• The study emphasizes knowledge graph-powered product iteration within an under-explored NPD domain of newer and less-established novel products. • An interpretable conceptual design knowledge graph (I-CDKG) is constructed to facilitate designers in generating innovative and cost-effective heuristic product ideations. • A hybrid method combining deep-learning ERNIE-BiGRU-CRF model, BIESO labeling mode, and triple-extracting algorithm is proposed to facilitate the I-CDKG construction. • The I-CDKG boasts both inherent and acquired interpretability reinforced by a Cluster-Relation-Nest organizational strategy for the intuitive locating of design knowledge. Novel products emerge over time to survive the competitive landscape as no existing product can perpetually satisfy all evolving customer expectations. These products are often characterized by groundbreaking solutions previously unavailable on the market. However, the swift imitation of successful novel products by competitors underscores the need for sustained iteration and continuous improvement. Designers increasingly face challenges in keeping up to date with the growing volume and fragmented nature of design information from diverse sources. While knowledge graphs show promise in structuring and organizing complex design information, their effective application in the ideation process remains limited due to difficulties in automatic knowledge extraction and the lack of interpretability aligned well with designers’ cognitive processes. This study proposes an integrated method to construct an interpretable conceptual design knowledge graph (I-CDKG) that features both inherent and acquired interpretability for heuristic product ideation. First, the schema layer models product design knowledge and governs the semantic connection of design information reinforced by design cognition principles to create a reasonable organizational framework to foster intuitive knowledge exploration. Second, the data layer mainly fulfills automatic and smooth design knowledge extraction for I-CDKG construction through the deep learning ERNIE-BiGRU-CRF model combined with BIESO labeling mode and triple-extracting algorithm. Third, the application layer empowers designers to visually delve into interpretable design knowledge to locate inspiration from cluster, relation, and nest levels and enable constant I-CDKG expansion as design schemes proliferate. A case study on the smart cat litter box demonstrates the feasibility of the proposed methodology. The evaluation results confirm the I-CDKG’s advantages as a productive design tool for inspiring creative, practical, and cost-effective product ideations, thereby empowering the iterative development of competitive novel products.

Eliciting user needs from mass online reviews is playing a significant role in the product iteration process. Efficient user needs elicitation does achieve considerable benefits for maintaining higher competitiveness and a speedier lifecycle. However, there is inevitably an online review scarcity about new products due to the short time on the market and low buyer recognition compared with commonly used products. This paper proposes a small sample data-driven method for user needs elicitation from online reviews in new product iteration. In the first stage, a scraped initial online review dataset is pre-processed roughly to improve the data quality. And then, reviews are classified into multiple categories according to different topics using ERNIE. In the second stage, each topic-based dataset is reprocessed in detail. Thereafter, the key user needs set is determined and facilitated by extracting key product information phrases from every single dataset using improved SIFRank. Moreover, the case study of a smart cat feeder is carried out to demonstrate the feasibility and potential of the ERNIE-ISIFRank methodology. Finally, comparative experiments are conducted to verify the advantages of the proposed method which is primarily based on the pre-trained language model to enhance the deep understanding of the semantics of online reviews. The experimental results confirm that the proposed method can assist in identifying key user needs with high efficiency.

Along with increasing the emphasis on cultural attributes, product design is not only satisfied with the realization of function and appearance, but also considers the embodiment of human emotion and social style. As a result, the number of creative product based on cultural style is increasing. However, existing product decision studies do not consider this style-oriented product- ranking problem, ignore the influence of cultural style and fuzzy decision-making environment on the limited psychological behavior of decision makers (DMs). Decision is a worthwhile research topic in order to facilitate ranking for cultural and creative products (CCPs). Therefore, this paper provides a decision framework based on intuitionistic fuzzy TODIM (IF-TODIM) method and group consensus reaching (GCR) model to fill this gap. Benefit from the theory of intuitionistic fuzzy set, IF-TODIM method can deal with the limited psychological behavior of DMs and the fuzziness of decision environment. This method bases on CCPs characteristics and cultural hierarchy theory (CHT) to select the decision criteria, and applies IF-TODIM method to quantify the relationship of DMs’ preferences and establish the dominance matrix for the alternatives. Furthermore, the GCP model is introduced to improve the group consensus of DMs, and the modified overall dominance matrixes are adopted to determine the alternatives scores and ranking results. The new Chinese style decorations are used as a case study to demonstrate the practicality and feasibility of the proposed method. Moreover, comparing with IF-TOPSIS method are further to verify its effectiveness and superiority.