Optimizing Innovation Decisions with Deep Learning: An Attention–Utility Enhanced IPA–Kano Framework for Customer-Centric Product Development

Abstract

This study employs deep learning techniques, specifically BERT and Latent Dirichlet Allocation (LDA), to analyze customer satisfaction and attribute-level attention from user-generated content. By integrating these insights with Kano model surveys, we systematically rank attribute preferences and enhance decision-making accuracy. Addressing the explicit attention–implicit utility discrepancy, we extend the traditional IPA–Kano model by incorporating an attention dimension, thereby constructing a three-dimensional optimization framework with eight decision spaces. This enhanced framework enables the following: (1) fine-grained classification of customer requirements by distinguishing between an attribute’s perceived salience and its actual impact on satisfaction; (2) strategic resource allocation, differentiating between quality enhancement priorities and cognitive expectation management to maximize innovation impact under resource constraints. To validate the model, we conducted a case study on wearable watches for the elderly, analyzing 12,527 online reviews to extract 41 functional attributes. Among these, 14 were identified as improvement priorities, 9 as maintenance attributes, and 7 as low-priority features. Additionally, six cognitive management strategies were formulated to address attention–utility mismatches. Comparative validation involving domain experts and consumer interviews confirmed that the proposed IPAA–Kano model, leveraging deep learning, outperforms the traditional IPA–Kano model in classification accuracy and decision relevance. By integrating deep learning with optimization-based decision models, this research offers a practical and systematic methodology for translating customer attention and satisfaction data into actionable innovation strategies, thus providing a robust, data-driven approach to resource-efficient product development and technological innovation.

1. Introduction

Value co-creation, as proposed in service-oriented logic [1], is an open innovation process that integrates knowledge, information, and skills through multi-agent participation, enabling entities to achieve sustainable competitive advantages [2]. Among these actors, customers play a dual role, as a key driver of innovation and a source of competitive advantage for enterprises [3]. The concept of user-centered design (UCD) [4] and participatory design across fields, including gerontechnology [5,6], exemplifies this trend by focusing on users’ needs.

With the increasing pace of global aging, elderly care has become both essential and increasingly important. Gerontechnology plays a critical role in improving the quality of life for older adults and their caregivers. However, older adults are generally perceived as less inclined to adopt new technologies compared to younger populations [7,8,9], and barriers to gerontechnology acceptance among this demographic remain persistent [10]. Research has highlighted that technology usability, user-friendliness, and social influences are significant predictors of gerontechnology acceptance [11]. Additionally, product and technical attributes are key determinants of adoption among elderly users [12]. Effective systems for promoting gerontechnology adoption should integrate both products and services to facilitate acceptance [13]. These factors are essential for advancing gerontechnology innovations aimed at improving acceptance and enhancing the quality of life for the elderly.

However, which technical characteristics should be improved, and how can innovation opportunities in gerontechnology be effectively identified? Few studies focus on identifying innovation opportunities and decision-making for attribute-level improvements from the perspective of user needs and market characteristics. Therefore, it is essential to adopt the philosophy of value co-creation for technological innovation by leveraging user-generated content (UGC), particularly electronic word-of-mouth (e-WOM), which reflects customer needs and satisfaction.

Researchers have analyzed user satisfaction through online reviews, with positive reviews reflecting customer satisfaction and negative reviews indicating dissatisfaction [14,15]. Aspect-based sentiment analysis (ABSA) is a crucial task in this domain, focusing on identifying user sentiment regarding specific aspects of an entity in text, where the aspect represents any characteristic or attribute of that entity [16]. Attribute extraction and attribute sentiment computation are the two subtasks involved in attribute-level sentiment analysis [17]. As for the attribute extraction task, research indicates that LDA [18] is widely recognized as an effective method for identifying product and service attributes from online reviews [19,20].

The task of sentiment computing can be handled by traditional methods, such as dictionary-based methods [21,22] and rule-based methods [23]. Machine learning methods [24], including deep learning methods [25,26], are developed with the tendency to use a convolutional neural network. With the advent of large models, pre-trained language models such as BERT [27] perform particularly well on ABSA tasks. These models are pre-trained on large-scale corpora and can then be fine-tuned to specific ABSA tasks.

For analyzing customer needs, the Kano model has been extensively adopted across industries as a reliable tool for understanding customer preferences [28,29,30]. Additionally, Kuo et al. [31] introduced the IPA–Kano model [32,33], which combines Importance–Performance Analysis and the Kano model to categorize service quality attributes and prioritize strategies accordingly.

While analyzing user-generated content in the context of technology products, we observe that attributes frequently discussed by consumers (e.g., exterior design) often have limited actual influence on user satisfaction. This reveals a misalignment between explicit attention and implicit utility, an inconsistency largely overlooked by traditional evaluation models such as IPA or Kano.

Drawing on the service gap theory [34], such mismatches can be attributed to cognitive distortions or incomplete market feedback mechanisms, which prevent decision-makers from accurately interpreting what truly drives satisfaction. This phenomenon becomes especially critical in resource-constrained decision contexts such as aging services, where misallocating effort to low-utility but high-attention attributes may hinder well-being outcomes.

To address this issue, we propose the IPAA–Kano model, an extension of IPA–Kano, which integrates an attention dimension to explicitly capture the perceptual salience of attributes. This three-dimensional framework enable the following: (1) fine-grained classification of attributes by aligning perceived attention with actual satisfaction impact; (2) dual-pathway decision strategies—either improving attribute quality or managing user perception—to resolve the attention–utility paradox.

To implement this model effectively, we employ a hybrid NLP pipeline leveraging LDA topic modeling, expert review, API-based sentiment labeling, and BERT-based sentiment evaluation—balancing automation with interpretability to support objective, scalable decision-making.

The contributions of this study are threefold: (1) Extension of the IPA–Kano framework. This study extends the traditional IPA–Kano model by introducing an attention dimension, enabling a three-dimensional framework that distinguishes between perceived salience and the actual utility of product attributes. This addresses the previously overlooked explicit attention–implicit utility inconsistency, resulting in a more nuanced, real-world-aligned decision-making model across eight defined decision spaces. (2) A practical analytical pipeline for attention-aware evaluation. By combining topic modeling (LDA), transformer-based sentiment evaluation (BERT), and expert validation, we introduce a semi-automated process to identify and label product attributes for fine-grained analysis. (3) Real-world application to wearable technology for the elderly. Applying the framework to 41 product attributes of elderly smartwatches, we demonstrate its effectiveness in prioritizing improvement strategies and uncovering overlooked but valuable features. This offers practical guidance for resource-optimized innovation design.

2. Methodology

2.1. Proposal of an Enhanced IPA–Kano Model

2.1.1. Challenges Brought by the “Explicit Attention–Implicit Utility Misalignment” Phenomenon to the IPA–Kano Model

The service gap theory [34] states that a perception gap—where firms fail to accurately identify customer needs due to inadequate market research or distorted information transfer—can lead to lower service quality and satisfaction.

With the increasing availability of online review data, we observe that the frequency of discussion or attention given to a product attribute does not always align with its actual impact on satisfaction—a phenomenon we term “explicit attention–implicit utility misalignment”. For example, the design aesthetics of a technology product may receive frequent mentions in reviews, yet its actual contribution to user satisfaction may be minimal.

This misalignment poses a critical challenge in customer needs classification: should firms improve the attribute’s quality or manage customer perception more effectively? Traditional IPA–Kano models neither account for nor address this issue. Given limited resources, precisely identifying the demand categories of different product attributes and implementing targeted response strategies is essential for effective decision-making.

2.1.2. Differentiating Attention and Importance

In previous IPA studies, “attention” has been used to reflect the relative “importance” of attributes based on how frequently they are mentioned in customer narratives, particularly in critical incident surveys [35]. These studies typically equate higher mention rates with greater importance.

However, in our study, which integrates IPA with the Kano model, we argue that such an equivalence between attention and importance can be misleading. Specifically, we distinguish between importance, derived from structured Kano surveys with a representative sample, capturing the psychological weight of attributes in shaping customer satisfaction, and attention, extracted from unstructured user-generated content (UGC), reflecting the public salience or visibility of an attribute based on mention frequency—but not necessarily its functional or psychological contribution to customer satisfaction.

This distinction is critical: users may devote high attention to certain attributes (e.g., because they are novel or controversial) even if those attributes do not significantly affect their overall satisfaction. Conversely, they may overlook attributes that are in fact essential drivers of satisfaction. This phenomenon, stated in Section 2.1.1 as “explicit attention–implicit utility misalignment”, illustrates the misalignment between what consumers focus on and what truly drives their satisfaction.

To operationalize this distinction in our model, attention is measured as the proportion of reviews mentioning a given attribute [14], as shown in Formula (1).$A_i$ represents the attention of the attribute $i$, $n_i$ represents the number of reviews containing the attribute $i$, and $M$ represents the total number of reviews. A higher value of $A_i$ indicates that the attribute is more frequently mentioned by customers.

$$A_i={\displaystyle \frac{n_i}{M}}$$

(1)

Importance is calculated from the Kano model based on satisfaction-related evaluations [36], as shown in Formula (2). The meaning of M, O, A, I, R, and Q represents the proportions of “Must-be”, “One-dimensional”, “Attractive”, “Indifferent”, “Reverse”, and “Doubtful” responses, respectively, in the Kano questionnaire.

$$\mathrm{I}\mathrm{m}\mathrm{p}\mathrm{o}\mathrm{r}\mathrm{t}\mathrm{a}\mathrm{n}\mathrm{c}\mathrm{e}={\displaystyle \frac{5\ast M+3\ast O+1\ast A+0\ast I}{A+O+M+I+R+Q}}$$

(2)

This dual-dimensional approach enables us to identify hidden or misaligned attributes—such as those with high attention but low satisfaction impact, or vice versa—thus enhancing the explanatory power of the traditional IPA–Kano framework and enabling the discovery of latent consumer priorities that might otherwise remain concealed.

2.1.3. Redefining Need Categories Based on the “Explicit Attention–Implicit Utility Misalignment”

To identify such misalignments, we propose a more precise classification of customer needs. Attributes with high attention but low contribution to satisfaction are defined as “Cognitive Bias” which require cognitive management. Attributes with low attention but high contribution to satisfaction are defined as “Potential Value Points” which are marketing optimization targets. Attributes with high attention and high contribution to satisfaction are classified as “Innovation Drivers”, which act as key breakthrough features. Attributes with low attention and low contribution to satisfaction are categorized as “Negligible” which need resource optimization. This classification framework is illustrated in Figure 1.

At the operational level, we adopt a two-dimensional quadrant-based classification using the median values of attention and satisfaction scores across all attributes as the splitting thresholds. Among the methods used to determine value points for constructing the two-dimensional grid that divides the matrix into four quadrants, the data-centered quadrant approach [37] is the most frequently applied [38,39,40,41] approach. We choose the medians of the values as the dividing lines for their high discriminative power and generalizability.

Building on the IPA–Kano model, we introduce the attention dimension to construct the three-dimensional IPAA–Kano decision model, forming eight decision spaces, as shown in the

Table 1. Three-dimensional QUAD reference table.

Quad Number	Dimension Combination
I	HP + HI + HA
II	HP + HI + LA
III	HP + LI + HA
IV	HP + LI + LA
V	LP + HI + HA
VI	LP + HI + LA
VII	LP + LI + HA
VIII	LP + LI + LA

. The abbreviations HP, HI, and HA represent high performance, high importance, and high attention, respectively, while LP, LI, and LA refer to low performance, low importance, and low attention, respectively.

Accordingly, three corresponding strategies were developed for the identified attribute demand classifications: 1. Enhancing product quality. Strengthen market education to increase user awareness of high-utility, low-attention features. 2. Cognitive management. Mitigate cognitive bias in high-attention, low-utility features through UI design and marketing guidance. 3. Smart selection. Simplify or optimize low-attention, low-utility features to reduce unnecessary product complexity.

Particularly, beyond identifying improvement and maintenance priorities, as addressed by the IPA–Kano model, this study proposes cognitive management strategies for different types of attention–utility misalignment: 1. High perceived importance, low attention, high satisfaction—enhance attribute exposure and consumer education to align perception with actual satisfaction. 2. High perceived importance, low attention, low satisfaction—avoid premature exposure until significant improvements are made; then, increase visibility and guide consumer attention. 3. Low perceived importance, high attention, high satisfaction—leverage early-stage exposure to attract attention, followed by consumer education to shift focus toward core needs. 4. Low perceived importance, low attention, low satisfaction—prioritize omission, as these attributes offer limited value.

2.1.4. Definition of the Improvement Coefficient

To dynamically assess the degree of attribute satisfaction and the fulfillment of technology products from a market perspective, we define the improvement coefficient as follows, where $w_i$ and $w_j$ represent the decision weight according to a specific standard. Both the importance and attention variables are normalized using the min–max normalization method.

$$P=w_i\times {\displaystyle \frac{\mathrm{I}\mathrm{m}\mathrm{p}\mathrm{o}\mathrm{r}\mathrm{t}\mathrm{a}\mathrm{n}\mathrm{c}\mathrm{e}\_\mathrm{n}\mathrm{o}\mathrm{r}\mathrm{m}\mathrm{a}\mathrm{l}\mathrm{i}\mathrm{z}\mathrm{e}\mathrm{d}}{Satisfaction}}+w_j\times {\displaystyle \frac{\mathrm{A}\mathrm{t}\mathrm{t}\mathrm{e}\mathrm{n}\mathrm{t}\mathrm{i}\mathrm{o}\mathrm{n}\_\mathrm{n}\mathrm{o}\mathrm{r}\mathrm{m}\mathrm{a}\mathrm{l}\mathrm{i}\mathrm{z}\mathrm{e}\mathrm{d}}{Satisfaction}}$$

(3)

First, assess customer demand preference using the Kano model. Next, evaluate market entities’ judgment on importance, satisfaction, and attention. Then, create the IPA quadrant analysis for each P-dimension. Finally, prioritize improvements or maintenance for each quadrant based on the improvement coefficient (P).

2.2. Clarifying the Remaining Variables in the Enhanced IPA–Kano Model

2.2.1. Definition of Preference Dimensions

The Kano model is widely used for classifying and prioritizing user needs, capturing the nonlinear relationship between product performance and user satisfaction. It categorizes product or service quality into five types: necessary attributes, desired attributes, attractive attributes, indifferent attributes, and reverse attributes [29]. The Kano model questionnaire consists of two pairs of questions: positive (functional, when the attribute is present) and negative (dysfunctional, when the attribute is absent). Each question is rated on a five-point scale: like very much, must-be, neutral, live-with, and dislike very much, abbreviated as like, necessary, neutral, unnecessary, and dislike. Attribute preferences are categorized based on the Kano survey, as summarized in

Table 2. The standard Kano evaluation.

Product/Service	Dysfunctional
		Like	Nec	Neu	Unnec	Dis
Functional	Like	Q	A	A	A	O
	Nec	R	I	I	I	M
	Neu	R	I	I	I	M
	Unnec	R	I	I	I	M
	Dis	R	R	R	R	Q

Note: A: attractive attribute, O: desired attribute, M: necessary attribute, I: indifference attribute, R: reverse attribute, Q: doubtful result.

.

2.2.2. Definition of Satisfaction Dimensions

IPA (Importance–Performance Analysis), introduced by Martilla et al. (1977) [42], utilizes a two-dimensional matrix of importance and performance to identify areas for improvement that can enhance productivity or increase customer satisfaction, as shown in Figure 2.

In this study, the average proportion of positive sentiment in each review mentioning a specific attribute is used to measure satisfaction (i.e., attribute performance), as shown in Formula (4) [15]. Specifically, for attribute $i$, let $n_i$ denote the number of online reviews that mention it. $S_i$ represents the satisfaction of the attribute $i$. In the $j$-th review containing the attribute $i$, $F_{j\_pos}$,$F_{j\_neg}$, and $F_{j\_neu}$, respectively, represent the positive, negative, and neutral sentiment scores associated with attribute $i$. These scores are normalized by the total sentiment strength in the review. The larger the value of $S_i$, the more positive the consumer sentiment toward attribute $i$, indicating a higher level of satisfaction.

$$S_i={\displaystyle \frac{1}{n_i}}{\sum }_{j=1}^{n_i}{\displaystyle \frac{F_{j\_pos}}{\left(F_{j_{\_pos}}+F_{j\_neg}+F_{j_{\_neu}}\right)}}$$

(4)

2.3. Methodological Pipeline of the Enhanced IPA–Kano Model

2.3.1. Data Sources: A Decoupled, Yet Complementary Integration of Surveys and UGC

In the Kano model, the importance derived from survey data is based on subjective ratings. In contrast, UGC data reflects customer behavior, offering greater objectivity and real-time dynamics. However, scholars have noted that UGC suffers from self-selection bias [43], which limits its representativeness and introduces potential sampling distortion. We explicitly acknowledge this representativeness gap and address it by integrating both data sources in a complementary and methodologically transparent manner.

Specifically, survey data—based on a representative sample—is used solely to calculate the “importance” variable in both the Kano and IPA models. UGC, in contrast, is used only to measure “attention” and “performance” (i.e., satisfaction), ensuring a clear separation between data sources across variables.

To enhance the breadth and relevance of UGC, we collected review data from multiple platforms, product categories, and time periods. This diversified sampling strategy increases the representativeness of UGC.

Accordingly, the Kano-based attribute classification provides a population-representative foundation, upon which UGC-based measurements of attribute satisfaction and attention are used for real-time dynamic monitoring. The two data types are used in a decoupled, yet complementary manner, enabling UGC to serve as a rapid exploratory layer in a convenience sampling manner within a representativeness-aware framework. The limited representativeness of UGC is inherently a trade-off for its strengths in efficiency, timeliness, and content richness. By explicitly acknowledging the representativeness gap and integrating survey data as a corrective baseline, this trade-off is managed in a scientifically controlled and transparent manner.

2.3.2. Modular Method Integration: Hybrid Attribute-Based Sentiment Evaluation Framework

To perform attribute-level sentiment evaluation, we first employed Latent Dirichlet Allocation (LDA) as an unsupervised topic modeling technique to extract latent attributes from user-generated reviews. This approach enables the automatic discovery of high-frequency and semantically coherent attribute candidates without relying on predefined keywords or subjective assumptions.

Firstly, following the LDA-based extraction, we invited domain experts to conduct a qualitative review and validation of the LDA output, including both topic terms and their representative review samples. Through expert evaluation, we refined and finalized a set of interpretable and representative product attributes.

Next, based on the validated attribute set, we retrieved corresponding review segments that mentioned each attribute and conducted sentiment annotation for these segments. Instead of relying solely on manual labeling or sentiment lexicons, we utilized the DeepSeek API—a large language model with strong contextual comprehension capabilities—to semi-automatically label the sentiment polarity (positive, neutral, negative) of the attribute-related reviews. This semi-automatic annotation approach provided a balanced trade-off between efficiency and consistency, especially in handling the large-scale and context-rich nature of user reviews.

Subsequently, we fine-tuned a pre-trained BERT model using the annotated dataset, enabling it to perform sentiment evaluation with attribute-level granularity. The BERT model captured nuanced expressions and contextual cues, which traditional rule-based or shallow machine learning models often fail to address. Finally, we applied the fine-tuned BERT model to the full review corpus to obtain sentiment distributions for each attribute, offering a detailed and robust basis for downstream analysis.

This hybrid framework—combining unsupervised attribute discovery, expert validation, semi-automatic labeling via a generative language model, and deep contextual sentiment classification—offers both interpretability and scalability. Compared to conventional sentiment analysis pipelines that rely solely on pre-defined attributes or sentence-level classification, our approach achieves more fine-grained and data-driven insights while maintaining robustness and generalizability across domains.

2.3.3. Outcome Integration: Strategic Fusion of Attribute Sentiment and Kano Classification

The resulting sentiment scores (attribute-level satisfaction) and frequency metrics (attention) were then combined with Kano-based importance categories to construct the results of attribute improvement, ignorance, and maintenance order dentification, which supports cognitive strategy development for design prioritization. This modular and adaptable pipeline ensures the interpretability and scalability of the IPAA–Kano framework, as illustrated in Figure 3.

This pipeline serves as the backbone of the IPAA–Kano framework, translating unstructured data into structured inputs for downstream strategic decision-making. Each module of the pipeline is interchangeable and adaptable, allowing for flexible adaptation to different product domains and data scales.

3. Case Study: Evaluating Elderly Smartwatch Attributes Through the Enhanced IPA–Kano Model

3.1. Data Collecting and Processing

To validate the model’s practical application, we used wearable watches for the elderly as a case study. Online reviews were collected from JD.com and Tmall, two major digital marketplaces in China, using Octopus Collector, a specialized data collection tool. A total of 12,527 reviews were gathered, with follow-up comments merged into single reviews. Python 3.8.0 was employed to remove duplicate comments and irrelevant characters during data cleaning.

3.2. Identifying Innovation Needs in Gerontechnology via Text Mining

3.2.1. Attribute Keyword Extraction Based on LDA Topic Model

LDA (Latent Dirichlet Allocation) is an unsupervised machine learning technique used for identifying latent topics in large document collections [18]. After constructing stop-word and custom dictionaries and performing word segmentation, we trained the LDA model using Gensim. The optimal number of topics was determined through both quantitative and qualitative methods. Perplexity, a widely used metric, was selected for its interpretability and applicability [44], while manual evaluation ensured topic interpretability [45]. Consistent with prior studies, we employed both perplexity and manual checks for validation. When the number of topics reached 18 or 19, the curve slope decreased sharply and leveled off (Figure 4). Keywords from 19 topics were chosen as the candidate pool for extracting attribute features of wearable watches for the elderly. The training results are shown in Figure 5 using pyLDAvis.

3.2.2. Mapping of Topic Keywords to Requirement Attribute Features by Human Interpretation

Following the methodology of Guo et al. (2017) [20] and Tirunillai et al. [19], each identified topic label was treated as an attribute of the product or service. This process yields a set of labeled topics (attributes) and their associated keywords. We mapped these keywords to the product attribute features, developing the attribute characteristics and evaluation indices for wearable watches for the elderly.

After LDA modeling, we extracted the top 30 keywords from each topic. Three trained annotators (with backgrounds in consumer behavior and product design) independently interpreted each topic by reviewing the keywords and a random sample of topic-representative documents. They then assigned a representative attribute label to each topic (e.g., “Battery”, “Appearance Design”, “Anti-loss”), which was later discussed and finalized in a consensus meeting. To ensure reliability, the attribute labels were independently assigned by three coders. Inter-rater agreement was measured using Cohen’s Kappa, which yielded a score of 0.78, indicating substantial agreement. Discrepancies were discussed and resolved by consensus in a follow-up meeting, resulting in a finalized list of 21 first-level functional attribute features and evaluation indicators, 41 s-level attribute indicators (34 functional and 7 evaluative). A total of 120 keywords are extracted, including sub-attributes or synonyms for the same attribute, as shown in

Table 3. Table showing attribute characteristics of wearable watches for the elderly and the extraction of evaluation indices.

No.	First-Level Functional Attributes	Secondary-Level Functional Attribute Features	Three-Level Extraction Keywords
1	Body index monitoring	Blood pressure monitoring	Blood pressure
		Heart rate monitoring	Heart rate
		Blood lipid monitoring	Blood lipid
		Blood sugar monitoring	Blood sugar
		Atrial fibrillation monitoring	Atrial fibrillation
		Respiratory rate monitoring	Respiratory rate
		Blood oxygen monitoring	Blood oxygen
		Uric acid monitoring	Uric acid
		Body temperature monitoring	Body temperature
		Sleep quality monitoring	Sleep
2	Anti-loss	Positioning	Positioning, GPS, Gps, gps
		Navigation	Navigation
		Electronic fence	Electronic fence, fence
3	Alarm	Fall alarm	Anti-fall alarm, fall alarm, tumble alarm, auto dialing
		One-click alarm	Alarm, one-click alarm, SOS, sos, Sos, One-click help
4	Call	Call	Phone, making a call, call, voice call
		Video	Video
		Sound	Volume, sound, sound quality, noise
		Signal	Signal
5	Data record	Exercise data record	Trajectory, exercise data, steps, step count
		Data sync to phone	Phone, data, APP, App, app, sync
6	Abnormal alert	Abnormal alert (heart rate, blood pressure, etc.)	Abnormal, abnormal alert
		Medication reminder	Medication reminder
		Sedentary reminder	Sedentary, sedentary reminder
		Warning notification	Warning
7	Remote operation	Guardian remote measurement command	Remote guardian
		Guardian phone alert for abnormal conditions
8	Smart interaction	Voice assistant	Voice assistant
		Message reminder	Message reminder
9	Battery	Battery	Battery, endurance, power, charging, charge once
			power-consuming
10	Screen	Screen	Screen, touch screen, display screen, touch screen, clear, font
11	Wearing comfort	Wearing comfort	Wearing, strap, material, texture, hand feeling
12	Appearance design	Appearance design	Color, style, design, pattern, facial attractiveness analysis, eye-catching, appearance
13	Daily life functions	Daily life functions	Payment, weather, weather forecast, timekeeping, alarm reminder
14	Entertainment functions	Entertainment functions	News, music, games, photography
15	Accuracy	Accuracy	Accuracy, error rate, precision, degree of precision, resolution, deviation, inaccuracy
16	Ease of operation	Ease of operation	operation, simplicity, easy to learn, convenient, convenience
17	Sensitivity	Sensitivity	Sensitive, responsive
18	Durability	Durability	Waterproof, sturdy, quality
19	Service	Service	Customer service, logistics
20	Price	Price	Price, performance–cost ratio
21	Negative reviews	Negative reviews	Ads, trash, bad, wasteful, superfluous, flaw, poor, bad review, does not match

.

Overall, the use of LDA helped guide the identification of high-frequency themes in user reviews. However, to ensure interpretability and contextual relevance, human judgment was applied for attribute mapping. The inclusion of multiple coders and inter-rater agreement assessment strengthened the reliability and reproducibility of this process.

3.3. Analysis of Attribute Feature Satisfaction and Attention Using BERT

3.3.1. Selection of Pre-Trained Models, Data Annotation, and Preprocessing

We employed an attribute-level sentiment analysis approach, a sub-form of Aspect-Based Sentiment Analysis (ABSA), to assess user sentiment toward specific product features. This method enables a fine-grained understanding of user preferences and dissatisfaction points.

BERT (Bidirectional Encoder Representations from Transformers) is a bidirectional transformer-based pre-trained model introduced by Google in 2018 [27]. It demonstrates superior performance in natural language tasks, including information retrieval, question answering, sentiment analysis, sequence labeling, and natural language inference.

Fine-tuning pre-trained BERT enhances performance and generalization across tasks while maintaining robustness against overfitting. Following prior research [46], we used a pre-trained BERT-based Chinese model. Emotional labeling was performed using the Deep Seek API, which annotates texts with negative, neutral, and positive sentiments. A systematic sampling method was applied, extracting 30% of the texts for each attribute. Following annotation, the dataset was partitioned into 80% training data, 10% validation data (for model tuning), and 10% test data (for final evaluation). To address class imbalance, data augmentation techniques such as oversampling [47] were applied. The final dataset included 17,188 training samples, 2149 validation samples, and 2149 test samples. The AdamW optimizer [48] and a learning rate scheduler were employed during training.

3.3.2. Selection of Model Performance Metrics

Consistent with previous studies [49,50,51], we used the weighted F1-score and the weighted ROC-AUC to evaluate model performance on imbalanced datasets, as it is a harmonic mean of precision and recall. For multi-class classification with imbalanced data, we employed weighted categorical cross-entropy loss [48] to compute the loss.

3.3.3. Fine-Tune Experimental Training and Sentiment Inference Results

Following the methods of Sun et al. (2019) [52] and Souza et al. (2022) [53], we first conducted single-parameter tuning—holding other parameters constant—to assess individual impacts on model performance. This helped define reasonable parameter ranges and narrow the search space. Text length analysis showed that 75% of the texts were under 115 characters, mostly between 50 and 120, so the maximum sequence length was set to 128. A grid search was then applied to optimize learning rate, batch size, and training epochs. Detailed settings and results are presented in

Table 4. Hyperparameter tuning and control strategies for BERT training experiments.

	Control Methods	Performance Metrics	Results
Learning Rate	Grid search over initial settings [5e−5, 2e−5], learning rate scheduler (linear decay, adaptive adjustment)	Adaptively adjusted based on validation loss, weighted F1-score and weighted ROC-AUC	5e−5
Batch Size	Grid search over batch sizes [8, 16, 32, 64]	Validation loss, weighted F1-score and weighted ROC-AUC	16
Maximum Sequence Length	Text length distribution calculation	Overwrites the text categories with the largest distribution ratio	128
Epoch	Experiments with epochs [1, 10], early stopping enabled	Validation loss, weighted F1-score, weighted PR-AUC, and weighted ROC-AUC	7

and

Table 5. Results of fine-tuning experiments on hyperparameter combinations using training datasets.

Maximum Sequence Length	Hyperparameter Combinations	Mean Weighted F1	Mean Weighted ROC-AUC	Variance of Weighted F1	Variance of Weighted ROC-AUC	Overall Variance
128	Learning rate: 5e−5, batch size: 16, epoch: [1, 10]	0.9741	0.9805	6.66E−07	3.85E−07	5.25E−07
	Learning rate: 5e−5, batch size: 32, epoch: [1, 10]	0.9744	0.9807	3.75E−07	2.15E−07	2.95E−07
	Learning rate: 2e−5, batch size: 16, epoch: [1, 10]	0.9744	0.9807	4.63E−07	2.70E−07	3.66E−07
	Learning rate: 2e−5, batch size: 32, epoch: [1, 10]	0.9740	0.9804	2.46E−07	1.42E−07	1.94E−07
	Learning rate: 5e−5, batch size: 8, epoch: [1, 10]	0.9730	0.9797	5.72E−07	3.43E−07	4.57E−07
	Learning rate: 5e−5, batch size: 64, epoch: [1, 10]	0.9740	0.9804	3.81E−07	2.15E−07	2.98E−07

. In this paper, lowercase letters in scientific notation represent parameter settings, while uppercase letters indicate computed results.

Using 10 randomly generated seeds [780, 2429, 2588, 5067, 5675, 6308, 7252, 7504, 7926, 9880], along with seed 42, we conducted 11 training runs per hyperparameter setting, totaling 66 experiments. The mean and variance of the weighted avg F1 and ROC-AUC scores were calculated to evaluate performance and stability. The global mean score was 0.9772, indicating high effectiveness, and the low variance (3.56E−07) demonstrated strong stability and robustness.

We selected the trained model with the hyperparameter combination [maximum sequence length: 128, learning rate: 5e⁻⁵, batch size: 32] due to its optimal comprehensive performance and low variance. Specifically, the model from the eighth epoch with a random seed of 780 was chosen, as shown in Figure 6, achieving the following performance metrics: a weighted F1-score of 0.9754 and a weighted ROC-AUC of 0.9815. On the test set, the model achieved a weighted F1-score of 0.9791 and a weighted ROC-AUC of 0.9982, comparable to its validation performance, demonstrating strong generalization ability.

Finally, the selected model was used to predict sentiment evaluations for the entire comment dataset on attribute features. Considering the existence of fake reviews [54,55,56], we assume real review data with a 60% ratio, and that fake reviews are mainly positive comments. The satisfaction and attention inference results are presented in

Table 6. Satisfaction and attention toward attribute features.

Label	Feature	Satisfaction	Attention	Label	Feature	Satisfaction	Attention
1	Ease of operation	0.5757	0.1364	23	Negative reviews	0.3136	0.0074
2	Accuracy	0.5455	0.1112	24	Video	0.5333	0.0067
3	Blood pressure monitoring	0.5543	0.0854	25	Daily life functions	0.5426	0.0053
4	Data sync to phone	0.5592	0.0718	26	Uric acid monitoring	0.5615	0.0049
5	call	0.5439	0.0638	27	Remote operation	0.5301	0.0049
6	Blood sugar monitoring	0.5619	0.0583	28	Signal	0.533	0.0044
7	Heart rate monitoring	0.5724	0.0474	29	Blood lipid monitoring	0.5649	0.0036
8	Appearance design	0.5802	0.0448	30	Sedentary reminder	0.5749	0.0035
9	Positioning	0.55509	0.0438	31	Waterproof	0.5586	0.0031
10	Battery	0.4939	0.0377	32	Fall alarm	0.5575	0.0027
11	Durability	0.5752	0.0332	33	Navigation	0.5421	0.0024
12	Screen	0.5789	0.0312	34	Entertainment functions	0.5618	0.0023
13	Wearing comfort	0.5696	0.0301	35	Abnormal alert	0.5124	0.0019
14	Service	0.5564	0.0274	36	Ads	0.5167	0.0015
15	Price	0.5474	0.0231	37	Warning	0.2204	0.001
16	Sound	0.5396	0.0195	38	Payment	0.5467	0.001
17	Blood oxygen monitoring	0.5715	0.0156	39	Electronic fence	0.3907	0.0009
18	Sleep monitoring	0.5732	0.0152	40	Respiratory rate monitoring	0.6	0.0007
19	Sensitivity	0.55635	0.0134	41	Message reminder	0.6	0.00068
20	Exercise data record	0.5495	0.0128	42	Medication reminder	0.5308	0.00055
21	One-click alarm	0.5486	0.0106	43	Atrial fibrillation monitoring	0.6	0.0001
22	Body temperature monitoring	0.5719	0.0077	44	Voice assistant	0.6	4.23E−05

.

The Pearson correlation coefficient between satisfaction and attention was 0.1117, with a p-value of 0.4651, indicating no significant correlation. This suggests that the frequency of mentions or attention given to an attribute is unrelated to actual satisfaction with that attribute.

3.4. Analysis of Satisfaction Importance and Preferences Based on Kano

3.4.1. Kano Questionnaire Design and Basic Analysis

Based on the attribute features outlined in Section 3.2, a Kano questionnaire was designed to assess the attributes. The survey targeted elderly individuals or their guardians with experience using or purchasing wearable watches for the elderly, as well as industry professionals. A total of 157 questionnaires were collected, including eight invalid and 149 valid responses, resulting in an effective response rate of 94.9%. The reliability and validity of the questionnaire, tested for both positive and negative questions using SPSS 22.0, are shown in

Table 7. Reliability analysis.

Cronbach’s Alpha	Number of Items
0.92	90

and

Table 8. Validity analysis.

positive items	KMO	0.911
	Sig.	0.000
negative items	KMO	0.914
	Sig.	0.000

.

3.4.2. Categorization of Attribute Demand Preferences

Using Python 3.8, we counted the total occurrences of each of the six Kano attribute types for each attribute and selected the type with the highest count. We then applied the relevant formulas to calculate the better, worse, and importance values for each attribute and generated a quadrant diagram. The results are presented in

Table 9. Quadrant classification based on attribute preference.

Attribute Label	Name	Q	A	O	I	R	M	Type	Better	Worse	Importance	QUAD
23	Call	0	33	49	38	2	27	O	0.5578	0.517	2.1141	I
34	Waterproof	1	30	51	34	2	31	O	0.5548	0.5616	2.2685	I
44	Durability	0	18	60	31	5	35	O	0.5417	0.6597	2.5034	I
35	Wearing comfort	0	26	55	31	2	35	O	0.551	0.6122	2.4564	I
37	Touchscreen	1	30	45	41	1	31	O	0.5102	0.517	2.1477	I
39	Service	1	15	64	33	5	31	O	0.5524	0.6643	2.4295	I
22	Data sync to Phone	1	35	44	44	1	24	O	0.5374	0.4626	1.9262	I
3	Heart rate monitoring	3	38	40	40	0	28	O	0.5342	0.4658	2	I
38	Screen display clarity	1	24	59	31	2	32	O	0.5685	0.6233	2.4228	II
41	Accuracy	1	28	59	31	3	27	O	0.6	0.5931	2.2819	II
42	Sensitivity	0	23	64	32	2	28	O	0.5918	0.6259	2.3826	II
12	Abnormal reminder to the guardian	2	39	53	26	2	27	O	0.6345	0.5517	2.2349	II
30	Signal	2	31	56	36	1	23	O	0.5959	0.5411	2.1074	II
27	Battery	0	36	60	30	1	22	O	0.6486	0.5541	2.1879	II
16	Positioning	3	39	50	31	1	25	O	0.6138	0.5172	2.1074	II
17	Fall alarm	3	41	52	34	1	18	O	0.6414	0.4828	1.9262	II
18	One-click alarm	6	33	55	30	1	24	O	0.6197	0.5563	2.1342	II
24	Video	1	35	51	44	2	16	O	0.589	0.4589	1.7987	II
40	Ease of operation	2	17	67	25	4	34	O	0.5874	0.7063	2.604	II
11	Abnormal alert	4	43	47	25	2	28	O	0.6294	0.5245	2.1745	II
43	Warning	0	44	43	44	1	17	A	0.5878	0.4054	1.7315	III
25	Voice assistant	0	39	45	45	1	19	O	0.5676	0.4324	1.8054	III
45	Price	0	48	40	45	1	15	A	0.5946	0.3716	1.6309	III
13	Remote operation	2	44	43	39	1	20	A	0.5959	0.4315	2.1141	III
19	Electronic fence	5	44	36	40	4	20	A	0.5714	0.4	1.8322	III
15	Sedentary reminder	2	53	36	40	0	18	A	0.6054	0.3673	1.6913	III
4	Atrial fibrillation monitoring	4	54	29	44	0	18	A	0.5724	0.3241	1.6846	III
14	Medication reminder	2	50	46	30	2	19	A	0.6621	0.4483	1.5503	III
7	Blood lipid monitoring	3	50	34	42	1	19	A	0.5793	0.3655	1.8993	III
21	Exercise data Record	0	39	46	45	1	18	O	0.5743	0.4324	1.6577	III
2	Blood pressure monitoring	5	45	35	39	0	25	A	0.5556	0.4167	1.7919	IV
5	Respiratory rate monitoring	4	49	32	47	0	17	A	0.5586	0.3379	1.8456	IV
6	Blood oxygen monitoring	4	43	34	45	0	23	I	0.531	0.3931	1.5436	IV
8	Uric acid monitoring	3	51	30	49	2	14	A	0.5625	0.3056	1.745	IV
9	Body temperature monitoring	3	41	33	46	2	24	I	0.5139	0.3958	1.4161	IV
20	Navigation	2	39	41	46	0	21	I	0.5442	0.4218	1.745	IV
10	Sleep monitoring	3	42	39	38	2	25	A	0.5625	0.4444	1.7919	IV
33	Entertainment Functions	0	32	36	62	3	16	I	0.4658	0.3562	1.906	IV
32	Daily life functions	1	40	38	50	1	19	I	0.5306	0.3878	1.4765	IV
31	Payment	1	70	0	67	11	0	A	0.5109	0	1.6711	IV
29	Sound loud	3	36	42	51	2	15	I	0.5417	0.3958	0.4698	IV
28	Sound clear	1	31	46	50	2	19	I	0.5274	0.4452	1.5906	IV
26	Message reminder	1	41	40	45	0	22	I	0.5473	0.4189	1.7718	IV
36	Appearance design	1	41	36	55	2	14	I	0.5274	0.3425	1.8188	IV
1	Blood sugar monitoring	9	41	23	53	3	20	I	0.4672	0.3139	1.4698	IV

and Figure 7.

The Pearson correlation coefficient between importance and attention was 0.3784, with a p-value of 0.0104, showing a significant but moderately weak positive correlation. This indicates that importance and attention reflect different aspects of user needs.

As shown in the table and figure above, eight attributes—call, waterproof, durability, wearing comfort, touchscreen, service, data sync to phone, and heart rate monitoring—are classified as must-be attributes.

Twelve attributes, including screen display clarity, accuracy, sensitivity, abnormal reminder to the guardian, signal, battery, positioning, fall alarm, one-click alarm, video, ease of operation, and abnormal alert, are identified as one-dimensional attributes.

Ten attributes—warning, voice assistant, price, remote operation, electronic fence, sedentary reminder, atrial fibrillation monitoring, medication reminder, blood lipid monitoring, and exercise data record—are categorized as attractive (delighter) attributes. The remaining attributes, classified as indifferent, will be excluded from further analyses.

In summary, the Kano model has effectively filtered and categorized the diverse set of features found in wearable smartwatches for the elderly. This provides a solid foundation for subsequent product development and functional enhancement.

3.5. Integration of Sentiment and Kano Results

Building on the analysis above, the three-dimensional decision model IPAA–Kano was developed by integrating the Kano and IPA models with attention. A total of 14 attributes were identified as needing improvement, nine as maintenance attributes, and seven as attributes that can be deprioritized. Six cognitive management strategies were proposed for attributes with attention–utility discrepancies. The results are shown in Figure 8 and

Table 10. Decision results for each P-dimension using the IPAA–Kano model with a weight configuration of (1, 0).

Attribute Label	Attribute Name	P-Dimension	QUAD	Improvement Priority Coefficient	Improvement Priority	Ignorance Priority	Cognitive Management	Maintenance Priority
44	Durability	Must-be	I	1.6566	/	/	/	1
35	Wearing comfort		I	1.644	/	/	/	2
37	Touchscreen		I	1.3801	/	/	/	3
3	Heart rate monitoring		I	1.2525	/	/	/	4
34	Waterproof		II	1.5087	/	/	High performance, high importance, but low attention; because of the must-be attributes, it does not have the propagation valve	5
39	Service		V	1.6775	1	/	/	/
23	Call		V	1.4165	2	/	/	/
22	Data sync to phone		V	1.2311	3	/	/	/
40	Ease of operation	One-dimensional	I	1.7369	/	/	/	6
38	Screen display clarity		I	1.6064	/	/	/	7
17	Fall alarm		II	1.2240	/	/	Raise awareness	8
27	Battery		V	1.6298	4	/	/	/
42	Sensitivity		V	1.611	5	/	/	/
41	Accuracy		V	1.5565	6	/	/	/
18	One-click alarm		V	1.4216	7	/	/	/
16	Positioning		V	1.3823	8	/	/	/
12	Abnormal reminder to the guardian		VI	1.5524	9	/	Reduce attention before improvement, raise awareness after improvement	/
11	Abnormal alert		VI	1.546	10	/	Reduce attention before improvement, raise awareness after improvement	/
30	Signal		VI	1.4396	11	/	Reduce attention before improvement, raise awareness after improvement	/
24	Video		VIII	1.1675	/	7	/	/
45	Price	Attractive	III	0.9398	/	/	/	9
25	Voice assistant		IV	1.043	/	6	/	/
15	Sedentary reminder		IV	0.9901	/	5	/	/
7	Blood lipid monitoring		IV	0.9853	/	4	/	/
4	Atrial fibrillation monitoring		IV	0.8438	/	3	/	/
14	Medication reminder		VI	1.262	12	/	Reduce attention before improvement, raise awareness after improvement	/
13	Remote operation		VI	1.2041	13	/	Reduce attention before improvement, raise awareness after improvement	/
21	Exercise data record		VII	1.1274	14	/	Pseudo-demand, reduce attention	/
19	Warning		VIII	1.0815	/	2	/	/
43	Electronic fence		VIII	1.0557	/	1	/	/

.

Among the improvement attributes, service ranks as the top dimension, requiring enhancement for elderly-focused technology products, followed by call, data sync to phone, battery, sensitivity, accuracy, one-click alarm, positioning, abnormal alert, abnormal reminder to the guardian, remote operation, medication reminder, and exercise data record. This indicates that for wearable smartwatches for the elderly, apart from service, core health alert functionalities related to the device itself—including sensitivity, accuracy, and alarms/reminders in different scenarios—are the most urgently needed improvements.

For the maintenance attributes, essential functions such as durability and water resistance rank at the top, followed by ease of use, screen, and price. It is worth noting that fall alarm, due to its high satisfaction, importance, and performance but low attention, is also categorized as a maintenance attribute. In terms of cognitive management, it is recommended to enhance market communication to raise consumer awareness and perceived value of this feature.

In contrast, attributes such as abnormal alert, which are highly important and contribute significantly to satisfaction, but perform poorly and currently receive little attention, should first undergo functionality improvements before cognitive marketing efforts are introduced to raise consumer awareness.

For the exercise data record attribute, both satisfaction and importance scores are low, but it receives relatively high consumer attention. This type of attribute can be considered a pseudo-demand—a function that attracts consumer interest yet contributes little actual utility. As such, it should be temporarily categorized as an improvement attribute, with targeted cognitive management interventions warranted due to its high attention level.

3.6. Strategic Implications and Practical Interpretation

Except for the one-dimensional attribute video, all deprioritized attributes fall under the attractive (delighter) category, including atrial fibrillation monitoring, sedentary reminder, blood lipid monitoring, voice assistant, warning, and electronic fence. This suggests that current consumers are more focused on the core functionalities of wearable smartwatches for the elderly, while additional attractive features—originally intended by manufacturers to appeal to consumers—are rated relatively low in satisfaction, importance, and attention. This may be related to consumers’ needs or their cognitive understanding of the product.

These findings align with the fact that elderly individuals’ acceptance of technology is affected by cognitive and technological barriers. The implications for the development and innovation of gerontechnology products are that elderly individuals’ cognitive and acceptance barriers to technology suggest that the design of elderly-focused technology products, such as wearable watches, should prioritize core functionalities, consolidate similar functions, and ensure ease of operation.

At the same time, aligning with the conclusion that service improvement is the top priority, social support factors during the technology adoption process for elderly individuals are also crucial. Therefore, the design of gerontechnology products must pay special attention to enhancing accompanying services, while also emphasizing the importance of cognitive management and education for elderly users regarding technology products.

Overall, improvement attributes are concentrated in the one-dimensional category, along with a few must-be attributes; maintenance attributes are primarily in the must-be category (8), with a few in the one-dimensional (3) and attractive (3) categories; most deprioritized attributes fall under the attractive category (6). These evaluation results demonstrate that, when compared against the mature Kano model classifications, the IPPA–Kano model’s identification of improvement, maintenance, and low-priority attributes shows high alignment with the Kano model’s structure. This aligns well with general factual knowledge and consumer intuition, indirectly validating the model’s effectiveness and reliability. Moreover, the model demonstrates strong practical interpretability and support, confirming its applicability and operational value. The next section will further strengthen the model’s validation from a quantitative empirical perspective.

4. Model Comparison and Validation Discussion

4.1. Comparison Results Between the IPAA–Kano Model and the IPA–Kano Model

To compare the IPA–Kano and IPAA–Kano models in supporting real-world decision-making, the IPA–Kano model was applied, identifying 17 improvement and 13 maintenance attributes, shown in

Table 11. Decision results for each P-dimension using the IPA–Kano model.

Attribute Name	P-Dimension	QUAD	Priority Improvement Coefficient	Improvement Priority	Maintenance Priority
Service	Must-be	I	1.6775	1	/
Call		I	1.4165	2	/
Data sync to phone		I	1.2311	3	/
Durability		II	1.6566	/	1
Wearing comfort		II	1.644	/	2
Waterproof		II	1.5087	/	3
Touchscreen		II	1.3801	/	4
Heart rate monitoring		II	1.2525	/	5
Battery	One-dimensional	I	1.6298	4	/
Sensitivity		I	1.611	5	/
Accuracy		I	1.5565	6	/
Abnormal reminder to the guardian		I	1.5524	7	/
Abnormal alert		I	1.546	8	/
Signal		I	1.4396	9	/
One-click alarm		I	1.4216	10	/
Positioning		I	1.3823	11	/
Ease of operation		II	1.7369	/	6
Screen display clarity		II	1.6064	/	7
Fall alarm		II	1.224	/	8
Video		IV	1.1675	12	/
Medication reminder	Attractive	I	1.262	13	/
Remote operation		I	1.2041	14	/
Voice assistant		III	1.043	/	9
Sedentary reminder		III	0.9901	/	10
Blood lipid monitoring		III	0.9853	/	11
Price		III	0.9398	/	12
Atrial fibrillation monitoring		III	0.8438	/	13
Exercise data record		IV	1.1274	15	/
Warning		IV	1.0815	16	/
Electronic fence		IV	1.0557	17	/

. And the attributes scatters are showed respectively in Figure 9 and Figure 10. While both models showed overlapping results—confirming the stability of the enhancement framework—the IPAA–Kano model, by incorporating the attention dimension, offered more nuanced insights. It could identify attributes that should be initially overlooked and those requiring cognitive management—capabilities absent in the IPA–Kano model. This comparative analysis highlights the IPAA–Kano model’s advantage in evaluation precision and strategic decision-making.

Specifically, the IPAA–Kano model identified seven dimensions as prioritized-to-be-neglected attributes, which intuitively aligns with our understanding and will be further validated through empirical studies. The number of improvement attributes decreased from 17 to 14, and maintenance attributes decreased from 13 to 9. These changes demonstrate that the inclusion of the attention dimension allowed the model to uncover previously hidden attributes under the attention–utility inconsistency condition and provided corresponding cognitive strategies for such dimensions. This improvement is meaningful not only in terms of decision-making granularity but also in enabling more efficient resource allocation under resource constraints.

Furthermore, unlike the weighted IPA–Kano and MCDA approaches (e.g., AHP, TOPSIS), which emphasize the assignment of weights—often based on expert judgment—the key innovation of the IPAA–Kano model lies in introducing attention as a new, independently derived metric. This allows for the identification of hidden attributes overlooked by traditional models and the development of cognitive strategies without relying on subjective assessments. Crucially, the attention metric is extracted from user-generated behavioral data, offering a more objective, behavior-driven basis for fine-grained attribute prioritization and decision support.

4.2. Validation of the IPPA–Kano Model’s Effectiveness

To evaluate the practical value of the proposed IPAA–Kano model, we conducted a stakeholder survey involving ten industry experts and forty end users. Participants assessed the outputs of both the traditional IPA–Kano model and the proposed IPAA–Kano model on three key dimensions using a five-point Likert scale: (1) practical relevance and consistency with real-world experience, (2) usefulness for decision-making, and (3) ease of use and acceptability. The evaluation results and paired t-test statistics are presented in

Table 12. Evaluation of model effectiveness: paired t-test results.

Evaluation Dimension	IPA–Kano Model Score (Mean ± Standard Deviation)	IPAA–Kano Model Score (Mean ± Standard Deviation)	t-Statistic	p-Value	Mean Difference
Practical relevance	3.32 ± 0.59	4.02 ± 0.65	7.65	<0.001	0.7
Decision-making support	3.16 ± 0.62	4.06 ± 0.68	9.84	<0.001	0.9
Ease and acceptability	3.4 ± 0.57	3.92 ± 0.67	5	<0.001	0.52

.

Statistical analysis confirms that the IPAA–Kano model significantly outperforms the traditional IPA–Kano across all evaluation dimensions (p < 0.001). Notably, the greatest improvement was observed in the “decision-making support” dimension, where the IPAA–Kano model achieved a mean score of 4.06, compared to 3.16 for IPA–Kano. These results provide robust evidence of the enhanced practical value, decision relevance, and stakeholder acceptability of the proposed model.

4.3. Stakeholder Feedback on Practical Use

Beyond statistical validation, we collected qualitative feedback from five stake holders (e.g., product managers and UX designers in the elderly smart device sector). Using a five-point Likert scale, they rated the model’s clarity, decision-making value, and relevance. Scores averaged 4.2 for decision support and 4.0 for clarity. Notably, stakeholders emphasized the model’s ability to reveal high-attention but low-utility attributes as crucial for guiding feature prioritization and resource allocation under budget constraints.

4.4. Sensitivity and Robustness Analysis of Misalignment Classification

The results of the sensitivity analysis indicate that using the median as the threshold for quadrant classification demonstrates strong stability under small fluctuations of ±3% and ±5%, with classification consistency maintained within the range of 70.73% to 82.93%, as shown in

Table 13. Sensitivity analysis showing consistency rates of attribute quadrant classification under different threshold perturbations (±3%, ±10%) from the median.

Compared with	Total Attributes	Consistent	Inconsistent	Consistency Rate (%)
plus_3%	41	33	8	80.49
minus_3%	41	34	7	82.93
plus_5%	41	29	12	70.73
minus_5%	41	32	9	78.05
plus_10%	41	20	21	48.78
minus_10%	41	26	15	63.41

. This suggests that the threshold-setting method based on the median possesses good structural robustness and practical applicability. Even under more extreme fluctuations of ±10%, the classification consistency, though slightly reduced, remains within a reasonable range, further highlighting the resilience of the median approach when applied to real-world survey data, which may present skewed distributions (see Figure 11).

To further evaluate the robustness of the median-based threshold across different data distribution scenarios, we conducted a simulation-based sensitivity test using synthetically generated datasets following normal, lognormal, uniform, and bimodal distributions. The results revealed that, when a consistency rate of 60% is used as the baseline, the median threshold exhibits relatively high stability across lognormal and uniform distributions, with fluctuations ranging from ±3% to ±10%. In contrast, for normal and bimodal distributions—where data concentration is more pronounced—stability is observed only within a narrower fluctuation range of ±3% to ±5%. Among these, the uniform distribution exhibits the highest consistency levels under small threshold variations, indicating that in datasets without a clear central tendency, slight shifts in the classification boundary exert minimal impact on the outcome. These results are presented in

Table 14. Sensitivity analysis of quadrant classification consistency for bimodal data.

Compared with	Total Attributes	Consistent	Inconsistent	Consistency Rate (%)
plus_3%	41	37	4	90.24
minus_3%	41	39	2	95.12
plus_5%	41	31	10	75.61
minus_5%	41	39	2	95.12
plus_10%	41	24	17	58.54
minus_10%	41	39	2	95.12

,

Table 15. Sensitivity analysis of quadrant classification consistency for lognormal data.

Compared with	Total Attributes	Consistent	Inconsistent	Consistency Rate (%)
plus_3%	41	35	6	85.37
minus_3%	41	39	2	95.12
plus_5%	41	31	10	75.61
minus_5%	41	36	5	87.8
plus_10%	41	27	14	65.85
minus_10%	41	33	8	80.49

,

Table 16. Sensitivity analysis of quadrant classification consistency for normal data.

Compared with	Total Attributes	Consistent	Inconsistent	Consistency Rate (%)
plus_3%	41	32	9	78.05
minus_3%	41	35	6	85.37
plus_5%	41	25	16	60.98
minus_5%	41	30	11	73.17
plus_10%	41	10	31	24.39
minus_10%	41	22	19	53.66

and

Table 17. Sensitivity analysis of quadrant classification consistency for uniform data.

Compared with	Total Attributes	Consistent	Inconsistent	Consistency Rate (%)
plus_3%	41	37	4	90.24
minus_3%	41	39	2	95.12
plus_5%	41	32	9	78.05
minus_5%	41	36	5	87.8
plus_10%	41	29	12	70.73
minus_10%	41	34	7	82.93

.

However, when the fluctuation exceeds ±15%, all distribution types experience a sharp decline in classification consistency, underscoring the high sensitivity of the method to threshold variation. This effect is particularly evident in distributions with strong central tendencies—such as normal and bimodal—where the method shows structural dependence on boundary positioning and is affected by the nonlinear response induced by data concentration patterns. These findings suggest that in practical applications, the range of boundary adjustments should be carefully constrained to avoid structural misclassifications, thereby ensuring the reliability of quadrant-based decision frameworks.

4.5. Comparison of Characteristics Between the IPP–Kano Model and the IPAA–Kano Model

We compared the characteristics of the models based on decision variables, importance calculation methods, data sources, attribute ranking results, and the specific contributions of the IPAA–Kano model, as shown in

Table 18. Comparison of the IPA–Kano and IPPA–Kano models.

Comparison Dimension	IPA–Kano Model	IPAA–Kano Model	Contribution
Decision Variables	Satisfaction	Satisfaction + Attention	Solution for “cognitive misalignment” between attention to attributes and satisfaction
Importance Calculation	Derived from separate survey	Derived from Kano preferences (contribution-based)	Ensures internal consistency
Data Sources	Structured survey only	Survey + Online Reviews (UGC)	Improves real-world relevance and dynamic reflection
Attribute Ranking Results	Uniform across weights	Flexibility under different decision-weight scenarios; identification and development of neglected priorities; and cognitive management strategies	Supports fine-grained contextual decision-making; cognitive management of “cognitive misalignment” attributes

.

5. Conclusions and Future Work

Building on value co-creation and leveraging deep learning-based natural language processing (NLP), this study employs BERT and Latent Dirichlet Allocation (LDA) for a data-driven, fine-grained analysis of user demands and attribute preferences. By integrating these insights with the Kano–IPA framework, we introduce attention and priority improvement coefficients, forming the IPAA–Kano model to optimize decision-making in customer-centric innovation.

Addressing the misalignment between explicit attention and implicit utility, the model extends the traditional IPA–Kano framework into a three-dimensional structure with eight decision spaces, enhancing its ability to (1) accurately classify product attributes based on their impact on satisfaction and user attention; (2) optimize resource allocation by distinguishing between quality improvement priorities and cognitive expectation management; (3) systematically deprioritize low-value attributes, increasing decision efficiency under resource constraints.

The empirical validation of wearable devices for the elderly, using 12,527 user-generated reviews, confirms the superior accuracy and decision relevance of the deep learning-enhanced IPAA–Kano model compared to traditional approaches. Additionally, domain expert validation and consumer feedback further support its practical effectiveness in guiding technology product development and optimization strategies.

However, this study has several limitations, which present opportunities for future research: (1) Data dependence: The satisfaction analysis primarily relies on user-generated content, which may introduce bias and limit the generalizability of attribute analysis. (2) Generalization constraints: The case study focuses on wearable devices for the elderly; further validation across diverse industries and product categories is necessary to broaden applicability. (3) Market dynamics: The model’s effectiveness may be affected by rapid market shifts, necessitating continuous model updates to capture emerging trends and evolving customer preferences. (4) Deep learning enhancements: While this study utilizes BERT and LDA, future research could explore the integration of advanced deep learning techniques, such as hybrid models, reinforcement learning, or neural network-based attention mechanisms, to further enhance classification accuracy and decision depth.

This study contributes to both methodological advancements in deep learning-assisted decision modeling and practical strategies for optimizing innovation under resource constraints, providing a scalable, intelligent framework for guiding technology-driven product development and strategic innovation.