Abstract
In response to the growing demand for smart household products, this study focused on the intelligent design optimization of a wooden dressing mirror that integrates traditional wood craftsmanship with smart home technologies. A data-driven design framework was developed by combining the PESTEL model, Kano demand analysis, and the Function-Behavior-Structure (FBS) model to address the personalized requirements of young single adults. PESTEL analysis identified market opportunities in sustainable wooden products and digital home integration, while the Kano model quantified and categorized user demands. The FBS model then transferred these demands into functional attributes, forming a comprehensive demand–function mapping framework. An empirical study with 16 young single adults employed semi-structured interviews and questionnaires. The results showed that (1) the Kano questionnaire had high reliability and validity (KMO > 0.92, Bartlett’s p < 0.001, Cronbach’s α > 0.92); (2) seventeen needs were classified as Must-be, One-dimensional, Attractive, and Indifferent, with “virtual fitting” showing the highest satisfaction sensitivity (0.734), and (3) the FBS model effectively guided the functional translation into hardware and software. The proposed PESTEL–Kano–FBS framework provides a practical approach for developing sustainable, user-centered smart wooden household products.
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A Data-Driven Design Framework for a Smart Wooden Dressing Mirror Integrating PESTEL-Kano-FBS Models
Rongrong Shi, Yao Luo, Rongxiang Chen, Luxi He 
*, and Jiaojiao Lin *
In response to the growing demand for smart household products, this study focused on the intelligent design optimization of a wooden dressing mirror that integrates traditional wood craftsmanship with smart home technologies. A data-driven design framework was developed by combining the PESTEL model, Kano demand analysis, and the Function-Behavior-Structure (FBS) model to address the personalized requirements of young single adults. PESTEL analysis identified market opportunities in sustainable wooden products and digital home integration, while the Kano model quantified and categorized user demands. The FBS model then transferred these demands into functional attributes, forming a comprehensive demand–function mapping framework. An empirical study with 16 young single adults employed semi-structured interviews and questionnaires. The results showed that (1) the Kano questionnaire had high reliability and validity (KMO > 0.92, Bartlett’s p < 0.001, Cronbach’s α > 0.92); (2) seventeen needs were classified as Must-be, One-dimensional, Attractive, and Indifferent, with “virtual fitting” showing the highest satisfaction sensitivity (0.734), and (3) the FBS model effectively guided the functional translation into hardware and software. The proposed PESTEL–Kano–FBS framework provides a practical approach for developing sustainable, user-centered smart wooden household products.
DOI: 10.15376/biores.21.3.7264-7303
Keywords: Wooden household products; Data-driven design; PESTEL-Kano-FBS model; Dressing mirror design; Single young adults
Contact information: Fujian Agriculture and Forestry University, Fuzhou, 350100, China;
* Corresponding authors: heluxi@fafu.edu.cn, 000q151028@fafu.edu.cn
Graphical Abstract
INTRODUCTION
Wooden household products have long been among the most widely used and economically significant categories of home products worldwide (Global Growth Insights 2024). The common categories of wooden household products are clocks, coat racks, decorative frames, and mirrors, all of which demonstrate wood’s versatility in both structural and aesthetic applications. Among them, wooden mirrors represent a distinctive intersection between utility and artistry, functioning not only as reflective tools but also as expressive interior elements that shape spatial ambience. In both Eastern and Western interior design traditions, wooden frames have been preferred for mirrors. In medieval Europe, intricately carved wooden frames symbolized craftsmanship and social status (Shephard 2013), while in traditional Chinese households, hardwoods such as rosewood or elm were prized for their stability, tactile comfort, and compatibility with lacquer or inlay techniques, producing mirrors that blended durability with cultural refinement (Wang 1986). Compared with those framed in metal, plastic, or glass, wooden mirrors offer a favorable strength-to-weight ratio, high processability, and natural warmth that create a distinctive tactile and visual experience (Ross 2010). These material qualities, together with wood’s environmental friendliness, have ensured the enduring popularity of wooden mirrors in everyday life. Their natural grain, soft color tone, and organic texture further enrich interior spaces with a sense of warmth and aesthetic harmony, enhancing both visual comfort and emotional attachment (Burnard and Kutnar 2015).
However, despite these advantages, traditional wooden designs often fall short of meeting the growing demand for intelligent and responsive living environments (Feng and Su 2025). As lifestyles evolve toward greater individualization and digitalization, the need for smart, user-adaptive wooden household products and related home products has become increasingly prominent (Fewella 2024). The global rise in solo living has significantly influenced household composition. Recent data indicate that single-person households now exceed 40% in several Northern European countries and account for more than 30% in many Western European and North American nations (Eurostat 2020; OECD 2021). In the United States, such households represented 28% of all households in 2020, which is twice the proportion recorded in 1960 (U.S. Census Bureau 2021). Similar trends are evident in East Asia, where over 40% of young residents in Tokyo live alone (MIC 2020), and South Korea anticipates continued growth in single-person households (KOSIS 2022). Although this lifestyle offers greater independence, it is also closely associated with loneliness and psychological stress (Park and Lee 2019; Lim et al. 2023; Hwang 2021), fueling the emergence of the “loneliness economy,” which encompasses smart home technologies designed to provide emotional support (Zhou et al. 2020; Han and Kim 2021). Within this context, young adults living alone increasingly seek intelligent household products that not only support daily routines but also enhance emotional well-being (Vuohijoki et al. 2023). For dressing mirrors in particular, user expectations have moved beyond the conventional role of traditional wooden mirrors toward designs that integrate personalized feedback, emotional interaction, and adaptive smart functions (SMART PRODUCTS: Technological Applications vs., n.d.).
The smart home market is expanding rapidly and is projected to surpass USD 230 billion by 2029 (Seo et al. 2016; Markets and Markets 2023). This trend is driven in part by the demand for personalized and emotionally engaging products (Fortune Business Insights 2026). Smart mirrors are evolving from simple reflective devices into multifunctional interactive platforms. Earlier applications primarily focused on fitness and beauty (Fatima et al. 2024), but recent developments have extended their functions to include health monitoring, telemedicine integration (Huang et al. 2021), skincare analysis (Lee et al. 2022), and even fall detection for the elderly (Chiu and Lee 2018). These systems are transitioning from “passive displays” to “proactive services” that support daily life, health management, and emotional well-being (Liu and Zhang 2023). However, key limitations persist, including insufficient personalization, superficial emotional interaction, and inadequate attention to user privacy (Storr et al. 2017; Li et al. 2022).
In recent years, researchers in product design and consumer behavior have employed diverse analytical frameworks to improve the systematicity and explanatory power of design research, bridging macro-environmental factors, user needs, and conceptual design. At the macro level, the PESTEL model, a critical tool for environmental context analysis, has been used to identify how political, economic, social, technological, environmental, and legal factors shape design and market dynamics. De Sousa et al. (2022) refined each PESTEL dimension by decomposing macro-environmental variables into specific elements such as production costs, consumer subsidy policies, and local government incentives, thereby identifying potential “bottlenecks” and “breakthroughs.” Bilgram and Laarmann (2023) applied the PESTEL framework to examine the influence of generative AI during product prototyping, while Morris et al. (2021) used PESTEL analysis to categorize barriers in wastewater reuse projects, emphasizing the need to consider all six dimensions in design and implementation.
At the user level, the Kano model, a classic method for identifying and classifying user needs—has been continually adapted to emerging design contexts. Shi and Peng (2021) integrated satisfaction data from online reviews into the Kano model to improve need classification accuracy in product design. Tandiono and Rau (2023) developed an enhanced framework combining the Kano model, environment-oriented QFD, and TRIZ (Theory of Inventive Problem Solving) at the component level. Kang and Qu (2021) coupled Kano categorization with a QFD optimization model to balance functional performance and affective experience. Addressing the inefficiencies of traditional survey-based Kano models, Joung and Kim (2022) introduced explainable neural networks to automate the classification of product features. Similarly, Soenandi et al. (2021) integrated Kansei Engineering with the Kano model to capture users’ emotional responses to desktop organizers and identify key satisfaction attributes.
At the conceptual design level, the Function-Behavior-Structure (FBS) model has been widely applied to establish logical mappings among function, behavior, and structure, enhancing systematic reasoning and knowledge transfer in design processes. Luo et al. (2023) emphasized that explicitly mapping these relationships ensures logical coherence and adaptability. Through fuzzy reasoning, the model can handle uncertain or incomplete information, yielding flexible and adaptive design solutions. Guo et al. (2021) applied FBS theory to improve functional resilience in uncertain environments by enabling structural reasoning and mapping. Han et al. (2021) used the FBS model as a structured semantic network to organize design concepts through the three-tiered hierarchy of function, behavior, and structure, thereby facilitating more effective retrieval, analogy, and concept generation. Russo and Spreafico (2023) further demonstrated how a macro-level FBS model could guide structural innovation in greenhouse cover systems through topological triggers.
The PESTEL, Kano, and FBS models each demonstrate unique strengths in macro-environmental analysis, user need identification, and conceptual design, respectively, forming a multi-level analytical foundation for sustainable product design research. Smart mirrors are currently transitioning from utilitarian products to emotional companions; however, existing studies still exhibit gaps in emotional interaction and user behavior integration. For young adults living alone, psychological and social needs remain insufficiently addressed (Seo et al. 2016; Luo et al. 2023). Prior research has often focused on the isolated application of these models, lacking an integrated, cross-level perspective that combines external environmental factors (PESTEL), user-perceived needs (Kano), and product conceptual mapping (FBS).
In existing studies, PESTEL analysis is frequently employed as a background tool for assessing market feasibility or industrial environments, with conclusions often remaining descriptive and difficult to operationalize in design decision-making (Schomaker and Sitter 2020). In contrast, this study treats each PESTEL dimension as an exogenous variable with both constraining and guiding effects, and converts them into actionable design input conditions through a systematic mapping mechanism. Within China’s policy and legal environment, initiatives such as “digital living,” “proactive health,” and personal information protection legislation are not abstract slogans; they directly delimit the functional boundaries, data processing modes, and interaction logic of the smart dressing mirror. At the socio-cultural level, young single adults’ high mobility, fast-paced lifestyles, and needs for emotional companionship not only shape functional directions but also profoundly influence product morphology, modular configuration, and modes of emotional interaction. Through this transformation process, PESTEL evolves from a macro-level explanatory tool into a pre-decision mechanism that actively drives innovation direction.
After incorporating macro constraints into the design system, this study further applies the Kano Model to structurally stratify young users’ needs, thereby avoiding the common problem of “feature-stacking” design. Unlike conventional approaches in which the Kano Model directly guides feature addition or reduction (Zhang et al. 2024), in this research it functions as a mechanism for demand screening and priority restructuring under constrained conditions. Once PESTEL constraints are clarified, certain features that might initially appear attractive, such as cloud-dependent facial recognition services, may be intentionally deprioritized due to privacy risks or regulatory uncertainty. At the same time, functions that are typically considered supplementary in smart home products, including privacy visualization controls and physical shielding mechanisms, may be reinterpreted as one-dimensional or even essential qualities within particular legal and social contexts. Thus, the Kano Model no longer merely reflects subjective user preferences but co-constructs a more realistic and feasible demand structure together with the macro-institutional environment.
Within this study, the FBS Model plays a critical role in translating the above analytical outcomes into concrete design solutions. Unlike prior research that primarily applies FBS as a conceptual design analysis tool (Fu et al. 2024), this study emphasizes its capacity for parameter mapping under complex constraints. Specifically, core demands filtered through the PESTEL-Kano dual mechanism are first defined at the functional level, such as health trend feedback, outfit decision support, and emotional interaction. These functions are subsequently translated into assessable behavioral objectives, including local data processing, low learning-cost interaction, and adaptation to high-frequency short-duration use. Finally, they are implemented at the structural level through design parameters such as modular folding structures, physically controllable sensor components, and portable installation mechanisms. This process ensures that design outcomes are not driven by intuition or stylistic preference but are rationally derived under multiple real-world constraints.
To address these gaps, the present study proposes an integrated PESTEL–Kano–FBS framework that enables the quantification and transformation of emotional needs into design attributes for smart wooden household products. Specifically, the PESTEL analysis identifies market opportunities and external drivers at the macro level, the Kano model stratifies users’ emotional and functional needs, and the FBS model maps these needs to corresponding functional, behavioral, and structural attributes. Based on these analytical linkages, the study further applies the framework to the intelligent enhancement of a traditional wooden dressing mirror, transforming it from a passive reflective object into an interactive platform that integrates emotional feedback and adaptive smart functions. This integration establishes a closed-loop mechanism from need identification to design implementation, enabling emotional and intelligent factors to be systematically embedded within the wooden household products design process.
In the context of the parallel advancement of China’s “Dual Carbon” strategy, the digital economy, and platform-based consumption, single-dimensional user demand analysis is no longer sufficient to support design research with practical feasibility. By systematically integrating the macro-institutional environment, users’ subjective experiences, and design engineering logic, design innovation is not merely a passive response to external constraints; rather, through structured methodologies, constraints can be transformed into directional innovation resources.
The core of this study lies in constructing a systematic design decision-making framework that progressively translates macro-contextual constraints (PESTEL) into meso-level demand structures (Kano Model) and further into micro-level design parameters (FBS Model). This framework overcomes the fragmentation commonly observed in prior design research, where macro-environmental analysis, user demand analysis, and specific design solutions remain disconnected, thereby establishing a logical closed loop from policy context to product structure. From a methodological perspective, the value of the PESTEL-Kano-FBS framework extends beyond a single product case. It provides a transferable and reusable research pathway for innovation within complex socio-technical systems. The framework is particularly applicable to intelligent products heavily influenced by policies and regulations, interactive systems involving sensitive issues such as privacy, health, or emotion, and products targeting specific social groups whose needs vary significantly across contexts. By constructing a macro-meso-micro transmission logic, this study offers a systematic paradigm for building a verifiable and interpretable theoretical bridge between institutional constraints and technological innovation in design research.
EXPERIMENTAL
Research Subject
This study investigated the needs, pain points, and expectations of young adults living alone with respect to smart dressing mirrors, particularly focusing on the intelligent enhancement of traditional wooden mirrors in the context of clothing coordination. To this end, semi-structured interviews were conducted with 16 young adults representing diverse demographic backgrounds and living arrangements. Detailed participant characteristics are provided in Table 1.
PESTEL Method
The PESTEL macro-environmental analysis method is a key tool for driving product innovation (De Sousa et al. 2022). It assists designers in systematically identifying product opportunities by examining the external environment across six dimensions: political, economic, social, technological, environmental, and legal. These dimensions together constitute the PESTEL framework (Fig. 1).
Questionnaire Survey
This questionnaire survey targeted young adults living alone, encompassing diverse occupations, income levels, and lifestyles. In addition to collecting basic demographic data, the questionnaire adopted a dual-question format derived from the Kano model, asking respondents about their feelings when a feature is present and their reactions when it is absent (Fig. 2). This design enabled the evaluation of user perceptions toward core features such as virtual try-on and outfit-sharing interactions.
Table 1. Participant Characteristics
Data collection employed both online and offline approaches. Online questionnaires were distributed through the Wenjuangxing platform, while offline surveys were administered in youth apartments and office areas to better capture the group’s specific needs and purchasing capacity.
Face-to-face communication during the offline phase allowed researchers to clarify questions in real time, thereby improving response quality and data reliability. A total of 200 questionnaires were distributed, with 177 valid responses obtained, resulting in an effective response rate of 88.5%.
Fig. 1. PESTEL Model
Fig. 2. Two-way questioning structure based on the Kano model
Kano Model
The Kano model, proposed by Professor Noriaki Kano of Tokyo Institute of Technology (Kano et al. 1984), is a widely recognized method for classifying and prioritizing user needs. It provides a systematic approach to understanding user requirements, optimizing product design, and improving user satisfaction and loyalty. Based on the relationship between demand attributes and satisfaction levels, the model classifies user needs into five categories: Must-be (M), One-dimensional (O), Attractive (A), Indifferent (I), and Reverse (R). The primary strength of the Kano model lies in its ability to elucidate how different types of product attributes influence overall user satisfaction (Fig. 3).
Must-be attributes represent the basic expectations that customers take for granted, their absence leads to strong dissatisfaction, yet their fulfillment merely prevents complaints rather than enhancing satisfaction. One-dimensional attributes exhibit a linear relationship with satisfaction, where improved performance proportionally increases satisfaction and poor performance results in dissatisfaction. Attractive attributes correspond to unexpected or novel features that delight users when present but do not cause dissatisfaction when absent. Indifferent attributes have little or no impact on customer satisfaction, as users remain largely unaffected by their inclusion or omission. In contrast, Reverse attributes decrease satisfaction when present, often due to unnecessary complexity, redundancy, or a mismatch with user expectations and preferences (Berger et al. 1993; Matzler and Hinterhuber 1998).
Fig. 3. The relationship between Kano demand attributes and user satisfaction
In practical research and application, the Kano model typically employs questionnaire surveys to collect user feedback. In recent years, online questionnaires have gained widespread adoption due to their broad coverage and high response efficiency, enabling more effective acquisition of demand perceptions across different user groups, thereby enhancing the accuracy and representativeness of model analysis. The computational formula is commonly expressed as membership degree, which quantifies the relationship between user needs and satisfaction. The expression for membership degree K is as follows,
(1)
where A, O, M, and I are the total frequencies of responses for the Attractive, One-dimensional, Must-be, and Indifferent categories, respectively. The A, M, O, I in the Better-Worse coefficients appearing below are the same as this.
The relationship between different types of needs and user satisfaction, often referred to as the Better-Worse coefficient, is commonly expressed by the following formulas.
(2)
(3)
(4)
Kaiser-Meyer-Olkin (KMO) Test
Before conducting factor analysis, the Kaiser–Meyer–Olkin (KMO) test is a necessary step to assess whether the data are suitable for factor analysis and to avoid invalid analytical results (Kaiser 1974). The KMO statistic ranges from 0 to 1, with values closer to 1 indicating stronger correlations among variables and greater suitability of the data for factor analysis (Cerny and Kaiser 1977). The Cronbach’s alpha (α) coefficient serves as a key indicator for measuring the internal consistency reliability of scales or questionnaires, and it is widely applied in fields such as market research. Its value also ranges from 0 to 1, with higher values indicating stronger inter-item correlations and better reliability (Cronbach 1951),
(5)
where k denotes the total number of items in the scale, 
represents the variance of the score for the -th item, and 
denotes the variance of the total score across all items.
In practical applications, a standardized Cronbach’s α value greater than or equal to 0.7 is generally considered to indicate acceptable reliability of the scale. The corresponding calculation is as follows,
(6)
where k denotes the total number of items in the scale, and 
represents the average inter-item correlation coefficient.
Function-Behavior-Structure (FBS) Model
The Function-Behavior-Structure (FBS) model provides an effective framework for translating user requirements into design solutions. Originally proposed by Gero (1990), it has become a foundational tool for describing the conceptual design process and remains highly influential in product design research (Gero and Kannengiesser 2004). The model consists of three interrelated and hierarchically organized components: Function, Behavior, and Structure. Together, these components form the logical foundation for transforming abstract requirements into concrete design representations (Fig. 4). When applied under complex design conditions, the FBS framework enables designers to systematically analyze how functional objectives are achieved through behavioral mechanisms and structural configurations.
Within the FBS model, a strong logical relationship exists among the three elements. Function defines behavior, and behavior determines structure. In other words, the functional requirements of a product define the behaviors needed to achieve those functions, and the corresponding structure is then designed based on these behaviors. At the same time, structure facilitates behavior, and behavior enables the implementation of function. The structure of a product dictates the behaviors it can exhibit, and these behaviors ultimately fulfill the intended functions. This interconnected and mutually reinforcing relationship forms a closed loop that guides product design from user needs to tangible product forms, ensuring alignment with user expectations (Gero and Kannengiesser 2004). Whereas conventional models primarily establish connections between function and behavior, the FBS model emphasizes the role of structure, which not only enables behavior but also provides the foundation for exploring a broader spectrum of potential behaviors (Fig. 5).
Fig. 4. FBS mapping framework
Fig. 5. FBS operation phase
Research Framework
This study proposes a “PESTEL-Kano-FBS” triadic integration framework (Fig. 6), which is structured as follows: PESTEL environmental scanning, followed by Kano-based demand grading, FBS-driven functional implementation, prototype validation, and finally, design practice. The framework integrates PESTEL macro-environmental analysis to define design boundaries, the Kano model to prioritize user requirements, and the FBS model to translate those requirements into functional solutions, thereby establishing a systematic and stepwise design methodology.
Focusing on the PESTEL-Kano-FBS model, the study applies this integrated framework to the intelligent enhancement of a traditional wooden dressing mirror for single young adults. Product opportunities and market gaps were initially identified through a PESTEL analysis, followed by a classification of user needs using the Kano model. A “Requirement–Functionality” mapping framework was subsequently developed. Based on this framework, user research was conducted through semi-structured interviews with 16 representative single young adults, complemented by a survey of 200 questionnaires, which yielded an effective response rate of 88.5%.
This process identified core user needs, including virtual try-on, outfit recommendations, and social sharing, while also establishing functional priorities. Based on these insights, a multifunctional smart wooden dressing mirror was developed, integrating virtual try-on, health data tracking, intelligent styling, and social interaction. The results highlight the practical and theoretical value of the PESTEL-Kano-FBS model in guiding the intelligent transformation of traditional wooden household products, offering a robust methodological reference for user-centered innovation and sustainable smart home product development.
Fig. 6. Research design
RESULTS
PESTEL Environmental Scan
To ensure that the design solution for the smart dressing mirror tailored to single young adults integrates strategic foresight and market competitiveness, an in-depth environmental assessment was conducted using the PESTEL model (Table 2). This analytical framework facilitated the identification of key external drivers shaping product and supported the derivation of targeted design strategies and potential market opportunities.
Behavioral Patterns and Emotional Needs of Single Young Adults
This study conducted in-person, semi-structured interviews with sixteen young adults living alone. The discussions were organized around three primary themes. The first theme examined clothing-related behaviors, including purchasing channels and challenges in outfit coordination. The second explored patterns of smart product usage, addressing the types of devices used, frequency of use, satisfaction levels, and expectations. The third focused on perceptions and expectations of smart dressing mirrors, assessing participants’ familiarity with such products, desired functionalities, and preferences regarding form and interaction methods. The semi-structured approach ensured comprehensive topic coverage while allowing participants flexibility for open expression. Each interview lasted approximately 30 minutes and was systematically documented.
Through interviews, this study identified the typical usage pathway of smart dressing mirrors among young adults living alone in pre-departure scenarios. This pathway is structured chronologically into five stages: the awareness activation stage, the screening and matching stage, the interactive try-on stage, the final confirmation stage, and the departure stage (Fig. 7). At each stage, the figure presents users’ specific behaviors, emotional fluctuations, contextual characteristics, and corresponding pain points, thereby constructing a comprehensive scenario-based emotional framework. At the behavioral level, users transition from low to high engagement. The process begins with checking the weather while still in bed, followed by browsing clothing in front of the wardrobe, repeatedly trying on outfits, adjusting details, and ultimately confirming their overall appearance. In terms of emotional dynamics, users’ affective states are not linearly stable; rather, they shift dynamically as tasks progress, reflecting the continuous psychological impact of outfit decision-making under time pressure. Regarding contextual characteristics, different stages exhibit differentiated situational conditions. The early stage is marked by high time pressure, fragmented information, and heightened spatial awareness. The middle stage emphasizes embodied human-computer interaction with greater physical participation. The later stage places greater emphasis on aesthetic confirmation as well as the sense of security and preparedness that emerges from the integration of functional performance and evaluative feedback. Accordingly, user pain points demonstrate a progressive evolution across different stages, beginning with challenges in integrating fragmented information, followed by the high costs associated with repeated try-on processes, and ultimately culminating in constraints related to the observation of fine details. By systematically juxtaposing user behaviors, emotional fluctuations, and specific usage contexts, this analysis establishes a situational foundation for the subsequent identification of user needs and elucidates the differentiated functional and emotional roles undertaken by the smart dressing mirror across diverse usage scenarios.
Table 2. Macroeconomic Environment Analysis Based on the PESTEL Model
