Model Of Consumer Preferences Research Articles

A Novel Consumer Preference Model Based on Blockchain and Topic Similarity Clustering in Cross-Border E-Commerce

Nowadays, countries in the world have frequent economic exchanges, and the scale of cross-border E-Commerce (CBEC) is getting larger and larger. CBEC refers to trading, payment, logistics, customs clearance, and other transactions. It also refers to the services between countries or regions through Internet technology and e-commerce platforms. In this article, the authors proposed a consumer preference model by blockchain and topic similarity clustering to obtain more consumer preference information. First, this article builds a blockchain-based consumer information collection system for CBEC to extract various features of consumers in CBEC. Secondly, by improving the performance of multiple characteristics of consumers, the accuracy of consumer preference prediction is improved. Finally, a method of consumer preference prediction based on topic similarity clustering is proposed to obtain consumers' purchase preference types. Experimental results show that the method can reach 84.3% of the H-mean, getting the best predictive performance and assisting CBEC by predicting consumer preferences.

Using consumer preference data in forecasting demand in apparel retailing

PurposeThis study was carried out to analyze the importance of consumer preference data in forecasting demand in apparel retailing.MethodologyTo collect preference data, 729 hypothetical stock keeping units (SKU) were derived using a full factorial design, from a combination of six attributes and three levels each. From the hypothetical SKU's, 63 practical SKU's were selected for further analysis. Two hundred two responses were collected from a store intercept survey. Respondents' utility scores for all 63 SKUs were calculated using conjoint analysis. In estimating aggregate demand, to allow for consumer substitution and to make the SKU available when a consumer wishes to buy more than one item in the same SKU, top three highly preferred SKU's utility scores of each individual were selected and classified using a decision tree and was aggregated. A choice rule was modeled to include substitution; by applying this choice rule, aggregate demand was estimated.FindingsThe respondents' utility scores were calculated. The value of Kendall's tau is 0.88, the value of Pearson's R is 0.98 and internal predictive validity using Kendall's tau is 1.00, and this shows the high quality of data obtained. The proposed model was used to estimate the demand for 63 SKUs. The demand was estimated at 6.04 per cent for the SKU cotton, regular style, half sleeve, medium priced, private label. The proposed model for estimating demand using consumer preference data gave better estimates close to actual sales than expert opinion data. The Spearman's rank correlation between actual sales and consumer preference data is 0.338 and is significant at 5 per cent level. The Spearman's rank correlation between actual sales and expert opinion is −0.059, and there is no significant relation between expert opinion data and actual sales. Thus, consumer preference model proves to be better in estimating demand than expert opinion data.Research implicationsThere has been a considerable amount of work done in choice-based models. There is a lot of scope in working in deterministic models.Practical implicationThe proposed consumer preference-based demand estimation model can be beneficial to the apparel retailers in increasing their profit by reducing stock-out and overstocking situations. Though conjoint analysis is used in demand estimation in other industries, it is not used in apparel for demand estimations and can be greater use in its simplest form.Originality/valueThis research is the first one to model consumer preferences-based data to estimate demand in apparel. This research was practically tested in an apparel retail store. It is original.

Visitor satisfaction with WMAs: A case study from Oklahoma

Analyzing visitor satisfaction has been an important practice for outdoor recreation managers, as satisfaction influences visitor behavior including the decision to visit in the future. This study analyzed visitors' level of satisfaction relative to their overall recreational experience at the last Oklahoma Wildlife Management Area (WMA) they visited. Satisfaction with WMA characteristics such as accessibility, facility availability and condition, scenery, abundance of wildlife, and a feeling of privacy and safety were also analyzed. To accomplish the study objective, WMA visitor surveys were administered among Oklahoma hunting and fishing license and conservation passport holders during the 2020–2021 hunting season. CUB (Covariates in a Uniform and shifted Binomial mixture) models were used to analyze the effects of visitors' characteristics on the level of satisfaction. Results suggested that compared to their non-consumptive counterparts, consumptive visitors (hunters and anglers) reported a higher level of satisfaction. In contrast, WMA visitors generally had lower satisfaction and seemed to express higher consternation about their privacy and safety than other aspects of visitations. This study demonstrates the applicability of a relatively new consumer preference model for analyzing visitor satisfaction within the outdoor recreation field. Furthermore, it provides valuable visitor satisfaction results to help management agencies analyze WMA visitation levels and determine the best management practices.

Nonlinear Time Series Fuzzy Regression for Developing Explainable Consumer Preferences’ Models Based on Online Comments

In modeling of consumer preferences based on online comments, nonlinearity and fuzziness exist in the relationship between the product design attribute and the consumer preference. On the other hand, consumer preferences are not static and changing over the time. Previous studies have proposed few approaches to model the variational consumer preferences based on online comments. However, the obtained models have black box problems and are not easy to be understood by humans as explicit models cannot be shown, which give rise to the research area of explainable artificial intelligence. Therefore, it is necessary to develop understandable and accurate consumer preferences’ models. In this article, a nonlinear time series fuzzy regression method is proposed to model the variational consumer preference based on online comments, which can generate a fuzzy dynamic consumer preference model with interactive terms, second-order and/or higher order terms. The datasets are first extracted from online comments using the sentiment analysis. Then, the polynomial structure of the fuzzy dynamic consumer preference model is established by using multiobjective chaos optimization algorithm. Then, the fuzzy regression method is used to allocate the fuzzy coefficients of each item of the model. Using sweeping robot as a case study, the validation results from the proposed approach are compared with those from fuzzy least squares regression, time series fuzzy least squares regression, fuzzy regression, and time series fuzzy regression, and it is found that the proposed approach performs better than the other four approaches in terms of mean relative errors and mean system credibility.

Model variational consumer preferences based on online reviews using sentiment analysis and PSO-based DENFIS approaches

Previous studies developed consumer preference models mainly through customer surveys, ignoring the variability of consumer preferences over time. In addition, it is difficult to obtain time series data based on the customer surveys. In recent years, some previous studies tried to analyse consumer preferences based on online reviews. However, they have not solved the problems of modelling variational consumer preference based on time series data with the consideration of the ambiguity of emotions expressed by customers in online reviews. To solve the above problems, this article proposes the particle swarm optimization (PSO) based dynamic evolving neural-fuzzy inference system (DENFIS) method to model variational consumer preferences based on online customer reviews. Using the time series data mined by the sentiment analysis method and the product attribute settings of the review products, the PSO-based DENFIS method is offered to dynamically model consumer preferences, in which PSO is used to adjust DENFIS parameters adaptively.

Data Marketing Optimization Method Combining Deep Neural Network and Evolutionary Algorithm

The design and optimization of personalized marketing strategy have become an important direction for e-commerce enterprises to meet the differentiated needs of consumers, innovate service content, and improve their core competitiveness. It is important to analyze the characteristics of personalized marketing in the e-commerce environment and to study and establish the optimization model of personalized marketing strategy and the model solving method, which are suitable for the application environment. In order to develop a new e-commerce model for consumers, innovate the online service content of enterprises, and improve consumer satisfaction, this paper improved two topological weight evolution methods of evolutionary neural networks and used them as tools for model solving, with the objectives of attracting potential consumers, improving consumer satisfaction, and maximizing revenue. The results of the experiments show that the proposed model is a good one. The experimental results show that the optimization model and model solving method proposed in this paper can efficiently build consumer demand and preference models from large-scale data and can help e-commerce enterprises to formulate accurate personalized promotion and pricing strategies to maximize their profits.

Consumer Preference Model for Coffee Beverages

Consumer Preference Model for Coffee Beverages

MODEL PREFERENSI KONSUMEN TERHADAP PERBANKAN SYARIAH

This study aims to build a model of consumer preference in choosing Islamic banking. This study uses a comparative study between people's understanding of Islamic banking and conventional banking which is influenced by several factors. The sample in this study amounted to 200 people who had different backgrounds. Existing data were analyzed using Structural equation models (SEM). The research results show that there is a level of understanding and preference of the community towards different Islamic banking which is influenced by several factors developed in this research model. In the next research, it is expected to conduct an assessment of the decision to choose Islamic and conventional banking products that concentrate more on specific products in Islamic banking. In addition, further research is expected to take into account environmental factors and government policies as well as other factors that have not been studied in this study so that it is expected to enrich the knowledge in the field of management, especially consumer preferences and decision making using Islamic banking products

ЗАРУБІЖНИЙ ДОСВІД ФІНАНСУВАННЯ СИСТЕМ ОХОРОНИ ЗДОРОВ'Я

The article is devoted to the study of foreign experience of health systems' financial support in different countries. The results of its comparative analysis are presented. The main features of the fundraising and financing in the health sector are emphasized. As a result of the comparison, it is proved that there is no one-size-fits-all model. Each country has to take into account the needs and opportunities for social development while building a system of economic relations. It is shown that in 2017 expenditures of Ukrainian consolidated budget for health care 3,4% of gross domestic product. Charitable funds and humanitarian aid providers were also engaged in health system financing. However, official funding coexists with shadow or informal out-of-pocket payments for healthcare services. It is established that the amount of healthcare financing in Ukraine is close to the European one, but the constitutional principle of equal access to health care for different segments of the population is violated. As a solution, we offer to use the experience of the developed countries with health systems recognized as successful. After all, the accumulated experience of different countries indicates the dependence between the effectiveness of the health care system and its ability to use resources rationally. An innovative model of health financing, providing the combined financial support, is offered and developed. It is concluded that in today's society there are four models of health financing. These are the consumer preference model (out-of-pocket health financing system), the Bismarck model (socially oriented health financing system), the Beveridge model (governmental health financing system) and the Semashko model (state budget with tight centralization of industry management). Each of the models analyzed has its advantages and disadvantages. Choosing a health financing model is a strategically important task for every country, which have to build a system of economic relations that takes into account objective needs and opportunities, and is aware of the ever-increasing economic and social value of a healthy society.

Mobile Payments—Why They Are So Successful?

Mobile payment service is exploding at an impressive rate as the global mobile payment revenue almost tripled over the last five years. But the reasons behind this phenomenon are still not clear. This paper thus focuses on identifying the key factors that contribute to the success of mobile payments. We firstly take a look at the current status of mobile payments. With this brief background in mind, we then address several questions associated with three key actors of mobile payment ecosystem: m-payment service provider, consumer, merchants. Specifically, the business model of mobile payments firms (including value proposition, market contribution, revenue source), consumer preferences and merchant. Conclusions: Mobile payment is gradually developing towards diversification and cross-border integration with highly sophisticated technology behind; the success of mobile payments application is based on aprofitable and valuable business model; in order to gain a competitive advantage when facing with fierce competition in the market, firms should respond best to consumer change and merchant feedback.

Beyond cost saving. Other factor consideration in online purchases of used electronic goods: a conjoint analysis approach

Abstract The market for second-hand and refurbished goods continues to record strong growth in most economies around the world. One obvious motivating factor in consumer choice of second-hand goods is price affordability. However, as the used market evolves, especially with the adoption of online e-commerce platforms, consumers’ inclination for used goods have also become complex. This paper investigates other appealing factors beyond price affordability (cost saving) in consumer choice of usable electronic goods. To do this, a consumer preference model was designed to reveal the key factors that drive consumer choices in the used electronic goods market. A case study using an emerging economy (Czech Republic) where used goods outlets are rampant coupled with the propensity for used electronic gadgets. The study adopted a simple random technique geared towards all the regions in the Czech Republic. The aggregate conjoint analysis method was used to model consumer preferences to determine the importance they attach to attributes most considered by consumers in the online used goods transactions. The result indicates that in respect of online purchases of used electronic appliances, a product’s ‘number of years in use’ is the next most important motivating factor after cost saving (price affordability). The study would help give a general insight into consumer preferences in the used electronic goods market.

Application of Aesthetic Principles to the Study of Consumer Preference Models for Vase Forms

The factors that affect a consumer when making a purchase decision include product function, quality, and price. Different products might feature similar functional performance although they are designed and manufactured by different vendors. The most decisive factor for a consumer in buying a product is no longer its physical functions. Nowadays, consumers care more about the aesthetics that a product can deliver. Designers should make any extra effort to improve the aesthetic design of their products. Therefore, it is critical for a designer to know the aesthetic tastes of his/her target consumer group in order to create products with the preferred style. Furthermore, the idea of design differentiation needs to be incorporated so that a product can present different aesthetic perceptions to consumers of different tastes. The objective of this study is to investigate consumers’ preference models from their preferred product forms. The aesthetic principles of Symmetry, Minimalism, and Cohesion are applied to the case study of the design of various vase forms. A quantitative approach of evaluating vase forms by the aesthetic principles is proposed. A conjoint analysis of the vase features and attributes was carried out in order to determine their correlation with consumer preferences. Consumers are classified into 6 groups of different aesthetic conceptions by cluster analysis. The consumers’ preference model can be determined by indices including Pearson’s R and Kendall’s tau. This approach provides designers with an effective way of determining the right direction for new form designs and renovation.

Agricultural research recommendation algorithm based on consumer preference model of e-commerce

Computer technology in agricultural e-commerce has great potentiality of development, but agricultural e-commerce platform market is still inefficient, making the agricultural recommendation not ideal. This paper studies the application of computer algorithms in agricultural products electricity supplier, introduces agricultural products with its e-commerce and the accurate mining algorithms, constructs the characteristic information of consumer preference model, and constructs the agricultural product feature model and recommendation algorithm. In four aspects, an in-depth study of the exact recommendation algorithm was conducted. Based on the characteristics of agricultural products, the optimization of the traditional product recommendation process is a great help to improve the recommendation accuracy.

A forward selection based fuzzy regression for new product development that correlates engineering characteristics with consumer preferences

Fuzzy regression models have commonly been used to correlate engineering characteristics with consumer preferences regarding a new product. Based on the models, product developers can determine optimal engineering characteristics of the new product in order to satisfy consumer preferences. However, they have a common limitation in that they cannot guarantee to include significant regressors with significant engineering characteristics or significant nonlinear terms. The generalization capability of the model can be reduced, when too few significant regressors are included and too many insignificant regressors are included. In this paper, a forward selection based fuzzy regression (FS-FR) is proposed based on the statistical forward selection to determine significant regressors. After the significant regressors are determined, the fuzzy regression is used to generate the fuzzy coefficients which address the uncertainties due to fuzziness and randomness caused by consumer preference evaluations. The developed model includes only significant regressors which attempt to improve the generalization capability. A case study of a tea maker design demonstrated that the FS-FR was able to generate consumer preference models with better generalization capabilities than the other tested fuzzy regressions. Also simpler consumer preference models can be provided for the new product development.

Retail Participation in Health Insurance: A Model of Consumer Preferences

Several countries are realizing that, with the increasing cost of health care, commercial health insurance is a key solution to meet health care financing needs of their citizens. India is one of the countries that allowed retail health insurance to begin operations, but the industry sector is still nascent in India with poor retail participation and hence there is an urgent need to understand consumer preferences, so as to significantly increase retail participation. This article has looked at the extant literature in modelling consumer and patient preferences in health industry, and this knowledge base was used to model consumer preferences in retail health insurance. The methodology included a detailed literature review and a primary data survey of 495 respondents, based on a systematic random sampling methodology. From a total of 58 variables, the variables were reduced using factor analysis and then the consumer preferences were modelled using structured equations modelling (SEM). The study provides key consumer perspectives that will be useful for researchers and practitioners facing similar health care market scenarios. The consumer preferences model will help the health stakeholders to design products that will be more suited to consumer needs and improve retail participation, which is a key building block to bridge the health financing gap that most of the countries are facing.

Effects of Brand on Consumer Preferences: A study in Turkmenistan

Using a measurement model of brand name and consumer preferences, this study aims to investigate the effects of brand name on consumer preferences in Turkmenistan. This study sought to investigate specifically, the influence of brand name on consumer’s preferences by utilizing structural equation modeling (SEM) technique. Moreover, all possible correlations between these dimensions or domains of brand name and consumer preferences are also empirically tested. In the context of the study, the introduced model was tested by a questionnaire instrument with 10 items excluding the demographic variables. A total of 422 completed copies of questionnaires were evaluated for analysis. The results suggest that, brand name variable have statistically significant relationships with consumer preferences variable. The findings of the study indicated positive correlations among the two variables with high factor loadings. Brand name of a product has significant impact on the overall preferences of the consumers.

Development and Validation of Producer and Consumer Preference Models for Rice Varieties in Bangladesh

Rice production depends on both producers’ and consumers’ preference. The consumption of rice depends on consumers’ taste and habits. The objectives of this study were to develop and validate mathematical models for producers’, consumers’ and producers-cum-consumers’ preference to rice varieties and to evaluate the factors affecting both producers’ and producer-cum-consumers’ decision on varieties for rice cultivation and can provide an indication of the factors affecting consumers’ preferences to rice varieties in Dhaka, Gazipur, Dinajpur and Bhola districts of Bangladesh. Chi-square (X2) tests were used to explore the significant difference of preferring rice varieties among the groups of people and compared the results with the proposed models for validation. Producers and producers-cum-consumers preferred BR11, BR22 and BRRI dhan32 in T. Aman; BR16, BRRI dhan28 and BRRI dhan29 in Boro and BR9, BR16 and BR20 in Aus seasons respectively. The specific grain quality characteristics such as whiteness, brokens, shape, amylose (%), aroma, cooking quality, hardness and chalkiness influenced the consumers and producers preference. Furthermore, pure consumers also preferred rice varieties on the basis of its tastiness and fineness.Bangladesh Rice j. 2015, 19(1): 63-71

A Stepwise-Based Fuzzy Regression Procedure for Developing Customer Preference Models in New Product Development

Fuzzy regression methods have commonly been used to develop consumer preferences models, which correlate the engineering characteristics with consumer preferences regarding a new product; the consumer preference models provide a platform, whereby product developers can decide the engineering characteristics in order to satisfy consumer preferences prior to developing the products. Recent research shows that these fuzzy regression methods are commonly used to model customer preferences. However, these approaches have a common limitation in that they do not investigate the appropriate polynomial structure, which includes significant regressors with only significant engineering characteristics; also, they cannot generate interaction or high-order regressors in the models. The inclusion of insignificant regressors is not an effective approach when developing the models. Exclusion of significant regressors may affect the generalization capability of the consumer preference models. In this paper, a novel fuzzy modeling method is proposed, namely fuzzy stepwise regression (F-SR), in order to develop a customer preference model which is structured with an appropriate polynomial, which includes only significant regressors. Based on the appropriate polynomial structure, the fuzzy coefficients are determined using the fuzzy least-squares regression. The developed fuzzy regression model attempts to obtain a better generalization capability using a smaller number of regressors. The effectiveness of the F-SR is evaluated based on two design problems, namely a tea maker design and a solder paste dispenser design. Results show that better generalization capabilities can be obtained compared with the fuzzy regression methods commonly used for new product development. In addition, smaller scale consumer preference models with fewer engineering characteristics can be obtained. Hence, a simpler and more effective product development platform can be provided.

Bayesian analysis of color preferences: An application for product and product line design

AbstractWhen choosing which colors to offer in their product lines, firms often rely upon consumer preference models that do not account for the heterogeneity of their target market and do not consider the trade‐offs consumers are willing to make for different color options. For this research we used visual conjoint analysis to assess preference for backpack color and then modeled respondent utilities with a Bayesian hierarchal multinomial logit model. This provided counter intuitive results in which product line color options are not additive but each color changes depending on the number of options the firm is willing to offer and that colors which seem to dominate secondary preferences within a target market may not be the best colors to choose for product line expansion. © 2015 Wiley Periodicals, Inc. Col Res Appl, 41, 445–456, 2016

Optimal pricing for new and remanufactured products

This work investigates the optimal pricing of new and remanufactured products using a model of consumer preferences based on extensive experimentation. The experimental investigation reveals two distinct segments of consumers. One segment is relatively indifferent between new and remanufactured products and displays high sensitivity to price discounts. The second segment shows strong preferences for new products—with an accompanying aversion to remanufactured products—and realtively low sensitivity to price discounts. The pricing analysis examines several scenarios involving a new product manufacturer, ranging from a simple monopolist scenario to a more complex scenario involving competition with third-party remanufacturers. In contrast to the usual finding that new product prices should decrease when competitive remanufactured products enter the market, the introduction of market segments reveals a robust finding across all scenarios: when remanufactured products enter the market, the optimal price of the new product should increase. Through appropriate pricing of new products, the OEM can mitigate the effects of cannibalization and increase profitability.