How Artificial Intelligence Matches Services for a Product? An event-related potential perspective

How Artificial Intelligence Matches Services for a Product? An event-related potential perspective | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article How Artificial Intelligence Matches Services for a Product? An event-related potential perspective Meina Zhao, Mingming Xu This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8305425/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 8 You are reading this latest preprint version Abstract The application of artificial intelligence provides new approaches for intelligent product-service systems, and an increasing number of service-oriented manufacturing enterprises are leveraging artificial intelligence (AI)to achieve value co-creation with customers. Service-dominant logic posits that interactions between consumers and providers constitute the primary source of value creation, highlighting the need to explore methods for matching products with appropriate services. In the process of intelligently matching additional services to products, whether the “similarity principle” or the “heterogeneity principle” better aligns with customer expectations remains to be explored. This study employs event-related potential (ERP) methodology to examine how product-service compatibility influences customer cognitive engagement and purchase intention by analyzing ERP components associated with cognitive processing. By proposing a method to determine product-service compatibility, this study provides insights for matching intelligent product-service combinations in an AI-driven context. The findings offer guidance for delivering personalized product-service combinations in platform economies and AI ecosystems, enabling enterprises to achieve value co-creation by enhancing product-service compatibility. This research contributes to the design and optimization of intelligent product-service systems. Business and commerce/Business and management Social science/Business and management Business and commerce/Information systems and information technology Physical sciences/Mathematics and computing artificial intelligence smart product-service systems event-related potentials Figures Figure 1 Figure 2 1. Introduction The development and application of digital technologies have accelerated the growth of digital services, and manufacturing enterprises increasingly prioritize creating commercial value through smart products and digital services. Wang et al. (2023) suggest that advancements in digital technology provide both technical support and practical conditions for manufacturers to directly collect consumer preferences. Ayala et al. (2025) highlight the role of artificial intelligence in digital service transformation, such as Tesla's use of AI to predict and notify owners of maintenance needs, which raises questions about how AI enables digital service transformation across both front-end and back-end operations. They propose methods to enhance digital service transformation across foundational, intermediate, and advanced service tiers. Companies employ product customization strategies to deliver goods aligned with consumer preferences (Turner et al., 2020). Because product-service combinations involve innovations in product attributes, service content (Oliver, 2015), and compatibility between products and services, their forms are diverse. This complexity makes it challenging to apply universal rules or patterns when using digital technologies to match product-service combinations with customer preferences. Yan et al. (2025) observed that during the digital technology-driven transition from Industry 4.0 to human-centered Industry 5.0, artificial intelligence applications in manufacturing still face issues when algorithmic performance does not align with human needs. The adoption of digital product-service systems (Tunn et al., 2020) has heightened manufacturers' attention to the role of customer perception in value creation (Song and Sakao, 2017). Scholars have consistently emphasized that diverse models can deliver products and services aligned with consumer preferences. Xu et al. (2026) observed that consumer acceptance levels can hinder the advancement of corporate product customization initiatives, whereas Xu et al. (2026) also suggest that aligning innovative approaches with customization models may enhance purchase intention among consumers with low product engagement. However, service-oriented products require more radical innovation from enterprises to increase consumers’ willingness to buy. Findings indicate that service-oriented enterprises should prioritize delivering innovative experience designs, whereas product-oriented enterprises should focus on achieving synergies between customization models and innovation types (Dabholkar, et al., 2000; Dong & Sivakumar, 2015; Dabholkar & Sheng, 2012 ). Thus, when offering product-service combinations, enterprises should tailor development strategies based on the type and characteristics of these combinations, such as product-oriented versus outcome or utility-oriented approaches (Tukker, 2004). Artificial intelligence will also profoundly influence traditional e-commerce purchasing patterns, triggering major transformations in shopping behavior. Competition among e-commerce platforms will shift from product- and price-based rivalry to the ability to deliver effective AI services. For instance, in August 2025, China's Taobao officially launched its “AI Universal Search Service,” which offers users styling guides, product reviews, and shopping strategies. However, consumers' perception of the intelligence of these shopping guide services has not yet been effectively measured, and this factor is a primary determinant of whether artificial intelligence can function effectively. When leveraging AI for intelligent recommendations, the diversity of product attributes and service content creates a cognitive “black box” regarding how to rank highly compatible product-service combinations, making it difficult to determine the optimal match between services and products. During the process of adding intelligent matching services to products, it remains necessary to explore whether the law of similarity (Ayala et al. ,2025) or the law of heterogeneity better aligns with customer expectations. Specifically, determining whether combinations with high product attributes and low service value are more appealing or whether high product attributes paired with high service value better match consumer cognition requires further investigation. Recognizing product attributes engages cognitive resources (Han et al., 2014), online services can induce positive emotions (Zhao et al., 2015), and customer cognitive abilities influence the perceived value of product-service combinations (Zhao et al., 2017). This paper proposes a method for measuring the compatibility between products and services by assessing customer engagement and attention levels during AI-driven smart matching. It also examines the impact of this compatibility on value co-creation. This approach aims to reduce development costs for AI applications in manufacturing and related industries while guiding platform economies toward delivering more personalized services. As competition among e-commerce platforms shifts from product- and price-based rivalry to generative AI services, platforms offering AI capabilities will attract a subset of customers. Furthermore, once all platforms provide AI search services, those best able to match consumer needs will gain a competitive advantage. Clearly, identifying optimal services for specific products remains an area requiring further exploration. Establishing a method to determine product-service relationships holds significant theoretical and practical importance for delivering personalized product-service systems in the AI era. 2. Literature Review and Research Hypotheses 2.1 Artificial Intelligence and Smart Product-Service Systems Artificial intelligence encompasses diverse tool types and application domains. Davenport and Ronanki (2018) categorized AI applications based on business needs into process automation, cognitive insight, and cognitive engagement. They further classified AI functions into front-end activities (direct customer interactions) and back-end activities (non-customer interactions). AI plays a role across numerous domains. Ayala et al. (2025) highlighted the role of AI in digital service transformation in manufacturing, manifesting both in front-end user interactions and in back-end programming and data analysis. AI facilitates the creation of high-value digital services (Rabetino et al., 2024; Shen et al., 2023). Queiroz et al. (2025) proposed a pathway for crowdsourced AI to generate digital service value by strengthening customer relationships, improving production and operations, and advancing product and service development. Smart product-service systems focus on value co-creation. Valencia et al. (2015) observed that these systems integrate intelligent products with digital services to meet consumers' personalized needs. Their characteristics include consumer involvement, personalized services, service participation, and shared personalized experiences (Song et al., 2021; Song, 2017). Ayala et al. (2025) proposed methods to enhance the digital service transformation of manufacturing across different service levels. Manufacturing enterprises undergo digital service transformation based on the distinct functions of AI and the varying service tiers it provides. For instance, intermediate services rely on back-end AI cognitive insight capabilities, using machine learning to analyze data and predict demand to support customer decision-making (Ayala et al., 2025). Personalized product and service development significantly influence value creation in intelligent product-service systems. When applying AI for digitalization across different service types, accurately learning and identifying user needs is essential, achieved through front-end and back-end AI configurations. Matching services to different product types using tailored tools and algorithms can reduce enterprise AI development costs. Therefore, within the context of AI and platform economies, further exploration is needed regarding how the user assesses the perceived value of enterprise-provided intelligent product-service systems and the degree to which these systems align with user needs. Black-box issues persist in AI's role within the design of intelligent product-service systems. 2.2 Product-Service Compatibility Product-service integration can take multiple forms (Tukker, 2004). The value composition of product-service combinations constitutes a primary research focus within product-service systems (Mont, 2002; Ulaga & Loveland, 2014), encompassing the content and form of these combinations, as well as consumers' perceived value of them (Beuren et al., 2013; Gaiardelli et al., 2013). Within product-service systems, the compatibility between products and services (Xu et al., 2026) can be defined as the alignment of service design with product attributes. This concept also encompasses the dynamic matching process between product attributes and service design under changing conditions (Neely, 2009; Baines, et al.2009; Lightfoot et al.,2013), such as technological advancements and shifts in consumer cognitive capabilities. Jovanovic et al. (2016) emphasize that manufacturers must design service configurations during servitization based on specific product attributes, ensuring compatibility by aligning service delivery with product functionality and operation. The authors further highlight the interdependence between products and services within product-service systems. Although analyzing factors that influence product-service compatibility has been a scholarly focus, the key determinants shaping companies' decisions on service types remain underexplored (Eggert, Thiesbrummel, and Deutscher 2015). Product-oriented services focus primarily on enhancing product performance (Oliva & Kallenberg, 2003; Vandermerwe & Rada, 1988), whereas customer-oriented services require matching business models to address issues such as product usage (Raddats, et al., 2015; Spring & Araujo 2013). In outcome-oriented product-service systems, service scheduling constitutes a critical component of product-service integration (Vargo & Lusch, 2004). Specifically, product-service scheduling with service matching represents a complex scheduling problem that involves integrating multiple service types (Liu et al., 2020; Maguire & Geiger 2015; Verhoef, et al., 2004). Liu et al. (2020) highlighted the complexity of service matching in multi-service-type integrated product-service systems and proposed a method for addressing product-service scheduling with service matching. This approach employs tabu search to provide insights for solving complex scheduling problems. Thus, product-service compatibility varies across different product-service systems. Xu et al. (2026) highlighted the matching effect between product attributes and customer perceptions during product customization. Building on cognitive theories that explain consumer choice (Yi et al., 2021; Kim et al., 2015), they proposed that consumer decisions arise from how individuals cognitively process matching combinations, while matching experiences are influenced by subconscious factors (Xu et al., 2026). However, this has not yet been interpreted from a neuroscience perspective. Zhao (2022) employed neuromarketing methods to demonstrate differences in event-related potentials induced by product-service systems with varying degrees of product-service matching but did not specify principles or methods for enhancing product-service compatibility. Therefore, further investigation is needed into the adaptability between products and services within product-service systems, particularly regarding the establishment of a methodology for defining product-service relationships. This carries significant theoretical and practical implications for delivering personalized product-service systems to users in the context of artificial intelligence. Service value perception differs from product attributes because it is influenced by cognitive abilities and consumer emotions (Zhao et al., 2015; Zhao et al., 2017; Zhao et al., 2021). Excessively high perceived service value may induce cognitive dissonance (Zhao, 2022), thereby reducing customers' perceived service value. Based on the above research, this paper proposes Research Hypothesis 1: H 1 : High product attributes paired with low-value services exhibit higher compatibility than when paired with high-value services in product-oriented product-service systems. 2.3 Neuromarketing and Customer Cognitive Engagement Research on human cognition and psychology through neuroscience has become one of the most cutting-edge areas in contemporary scientific inquiry (Davidson, 2004; Matsuda & Nittono 2015). Advances in brain imaging techniques and functional localization of brain regions (Huth et al., 2016) have established brain-based measurements as relatively objective assessment methods. Event-related potentials (ERPs) allow for the analysis of consumer behaviors such as purchasing decisions and brand perception (Ma et al., 2006; Du Jiangang, 2012; Zhao et al., 2019). Ma and Wang (2006) explored feasible pathways for integrating neuroscience into management science, introducing the concept of neuromanagement and discussing its potential applications in brand perception and purchasing decision-making. Regarding methods for determining product-service compatibility, scholars have shown a shift from relying on cognitive theories to incorporating neuroscience when explaining the matching between product attributes and customer responses. Han et al. (2014) applied an ERP-based method, demonstrating that product performance combinations that meet customer expectations can induce the P300 component in the occipital-parietal region of the brain. It is evident that ERP components related to cognition can be used to analyze customers' cognitive engagement (Erk, et al., 2002). The P2 component is a positive wave with a latency of approximately 200 ms that reflects attention and cognitive processing. Extensive research indicates that stimuli that attract greater participant attention yield larger P2 amplitudes, signifying heightened attention allocation. Wang et al. (2012) experimentally demonstrated that aesthetically appealing images generate larger P2 components, suggesting that stimuli aligned with participants' aesthetic preferences influence brain activity. Thus, the P2 wave serves as an effective indicator of attention: shorter P2 latency reflects earlier attentional engagement with the target stimuli, whereas greater P2 amplitude indicates higher attention resources allocated to the stimulus. When consumers browse product-service combinations, those that align with customer preferences and needs activate the striatum, particularly the nucleus accumbens. This indicates that appealing combinations are processed as reward-related stimuli in the brain. Based on neurological research into the brain mechanisms of utility and the localization of utility-related brain regions, the compatibility of products and services positively influences cognitive, emotional, and behavioral engagement, thereby facilitating value co-creation. Based on this, the following hypotheses are proposed: H 2 : Highly compatible product-service combinations induce higher P2 components, indicating greater cognitive engagement among customers. H 3 : The relative purchase rate of highly compatible product-service combinations exceeds that of low-compatibility combinations. 3. Experimental Method 3.1 Experimental Design This study employs ERP methodology to investigate the mechanisms by which different product-service combinations influence consumer engagement and purchase intention. Using E-prime software, we designed an experiment simulating consumer purchasing scenarios. Participants were exposed to stimuli representing various product-service combinations to induce relevant ERP components. Electroencephalography (EEG) data were collected using brain-monitoring equipment while participants browsed the different product-service combinations. Based on Tukker's (2004) classification of product-service systems, five product-service combinations were selected (laptop computers, smartphones, portable hard drives, shared bicycles, and shared mobility services). Product attributes were categorized into three types (e.g., laptop attributes: memory, battery life, color), with each attribute offering three configuration levels: low, medium, and high (P1: low configuration; P2: medium configuration; P3: high configuration). Building upon Baines et al.'s (2013) framework for manufacturing servitization, which categorizes supplementary services into basic, intermediate, and advanced tiers, this study adopts the basic and intermediate service types. Basic services focus on supporting product delivery (e.g., installation and warranty), while intermediate services maintain product condition (e.g., periodic maintenance and technical support). Service selection was determined by the characteristics of the product-service combination and stimulus presentation frequency requirements. Specifically, service content was categorized into two types (S1: Basic Services; S2: Intermediate Services). Each product-attribute configuration and service content were paired to form distinct product-service combinations, resulting in six categories: P1S1, P1S2, P2S1, P2S2, P3S1, and P3S2. Among these: P1S1 represents high product-attribute matching with low service value, constituting a high-compatibility product-service combination. P2S1 represents high product-attribute matching with high service value, constituting a low-compatibility product-service combination. The stimulus materials for the product-service combinations comprised 180 trials, with 36 trials per product type. Each stimulus category contained 30 trials, meeting the minimum trial requirement for EEG experiments. 3.2 Participants This study recruited 22 undergraduate and graduate students, comprising 10 males and 12 females, aged between 22 and 26 years (mean age: 22.3 years). All participants had normal or corrected-to-normal vision. Participation was voluntary. Prior to the experiment, participants were informed about the experimental procedures and precautions and signed informed consent forms. Data from two female participants were excluded owing to excessive information disturbance during the experiment, resulting in anomalous data. The final dataset comprised 10 female and 10 male participants. 3.3 Experimental Procedure The experiment was conducted in the Behavior and Human Factors Laboratory at the School of Economics and Management, Beihang University. After receiving instructions, participants donned electrode caps, sat in chairs adjusted to their most comfortable position, followed on-screen prompts, and placed their fingers on the keyboard (left index finger on F, right index finger on J) to prepare for purchase decisions. The experiment employed a “priming-probe” paradigm. Participants first viewed the experimental instructions. The experimental stimuli consisted of 180 distinct product-service combinations across six categories. The sequence of stimulus presentation is illustrated in Fig. 1. First, a “+” image appeared for 2000 ms, followed by product and price images, which were displayed for 2000 ms. Then, product-attribute configurations and service details were added to form the product-service combination image, presented for 2000 ms. Finally, the purchase selection image was displayed for 4000 ms. If the participant did not respond, the system proceeded directly to the next trial. Participants made purchase decisions by reviewing product-attribute configurations and additional service content. They pressed the F key to select “indicate purchase” and the J key to decline. Before formal data collection, participants completed 10 practice trials. Each participant independently completed 180 trials in the formal test. Figure 1. Illustration of the experimental design 3.4 EEG Recording and Analysis Experimental stimuli were presented using E-prime software, and EEG data were recorded and analyzed using the Net Station EEG system. Participants wore a 64-channel electrode cap during the experiment, with Cz as the reference electrode and a sampling frequency of 250 Hz. E-prime software recorded behavioral data, including reaction times and final purchase choices. The EEG device collected brainwave signals, which were processed using the Net Station EEG recording and analysis system to generate ERPs elicited by different product-service combinations. The EEG data processing workflow follows standardized procedures: a phase-shift-free digital low-pass filter at 40 Hz was applied, and the EEG data were then segmented by service content labels into six stimulus-type segments. Each segment captured 200 ms before and 1500 ms after stimulus onset. Data containing artifacts such as eye movements (+/−55 µV) and blinks (+/−140 µV) were excluded. The segmented data were subsequently averaged to derive ERPs elicited by different product-service combinations, with baseline correction referenced to the 200 ms pre-stimulus EEG data. To examine the neural mechanisms underlying consumers' perceptions of product-service combination utility, this study employed within-subjects one-way analysis of variance (ANOVA) to compare ERP amplitudes across the six product-service combination conditions. Factors analyzed included experimental condition (high-compatibility vs. low-compatibility product-service combinations) and electrode location ( F3, FC1, FC3, FC5). 4. Data Analysis 4.1 EEG Data Analysis When participants browsed different product-service combinations, P2 waves were elicited in the left frontal region (F3), and left frontocentral junction (FC1, FC3, FC5). Figure 2 displays the averaged ERP waveforms at the left frontal electrode across the 100 ms pre-stimulus to 600 ms post-stimulus time window for various product-service combinations. The P2 evoked by the high-compatibility product-service combination (P3S1) was larger than that elicited by the low-compatibility combination (P3S2). A 2 (high emotional value vs. low emotional value) × 4 (electrode sites) within-subjects repeated measures ANOVA was conducted to compare amplitude differences of the positive component during the 192–292 ms interval under both conditions (high-compatibility and low-compatibility). Significant differences were observed between high-compatibility and low-compatibility combinations during the 192–292 ms interval. Table 1 presents the ANOVA results for the relevant electrode sites for both product-service combinations. Both combinations elicited significant P2 components. Analysis confirmed that within the 192–292 ms time window, the P2 amplitude evoked by the high-compatibility PSS (P3S1) was greater than that evoked by the low-compatibility PSS (P3S2), supporting Hypothesis 2. Table 1 . Descriptive statistics (mean ± SD) of the averaged wave P2 amplitude in the left frontal region and left frontal-central junction region for PSSs with different adaptability levels (192–292 ms) Figure 2. Total average ERP waveforms elicited in the left frontal region and the frontal-central combined region (F3, FCI, FC3, FC5) by product-service combinations with varying levels of adaptability. 4.2 Behavioral Data Analysis During the process of browsing and comparing product-service bundles, consumers evaluate the product attributes and bundled service contents to guide their purchase decisions. Behavioral data recorded the purchase rates of six product-service bundle combinations, revealing significant differences among the bundles. The purchase rates for the six product-service combinations P1S1, P1S2, P2S1, P2S2, P3S1, and P3S2 were 14%, 25%, 27%, 42%, 50%, and 59%, respectively, indicating distinct purchase rates across each combination. Since intermediate services induce higher positive emotions (Zhao et al., 2015), the absolute purchase rate of P3S2 (a combination featuring high product-attribute matching with intermediate services) reached 59%. Meanwhile, P3S1, a high-compatibility combination, achieved a purchase rate of 50%. However, the relative purchase rate of high-compatibility combinations defined as the incremental purchase rate increase resulting from factor changes exceeded that of low-compatibility combinations. Relative purchase rates differ across PSSs with varying compatibility, defined as the increase in purchase rate after product-attribute enhancement: Relative Purchase Rate = (Purchase Rate of Enhanced Combination − Original Combination Purchase Rate) / Original Combination Purchase Rate. The purchase rate for P2S1 was 27%, while P3S1 reached 50%. The relative purchase rate for high-compatibility PSSs was 85.19%. The purchase rate for product-service combination (P2S2) was 42%, whereas P3S2 reached 59%. The relative purchase rate for low-compatibility PSSs, representing the increase in purchase rate after product-attribute enhancement, was 40.48%. These findings demonstrate that the relative purchase rate for high-compatibility product-service combinations exceed that of low-compatibility combinations, thereby supporting Hypothesis 3. In the experimental design, P3S1 represents high product attributes paired with low-value services, whereas P3S2 denotes high product attributes paired with high-value services. The experimental results indicate that P3S1 elicits a greater P2 effect than P3S2, suggesting that P3S1 engages consumers at a higher cognitive level. P3S1 also achieves a higher relative purchase rate than P3S2, reflecting greater consumer acceptance of P3S1. Therefore, P3S1 demonstrates greater adaptability than P3S2, supporting Hypothesis 1. 5. Discussion of Experimental Results 5.1 P2 and Perception of Product-Service Combinations Analysis of brainwave patterns elicited by different product-service combinations revealed that the EEG findings aligned with behavioral data conclusions: ERPs induced by high-compatibility combinations were significantly greater than those elicited by low-compatibility combinations. Specifically, these differences were reflected in the P2 component recorded in the left frontal, left parietal, and left-central association regions. P2 represents the ERP component associated with cognitive processing when consumers encounter product-service combinations. Behavioral data revealed that cognitive regulation plays a crucial role in shaping purchase intention toward product-service combinations. Although these combinations also evoked positive emotional responses in consumers, which can enhance purchase intention, the increase attributable to emotional regulation was smaller than that from cognitive regulation. This finding holds practical value for designing complex, multi-element product-service systems, particularly in matching service content. These results suggest that supplementary services can enhance purchase intention but only to a limited extent and under specific threshold conditions. When premium services mismatch with customers' cognitive capabilities, they may generate cognitive conflict and negatively influence purchase intention. 5.2 Cognitive-Emotional Interaction and Value Co-Creation Analysis of brainwave patterns elicited by different product-service combinations revealed that EEG findings were consistent with behavioral data conclusions: ERPs elicited by highly compatible product-service combinations were significantly higher than those elicited by low-compatibility combinations. This effect was reflected in differences in the P2 component across the left frontal, left parietal, and left-central association regions. The P2 component represents an ERP index associated with cognitive processing when consumers evaluate product-service combinations. Behavioral data indicated that cognitive regulation plays a crucial role in shaping purchase intention toward product-service combinations. Although such combinations also elicited positive emotional responses that can enhance purchase intention, the increase in purchase rate attributable to emotional regulation was smaller than that driven by cognitive regulation. These findings suggest that supplementary services can enhance purchase intention but only within a limited range and under threshold conditions. When premium services mismatch with customers' cognitive capabilities, they may induce cognitive conflict, thereby negatively impacting purchase intention. These findings hold practical value for designing complex product-service systems with multiple elements, particularly in ensuring the alignment of service offerings. 6. Research Findings and Significance 6.1 Research Findings The application of digital technologies, particularly artificial intelligence, has significantly influenced consumer shopping behavior. Establishing measurement methods and matching rules plays a crucial role in enabling smarter services for AI-assisted purchasing. By designing the AI front end based on cognitive engagement and categorizing service types according to consumer data acquisition patterns, businesses can achieve deeper cognitive insights and enhance the competitiveness of AI-driven services. This study proposes a neural mechanism underlying compatibility with product-service combinations, providing support for aligning AI algorithms with customer needs. The P2 component serves as a neurological indicator for consumers to evaluate product-service combinations, explaining how variations in these combinations and their elements influence user engagement and purchase intention. Analysis of P2 amplitude demonstrated that different product-service combinations, because of variations in attribute matching, occupy varying degrees of cognitive resources and evoke differing levels of positive emotional responses. These findings confirm that consumer purchase intentions vary across different product-service combinations because of both cognitive resource allocation and positive emotional activation, thereby establishing a neurological basis for product-service matching. Therefore, this approach can assist artificial intelligence algorithms. Within the platform economy context, data empowerment supports the delivery of personalized digital services, fostering the emergence of product-service bundles as a new form of e-commerce. Although platforms such as JD.com offer supplementary services for products, these bundles have yet to become a dominant shopping model. AI-generated personalized shopping lists of product-service bundles can help overcome consumers' initial resistance to purchasing services, creating a consumption experience that enhances cognitive engagement. This approach has the potential to substantially increase related service purchases and address the issue of low consumer uptake for product-related services. For platforms, AI-powered shopping assistance must deliver a concrete sense of intelligence to achieve a meaningful impact—a capability that remains underdeveloped. Realizing this goal requires back-end cognitive insights and algorithm optimization to effectively stimulate consumer demand. For manufacturers, emphasizing the critical role of product attributes is essential. During service and digital transformation, it is vital to recognize the significant influence of consumer perception of product attributes on decision-making. Specifically, product attributes should be developed across multiple dimensions beyond service levels. Rather than solely enhancing service quality, manufacturers should first elevate product attributes to a certain level before offering premium supplementary services. This approach aligns with consumer cognitive needs. During service transformation, companies may encounter bottlenecks when attempting to boost competitiveness through supplementary services, as promoting advanced services necessitates substantial improvements in product attributes. The design of product-service combinations incorporates both customization concepts and principles of product-service alignment. This approach requires exploring pricing methodologies, and the research findings suggest that providing adaptive matching solutions may offer novel approaches for personalized pricing. 6.2 Practical Implications Digital platforms guide AI-driven transformations in sales and shopping models by tailoring product-service offerings to customer knowledge levels and enhancing perceived value through service experiences. For instance, in 2021, Brazil's KITCHEN launched the world's first smart connected stove with IoT and AI capabilities. By leveraging artificial intelligence, it delivers personalized recommendation services, suggesting recipes and complementary ingredients based on individual preferences and past cooking behaviors (Ayala et al., 2025). Taobao currently offers its “AI Universal Search” service, which generates personalized shopping lists based on purchasing habits and provides product recommendations using user data. If most platforms develop AI services, this will transform traditional online shopping models. However, the functionality of matching complex products with supplementary services poses significant challenges to AI model development costs, potentially offering insights for creating competitive advantages for certain platforms. Manufacturing servitization still faces numerous challenges in the digitalization process, such as the development costs of artificial intelligence and the design of intelligent product-service systems that meet personalized needs, which require further exploration. This research contributes to providing a method for rapidly ranking product-service combinations in the context of artificial intelligence. Through algorithmic configuration and optimization, the approach can reduce the time costs of artificial intelligence, directly providing matching solutions that better align with consumer needs and providing a neurological reference and basis for digitalization. With the advancement of neuromanagement, brain imaging technology offers scientific tools to observe consumer brain activity during purchasing decisions. The research findings can be applied to digital product-service system platform design and intelligent recommendations for products and services on e-commerce platforms, boosting online service sales. For instance, while JD.com offers supplementary online services for products, achieving significant sales of these services remains challenging after years of operation, with consumer service selection being a key hurdle. When AI matches appropriate services to shopping platform products, it can enhance sales of product-service combinations. For manufacturers, since premium services require higher-tier product attributes operating on a principle akin to “like repels, unlike attracts” research findings support manufacturers in offering products with elevated attributes. Providing intelligent product-service systems tailored to customer needs also informs smart pricing strategies. For instance, in Brazil's SCREWS intelligent supply chain solution, AI identifies distinct customer consumption profiles and delivers personalized quotes based on their requirements (Ayala et al., 2025). Manufacturers can establish differentiation between product attributes and service value, replacing uniformity with differentiation within AI algorithms by developing distinct AI capabilities. This enhances service customization and personalization, such as delivering premium services through generative AI (Wamba et al., 2023) and offering tailored services like expert consultations (Cook et al., 2024), thereby advancing intelligent pricing. 6.3 Research Limitations and Future Directions This study employs a laboratory research methodology and thus has inherent limitations. First, the sample consisted of students, which may limit the applicability of the findings to consumers from diverse professional backgrounds, warranting further investigation. Second, within the digital economy, consumers' cognitive capabilities are rapidly evolving, while service types and their relationships with product attributes are constantly changing. This dynamic alters the perceived value of product-service combinations, making it challenging to quantify high-value versus low-value services. Specifically, as consumer cognition advances, premium services may transition into basic offerings, suggesting that the service categories defined in this study will shift over time. Third, if subjects participate in EEG experiments for extended periods, fatigue may compromise experimental outcomes. Consequently, the range of products and services presented as stimuli in the experiment is limited. To address these limitations, future research should explore areas including dynamic product-service system pricing grounded in psychological and neural mechanisms, as well as the supply and design of personalized intelligent services. Declarations Declaration of competing interest The authors declare no conflict of interest. Ethical approval Approval was obtained from the ethics committee of Beihang University on 17th, September, 2017 (No. BUAA-2017-09-10). The procedures used in this study adhere to the tenets of the Declaration of Helsinki. Informed consent This study meticulously secured informed consent from every participant involved. The process took place in October 2017. The original informed consent document was crafted in Chinese. Before initiating the data collection process, all individuals were required to thoroughly review and acknowledge their comprehension of the informed consent documentation. This material explicitly conveyed the voluntary nature of participation and guaranteed the anonymity of responses, emphasizing their sole utilization for scholarly inquiry. References Ayala, N. F., et al. (2025). Artificial Intelligence capabilities in Digital Servitization: Identifying digital opportunities for different service types. International Journal of Production Economics, 284:109604. Queiroz, M. M., Beatriz, A. & Bagherzadeh, M. (2025). Crowdsourcing-enabled AI: Unlocking value in digital services. International Journal of Production Economics, 283, 109586. Turner, F., Merle, A., Gotteland, D., (2020). Enhancing consumer value of the co-design experience in mass customization. J. Bus. Res. 117, 473–483 Wang, Y., Mo, D.Y., Ma, H.L., (2023). Perception of time in the online product customization process. Ind. Manag. Data Syst. 123, 369–385 Xu, Q., Wang, X. & Sun, Q. (2026). When customization mode meets product innovativeness: How their match drives consumer purchase. Journal of Retailing and Consumer Services, 88:104483. Davenport, T.H., Ronanki, R., 2018. Artificial intelligence for the real world: don’t start with moon shots. Harv. Bus. Rev. 96 (January–February), 108–116. Yan, J., et al. (2025). Human-centric artificial intelligence towards Industry 5.0: retrospect and prospect. Journal of Industrial Information Integration, 47:100903. Baines, T., et al. (2009). Towards an operations strategy for product-centric servitization. International Journal of Operations & Production Management, 29(5):494–519. Beuren, F.H., Ferreira, M.G., & Miguel, P.A. (2013). Product-service systems: a literature review on integrated products and services. Journal of Cleaner Production, 47: 222–231. Benoit, S., Scherschel, K., Ates, Z., Nasr, L., & Kandampully, J. (2017). Showcasing the diversity of service research: Theories, methods, and success of service articles. Journal of Service Managment, 28: 810–836. Catulli, M., Cook, M., & Potter, S. (2017). Consuming use orientated product service systems: A consumer culture theory perspective. Journal of Cleaner Production, 141: 1186–1193. Davidson, R.J. (2004). What does the prefrontal cortex ‘‘Do’’ in affect: perspectives on frontal EEG asymmetry research. Biological Psychology, 67(1): 219–234. Dabholkar, P., Shepherd, C.D., & Thorpe, D.I. (2000). A comprehensive framework for service quality: an investigation of critical conceptual and measurement issues through a longitudinal study. Journal of Retailing, 76(2), 139–173. Dong, B., & Sivakumar, K. (2015). A process-output classification for customer participation in services. Journal of Service Management, 26:726–750. Dabholkar, P.A., & Sheng, X. (2012). Consumer participation in using online recommendation agents: Effects on satisfaction, trust, and purchase intentions. The Service Industries Journal, 32:1433–1449. Erk S., et al.(2002). Cultural Objects Modulate Reward Circuitry. Neuroreport, 13(18):2499–2503. Gaiardelli, P., et al. (2013). A classification model for product-service offerings. Journal of Cleaner Production, 11:1–13 Huth, A.G., et al. (2016). Where words make sense: A semantic atlas of the cerebral cortex. Nature, 532 (7600):453–459. Han, W., Wang, X., Petropoulos, F., & Wang, J. (2019). Brain imaging and forecasting: Insights from judgmental model selection. Omega, 87:1–9. Khandeparkar, K. (2014). The role of complementarity and partner brand price level in new product introduction strategy using bundle offers: A study on the quality perception of bundle components. Journal of Retailing and Consumer Services, 21:992–1000. Kuijken, B., Gemser G., & Wijnberg N. M. (2017). Effective product-service systems: A value-based framework. Industrial Marketing Management. 60:33–41. Karwatzki, S., Dytynko, O., Trenz, M., & Veit, D. (2017). Beyond the personalization-privacy paradox: privacy valuation, transparency features, and service personalization. Journal of Management Information Systems. 34(2): 369–400. Kaiser, U., Schreier, M., & Janiszewski, C. (2017). The self-expressive customization of a product can improve performance. Journal of Marketing Research. 54(5):816–831. Lightfoot H., Baines T., & Smart, P. (2013). The servitization of manufacturing: a systematic literature review of interdependent trends. International Journal of Operations & Production Management, 33: 11–12 Mont O.K. (2002). Clarifying the Concept of Product-Service System. Journal of Cleaner Production, 11:237–245 Maguire, L., & Geiger, S., (2015). Emotional Timescapes: the temporal perspective and consumption emotions in services. Journal of Services Marketing, 29(3):211–223. Matsuda, I., & Nittono, H. (2015). Motivational significance and cognitive effort elicit different late positive potentials. Neurophysiologie Clinique, 126: 304–313. Mazaheri, E., Richard, M. O., Laroche, M., & Ueltschy, L. C. (2014). The influence of culture, emotions, intangibility and atmospheric cues on customer online behavior. Journal of Business Research, 67 (3): 253–259. Neely, A. (2009). Exploring the financial consequences of the servitization of manufacturing. Operations Management Research, 1(2): 103–118. Oliver, J. (2015). The consumer’s perspective on evaluating products: service is the key. Journal of Services Marketing, 29 (3):200–210. Oliva, R., & Kallenberg R. (2003). Managing the transition from products to services. International Journal of Service Industry Management, 14 (2):160–172. Song, W. (2017). Requirement management for product-service systems: Status review and future trends. Computers in Industry, 85:11–22. Spring, M., & Araujo, L. (2013). Beyond the service factory: Service innovation in manufacturing supply networks. Industrial Marketing Management, 42(1), 59–70. Song, W., & Sakao, T. (2017). A customization-oriented framework for design of sustainable product/service system. Journal of Cleaner Production, 140: 1672–1685. Shao, Z., Li, X., Guo, Y., & Zhang, L. (2020). Influence of service quality in sharing economy: Understanding customers’ continuance intention of bicycle sharing. Electronic Commerce Research and Applications, 40: 100944. Tukker, A. (2004). Eight types of product-service system: Eightways to sustainability? Experiences from SusProNet. Business Strategy and the Environment, 13(4), 246–260. Tunn, V. S. C., van den Hende, E. A., Bocken, N. M. P., & Schoormans, J. P. L. (2020). Digitalised product-service systems: Effects on consumers’ attitudes and experiences. Resources, Conservation and Recycling, 162: 105045. Thomas, A. W., Molter, F., Krajbic, I., Heekeren H. R., & Mohr., P.N.C. (2019). Gaze Bias Differences Capture Individual Choice Behavior. Nature Human Behaviour, 3 (6): 625–635. Ulaga, W., & Loveland, J. M. (2014). Transitioning from product to service-led growth in manufacturing firms: Emergent challenges in selecting and managing the industrial sales force. Industrial Marketing Management, 43(1), 113–125. Vargo, S. L., & Lusch, R. F. (2004). Evolving to a new dominant logic for marketing. Journal of Marketing, 68(1), 1–17. Vandermerwe, S., & Rada, J. (1988). Servitization of business: adding value by adding services. European Management Journal, 6(4):314–324. Verhoef, P. C., Antonides, G, & Hoog, A. N. (2004). Service encounters as a sequence of events: the importance of peak experiences. Journal of Service Research, 7(1): 53–64. Wang, J., Zhao, M., & Zhao, G. (2017). The impact of customer cognitive competence on online service decision-making: An event-related potentials perspective. The Service Industries Journal, 37(5–6):363–380. Zhao, M., Wang, J., & Han, W. (2015). The impact of emotional involvement on online service buying decisions: an event-related potentials perspective. Neuroreport, 26(17):995–1002. Song, W., et al., Design concept evaluation of smart product-service systems considering sustainability: An integrated method. Computers & Industrial Engineering.2021.159:107485 Ma, Q., Wang, X. (2006). From Neuroeconomics and Neuromarketing to Neuromanagement. Journal of Management Engineering, 20, 129–132. Valencia, A., Mugge, R., Schoormans, J., & Schifferstein, H. (2015). The design of smart product-service systems (PSSs): An exploration of design characteristics. International Journal of Design, 9(1):13–28. Wang X., Huang Y., Ma Q., Li N. Event-related Potential P2 correlates of Implicit Aesthetic Experience. Neuroreport, 2012, 23: 862–866 Cook, S., Hagiu, A., Wright, J., 2024. Turn generative AI from an existential threat into a competitive advantage. Harv. Bus. Rev. 102 (1), 118–125. Wamba, S.F., Queiroz, M.M., Jabbour, C.J.C., Shi, C.V., 2023. Are both generative AI and ChatGPT game changers for 21st-Century operations and supply chain excellence? Int. J. Prod. Econ. 265, 109015. Table 1 Table 1 . Descriptive statistics (mean ± SD) of the averaged wave P2 amplitude in the left frontal region and left frontal-central junction region for PSSs with different adaptability levels (192–292 ms) Channel Scenario 1 Scenario 2 F-value P-value F3 2.51（0.13） 2.29（0.23） 17.929 .000 FC1 4.32 (0.79) 3.42 (0.41) 26.500 .000 FC3 3.51 (0.42) 2.76 (0.28) 55.585 .000 FC5 3.59 (0.22) 2.33 (0.22) 429.410 .000 Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Reviews received at journal 16 Apr, 2026 Reviewers agreed at journal 20 Mar, 2026 Reviews received at journal 18 Mar, 2026 Reviewers agreed at journal 18 Mar, 2026 Reviewers invited by journal 17 Mar, 2026 Editor assigned by journal 13 Jan, 2026 Submission checks completed at journal 05 Jan, 2026 First submitted to journal 05 Jan, 2026 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-8305425","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":608011370,"identity":"5cce0629-3f3b-477e-baf4-741230110d2f","order_by":0,"name":"Meina Zhao","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAvklEQVRIie3PMQrCMBiG4YRAXFLnSsB4hJQOLh4mItgldc4qwj+JoxQKegvnlIJTewKXghfo2E1dHcTfzSHP/L3DR0gQ/CfaGb2Y8tHOoxOme7dOx+Jq0AmfFE29PMV2hturY+tlBCwDYgkZ3OV7QsuNSSPgOZDW031z+54wafUqApEDPRhGAZHwV1JHEGecCY1LhLTJtmi04RybxNKmpHcmAcFNhfqiSjsfjH4odb5X3eAQyRv/4z4IgiD45AlbhzcBLDv5FgAAAABJRU5ErkJggg==","orcid":"","institution":"Heilongjiang University","correspondingAuthor":true,"prefix":"","firstName":"Meina","middleName":"","lastName":"Zhao","suffix":""},{"id":608011371,"identity":"2cd1fbc6-035d-4857-a7ac-19374fad6950","order_by":1,"name":"Mingming Xu","email":"","orcid":"","institution":"Beihang University","correspondingAuthor":false,"prefix":"","firstName":"Mingming","middleName":"","lastName":"Xu","suffix":""}],"badges":[],"createdAt":"2025-12-08 09:46:35","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8305425/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8305425/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":105013007,"identity":"70e84135-6ce2-4263-adf1-9d7531f41cb7","added_by":"auto","created_at":"2026-03-19 21:47:51","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":135691,"visible":true,"origin":"","legend":"\u003cp\u003eSee image above for figure legend\u0026nbsp;\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-8305425/v1/2d491ed020149a26e3b43861.png"},{"id":105035627,"identity":"d65c3bc7-e573-456b-a60b-9d3c09d00862","added_by":"auto","created_at":"2026-03-20 07:26:21","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":729006,"visible":true,"origin":"","legend":"\u003cp\u003eSee image above for figure legend\u0026nbsp;\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-8305425/v1/e96bc97bcf1f7ee109610dad.png"},{"id":105562741,"identity":"963d9969-988d-4e58-8d1b-ce79150089bb","added_by":"auto","created_at":"2026-03-27 12:44:28","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1613143,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8305425/v1/1a27ae0b-6924-4447-936b-69715189917d.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"\u003cp\u003eHow Artificial Intelligence Matches Services for a Product? An event-related potential perspective\u003c/p\u003e","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eThe development and application of digital technologies have accelerated the growth of digital services, and manufacturing enterprises increasingly prioritize creating commercial value through smart products and digital services. Wang et al. (2023) suggest that advancements in digital technology provide both technical support and practical conditions for manufacturers to directly collect consumer preferences. Ayala et al. (2025) highlight the role of artificial intelligence in digital service transformation, such as Tesla's use of AI to predict and notify owners of maintenance needs, which raises questions about how AI enables digital service transformation across both front-end and back-end operations. They propose methods to enhance digital service transformation across foundational, intermediate, and advanced service tiers. Companies employ product customization strategies to deliver goods aligned with consumer preferences (Turner et al., 2020). Because product-service combinations involve innovations in product attributes, service content (Oliver, 2015), and compatibility between products and services, their forms are diverse. This complexity makes it challenging to apply universal rules or patterns when using digital technologies to match product-service combinations with customer preferences. Yan et al. (2025) observed that during the digital technology-driven transition from Industry 4.0 to human-centered Industry 5.0, artificial intelligence applications in manufacturing still face issues when algorithmic performance does not align with human needs.\u003c/p\u003e \u003cp\u003eThe adoption of digital product-service systems (Tunn et al., 2020) has heightened manufacturers' attention to the role of customer perception in value creation (Song and Sakao, 2017). Scholars have consistently emphasized that diverse models can deliver products and services aligned with consumer preferences. Xu et al. (2026) observed that consumer acceptance levels can hinder the advancement of corporate product customization initiatives, whereas Xu et al. (2026) also suggest that aligning innovative approaches with customization models may enhance purchase intention among consumers with low product engagement. However, service-oriented products require more radical innovation from enterprises to increase consumers\u0026rsquo; willingness to buy. Findings indicate that service-oriented enterprises should prioritize delivering innovative experience designs, whereas product-oriented enterprises should focus on achieving synergies between customization models and innovation types (Dabholkar, et al., 2000; Dong \u0026amp; Sivakumar, 2015; Dabholkar \u0026amp; Sheng, 2012 ). Thus, when offering product-service combinations, enterprises should tailor development strategies based on the type and characteristics of these combinations, such as product-oriented versus outcome or utility-oriented approaches (Tukker, 2004).\u003c/p\u003e \u003cp\u003eArtificial intelligence will also profoundly influence traditional e-commerce purchasing patterns, triggering major transformations in shopping behavior. Competition among e-commerce platforms will shift from product- and price-based rivalry to the ability to deliver effective AI services. For instance, in August 2025, China's Taobao officially launched its \u0026ldquo;AI Universal Search Service,\u0026rdquo; which offers users styling guides, product reviews, and shopping strategies. However, consumers' perception of the intelligence of these shopping guide services has not yet been effectively measured, and this factor is a primary determinant of whether artificial intelligence can function effectively. When leveraging AI for intelligent recommendations, the diversity of product attributes and service content creates a cognitive \u0026ldquo;black box\u0026rdquo; regarding how to rank highly compatible product-service combinations, making it difficult to determine the optimal match between services and products. During the process of adding intelligent matching services to products, it remains necessary to explore whether the law of similarity (Ayala et al. ,2025) or the law of heterogeneity better aligns with customer expectations. Specifically, determining whether combinations with high product attributes and low service value are more appealing or whether high product attributes paired with high service value better match consumer cognition requires further investigation. Recognizing product attributes engages cognitive resources (Han et al., 2014), online services can induce positive emotions (Zhao et al., 2015), and customer cognitive abilities influence the perceived value of product-service combinations (Zhao et al., 2017).\u003c/p\u003e \u003cp\u003eThis paper proposes a method for measuring the compatibility between products and services by assessing customer engagement and attention levels during AI-driven smart matching. It also examines the impact of this compatibility on value co-creation. This approach aims to reduce development costs for AI applications in manufacturing and related industries while guiding platform economies toward delivering more personalized services. As competition among e-commerce platforms shifts from product- and price-based rivalry to generative AI services, platforms offering AI capabilities will attract a subset of customers. Furthermore, once all platforms provide AI search services, those best able to match consumer needs will gain a competitive advantage. Clearly, identifying optimal services for specific products remains an area requiring further exploration. Establishing a method to determine product-service relationships holds significant theoretical and practical importance for delivering personalized product-service systems in the AI era.\u003c/p\u003e"},{"header":"2. Literature Review and Research Hypotheses","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Artificial Intelligence and Smart Product-Service Systems\u003c/h2\u003e \u003cp\u003eArtificial intelligence encompasses diverse tool types and application domains. Davenport and Ronanki (2018) categorized AI applications based on business needs into process automation, cognitive insight, and cognitive engagement. They further classified AI functions into front-end activities (direct customer interactions) and back-end activities (non-customer interactions). AI plays a role across numerous domains. Ayala et al. (2025) highlighted the role of AI in digital service transformation in manufacturing, manifesting both in front-end user interactions and in back-end programming and data analysis. AI facilitates the creation of high-value digital services (Rabetino et al., 2024; Shen et al., 2023). Queiroz et al. (2025) proposed a pathway for crowdsourced AI to generate digital service value by strengthening customer relationships, improving production and operations, and advancing product and service development.\u003c/p\u003e \u003cp\u003eSmart product-service systems focus on value co-creation. Valencia et al. (2015) observed that these systems integrate intelligent products with digital services to meet consumers' personalized needs. Their characteristics include consumer involvement, personalized services, service participation, and shared personalized experiences (Song et al., 2021; Song, 2017). Ayala et al. (2025) proposed methods to enhance the digital service transformation of manufacturing across different service levels. Manufacturing enterprises undergo digital service transformation based on the distinct functions of AI and the varying service tiers it provides. For instance, intermediate services rely on back-end AI cognitive insight capabilities, using machine learning to analyze data and predict demand to support customer decision-making (Ayala et al., 2025). Personalized product and service development significantly influence value creation in intelligent product-service systems. When applying AI for digitalization across different service types, accurately learning and identifying user needs is essential, achieved through front-end and back-end AI configurations. Matching services to different product types using tailored tools and algorithms can reduce enterprise AI development costs. Therefore, within the context of AI and platform economies, further exploration is needed regarding how the user assesses the perceived value of enterprise-provided intelligent product-service systems and the degree to which these systems align with user needs. Black-box issues persist in AI's role within the design of intelligent product-service systems.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2 Product-Service Compatibility\u003c/h2\u003e \u003cp\u003eProduct-service integration can take multiple forms (Tukker, 2004). The value composition of product-service combinations constitutes a primary research focus within product-service systems (Mont, 2002; Ulaga \u0026amp; Loveland, 2014), encompassing the content and form of these combinations, as well as consumers' perceived value of them (Beuren et al., 2013; Gaiardelli et al., 2013). Within product-service systems, the compatibility between products and services (Xu et al., 2026) can be defined as the alignment of service design with product attributes. This concept also encompasses the dynamic matching process between product attributes and service design under changing conditions (Neely, 2009; Baines, et al.2009; Lightfoot et al.,2013), such as technological advancements and shifts in consumer cognitive capabilities. Jovanovic et al. (2016) emphasize that manufacturers must design service configurations during servitization based on specific product attributes, ensuring compatibility by aligning service delivery with product functionality and operation. The authors further highlight the interdependence between products and services within product-service systems. Although analyzing factors that influence product-service compatibility has been a scholarly focus, the key determinants shaping companies' decisions on service types remain underexplored (Eggert, Thiesbrummel, and Deutscher 2015).\u003c/p\u003e \u003cp\u003eProduct-oriented services focus primarily on enhancing product performance (Oliva \u0026amp; Kallenberg, 2003; Vandermerwe \u0026amp; Rada, 1988), whereas customer-oriented services require matching business models to address issues such as product usage (Raddats, et al., 2015; Spring \u0026amp; Araujo 2013). In outcome-oriented product-service systems, service scheduling constitutes a critical component of product-service integration (Vargo \u0026amp; Lusch, 2004). Specifically, product-service scheduling with service matching represents a complex scheduling problem that involves integrating multiple service types (Liu et al., 2020; Maguire \u0026amp; Geiger 2015; Verhoef, et al., 2004). Liu et al. (2020) highlighted the complexity of service matching in multi-service-type integrated product-service systems and proposed a method for addressing product-service scheduling with service matching. This approach employs tabu search to provide insights for solving complex scheduling problems. Thus, product-service compatibility varies across different product-service systems.\u003c/p\u003e \u003cp\u003eXu et al. (2026) highlighted the matching effect between product attributes and customer perceptions during product customization. Building on cognitive theories that explain consumer choice (Yi et al., 2021; Kim et al., 2015), they proposed that consumer decisions arise from how individuals cognitively process matching combinations, while matching experiences are influenced by subconscious factors (Xu et al., 2026). However, this has not yet been interpreted from a neuroscience perspective. Zhao (2022) employed neuromarketing methods to demonstrate differences in event-related potentials induced by product-service systems with varying degrees of product-service matching but did not specify principles or methods for enhancing product-service compatibility. Therefore, further investigation is needed into the adaptability between products and services within product-service systems, particularly regarding the establishment of a methodology for defining product-service relationships. This carries significant theoretical and practical implications for delivering personalized product-service systems to users in the context of artificial intelligence. Service value perception differs from product attributes because it is influenced by cognitive abilities and consumer emotions (Zhao et al., 2015; Zhao et al., 2017; Zhao et al., 2021). Excessively high perceived service value may induce cognitive dissonance (Zhao, 2022), thereby reducing customers' perceived service value. Based on the above research, this paper proposes Research Hypothesis 1:\u003c/p\u003e \u003cp\u003eH\u003csub\u003e1\u003c/sub\u003e: High product attributes paired with low-value services exhibit higher compatibility than when paired with high-value services in product-oriented product-service systems.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3 Neuromarketing and Customer Cognitive Engagement\u003c/h2\u003e \u003cp\u003eResearch on human cognition and psychology through neuroscience has become one of the most cutting-edge areas in contemporary scientific inquiry (Davidson, 2004; Matsuda \u0026amp; Nittono 2015). Advances in brain imaging techniques and functional localization of brain regions (Huth et al., 2016) have established brain-based measurements as relatively objective assessment methods. Event-related potentials (ERPs) allow for the analysis of consumer behaviors such as purchasing decisions and brand perception (Ma et al., 2006; Du Jiangang, 2012; Zhao et al., 2019). Ma and Wang (2006) explored feasible pathways for integrating neuroscience into management science, introducing the concept of neuromanagement and discussing its potential applications in brand perception and purchasing decision-making. Regarding methods for determining product-service compatibility, scholars have shown a shift from relying on cognitive theories to incorporating neuroscience when explaining the matching between product attributes and customer responses. Han et al. (2014) applied an ERP-based method, demonstrating that product performance combinations that meet customer expectations can induce the P300 component in the occipital-parietal region of the brain.\u003c/p\u003e \u003cp\u003eIt is evident that ERP components related to cognition can be used to analyze customers' cognitive engagement (Erk, et al., 2002). The P2 component is a positive wave with a latency of approximately 200 ms that reflects attention and cognitive processing. Extensive research indicates that stimuli that attract greater participant attention yield larger P2 amplitudes, signifying heightened attention allocation. Wang et al. (2012) experimentally demonstrated that aesthetically appealing images generate larger P2 components, suggesting that stimuli aligned with participants' aesthetic preferences influence brain activity. Thus, the P2 wave serves as an effective indicator of attention: shorter P2 latency reflects earlier attentional engagement with the target stimuli, whereas greater P2 amplitude indicates higher attention resources allocated to the stimulus. When consumers browse product-service combinations, those that align with customer preferences and needs activate the striatum, particularly the nucleus accumbens. This indicates that appealing combinations are processed as reward-related stimuli in the brain. Based on neurological research into the brain mechanisms of utility and the localization of utility-related brain regions, the compatibility of products and services positively influences cognitive, emotional, and behavioral engagement, thereby facilitating value co-creation. Based on this, the following hypotheses are proposed:\u003c/p\u003e \u003cp\u003eH\u003csub\u003e2\u003c/sub\u003e: Highly compatible product-service combinations induce higher P2 components, indicating greater cognitive engagement among customers.\u003c/p\u003e \u003cp\u003eH\u003csub\u003e3\u003c/sub\u003e: The relative purchase rate of highly compatible product-service combinations exceeds that of low-compatibility combinations.\u003c/p\u003e \u003c/div\u003e"},{"header":"3. Experimental Method","content":"\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e3.1 Experimental Design\u003c/h2\u003e \u003cp\u003eThis study employs ERP methodology to investigate the mechanisms by which different product-service combinations influence consumer engagement and purchase intention. Using E-prime software, we designed an experiment simulating consumer purchasing scenarios. Participants were exposed to stimuli representing various product-service combinations to induce relevant ERP components. Electroencephalography (EEG) data were collected using brain-monitoring equipment while participants browsed the different product-service combinations.\u003c/p\u003e \u003cp\u003eBased on Tukker's (2004) classification of product-service systems, five product-service combinations were selected (laptop computers, smartphones, portable hard drives, shared bicycles, and shared mobility services). Product attributes were categorized into three types (e.g., laptop attributes: memory, battery life, color), with each attribute offering three configuration levels: low, medium, and high (P1: low configuration; P2: medium configuration; P3: high configuration). Building upon Baines et al.'s (2013) framework for manufacturing servitization, which categorizes supplementary services into basic, intermediate, and advanced tiers, this study adopts the basic and intermediate service types. Basic services focus on supporting product delivery (e.g., installation and warranty), while intermediate services maintain product condition (e.g., periodic maintenance and technical support). Service selection was determined by the characteristics of the product-service combination and stimulus presentation frequency requirements. Specifically, service content was categorized into two types (S1: Basic Services; S2: Intermediate Services).\u003c/p\u003e \u003cp\u003eEach product-attribute configuration and service content were paired to form distinct product-service combinations, resulting in six categories: P1S1, P1S2, P2S1, P2S2, P3S1, and P3S2. Among these: P1S1 represents high product-attribute matching with low service value, constituting a high-compatibility product-service combination. P2S1 represents high product-attribute matching with high service value, constituting a low-compatibility product-service combination. The stimulus materials for the product-service combinations comprised 180 trials, with 36 trials per product type. Each stimulus category contained 30 trials, meeting the minimum trial requirement for EEG experiments.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e3.2 Participants\u003c/h2\u003e \u003cp\u003eThis study recruited 22 undergraduate and graduate students, comprising 10 males and 12 females, aged between 22 and 26 years (mean age: 22.3 years). All participants had normal or corrected-to-normal vision. Participation was voluntary. Prior to the experiment, participants were informed about the experimental procedures and precautions and signed informed consent forms. Data from two female participants were excluded owing to excessive information disturbance during the experiment, resulting in anomalous data. The final dataset comprised 10 female and 10 male participants.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e3.3 Experimental Procedure\u003c/h2\u003e \u003cp\u003eThe experiment was conducted in the Behavior and Human Factors Laboratory at the School of Economics and Management, Beihang University. After receiving instructions, participants donned electrode caps, sat in chairs adjusted to their most comfortable position, followed on-screen prompts, and placed their fingers on the keyboard (left index finger on F, right index finger on J) to prepare for purchase decisions.\u003c/p\u003e \u003cp\u003eThe experiment employed a \u0026ldquo;priming-probe\u0026rdquo; paradigm. Participants first viewed the experimental instructions. The experimental stimuli consisted of 180 distinct product-service combinations across six categories. The sequence of stimulus presentation is illustrated in Fig.\u0026nbsp;1. First, a \u0026ldquo;+\u0026rdquo; image appeared for 2000 ms, followed by product and price images, which were displayed for 2000 ms. Then, product-attribute configurations and service details were added to form the product-service combination image, presented for 2000 ms. Finally, the purchase selection image was displayed for 4000 ms. If the participant did not respond, the system proceeded directly to the next trial. Participants made purchase decisions by reviewing product-attribute configurations and additional service content. They pressed the F key to select \u0026ldquo;indicate purchase\u0026rdquo; and the J key to decline. Before formal data collection, participants completed 10 practice trials. Each participant independently completed 180 trials in the formal test.\u003c/p\u003e \u003cp\u003e \u003cb\u003eFigure 1.\u003c/b\u003e Illustration of the experimental design\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003e3.4 EEG Recording and Analysis\u003c/h2\u003e \u003cp\u003eExperimental stimuli were presented using E-prime software, and EEG data were recorded and analyzed using the Net Station EEG system. Participants wore a 64-channel electrode cap during the experiment, with Cz as the reference electrode and a sampling frequency of 250 Hz. E-prime software recorded behavioral data, including reaction times and final purchase choices. The EEG device collected brainwave signals, which were processed using the Net Station EEG recording and analysis system to generate ERPs elicited by different product-service combinations.\u003c/p\u003e \u003cp\u003eThe EEG data processing workflow follows standardized procedures: a phase-shift-free digital low-pass filter at 40 Hz was applied, and the EEG data were then segmented by service content labels into six stimulus-type segments. Each segment captured 200 ms before and 1500 ms after stimulus onset. Data containing artifacts such as eye movements (+/\u0026minus;55 \u0026micro;V) and blinks (+/\u0026minus;140 \u0026micro;V) were excluded. The segmented data were subsequently averaged to derive ERPs elicited by different product-service combinations, with baseline correction referenced to the 200 ms pre-stimulus EEG data. To examine the neural mechanisms underlying consumers' perceptions of product-service combination utility, this study employed within-subjects one-way analysis of variance (ANOVA) to compare ERP amplitudes across the six product-service combination conditions. Factors analyzed included experimental condition (high-compatibility vs. low-compatibility product-service combinations) and electrode location ( F3, FC1, FC3, FC5).\u003c/p\u003e \u003c/div\u003e"},{"header":"4. Data Analysis","content":"\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003e4.1 EEG Data Analysis\u003c/h2\u003e \u003cp\u003eWhen participants browsed different product-service combinations, P2 waves were elicited in the left frontal region (F3), and left frontocentral junction (FC1, FC3, FC5). Figure\u0026nbsp;2 displays the averaged ERP waveforms at the left frontal electrode across the 100 ms pre-stimulus to 600 ms post-stimulus time window for various product-service combinations. The P2 evoked by the high-compatibility product-service combination (P3S1) was larger than that elicited by the low-compatibility combination (P3S2).\u003c/p\u003e \u003cp\u003eA 2 (high emotional value vs. low emotional value) \u0026times; 4 (electrode sites) within-subjects repeated measures ANOVA was conducted to compare amplitude differences of the positive component during the 192\u0026ndash;292 ms interval under both conditions (high-compatibility and low-compatibility). Significant differences were observed between high-compatibility and low-compatibility combinations during the 192\u0026ndash;292 ms interval. Table\u0026nbsp;1 presents the ANOVA results for the relevant electrode sites for both product-service combinations. Both combinations elicited significant P2 components. Analysis confirmed that within the 192\u0026ndash;292 ms time window, the P2 amplitude evoked by the high-compatibility PSS (P3S1) was greater than that evoked by the low-compatibility PSS (P3S2), supporting Hypothesis 2.\u003c/p\u003e \u003cp\u003e \u003cb\u003eTable\u0026nbsp;1\u003c/b\u003e. Descriptive statistics (mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD) of the averaged wave P2 amplitude in the left frontal region and left frontal-central junction region for PSSs with different adaptability levels (192\u0026ndash;292 ms)\u003c/p\u003e \u003cp\u003e \u003cb\u003eFigure 2.\u003c/b\u003e Total average ERP waveforms elicited in the left frontal region and the frontal-central combined region (F3, FCI, FC3, FC5) by product-service combinations with varying levels of adaptability.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003e4.2 Behavioral Data Analysis\u003c/h2\u003e \u003cp\u003eDuring the process of browsing and comparing product-service bundles, consumers evaluate the product attributes and bundled service contents to guide their purchase decisions. Behavioral data recorded the purchase rates of six product-service bundle combinations, revealing significant differences among the bundles. The purchase rates for the six product-service combinations P1S1, P1S2, P2S1, P2S2, P3S1, and P3S2 were 14%, 25%, 27%, 42%, 50%, and 59%, respectively, indicating distinct purchase rates across each combination.\u003c/p\u003e \u003cp\u003eSince intermediate services induce higher positive emotions (Zhao et al., 2015), the absolute purchase rate of P3S2 (a combination featuring high product-attribute matching with intermediate services) reached 59%. Meanwhile, P3S1, a high-compatibility combination, achieved a purchase rate of 50%. However, the relative purchase rate of high-compatibility combinations defined as the incremental purchase rate increase resulting from factor changes exceeded that of low-compatibility combinations.\u003c/p\u003e \u003cp\u003eRelative purchase rates differ across PSSs with varying compatibility, defined as the increase in purchase rate after product-attribute enhancement: Relative Purchase Rate = (Purchase Rate of Enhanced Combination\u0026thinsp;\u0026minus;\u0026thinsp;Original Combination Purchase Rate) / Original Combination Purchase Rate. The purchase rate for P2S1 was 27%, while P3S1 reached 50%. The relative purchase rate for high-compatibility PSSs was 85.19%. The purchase rate for product-service combination (P2S2) was 42%, whereas P3S2 reached 59%. The relative purchase rate for low-compatibility PSSs, representing the increase in purchase rate after product-attribute enhancement, was 40.48%. These findings demonstrate that the relative purchase rate for high-compatibility product-service combinations exceed that of low-compatibility combinations, thereby supporting Hypothesis 3.\u003c/p\u003e \u003cp\u003eIn the experimental design, P3S1 represents high product attributes paired with low-value services, whereas P3S2 denotes high product attributes paired with high-value services. The experimental results indicate that P3S1 elicits a greater P2 effect than P3S2, suggesting that P3S1 engages consumers at a higher cognitive level. P3S1 also achieves a higher relative purchase rate than P3S2, reflecting greater consumer acceptance of P3S1. Therefore, P3S1 demonstrates greater adaptability than P3S2, supporting Hypothesis 1.\u003c/p\u003e \u003c/div\u003e"},{"header":"5. Discussion of Experimental Results","content":"\u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003e5.1 P2 and Perception of Product-Service Combinations\u003c/h2\u003e \u003cp\u003eAnalysis of brainwave patterns elicited by different product-service combinations revealed that the EEG findings aligned with behavioral data conclusions: ERPs induced by high-compatibility combinations were significantly greater than those elicited by low-compatibility combinations. Specifically, these differences were reflected in the P2 component recorded in the left frontal, left parietal, and left-central association regions. P2 represents the ERP component associated with cognitive processing when consumers encounter product-service combinations. Behavioral data revealed that cognitive regulation plays a crucial role in shaping purchase intention toward product-service combinations. Although these combinations also evoked positive emotional responses in consumers, which can enhance purchase intention, the increase attributable to emotional regulation was smaller than that from cognitive regulation. This finding holds practical value for designing complex, multi-element product-service systems, particularly in matching service content. These results suggest that supplementary services can enhance purchase intention but only to a limited extent and under specific threshold conditions. When premium services mismatch with customers' cognitive capabilities, they may generate cognitive conflict and negatively influence purchase intention.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003e5.2 Cognitive-Emotional Interaction and Value Co-Creation\u003c/h2\u003e \u003cp\u003eAnalysis of brainwave patterns elicited by different product-service combinations revealed that EEG findings were consistent with behavioral data conclusions: ERPs elicited by highly compatible product-service combinations were significantly higher than those elicited by low-compatibility combinations. This effect was reflected in differences in the P2 component across the left frontal, left parietal, and left-central association regions. The P2 component represents an ERP index associated with cognitive processing when consumers evaluate product-service combinations. Behavioral data indicated that cognitive regulation plays a crucial role in shaping purchase intention toward product-service combinations. Although such combinations also elicited positive emotional responses that can enhance purchase intention, the increase in purchase rate attributable to emotional regulation was smaller than that driven by cognitive regulation. These findings suggest that supplementary services can enhance purchase intention but only within a limited range and under threshold conditions. When premium services mismatch with customers' cognitive capabilities, they may induce cognitive conflict, thereby negatively impacting purchase intention. These findings hold practical value for designing complex product-service systems with multiple elements, particularly in ensuring the alignment of service offerings.\u003c/p\u003e \u003c/div\u003e"},{"header":"6. Research Findings and Significance","content":"\u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003e6.1 Research Findings\u003c/h2\u003e \u003cp\u003eThe application of digital technologies, particularly artificial intelligence, has significantly influenced consumer shopping behavior. Establishing measurement methods and matching rules plays a crucial role in enabling smarter services for AI-assisted purchasing. By designing the AI front end based on cognitive engagement and categorizing service types according to consumer data acquisition patterns, businesses can achieve deeper cognitive insights and enhance the competitiveness of AI-driven services. This study proposes a neural mechanism underlying compatibility with product-service combinations, providing support for aligning AI algorithms with customer needs. The P2 component serves as a neurological indicator for consumers to evaluate product-service combinations, explaining how variations in these combinations and their elements influence user engagement and purchase intention. Analysis of P2 amplitude demonstrated that different product-service combinations, because of variations in attribute matching, occupy varying degrees of cognitive resources and evoke differing levels of positive emotional responses. These findings confirm that consumer purchase intentions vary across different product-service combinations because of both cognitive resource allocation and positive emotional activation, thereby establishing a neurological basis for product-service matching. Therefore, this approach can assist artificial intelligence algorithms.\u003c/p\u003e \u003cp\u003eWithin the platform economy context, data empowerment supports the delivery of personalized digital services, fostering the emergence of product-service bundles as a new form of e-commerce. Although platforms such as JD.com offer supplementary services for products, these bundles have yet to become a dominant shopping model. AI-generated personalized shopping lists of product-service bundles can help overcome consumers' initial resistance to purchasing services, creating a consumption experience that enhances cognitive engagement. This approach has the potential to substantially increase related service purchases and address the issue of low consumer uptake for product-related services. For platforms, AI-powered shopping assistance must deliver a concrete sense of intelligence to achieve a meaningful impact\u0026mdash;a capability that remains underdeveloped. Realizing this goal requires back-end cognitive insights and algorithm optimization to effectively stimulate consumer demand.\u003c/p\u003e \u003cp\u003eFor manufacturers, emphasizing the critical role of product attributes is essential. During service and digital transformation, it is vital to recognize the significant influence of consumer perception of product attributes on decision-making. Specifically, product attributes should be developed across multiple dimensions beyond service levels. Rather than solely enhancing service quality, manufacturers should first elevate product attributes to a certain level before offering premium supplementary services. This approach aligns with consumer cognitive needs. During service transformation, companies may encounter bottlenecks when attempting to boost competitiveness through supplementary services, as promoting advanced services necessitates substantial improvements in product attributes. The design of product-service combinations incorporates both customization concepts and principles of product-service alignment. This approach requires exploring pricing methodologies, and the research findings suggest that providing adaptive matching solutions may offer novel approaches for personalized pricing.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec19\" class=\"Section2\"\u003e \u003ch2\u003e6.2 Practical Implications\u003c/h2\u003e \u003cp\u003eDigital platforms guide AI-driven transformations in sales and shopping models by tailoring product-service offerings to customer knowledge levels and enhancing perceived value through service experiences. For instance, in 2021, Brazil's KITCHEN launched the world's first smart connected stove with IoT and AI capabilities. By leveraging artificial intelligence, it delivers personalized recommendation services, suggesting recipes and complementary ingredients based on individual preferences and past cooking behaviors (Ayala et al., 2025). Taobao currently offers its \u0026ldquo;AI Universal Search\u0026rdquo; service, which generates personalized shopping lists based on purchasing habits and provides product recommendations using user data. If most platforms develop AI services, this will transform traditional online shopping models. However, the functionality of matching complex products with supplementary services poses significant challenges to AI model development costs, potentially offering insights for creating competitive advantages for certain platforms.\u003c/p\u003e \u003cp\u003eManufacturing servitization still faces numerous challenges in the digitalization process, such as the development costs of artificial intelligence and the design of intelligent product-service systems that meet personalized needs, which require further exploration. This research contributes to providing a method for rapidly ranking product-service combinations in the context of artificial intelligence. Through algorithmic configuration and optimization, the approach can reduce the time costs of artificial intelligence, directly providing matching solutions that better align with consumer needs and providing a neurological reference and basis for digitalization. With the advancement of neuromanagement, brain imaging technology offers scientific tools to observe consumer brain activity during purchasing decisions. The research findings can be applied to digital product-service system platform design and intelligent recommendations for products and services on e-commerce platforms, boosting online service sales. For instance, while JD.com offers supplementary online services for products, achieving significant sales of these services remains challenging after years of operation, with consumer service selection being a key hurdle. When AI matches appropriate services to shopping platform products, it can enhance sales of product-service combinations.\u003c/p\u003e \u003cp\u003eFor manufacturers, since premium services require higher-tier product attributes operating on a principle akin to \u0026ldquo;like repels, unlike attracts\u0026rdquo; research findings support manufacturers in offering products with elevated attributes. Providing intelligent product-service systems tailored to customer needs also informs smart pricing strategies. For instance, in Brazil's SCREWS intelligent supply chain solution, AI identifies distinct customer consumption profiles and delivers personalized quotes based on their requirements (Ayala et al., 2025). Manufacturers can establish differentiation between product attributes and service value, replacing uniformity with differentiation within AI algorithms by developing distinct AI capabilities. This enhances service customization and personalization, such as delivering premium services through generative AI (Wamba et al., 2023) and offering tailored services like expert consultations (Cook et al., 2024), thereby advancing intelligent pricing.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec20\" class=\"Section2\"\u003e \u003ch2\u003e6.3 Research Limitations and Future Directions\u003c/h2\u003e \u003cp\u003eThis study employs a laboratory research methodology and thus has inherent limitations. First, the sample consisted of students, which may limit the applicability of the findings to consumers from diverse professional backgrounds, warranting further investigation. Second, within the digital economy, consumers' cognitive capabilities are rapidly evolving, while service types and their relationships with product attributes are constantly changing. This dynamic alters the perceived value of product-service combinations, making it challenging to quantify high-value versus low-value services. Specifically, as consumer cognition advances, premium services may transition into basic offerings, suggesting that the service categories defined in this study will shift over time. Third, if subjects participate in EEG experiments for extended periods, fatigue may compromise experimental outcomes. Consequently, the range of products and services presented as stimuli in the experiment is limited. To address these limitations, future research should explore areas including dynamic product-service system pricing grounded in psychological and neural mechanisms, as well as the supply and design of personalized intelligent services.\u003c/p\u003e \u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eDeclaration of competing interest\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no conflict of interest.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthical approval\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eApproval was obtained from the ethics committee of Beihang University on 17th, September, 2017 (No. BUAA-2017-09-10). The procedures used in this study adhere to the tenets of the Declaration of Helsinki.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eInformed consent\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study meticulously secured informed consent from every participant involved. The process took place in October 2017. The original informed consent document was crafted in Chinese. Before initiating the data collection process, all individuals were required to thoroughly review and acknowledge their comprehension of the informed consent documentation. This material explicitly conveyed the voluntary nature of participation and guaranteed the anonymity of responses, emphasizing their sole utilization for scholarly inquiry.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eAyala, N. F., et al. (2025). Artificial Intelligence capabilities in Digital Servitization: Identifying digital opportunities for different service types. International Journal of Production Economics, 284:109604.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eQueiroz, M. M., Beatriz, A. \u0026amp; Bagherzadeh, M. (2025). Crowdsourcing-enabled AI: Unlocking value in digital services. International Journal of Production Economics, 283, 109586.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTurner, F., Merle, A., Gotteland, D., (2020). Enhancing consumer value of the co-design experience in mass customization. J. Bus. Res. 117, 473\u0026ndash;483\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWang, Y., Mo, D.Y., Ma, H.L., (2023). Perception of time in the online product customization process. Ind. Manag. Data Syst. 123, 369\u0026ndash;385\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eXu, Q., Wang, X. \u0026amp; Sun, Q. (2026). When customization mode meets product innovativeness: How their match drives consumer purchase. Journal of Retailing and Consumer Services, 88:104483.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDavenport, T.H., Ronanki, R., 2018. Artificial intelligence for the real world: don\u0026rsquo;t start with moon shots. Harv. Bus. Rev. 96 (January\u0026ndash;February), 108\u0026ndash;116.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYan, J., et al. (2025). Human-centric artificial intelligence towards Industry 5.0: retrospect and prospect. Journal of Industrial Information Integration, 47:100903.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBaines, T., et al. (2009). Towards an operations strategy for product-centric servitization. International Journal of Operations \u0026amp; Production Management, 29(5):494\u0026ndash;519.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBeuren, F.H., Ferreira, M.G., \u0026amp; Miguel, P.A. (2013). Product-service systems: a literature review on integrated products and services. Journal of Cleaner Production, 47: 222\u0026ndash;231.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBenoit, S., Scherschel, K., Ates, Z., Nasr, L., \u0026amp; Kandampully, J. (2017). Showcasing the diversity of service research: Theories, methods, and success of service articles. Journal of Service Managment, 28: 810\u0026ndash;836.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCatulli, M., Cook, M., \u0026amp; Potter, S. (2017). Consuming use orientated product service systems: A consumer culture theory perspective. Journal of Cleaner Production, 141: 1186\u0026ndash;1193.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDavidson, R.J. (2004). What does the prefrontal cortex \u0026lsquo;\u0026lsquo;Do\u0026rsquo;\u0026rsquo; in affect: perspectives on frontal EEG asymmetry research. Biological Psychology, 67(1): 219\u0026ndash;234.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDabholkar, P., Shepherd, C.D., \u0026amp; Thorpe, D.I. (2000). A comprehensive framework for service quality: an investigation of critical conceptual and measurement issues through a longitudinal study. Journal of Retailing, 76(2), 139\u0026ndash;173.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDong, B., \u0026amp; Sivakumar, K. (2015). A process-output classification for customer participation in services. Journal of Service Management, 26:726\u0026ndash;750.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDabholkar, P.A., \u0026amp; Sheng, X. (2012). Consumer participation in using online recommendation agents: Effects on satisfaction, trust, and purchase intentions. The Service Industries Journal, 32:1433\u0026ndash;1449.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eErk S., et al.(2002). Cultural Objects Modulate Reward Circuitry. Neuroreport, 13(18):2499\u0026ndash;2503.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGaiardelli, P., et al. (2013). A classification model for product-service offerings. Journal of Cleaner Production, 11:1\u0026ndash;13\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHuth, A.G., et al. (2016). Where words make sense: A semantic atlas of the cerebral cortex. Nature, 532 (7600):453\u0026ndash;459.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHan, W., Wang, X., Petropoulos, F., \u0026amp; Wang, J. (2019). Brain imaging and forecasting: Insights from judgmental model selection. Omega, 87:1\u0026ndash;9.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKhandeparkar, K. (2014). The role of complementarity and partner brand price level in new product introduction strategy using bundle offers: A study on the quality perception of bundle components. Journal of Retailing and Consumer Services, 21:992\u0026ndash;1000.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKuijken, B., Gemser G., \u0026amp; Wijnberg N. M. (2017). Effective product-service systems: A value-based framework. Industrial Marketing Management. 60:33\u0026ndash;41.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKarwatzki, S., Dytynko, O., Trenz, M., \u0026amp; Veit, D. (2017). Beyond the personalization-privacy paradox: privacy valuation, transparency features, and service personalization. Journal of Management Information Systems. 34(2): 369\u0026ndash;400.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKaiser, U., Schreier, M., \u0026amp; Janiszewski, C. (2017). The self-expressive customization of a product can improve performance. Journal of Marketing Research. 54(5):816\u0026ndash;831.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLightfoot H., Baines T., \u0026amp; Smart, P. (2013). The servitization of manufacturing: a systematic literature review of interdependent trends. International Journal of Operations \u0026amp; Production Management, 33: 11\u0026ndash;12\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMont O.K. (2002). Clarifying the Concept of Product-Service System. Journal of Cleaner Production, 11:237\u0026ndash;245\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMaguire, L., \u0026amp; Geiger, S., (2015). Emotional Timescapes: the temporal perspective and consumption emotions in services. Journal of Services Marketing, 29(3):211\u0026ndash;223.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMatsuda, I., \u0026amp; Nittono, H. (2015). Motivational significance and cognitive effort elicit different late positive potentials. Neurophysiologie Clinique, 126: 304\u0026ndash;313.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMazaheri, E., Richard, M. O., Laroche, M., \u0026amp; Ueltschy, L. C. (2014). The influence of culture, emotions, intangibility and atmospheric cues on customer online behavior. Journal of Business Research, 67 (3): 253\u0026ndash;259.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNeely, A. (2009). Exploring the financial consequences of the servitization of manufacturing. Operations Management Research, 1(2): 103\u0026ndash;118.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eOliver, J. (2015). The consumer\u0026rsquo;s perspective on evaluating products: service is the key. Journal of Services Marketing, 29 (3):200\u0026ndash;210.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eOliva, R., \u0026amp; Kallenberg R. (2003). Managing the transition from products to services. International Journal of Service Industry Management, 14 (2):160\u0026ndash;172.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSong, W. (2017). Requirement management for product-service systems: Status review and future trends. Computers in Industry, 85:11\u0026ndash;22.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSpring, M., \u0026amp; Araujo, L. (2013). Beyond the service factory: Service innovation in manufacturing supply networks. Industrial Marketing Management, 42(1), 59\u0026ndash;70.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSong, W., \u0026amp; Sakao, T. (2017). A customization-oriented framework for design of sustainable product/service system. Journal of Cleaner Production, 140: 1672\u0026ndash;1685.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eShao, Z., Li, X., Guo, Y., \u0026amp; Zhang, L. (2020). Influence of service quality in sharing economy: Understanding customers\u0026rsquo; continuance intention of bicycle sharing. Electronic Commerce Research and Applications, 40: 100944.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTukker, A. (2004). Eight types of product-service system: Eightways to sustainability? Experiences from SusProNet. Business Strategy and the Environment, 13(4), 246\u0026ndash;260.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTunn, V. S. C., van den Hende, E. A., Bocken, N. M. P., \u0026amp; Schoormans, J. P. L. (2020). Digitalised product-service systems: Effects on consumers\u0026rsquo; attitudes and experiences. Resources, Conservation and Recycling, 162: 105045.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eThomas, A. W., Molter, F., Krajbic, I., Heekeren H. R., \u0026amp; Mohr., P.N.C. (2019). Gaze Bias Differences Capture Individual Choice Behavior. Nature Human Behaviour, 3 (6): 625\u0026ndash;635.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eUlaga, W., \u0026amp; Loveland, J. M. (2014). Transitioning from product to service-led growth in manufacturing firms: Emergent challenges in selecting and managing the industrial sales force. Industrial Marketing Management, 43(1), 113\u0026ndash;125.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eVargo, S. L., \u0026amp; Lusch, R. F. (2004). Evolving to a new dominant logic for marketing. Journal of Marketing, 68(1), 1\u0026ndash;17.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eVandermerwe, S., \u0026amp; Rada, J. (1988). Servitization of business: adding value by adding services. European Management Journal, 6(4):314\u0026ndash;324.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eVerhoef, P. C., Antonides, G, \u0026amp; Hoog, A. N. (2004). Service encounters as a sequence of events: the importance of peak experiences. Journal of Service Research, 7(1): 53\u0026ndash;64.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWang, J., Zhao, M., \u0026amp; Zhao, G. (2017). The impact of customer cognitive competence on online service decision-making: An event-related potentials perspective. The Service Industries Journal, 37(5\u0026ndash;6):363\u0026ndash;380.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZhao, M., Wang, J., \u0026amp; Han, W. (2015). The impact of emotional involvement on online service buying decisions: an event-related potentials perspective. Neuroreport, 26(17):995\u0026ndash;1002.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSong, W., et al., Design concept evaluation of smart product-service systems considering sustainability: An integrated method. Computers \u0026amp; Industrial Engineering.2021.159:107485\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMa, Q., Wang, X. (2006). From Neuroeconomics and Neuromarketing to Neuromanagement. Journal of Management Engineering, 20, 129\u0026ndash;132.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eValencia, A., Mugge, R., Schoormans, J., \u0026amp; Schifferstein, H. (2015). The design of smart product-service systems (PSSs): An exploration of design characteristics. International Journal of Design, 9(1):13\u0026ndash;28.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWang X., Huang Y., Ma Q., Li N. Event-related Potential P2 correlates of Implicit Aesthetic Experience. Neuroreport, 2012, 23: 862\u0026ndash;866\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCook, S., Hagiu, A., Wright, J., 2024. Turn generative AI from an existential threat into a competitive advantage. Harv. Bus. Rev. 102 (1), 118\u0026ndash;125.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWamba, S.F., Queiroz, M.M., Jabbour, C.J.C., Shi, C.V., 2023. Are both generative AI and ChatGPT game changers for 21st-Century operations and supply chain excellence? Int. J. Prod. Econ. 265, 109015.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"},{"header":"Table 1","content":"\u003cp\u003e\u003cstrong\u003eTable 1\u003c/strong\u003e. Descriptive statistics (mean \u0026plusmn; SD) of the averaged wave P2 amplitude in the left frontal region and left frontal-central junction region for PSSs with different adaptability levels (192\u0026ndash;292 ms)\u0026nbsp;\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 111px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eChannel\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 111px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eScenario 1\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 111px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eScenario\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;2\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 111px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eF-value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 77px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eP-value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 111px;\"\u003e\n \u003cp\u003eF3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 111px;\"\u003e\n \u003cp\u003e2.51（0.13）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 111px;\"\u003e\n \u003cp\u003e2.29（0.23）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 111px;\"\u003e\n \u003cp\u003e17.929\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 77px;\"\u003e\n \u003cp\u003e.000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 111px;\"\u003e\n \u003cp\u003eFC1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 111px;\"\u003e\n \u003cp\u003e4.32 (0.79)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 111px;\"\u003e\n \u003cp\u003e3.42 (0.41)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 111px;\"\u003e\n \u003cp\u003e26.500\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 77px;\"\u003e\n \u003cp\u003e.000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 111px;\"\u003e\n \u003cp\u003eFC3\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 111px;\"\u003e\n \u003cp\u003e3.51 (0.42)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 111px;\"\u003e\n \u003cp\u003e2.76 (0.28)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 111px;\"\u003e\n \u003cp\u003e55.585\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 77px;\"\u003e\n \u003cp\u003e.000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 111px;\"\u003e\n \u003cp\u003eFC5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 111px;\"\u003e\n \u003cp\u003e3.59 (0.22)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 111px;\"\u003e\n \u003cp\u003e2.33 (0.22)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 111px;\"\u003e\n \u003cp\u003e429.410\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 77px;\"\u003e\n \u003cp\u003e.000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"humanities-and-social-sciences-communications","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"palcomms","sideBox":"Learn more about [Humanities \u0026 Social Sciences Communications](http://www.nature.com/palcomms/)","snPcode":"41599","submissionUrl":"https://submission.springernature.com/new-submission/41599/3","title":"Humanities and Social Sciences Communications","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Nature AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"artificial intelligence, smart product-service systems, event-related potentials","lastPublishedDoi":"10.21203/rs.3.rs-8305425/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8305425/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThe application of artificial intelligence provides new approaches for intelligent product-service systems, and an increasing number of service-oriented manufacturing enterprises are leveraging artificial intelligence (AI)to achieve value co-creation with customers. Service-dominant logic posits that interactions between consumers and providers constitute the primary source of value creation, highlighting the need to explore methods for matching products with appropriate services. In the process of intelligently matching additional services to products, whether the \u0026ldquo;similarity principle\u0026rdquo; or the \u0026ldquo;heterogeneity principle\u0026rdquo; better aligns with customer expectations remains to be explored. This study employs event-related potential (ERP) methodology to examine how product-service compatibility influences customer cognitive engagement and purchase intention by analyzing ERP components associated with cognitive processing. By proposing a method to determine product-service compatibility, this study provides insights for matching intelligent product-service combinations in an AI-driven context. The findings offer guidance for delivering personalized product-service combinations in platform economies and AI ecosystems, enabling enterprises to achieve value co-creation by enhancing product-service compatibility. This research contributes to the design and optimization of intelligent product-service systems.\u003c/p\u003e","manuscriptTitle":"How Artificial Intelligence Matches Services for a Product? An event-related potential perspective","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-03-19 21:47:46","doi":"10.21203/rs.3.rs-8305425/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"editorInvitedReview","content":"","date":"2026-04-16T13:24:38+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"103039130387802665075044967660846943529","date":"2026-03-20T07:18:23+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-03-19T03:49:32+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"22185344923430468639530544205424963616","date":"2026-03-18T05:50:47+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-03-17T15:09:35+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-01-13T12:26:51+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-01-06T04:29:09+00:00","index":"","fulltext":""},{"type":"submitted","content":"Humanities and Social Sciences Communications","date":"2026-01-06T04:23:51+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"humanities-and-social-sciences-communications","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"palcomms","sideBox":"Learn more about [Humanities \u0026 Social Sciences Communications](http://www.nature.com/palcomms/)","snPcode":"41599","submissionUrl":"https://submission.springernature.com/new-submission/41599/3","title":"Humanities and Social Sciences Communications","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Nature AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"170e0c3b-555d-4ac2-9a95-8dabbff7d3fe","owner":[],"postedDate":"March 19th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[{"id":64816457,"name":"Business and commerce/Business and management"},{"id":64816458,"name":"Social science/Business and management"},{"id":64816459,"name":"Business and commerce/Information systems and information technology"},{"id":64816460,"name":"Physical sciences/Mathematics and computing"}],"tags":[],"updatedAt":"2026-03-19T21:47:46+00:00","versionOfRecord":[],"versionCreatedAt":"2026-03-19 21:47:46","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8305425","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8305425","identity":"rs-8305425","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}