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Market evaluation of products is the basis for product innovation, yet traditional expert-based evaluation methods are highly dependent on the specialization of experts. There exist a lot of weak expert-generated texts on the Internet of their own subjective evaluations of products. Analyzing these texts can indirectly extract the opinions of weak experts and transform them into decision-support information that assists product designers in understanding the market.

In social networks, a subset of users, termed “weak experts”, possess specialized knowledge and frequently share their product experiences online. This study introduces a comparative opinion mining framework that leverages the insights of “weak experts” to analyze user opinions.

An automotive product case study demonstrates that evaluations based on weak expert insights offer managerial insights with a 99.4% improvement in timeliness over traditional expert analyses. Furthermore, in the few-shot sentiment analysis module, with only 10% of the sample, the precision loss is just 1.59%. In addition, the quantitative module of specialization weighting balances low-specialization expert opinions and boosts the weight of high-specialization weak expert views. This new framework offers a valuable tool for companies in product innovation and market strategy development.

This study introduces a novel approach to opinion mining by focusing on the underutilized insights of weak experts. It combines few-shot sentiment analysis with specialization weighting and AHP, offering a comprehensive and efficient tool for product evaluation and market analysis.

The purpose of the study is to propose a normative approach for market segmentation, profile and monitoring using computing and information technology to analyze User-Generated Content (UGC).

The specific steps include performing a structural analysis of the UGC and extracting the base variables and values from it, generating a consumer characteristics matrix for segmenting process, and finally describing the segments' preferences, regional and dynamic characteristics. The authors verify the feasibility of the method with publicly available data. The external validity of the method is also tested through questionnaires and product regional sales data.

The authors apply the proposed methodology to analyze 53,526 UGCs in the New Energy Vehicle (NEV) market and classify consumers into four segments: Brand-Value Suitors (32%), Rational Consumers (21%), High-Quality Fanciers (26%) and Utility-driven Consumers (21%). The authors describe four segments' preferences, dynamic changes over the past six years and regional characteristics among China's top five sales cities. Then, the authors verify the external validity of the methodology through a questionnaire survey and actual NEV sales in China.

The proposed method enables companies to utilize computing and information technology to understand the market structure and grasp the dynamic trends of market segments, which assists them in developing R&D and marketing plans.

This study contributes to the research on UGC-based universal market segmentation methods. In addition, the proposed UGC structural analysis algorithm implements a more fine-grained data analysis.

The multidimensional complexity of urban settlements is increasing and the problem of spaces and territories brought to the scale of smart cities is a critical global issue. This study aims to analyse the scientific production in the Web of Science (WoS) on the relationship between smart cities and the eight urban dimensions defined by the World Economic Forum (WEF) in the period 1990 to 2021, in order to establish which countries lead the knowledge related to the search for sustainable living conditions for people and how this knowledge contributes to improving stakeholders' decision-making.

The methodological steps followed in the study were: (1) Identification and selection of keywords. (2) Design and application of an algorithm to identify these selected keywords in titles, abstracts and keywords using WoS terms to contrast them. (3) Data processing was performed from Journal Citation Report (JCR) journals during the year 2022.

This study identified the authors, institutions and countries that publish the most globally on the topic of Smart Cities. The acceleration in the integration of new technologies and their impact on population conglomerates and their relationship with urban dimensions were also analysed. The evidence found indicates that the USA and China are leading in this field.

This bibliometric study was designed to analyse a knowledge space not addressed in the scientific literature referred to the relationship between the concept of smart cities and the urban dimensions established by the WEF, the identification of new technologies that are converging to promote developments of new ways of managing urban dimensions and propose new knowledge spaces.

This paper aims to explore blockchain integration strategies within a three-level livestock meat supply chain in which consumers have a preference for quality trust in livestock meat products. The paper investigates three questions: First, how does consumers’ preference for quality trust affect blockchain integration and transaction decisions among supply chain participants? Second, under what circumstances will retailers choose to participate in the blockchain? Finally, how can other factors such as blockchain costs and supplier–retailer partnership value affect integration decisions?

This paper formulates a supply chain network equilibrium model and employs the logarithmic-quadratic proximal prediction-correction method to obtain equilibrium decisions. Extensive numerical studies are conducted using a pork supply chain network to analyze the implications of blockchain integration for different supply chain participants.

The results reveal several key insights: First, suppliers’ increased blockchain integration, driven by higher quality trust preference, can negatively affect their profits, particularly, with excessive trust preferences and high blockchain costs. Second, an increase in consumers’ preference for quality trust expands the range of unit operating costs for retailers engaging in blockchain. Finally, the supplier–retailer partnership drives retailer blockchain participation, facilitating enhanced information sharing to benefit the entire supply chain.

This study provides original insights into blockchain integration strategies in an agricultural supply chain through the application of the supply chain network equilibrium model. The investigation of several key factors on equilibrium decisions provides important managerial implications for different supply chain participants to address consumers’ preference for quality trust and enhance overall supply chain performance.

This study aims to examine the relationship between the personality dimensions of consumers and celebrities; the effect of celebrity personality on attitude towards the celebrity; and the effect of attitude towards the celebrity on purchase intention.

Data were gathered from 400 respondents in the North of England to explore the connections between five consumer personality dimensions (agreeableness, extroversion, openness, conscientiousness and neuroticism) and nine celebrity personality dimensions (neuroticism, extroversion, openness, agreeableness, conscientiousness, sincerity, excitement, stylishness and positivity) and were analysed using structural equation modelling.

The findings suggested that some dimensions of consumer personality, i.e. conscientiousness, extroversion and openness, were significantly related to all the celebrity personality dimensions. Moreover, all the celebrity personality dimensions had a significant effect on consumers’ attitude towards the celebrity; however, only neuroticism, extroversion, openness, sincerity and positivity significantly affected purchase intention. Finally, attitude towards the celebrity had a significant effect on purchase intention.

The study introduces a celebrity personality scale and explores a topic that has not previously been researched.

Gamification is a strategic approach employed by practitioners to foster meaningful engagement and enhance the acceptance of recommendations. Gamification affordances (e.g. achievement, self-expression, interaction, and cooperation) catalyze significant psychological processes in consumers, leading to behavioral changes. Despite its application, a gap remains in understanding how these gamification affordances in e-commerce contexts impact customers' perceived values and drive recommendation acceptances.

Employing affordance theory and perceived value theory as our foundation, we have crafted a comprehensive framework that addresses the multifaceted nature of e-commerce gamification, thereby unifying the fragmented knowledge in this area. We implemented a quantitative research design to empirically test the proposed model.

The research reveals that the four principal affordances of gamification – achievement, self-expression, interaction, and cooperation – significantly enrich consumer values across hedonic, utilitarian, and social dimensions. This enrichment facilitates an increased propensity for accepting recommendations.

This study provides a novel lens through which to view the influence of gamification affordances on recommendation acceptance within gamified e-commerce settings. It delineates the effects of each affordance on consumers' perceived value and highlights the pivotal affordances that shape gamified e-commerce experiences. These insights yield actionable strategies for practitioners aiming to refine e-commerce gamification designs and cultivate more engaging consumer interactions.

The importance of the agri-food supply chain in both food production and distribution has made the issue of its development a critical concern. Based on configuration theory and congruence theory, this research investigates the complex impact of supply chain concentration on financial growth in agri-food supply chains.

The cluster analysis and response surface methodology are employed to analyse the data collected from 207 Chinese agri-food companies from 2010 to 2022.

The results indicate that different combination patterns of supply chain concentration can lead to different levels of financial growth. We discover that congruent supplier and customer concentration is beneficial for companies’ financial growth. This impact is more pronounced when the company is in the agricultural production stage of agri-food supply chains. Post-hoc analysis indicates that there exists an inverted U-shaped relationship between the overall levels of supply chain concentration and financial growth.

Our research uncovers the complex interplay between supply chain base and financial outcomes, thereby revealing significant ramifications for agri-food supply chain managers to optimise their strategies for exceptional financial growth.

This study proposes a combined approach of cluster analysis and response surface analysis for analysing configuration issues in supply chain management.

This study investigates whether the visualization of agricultural products influences consumers’ purchase intentions in the context of farmer-assisted livestreaming in China. Moreover, it explores the moderating effect of packaging functionality and the mediating effect of consumer trust.

Consumers in China from multiple social media platforms participated in this survey, which yielded 333 valid responses for analysis.

The results revealed a positive relationship between the video presentation about the agricultural production process and consumers’ purchase intention, which is mediated by consumers’ trust. Meanwhile, packaging functionality moderates the relationship between agricultural product visualization and consumers’ purchase intentions as well as the indirect effect of consumers’ trust.

This study extends the application of the stimulus-organism-response (SOR) model to the field of farmer-assisted livestreaming. By building a more detailed model, this study adds to knowledge on the influencing mechanisms of consumers’ purchase intentions in farmer-assisted livestreaming.

The rapid development of e-commerce live streaming is inseparable from the important role of live-streamer. However, research exploring the impacts of live-streamers’ characteristics on customer engagement in different types of live streaming rooms remains limited. This study aims to examine the impacts of live-streamers’ characteristics (i.e. expertise, moral reputation, popularity, responsiveness) on consumer engagement in e-commerce live streaming, and verify whether there are differences in the impact of live-streamers’ characteristics on consumer experience for different types of live-streamers.

Data were gathered from 266 Chinese adults who have experience watching e-commerce live streaming. Partial least squares techniques were used to verify the model.

The results show that expertise, moral reputation, popularity, and responsiveness of live-streamers have positive impacts on consumer engagement through perceived trust and perceived pleasure. Moreover, through multi-group analysis, this study found that compared with enterprise live-streamers, the moral reputation of individual live-streamers exerts a stronger effect on consumers’ perceived trust, and the popularity of individual live-streamers exerts a stronger effect on consumers’ perceived pleasure. Compared with individual live-streamers, the responsiveness of enterprise live-streamers exerts a stronger effect on consumers’ perceived pleasure.

This study contributes to e-commerce live streaming research by categorizing live-streamers into two types: individual live-streamers and enterprise live-streamers, and verifying the specific difference in the impacts of live-streamers’ characteristics on consumer experience for the two types of live-streamers.

This study delves into the intricate relationship between specific positive and negative emotions experienced by tourists during their vacations and the corresponding emotion regulation strategies they employ. Drawing from emotion regulation theory, we examine the nuanced impact of various strategies on tourists' emotional experiences, thereby advancing our understanding of emotion dynamics in the context of tourism.

Data were gathered through an online survey and travel diaries, subsequently analyzed using linear mixed-effects models.

Our findings underscore that emotion regulation strategies exert a significant influence on both positive and negative emotions. Furthermore, we identified that different strategies correlate uniquely with specific emotions. For instance, the deployment of Expressive Suppression, Savoring, and Stimulus Control strategies notably amplifies the intensity of joy.

This study recommends that tourism managers design experiences that evoke positive emotions through curated sensory cues, storytelling, and stress-free service offerings. Tourism managers should prioritize stress-free services, guide tourists in expressing themselves, and train service providers to manage emotions effectively, thus promoting positive emotional interactions and improving overall customer satisfaction.

Theoretically, this research enriches the emotion regulation literature by contextualizing it within the tourism domain, highlighting the differential effects of regulation strategies on diverse emotional experiences. From a practical standpoint, these insights can guide practitioners in crafting targeted marketing strategies and empower tourists with knowledge to select optimal strategies for enhancing their emotional well-being during vacations.