2.1. Systematic review using PRISMA-ScR

A scoping review aims to identify, analyze and categorize prior research within a given field by employing a systematic, transparent and comprehensive approach to searching, selecting and critically evaluating relevant studies. Unlike systematic reviews, which focus on meta-analysis and the quantitative synthesis of data, scoping reviews do not statistically aggregate results. Instead, they map the research landscape, classify key thematic areas and identify gaps in the literature, making them particularly valuable for emerging or complex research domains.

In this study, the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses; Scoping Review Extension) guidelines were employed to systematically search for and evaluate studies related to stress in design. Rather than focusing on quantitative outcomes, PRISMA-ScR facilitates thematic mapping and qualitative synthesis of existing research (McGowan et al. Reference McGowan, Straus, Moher, Langlois, O’Brien, Horsley, Aldcroft, Zarin, Garitty, Hempel, Lillie, Tunçalp and Tricco2020). Additionally, metasynthesis and grounded theory coding were employed for qualitative analysis, allowing for the extraction of core themes and conceptual patterns from the selected studies (Tong et al. Reference Tong, Flemming, McInnes, Oliver and Craig2012). The PRISMA-ScR flow diagram was used to illustrate the process of study identification and screening in this research. This diagram provides details regarding the number of initially identified studies, those excluded based on inclusion and exclusion criteria, and the final selection of studies for analysis. The four key steps of this process include: (1) identifying relevant studies, (2) initial screening, (3) detailed assessment of selected studies and (4) final data synthesis (Hay et al. Reference Hay, Duffy, McTeague, Pidgeon, Vuletic and Grealy2017). This structured approach enhances the transparency of study selection and ensures the comprehensiveness of the scoping review. To ensure methodological transparency, all searches in the final phase were restricted to the title field. This decision was made to maximize the specificity of the retrieved records and to ensure that only studies explicitly addressing stress within design contexts were included. While this approach may limit the retrieval of articles in which stress is discussed only in the abstract or keywords, it enhances the precision of the dataset and reduces false positives. Accordingly, the comprehensiveness of the review refers to the systematic and exhaustive application of the predefined search strategy within these parameters. This methodological choice is consistent with scoping review standards, where clarity, reproducibility and conceptual mapping take precedence over exhaustive aggregation of all potentially related literature.

2.2. Scientometric analysis

Various methods are available for examining and analyzing the quantitative aspects of a collection of articles related to a specific journal, country or topic. One widely used method is scientometric analysis, which involves conducting a scientometric analysis to obtain quantitative insights (Rialp et al. Reference Rialp, Merigó, Cancino and Urbano2019). Scientometric analysis falls within the realm of library and information science research and focuses on the quantitative study of bibliographic documents (Rialp et al. Reference Rialp, Merigó, Cancino and Urbano2019; Laengle et al. Reference Laengle, Lobos, Merigó, Viedma, Cobo and Baets2021). This technique serves as a comprehensive and precise method for analyzing and sorting extensive scientific data. It aims to provide a shared understanding of the relationships between articles and offer an updated summary of the current or evolving state in a specific research field (Kuzior & Sira Reference Kuzior and Sira2022; Tamala et al. Reference Tamala, Maramag, Simeon and Ignacio2022). Researchers use this analysis for various reasons, including discovering content and intellectual frameworks in the literature, identifying new patterns in article and journal performance and understanding collaboration patterns among researchers (Leal Filho et al. Reference Leal Filho, Setti, Azeiteiro, Lokupitiya, Donkor, Etim, Matandirotya, Olooto, Sharifi, Nagy and Djekic2022).

2.3. VOSviewer software

Data visualization is a method that supports the understanding of structures and connections among thousands of documents. Specialized tools have been developed for this purpose, including the VOSviewer software. VOSviewer, which stands for “Visualization of Similarities,” is used to study the links between articles and apply clustering in reference databases. This software primarily focuses on visualizing scientometric networks using specialized algorithms for labeling and employing visual metaphors to represent data density (Laengle et al. Reference Laengle, Lobos, Merigó, Viedma, Cobo and Baets2021; Barroso & Laborda Reference Barroso and Laborda2022; Mallick & Debasish Reference Mallick and Debasish2022). It is renowned for its ability to efficiently visualize and create maps from scientometric data, allowing for the compilation of literature and mapping of relationships between selected studies (Kuzior & Sira Reference Kuzior and Sira2022). Additionally, the tool can generate keyword co-occurrence maps and is compatible with databases such as WOS, PubMed, Scopus, CrossRef JSON and RIS (Barroso & Laborda Reference Barroso and Laborda2022).

2.4. Grounded theory

Grounded theory is an analytical tool used to extract new concepts from data collected during research systematically. Rather than testing existing theories, researchers use this approach to develop their own theories (Strauss & Corbin Reference Strauss and Corbin1990). The process includes open coding, axial coding and selective coding, through which each article is carefully analyzed to identify key topics and concepts. In the open coding phase, each idea is assigned a code and label that reflect its content and meaning, with the codes being directly derived from the collected data (Miles & Huberman Reference Miles and Huberman2002). In the subsequent phase, known as axial coding, the data are summarized under the primary categories of these concepts. This process involves categorizing similar codes into related groups through iterative reading and re-reading of the texts (Goulding Reference Goulding2002; Boeije Reference Boeije2010). Axial coding focuses on conceptualizing and moves from an open to a more specialized form, where the principal axes within the data are identified and future steps of the research are organized around these axes (Strauss & Corbin Reference Strauss and Corbin1990).

Selective coding, the third step in grounded theory analysis and closely related to axial coding, focuses on the central axis of concepts. This step involves refining, integrating and linking concepts (Strauss & Corbin Reference Strauss and Corbin1990; Lee Reference Lee2001), organizing them into a coherent theory through a specific arrangement of categories. In grounded theory, a new theory is developed by meticulously extracting data from study texts and categorizing it into three types of coding: open, axial and selective.

This theory can be presented in three different forms: as a diagrammatic representation, an explanatory and narrative description or a set of propositions (Creswell Reference Creswell2005). Overall, this method involves designing and constructing a documented and systematic theory, approach and research domain through the systematic collection and inductive analysis of data (Faregh & Ashari Reference Faregh and Ashari2022, p. 7).

In this study, two independent researchers conducted all analyses manually. To ensure accuracy, each article was reviewed multiple times. The initial agreement between the coders was 85%, indicating a high level of concordance. To account for potential chance agreement, Cohen’s kappa was calculated at 0.82, reflecting substantial interrater reliability. Any discrepancies between the coders were resolved through discussion and consensus. For greater clarity and consistency in the analytical process, the coding results were organized into a Codebook, which is presented in the study’s tables. This Codebook included all codes, even those that appeared only once, reflecting the exploratory and theory-generating nature of the research. The aim was to provide a comprehensive representation of all concepts related to stress in design research. Through both quantitative and qualitative analyses, along with processes of inference, comparison, classification and synthesis of the existing literature in this area (based on keywords, themes, theories and orientations of the reviewed studies), a new research domain was created. This framework, which includes both conceptual and theoretical dimensions, encourages a deeper understanding of the subject. It offers valuable insights for researchers in this field and helps address existing knowledge gaps. A detailed overview of the research methodology is illustrated in the Figure 3.

Figure 3.

Research methodology, Section 3: Article identification process according to PRISMA-ScR protocol.

Figure 3. Long description

The diagram runs Section 3 to ’The Process of Identifying Articles’ to PRISMA to ’Keywords’, which splits into ’Stress’ and ’Design studies’. ’Design studies’ branches into five columns of sub-fields: (1) Engineering Design, Industrial Design, Architectural Design, Product Design, Co-Design; (2) Service Design, Interior Design, Graphic Design, Conceptual Design, Design Cognition; (3) Thinking Design, Design Process, Creativity Design, Strategic Design, Systemic Design; (4) Fashion Design, HCI, Interaction Design, Education Design, Design Research; (5) Methodology Design, Design Decision-making, User Experience Design, Design Problem solving. Both ’Stress’ and ’Design studies’ converge into ’Reputable International Scientific Databases’: WOS, Scopus, Science Direct, IEEE, and ACM.

Through the combined use of a scoping review (as a subtype of systematic review), scientometric analysis, grounded theory coding and qualitative content analysis, this study integrates quantitative mapping with in-depth conceptual synthesis. This multimethod approach enables the identification of conceptual clusters, theoretical orientations and emerging patterns within the literature on stress in design. By systematically mapping the field and synthesizing its underlying themes, the study establishes the foundations of design stress as a distinct and emerging research domain. This integrative methodology not only clarifies the current state of knowledge but also highlights conceptual gaps, methodological challenges and opportunities for future research. Within this integrated framework, metasynthesis operates as the interpretive mechanism that connects structural mapping with conceptual theory building, ensuring coherence across the different analytical layers.

2.5. Qualitative content analysis

Berleson, a pioneer in content analysis, introduces this technique as a research method for systematically and quantitatively examining the manifest content of messages (Berleson Reference Berleson1952). Similarly, Weber (Reference Weber1991) defines content analysis as a research method for extracting reliable and replicable results from data derived from texts. This method is used to analyze textual and non-numerical data, identifying themes and meanings within the data (Creswell Reference Creswell, Dutcher, Klein, Harris and Levine2013). Typically, an inductive approach is used in conjunction with content analysis to uncover underlying themes and categories through coding, which is derived directly from systematically collected content throughout the research process. Coding (initial and core concepts), a key step in this method, enables researchers to identify and categorize important textual segments with specific codes, grouping them into related conceptual categories (Braun & Clarke Reference Braun and Clarke2006). Finally, the researcher analyzes and presents the results. Due to its focus on uncovering deeper and hidden meanings within data, this method is frequently employed in the social sciences and humanities, contributing to a more comprehensive understanding of research topics (Saldana Reference Saldana2015).

The methodological framework of this study follows a sequential exploratory–explanatory logic. First, the scoping review (PRISMA-ScR) establishes the complete and conceptually relevant corpus of studies on stress within design contexts. The scientometric analysis then maps the structural patterns of this corpus (such as keyword co-occurrence, thematic clusters and coauthorship networks), providing a quantitative overview of how the field is organized. Building on this structural mapping, grounded theory coding is applied to the full-text articles to inductively derive topics, themes and higher order categories that capture the conceptual substance of the literature. Finally, qualitative content analysis interprets these categories in relation to the underlying theories, assumptions and conceptual orientations present in the studies. Together, these steps form an integrated, sequential analytical process in which quantitative mapping and qualitative synthesis mutually reinforce one another, enabling the identification and conceptualization of design stress as an emerging research domain.

3.1. Exploratory search phase (2022): broad mapping of the field

The initial phase aimed to map the breadth of research connecting design and stress-related emotional experiences. Between January and June 2022, we conducted a broad exploratory search across major scientific databases using combinations of three core design-related terms (design, creativity and problem-solving) paired with a wide set of stress-related emotional terms, including stress, anxiety, worry, fear, emotion, excitement, strain, tension, arousal, mediator and stress + mediator. This phase generated a very large corpus of records, reflecting the conceptual diversity of the field. All retrieved studies were categorized thematically and documented. Insights from this phase informed a separate review article focusing on negative emotional experiences in design (Ashari et al. Reference Ashari, Amraei, Schmidt and Cascini2024). More importantly, this exploratory mapping provided the empirical basis for refining the search strategy for this study.

3.2. Derivation of the final keyword set (integrated version)

The refinement of the final keyword set was informed by insights from the exploratory search conducted in 2022. In that initial phase, a broad set of design-related terms (e.g. design, creativity, problem-solving) was combined with stress-related emotional concepts to map the landscape of research linking design and negative affect. A preliminary thematic overview of the retrieved literature, together with conceptual insights from related disciplines, revealed recurring clusters of topics and helped narrow the scope of the present review.

To ensure methodological rigor and reproducibility, the broad keyword pool from the exploratory phase was systematically reduced. Three criteria guided this refinement. First, controlled vocabulary alignment was applied by cross-checking candidate terms with database-specific indexing systems (e.g. Web of Science Categories, Scopus Subject Areas). Second, thematic clustering was used to group terms according to recurring conceptual patterns observed in the exploratory corpus, such as design domains, cognitive processes and interaction-related constructs. Third, relevance saturation was assessed to remove terms that consistently retrieved irrelevant or nondesign-related literature.

Through this structured process, a set of 23 domain-specific design keywords was derived. Each keyword was paired with the term stress to form the final search expressions. These terms collectively represent major disciplinary areas (e.g. engineering design, product design, interior design), cognitive and process-related constructs (e.g. design thinking, design cognition) and emerging subfields (e.g. codesign, HCI, systemic design). This refined set ensured both conceptual coverage and search efficiency, enabling the retrieval of studies directly relevant to the research aims.

3.3. Final search phase (2024): reproducible title-field search across five databases

The refined search was conducted between 1 and 5 February 2024, across five major academic databases: Web of Science (WOS), Scopus, ScienceDirect, IEEE Xplore and the ACM Digital Library. All searches were:

• • limited to the title field,

• • restricted to English-language publications,

• • limited to peer-reviewed journal articles and conference papers,

• • conducted with no date restrictions.

Database-specific syntax constraints required tailored Boolean expressions:

• • Web of Science: Query Builder interface using TI = Title.

• • Scopus: Advanced Search using TITLE().

• • ScienceDirect: Advanced Search using Title (no wildcard support).

• • IEEE Xplore: Advanced Search using Document Title (exact-match terms; wildcards not supported).

• • ACM Digital Library: Search Within → Title (wildcards supported).

The complete Boolean strings, field tags and filters for all databases are provided in Supplementary Table S0.

3.4. Initial retrieval and first-level screening

Using the 23 refined keywords, the search yielded 326 records from Web of Science, 395 from Scopus, 96 from ScienceDirect, 46 from IEEE Xplore and 7 from the ACM Digital Library. A first-level screening was then conducted to remove within-database duplicates, identified through overlapping title keywords (four or more shared terms). After this step, the number of retained records was reduced to 292 in Web of Science, 308 in Scopus, 106 in ScienceDirect, 47 in IEEE Xplore and 7 in the ACM Digital Library. These results are summarized in Supplementary Tables S1 and S2.

3.5. Second-level screening: cross-database deduplication

A second-level screening was then conducted to remove duplicates across all databases. This cross-database comparison eliminated 283 redundant records and resulted in a set of 516 unique articles (Figure 4). These records were subsequently assessed for eligibility for quantitative and qualitative analysis. Specific inclusion and exclusion criteria were applied, and the full text of each article was carefully reviewed. Studies that met the inclusion criteria were retained, while those that did not were excluded with justification based on predefined exclusion standards. Through this rigorous evaluation process, 498 articles were excluded, leaving 19 studies that satisfied all eligibility requirements and proceeded to the final step of analysis (Table 3).

Figure 4.

PRISMA-ScR flow diagram.

Figure 4. Long description

Three row labels run down the left: Identification, Screening, and Included, with two columns: ‘Identification of studies via databases and registers’ (left) and ‘Identification of studies through other methods’ (right). Left column - Step 1: records identified from WOS (n=326), Scopus (n=395), ScienceDirect (n=96), IEEE (n=46), ACM (n=7); arrow to ‘Records removed - first screening: duplicate articles sharing common keywords’. Step 2: records after first screening - WOS (n=292), Scopus (n=308), ScienceDirect (n=106), IEEE (n=47), ACM (n=7); arrow to ‘Records removed - second screening: 283 duplicate records eliminated across all databases’. Step 3: 517 unique records assessed for eligibility, leading to 498 records excluded with justification, leaving 19 studies that satisfied all eligibility requirements. Step 4: reports assessed for eligibility. Step 5 (Included): studies included in review. Right column: records identified from reference list searches (n=48), then records excluded, reports not retrieved, and reports sought for retrieval (n=9), which also feeds into Step 5.

3.6. Inclusion and exclusion criteria

In line with the PRISMA protocol and to improve transparency, reduce subjectivity in the selection process and ensure reproducibility, we clearly defined the inclusion and exclusion criteria. These criteria made sure that only studies related to design research were considered for screening. Eligible studies were chosen based on the following inclusion criteria, while those that did not meet these standards were excluded according to the defined exclusion criteria.

Inclusion criteria

1. 1. Articles that explicitly addressed stress within the professional and disciplinary context of design studies, including its related concepts, fields and subdomains.

2. 2. Studies focusing on stress at one or more steps of the design process.

3. 3. Research involving designers and design students across different specializations.

4. 4. Publications appearing in peer-reviewed journals indexed in reputable scientific databases, to ensure scientific rigor and credibility of the findings.

Exclusion criteria

1. 1. Studies addressing concepts unrelated to the disciplinary scope of design research (e.g. organizational innovation, general education or nonspecialized uses of the term “design”).

2. 2. Studies primarily focusing on domains such as cognitive science, engineering, psychology, management or business.

3. 3. Physiological or quantitative studies of decision-making stress, as well as studies addressing decision-making stress outside the domain of design research, were not included in this analysis.

4. 4. Non-English articles are required to maintain linguistic consistency and enable precise evaluation of all texts.

5. 5. Gray literature was excluded due to the absence of standardized quality controls and the challenges it poses for reproducibility.

6. 6. Studies lacking relevant data on stress in the design process or employing inappropriate or nonrigorous methodologies.

7. 7. Studies for which only abstracts were available and no full text could be retrieved.

To identify all relevant articles for the current research and improve accuracy, additional study records were also reviewed. This review was conducted by examining the reference lists of the identified articles. At this step, 48 articles were identified, of which 17 articles, previously identified through keyword searches and deemed duplicates, were removed during the initial screening. Subsequently, the full texts of the remaining 22 articles were carefully reviewed and based on the inclusion and exclusion criteria outlined, 13 articles were excluded, leaving only 9 articles deemed suitable for evaluation. Ultimately, the number of articles considered as eligible for final assessment and analysis, which were identified through keyword searches in academic databases and additional record reviews, totals 21 articles. These articles fall within the scope of design studies and address the topic of stress and its impact on designers, the design profession, design products and design creativity in the areas of product design, engineering design, interior design and architectural design (Figure 4).

4.1. Co-occurrence analysis

In this analysis, the 28 identified articles in the field of design related to stress were obtained in Excel format from scientific databases and uploaded into the VOSviewer software. This analysis is of the co-occurrence type, which examines the simultaneous occurrence of three keywords among all keywords in the identified articles in this research area. According to the results of this analysis, 12 keywords were identified in the fields of design and stress, with the co-occurrence links between these keywords calculated. Twenty-five percent of the total keywords (three keywords), namely “stress,” “mental stress” and “cognitive stress experience,” pertain to the field of stress. In comparison, 75% of the total keywords (nine keywords), including “design,” “design activity,” “engineering design,” “product design,” “conceptual design,” “design process,” “design creativity,” “conceptual ideation” and “conceptual selection,” belong to the field of design.

The keyword “mental stress” has the highest link strength of 30 and a co-occurrence frequency of 10, ranking first in the studies. Following this, the keywords “design” with a link strength of 21 and a co-occurrence frequency of 5 are ranked second, while “conceptual ideation” and “conceptual selection” with a link strength of 16 and a co-occurrence frequency of 3 are ranked third. Figure 6 provides an overview of the co-occurrence network visualization, which illustrates the simultaneous occurrence of three items (co-occurrence) and all keywords. It demonstrates that, in the field of design, researchers’ focus up until 2015 was primarily on mental stress; however, in recent years, there has been an increased attention to cognitive stress experience and its related concepts within this field (Figure 6).

Figure 6.

Visualization of the co-occurrence network analysis in the field of design related to stress.

Figure 6. Long description

The map shows ’mental stress’ as the largest node, positioned centrally, connected by curved lines to nine other nodes: ’design process’ and ’conceptual design’ (upper area, purple/blue, earlier years), ’design activity’ and ’design’ (centre, teal), ’concept generation’, ’concept selection’, ’cognitive stress’, and ’engineering design’ (lower left, yellow-green, more recent years), and ’design creativities’ and ’product design’ (lower right). A colour scale at bottom right runs from purple (2014) through teal and green to yellow (2022), the VOSviewer logo appears at bottom left.

4.2. Coauthorship analysis of studies on design stress based on authors

To make sure the findings of the co-occurrence network were not heavily influenced by one research group, we conducted a coauthorship analysis. Coauthorship looks at the collaborative relationships among authors across various scientific works, showing their teamwork in creating scholarly output. This analysis included all authors who published at least one paper in the field of design stress and highlighted their coauthorship connections. We identified a total of 65 different authors. Yong Zeng is the most influential contributor, with 7 publications, 173 citations and a total link strength of 13. Thang An Nguyen follows, having 4 publications, 142 citations and a total link strength of 6; most of their work is from earlier years. Recently, Hannah Nolte and Christopher McComb have become significant contributors, each with 4 publications, 19 citations and a total link strength of 6, showing their strong involvement in current research on design stress (Figure 6).

The other 61 authors contributed only one publication each in this area. This pattern shows that while a few researchers have played a key role in shaping the conversation, many other scholars have also contributed to knowledge production. Therefore, the co-occurrence and coauthorship analyses not only highlight the collaborations of a few key researchers but also show a wider trend of active scholarly participation. This analysis helps identify collaborative networks and key research topics, offering a clearer view of the scientific structure and activity distribution within the field of design stress. As shown in the density visualization map, the researchers who have contributed the most to design stress studies fall into two distinct groups. The first group includes Yong Zeng and Tang Nguyen, along with their collaborators Rajiv Yadav, Harshad Petkar, Shivangi Dande, Mengting Zhao, Shan Xu, Shenjie Yu and Xiaoying Wang. Together, they have published seven articles in this field, which is the highest number of publications in this area (Yao & Zeng Reference Yao and Zeng2011; Tang & Zeng Reference Tang and Zeng2009; Nguyen & Zeng Reference Nguyen and Zeng2012, Reference Nguyen and Zeng2014; Nguyen et al. Reference Nguyen, Xu and Zeng2013; Zhao & Zeng Reference Zhao and Zeng2019). The second group, headed by Hanna Nolte and Christopher McComb, along with Jacqueline Huff, Nicholas F. Soria Zurita and Elizabeth Starkey, has published four articles in recent years (Nolte & McComb Reference Nolte and McComb2020, Reference Nolte and McComb2021; Nolte et al. Reference Nolte, Huff and McComb2022, Reference Nolte, Soria Zurita, Starkey and McComb2023) (see Figure 7).

Figure 7.

Coauthorship analysis of articles in the domain of design stress studies, mapped according to author collaboration patterns.

Figure 7. Long description

The map shows six mostly separate clusters. The largest, in teal, centres on ’Zeng, Yong’, linked to ’Wang, Xiaoying’, ’An Nguyen, Thanh’, ’Xu, Xu’, ’Zhao, Mengting’, ’Yao, Shengji’, and a linked sub-group of ’Dande, Shivangi’ and ’Yadav, Rajeev’. A second cluster (teal) links ’Fich, Lars Brorson’, ’Garde, Anne Helene’, and ’Hansen, Åse’. A third cluster (yellow-green) links ’Khan, Atif Muhammad’, ’Sajid, Muhammad’, ’Hussain, Salman’, and ’Jahanzaib, Mirza’. A fourth cluster (teal-yellow) links ’Dayan, Mumin’, ’Di Benedetto, C. Anthony’, ’Ceylanlı, Zeynep’, ’Bengü, M.’, ’McComb, C.’, ’Gümüşburun Ayalp, Gülden’, ’Kounios, John’, and ’Duan, Haijun’. A fifth, smaller cluster (yellow) links ’Fitton, Dan’, ’Phillips, Charlie’, and ’Knight, John’. The sixth cluster (yellow), at the bottom, links ’Huff, Jacquelyn’, ’McComb, Christopher’, and ’Nolte, Hannah’, with the latter two as the largest nodes in this cluster.

4.2.1. Group 1: Yong Zeng and Tang Nguyen with collaborators

The main focus of their publications is the relationship between designers’ mental stress during the engineering design process, conceptual design and design creativity. They used various methods to measure designers’ mental stress, including heart rate variability (HRV) (Nguyen et al. Reference Nguyen, Xu and Zeng2013; Nguyen & Zeng Reference Nguyen and Zeng2014; Zhao & Zeng Reference Zhao and Zeng2019), electroencephalography (EEG) (Petkar et al. Reference Petkar, Dande, Yadav, Zeng and Nguyen2009; Nguyen & Zeng Reference Nguyen and Zeng2014), audio and video recording with retrospective protocol analysis (Yao & Zeng Reference Yao and Zeng2011), eye-tracking (Petkar et al. Reference Petkar, Dande, Yadav, Zeng and Nguyen2009), interviews (Zhao & Zeng Reference Zhao and Zeng2019) and qualitative and theoretical methods (Nguyen & Zeng Reference Nguyen and Zeng2012; Nguyen & Zeng Reference Nguyen and Zeng2014). These approaches showed that mental stress can significantly affect designers’ performance, creativity and information-gathering strategies during problem formulation in design processes (see Figure 8).

Figure 8.

Visualization of the coauthorship network of Nguyen and Zeng.

Figure 8. Long description

’Zeng, Yong’ (large green, central node) is directly linked by a thick line (indicating frequent collaboration) to ’Nguyen, Thanh An’ (large green node), and also to ’Xu, Xu’, ’Wang, Xiaoying’, ’Zhao, Mengting’ (green/yellow nodes) and ’Yao, Shengji’ (blue node, top). A separate red-coloured cluster on the right connects ’Dande, Shivangi’, ’Yadav, Rajeev’, ’An Nguyen, Thanh’, and ’Patkar, Harshad’ to each other and to the main cluster.

These scholars studied designers’ mental stress during the engineering design process (Petkar et al. Reference Petkar, Dande, Yadav, Zeng and Nguyen2009; Nguyen & Zeng Reference Nguyen and Zeng2012) and the conceptual design process (Yao & Zeng Reference Yao and Zeng2011; Nguyen et al. Reference Nguyen, Xu and Zeng2013; Nguyen & Zeng Reference Nguyen and Zeng2014; Nguyen et al. Reference Nguyen, Nguyen, Zeng, Weber, Husung, Cascini, Cantamessa, Marjanovic and Bordegoni2015; Zhao & Zeng Reference Zhao and Zeng2019). They defined mental stress as a dynamic, nonlinear and spiral process, based on their measurements during conceptual design (Yao & Zeng Reference Yao and Zeng2011). To assess this stress and how it spreads throughout the conceptual design process, they used methods like EEG, HRV and eye tracking (Petkar et al. Reference Petkar, Dande, Yadav, Zeng and Nguyen2009; Nguyen & Zeng, Reference Nguyen and Zeng2014; Nguyen et al. Reference Nguyen, Xu and Zeng2013). Their results showed a significant link between designers’ physiological signals and their emotional states (Petkar et al. Reference Petkar, Dande, Yadav, Zeng and Nguyen2009). They also introduced a framework to explore the relationship between mental stress and cognitive effort (Nguyen & Zeng Reference Nguyen and Zeng2014). In addition, they looked into how design creativity relates to mental stress, finding an inverted U-shaped relationship. This means that mental stress can have both positive and negative effects on creativity. Based on this, they proposed a theoretical model of creative design. This model highlights its dynamic and nonlinear nature and the role of mental stress (Nguyen & Zeng Reference Nguyen and Zeng2012; Yao & Zeng Reference Yao and Zeng2011). In later studies, they quantitatively examined the connection between information-gathering strategies (Nguyen et al., Reference Nguyen, Nguyen, Zeng, Weber, Husung, Cascini, Cantamessa, Marjanovic and Bordegoni2015; Zhao & Zeng Reference Zhao and Zeng2019), mental stress and how these factors together affect creativity through designers’ stress levels (Nguyen et al. Reference Nguyen, Xu and Zeng2013).

The objectives of their research can be grouped into three main categories:

1. 1. Development of theoretical models and conceptual analyses focusing on the relationship between designers’ mental stress during the design process and design creativity (Yao & Zeng Reference Yao and Zeng2011; Nguyen & Zeng Reference Nguyen and Zeng2012, Reference Nguyen and Zeng2014).

2. 2. Assessment of designers’ mental stress during conceptual design using physiological data (Petkar et al. Reference Petkar, Dande, Yadav, Zeng and Nguyen2009; Nguyen & Zeng Reference Nguyen and Zeng2014).

3. 3. Examination of the influence of information-gathering strategies in the design process on both mental stress and creativity (Nguyen & Zeng Reference Nguyen and Zeng2014; Zhao & Zeng Reference Zhao and Zeng2019).

4.2.2. Group 2: Hannah Nolte, Christopher McComb and collaborators

As mentioned earlier, this group has published four articles in recent years on design stress. Three of these appeared in the Design Science journal. Their research has specifically examined mental stress in three key activities of the engineering design process: concept generation, concept selection and physical prototyping. This work involved 73 first-year engineering design students, who are novice designers. They collected data through surveys, interviews and online questionnaires. In their first two studies (Nolte & McComb Reference Nolte and McComb2020, Reference Nolte and McComb2021), they looked into the cognitive experiences of novice engineering design students and the sources of stress they perceived during these three main design activities. In their later work (Nolte et al. Reference Nolte, Huff and McComb2022, Reference Nolte, Soria Zurita, Starkey and McComb2023), they studied the effects of a brief mindfulness-based cognitive intervention on students’ stress. They also explored the connection between mindfulness, stress and creativity in engineering design. The main goal of their research was to identify mental stress during key steps of engineering design and to examine how mindfulness practices affect stress and creativity among novice designers. Their findings confirmed that mental stress exists throughout the three primary design activities. They showed that each step of the engineering design process provides a unique cognitive experience with different effects noticeable across the entire design process (Nolte & McComb Reference Nolte and McComb2020, Reference Nolte and McComb2021; Nolte et al. Reference Nolte, Huff and McComb2022, Reference Nolte, Soria Zurita, Starkey and McComb2023). Additionally, their work demonstrated that mindfulness affected the cognitive stress experiences of designers. Students reported slight increases in mindfulness, particularly in decentering. However, they found no noticeable effect of mindfulness on stress levels (Nolte et al. Reference Nolte, Huff and McComb2022, Reference Nolte, Soria Zurita, Starkey and McComb2023). The authors also pointed out that students often do not recognize the physical changes linked to stress during the main design activities. Instead, they struggle with design fixation. They recommended that future studies should use physiological sensors to provide more objective and reliable measurements of physical stress, ideally captured right after rest periods (Nolte & McComb Reference Nolte and McComb2020, Reference Nolte and McComb2021; Nolte et al. Reference Nolte, Huff and McComb2022, Reference Nolte, Soria Zurita, Starkey and McComb2023) (Figure 9).

Figure 9.

Visualization of the coauthorship network of Hannah Nolte, Christopher McComb and their collaborators.

Figure 9. Long description

‘McComb, Christopher’ and ‘Nolte, Hanna’ are shown as large red nodes joined by a thick link. ‘Huff, Jacquelyn’ (small red node) is connected to both. A separate green pair, ‘Starkey, Elizabeth’ and ‘Soria Zurita, Nicolás F.’, are linked to each other and loosely connected toward the red cluster.

The scientometric findings provide an essential empirical foundation for the conceptual synthesis developed in later sections. Specifically, the dominance of “mental stress” as a keyword and the clustering of design-related terms around cognitive and process-oriented nodes indicate that stress in design is primarily framed within cognitive-processual boundaries rather than purely occupational contexts. This structural pattern directly supports the later grounded theory results, which identify “design process stress” as the core category of the emerging design stress domain. Accordingly, scientometric analysis not only maps the intellectual structure of the field but also empirically validates the conceptual emergence of design stress as a distinct research domain.

6.1. Existing researchers’ perspectives in the field of design stress studies

Stress is widely recognized in the literature as a common and impactful issue among design professions. Prior studies have pointed out its effects on designers’ health and stressed the importance of understanding stress experiences connected to the design process, especially given its high frequency in professional practice (Nolte & McComb Reference Nolte and McComb2020). Research suggests that if not managed well, stress may become chronic (Nguyen & Zeng Reference Nguyen and Zeng2017) and can lead to serious long-term health issues for designers (Nolte & McComb Reference Nolte and McComb2020). As a result, looking into mental stress and its effects during the design process is crucial. Several authors highlight the need to effectively measure these stress factors to create reliable design models. For instance, the Yerkes–Dodson law illustrates the connection between performance and mental stress as an inverted U-shaped curve, showing how the design process can affect stress levels. Literature also shows that a well-organized and suitable design process can help lessen cognitive workload and thus improve designers’ well-being. On the other hand, when the process does not match designers’ thinking or working styles, it can raise stress levels. Therefore, keeping stress within an optimal range is viewed as essential for improving designer performance. This highlights the need to model the relationship between mental stress and performance (Petkar et al. Reference Petkar, Dande, Yadav, Zeng and Nguyen2009).

In previous studies, researchers have found that design is a highly cognitive activity characterized by mental stress (Dym et al. Reference Dym, Agogino, Eris, Frey and Leifer2005; Zhu et al. Reference Zhu, Yao and Zeng2007; Petkar et al. Reference Petkar, Dande, Yadav, Zeng and Nguyen2009; Nguyen et al. Reference Nguyen, Xu and Zeng2013; Nolte & McComb Reference Nolte and McComb2020, Reference Nolte and McComb2021; Nolte et al. Reference Nolte, Huff and McComb2022). This stress is induced throughout the engineering design process (Zhu et al. Reference Zhu, Yao and Zeng2007; Petkar et al. Reference Petkar, Dande, Yadav, Zeng and Nguyen2009; Tang & Zeng Reference Tang and Zeng2009; Nguyen et al. Reference Nguyen, Xu and Zeng2013; Nguyen & Zeng Reference Nguyen and Zeng2014, Reference Nguyen and Zeng2017; Nolte & McComb Reference Nolte and McComb2020, Reference Nolte and McComb2021; Nolte et al. Reference Nolte, Huff and McComb2022) and various aspects of the process often lead to stress (Tang & Zeng Reference Tang and Zeng2009; Nguyen & Zeng Reference Nguyen and Zeng2014, Reference Nguyen and Zeng2017; Nolte & McComb Reference Nolte and McComb2020, Reference Nolte and McComb2021). Excessive stress during engineering design problem-solving tasks results in decreased designer performance (Dyregrov, Solomon & Bassøe Reference Dyregrov, Solomon and Bassøe2000; Bourne & Yaroush Reference Bourne and Yaroush2003; Zhu et al. Reference Zhu, Yao and Zeng2007; Nolte & McComb Reference Nolte and McComb2020, Reference Nolte and McComb2021; Nolte et al. Reference Nolte, Huff and McComb2022) and reduces their creativity (Shanteau & Dino Reference Shanteau, Dino, Svenson and Maule1993; Nguyen & Zeng Reference Nguyen and Zeng2012, Reference Nguyen and Zeng2014, Reference Nguyen and Zeng2017; Sandi Reference Sandi2013; Håkansson & Törlind Reference Håkansson and Törlind2014; Nolte & McComb Reference Nolte and McComb2020; Pyykkonen Reference Pyykkonen2021; Nolte et al. Reference Nolte, Huff and McComb2022, Reference Nolte, Soria Zurita, Starkey and McComb2023). Stress also affects their motivation and cognitive abilities, disrupting their decision making (Robledo Yamamoto et al. Reference Robledo Yamamoto, Cho, Voida and Voida2023) and their ability to perform tasks that require complex reasoning and flexibility; skills essential for successful design (Sandi Reference Sandi2013; Nolte et al. Reference Nolte, Huff and McComb2022). While moderate levels of stress can potentially enhance designer performance, leading to increased motivation, creativity and focus (Gutnick et al. Reference Gutnick, Walter, Nijstad and De Dreu2012; Nguyen & Zeng Reference Nguyen and Zeng2014; Degroote et al. Reference Degroote, Schwaninger, Heimgartner, Hedinger, Ehlert and Wirtz2020), other studies have shown that the relationship between stress and creativity is influenced by factors such as the type of stress experienced, its intensity and individual differences (Byron, Khazanchi & Nazarian Reference Byron, Khazanchi and Nazarian2010; Knight et al. Reference Knight, Fitton, Phillips and Price2019). These studies present a complex picture of how stress affects creative cognition (Duan et al. Reference Duan, Wang, Hu and Kounios2019). A review study (Byron et al. Reference Byron, Khazanchi and Nazarian2010) discussed three types of outcomes: (1) sometimes stressors decrease creative performance (Beversdorf et al. Reference Beversdorf, Hughes, Steinberg, Lewis and Heilman1999; Probst et al. Reference Probst, Stewart, Gruys and Tierney2007; Lovelace & Hunter Reference Lovelace and Hunter2013); (2) sometimes stressors increase creative performance (Baas, De Dreu & Nijstad Reference Baas, De Dreu and Nijstad2008; Ohly & Fritz Reference Ohly and Fritz2010) and (3) sometimes stressors are related to creative performance in a manner represented by an inverted U function (Suedfeld & Vernon Reference Suedfeld and Vernon1965; Baer & Oldham Reference Baer and Oldham2006).

Quantitative analysis of the relationship between information-gathering strategies and designers’ cognitive stress has demonstrated that quantifying cognitive stress contributes to a better understanding of creative and design processes (Zhu et al. Reference Zhu, Yao and Zeng2007; Zhao & Zeng Reference Zhao and Zeng2019). Therefore, understanding designers’ cognitive stress is crucial for developing a more effective design process (Bourne & Yaroush Reference Bourne and Yaroush2003; Zhu et al. Reference Zhu, Yao and Zeng2007). In this context, using the recurrent object modeling (ROM) approach as a tool to represent designers’ mental states at each step of the conceptual design process facilitates the examination of how different information-gathering strategies impact design creativity. These studies illustrate how these strategies, by affecting cognitive stress, can influence design creativity. These findings are based on theoretical analyses that explore the recursive relationship between the design process and the interplay between creativity and cognitive stress (Wang et al. Reference Wang, Nguyen and Zeng2015).

On the other hand, research has shown that acute stress can negatively impact two types of creative problem-solving strategies (namely, convergent and divergent) leading to reduced flexibility in problem-solving. Stress, especially in convergent problem-solving, generates quick but less accurate responses. In divergent problem-solving, it reduces flexibility in developing solutions, which in turn decreases the variety of proposed solutions (Duan et al. Reference Duan, Wang, Hu and Kounios2019).

Since design activities are primarily based on cognitive processes, understanding the impact of stress on the design process and finding effective methods to reduce stress in workplace, educational and academic environments is crucial. To date, there has been limited research on identifying cognitive stress within design teams (Wasim et al. Reference Wasim, Siddique, Sajid, Hussain, Jahanzaib and Khan2019). However, the productivity of design teams is significantly affected by mental stress (Wasim et al. Reference Wasim, Siddique, Sajid, Hussain, Jahanzaib and Khan2019), which may negatively impact intuitive decision making, creativity and ultimately their performance (Dayan & Di Benedetto Reference Dayan and Di Benedetto2011). The role of team experience and stress as moderating factors in the impact of team intuition on product creativity in new product development (NPD) processes has been examined. The results indicate that team intuition positively affects product creativity, but this effect is moderated by team experience and stress (Dayan & Di Benedetto Reference Dayan and Di Benedetto2011). Designers experience high levels of stress due to their intense focus on their work (Dym et al. Reference Dym, Agogino, Eris, Frey and Leifer2005; Nolte & McComb Reference Nolte and McComb2020). The inherent complexity of design issues, such as poorly defined or constantly evolving problems (Dym et al. Reference Dym, Agogino, Eris, Frey and Leifer2005), extended brainstorming session durations (Tsenn et al. Reference Tsenn, Atilola, McAdams and Linsey2014) and the multiple skills designers must employ to solve design problems, including analytical skills, decision making and creativity (Dym et al. Reference Dym, Agogino, Eris, Frey and Leifer2005), contribute to this stress. Additionally, the methods used for problem-solving (Wang et al. Reference Wang, Nguyen and Zeng2015; Zhao & Zeng Reference Zhao and Zeng2019; Nolte et al. Reference Nolte, Huff and McComb2022), various additional factors such as design requests (Nguyen & Zeng Reference Nguyen and Zeng2012), problem-solving strategies (Wang et al. Reference Wang, Nguyen and Zeng2015; Zhao & Zeng Reference Zhao and Zeng2019), individual differences (Nolte et al. Reference Nolte, Soria Zurita, Starkey and McComb2023) and time constraints on design activities (Nolte et al. Reference Nolte, Huff and McComb2022), all contribute to the increased cognitive load and stress experienced by designers (Nolte & McComb Reference Nolte and McComb2021; Nolte et al. Reference Nolte, Huff and McComb2022). Furthermore, intensive schedules, complex tasks or other severe stressors may cause designers to experience stress, potentially leading to reduced performance or even failure (Dyregrov et al. Reference Dyregrov, Solomon and Bassøe2000; Bourne & Yaroush Reference Bourne and Yaroush2003; Zhu et al. Reference Zhu, Yao and Zeng2007).

Past research has observed an increase in stress among professionals and students in the fields of engineering and architectural design. These studies have shown that designers experience considerable levels of stress (Anthony Reference Anthony1991; Foster & Spencer Reference Foster and Spencer2003; Ipsen & Jensen Reference Ipsen and Jensen2012; Kirkpatrick Reference Kirkpatrick2018; Xie et al. Reference Xie, Yaqoob, Mansell and Tai2019; Nolte & McComb Reference Nolte and McComb2020; Quam et al. Reference Quam, Ladjahasan, Kang and Faber2022), which can also be influenced by various contextual factors (Greenough et al. Reference Greenough, Black and Wallace1987; Lobel & Dunkel-Schetter Reference Lobel and Dunkel-Schetter1990; Nolte & McComb Reference Nolte and McComb2020) Consequently, research emphasizes the necessity of finding methods to improve mental conditions, reduce stress and enhance the quality of students’ educational experiences (Gümüşburun Ayalp & Çivici Reference Gümüşburun Ayalp and Çivici2021; Quam et al. Reference Quam, Ladjahasan, Kang and Faber2022; Robledo Yamamoto et al. Reference Robledo Yamamoto, Cho, Voida and Voida2023). Furthermore, other studies have examined stress in creative educational programs, particularly in the context of interior architectural design and have demonstrated that stress can affect students’ performance (Quam et al. Reference Quam, Ladjahasan, Kang and Faber2022) and well-being (Smith & Lilly Reference Smith and Lilly2016). Stress at a low-to-moderate level is experienced during most steps of conceptual design (Nguyen et al. Reference Nguyen, Xu and Zeng2013; Nguyen & Zeng Reference Nguyen and Zeng2014; Nolte et al. Reference Nolte, Soria Zurita, Starkey and McComb2023). Acute stress, resulting from involvement in engineering design, also fluctuates throughout the design process (Zhu et al. Reference Zhu, Yao and Zeng2007; Nguyen et al. Reference Nguyen, Xu and Zeng2013; Nguyen & Zeng Reference Nguyen and Zeng2014; Nolte & McComb Reference Nolte and McComb2021; Nolte et al. Reference Nolte, Huff and McComb2022, Reference Nolte, Soria Zurita, Starkey and McComb2023) and contributes to design stabilization (Nguyen & Zeng Reference Nguyen and Zeng2017).

Additionally, some studies have indicated that physical modeling is one of the most stressful steps in the design process, whereas activities such as concept generation and concept selection have similar levels of stress (Nolte & McComb Reference Nolte and McComb2021; Nolte et al. Reference Nolte, Huff and McComb2022). These findings emphasize that cognitive stress is related to the design activity itself, rather than the design subject (Nolte & McComb Reference Nolte and McComb2021; Nolte et al. Reference Nolte, Huff and McComb2022).

Similarly, a limited number of studies have shown that certain aspects of engineering design (Greenough et al. Reference Greenough, Black and Wallace1987; Lobel & Dunkel-Schetter Reference Lobel and Dunkel-Schetter1990; Nolte & McComb Reference Nolte and McComb2020) and interaction design can contribute to increased stress. These studies have examined how cognitive load and user stress can be reduced, as well as strategies for managing and mitigating them (Paredes & Chan Reference Paredes and Chan2011; Kiss & Schmidt Reference Kiss and Schmidt2019; Bampouni & Kaptelinin Reference Bampouni and Kaptelinin2020; Zaharuddin et al. Reference Zaharuddin, Ibrahim, Yusof, Mahidin and Rusli2020; Robledo Yamamoto et al. Reference Robledo Yamamoto, Cho, Voida and Voida2023). Other research indicates that mental stress is positively associated with cognitive workload and negatively associated with cognitive capacity (Tang & Zeng Reference Tang and Zeng2009; Nguyen & Zeng Reference Nguyen and Zeng2012), which is further influenced by the designer’s combination of knowledge and skill (Nguyen & Zeng Reference Nguyen and Zeng2012; Nolte et al. Reference Nolte, Soria Zurita, Starkey and McComb2023).

Other studies have shown that each design activity creates a unique cognitive experience, with stress levels present and consistent across design activities (Nolte & McComb Reference Nolte and McComb2021). It should be noted that differences in design experience do not contribute to variations in cognitive stress experience, as each design activity has its own distinct stressors, which are generally different from those of other activities and the ranking of perceived stressors varies for each design activity (Nolte & McComb Reference Nolte and McComb2021; Nolte et al. Reference Nolte, Huff and McComb2022). Additionally, different levels of stress can affect the cognitive experience of the designer during the design process and the quality of design outcomes (Nolte et al. Reference Nolte, Soria Zurita, Starkey and McComb2023). Therefore, managing long-term stress in designers and mitigating its adverse effects is crucial (Nolte & McComb Reference Nolte and McComb2021). Recognizing the characteristics of stress throughout the design process enables the provision of appropriate training to reduce designers’ stress and enhance the quality of final products (Nolte & McComb Reference Nolte and McComb2020). Studies have shown that mindfulness-based stress reduction (MBSR) approaches not only decrease stress and anxiety levels in design students but also improve focus, motivation, reduce perfectionism (Purnaumbara & Khasna Reference Purnaumbara and Khasna2023), enhance sleep patterns and decrease chronic fatigue and pain (Ceylanli, Engin & Uluengin Reference Ceylanli, Engin and Uluengin2021). From another perspective, stress is recognized not only as an individual issue but also as a social challenge with widespread negative impacts on individuals and communities. Researchers have proposed strategies to help designers create products and services that alleviate stress. These strategies include identifying sources of stress, adopting coping approaches, assessing stress levels and enhancing individual capabilities (Stoop & Snelders Reference Stoop, Snelders, Maier, Škec, Kim, Kokkolaras, Oehmen, Fadel, Salustri and Van der Loos2017). Designers can utilize these existing stress management strategies to transform them into valuable products and services that can support individuals affected by stress. Based on the existing literature in this field, a model called “Design for Serenity” has been developed. Designers can utilize this model to create future products and services that address excessive stress levels (Stoop & Snelders Reference Stoop, Snelders, Maier, Škec, Kim, Kokkolaras, Oehmen, Fadel, Salustri and Van der Loos2017).

6.2. Analysis of theoretical perspectives in the field of design studies on stress

After gathering all theoretical contributions for this research, the next step was to extract initial concepts and code them using a qualitative content analysis method. In this phase, through a multistep process, we identified, extracted and coded meaning units, sentences and expressions relevant to the research area, as mentioned in the reviewed theories (Table 5). The main focus was on uncovering patterns and hidden meanings within the texts. After compiling all perspectives on “design stress,” we read the full texts of the articles multiple times, line by line, to find meaning units with theoretical or empirical importance. Each sentence or expression that reflected researchers’ views on the link between stress and the design process received an initial code. At the same time, we wrote analytic memos to document the ongoing development of concepts throughout the coding process. These memos were essential for capturing immediate insights and emerging patterns based on the data.

Table 5.

Semantic units embedded in researchers’ perspectives in the field of design studies on stress

Table 5. Long description

The table is organized into four columns: Code, References, Primary concepts, and Main concepts. It contains 81 rows of data.

* The Code column uses alphanumeric identifiers from B 1 to B 81.

* The References column lists academic citations, frequently citing Nolte and McComb (2020, 2021), Petkar et al. (2009), and Nguyen and Zeng (2014, 2017).

* The Primary concepts column describes specific research focuses, such as ‘Stress in design professions’ (B 1), ‘Impact of mental stress on the creative design process’ (B 8), and ‘Design fixation caused by acute stress’ (B 57).

* The Main concepts column categorizes these into broader themes, such as ‘Stress caused by the design process’, ‘Impact of stress on designers’ performance’, ‘Relationship between stress and creativity’, and ‘Stress management’.

Key data points include:

* B 1 to B 4 focus on stress within design professions and its impact on health.

* B 5 to B 16 detail the impact of mental stress on the design process and management.

* B 17 to B 28 explore the relationship between stress, problem-solving, and creativity.

* B 32 specifically mentions a ‘U-shaped relationship between stress and creative performance’.

* B 51 identifies ‘Stress caused by time constraints’.

* B 66 notes ‘Acute stress resulting from architectural design’.

* B 74 to B 77 focus on coping mechanisms and stress-reducing products.

* B 78 addresses ‘Increased user stress in product interaction’.

Following the initial coding, we selected a subset of articles to create the first draft of the codebook. In collaborative sessions with two independent coders, we carefully reviewed the definitions of each code, illustrative quotations and criteria for assigning codes to meaning units. Once finalized, we recoded the entire set of articles according to this shared framework. The intercoder reliability (Cohen’s κ), which started at about 0.78, improved to around 0.85 after we resolved discrepancies and refined code definitions.

Subsequently, to ensure greater transparency in data analysis, the process of theme extraction was conducted systematically from the raw data to the higher order concepts. Each quotation or finding was assigned to an initial idea, and these concepts were then grouped into broader categories based on similarities and conceptual overlaps. This process enabled the emergence of overarching themes. Through comparative analysis and conceptual mapping, hundreds of initial codes (primary concepts) were consolidated into dozens of secondary codes (main ideas). For example:

• • Quotation B1 (Nolte & McComb Reference Nolte and McComb2020) was assigned the initial concept “stress in design professions” and subsequently categorized under the central idea “stress arising from professional design practices.”

• • Similarly, quotation B7 (Petkar et al. Reference Petkar, Dande, Yadav, Zeng and Nguyen2009; Quam et al. Reference Quam, Ladjahasan, Kang and Faber2022) was coded as “the impact of mental stress on designers’ performance” and classified under the central concept “effects of stress on design performance and mental stress.”

This procedure was systematically applied to the entire dataset, ensuring a transparent documentation of the relationships among raw data, initial codes and core concepts. Table 5 illustrates an example of this process and outlines the step-by-step pathway through which themes were derived. Furthermore, Figure 13 visualizes the frequency of each theme across the analyzed articles (Figure 15).

Figure 15.

Frequency distribution of main concepts underlying design research.

Figure 15. Long description

The chart lists around 30 categories along the bottom axis, ordered from lowest to highest frequency (vertical axis 0-14). The lowest-frequency categories (count of 1) include topics such as stress management with products and various impacts of stress on designers’ cognitive or physical health. Frequency increases through the middle of the chart (counts of 2-5) for categories such as stress caused by information gathering and design creativity stress. The highest-frequency categories at the right are stress caused by the design process (count 10-11 range) and ’Mental stress’ (count 13), the single most frequent category. The full underlying data is presented in Table 5.

In the next step, the core concepts extracted from Table 5 were organized into a set of second-order conceptual groupings to construct a deeper conceptual structure. This step aimed to produce a conceptual synthesis, enabling the scattered ideas found in the literature to be represented as a coherent set of higher order categories. Through this process, the core concepts were integrated based on their semantic similarities, theoretical roles and their contribution to explaining the phenomenon of design stress. These concepts were subsequently merged into several higher level groups, including process-internal stressors, contextual/professional stressors, environmental/educational stressors, types and intensity of stress, designer-level mediators, the bidirectional relationship between stress and design, effects of stress on designers and on design and coping strategies for design-related stress. This structure reveals that existing theories predominantly focus on dimensions such as mental stress, acute and chronic stress, cognitive load, steps of the design process, problem-solving, creativity, decision making and the cognitive and performance-related effects of stress. Table 6 presents the final version of these groupings, which not only clarifies the underlying relationships among concepts but also provides a coherent framework for analyzing theoretical perspectives within the field of design stress (Table 6).

Table 6.

Classification of main concepts extracted from researchers’ perspectives in the field of design studies

Table 6. Long description

The table consists of four columns: Article codes, Main concepts, Second-order conceptual category, and Central theoretical claim.

Key rows include:

* B 6 through B 74: Stress caused by design; Intrinsic design stressors; Design is inherently stress-inducing.

* B 1 through B 4: Stress caused by design professions; Contextual or professional stressors; Professional demands create persistent stress.

* B 3 through B 71: Stress caused by the design process; Process-internal stressors; Different steps generate cognitive load.

* B 51: Stress caused by time constraints; Situational stressors; Time pressure increases stress.

* B 80: Stress caused by Architectural Design Education; Environmental or educational stressors; Educational environments can be stress inducing.

* B 7 through B 65: Mental stress; Types and intensity of stress; Mental stress affects cognition and performance.

* B 27 through B 35: Relationship between stress and design creativity; Bidirectional relationship; Stress can reduce or enhance creativity.

* B 2 through B 68: Impacts on designers’ health; Effects of stress on designers; Stress undermines physical and mental health.

* B 15 through B 73: Impacts on design quality; Effects of stress on design; Stress reduces the quality of design outcomes.

* B 19 through B 30: Positive impacts of stress; Positive effects of stress; Moderate stress can enhance performance.

* B 76: Stress reduction through mindfulness; Coping strategies; Mindfulness practices reduce design-related stress.

The pie chart below illustrates the frequency distribution of the thematic categories derived from Table 6. As shown, the highest concentration of studies falls under the category of design stress, with 37 publications addressing stress as an inherent and unavoidable aspect of design activities and professional practice. This is followed by 31 studies focusing on types and intensity of stress in design and 21 studies examining the effects of stress on design, highlighting the centrality of these dimensions within the existing literature.

In contrast, categories such as the bidirectional relationship between stress and design (eight studies), positive effects of stress (five studies), coping strategies (five studies) and designer-level mediators (one study) appear less frequently. This distribution suggests that while prior research has predominantly emphasized broader conceptualizations of design-related stress, more specific aspects (particularly mediating mechanisms, pathways through which stress exerts its effects and coping strategies) remain underexplored and warrant further investigation in future studies (Figure 16).

Figure 16.

Frequency distribution, essence of researchers’ perspectives of design research.

Figure 16. Long description

The pie chart is divided into seven slices, arranged clockwise from largest: Design Stress, 37 studies (30%); Types and intensity of stress in design, 31 studies (25%); Effects of stress on design, 21 studies (17%); Effects of stress on designers, 16 studies (13%); Bidirectional relationship between stress and design, 8 studies (7%); Coping with design stress, 5 studies (4%); Positive effects of stress on design, 5 studies (4%).

After extracting the core concepts and organizing them into second-order conceptual groupings (Table 6), the next step involved identifying the overarching thematic structure. In this step, the second-order concepts were integrated into several thematic categories based on their roles in explaining the phenomenon of design stress (Table 7). These categories illustrate that design stress is not only an inherent and multidimensional phenomenon but also one that produces extensive consequences for both designers and the design process, while at optimal levels it may also play a facilitative role. The final thematic structure comprises six major domains: design stress, types and intensity of stress, the bidirectional relationship between stress and design, effects of stress on designers, effects of stress on design and coping strategies for design-related stress.

Table 7.

Thematic classification of core concepts in the field of design stress studies

Table 7. Long description

The table has four columns (illustrative statement, Explanation, Thematic Category, number) and six rows: (1) ’Stress as an inevitable part of the design process’ - Design stress - studies addressing stress as an inherent, unavoidable phenomenon in design activities and practice. (2) ’Designers experience acute stress under time pressure’ - Types and intensity of stress in design - research classifying stress types (mental, physiological, ethical, environmental) and intensity (acute, chronic, mild). (3) ’High stress leads to reduced design quality’ - Bidirectional relationship between stress and design - studies on the two-way interaction where design induces stress and stress affects design outcomes. (4) ’Increased heart rate and reduced mental focus’ - Effects of stress on designers - effects on designers’ health, cognition, motivation, concentration, and performance. (5) ’Stress exerts both positive and negative effects on design’ - Effects of stress on design - effects on design process, quality, creativity, problem-solving, and fixation, both beneficial and detrimental. (6) ’Mindfulness reduces stress’ - Coping with design stress - strategies including cognitive techniques, educational/environmental interventions, mindfulness, and stress-mitigation tools.

This thematic organization provides a coherent conceptual framework for analyzing designers’ experiences and enables the identification of underlying connections among concepts, thereby supporting the development of a theoretical foundation for future research on design stress.