Smart Outdoor Furniture in Tourism-Oriented Rural Villages: Pathways Towards Becoming Inclusive and Sustainable

Abstract

As the development of “smart villages” and “sustainable rural tourism” increasingly becomes a focal point on the global policy agenda, tourism-oriented villages are experiencing a growing demand for digital infrastructure transformation. Against this backdrop, smart outdoor furniture emerges as a noteworthy intervention. However, existing designs for smart outdoor furniture predominantly originate from urban contexts, often failing to align with the distinct preferences, behavioural patterns, and cultural identity of rural users. This study employs a mixed-methods approach, combining Q-methodology with an extended Technology Acceptance Model (TAM), to explore rural users’ technology acceptance pathways. Through Q-sorting, four typical attitude structures were identified: Pragmatic Function-Oriented, Cultural Concern-Oriented, Smart Enhancement-Oriented, and Technology Anxiety-Oriented. These qualitative insights were integrated into an extended TAM framework and validated through a structured survey (n = 319) using Partial Least Squares Structural Equation modelling (PLS-SEM). Findings confirm that Perceived Usefulness and Perceived Ease of Use remain the strongest predictors of user attitude and behavioural intention. Among contextual factors, Function Configuration exerts significant positive influence on both PU and PEOU; Cultural Adaptation significantly enhances PU; Social Influence primarily affects PEOU; Smart Features moderately influence both dimensions; and Perceived Cost Structure affects only PU. This research extends the applicability of the TAM model within rural socio-technical contexts. It provides empirical reference for inclusive and sustainable digital infrastructure design in tourism-oriented villages, while offering practical insights and dissemination pathways for smart design strategies in public spaces within similar socio-cultural environments.

1. Introduction

Driven by both the “Smart Village” [1,2] and “Sustainable Rural Tourism” [3,4] policies, enhancing the digitalisation and liveability of rural public spaces is increasingly becoming a key pathway for advancing rural revitalisation, improving people’s livelihoods, and promoting the integrated development of culture and tourism [5]. Outdoor furniture, as an indispensable component of public spaces [6,7]. Its flexible deployment [8], high usage frequency [9], and relatively low maintenance costs [10] are key attributes, has emerged as a pivotal element within spatial governance and service systems [11]. Its role extends beyond shaping spatial aesthetics, increasingly encompassing multifaceted intelligent functions such as cultural display, information exchange, and environmental perception. Against this backdrop, smart outdoor furniture is widely regarded as a promising vehicle for advancing digital infrastructure development in rural public spaces [12]. Concurrently, the global advancement of “digital village” strategies [13,14,15,16] has incorporated intelligent facilities as a critical element in integrated urban-rural development [17]. However, current smart outdoor furniture (such as smart lampposts, smart litter bins, and integrated stations) predominantly originates from urban contexts [18]. Its promotion and application in rural environments face significant challenges, including insufficient cultural adaptability [19], high operational and maintenance costs [20], and ambiguous user perceptions coupled with limited acceptance [21]. The pronounced disparities between urban and rural areas in spatial form, technological foundations, and social structures mean that simply replicating urban solutions cannot achieve genuine technological inclusion. Therefore, establishing a systematic bridge between technical feasibility and social acceptability is urgently needed to effectively integrate smart technologies with rural spatial, social, and cultural systems.

At the academic research level, current discussions on smart outdoor furniture predominantly focus on aspects such as technological implementation [22], material design [23,24,25,26], functional development [27,28], sustainability [29,30,31], planning and design [32,33], and management systems [34]—that is, the “object” dimension. However, the essence of outdoor furniture lies in serving people [12]. Compared to the rapid iteration of technological offerings, research into the human dimension, specifically regarding user needs and acceptance levels, remains relatively underdeveloped. Particularly within rural contexts, insights into user value perceptions, behavioural motivations, and cultural preferences remain scarce [35]. Existing literature further indicates significant heterogeneity between urban and rural users in terms of cognitive levels, behavioural habits, and technology adoption pathways [36,37,38]. Failure to adequately identify and address these differences may result in technological underutilisation and resource wastage. Therefore, systematically exploring rural users’ cognitive structures and acceptance attitudes towards smart outdoor furniture, and subsequently proposing people-centred, contextually appropriate design strategies, holds significant practical importance for advancing genuine technological inclusivity in rural areas and sustainable spatial governance [39].

Existing research exploring user needs typically draws upon Maslow’s hierarchy of needs theory [40,41], motivation theories [42,43,44], or employs methodologies such as co-design [45], the Kano model [46,47], and grounded theory [48,49,50]. From a methodological perspective, the Technology Acceptance Model (TAM) has been extensively employed in technology adoption studies [51,52] due to its strong explanatory power and model extensibility. In recent years, it has also begun to take shape in outdoor furniture technology research [53]. However, as a variance model based on quantitative questionnaires (e.g., Likert scales), TAM often presupposes cognitive homogeneity within user groups. This makes it challenging to capture and explain the deep-seated, heterogeneous subjective attitude structures and their formation mechanisms within user groups [54], particularly among diverse rural user groups, where its explanatory power faces theoretical limitations. To address this fundamental shortcoming, this study innovatively introduces Q methodology [55]. This approach emphasises constructing “attitude types” from participants’ perspectives, shifting the analytical focus from traditional “group representativeness” to identifying “subjective viewpoint structures”. It effectively reveals the root causes of attitude heterogeneity that TAM struggles to capture. Taking Jingshan Village, a prototypical tourism-oriented rural community in Zhejiang Province, as the field research site, this study systematically identifies value perceptions and acceptance attitudes towards smart outdoor furniture among diverse rural stakeholders. Rather than detailing specific product design outcomes, this study adopts an exploratory methodological approach to investigate the socio-technical mechanisms underpinning rural user acceptance. It constructs an extended TAM model incorporating key external variables and explores pathway relationships. Consequently, more targeted and inclusive design strategies are proposed to facilitate the sustainable implementation of smart facilities within tourism-oriented rural settings.

This study focuses on the following three questions:

• RQ1: In the acceptance process of smart outdoor furniture, what distinct subjective attitude configurations emerge regarding functional requirements and value perceptions among different core stakeholders (e.g., villagers, tourists, managers) in rural settings? (Q-methodology exploration)
• RQ2: How do context-specific variables unique to rural settings (e.g., Functional Configuration, Cultural Adaptability) influence perceived usefulness (PU) and perceived ease of use (PEOU), thereby affecting behavioural intention (BI)? (Extended TAM model validation)
• RQ3: Based on findings from RQ1 exploration and RQ2 validation, how can design optimisation strategies be formulated that simultaneously achieve ‘high acceptance’ and ‘low usage barriers’ to facilitate the effective implementation of smart facilities in tourism-oriented rural areas? (Strategy Derivation)

By integrating Q-methodology’s capability to identify user attitude heterogeneity with TAM path modelling’s predictive power, this study seeks to build an analytical bridge between ‘user logic’ and ‘technical logic’, providing theoretical underpinnings and practical guidance for the sustainable rural application of smart outdoor furniture.

2. Materials and Methods

2.1. Study Area

This study selects Jingshan Village in Yuhang District, Hangzhou City, Zhejiang Province as its empirical case. Jingshan Village comprises 515 farming households with a permanent population of approximately 1610. As a popular tourist destination, it receives up to 300,000 visitors annually, including numerous overseas tourists. The village stands as a model for China’s “Beautiful Countryside” initiative, possessing significant exemplary value due to its deep integration of green ecology and digital technology within “Future Village” development (Figure 1). Its representativeness manifests primarily in four aspects: Firstly, the forward-looking nature of policy support. The village was included in the inaugural cohort of the “China Zero-Carbon Towns and Villages Promotion Project” in 2025, its development trajectory reflecting national-level policy orientations for rural construction. Second, transitional industrial form. While preserving traditional agricultural culture and strong community cohesion, the village actively develops tourism and introduces the digital economy, forming an industrial structure and social form that blends tradition with modernity. Third, pioneering digital infrastructure. The village has deployed multiple smart public facilities, including smart bus stops, smart streetlights, photovoltaic benches, and smart waste bins with early warning systems, providing rich real-world samples for studying the rural application of smart technologies. Fourth, social structural diversity. Long-term residents, migrant entrepreneurs, tourists, and village administrators coexist as diverse stakeholders, offering an ideal social arena for examining differences in acceptance of intelligent technologies among various user groups. In summary, Jingshan Village uniquely combines traditional character with modern functionality, technological application with social complexity, making it an ideal case study for exploring how smart technologies can be embedded within and reshape rural living spaces. Its research findings hold significant implications for China and other regions facing similar transformative challenges.

2.2. Methodological Approach

2.2.1. Q Methodology

Q Methodology is a hybrid research approach for systematically exploring subjectivity, philosophically grounded in operantism. It aims to scientifically identify and interpret the diverse viewpoint configurations inherent in any subject matter [56]. This methodology can profoundly reveal consensus and divergence within complex social issues using relatively small purposive samples (P-sets), proving particularly suitable for examining topics involving value judgements such as societal acceptance of emerging technologies and policy preferences [57]. It is now widely applied across social sciences [58], educational research [59], environmental and sustainability studies [60], and public administration [61].

Its research process typically follows a standardised sequence of steps [62]: First, construct the Q-set: Establish a Concourse—a repository of statements encompassing all potential viewpoints on the research topic—through literature analysis, interviews, and field investigations. Select representative statements from this Concourse to form the Q-set. Secondly, implement Q-sorting: participants (P-set) with diverse backgrounds are invited to rank statements from the Q-set according to their degree of internal agreement (from “Most Prohibitive” to “Most Imperatice”) using a forced-normality distribution. This converts their subjective attitude structure into quantifiable data. Finally, data analysis and interpretation: Extracting significant factors through methods such as Principal Component Analysis (PCA), and clarifying the factor structure using Varimax rotation. Each extracted factor represents a shared subjective viewpoint structure or configuration. These are narratively named and interpreted by combining their signature statements (high-loadings and low-loadings) with participant interviews.

Unlike traditional questionnaire methods that merely reveal “how many people hold a certain attitude”, Q-methodology further addresses “what distinct perspectives people actually hold” and “why they hold them”. This characteristic aligns closely with the first-phase objective of this study: not to quantify the prevalence of individual attitudes, but to systematically identify and understand the diverse, yet under-explored, patterns of subjectivity regarding smart outdoor furniture within rural contexts. This provides a contextualised, empirically grounded theoretical foundation for subsequently expanding the construction of the Technology Acceptance Model (TAM).

2.2.2. Technology Acceptance Model (TAM)

The Technology Acceptance Model (TAM), proposed by Davis [63], serves as the core theoretical framework within the field of information systems for explaining and predicting users’ technology adoption behaviour. Originally rooted in the Theory of Reasoned Action (TRA), its foundational framework established Perceived Usefulness (PU) and Perceived Ease of Use (PEOU) as key antecedents of Behavioural Intention (BI), thereby predicting actual technology usage behaviour. Renowned for its robust explanatory power and structural simplicity, the model has undergone significant theoretical refinement and contextual expansion. Successive iterations include TAM2 [64] incorporating social influence and cognitive process variables, TAM3 [65] integrating anchoring and adjustment factors, and the more comprehensive Unified Theory of Acceptance and Use of Technology (UTAUT) [66]. It finds extensive application across domains including healthcare [67], construction [68], information technology [69], and intelligent technologies [70].

The standard research process for TAM comprises: first, constructing a hypothetical model incorporating core variables and their antecedents based on theoretical literature and research context; subsequently, developing or adapting measurement scales for latent variables, typically employing Likert-type multi-level scales; Subsequently, large-scale questionnaire surveys are conducted among target user groups to collect data; finally, Structural Equation Modelling (SEM) is primarily employed to analyse the data, validating the pre-set path hypotheses and assessing the overall model fit [71]. However, the classical TAM and its extensions often rely on researcher-preset variables, potentially overlooking critical factors specific to particular cultures or contexts (such as the rural-specific ‘human touch’ in this study) [72], and struggle to capture the complex, multifaceted subjective motivational structure underlying attitudes.

To overcome these limitations, this study will construct a contextualised extended TAM model. Building upon core constructs such as Perceived Utility (PU), Perceived Ease of Use (PEOU), Attitude Toward Using (ATU), and Behavioural Intentions (BI), this model integrates key antecedent variables derived from Phase I Q-methodology research. These include specific perceptual dimensions of: Functional Configuration (FC), Cultural Adaptability (CA), Social Influence (SI), Perceived Cost Structure (PCS), and Smart Features (SF).

2.3. Integrated Q–TAM Research Framework

This study employs a Sequential Exploratory Design as a mixed-methods approach [73], systematically integrating Q-methodology with an extended Technology Acceptance Model (TAM) to achieve both in-depth exploration and broad validation of the research questions.

The research comprises two distinct phases (Figure 2):

Exploratory Phase: Employing Q-methodology, a person-centred exploratory technique, to identify, in a bottom-up approach, the primary subjective attitude configurations held by diverse rural stakeholders towards smart outdoor furniture. This aims to answer “What distinct patterns of views exist?” and “What is the inherent logic of each perspective?” (RQ1).

Validation Phase: Building upon Phase One’s qualitative findings, an extended TAM theoretical model grounded in the local context is constructed. This undergoes quantitative validation through a large-scale questionnaire survey, addressing “how the path relationships between influencing factors operate” and “what drives behavioural intentions across different perspective groups” (RQ2).

This design effectively integrates the strengths of both methodologies: Q-methodology discoveries provide the TAM model with contextual depth and validity, ensuring expanded variables are not preconceived theoretical assumptions; while TAM’s quantitative analysis compensates for Q-methodology’s limitations in small samples, enhancing the generalisability and extrapolation power of research findings. Ultimately, through methodological Triangulation, this study constructs a comprehensive explanatory model that both profoundly understands users’ subjective motivations and possesses statistical persuasiveness. This model informs the derivation of innovative and feasible design strategies, thereby guiding design practice (RQ3).

3. Model Construction and Analytical Process

3.1. Exploring User Attitudes via Q Methodology

3.1.1. Q Sorting Design and Implementation

This study strictly adheres to the standardised operational procedures of Q methodology. The construction of the Q-set integrates in-depth fieldwork with systematic literature analysis to ensure the comprehensiveness and representativeness of the statements. Firstly, fieldwork was conducted in Jingshan Village, Zhejiang Province, focusing on three core stakeholder groups: villagers, tourists, and village administrators. Nine semi-structured interviews and participatory observations were completed, extensively gathering raw perspectives and discourse on smart outdoor furniture to preliminarily construct the original “discourse domain” (Concourse) for the research topic. Subsequently, a systematic review of literature on rural revitalisation, outdoor furniture, smart technologies, and technology acceptance was conducted to achieve theoretical saturation. Finally, the original statements underwent thematic categorisation, deduplication, and refinement. Two Q-methodology scholars and three design experts participated in multiple rounds of deliberation. This process yielded a formal Q-sample comprising 32 statements selected from the initial 60 (

Table 1. Q-set for Rural Outdoor Smart Furniture.

Categories	Keyword	Q-Set	Source
Functionality and practical value	Customer Care	Q1: The automatic seat heating in winter is particularly beneficial for the elderly and children.	Village residents’ corpus
	Improving Quality of Life	Q2: Smart bin with automatic lid opening and sorting saves me time.	Village residents’ corpus
	Attraction	Q3: High-tech attractions draw young travellers.	[74]
	Sense of Security	Q4: The automatic emergency call function enhances the sense of security.	[75]
	Questioning Non-Core Functions	Q5: The display-equipped features look rather flashy but offer little practical benefit to me.	Village residents’ corpus
	Village Residents’ Requirements	Q6: Smart furniture is not as hassle-free as traditional furniture.	Village residents’ corpus
Ease of Use and Learning	Simple Operation Requirements	Q7: Language control simplifies operation.	[76]
	Learning Difficulties in Older Adults	Q8: At my age, I’m afraid of pressing the wrong button or breaking it.	Village residents’ corpus
	Training/Support Requirements	Q9: If someone in the village could provide instruction or a simple explanation, that would be rather helpful.	Village residents’ corpus
Privacy and Security	Concerns Regarding Data Collection	Q10: I’m concerned it will record when I used it and for how long.	Village residents’ corpus
	Technical Understanding	Q11: Furniture that speaks might make me feel uneasy.	Village residents’ corpus
Emotional and behavioural intentions	Pleasant Experience	Q12: Being able to connect to WiFi and video call my child while out and about feels rather nice.	Village residents’ corpus
	Emotional Detachment	Q13: Having such cold, high-tech equipment in the village is no match for the warmth of sitting on a wooden bench.	Village residents’ corpus
	Willingness to Use	Q14: If the village installed this sort of smart outdoor furniture, I’d be quite keen to give it a go.	[66]
	Refusal of Consent	Q15: Even if it’s free to install, I find it too much of a faff and wouldn’t want to bother with it.	Village residents’ corpus
Reliability and Maintenance	Accessibility	Q16: Fault self-diagnosis prompts reduce the difficulty of repairs.	[77]
	Durability concerns	Q17: Exposed to sun and rain, the smart outdoor furniture in the village becomes mere decoration once damaged.	[78]
	Serviceability	Q18: If damaged, repairs are difficult and time-consuming.	Village residents’ corpus
Cost and Economic Efficiency	Value Perception	Q19: Solar power eliminates electricity bills, proving cost-effective in the long run.	[79]
	Maintenance costs	Q20: Such smart home appliances are expensive to begin with, and the cost of repairs is simply unaffordable.	Village residents’ corpus
	Policy Benefits	Q21: Government subsidies can alleviate my cost concerns.	[80]
Cultural Adaptation and Appearance	Landscape Harmony	Q22: The smart street lamps, featuring an antique-inspired design, harmonise with the village’s aesthetic.	[81]
	Material Localisation	Q23: Local materials paired with smart features for a more approachable feel.	Village residents’ corpus
	Material Conflict	Q24: Modern metal materials have disrupted the village’s rustic atmosphere.	[82]
	Cultural Identity	Q25: Smart public facilities displaying village history information are more readily accepted by the elderly.	[83]
	Cultural Value	Q26: Digital patterns lack the depth of hand-carved designs.	Village residents’ corpus
	Traditional vs. Modern	Q27: Smart home furnishings should ideally be thoughtfully integrated with traditional village craftsmanship elements.	[84]
Social Impact and Norms	Authoritative Influence	Q28: If the Village Administrator and neighbours take the lead, I’ll follow suit.	[85]
	External Image Enhancement	Q29: The praise from visitors for our smart outdoor furniture fills me with pride.	Village residents’ corpus
	Intergenerational Communication	Q30: Young people teach older people to foster intergenerational exchange.	[86]
	Reduced Social Anxiety	Q31: Over-reliance on technology diminishes interaction among villagers.	Village residents’ corpus
	Future Outlook	Q32: Smart home appliances may transform our way of life.	Village residents’ corpus

). This sample comprehensively covers eight core dimensions: Functionality and practical value, Ease of Use and Learning, Privacy and Security, Emotional and behavioural intentions, Reliability and Maintenance, Cost and Economic Efficiency, Cultural Adaptation and Appearance and Social Impact and Norms. It balances the distribution of positive, negative, and neutral perspectives, ensuring the systematic capture of all subjective cognitive categories relevant to the research topic.

P-set recruitment employed Purposive Sampling and Maximum Variation sampling strategies. In accordance with Q-methodology logic, its statistical power derives from the richness of variables (statements) rather than participant numbers. The study ultimately recruited 18 participants, comprising 10 males and 8 females, encompassing local villagers (n = 10), rural tourists (n = 3), and village administrators (n = 5). This sampling strategy ensured coverage of diverse user groups to maximise capture of attitudinal configuration heterogeneity. The research objective focused on revealing the diversity of subjective perspectives rather than statistical representativeness.

Data Collection employed a standardised Q-sort procedure. Under researcher guidance, each participant ranked 32 statement cards along a 9-point continuous forced-normal distribution grid, from “Most Imperatice” (+4) to “Most Prohibitive” (−4), based on their intrinsic level of agreement (Figure 3). Throughout the sorting process, researchers conducted continuous audio recording and encouraged participants to employ “think-aloud” techniques. This captured the underlying logic and judgement criteria behind their sorting decisions, providing qualitative support for subsequent factor interpretation.

Data analysis was conducted using Ken-Q Analysis. Principal Component Analysis (PCA) was employed to extract initial factors, followed by Varimax rotation to clarify the factor structure and identify latent configurations representing shared attitudes.

3.1.2. Factor Analysis and Configuration Interpretation

Based on the Kaiser-Guttman criterion (eigenvalue > 1) and supplemented by the scree plot analysis, four significant factors were ultimately extracted, cumulatively explaining 84% of the variance. This indicates the model possesses excellent explanatory power (Table 2). These four factors represent four distinct, shared configurations of subjective attitudes present within the participant group (Figure 4).

(1)

Pragmatic Function-Oriented

FI exhibits an eigenvalue of 8.53 and accounts for 47% of sample variance, representing the most influential perspective configuration. This factor correlates strongly with 6 participants (3 village administrators, 2 villagers, 1 family traveller). Its defining characteristic is a pronounced emphasis on the product’s core practical value and safety assurance. Ranking results indicate this group places paramount importance on user care (Q1: +3) and personal safety (Q4: +4) features, while also recognising their value in enhancing life efficiency (Q2: +2) and long-term cost-effectiveness (Q19: +3). One participant remarked: “Seat heating is tremendously considerate for the elderly and children—genuinely useful”. Simultaneously, they demand intuitive operation (Q7: +2) while explicitly rejecting flashy, overly complex features (Q5: −3), stating “functions with displays look gimmicky but offer little practical benefit”. They showed scant interest in features promoting social interaction (Q31: −2) or cultural symbolism (Q25: −2), with their decisions driven solely by practical utility. Notably, the village cadre participant also expressed concern for policy support (Q21: +1), reflecting their managerial perspective.

(2)

Cultural concern-oriented

FII exhibits an eigenvalue of 3.80 and accounts for 21% of sample variance. It correlates strongly with 5 participants (3 local villagers, 2 elders). The core of this construct is a profound concern for local cultural identity and the preservation of traditional character. They exhibited strong preferences for local materials (Q23: +3) and antique-inspired designs (Q22: +3), firmly believing products should blend into the rural ambience and retain “human warmth” (Q13: +4). One participant remarked: “Having such cold, high-tech stuff in the village is no match for the human warmth of sitting on a wooden stool”. In stark contrast, their cost concerns (Q20: +2) and doubts about durability (Q17: +2) are pronounced, alongside unease and resistance towards outward-facing functions like technological interaction (Q11: −2) and tourist attraction (Q3: −3). This preservation of cultural integrity stands as the paramount principle in their value hierarchy.

(3)

Smart Enhancement-Oriented

FIII exhibits strong correlations with 5 participants (2 village administrators, 2 tourists, 1 returning entrepreneur). This group represents technological optimists in rural development, prioritising the modern image, external attention (Q3: +3; Q29: +3), and developmental opportunities that smart furnishings can deliver. They actively acknowledge technology’s positive value in enhancing village appeal and facilitating intergenerational exchange (Q30: +3), appreciating smart outdoor furniture’s role in improving visitor experience and satisfaction, viewing it as a key element in adding value to rural cultural tourism. One participant remarked: “Visitors’ praise makes me feel proud”. Notably, visitor participants within this group highly value the convenience and enjoyment smart facilities bring to their touring experience, viewing them as a significant manifestation of the village’s modern image and appeal. Consequently, they reject the notion that technology diminishes social interaction (Q31: −2), show relatively low concern for traditional materials (Q24: −1) and costs (Q21: 0), and demonstrate a high willingness to recommend (Q14: +2). Their perspective embodies a forward-looking vision for driving rural development through smart technology.

(4)

Technology-Anxiety-Oriented Type

FIV showed strong correlations with 2 participants (1 elderly individual and 1 with low technical experience). This configuration reflects profound anxiety and alienation stemming from technological adaptation barriers. Its focus is entirely centred on operational complexity and learning costs (Q8: +4; Q9: +3), with a strong desire for simple, intuitive functionality (Q7: −2). One participant candidly stated: “At my age, I’m afraid of pressing the wrong button or breaking it”. They exhibited marked resistance (Q15: +1) and unease (Q10: +3) towards technologically advanced features, coupled with scepticism regarding the product’s long-term reliability (Q18: +2). Consequently, they struggled to derive pleasure from smart technology (Q12: −1), yielding predominantly negative evaluations. This factor highlights the imperative to prioritise addressing usability and inclusivity challenges in the pursuit of technological inclusion.

Table 2. Factor characteristics.

	Factor I	Factor II	Factor III	Factor IV
No. of Defining Variables	6	5	5	2
Eigenvalues	8.53	3.80	1.67	1.34
% Explained Variance	47	21	9	7
cumulative % explained variance	47	68	77	84
Avg. Rel. Coef.	0.8	0.8	0.8	0.8
Composite reliability	0.96	0.952	0.952	0.889
S.E. of Factor Z-scores	0.2	0.219	0.219	0.333

Table 2. Factor characteristics.

	Factor I	Factor II	Factor III	Factor IV
No. of Defining Variables	6	5	5	2
Eigenvalues	8.53	3.80	1.67	1.34
% Explained Variance	47	21	9	7
cumulative % explained variance	47	68	77	84
Avg. Rel. Coef.	0.8	0.8	0.8	0.8
Composite reliability	0.96	0.952	0.952	0.889
S.E. of Factor Z-scores	0.2	0.219	0.219	0.333

Figure 4. Radar chart of rounded statements scores for distinguishing statements of factor.

Figure 4. Radar chart of rounded statements scores for distinguishing statements of factor.

3.2. Extended TAM Model Development

3.2.1. Conceptual Mapping and Variable Translation

Building upon the core constructs of the classic Technology Acceptance Model (TAM), this study endeavours to construct an extended model better suited to the technological context of rural societies. While the classic TAM reveals the foundational influence of Perceived Usefulness (PU) and Perceived Ease of Use (PEOU) on technology adoption, its explanatory power proves insufficient in cross-disciplinary research concerning smart outdoor furniture—an emerging phenomenon—within the specific rural domain. To address this limitation, this study avoids introducing pre-determined external variables. Instead, it adopts a locally grounded approach: Through a systematic concept mapping process combined with existing literature research, we have transformed and conceptualised the core issues addressed by the four attitude configurations identified in the preliminary Q-methodology study into five contextualised antecedent variables. This transformation ensures the theoretical elements of the expanded model are deeply rooted in the genuine perceptions and value judgements of the target users, rather than the researchers’ a priori assumptions. The specific transformation logic is as follows.

• From “Pragmatic function-oriented (FI)” to “Functional Configuration (FC)”

The core characteristic of the FI configuration lies in its paramount emphasis on practical value, safety assurance, and enhanced living efficiency. Participants holding this perspective strongly endorsed statements related to user care (Q1), safety assurance (Q4), and efficiency improvement (Q2), while also being regarded as foundational needs by other factors. Consequently, we propose the Functional Configuration (FC) concept to measure users’ evaluations of the fundamental utility and safety functions of smart outdoor furniture.

2.

From “Cultural concern-oriented (FII)” to “Cultural Adaptability (CA)” and “Perceived Cost Structure (PCS)”

The FII configuration reveals users’ profound concern for local cultural identity and the preservation of traditional characteristics. This manifests as strong preferences for indigenous materials (Q23), aesthetic harmony (Q22), and preserving “humanistic warmth” (Q13). It correlates with F3’s emotional responses to aesthetics and value (Q3). Consequently, we derive the Cultural Adaptability (CA) variable to gauge users’ perceptions of how product form, materials, and symbolic meaning integrate with local culture.

Concurrently, FII participants exhibited significant anxiety regarding durability (Q17), serviceability (Q18), and maintenance costs (Q20). It also integrates FI’s focus on long-term utility (Q19) and FIII’s decision-maker perspective on the rationality of initial investment and its developmental benefits. These concerns are deeply intertwined with their desire to preserve tradition and avoid ephemeral modern interventions, forming the core foundation of the Perceived Cost Structure (PCS) variable for evaluating users full lifecycle economic value.

3.

From “Smart Gain-Oriented (FIII)” to “Social Influence (SI)”

Group FIII comprises technological optimists who value the modern image and developmental opportunities afforded by smart home appliances. Their perceptions are significantly influenced by external social factors, such as the demonstration effect of village administrators (Q28), external recognition from tourists (Q29), and intergenerational support from younger generations (Q30). We distil these social dynamics into the Social Influence (SI) variable, reflecting the role of authoritative figures, peer recognition, and family support within rural social hierarchies.

4.

Composite Variable: Integrating Diverse Perspectives into “Smart Features (SF)”

By synthesising interactions and comparisons across four configurations, we construct the composite dimension of Smart Features (SF), representing users’ holistic experience of the technology itself. This dimension integrates the complexity of FIV (Technology-anxiety-oriented) fears (Q11), the functionality emphasised by FI (Q2), the resistance to technological interaction within FII (Q6), and the appreciation of modernised imagery in FIII (Q3). Furthermore, drawing upon relevant literature on intelligent technologies, it ultimately embodies users’ holistic experience and evaluation of technological interaction fluidity, information presentation clarity, responsiveness sensitivity, and technological presence (such as stealth). Consequently, SF is not bound to any single Q configuration but constitutes a more comprehensive construct, capturing the delicate equilibrium perceived by diverse user groups between intelligent functionality, aesthetic modernity, and user-friendliness.

To ensure these newly constructed variables and their measurement items accurately capture the target constructs, the study subsequently undertook rigorous expert validity assessment. A panel comprising 12 senior design scholars and experts in the field of rural development reviewed the preliminary measurement model. Items were screened and optimised based on the Content Validity Ratio (CVR), ultimately establishing a measurement model comprising 9 latent variables and 30 observed variables (

Table 3. Conceptualisation and Project Implementation of Outdoor Smart Furniture in Rural Settings within this Model.

Variable Type	Latent Variable	Codes	Observable Variables	Source
External variables	Functional Configuration (FC)	FC1	Safety Assurance	FI-Q4, [87]
		FC2	Enhancing Quality of Life	FI-Q2, [88]
		FC3	User Care	FI-Q1, [39]
	Cultural Adaptability (CA)	CA1	Aesthetic Form Design	FII-Q22, [89]
		CA2	Material Localization Level	FII-Q23, [90]
		CA3	Perceived Cultural Identity	FII-Q13, [91]
	Social Impact (SI)	SI1	Cadre Demonstration Effect	FIII-Q28, [92]
		SI2	External Recognition-Driven	FIII-Q29, [93]
		SI3	Intergenerational Support-Driven	FIII-Q30, [94]
	Perceived Cost Structure (PCS)	PCS1	Perceived Acquisition Cost	FII-Q20
		PCS2	Perceived Maintenance Cost	FII-Q17, [95]
		PCS3	Perceived Value	FI-19
	Smart Features (SF)	SF1	Low-Profile Smart Design	FII-13, [96]
		SF2	Information Accessibility	FI-Q2, [97]
		SF3	Interactive Experience	FIII-Q3, [98]
		SF4	Sensing Effect	FIV-Q8, [99]
Internal variable	Perceived Usefulness (PU)	PU1	Enhanced Daily Convenience	[66]
		PU2	Emotional Belonging	[100,101]
		PU3	Autonomy Support	[101,102]
		PU4	Self-Efficacy	[103]
	Perceived Ease of Use (PEOU)	PEOU1	Operational Simplicity	[66,101]
		PEOU2	Learnability	[66,104]
		PEOU3	Age-Friendly Design	[105]
	Attitude Toward Using (ATU)	ATU1	Perceived Evaluation	[106]
		ATU2	Affective Tendency	[66]
		ATU3	Self-Initiated Learning	[107]
	Behavioural Intention (BI)	BI1	Usage Intention	[66,101,108]
		BI2	Recommendation Intention
		BI3	Continuance Intention

). All observed variables were measured using a five-point Likert scale (1 = “Strongly Disagree” to 5 = “Strongly Agree”). These measurement items primarily originated from two sources: for classic TAM constructs such as PU, PEOU, ATU, and BI, items were adapted from established scales by Davis and Venkatesh et al.; For the five newly introduced contextual constructs, items were directly derived from statements and interview transcripts in prior Q research, supplemented by semantic refinement using relevant literature from fields such as intelligent technology and rural sociology. This ensured the scale was both grounded in local experience and compliant with academic measurement standards (

Table 4. Variables and Measurement Items.

Variable	Measurement Items
FC	FC1: I believe it offers excellent safety assurances when in use.
	FC2: I believe it can effectively enhance my daily productivity (such as saving time through automated waste sorting).
	FC3: I find health care features (such as heated seats in winter) extremely useful.
CA	CA1: I believe its design harmonises perfectly with the traditional character of the village.
	CA2: I consider the use of local materials (such as timber and stone) to be a commendable practice.
	CA3: I believe its function can effectively enhance our cultural identity.
SI	SI1: If village officials lead by example in using it, I would be inclined to adopt it myself.
	SI2: Visitors’ appreciation will enhance my vision for its use.
	SI3: The assistance offered by the village youngsters makes me more inclined to use it.
PCS	PCS1: I consider its initial purchase price to be good value for money.
	PCS2: I consider its subsequent maintenance and repairs to be convenient and reasonably priced.
	PCS3: I believe the savings it delivers over the long term (such as energy efficiency) are well worth it.
SF	SF1: I consider its technology unobtrusive, possessing a degree of concealment (no intrusive screens or voice prompts).
	SF2: I find it very convenient to obtain and comprehend information from it.
	SF3: I find the interaction with it to be very smooth.
	SF4: I consider it to be highly sensitive and accurate in its environmental perception and response.
PU	PU1: Using smart outdoor furniture has helped me better fulfil my current needs (such as looking up information and recharging my mobile phone).
	PU2: I believe it has strengthened my sense of belonging as a member of this village.
	PU3: I believe it enables me to experience a wider range of features independently.
	PU4: I believe it has given me greater confidence in how I handle my daily life.
PEOU	PEOU1: I find its operational procedures straightforward and intuitive, making it easy to master.
	PEOU2: I don’t think it’s difficult to learn how to use all its features.
	PEOU3: I believe its design (such as typography and buttons) has been thoroughly considered with the needs of the elderly in mind.
ATU	ATU1: I think it’s a good idea to use smart outdoor furniture in the village.
	ATU2: My impression of smart outdoor furniture is favourable.
	ATU3: I shall take the time to learn how to use this smart outdoor furniture.
BI	BI1: If it is installed in the village, I would be happy to use it.
	BI2: I would be happy to recommend smart outdoor furniture to my neighbours.
	BI3: I anticipate making frequent use of this smart outdoor furniture in the future.

).

3.2.2. Model Structure and Hypotheses Formulation

Building upon the classical TAM theoretical framework and prior Q-methodology research findings, this study constructs an extended Q-TAM model and proposes the following research hypotheses:

H1a. 

Functional Configuration (FC) has a significant positive effect on Perceived Usefulness (PU).

H1b. 

Functional Configuration (FC) has a significant positive effect on Perceived Ease of Use (PEOU).

H2a. 

Cultural Adaptability (CA) has a significant positive effect on Perceived Usefulness (PU).

H2b. 

Cultural Adaptability (CA) has a significant positive effect on Perceived Ease of Use (PEOU).

H3a. 

Social Impact (SI) has a significant positive effect on Perceived Usefulness (PU).

H3b. 

Social Impact (SI) has a significant positive effect on Perceived Ease of Use (PEOU).

H4a. 

Perceived Cost Structure (PCS) has a significant positive effect on Perceived Usefulness (PU).

H4b. 

Perceived Cost Structure (PCS) has a significant positive effect on Perceived Ease of Use (PEOU).

H5a. 

Smart Features (SF) have a significant positive effect on Perceived Usefulness (PU).

H5b. 

Smart Features (SF) have a significant positive effect on Perceived Ease of Use (PEOU).

H6. 

Perceived Ease of Use (PEOU) has a significant positive effect on Perceived Usefulness (PU).

H7a. 

Perceived Usefulness (PU) has a significant positive effect on Attitude Toward Using (ATU).

H7b. 

Perceived Ease of Use (PEOU) has a significant positive effect on Attitude Toward Using (ATU).

H8. 

Attitude Toward Using (ATU) has a significant positive effect on Behavioural Intention (BI).

Based on the aforementioned hypotheses, this study constructs a Q-TAM theoretical model for the technology acceptance of smart outdoor furniture in rural settings, as illustrated in (Figure 5).

4. Model Validation and Results

Structural Equation Modelling (SEM) is a multivariate statistical method for analysing complex causal relationships among multiple variables. Its advantage lies in simultaneously estimating measurement relationships between latent variables and their observed indicators, alongside structural relationships among latent variables, while effectively handling measurement error. SEM primarily encompasses two estimation methods: Covariance-Based SEM (CB-SEM) and Partial Least Squares SEM (PLS-SEM). CB-SEM is suitable for theory validation, emphasising overall model fit; whereas PLS-SEM is a variance-based, predictive analysis technique particularly suited to scenarios with limited sample sizes, non-normally distributed data, or theoretical models in development and exploratory phases [109]. Compared to CB-SEM, PLS-SEM imposes more relaxed assumptions regarding model complexity and data distribution, avoids issues of identifiability and convergence, and is more suitable for exploratory research and constructing novel theoretical frameworks.

This study opted to employ PLS-SEM for analysis, primarily based on the following considerations: Firstly, the constructed extended TAM model exhibits considerable complexity, comprising 9 latent variables and 30 observed variables, with 14 hypothetical pathways. PLS-SEM demonstrates distinct advantages in handling such intricate models; Secondly, the research subjects were concentrated in typical rural case studies, resulting in a limited actual sample size (n = 319). PLS-SEM demonstrates good adaptability and stability for small sample situations; Thirdly, this study introduces five contextualised constructs (Functional Configuration, Cultural Adaptability, Social Impact, Perceived Cost Structure, and Smart Features) beyond the classical TAM. Given the exploratory nature of the theoretical model, PLS-SEM is better suited for such theoretical development and causal prediction tasks. In summary, PLS-SEM effectively supports the theoretical objectives and data conditions of this research, providing a robust analytical foundation for exploring the mechanisms of intelligent technology acceptance in rural settings.

4.1. Descriptive Statistics of the Sample

This study employs Jingshan Village in Zhejiang Province as its case site. A total of 340 paper questionnaires were distributed offline and fully recovered. Following rigorous screening, 319 valid questionnaires were ultimately selected for data analysis, yielding a valid recovery rate of 93.8%. Based on the village’s permanent population (approximately 1610 individuals), the valid sample size represents approximately 19.8%, demonstrating sound representativeness. The sample encompassed three key groups: residents, village administrators, and tourists, ensuring comprehensive research perspectives. Descriptive statistics were conducted on participants’ gender, age, educational attainment, and occupational composition (

Table 5. Participant characteristics.

	Demographic Characteristic	N	%
Gender	Male	164	51.4
	Female	155	48.5
Age	Under 18	5	1.6
	18–25	28	8.8
	26–30	84	26.3
	31–40	97	30.4
	41–50	40	12.5
	51–60	43	13.5
	Over 60	22	6.7
Academic qualifications	Junior secondary school and below	56	17.6
	Secondary school/Vocational college	69	21.6
	College diploma	92	28.8
	Undergraduate degree	71	22.3
	Postgraduate level and above	31	9.7
Occupation	Administrative	22	6.9
	Solicitor/Legal Officer	12	3.8
	Technical Development/Engineer	20	6.3
	Full-time homemaker	41	12.9
	Retirement	27	8.5
	Teacher	21	6.7
	Sole trader	65	20.4
	Designer	19	6.0
	Workers and labourers	41	12.9
	Freelancer	22	6.9
	Student	19	6.0
	Others	10	3.1
Identity Category	Local Residents	214	67.1%
	Tourists	81	25.4%
	Village Administrator	24	7.5%
Whether you are aware of or use	Yes	319	100

).

4.2. Measurement Model Assement

This study employed SmartPLS 4.0 software to assess the reliability and validity of the measurement model.

4.2.1. Reliability and Convergent Validity

The reliability of the questionnaire was assessed using Cronbach’s alpha (α), Composite Reliability (CR), and Average Variance Extracted (AVE). It is generally accepted that when α and CR exceed 0.7 respectively, and the AVE exceeds 0.5, the survey data demonstrates good stability and the measurement model’s reliability is acceptable. The provided values show all AVE values exceed 0.5 (minimum 0.714, maximum 0.784), all α values exceed 0.7 (minimum 0.800, maximum 0.887), CR values all exceeded 0.8 (minimum 0.882, maximum 0.922) (

Table 6. The constructs’ reliability validity.

Variable	Codes	Factor Loadings	Cronbach’s α	CR	AVE
FC	FC1	0.905	0.854	0.912	0.775
	FC2	0.854
	FC3	0.880
CA	CA1	0.861	0.821	0.983	0.736
	CA2	0.832
	CA3	0.879
SI	SI1	0.878	0.850	0.909	0.769
	SI2	0.868
	SI3	0.885
PCS	PCS1	0.834	0.862	0.916	0.784
	PCS2	0.930
	PCS3	0.891
SF	SF1	0.872	0.872	0.912	0.723
	SF2	0.825
	SF3	0.844
	SF4	0.859
PU	PU1	0.880	0.887	0.922	0.747
	PU2	0.846
	PU3	0.882
	PU4	0.848
PEOU	PEOU1	0.848	0.847	0.907	0.766
	PEOU2	0.890
	PEOU3	0.887
ATU	ATU1	0.871	0.860	0.915	0.781
	ATU2	0.914
	ATU3	0.866
BI	BI1	0.853	0.800	0.882	0.714
	BI2	0.848
	BI3	0.835

). These metrics fully demonstrate that the measurement model employed in this study exhibits excellent internal consistency and convergent validity [110].

4.2.2. Discriminant Validity

This study employed both the Fornell-Larcker criteria and the Heterogeneity-to-monotonicity ratio (HTMT) to jointly examine discriminant validity. Firstly, the square root of the AVE for all latent variables (diagonal values, minimum 0.845) was significantly greater than their respective correlations with other variables (non-diagonal values) (

Table 7. The discriminant validity—Fornell-Larcker criterion.

	FC	CA	SI	PCS	SF	PU	PEOU	ATU	BI
FC	0.880
CA	0.491	0.858
SI	0.470	0.313	0.877
PCS	0.280	0.253	0.378	0.886
SF	0.386	0.342	0.345	0.407	0.850
PU	0.680	0.594	0.560	0.486	0.570	0.864
PEOU	0.556	0.393	0.511	0.306	0.419	0.647	0.875
ATU	0.502	0.379	0.361	0.308	0.326	0.629	0.609	0.884
BI	0.373	0.278	0.339	0.259	0.217	0.455	0.391	0.668	0.845

Note: The bolded diagonal values represent the square root of the AVE.

), satisfying the Fornell-Larcker criterion [111]. Secondly, all HTMT values fell below the conservative threshold of 0.85 (

Table 8. HTMT Ratio for Discriminant Validity.

	FC	CA	SI	PCS	SF	PU	PEOU	ATU	BI
FC
CA	0.579
SI	0.544	0.373
PCS	0.322	0.299	0.456
SF	0.446	0.403	0.399	0.468
PU	0.780	0.692	0.639	0.553	0.646
PEOU	0.649	0.468	0.594	0.351	0.481	0.743
ATU	0.577	0.436	0.418	0.350	0.367	0.711	0.712
BI	0.452	0.341	0.408	0.311	0.258	0.541	0.473	0.800

). The combined results of these two tests confirm that the measurement model possesses sound discriminant validity.

4.3. Structural Model Analysis and Hypothesis Testing

4.3.1. Model Fit and Predictive Power

Prior to testing hypotheses, the integrity and predictive capability of the structural model were first evaluated.

This study employed the Variance Inflation Factor (VIF) to examine potential multicollinearity within the model. The VIF values for all paths in the model were less than 3 (

Table 9. Collinearity Statistics (VIF).

	PU	PEOU	ATU	BI
FC	1.770	1.598
CA	1.395	1.380
SI	1.569	1.444
PCS	1.325	1.324
SF	1.418	1.371
PU			1.721
PEOU	1.736		1.721
ATU				1.000

). The multicollinearity issue among the dependent variables can be disregarded, meeting the criteria requirements.

After confirming the measurement model met the criteria, the structural model and research hypotheses were tested using Bootstrapping (n = 5000 iterations) (Figure 6).

The determination coefficients R² and predictive correlations Q² for the four endogenous variables are as follows: Perceived Usefulness (PU): R² = 0.715, Q² = 0.524; Perceived Ease of Use (PEOU): R² = 0.424, Q² = 0.307; Attitude Towards Use (ATU): R² = 0.466, Q² = 0.352; Behavioural intention (BI): R² = 0.466, Q² = 0.314. All R² values exceed 0.4 and all Q² values exceed 0, indicating the model possesses good explanatory power and strong predictive correlation [112].

4.3.2. Path Coefficients and Hypothesis Testing

Path coefficients of the structural model and hypothesis testing results (

Table 10. Bootstrapping test and hypotheses testing.

Path	β	STDEV	T Values	p Values	Hypotheses Testing
FC → PU	0.272	0.041	6.625	0.000	H1a Valid
FC → PEOU	0.315	0.060	5.249	0.000	H1b Valid
CA → PU	0.233	0.035	6.654	0.000	H2a Valid
CA → PEOU	0.093	0.054	1.718	0.086	H2b Invalid
SI → PU	0.121	0.040	3.000	0.003	H3a Valid
SI → PEOU	0.268	0.055	4.908	0.000	H3b Valid
PCS → PU	0.160	0.041	3.951	0.000	H4a Valid
PCS → PEOU	0.021	0.059	0.359	0.719	H4b Invalid
SF → PU	0.188	0.045	4.222	0.000	H5a Valid
SF → PEOU	0.164	0.059	2.787	0.005	H5b Valid
PEOU → PU	0.215	0.035	6.221	0.000	H6 Valid
PU → ATU	0.405	0.052	7.837	0.000	H7a Valid
PEOU → ATU	0.346	0.048	7.269	0.000	H7b Valid
ATU → BI	0.668	0.038	17.351	0.000	H8 Valid

). Of the 14 hypotheses proposed in this study, 12 were supported.

We proposed the first set of hypotheses (H1a,b) to examine the influence of Functional Configuration (FC) on core variables of technology acceptance. The analysis results supported this set of hypotheses: FC exerted a significant positive influence on both PU (β = 0.272, p < 0.05) and PEOU (β = 0.315, p < 0.05).

We proposed the second set of hypotheses (H2a,b) to examine the role of Cultural Adaptability (CA). Results indicate that CA exerts a significant positive effect on PU (β = 0.233, p < 0.05), confirming H2a; however, its influence on PEOU (β = 0.093, p > 0.05) failed to reach statistical significance, thus rejecting H2b.

We proposed a third set of hypotheses (H3a,b) to explore the mediating role of Social Influence (SI). Results indicated that SI exerted significant positive effects on both PU (β = 0.121, p < 0.05) and PEOU (β = 0.268, p < 0.05), confirming both H3a and H3b.

We proposed a fourth set of hypotheses (H4a,b) to validate the role of Perceived Cost Structure (PCS). PCS exerted a significant positive effect on PU (β = 0.160, p < 0.05), confirming hypothesis H4a; however, its effect on PEOU (β = 0.021, p > 0.05) was insignificant, thus rejecting hypothesis H4b.

We proposed a fifth set of hypotheses (H5a,b) to assess the influence of Smart Features (SF). SF exerted a significant positive effect on both PU (β = 0.188, p < 0.05) and PEOU (β = 0.164, p < 0.05), confirming both H5a and H5b.

Regarding the traditional TAM pathways, all hypotheses were supported: PEOU significantly and positively influenced PU (β = 0.215, p < 0.05); both PU (β = 0.405, p < 0.05) and PEOU (β = 0.346, p < 0.05) significantly and positively influenced ATU; and ATU exerted a strong positive influence on BI (β = 0.668, p < 0.05). These findings reaffirm the robustness of the core TAM structure within the context of technology acceptance in rural settings.

5. Discussion

5.1. Comparison with Existing Studies

Through the theoretical lens of the TAM extension model, we explore how external variables such as Functional Configuration and Cultural Adaptability influence rural users’ acceptance of smart outdoor furniture.

Regarding the influence of external variables on the core TAM construct:

Functional configuration (FC) is the core factor exerting the strongest influence among all external variables. FC exerts a strong positive effect on PU (β = 0.272, p < 0.05). This indicates that the core practical functions provided by smart outdoor furniture, such as user care (e.g., seat heating), safety assurance (e.g., emergency call), and efficiency enhancement (e.g., automatic sorting), are directly perceived by users as the primary source of its value. These findings resonate with Gangyong Jia’s [27] conclusion that fundamental functions like energy efficiency and safety are indispensable for street lamps. Concurrently, Anna M. Grabiec et al. [35] demonstrated that furniture popularity hinges on its suitability to user needs. Such functionalities effectively address practical pain points in daily life, thereby significantly enhancing users’ assessment of “whether this product is useful”. Notably, the impact of functional configuration on PEOU is even stronger (β = 0.315, p < 0.05). This finding holds significant theoretical importance, as users do not necessarily equate “feature-rich” with “operationally complex”. Conversely, well-designed, purpose-driven practical functions (such as simple voice control or one-touch calling) are perceived by users as key components of a product’s ease of use. This indicates that intuitive functionality and problem-oriented design are crucial factors in lowering user barriers to adoption. This aligns strongly with the conclusions of prior research by Robert Frischer et al. [113], who argued that smart home functions should remain embedded and intuitive; otherwise, technological layering increases usage burden, with particularly pronounced effects on elderly users. It also supplements Davis’s (1989) perspective within the classic Technology Acceptance Model, which posits that increased functionality may diminish usability [63]. Comparative analysis reveals that FC exerts a stronger influence on PEOU (β = 0.315) than on PU (β = 0.272). This disparity indicates users perceive that practical functional settings not only enhance perceived functionality acquisition but, more critically, substantially reduce operational difficulty. This finding challenges the conventional notion that increased functionality inevitably leads to operational complexity, emphasising the potential for synergistic optimisation of functional utility and operational simplicity in smart product design.

Cultural Adaptability (CA) influences PU (β = 0.233, p < 0.05). When smart outdoor furniture integrates with rural traditional aesthetics in terms of form, materials, and cultural symbolism, users not only develop emotional affinity but tangibly perceive enhanced functional value. By strengthening sense of place, historical continuity, and social value, it significantly contributes to positive evaluations of the product’s core utility. This aligns with findings from Esra Köksaldı et al. [26] on urban outdoor furniture, which concluded that as integral components of public spaces, urban furniture not only reinforces spatial functionality and cultural identity but must also incorporate local culture, utilise traditional materials, and harmonise with historical environments to effectively enhance users’ sense of belonging. However, this study found that the role of cultural adaptation is more pronounced in rural contexts. Compared to urban users, rural users exhibit greater sensitivity to cultural elements, tending to confirm a product’s value and sense of belonging through local materials, indigenous craftsmanship, and traditional symbols. Consequently, cultural adaptation is not merely an aesthetic consideration but becomes a key factor driving rural users’ acceptance of smart outdoor furniture, with its influence extending from emotional identification to the recognition of functional value. CA exerts a weak influence on PEOU (β = 0.093, p < 0.05). This indicates that while traditional cultural elements (such as indigenous materials and antique-inspired designs) enhance emotional resonance and perceived usefulness, they do not significantly improve users’ practical assessments of a product’s ease of operation.

Social influence (SI) exerts a significant positive effect on PU (β = 0.121, p < 0.05), though the effect size remains relatively modest. This indicates that while external social factors, such as demonstrations by village administrators, visitor appreciation, and children’s recommendations, may positively influence users’ perception of a product’s practicality, they are not regarded as the fundamental basis for value judgement. In contrast, SI exerts a markedly stronger promotional effect on PEOU (β = 0.268, p < 0.05). This notable discrepancy reveals a distinctive mechanism within China’s rural socio-cultural context. Within the differential pattern that emphasises collective identity and relational networks, external social influence primarily functions to lower the psychological barriers to technology adoption. Wei et al. (2023) extended the TAM model to find that grandchildren’s demonstration and guidance significantly increased elderly community members’ willingness to adopt smart home technology [86]. This also aligns with Lei Luo et al.’s [85] rural study conclusion that authoritative demonstration and centralised education within farmers’ cooperatives significantly promoted adoption of green pest control technology among farmers.

Smart features (SF) exert a robust, moderately strong positive influence on PU (β = 0.188, p < 0.05), indicating that users recognise the functional value inherent in smart technology itself. Features including environmental awareness sensitivity, clarity of information display, and fluidity of interactive processes directly enhance a product’s practicality and experiential quality, thereby strengthening users’ perception of its utility. Concurrently, SF exert a significant moderate influence on PEOU (β = 0.164, p < 0.05), though this effect is marginally weaker than that on PU. This outcome reflects the dual-edged nature of smart features. On the one hand, well-designed intelligent interactions (such as seamless sensing and intuitive interfaces) can substantially simplify operational workflows and enhance ease of use. This aligns with Wencong Wang’s [114] proposal that optimising human-computer interfaces through deep learning and spatial computing can streamline smart home operation processes, significantly boosting user autonomy and convenience. Conversely, certain overly technical or jarringly designed intelligent functions (such as complex voice commands) may potentially increase usage complexity, thereby partially diminishing users’ perceptions of ease of use. This aligns with research by Alex Sciuto et al. [115], who found users often abandon certain features due to excessively complex voice commands or excessive memory burden. Consequently, while intelligent features exert a net positive influence on usability overall, their effect strength remains marginally lower than that of FC prioritising operational simplification. In summary, intelligent features can concurrently enhance users’ perceptions of both functional value and operational convenience, yet their ultimate efficacy remains highly contingent upon the appropriateness and human-centredness of intelligent function design.

Perceived Cost Structure (PCS) also exerts a significant positive influence on PU (β = 0.160, p < 0.05). This indicates that users’ rational assessment of a product’s full lifecycle economic costs, including acquisition, maintenance, and long-term value, directly impacts their judgement of whether the product is useful. This research aligns with findings by Cai Yuchao et al. [116], indicating that in multicultural urban settings, urban furniture design must deeply integrate macro-level economic, social, and cultural factors to harmonise with the city’s cultural heritage and future development. When users perceive smart furniture as offering value for money, with reasonable maintenance costs and potential for long-term savings, they are more inclined to affirm its functional utility. This finding underscores the importance of incorporating economic rationality dimensions into rural technology acceptance research, as rural users’ assessments of technological value deeply integrate dual criteria of practicality and cost-effectiveness. PCS exerted negligible influence on PEOU (β = 0.021, p < 0.05), indicating that users’ judgements regarding a product’s economic viability and its perceived ‘ease of use’ constitute two relatively independent cognitive dimensions.

Regarding the traditional TAM pathway:

Attitude towards use (ATU) exhibits a highly significant predictive effect on behavioural intention (BI) (β = 0.668, p < 0.05), constituting the most influential pathway within the model. This fully validates the centrality of the classical hypothesis that “attitude determines behavioural intention” within the context of this study.

PU (β = 0.405) and PEOU (β = 0.346, p < 0.05) jointly serve as key antecedents to ATU, with usefulness exerting a slightly dominant influence. This indicates that whether rural users ultimately develop a positive attitude depends both on the product’s PU and its PEOU. PEOU exerts a significant positive influence on PU (β = 0.215, p < 0.05), aligning with the classic TAM theory expectation that the more user-friendly the furniture, the more readily users perceive its functional value. This finding not only aligns with TAM’s classical framework but further demonstrates that operational simplicity can equally enhance users’ perception of functional value within rural contexts. Consequently, it successfully extends the applicability of TAM to research on rural public facilities and smart home appliances.

5.2. Implications for Inclusive Design Strategies

Based on the four user attitude configurations revealed by Q-methodology and the path testing results of the Q-TAM model, this study summarises the following inclusive and sustainable design optimisation strategies for rural smart outdoor furniture. These strategies aim to achieve technological equity among diverse users, lower usage barriers, enhance environmental integration, and improve overall acceptance and long-term sustainability.

(1)

Function-Oriented Configuration: Intuitive design centred on problem-solving.

Research findings indicate that Functional Configuration exerts a significant positive influence on both PU and PEOU, with the latter effect being more pronounced. This suggests designs should focus on essential needs, emphasising core functions that are “fewer but more refined” (FC1). Concurrently, functions with clear lifestyle benefits, such as winter seat heating, emergency call systems, and one-touch operation (FC2, FC3), should be prioritised and enhanced with intuitive interactions to support technological accessibility and digital inclusiveness. For “Pragmatic function-oriented” users, such designs significantly boost confidence in usage; they also help alleviate complexity concerns among the “Technology-anxiety-oriented” group. This strategy balances efficiency and equity, facilitating equitable user experiences across different age groups and levels of technological literacy.

(2)

Cultural Adaptability Orientation: Embedding Local Culture and Rural Aesthetics.

Cultural Adaptability markedly enhances PU but has limited impact on PEOU. This indicates that smart outdoor furniture incorporating local culture through form (CA1), materials, and symbols (CA2) can strengthen villagers’ sense of belonging and social value (CA3). Specifically, adopting a design philosophy that emphasises a traditional exterior with an intelligent core, by using local materials such as timber and stone in combination with traditional craftsmanship or elements of village history, helps to prevent perceptions of technological alienation and cultural disconnection. For users exhibiting “Cultural concern-oriented”, such strategies are pivotal in overcoming resistance and boosting acceptance. They foster emotional resonance while demonstrating respect for local culture and ecosystems, aligning with principles of cultural sustainability.

(3)

Social Influence Orientation: Activating demonstration and mutual aid mechanisms within hierarchical social structures.

Research indicates that differentiated Social Influence exerts limited impact on PU but significantly influences PEOU. This reveals the behavioural logic of “observing others to learn usage” within rural communities: when village administrators, young people, or visitors demonstrate proficient use, it significantly lowers psychological barriers for other groups. Therefore, design and promotion should synchronously integrate social mobilisation strategies, such as having village administrators demonstrate first or youth volunteers provide operational guidance, thereby forming intergenerational mutual aid mechanisms (DSI1, DSI3) to enhance overall group willingness to use. Concurrently, outdoor furniture itself can foster acceptance by enhancing its social attributes. For instance, integrating USB charging ports into smart benches or installing village information display screens transforms these elements into new social nodes (DMOA2). For “Smart enhancement-oriented”, symbolic design can stimulate engagement by crafting products as “village calling cards” that blend technological sophistication with environmental harmony, thereby establishing iconic symbols of rural modernisation. Such strategies facilitate cross-generational learning networks, driving inclusive technological diffusion within communities.

(4)

Cost Structure Orientation: Mitigate economic concerns through lifecycle optimisation and policy subsidies.

Whilst Perceived Cost Structure exerts limited influence on PEOU, it significantly impacts PU. This indicates users prioritise the reasonableness of acquisition and maintenance costs when evaluating product value. Consequently, design should emphasise weather resistance and maintainability (PCS2), avoiding scenarios of “high investment, short lifespan” (PCS3). Concurrently, the integration of sustainable fiscal policies and collective collaboration mechanisms, such as government subsidies or cooperative co-construction models (PCS2), can effectively alleviate the financial burdens faced by villagers. This proves particularly crucial for “Cultural concern-oriented” groups, as such strategies mitigate their aversion to high costs while enhancing technological equity and economic accessibility.

(5)

Smart features Orientation: Reinforcing implicit intelligence and human-centred interaction.

Smart Features exert a positive influence on both PU and PEOU. While their impact intensity is slightly lower than that of functional configurations, this demonstrates that the value of smart design lies not in technical showmanship or excessive technological complexity, but in tangibly enhancing user experience and interaction quality. Therefore, the principle of “subtle intelligence” (SF1) should be reinforced, ensuring technology serves user needs seamlessly without appearing intrusive. Integrating interaction experience (SF3), complex touchscreens or verbose voice assistants should be dispensed with, favouring context-aware technology and simplified natural language commands. For instance, smart benches could incorporate concealed, weather-resistant sensors that automatically activate gentle seat heating when users are detected in cold conditions, eliminating manual operation. Voice prompts should be concise and intuitive (e.g., saying “call for assistance” activates an emergency alert), avoiding multi-step dialogue processes that may confuse elderly or technologically inexperienced users. For information accessibility (SF2), enhance convenience by providing clear, multi-sensory information channels. Large, high-contrast icons combined with tactile Braille guide visually impaired users. Audible feedback (e.g., gentle chimes confirming successful operations like waste sorting) supplements visual cues. Smart kiosks announce functions using clear pictograms paired with brief, looping environmental audio, enabling information retrieval without reading or touching. Perceptual effects (SF4) should reinforce immediate, unambiguous physical feedback to eliminate user uncertainty. This includes tactile buttons with distinct “clicks” and bright, persistent indicator lights that intuitively convey device status (e.g., a steady green light signifies an available charging port, while a slow-flashing red light indicates maintenance is required). Such direct feedback is crucial for building trust, particularly among “technology-anxiety-oriented” user groups. These strategies also align with the core demand for “appropriateness and humanisation” across all attitude configurations.

5.3. Limitations and Directions for Future Research

Despite the methodological and conceptual innovations presented in this study, several limitations remain, which provide directions for future research.

(1)

Limitations of the case setting.

This study selected Jingshan Village in Zhejiang Province as the sole empirical case. While the village demonstrates certain typical characteristics in terms of policy support, digital infrastructure, and social structure, it is important to note the wide diversity among Chinese rural areas. Significant variations exist in geographical conditions, economic development levels, and cultural traditions. Therefore, the generalizability of the findings to other regions should be approached with caution. Future research may adopt a multi-case comparative approach across different regions to examine the heterogeneity of user attitudes and adoption pathways in diverse rural contexts, thereby enhancing the external validity and practical applicability of the model.

(2)

Lack of dynamic and long-term perspectives.

This study employed cross-sectional data, capturing user attitudes and behavioural intentions at a single point in time. However, technology adoption is a dynamic and evolving process, and users’ perceptions and emotions may shift over time due to accumulated experience, changing social norms, and policy adjustments. Future research could adopt longitudinal study designs to investigate the evolution of user acceptance across different stages. Furthermore, it is crucial to explore whether inclusive design strategies can maintain sustained engagement and collaborative mechanisms over time, thereby promoting technological equity and social integration in rural settings.

(3)

Insufficient differentiation across user groups and regional contexts.

To enhance external validity and uncover group-specific acceptance mechanisms, future studies should adopt a multi-case comparative approach across regions with varying developmental levels and cultural backgrounds. Additionally, within a given case, Multi-Group Analysis (MGA) using Partial Least Squares (PLS) can be employed to rigorously compare the path coefficients of our Q-TAM model across key stakeholder groups, such as villagers, tourists, and administrators. This analysis could reveal, for instance, whether “Social Influence” is a stronger driver for villagers than for tourists, or if “Cultural Adaptability” weighs more heavily for local residents than for managers. Such insights are crucial for developing finely-targeted, group-specific implementation strategies

6. Conclusions

This study adopts an integrated approach combining Q methodology with structural equation modelling (SEM) to systematically explore user acceptance mechanisms for smart outdoor furniture in rural settings. The key findings are as follows:

First, the Q methodology identified four distinct patterns of subjective user attitudes toward smart outdoor furniture in rural areas: Function-Oriented Pragmatists, Cultural Identity Concerned, Smart-Technology Enthusiasts, and Technological Anxiety Holders. These configurations reveal significant differences in how rural users prioritise values, perceive cultural identity, and relate to technology. These differences are not only reflected in functional preferences but also in the expected integration between technology and local culture. Accordingly, this study translated the core concerns embedded in each user configuration into contextual antecedent variables of an extended Technology Acceptance Model (TAM), thereby achieving a theoretical linkage from qualitative insight to quantitative validation (RQ1).

Second, the empirical validation of the extended TAM model confirms that Perceived Usefulness (PU) and Perceived Ease of Use (PEOU) remain central to determining Attitude Toward Use (ATU) and Behavioural Intention (BI). Meanwhile, five newly introduced contextual antecedents—Functional Configuration (FC), Cultural Adaptability (CA), Social Influence (SI), Perceived Cost Structure (PCS), and Smart Features (SF)—exerted differentiated and significant effects on PU and PEOU. These findings not only further validate the explanatory power of classic TAM in specific sociocultural contexts but also extend its applicability to research on smart infrastructure in rural areas, offering a theoretical reference for future cross-contextual model development (RQ2).

Third, based on the path coefficients of the Q-TAM model and the distinct user configurations, this study proposes design optimisation strategies aimed at inclusivity and sustainability, emphasising multidimensional and collaborative design thinking. These strategies include: enhancing user confidence through intuitive functional configurations; strengthening emotional identification through cultural embedding; lowering usage barriers via social demonstration and intergenerational assistance; mitigating economic concerns through lifecycle optimisation and policy support; and reducing technological pressure through implicit intelligence and user-friendly interaction design. Collectively, these strategies point to a central design objective—achieving high acceptance with low usage threshold for rural smart public products, offering a viable pathway toward equitable and sustainable adoption of digital technology in rural communities (RQ3).

In retrospect, this study represents an exploratory yet significant stride in integrating methodological innovation with the development of contextualised models for rural digitalisation. By synthesising the qualitative depth of Q-methodology with the predictive power of the Technology Acceptance Model (TAM), we have established a more nuanced framework for understanding technology adoption, one deeply rooted in user subjectivity. The identified user archetypes and their corresponding influence pathways reveal a pivotal paradigm: the success of smart outdoor furniture in rural settings hinges not on technological sophistication per se, but on its alignment with local functional needs, cultural values, and social structures. However, as outlined in the study’s limitations, future research must enhance regional diversity through multi-case and multi-group comparisons, while employing longitudinal studies to capture the dynamic process of technology adoption. We believe that exploring these avenues will not only enhance the theoretical universality of the Q-TAM framework but also provide actionable and scalable policy and design insights for local governments and rural governance institutions. Ultimately, the path towards inclusive and sustainable rural design is one of continuous learning and adaptation, and this study aims to provide a foundational, user-centred perspective for this endeavour.

6.1. Theoretical Contributions

This study integrates Q methodology with the Technology Acceptance Model (TAM) to address the limited generalizability of conventional technology acceptance theories in rural contexts. The theoretical contributions are threefold:

• Proposing the “Q-TAM” framework to extend the contextual adaptability of TAM. Previous TAM-based studies have predominantly focused on urban users or specific digital products, often overlooking the cultural and contextual diversity of users in non-standard settings. Drawing on four user attitudinal archetypes identified through Q methodology, this study introduces five context-specific antecedent variables—Function Configuration (FC), Cultural Adaptation (CA), Social Influence (SI), Perceived Cost Structure (PCS), and Smart Features (SF)—to enrich TAM’s explanatory power in rural social structures and cultural landscapes. This extension offers a pathway for evolving TAM from a general model into a context-sensitive theory.
• Achieving theoretical integration from subjective cognition to quantitative modelling. Unlike traditional TAM studies that rely on researcher-defined constructs, this study builds its model from the bottom up—transforming user attitudes distilled from Q-sorting into latent variables tested in structural equation modelling (SEM). This approach establishes a methodological chain from “user archetypes” to “construct modeling” to “path validation”, enhancing the alignment between theoretical constructs and empirical realities, while contributing to a novel cross-method modelling paradigm.
• Responding to concerns of digital equity and sustainability. By proposing a “high acceptance–low threshold” design logic for rural smart infrastructure, this study highlights mechanisms of structural inequality in technology adoption—such as cognitive barriers, cultural disconnects, and intergenerational gaps. The Q-TAM framework offers a theoretical lens to analyse and bridge these divides, contributing to a broader understanding of digital inclusion within the sustainability discourse.

6.2. Practical Implications

The findings of this study offer concrete guidance for the design and implementation of inclusive smart public products in rural settings, with practical implications in the following three areas:

• Providing an inclusive design strategy for rural smart furniture. Addressing user heterogeneity in terms of functional needs, cultural sensitivities, and technology-related anxiety, the study proposes a multi-dimensional strategy framework that incorporates functional configuration, cultural embedding, social mobilisation, cost optimisation, and adaptive smart features. This framework assists design teams in anticipating and accommodating diverse user needs early in the product development process, avoiding one-size-fits-all approaches and enhancing long-term adoption.
• Informing rural governance and public policy formulation. The findings indicate that perceived ease of use among different social groups is strongly shaped by social networks and demonstration effects. Local governance actors are thus encouraged to activate soft support mechanisms, such as leadership modelling, intergenerational mentoring, and volunteer guidance, to lower adoption thresholds. Moreover, the significance of cost-related variables underscores the importance of financial subsidies and cooperative ownership models in ensuring equitable access to digital infrastructure.
• Providing a transferable model for sustainable infrastructure implementation. Using smart public furniture as an entry point, the Q-TAM framework and corresponding design strategies are applicable to other types of rural smart infrastructure (e.g., smart streetlights, digital kiosks, shared devices). By emphasising alignment between technology, culture, and social structure, this model offers a replicable framework for promoting inclusive, equitable, and sustainable digital transitions in rural and under-resourced regions.