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For a better understanding of the preferences and differences of young consumers in emerging wine markets, this study aims to propose a clustering method to segment the super-new generation wine consumers based on their sensitivity to wine brand, origin and price and then conduct user profiles for segmented consumer groups from the perspectives of demographic attributes, eating habits and wine sensory attribute preferences.

We first proposed a consumer clustering perspective based on their sensitivity to wine brand, origin and price and then conducted an adaptive density peak and label propagation layer-by-layer (ADPLP) clustering algorithm to segment consumers, which improved the issues of wrong centers' selection and inaccurate classification of remaining sample points for traditional DPC (DPeak clustering algorithm). Then, we built a consumer profile system from the perspectives of demographic attributes, eating habits and wine sensory attribute preferences for segmented consumer groups.

In this study, 10 typical public datasets and 6 basic test algorithms are used to evaluate the proposed method, and the results showed that the ADPLP algorithm was optimal or suboptimal on 10 datasets with accuracy above 0.78. The average improvement in accuracy over the base DPC algorithm is 0.184. As an outcome of the wine consumer profiles, sensitive consumers prefer wines with medium prices of 100–400 CNY and more personalized brands and origins, while casual consumers are fond of popular brands, popular origins and low prices within 50 CNY. The wine sensory attributes preferred by super-new generation consumers are red, semi-dry, semi-sweet, still, fresh tasting, fruity, floral and low acid.

Young Chinese consumers are the main driver of wine consumption in the future. This paper provides a tool for decision-makers and marketers to identify the preferences of young consumers quickly which is meaningful and helpful for wine marketing.

In this study, the ADPLP algorithm was introduced for the first time. Subsequently, the user profile label system was constructed for segmented consumers to highlight their characteristics and demand partiality from three aspects: demographic characteristics, consumers' eating habits and consumers' preferences for wine attributes. Moreover, the ADPLP algorithm can be considered for user profiles on other alcoholic products.

The purpose of this paper is to develop a common remaining shelf life prediction model that is generally applicable for postharvest table grape using an optimized radial basis function (RBF) neural network to achieve more accurate prediction than the current shelf life (SL) prediction methods.

First, the final indicators (storage temperature, relative humidity, sensory average score, peel hardness, soluble solids content, weight loss rate, rotting rate, fragmentation rate and color difference) affecting SL were determined by the correlation and significance analysis. Then using the analytic hierarchy process (AHP) to calculate the weight of each indicator and determine the end of SL under different storage conditions. Subsequently, the structure of the RBF network redesigned was 9-11-1. Ultimately, the membership degree of Fuzzy clustering (fuzzy c-means) was adopted to optimize the center and width of the RBF network by using the training data.

The results show that this method has the highest prediction accuracy compared to the current the kinetic–Arrhenius model, back propagation (BP) network and RBF network. The maximum absolute error is 1.877, the maximum relative error (RE) is 0.184, and the adjusted R² is 0.911. The prediction accuracy of the kinetic–Arrhenius model is the worst. The RBF network has a better prediction accuracy than the BP network. For robustness, the adjusted R² are 0.853 and 0.886 of Italian grape and Red Globe grape, respectively, and the fitting degree are the highest among all methods, which proves that the optimized method is applicable for accurate SL prediction of different table grape varieties.

This study not only provides a new way for the prediction of SL of different grape varieties, but also provides a reference for the quality and safety management of table grape during storage. Maybe it has a further research significance for the application of RBF neural network in the SL prediction of other fresh foods.

The purpose of this paper is to propose an optimized hybrid model based on artificial intelligence methods, use the method of time series forecasting, to deal with the price prediction issue of China’s table grape.

The approaches follows the framework of “decomposition and ensemble,” using ensemble empirical mode decomposition (EEMD) to optimize the conventional price forecasting methods, and, integrating the multiple linear regression and support vector machine to build a hybrid model which could be applied in solving price series predicting problems.

The proposed EEMD-ADD optimized hybrid model is validated to be considered satisfactory in a case of China’ grape price forecasting in terms of its statistical measures and prediction performance.

This study would resolve the difficulties in grape price forecasting and provides an adaptive strategy for other agricultural economic predicting problems as well.

The paper fills the vacancy of concerning researches, proposes an optimized hybrid model integrating both classical econometric and artificial intelligence models to forecast price using time series method.

The purpose of this paper is to provide reference for researchers by reviewing the research advances and trend of agricultural product price forecasting methods in recent years.

This paper reviews the main research methods and their application of forecasting of agricultural product prices, summarizes the application examples of common forecasting methods, and prospects the future research directions.

1) It is the trend to use hybrid models to predict agricultural products prices in the future research; 2) the application of the prediction model based on price influencing factors should be further expanded in the future research; 3) the performance of the model should be evaluated based on DS rather than just error-based metrics in the future research; 4) seasonal adjustment models can be applied to the difficult seasonal forecasting tasks in the agriculture product prices in the future research; 5) hybrid optimization algorithm can be used to improve the prediction performance of the model in the future research.

The methods from this paper can provide reference for researchers, and the research trends proposed at the end of this paper can provide solutions or new research directions for relevant researchers.

The purpose of this study is to propose a new consumer value segmentation method for low-dimensional dense market datasets to quickly detect and cluster the most profitable customers for the enterprises.

In this study, the comprehensive segmentation bases (CSB) with richer meanings were obtained by introducing the weighted recency-frequency-monetary (RFM) model into the common segmentation bases (SB). Further, a new market segmentation method, the CSB-MBK algorithm was proposed by integrating the CSB model and the mini-batch k-means (MBK) clustering algorithm.

The results show that our proposed CSB model can reflect consumers' contributions to a market, as well as improve the clustering performance. Moreover, the proposed CSB-MBK algorithm is demonstrably superior to the SB-MBK, CSB-KMA and CSB-Chameleon algorithms with respect to the Silhouette Coefficient (SC), the Calinski-Harabasz (CH) Index , the average running time and superior to the SB-MBK, RFM-MBK and WRFM-MBK algorithms in terms of the inter-market value and characteristic differentiation.

This paper provides a tool for decision-makers and marketers to segment a market quickly, which can help them grasp consumers' activity, loyalty, purchasing power and other characteristics in a target market timely and achieve the precision marketing.

This study is the first to introduce the CSB-MBK algorithm for identifying valuable customers through the comprehensive consideration of the clustering quality, consumer value and segmentation speed. Moreover, the CSB-MBK algorithm can be considered for applications in other markets.

The selection of purchasing channels by wine consumers indirectly affects buying experience and satisfaction, therefore, it is of great practical significance to study consumers' preference on channel selection. The purpose of this study is to investigate the current state of consumer selection for purchasing channel and the corresponding influencing factors.

A total of 2,976 valid questionnaires were collected by convenience sampling from 34 provinces, municipalities and autonomous regions of China in 2020 via the Internet, yielding a response rate of 82.2%. A categorical statistical approach was used to understand consumer's selection for each channel. Besides, binary logistic regression model was used to analyze the factors affecting consumers' channel selection.

The results show that Chinese wine consumers' main purchasing channels are as follows: supermarket/mall, wine specialty stores, comprehensive e-business flagship stores, comprehensive e-business individual stores, restaurants and short video and live streaming platforms. Estimation results showed that among the 12 influencing factors in 4 categories, consumers' education and some other factors significantly influenced consumers' decision on wine purchasing channels.

Limited by time and experimental conditions, this study did not analyze the trend of wine consumers' purchasing channel selection. Future work would concentrate on multi-year data and conduct longitudinal comparative analysis.

This study innovatively subdivides the currently popular wine sales channels in Chinese market and conducts research related to consumer channel selection. The results of the study can provide reference for wine producers and distributors to update their strategic layout and also help various channels to understand the characteristics of their customer groups for targeted marketing.

The purpose of this paper is to analyze and identify Chinese consumers’ behavior and preference characters toward table grapes, especially to explore the variance of consumption preference in recent five years.

Two representative China-wide questionnaire surveys were conducted by face-to-face and online questionnaire survey, respectively, in 2009 and 2014. Comparative study was adopted to dig the changes of consumers’ preferences and habits. ANOVA was adopted to explore the statistically difference among consumers’ behavior and preferences.

The results indicate that Chinese consumers are rational-motivation-driven grape consumers, they prefer to sweet taste, seedless and medium priced grapes. Safety and quality characters (clean appearance, freshness and taste) were ranked as the most important grape attributes. As a whole, it was found that consumers are more quality-focussed and safety-conscious five years later, so some variances was showed in their purchase habits and preferences, such as the choice of purchase place, attitude to special grapes and willingness to pay to safe and genetically modified grapes.

This research not only indicates some stable preferences of Chinese consumers toward table grape, but also finds out some significant changes in consumers’ behavior before and after five years based on a comparative study in 2009 and 2014.

The paper aims to understand the cost-benefits, financial performance and relationships of the key actors in the table grape supply chain by using value chain analysis method.

This study presents the result of analysis on financial performance of key actors in three table grape supply chains and the relationship among them using value chain analysis. The table grape supply chain was analyzed using data collected from a field survey in 5 regions of China. The key actors of these chains were interviewed. The costs, benefits, operational and financial performance of supply chain actors were analyzed.

The results show that, despite receiving the highest proportion of total net profit and making the highest value creation, vinegrowers are facing fluctuant and uncertain commercial returns due to the production and market risks, fluctuation of farm-gate price and the buyer dominant relationship with wholesalers. Moreover, although it is profitable to all key actors, table grape supply chain still faces several challenges including unorganized and dispersed production systems, power asymmetry and lack of information sharing, all of which are the barriers to the improvement of the whole chain performance and the long term sustainability of this important industry.

This paper not only helps to understand the relationship among the key actors and the critical factors impacting on the whole chain performance, but also offer useful guidance for the policy makers and key actors to achieve further improvement of the table grape supply chain performance and provide valuable implications for the future studies on agriculture products.

Chinese consumers have increased their consumption of grape products in recent years due to recognition of the nutritional value of grapes. At the same time, consumers are paying more attention to food safety because of the occasional occurrence of food safety scares. Based on a survey of consumers in Zhejiang, China, this paper aims to understand and provide information on consumers' perceptions of quality and safety for grape products, purchasing behavior, and willingness to pay (WTP) for grape products.

This paper presents the results of empirical research. The survey method was a direct (face‐to‐face) interview based on a standardized questionnaire. Consumers' perceptions of the quality and safety of grape products were examined, together with consumers' demographic characteristics.

The results indicate that quality and safety, rather than price, are considered the most important factors that affect consumers' purchasing decisions for grape products. Consumer's educational level, the average price of grapes and the perception of safety are the main factors that determine consumers' WTP for grape products. As price increases, the consumer's WTP for safe and high quality grapes decreases.

This research provides a chance to understand consumers' demand and WTP for the quality and safety of grape products in Zhejiang, China. Further understanding was gained regarding factors affecting consumers judging grape products, which in turn may influence their purchase decisions. The results also could guide grape growers to supply products that better meet consumers' needs. It is proposed that the Chinese government pay more attention to taking measures to improve the quality and safety of grape products.

The paper aims to understand consumers' perception and cognition toward table grapes, which focuses on the most important attributes, consumption consequences and personal values of grape consumption and the relations among them.

The study related grape consumption with consumers based on Means-end chain analysis. The data were gathered with both laddering interviews (n=37) and questionnaire survey returned by 843 individuals in 31 provinces of China. The data were firstly processed by SPSS software and descriptive statistics was adopted to evaluate consumers' awareness. Furthermore, MECanalyst software was opted for, to construct the hierarchical relations of table grape consumption.

The findings are that consumers consume grapes based on their functional cognition and sensory perception. The quality-orientated characters are deemed to be the most important grape attributes, healthy and practical function are the main expected benefits and desired values in table grape consumption.

The findings can provide some implications to producers, administers and operators in the grape industry and inspire them to design products that will better satisfy consumers. Stakeholders in grape industries should take measures to inform consumers of grape benefits so as to increase grape consumption and expand the market.

The paper fulfils the combinations of laddering interview and structured questionnaire to conduct the large-scale samples survey about consumers' perception based on MEC analysis. Findings from this study reinforce the importance of research into the relationship between products and consumers.