Consumer Preference Information Research Articles

A data-driven approach to predicting consumer preferences for product customization

Product customization is a complex task that relies heavily on consumer preferences. Eliciting these preferences can be challenging for firms looking to develop novel products and require significant investments of both time and effort. Prediction models can serve to replace traditional methods of understanding consumer preferences such as elicitation, focus groups or the designer’s intuition, while speeding up the production cycle and saving cost. Current prediction models generally focus on one specific product type and require large amounts of data or historical product data. The idea of predicting consumer preferences for products based on the product type and its features using a clustering approach has not been explored in literature. This paper presents a proof-of-concept consumer preference prediction and decision support model based on a data-driven approach to design for product customization. First, consumer preference information is crowdsourced using surveys with 307 individual responses that are collected for a data set of thirty-seven training products and three validation products. Second, clustering techniques are assessed for user-generated clustering variables along with features that are extracted with artificial intelligence (ChatGPT). A threshold metric is proposed to evaluate the accuracy of different clustering algorithms. Third, a recommendation model is developed for customization decisions, and it is validated with three different products with an average accuracy of 70%. Areas for future work to improve the accuracy and expand the scope of the model are discussed including the use of a larger training data set, different machine learning approaches, and the improved use of ChatGPT.

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.

Information sharing and production timing strategy in a platform‐based supply chain

AbstractThe e‐commerce platform has been adopting the agency mode to sell the products of the national brand manufacturer (NBM) while introducing its own store brand and obtaining the consumer's preference information (CPI) to reinforce the competitiveness of the store‐brand products. This paper investigates the platform's decisions on whether to share the CPI with the NBM and its impact on the production timing decisions of the platform and the NBM. The results show that the platform may benefit from CPI sharing which may also change the equilibrium production timing of the platform and the NBM from sequential decision to simultaneous decision. In addition, the equilibria of the joint decision between the CPI sharing and the production timing are obtained, which is related to the market acceptance uncertainties of the products and the competitiveness of the national brands.

Entrepreneurial orientation and vertical knowledge acquisition by smallholder agricultural firms in transitional economies: The role of interfirm collaboration in value-chains

This study extends the understanding of entrepreneurial orientation (EO), vertical knowledge acquisition, and actor collaboration to a “whole of value-chain” perspective in transitional economies by investigating a subsistence smallholder agricultural value-chain. It examines the relationships between EO and vertical knowledge acquisition and the role of value-chain collaboration. It also contributes to the understanding of entrepreneurship as an economic development tool by considering how EO moderates the effect of value-chain actor collaboration on acquiring market and consumer knowledge by subsistence upstream actors within value-chains. Data were collected through a survey of smallholder value-chain shifting from selling to local wet markets to selling to cattle collectors and traders and through a value-chain to retailers. The findings suggest that higher levels of EO by smallholders and SME agribusinesses enhance the acquisition of vertical knowledge such as market and consumer preference information throughout the value-chain, and interfirm collaboration positively moderates this relationship.

Pricing rules of Green Supply Chain considering Big Data information inputs and cost-sharing model

Big Data provide an opportunity for decision makers to implement green production and order plans with accurate and timely consumer demand information. Thus, many enterprise begun to invest in consumer preference information based on big data (CPIBD) and green production technology. These will add their extra costs, and cost-sharing model is an effective way to improve chain members’ benefits. However, in the new environment, how to price can improve their outcomes in different cost-sharing models? And which model is the best? Aims of this paper are to solve the proposed issues considering CPIBD input and the green technology R&D cost. We chose a green supply chain with one green manufacturer and one retailer as study subject. Then, based on game theory, we proposed three cost-sharing models, and their benefit functions were developed. Using the reverse induction, we analyzed and discussed the change rules of the retail price and the wholesale price with the product green degree and the unit CPIBD cost. Then, using Matlab2014, a numerical example based on actual data was implemented. Findings: (1) with the increase of the unit CPIBD cost, the optimal retail prices will grow in the proposed three models, and the change trend of the wholesale price has a relationship with the situation whether the retailer undertakes the unit CPIBD cost or the green technology R&D cost. (2) With the growth of the product green degree, the optimal retail price and the optimal wholesale price in the three models will decrease. (3) With the increase of the unit CPIBD cost and the product green degree, benefits of supply chain members in the proposed three models will reduce. If the retailer can undertake the unit CPIBD cost or the green technology R&D cost, benefits of supply chain will be higher.

Pricing and Carbon Emission Reduction Decisions Considering Fairness Concern in the Big Data Era

Facing the increase of consumer heterogeneous demand, green manufacturers and retailers need to get accurate and timely consumer preference information to better meet the consumer demand. In the big data era, a massive amount of demand data can be collected by advanced technologies, which can help manufacturers and retailers predict consumer preferences more accurately. However, big data technology also brings additional information cost to low-carbon supply chains. Therefore, it is necessary to rethink the pricing and carbon emission reduction decisions in the new situations. The purpose of this paper is to explore the influence of big data technology on pricing and carbon emission reduction decisions in low-carbon supply chain considering the retailers’ fairness concerns. By constructing Stackelberg game models, the optimal pricing and carbon emission reduction decisions are presented. Results indicate that the use of big data technology can increase both the profit of manufacturer and the utility of retailer, and can promote manufacturer to improve the carbon emission reduction level.

Pricing policies and coordination of low-carbon supply chain considering targeted advertisement and carbon emission reduction costs in the big data environment

In the Big Data era, targeted advertising occurs some changes in increasing the marketing accurateness and its valuation model. When the low-carbon product traders adopt targeted advertising, consumer preference information also gets their attentions. In this process, the pricing rules and coordination issues become their attention problems considering targeted advertising and consumer performance information in the new background, but efforts about it are still a gap. Thus, to explore these problem, a low-carbon supply chain with one retailer and one low-carbon manufacturer was chosen. Afterwards, the demand function was revised considering the targeted advertising and the carbon emission level, and four common cost-sharing models and its pricing rules were put forward and analyzed. Finally, revenue-sharing contract was used to coordinate supply chain in the proposed four models. Results indicates that sharing the BDAT inputs, the costs for obtaining consumer preference information and the carbon emission reduction costs can help the retailer obtain a low wholesale price and gain more benefits. When the value of revenue-sharing coefficient is bigger than 0.25 and lower than 0.5, the revenue-sharing contract can achieve the low-carbon supply chain coordinate.

Investment decision-making and coordination of a three-stage supply chain considering Data Company in the Big Data era

In the Big Data era, Data Company as the Big Data information (BDI) supplier should be included in a supply chain. To research the investment decision-making problems of BDI and its effects on supply chain coordination, a three-stage supply chain with one manufacturer, one retailer, and one Data Company was chosen. Meanwhile, considering the manufacturer contained the internal BDI and the external BDI, four benefit models about BDI investment were proposed and analyzed in decentralized and centralized supply chains. Meanwhile, a revenue sharing contract was used to coordinate the decentralized supply chain after investing in BDI. Findings: (1) the Big Data investment threshold of the Data Company was determined by the cost improvement coefficient, meanwhile, Data Company’s benefit was influenced by the consumer preference information conversion coefficient. (2) Whether the manufacturer was suitable to invest in BDI, it was influenced by the cost improvement coefficient. (3) When revenue sharing coefficient could meet a certain range, the revenue sharing contract could make the supply chain coordinate. Moreover, the benefits of supply chain members were same after coordination.

Delivering Person-Centered Care: Important Preferences for Recipients of Long-term Services and Supports

ObjectivesAlthough assessing individual consumer preferences are an important first step in providing person-centered care, the purpose of this study was to identify the top 10 shared preferences that are important to a majority of consumers receiving long-term services and supports. DesignA cross-sectional survey design was used. Setting and participantsPreference assessment interviews were conducted with 255 nursing home (NH) residents and 528 older adults receiving home and community-based services (HCBS). MeasurementsThe Preferences for Everyday Living Inventory (PELI) was used to collect consumer preference information. Two versions of the PELI were used—the PELI-NH for NH residents and the PELI-HC for clients receiving HCBS and analysis focused on 41 shared items between the 2 versions. All respondents answered PELI questions independently and rated the importance of psychosocial preference items on a scale from not at all to a lot/very important. ResultsTen preferences were shared as being important or very important by NH residents and older adults receiving HCBS. Most notably, more than 90% of respondents in each group rated “having regular contact with family” as an important priority. Having privacy, choices about what to eat, when to bathe, and activity options also were important preferences for a majority (77%-93%) in both settings. ConclusionProviders seeking to incorporate preference-based care can utilize study results as a foundation to incorporating important preferences into the care delivery process at the organizational level across care settings. For example, assessing all consumers on this core set of 10 shared preferences can assist with relationship building, transitions in care, and quality improvement. However, preferences with aggregate low-rated levels of importance in this study should not be discredited or eliminated. It is important for providers to understand the unique preference inventory of each older adult, which can then be targeted toward meeting goals for preference fulfillment. This can aid in bringing preferences into practice to improve the quality of care and quality of life to best meet the psychosocial needs of each person.

Predicting the Incremental Benefits of Online Information Search for Heterogeneous Consumers

ABSTRACTConsumer information search (CIS), the process by which a consumer browses and inspects a shopping environment for appropriate information to select a product or service from available options, has been a research focus in the context of online business. One of the key questions related to CIS is how to determine how much information to search (i.e., when to stop searching). Extensive literature on behavioral science has revealed that consumers often search either “too little” or “too much,” even with the help of existing consumer decision support systems (CDSSs). To address this issue, this article introduces a new method of CDSSs that provides effective estimation of incremental search benefits. The method, called the personalized distribution‐based prediction method (PDM), is developed from the perspective of machine learning and utilizes consumer preference information generated by collaborative filtering (CF) algorithms. In contrast to related methods that assume that all consumers follow the same distribution function in terms of product preference, the PDM method is designed to consider the diversified search behaviors of consumers through the incorporation of heterogeneous preference distribution functions. Experiments based on data provided by Netflix illustrate that the proposed method is effective and advantageous over existing applicable techniques. Theoretical analyses are also provided to explain the advantageous performance of PDM.

Short Selling and the Product Market: Evidence from SHO

I study the effect of short selling on product market performance using exogenous variations in short selling threats generated by the pilot program of Regulation SHO. I find that the decrease in short selling costs of firms in the pilot program has a positive and causal effect on firms' product market shares. The positive effect is stronger if firms face ex ante obstacle in effectively obtaining product related consumer preference information. The result is consistent with the product information transmission effect of short selling, i.e., short sellers transmit information about customers' preferences through their trading decisions. I find some weak evidence consistent with the disciplining effect of short selling.

Using Consumer Preference Information to Increase the Reach and Impact of Media-Based Parenting Interventions in a Public Health Approach to Parenting Support

Within a public health approach to improving parenting, the mass media offer a potentially more efficient and affordable format for directly reaching a large number of parents with evidence-based parenting information than do traditional approaches to parenting interventions that require delivery by a practitioner. Little is known, however, about factors associated with parents’ interest in and willingness to watch video messages about parenting. Knowledge of consumer preferences could inform the effective design of media interventions to maximize parental engagement in the parenting messages. This study examined parents’ preferred formats for receiving parenting information, as well as family sociodemographic and child behavior factors that predict parents’ ratings of acceptability of a media-based parenting intervention. An ethnically diverse sample of 162 parents of children ages 3–6years reported their preferences for various delivery formats for parenting information and provided feedback on a prototype episode of a video-format parenting program based on the Triple P Positive Parenting Program. Parents reported the strongest preference for self-administered delivery formats such as television, online programs, and written materials; the least preferred formats were home visits, therapists, and multiweek parenting groups. Parents’ ratings of engagement, watchability, and realism of the prototype parenting episode were quite strong. Parents whose children exhibited clinical levels of problem behaviors rated the episode as more watchable, engaging, and realistic. Mothers also rated the episodes as more engaging and realistic than did fathers. Lower income marginally predicted higher watchability ratings. Minority status and expectations of future problems did not predict acceptability ratings. The results suggest that the episode had broad appeal across groups.

Consumer preferences for cowpea in Cameroon and Ghana

AbstractConsumer preference information is essential to targeting research. This paper reports an effort of a multi‐disciplinary team to measure the market value of cowpea characteristics. Five samples were purchased once per month in seven markets in Ghana and Cameroon starting in September 1996. In the market, price and vendor characteristics were noted. In the laboratory, size of grains, testa color, testa texture, eye color and damage levels were recorded. A hedonic pricing regression model was used. Results indicate that grain size is the most important characteristic. Consumers seem more sensitive to bruchid (Callosobruchus maculates) damage than hypothesized. Cowpeas with white testa command a clear premium only in one of the Ghanaian markets. In Ghana, black eyes sell at a premium, but in Cameroon black eyes are discounted. In general, this study indicates that quality characteristics are very important in West African food markets. Even low income consumers are willing to pay a premium for products that match their preferences, and they are vigilant in identifying products that do not meet their standards. Purchasing samples on a regular basis and hedonic pricing offers a practical way for biological scientists and economists to work together to measure these consumer preferences.

Consumer preferences for cowpea in Cameroon and Ghana

Consumer preference information is essential to targeting research. This paper reports an effort of a multi-disciplinary team to measure the market value of cowpea characteristics. Five samples were purchased once per month in seven markets in Ghana and Cameroon starting in September 1996. In the market, price and vendor characteristics were noted. In the laboratory, size of grains, testa color, testa texture, eye color and damage levels were recorded. A hedonic pricing regression model was used. Results indicate that grain size is the most important characteristic. Consumers seem more sensitive to bruchid (Callosobruchus maculates) damage than hypothesized. Cowpeas with white testa command a clear premium only in one of the Ghanaian markets. In Ghana, black eyes sell at a premium, but in Cameroon black eyes are discounted. In general, this study indicates that quality characteristics are very important in West African food markets. Even low income consumers are willing to pay a premium for products that match their preferences, and they are vigilant in identifying products that do not meet their standards. Purchasing samples on a regular basis and hedonic pricing offers a practical way for biological scientists and economists to work together to measure these consumer preferences.

Experimental Strategies for Preference Information Acquisition: A Lattice Path Treatment

This paper investigates strategies for the acquisition of consumer preference information by a market researcher. Such preference information is obtained by the market researcher through an experimental process that entails polling consumers about their preference profiles with a view to completely specifying the preference order. In this paper, we limit the focus to one or two consumers.

A choice set-sensitive analysis of preference information acquisition about discrete resources

The theory of questionnaires deals with the design of experiments for eliciting information with a view to making a decision. The need for eliciting information arises in the problem of allocation of discrete and indivisible scarce resources among consumers by a coordinator. Resource allocation is carried out based on consumer preference information about the discrete resources. This information is acquired by the coordinator by administering a questionnaire, a sequential experimental process that allows the mapping of consumer preference profiles. Questionnaires are comprised of paired comparison experiments. The research questions posed in the paper are akin to those asked in the marketing literature of tailored interviewing: which experiment should be conducted first, and given the answer to the first, which experiment should be conducted next. The focus of this paper is the analysis of the process of preference information acquisition in a choice set-sensitive scenario. A general nonparametric measure of entropy is used for the analysis.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">&gt;</ETX>

A language and procedure for the sensory assessment of cox's orange pippin apples

AbstractTo relate consumer preference information with physico‐chemical data, necessary to understand the causes of flavour quality in foods and beverages requires detailed objective sensory descriptions to act as the link between the two. This paper describes the development of a language and assessment procedure for evaluating the sensory attributes of the apple cultivar, Cox 's Orange Pippin. It covers aspects of appearance, external and internal aroma, feel of the apple in the hand, taste, texture and aftertaste, terminology where possible being defined in terms of standards or by analogy with other foods. The problems associated with the sensory assessment of apples are also discussed.