, capability-gap assessment, and implementation roadmaps. Participant evaluations indicate high perceived clarity and practical utility. By bridging foresight analysis with participatory design, Trendiation enables organizations to proactively cultivate adaptive capabilities and build learning cultures aligned with future work ... <\/div>\n <\/div>\n
    Industry 4.0 Science<\/strong> | Volume 42 | 2026 | Edition 2 | Pages 22-29 | DOI 10.30844\/I4SE.26.2.22<\/a><\/div> <\/div>\n <\/div>\n <\/a>\n <\/div>\n
    \n \n
    \n \n \n \n \"Operationalizing\n <\/picture>\n <\/div>\n
    \n \t \n
    \t\t

    Operationalizing Ethical AI with tachAId<\/h4>\n
    Validating an interactive advisory tool in two manufacturing use cases<\/div>
    Pavlos Rath-Manakidis<\/a>, Henry Huick<\/a>, Bj\u00f6rn Kr\u00e4mer<\/a> \n \"ORCID<\/a>, Laurenz Wiskott<\/a> \n \"ORCID<\/a><\/div>\n <\/td>\n <\/tr>\n <\/table>\n
    \n Integrating artificial intelligence (AI) into workplace processes promises significant efficiency gains, yet organizations face numerous ethical challenges that stakeholders are often initially unaware of\u2014from opacity in decision-making to algorithmic bias and premature automation risks. This paper presents the design and validation of tachAId, an interactive advisory tool aimed at embedding human-centered ethical considerations into the development of AI solutions. It reports on a validation study conducted across two distinct industrial AI applications with varying AI maturity. tachAId successfully directs attention to critical ethical considerations across the AI solution lifecycle that might be overlooked in technically-focused development. However, the findings also reveal a central tension: while effective in raising awareness, the tool\u2019s non-linear design creates significant usability challenges, indicating a user preference for more structured, linear guidance, especially ... <\/div>\n <\/div>\n
    Industry 4.0 Science<\/strong> | Volume 42 | 2026 | Edition 1 | Pages 50-59 | DOI 10.30844\/I4SE.26.1.48<\/a><\/div> <\/div>\n <\/div>\n <\/a>\n <\/div>\n
    \n \n
    \n \n \n \n \"JOCAT\n <\/picture>\n <\/div>\n
    \n \t \n
    \t\t

    JOCAT (Job Change Acceptance Toolbox)<\/h4>\n
    A change management approach for implementing AI systems ethically and sustainably<\/div>
    Sophie Berretta<\/a> \n \"ORCID<\/a>, Pauline Nolte<\/a>, Annette Kluge<\/a> \n \"ORCID<\/a>, Skrolan Kopka<\/a><\/div>\n <\/td>\n <\/tr>\n <\/table>\n
    \n AI systems challenge conventional change management due to their dynamic, opaque, and ethically sensitive nature. This article applies insights from established change models to AI-specific challenges, illustrated by a real-world use case. The resulting propositions are substantiated by six expert interviews, which integrate practical perspectives. Together, they inform the development of the Job Change Acceptance Toolbox (JOCAT), a modular, practice-oriented resource designed to support the implementation of human-centered, ethical, and sustainable AI-related change processes. <\/div>\n <\/div>\n
    Industry 4.0 Science<\/strong> | Volume 42 | Edition 1 | Pages 80-91 | DOI 10.30844\/I4SE.26.1.74<\/a><\/div> <\/div>\n <\/div>\n <\/a>\n <\/div>\n
    \n \n
    \n \n \n \n \"Co-Determination\n <\/picture>\n <\/div>\n
    \n \t \n
    \t\t

    Co-Determination Dialogues<\/h4>\n
    A tool for human-centered AI implementation<\/div>
    Manfred Wann\u00f6ffel<\/a> \n \"ORCID<\/a>, Fabian Hoose<\/a> \n \"ORCID<\/a>, Alexander Ranft<\/a>, Claudia Niewerth<\/a> \n \"ORCID<\/a>, Dirk St\u00fcter<\/a><\/div>\n <\/td>\n <\/tr>\n <\/table>\n
    \n As part of the regional competence center humAIne, funded by the Federal Ministry of Research, Technology, and Space (BMFTR), a process was developed using co-determination dialogues to establish a common understanding of the challenges involved in the introduction of artificial intelligence (AI) between management, employees, and interest groups. Experiences from project partner companies such as Doncasters Precision Castings in Bochum GmbH (DPC) exemplify how co-determination dialogues not only help to develop legally binding regulations for manageable, operationally anchored, sustainable AI use but also initiate continuous qualification processes for all stakeholder groups in accordance with Articles 4 and 5 of the EU AI Act. <\/div>\n <\/div>\n
    Industry 4.0 Science<\/strong> | Volume 42 | Edition 1 | Pages 92-98 | DOI 10.30844\/I4SE.26.1.84<\/a><\/div> <\/div>\n <\/div>\n <\/a>\n <\/div>\n
    \n \n
    \n \n \n \n \"Human-Centered\n <\/picture>\n <\/div>\n
    \n \t \n
    \t\t

    Human-Centered AI in Companies with Employee Representation<\/h4>\n
    Using the HUMAINE model for a company-specific works agreement<\/div>
    Alexander Ranft<\/a>, Fabian Hoose<\/a> \n \"ORCID<\/a>, Claudia Niewerth<\/a> \n \"ORCID<\/a>, Mathias Preu\u00df<\/a>, Manfred Wann\u00f6ffel<\/a> \n \"ORCID<\/a><\/div>\n <\/td>\n <\/tr>\n <\/table>\n
    \n The introduction of artificial intelligence (AI) in companies poses new challenges for regulation and co-determination. Binding requirements have been in force since the 2025 EU AI Act, which must be linked nationally with the Works Constitution Act (BetrVG). The regional competence center humAine has developed a model works agreement on AI (MBV KI) in accordance with Section 77 BetrVG, which strengthens co-determination rights in companies and implements European regulations in a practical way. Flanked by co-determination dialogues, the MBV KI enables company-specific adaptation for responsible and human-centered AI use. Using selected parts of the MBV KI as examples, this article shows how a framework works agreement on AI can be designed and discusses its transferability to companies without a works council. The MBV KI presented here contributes to the sustainable, socially secure design of the digital transformation. <\/div>\n <\/div>\n
    Industry 4.0 Science<\/strong> | Volume 42 | Edition 1 | Pages 14-21 | DOI 10.30844\/I4SE.26.1.14<\/a><\/div> <\/div>\n <\/div>\n <\/a>\n <\/div>\n
    \n \n
    \n \n \n \n \"Ethical\n <\/picture>\n <\/div>\n
    \n \t \n
    \t\t

    Ethical AI in the Workplace Through Value-Based Labels?<\/h4>\n
    Lessons learned from applying the VCIO framework to an AI-based assistant<\/div>
    Natalie Martin<\/a> \n \"ORCID<\/a>, Tobias Kopp<\/a> \n \"ORCID<\/a>, Natalie Beyer<\/a>, Jochen Wendel<\/a> \n \"ORCID<\/a>, Steffen Kinkel<\/a> \n \"ORCID<\/a><\/div>\n <\/td>\n <\/tr>\n <\/table>\n
    \n The AI Ethics Label represents a promising approach to promoting ethical AI and appropriate trust in AI systems. However, its practical application reveals some challenges due to its conservative assessment approach, limited context sensitivity, lack of benchmarks, and interpretation aids. Improvements are needed to unlock its full potential. <\/div>\n <\/div>\n
    Industry 4.0 Science<\/strong> | Volume 42 | Edition 1 | Pages 30-38 | DOI 10.30844\/I4SE.26.1.30<\/a><\/div> <\/div>\n <\/div>\n <\/a>\n <\/div>\n<\/div>\n\n","protected":false},"excerpt":{"rendered":"

    Originally developed in the software industry, a method inspired by the mindset of designers has proven to be a broadly applicable approach to solving complex problems. Design thinking holds the potential to contribute positively to social and economic change. A look at its development offers valuable insights for a deeper understanding.<\/p>\n","protected":false},"featured_media":109789,"menu_order":0,"template":"","categories":[79167,79298],"tags":[84351,79625],"product_cat":[79304],"topic":[79491],"technology":[],"knowhow":[],"industry":[],"writer":[83750,83751],"content-type":[],"potential":[],"solution":[],"glossary":[],"class_list":["post-110104","article","type-article","status-publish","has-post-thumbnail","category-design-en","category-typeset","tag-change-management-en","tag-design-thinking-en","product_cat-articles","topic-change-management-en","writer-benjamin-maerz-en","writer-nick-ackerhans-en","product","first","instock","downloadable","virtual","sold-individually","taxable","purchasable","product-type-article"],"uagb_featured_image_src":{"full":["https:\/\/industry-science.com\/wp-content\/uploads\/2025\/08\/Ackerhans.jpg",1400,788,false],"thumbnail":["https:\/\/industry-science.com\/wp-content\/uploads\/2025\/08\/Ackerhans-150x150.jpg",150,150,true],"medium":["https:\/\/industry-science.com\/wp-content\/uploads\/2025\/08\/Ackerhans-666x375.jpg",666,375,true],"medium_large":["https:\/\/industry-science.com\/wp-content\/uploads\/2025\/08\/Ackerhans-768x432.jpg",768,432,true],"large":["https:\/\/industry-science.com\/wp-content\/uploads\/2025\/08\/Ackerhans-1024x576.jpg",1020,574,true],"front-page-entry":["https:\/\/industry-science.com\/wp-content\/uploads\/2025\/08\/Ackerhans-1032x320.jpg",1032,320,true],"post-entry":["https:\/\/industry-science.com\/wp-content\/uploads\/2025\/08\/Ackerhans-764x376.jpg",764,376,true],"post-teaser":["https:\/\/industry-science.com\/wp-content\/uploads\/2025\/08\/Ackerhans-392x320.jpg",392,320,true],"post-teaser-mobile":["https:\/\/industry-science.com\/wp-content\/uploads\/2025\/08\/Ackerhans-608x496.jpg",608,496,true],"post-custom-size":["https:\/\/industry-science.com\/wp-content\/uploads\/2025\/08\/Ackerhans-640x325.jpg",640,325,true],"whitepaper-teaser":["https:\/\/industry-science.com\/wp-content\/uploads\/2025\/08\/Ackerhans-274x376.jpg",274,376,true],"card-big":["https:\/\/industry-science.com\/wp-content\/uploads\/2025\/08\/Ackerhans-514x292.jpg",514,292,true],"card-portrait":["https:\/\/industry-science.com\/wp-content\/uploads\/2025\/08\/Ackerhans-320x440.jpg",320,440,true],"card-big-company":["https:\/\/industry-science.com\/wp-content\/uploads\/2025\/08\/Ackerhans-514x289.jpg",514,289,true],"gp-listing":["https:\/\/industry-science.com\/wp-content\/uploads\/2025\/08\/Ackerhans-196x180.jpg",196,180,true],"1536x1536":["https:\/\/industry-science.com\/wp-content\/uploads\/2025\/08\/Ackerhans.jpg",1400,788,false],"2048x2048":["https:\/\/industry-science.com\/wp-content\/uploads\/2025\/08\/Ackerhans.jpg",1400,788,false],"woocommerce_thumbnail":["https:\/\/industry-science.com\/wp-content\/uploads\/2025\/08\/Ackerhans-510x510.jpg",510,510,true],"woocommerce_single":["https:\/\/industry-science.com\/wp-content\/uploads\/2025\/08\/Ackerhans-510x287.jpg",510,287,true],"woocommerce_gallery_thumbnail":["https:\/\/industry-science.com\/wp-content\/uploads\/2025\/08\/Ackerhans-100x100.jpg",100,100,true],"dgwt-wcas-product-suggestion":["https:\/\/industry-science.com\/wp-content\/uploads\/2025\/08\/Ackerhans-64x36.jpg",64,36,true]},"uagb_author_info":{"display_name":"Florian Goldmann","author_link":"https:\/\/industry-science.com\/en\/author\/"},"uagb_comment_info":0,"uagb_excerpt":"Originally developed in the software industry, a method inspired by the mindset of designers has proven to be a broadly applicable approach to solving complex problems. Design thinking holds the potential to contribute positively to social and economic change. A look at its development offers valuable insights for a deeper understanding.","_links":{"self":[{"href":"https:\/\/industry-science.com\/en\/wp-json\/wp\/v2\/article\/110104","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/industry-science.com\/en\/wp-json\/wp\/v2\/article"}],"about":[{"href":"https:\/\/industry-science.com\/en\/wp-json\/wp\/v2\/types\/article"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/industry-science.com\/en\/wp-json\/wp\/v2\/media\/109789"}],"wp:attachment":[{"href":"https:\/\/industry-science.com\/en\/wp-json\/wp\/v2\/media?parent=110104"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/industry-science.com\/en\/wp-json\/wp\/v2\/categories?post=110104"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/industry-science.com\/en\/wp-json\/wp\/v2\/tags?post=110104"},{"taxonomy":"product_cat","embeddable":true,"href":"https:\/\/industry-science.com\/en\/wp-json\/wp\/v2\/product_cat?post=110104"},{"taxonomy":"topic","embeddable":true,"href":"https:\/\/industry-science.com\/en\/wp-json\/wp\/v2\/topic?post=110104"},{"taxonomy":"technology","embeddable":true,"href":"https:\/\/industry-science.com\/en\/wp-json\/wp\/v2\/technology?post=110104"},{"taxonomy":"knowhow","embeddable":true,"href":"https:\/\/industry-science.com\/en\/wp-json\/wp\/v2\/knowhow?post=110104"},{"taxonomy":"industry","embeddable":true,"href":"https:\/\/industry-science.com\/en\/wp-json\/wp\/v2\/industry?post=110104"},{"taxonomy":"writer","embeddable":true,"href":"https:\/\/industry-science.com\/en\/wp-json\/wp\/v2\/writer?post=110104"},{"taxonomy":"content-type","embeddable":true,"href":"https:\/\/industry-science.com\/en\/wp-json\/wp\/v2\/content-type?post=110104"},{"taxonomy":"potential","embeddable":true,"href":"https:\/\/industry-science.com\/en\/wp-json\/wp\/v2\/potential?post=110104"},{"taxonomy":"solution","embeddable":true,"href":"https:\/\/industry-science.com\/en\/wp-json\/wp\/v2\/solution?post=110104"},{"taxonomy":"glossary","embeddable":true,"href":"https:\/\/industry-science.com\/en\/wp-json\/wp\/v2\/glossary?post=110104"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}