Method for supporting evaluation and evaluation support system

Abstract

To quantitatively evaluate a degree to which various factors relating to human sensitivity, aroused from a form and other attributes possessed by an evaluation target, affect a sense of price that a human has for the evaluation target.SOLUTION: The evaluation support method includes: a presenting step of presenting an evaluation object to a subject; an acquiring step of acquiring answers of the subject to a group of questions; a model creating step of creating an association model including (1) first information relating to impression, (2) second information relating to attitude, and (3) third information relating to a sense of price; and an evaluating step of evaluating the degree to which at least one of the impression and the attitude relates to the sense of price. Evaluation words include words relating to the impression, words relating to the attitude, and words relating to the sense of price.SELECTED DRAWING: Figure 1

Description

[Technical Field]

[0001] The present invention relates to an evaluation support method and an evaluation support system for creating an association model that can identify the degree of relationship between elements such as perception, impression, and emotion regarding an evaluation target and price perception. [Background technology]

[0002] A method for analyzing the relationship between elements such as the form of an evaluation object and the human sensibility toward that evaluation object has been known. This method quantitatively links elements such as the form of an evaluation object with human sensibility by assuming a hierarchical perceptual and cognitive structure in which "people receive some kind of impression from external stimuli such as products and experiences, which in turn evokes certain emotions."

[0003] In addition, factors such as the shape of the object of evaluation and the human sensitivity to the object of evaluation are thought to affect the price that people perceive for the object of evaluation. For example, cited document 1 describes an invention that determines the selling price on the EC by taking into account specific human emotions. [Prior art documents] [Patent documents]

[0004] [Patent Document 1] Japanese Patent Application Publication No. 2020-177498 Summary of the Invention [Problem to be solved by the invention]

[0005] Although factors such as the shape of the object of evaluation and the human sensitivity to the object of evaluation are thought to affect the price perception of the object of evaluation, it has been difficult to quantitatively analyze the extent to which this influence can occur.

[0006] The present invention aims to quantitatively evaluate the degree to which each of the elements of an evaluation object, such as its form, the impression a person has of the evaluation object, and human sensibilities, including attitudes such as emotions, cognition, and behavioral tendencies, relate to the price perception a person has of the evaluation object. [Means for solving the problem]

[0007] In order to solve the above-mentioned problems, an evaluation support method according to one aspect of the present invention includes a presenting step of presenting an evaluation object having a plurality of characteristics to a subject; an acquisition step of acquiring, by a computer, answers from the subject to a group of questions about the evaluation object, the group of questions including a question sentence related to each of the plurality of characteristics and answer options to the question sentence, wherein at least one of the question sentence and the answer options includes at least one evaluation term related to the evaluation object; and an acquisition step of acquiring, by a computer, answers from the subject to a group of questions including each of the plurality of characteristics, words indicating factors or components common to the evaluation terms included in the group of questions, and ... and The method includes a model creation process for creating an association model including (1) first information regarding the impression the subject receives from the evaluation object based on each of the plurality of characteristics, (2) second information regarding the subject's attitude toward the evaluation object according to the impression, and (3) third information regarding the subject's price perception toward the evaluation object according to the impression or the attitude, and an evaluation process for evaluating the degree to which at least one of the impression and the attitude is related to the price perception based on the created association model, wherein the evaluation words include words related to the impression, words related to the attitude, and words related to the price perception.

[0008] In order to solve the above-mentioned problems, an evaluation support system according to one aspect of the present invention includes an acquisition unit that acquires answers from the subject to a group of questions that includes a question sentence related to each of a plurality of characteristics of an evaluation target that has the plurality of characteristics and answer options to the question sentence, and at least one of the question sentence and the answer options includes at least one evaluation term related to the evaluation target; and an acquisition unit that acquires answers from the subject to the group of questions that includes a question sentence related to each of the plurality of characteristics and answer options to the question sentence, and based on each of the plurality of characteristics, words indicating factors or components common to the evaluation terms included in the group of questions, and the answers from the subject, (1) and a model creation unit that creates an association model including (1) first information regarding the impression the subject receives from the evaluation object based on each of the information, (2) second information regarding the subject's attitude toward the evaluation object according to the impression, and (3) third information regarding the subject's price perception toward the evaluation object according to the impression or the attitude, and the model creation unit evaluates the degree to which at least one of the impression and the attitude is related to the price perception based on the created association model, and the evaluation words include words related to the impression, words related to the attitude, and words related to the price perception.

[0009] An evaluation support system capable of executing the evaluation support method according to each aspect of the present invention may be realized by a computer. In this case, the control program for the evaluation support system that causes the computer to operate as each part (software element) of the evaluation support system to realize the evaluation support system, and the computer-readable recording medium on which the control program is recorded, also fall within the scope of the present invention. [Effects of the Invention]

[0010] According to one aspect of the present invention, it is possible to quantitatively evaluate the degree to which human sensibilities, such as elements that indicate the characteristics of the object of evaluation, such as the shape of the object, or the impressions and attitudes that people have toward the object of evaluation, are related to the price perception of the object of evaluation. [Brief explanation of the drawings]

[0011] [Figure 1] 1 is a flowchart illustrating an example of the flow of an evaluation support method according to the first embodiment of the present disclosure. [Figure 2] FIG. 1 is a diagram illustrating an outline of an association model. [Figure 3] 1 is a block diagram showing an example of the configuration of an evaluation support system according to a first embodiment. [Figure 4] FIG. 10 is a diagram showing a specific example of an association model. [Figure 5] 10 is a flowchart illustrating an example of the flow of an evaluation support method according to a second embodiment of the present disclosure. [Figure 6] FIG. 10 is a block diagram showing an example of the configuration of an evaluation support system according to a second embodiment. DETAILED DESCRIPTION OF THE INVENTION

[0012] [Embodiment 1] Hereinafter, one embodiment of the present invention will be described in detail.

[0013] <Outline of evaluation support method> In this embodiment, a method for creating an association model for analyzing factors related to a subject's price perception of an evaluation target and the degree of association between price perception and each factor using a group of questions will be described.

[0014] The evaluation object is an object to be evaluated by a subject. The evaluation object may be the object itself, such as a product, or an experience related to the object being evaluated (e.g., an image or description of a product, or a service received in a certain environment). The subject is presented with the evaluation object and answers questions about the evaluation object.

[0015] As used herein, elements refer to value judgment elements such as a subject's impression of the evaluation object, their cognition, emotions, and behavioral tendencies toward the evaluation object, and their perceived value toward the evaluation object, and are expressed by words that represent these elements. Elements refer to the subject's responses to a group of questions containing evaluation terms related to the evaluation object, or factors or components derived by statistically analyzing those responses, and may be expressed by words that represent the layers of the association model or words included in each layer. Elements used in the method according to the present disclosure may include impression elements, attitude elements, and value judgment elements. Elements used in the method according to the present disclosure may also include characteristic elements that indicate the characteristics of the evaluation object. Elements used herein are numerical values ​​or words, and are labeled with words that represent each element.

[0016] The multiple features of the evaluation object are features related to the five senses and other senses, and the feature elements may be either measurement results related to the properties of the evaluation object or evaluation results obtained by evaluating the features based on predetermined criteria. Each feature is represented by a corresponding term. For example, the features of the evaluation object may be morphological characteristics of the evaluation object, such as the shape, size, weight, color, temperature, friction, volume, and gloss of the evaluation object, which can be evaluated by measuring the evaluation object. The features of the evaluation object may also be evaluation results indicating whether the characteristics of the evaluation object correspond to one of several pre-defined levels, or whether they are present or absent. Note that the features are not limited to morphological characteristics of the evaluation object. For example, the features may be the sound emitted by the evaluation object, the taste of the evaluation object, the smell of the evaluation object, information recorded on the evaluation object, etc.

[0017] Impression elements are elements that indicate the impression that a subject has of an evaluation object. Impression elements may include instinctive impressions based on the appearance, feel, etc. of the evaluation object, and elements that indicate impressions of functions such as ease of use and performance. Impression elements may also include elements that indicate introspective impressions linked to personal memories, imagination, or associations. Furthermore, impression elements may be classified into elements that indicate impressions that are relatively closer to the subject's emotions (higher-order impressions) and elements that are relatively closer to the formal characteristics of the evaluation object (lower-order impressions). Attitude elements are elements that indicate the subject's attitude toward the evaluation object. Attitude elements may include elements that indicate the subject's cognition of the evaluation object, the emotions the subject has toward the evaluation object, and the behavioral tendencies of the subject evoked by the evaluation object.

[0018] Value judgment factors are factors that indicate the subject's perception of the price of the evaluated item. Value judgment factors are expressed by the manufacturer's suggested retail price or expected retail price of the evaluated item based on the subject's response, or the willingness to pay amount, which indicates how much the subject is willing to pay for the evaluated item.

[0019] Furthermore, the factors used in the method according to the present disclosure may further include other factors. For example, the factors may include factors indicating economic situation, recommendations from others, strength of interest in the product, confidence in being able to use the product, etc. These may be expressed as factors indicating "subjective norms" or "sense of behavioral control" proposed in the theory of planned behavior in consumer behavior research.

[0020] 1 is a flowchart illustrating an example of the flow of an evaluation support method according to the first embodiment of the present disclosure. As shown in FIG. 1, the evaluation support method according to the present disclosure includes a presentation step (S1), an acquisition step (S2), a model creation step (S5), and an evaluation step (S6). The evaluation method may be performed using a computer such as a general-purpose computer.

[0021] The presenting step is a step of presenting an evaluation target having a plurality of characteristics to a subject. The acquiring step is a step of acquiring answers to a plurality of subjects as a result of presenting a group of questions related to the evaluation target to the subjects. The subjects may be people who are in regular contact with the evaluation target, or people who are desired to be exposed to the evaluation target.

[0022] The question includes at least one evaluation term related to the evaluation object in at least one of the question sentence and answer options. The evaluation term is a word used to evaluate the evaluation object, and is selected in advance by the evaluator according to the evaluation object, and is related to the characteristics, impression, attitude when interacting with the evaluation object, and the price perception of the evaluation object, and is pre-classified according to the nature of the word. For example, the evaluation term includes words related to the impression of the evaluation object, words related to the attitude toward the evaluation object, and words related to the price perception of the evaluation object.

[0023] The extraction process is a process of extracting elements from the answers acquired in the acquisition process to be used in the association model created in the subsequent model creation process. In the extraction process, factors or components common to the evaluation terms included in the group of questions are extracted for each layer. The layers are multiple layers that make up the association model created in the model creation process, and each layer and element is classified in advance by the properties of the words used to classify the evaluation terms, depending on the words that represent each layer and element.

[0024] The model creation step is a step of creating an association model using the answers acquired in the acquisition step and elements extracted based on the answers, and acquiring information indicating the relationships between elements.

[0025] FIG. 2 is a diagram illustrating an overview of an association model created by the method according to the first embodiment of the present disclosure. As shown in FIG. 2, the association model (reference numeral 201 in FIG. 2) created by the method according to the first embodiment of the present disclosure includes the first information, second information, and third information shown in the following (1) to (3). The first information (reference numeral 202 in FIG. 2) is related to impression elements, the second information (reference numeral 203 in FIG. 2) is related to attitude elements, and the third information (reference numeral 204 in FIG. 2) is related to value judgment elements. When the attitude elements include elements indicating cognition, emotion, and behavioral tendencies, these elements may be arranged at different levels within the attitude element hierarchy or at the same level. Furthermore, a hierarchical structure may be assumed within a single element. Furthermore, the impression elements may be divided into elements indicating low-level impressions and elements indicating high-level impressions, and a hierarchical structure from low-level impressions to high-level impressions may be shown. Furthermore, as shown in FIG. 2, the association model may include feature information (reference numeral 205 in FIG. 2) shown in the following (4).

[0026] (1) First information: Information about the impression that the subject receives from the evaluation target based on each of the multiple characteristics. (2) Secondary information: Information about the subject's attitude toward the subject based on characteristics or impressions (3) Third information: Value judgments of the subject based on characteristics, impressions, or attitudes, such as price perceptions (4) Feature information: Information indicating each of the multiple features possessed by the evaluation target, related to the feature elements The association model may also include a fourth piece of information other than the characteristic information and the first to third pieces of information, such as subjective norms, a sense of control, or the environment or situation in which the subject finds himself, which are determined independently of the subject's sensibilities. The environment or situation in which the subject finds himself may be, for example, the subject's prior knowledge of the evaluation target, the subject's interest in the evaluation target, or the subject's purchasing ability.

[0027] Furthermore, the association model includes information indicating the degree of association between elements included in lower-level information and elements included in higher-level information.

[0028] Specifically, the association model includes information indicating the degree of association between a feature element of the evaluation target included in the feature information and at least one of an impression element included in the first information, an attitude element included in the second information, and a value judgment element included in the third information. Note that feature information is not required in the association model. The association model also includes information indicating the degree of association between an impression element included in the first information and at least one of an attitude element included in the second information and a value judgment element included in the third information. Furthermore, the association model includes information indicating the degree of association between an attitude element included in the second information and a value judgment element included in the third information. Furthermore, if the association model includes fourth information, the association model may also include information indicating the degree of association between an element included in the fourth information and at least one of a value judgment element, an attitude element, and an impression element.

[0029] The evaluation step is a step of evaluating the degree to which each of a plurality of characteristics, impressions, and attitudes are related to price perception based on the created association model.

[0030] It has been known that human sensibility, such as the characteristics of an evaluation target and the impression and attitude toward those characteristics, is related to the value judgment of the evaluation target. However, it has not been possible to quantitatively evaluate the relationship between these elements that indicate sensibility. For example, the feeling that a person has about a certain product that is an evaluation target, such as "good" or "cold," can affect the price perception that a person has about the product, but it has not been known how these elements specifically relate to price perception.

[0031] The association model created using the model creation method of the present invention includes information indicating the results of evaluating the degree to which each of the multiple features of the evaluation object and the impressions and attitudes toward the evaluation object relate to the price perception of the evaluation object. Furthermore, by applying this information to a specific monetary amount, it is possible to express the specific amount by which the price perception of the evaluation object is determined based on people's impressions and attitudes. Using such an association model, it is possible to grasp the degree to which factors such as the features of the evaluation object and the impressions and attitudes that subjects receive from the evaluation object relate to the price perception of the evaluation object. Furthermore, because the degree of relationship to price perception is evaluated using a common measure, monetary amount, it is possible to grasp the specific value of each factor in relation to price perception. Therefore, the association model allows the degree of relationship between each of multiple factors and price perception to be grasped using a unified standard, price.

[0032] Based on the association model created by the method disclosed herein, product designers can analyze which elements of the impressions and attitudes evoked by the evaluation object can improve or decrease the price perception of the evaluation object. Product designers can also understand the characteristics that improve consumers' price perception based on the association model and use this information in product design. Therefore, the evaluation support method disclosed herein can also be applied to product design methods.

[0033] <Evaluation Support System 100> The evaluation support method according to this embodiment may be performed by a system capable of executing each step of the method. The evaluation support method according to this embodiment is executed by one or more computers (including tablet terminals and smartphones) capable of executing each step of the method. FIG. 3 is a block diagram showing an example of the configuration of an evaluation support system 100 capable of executing the evaluation support method according to the first embodiment. As shown in FIG. 3, the evaluation support system 100 includes an input device 1 and a model creation device 2. The input device 1 and the model creation device 2 may be separate computers or may be a single computer. For example, the functions of the input device 1 and the model creation device 2 may be implemented by a single computer. The functions of the model creation device 2 may also be implemented by multiple computers connected to each other so that they can communicate with each other. The input device 1 may also include a display unit (corresponding to the display unit 22 of the model creation device 2, described below). The input device 1 is a device for inputting information indicating a response to a question by a subject. The response may be input using the input device 1 by the subject who is asked the question, or by an analyst who hears the subject's response.

[0034] 3, the model creating device 2 includes a control unit 21 and a display unit 22. The model creating device 2 may also include a storage unit 23 that stores various information used in the model creating device 2, such as information indicating a group of questions, information indicating input answers, and information indicating analysis results based on the answers. Answers obtained by presenting a group of questions are input to the model creating device 2. The answers may be input using an input device 1 such as a keyboard.

[0035] The control unit 21 includes an acquisition unit 211, an analysis unit 212, and a model creation unit 213. The acquisition unit 211 acquires information indicating answers to a group of questions. The analysis unit 212 performs analysis based on the acquired answers and extracts factors or component loadings or scores representing the evaluation terms included in each stratum, or elements representing the evaluation terms included in each stratum. Details of the analysis performed by the analysis unit 212 will be described later.

[0036] The model creation unit 213 selects components of the association model based on the analysis results by the analysis unit 212, and creates the association model. Details of the method for creating the association model will be described later. The model creation unit 213 displays the created association model on the display unit 22. Note that the model creation unit 213 may transmit the association model to another device connected to the model creation device 2 so that it can communicate with the model creation device 2.

[0037] The association model created by the model creation unit 213 includes the first information to the third information. The association model may also include feature information. The association model may also include fourth information. The association model also includes information indicating the degree of association from an element included in one layer to an element included in a higher layer in the association model. For example, the association model includes information indicating the degree of association of the feature element of the evaluation target with the impression element, the degree of association of the impression element evoked by the evaluation target with the attitude element or the value judgment element, and the degree of association of the attitude element with the value judgment element.

[0038] As described above, the model creation device 2 can create an association model that can grasp how much the characteristics of the evaluation object and the impressions and attitudes evoked by the evaluation object correspond to in the subject's price perception of the evaluation object, based on the subject's answers to a group of questions about the evaluation object. A person who has checked the association model, such as a product designer, can analyze which elements of the characteristics of the evaluation object and the impressions and attitudes evoked by the evaluation object relate to the consumer's value judgment and how they relate to the consumer's value judgment.

[0039] <Evaluation support method> The evaluation support method will be described in detail below, again with reference to FIG. 1. As an example, the evaluation support method is performed using a model creation device 2. Note that a part of the evaluation method may be performed manually. For example, in the evaluation support method, the model creation step may be performed by an analyst who has acquired the model in the acquisition step and understood the analysis results in the analysis step.

[0040] First, in the evaluation support method, the evaluation target is presented to a subject who will evaluate the evaluation target (S1: presentation step). In the presentation step, the evaluation target is presented to the subject while the subject is imagining a situation in which the subject is considering purchasing the evaluation target. In addition to the purchase of the evaluation target, the presentation step may also involve imagining other situations related to the evaluation target. For example, in the presentation step, the subject may be asked to imagine a situation in which they "choose their own smartphone cover within a budget of x yen," and then a cover attached to a smartphone mockup may be presented as the evaluation target.

[0041] "Presenting an evaluation target" may mean showing the actual evaluation target or allowing the subject to have an experience related to the evaluation target. For example, "presenting an evaluation target" may mean having the subject experience something that includes showing a photograph of the evaluation target, letting the subject hear sounds emitted from the evaluation target, letting the subject smell the evaluation target, letting the subject eat the evaluation target, letting the subject touch the evaluation target, or presenting information recorded on the evaluation target. Specifically, if the evaluation target is wood, the subject may be shown the actual wood and allowed to touch it. Also, if the evaluation target is a music CD, the presentation step may show the appearance of the evaluation target and allow the subject to listen to the music recorded on the CD.

[0042] The subject may be presented with one or more evaluation targets. For example, the subject may be asked to imagine a situation in which he or she must compare evaluation targets, and multiple comparable evaluation targets may be presented. Alternatively, the subject may be presented with one item, multiple conditions related to the item may be set, and each of these multiple conditions may be multiple evaluation targets. The order in which these evaluation targets are presented may be different (random) for each subject, or may be the same order.

[0043] After the presentation step, a set of questions related to the evaluation object is presented to the subject, and a response is obtained. The set of questions includes a plurality of sets of questions, each set including a question sentence related to the impression / attitude toward the evaluation object and the price perception toward the evaluation object, and an answer option for the question sentence. Furthermore, the question includes at least one evaluation term related to the evaluation object in at least one of the question sentence and the answer option. The method of presenting the set of questions is not particularly limited. For example, the set of questions may be displayed on a presentation unit such as a display, may be printed on paper, or may be communicated to the subject orally.

[0044] The evaluation words included in the question set include (1) words related to the subject's impression of the evaluation object, (2) words related to the subject's attitude based on the impression, and (3) words related to the subject's value judgment of the evaluation object based on the impression or attitude, such as words indicating the perceived value of the evaluation object. Furthermore, if characteristic elements are collected by asking questions to respondents, the evaluation words included in the question set may include words related to the characteristics of the evaluation object. Furthermore, as fourth information, words related to information determined unrelated to the subject's sensibilities or other information such as information about the subject's environment or situation may also be included.

[0045] The evaluation words can be collected through preliminary interviews with subjects, can be taken from existing related literature, or can be decided through consensus among the parties involved. The interviews can be conducted in any way, or can follow existing methods such as the evaluation grid method.

[0046] Words relating to impressions may include words relating to high-level impressions such as "beautiful," "cool," and "novel," as well as words relating to low-level impressions such as "smooth," "silky," and "hard to scratch."

[0047] Emotional words may be words such as "like," "dislike," "enjoy," "pleasant," and "arousing." Perception words may be words such as "good," "bad," "matches the image," and "affordable." Behavioral tendency words may be words such as "want," "don't want," "want to buy," and "want to go."

[0048] Words indicating a value judgment may be, for example, words asking the subject about the manufacturer's suggested retail price or retail price of the item being evaluated, or about the subject's willingness to pay, which indicates how much the subject would be willing to pay for the item being evaluated.

[0049] The method of answering the questions may be a method in which the subject selects a number from any range, such as 1 to 5, to indicate the relevance of the evaluation word to the evaluation target. For example, when a question regarding the subject's cognition is presented, such as "Do you think the evaluation target is good?", numbers from 1 to 5 may be presented as answer options. The subject may select 1 if they perceive the evaluation target as "bad" and 5 if they perceive the evaluation target as "good." Furthermore, if the subject perceives the evaluation target as somewhere between "good" and "bad," they may select 2 to 4 depending on the degree of goodness. Note that the numerical value does not necessarily have to correspond to the quality of the evaluation. The subject may select 5 if they perceive the evaluation target as good and 1 if they perceive the evaluation target as bad, or 5 if they perceive the evaluation target as good and 1 if they perceive the evaluation target as bad. Furthermore, the question format may present two opposing words as described above, or may be a format in which a single word is presented and the subject responds numerically to the degree of agreement between that word and the evaluation target.

[0050] Alternatively, the subject may answer the question by selecting one of a plurality of words for a question containing one evaluation term for the evaluation target. For example, if the question "Do you think the evaluation target is good?" is presented as a question regarding the subject's cognition, the answer options may be "bad," "slightly bad," "average," "slightly good," or "good." The subject may answer by selecting one of the options based on their own sensibilities.

[0051] In addition, the method of answering questions regarding value judgments may be to have the subject answer a numerical value indicating the price of the item being evaluated, or to present multiple price perception options and have the subject select one.

[0052] When multiple evaluation targets are presented to the subject, the subject may respond to each of the multiple evaluation targets, or may respond to the entire group of multiple evaluation targets at once.

[0053] The acquisition unit 211 acquires answers resulting from presenting a set of questions to multiple subjects (S2: acquisition step). Note that the presentation step and acquisition step may be repeated multiple times in one association model creation. When the presentation step and acquisition step are performed multiple times, the set of questions and answer options presented in the presentation step may be the same or different.

[0054] The acquisition unit 211 identifies evaluation terms included in the group of questions, classifies the evaluation terms into layers, and outputs information indicating the classification results and information indicating the answers to the analysis unit 212. Here, layers indicate word properties such as price perception, attitude, impression (high-level impression), and impression (low-level impression). Classifying evaluation terms into layers means classifying the evaluation terms into word properties. Layers are used to represent each element in a hierarchical structure in the association model described below. In the association model, a hierarchical structure may be assumed that is divided into four layers, for example, a layer including characteristic elements, a layer including impression elements, a layer including attitude elements, and a layer including value judgment elements. Furthermore, the layer including impression elements may be further divided into two layers: a layer including high-level impression elements and a layer including low-level impression elements. Furthermore, the layer including attitude elements may be divided into three layers: a layer including cognitive elements, a layer including emotional elements, and a layer including elements indicating behavioral tendencies.

[0055] It may be predetermined which element each evaluation word corresponds to and which layer it is classified into. The acquisition unit 211 may output the pre-classified evaluation words to the analysis unit 212 together with the response information.

[0056] Upon acquiring information indicating the evaluation terms classified by layer, the analysis unit 212 extracts factors or components common to the evaluation terms included in one layer (S3: extraction step). The extraction of factors or components is performed, for example, using statistical analysis. The analysis unit 212 performs this extraction for each layer. The term indicating the factor or component may be one of the evaluation terms included in the layer, or another term representing the evaluation terms included in the layer. Furthermore, one factor or component may be extracted for one layer, or multiple factors or components may be extracted for one layer. The extraction step is performed only when many evaluation terms are acquired, and may be omitted if few evaluation terms are acquired. The number of evaluation terms may be appropriately determined by an analyst involved in creating the association model. For example, if the analyst determines that there are many evaluation terms, the analysis unit 212 accepts instructions from the analyst and extracts factors or components common to the evaluation terms included in one layer. However, if it is determined that there are few evaluation terms, the extraction step may be omitted.

[0057] If all evaluation terms contained in a group of questions, or if feature elements are included, all of those feature elements are used as is to create an association model, the number of terms (elements) may be so large that it may be difficult to analyze the relationships between layers. By having the analysis unit 212 extract factors or components from the evaluation terms, it is possible to reduce the number of elements used to create an association model and create an association model that makes it easier to analyze the relationships between elements. Here, factors refer to elements that influence the events and concepts expressed by evaluation terms and are extracted using factor analysis. Furthermore, components refer to elements expressed by a set of events and concepts expressed by evaluation terms and are integrated using principal component analysis.

[0058] In particular, for evaluation terms that indicate impressions, which tend to be numerous, a small number of latent factors may be extracted using factor analysis, quantification theory type III, or the like. This allows for the construction of a more reliable model by eliminating similarities between evaluation terms. For example, when factor analysis is performed, maximum likelihood method or the like can be used for factor extraction, and promax rotation or the like can be used for factor rotation.

[0059] The analysis unit 212 performs an analysis using information indicating the factors or components and information indicating the subject's responses, and quantitatively determines a loading that indicates the degree of influence that the evaluation terms included in the stratum have on the factor or component that represents the stratum, and a score that indicates the degree of influence that the evaluation target has on the factor or component (S4). Here, the analysis may be performed using information indicating the characteristics of the evaluation target. The analysis unit 212 outputs information indicating the analysis results to the model creation unit 213.

[0060] For example, the analysis unit 212 performs factor analysis on the response data obtained from the subjects, and extracts the factor loadings and factor scores of factors that represent the evaluation words included in each stratum by performing a process of expressing the response data matrix as the product of a factor loading matrix and a factor score matrix.

[0061] Furthermore, for example, the analysis unit 212 may perform principal component analysis instead of factor analysis to determine principal component loadings and scores, or may employ a method such as quantification theory type III depending on the nature of the data (scale level) of the elements. Alternatively, as an alternative to these methods, various cluster analyses (k-means or hierarchical clustering) or multidimensional scaling may be used to summarize multiple elements into a small number of representative elements.

[0062] Upon receiving the analysis results from the analysis unit 212, the model creation unit 213 creates an association model based on the analysis results (S5: model creation step). The analysis results from the analysis unit 212 include, for example, loadings indicating the degree of influence between the evaluation terms and the common factors or principal components obtained based on the answers from the subjects, scores indicating the degree of influence between the evaluation target and the common factors or principal components, or representative values ​​of answers to multiple questions with similar answering tendencies from the subjects. Note that the analyst may assign appropriate words to the common factors, principal components, or representative values. The model creation unit 213 obtains information indicating words corresponding to the common factors, principal components, or representative values ​​from, for example, the input device 1 operated by the analyst.

[0063] The model creation step can also be expressed as a step of creating an association model containing information, expressed in words, indicating the relationship between the elements extracted for each layer, based on the extracted elements (common factors, principal components, or representative values) and the responses from the subjects. The association model created by the model creation unit 213 is a hierarchical model created using any of statistical modeling methods such as multiple regression, covariance structure analysis, and Bayesian network, or other modeling methods such as rough sets and network analysis.

[0064] For example, the model creation unit 213 performs, for example, structural analysis of covariance (SEM) using the elements (common factors, principal components, or representative values) identified by the analysis unit 212. As a result, the model creation unit 213 determines a value indicating the degree of association between each element and a higher-level element.

[0065] For example, based on the analysis results of the analysis unit 212, the model creation unit 213 identifies the degree of association between each of the elements included in the layer indicating characteristics and the elements included in the layer indicating impressions. Similarly, the model creation unit 213 identifies the degree of association between impression elements and attitude elements, and between attitude elements and value judgment elements. Note that the linking of one element to another performed by the model creation unit 213 is not limited to the above. For example, the model creation unit 213 may identify the degree of association between each of the elements included in the layer indicating characteristics and the elements included in the layer indicating attitudes, or the elements included in the layer indicating value judgments, which is located at a higher level. Alternatively, when impression elements are divided into lower-level impression elements and higher-level impression elements, the model creation unit 213 may identify the degree of association between lower-level impression elements and higher-level impression elements, or the elements included in the layer indicating attitudes or value judgments, which is located at a higher level.

[0066] Based on the results of the above identification, the model creation unit 213 creates an association model including information indicating the relationships between multiple elements. The association model created in this way includes (1) information indicating impression elements, (2) information indicating attitude elements, and (3) information indicating value judgment elements. The association model may also include information indicating each of multiple features as feature information. The association model may also include other information as fourth information.

[0067] The information indicating impression elements may include information indicating low-order impression elements and high-order impression elements, and the information indicating attitude elements may include information indicating cognitive elements, emotional elements, and behavioral tendency elements.

[0068] Furthermore, the association model includes information indicating the degree of relationship between elements included in a lower layer and elements included in a higher layer. For example, in the association model, the feature information includes information indicating each of a plurality of feature elements and the degree of association with an impression element. Furthermore, the first information includes information indicating each of the impression elements and the degree of association with an attitude element. Furthermore, the second information includes information indicating each of the attitude elements and the degree of association with a value judgment element. Note that the first information, the second information, and the feature information may further include information indicating the degree of association with other elements. For example, the feature information may include information indicating the degree of association between each of a plurality of feature elements and an attitude element or a value judgment element.

[0069] Furthermore, when the association model includes the fourth information, the fourth information may include information indicating the degree of association between elements other than those included in the first to third information and the characteristic information and the value judgment element, the attitude element, or the impression element. For example, the fourth information may include an element indicating the subject's economic situation and financial resources and information indicating the degree of association with the value judgment element.

[0070] Furthermore, the model creation unit 213 evaluates the degree to which each of the multiple features, impression, and attitude elements relates to the value judgment elements based on the created association model (S6: evaluation process). Given a specific price of the evaluation target, the degree to which at least one of the impression and attitude elements relates to price perception may be expressed as a numerical value indicating the extent to which the price of the evaluation target varies due to the element. The degree may also be expressed as a value indicating the price of the evaluation target after varying the price due to the element, or as a numerical value representing the proportion of the price of the evaluation target that the element accounts for, expressed in terms of price. The specific price of the evaluation target may be, for example, the subject's predicted manufacturer's suggested retail price or retail price of the evaluation target, or a willingness-to-pay amount indicating how much the subject is willing to pay for the evaluation target. For example, if the price of the evaluation target is 500 yen, the degree to which any impression element relates to price perception may be expressed as a value such as +20 yen, 520 yen, or 20 yen. This makes it possible to understand that the impression factor will increase the price perception of the evaluation target by 20 yen, or that the impression factor will account for 20 yen of the price perception of the evaluation target.

[0071] The model creation unit 213 may also confirm the validity of the model based on several goodness-of-fit indices obtained as a result of the statistical modeling method. In this case, the model creation unit 213 may specify the coefficient of determination of each element if a regression statistical method is used, or may additionally specify the values ​​of GFI (AGFI) and RMSEA if covariance structure analysis is used. The model creation unit 213 may adopt, as the association model for the evaluation target, a model in which the coefficient of determination or GFI is as large as possible within the range of 0 to 1 and the RMSEA is as small as possible.

[0072] After the model creation step, the model creation unit 213 outputs the created association model (S7). By checking the output association model, an analyst such as a product designer can grasp the degree of relationship between each element and the value judgment element using the unified measure of price.

[0073] <Specific examples of correlation models> Fig. 4 is a diagram showing a specific example of an association model created using the evaluation support method according to the present disclosure. The model creation unit 213 may create and output a diagram (reference numeral 401 in Fig. 4) showing the association model as shown in Fig. 4. A specific example of a method for creating an association model will be described below with reference to Fig. 4.

[0074] The evaluation support method first performs a presentation step. In this specific example, a group of evaluation targets including a plurality of smartphone covers is presented in the presentation step. In this specific example, the plurality of smartphone covers included in the group of evaluation targets differ from one another in at least one of appearance, material, and price. After the presentation step, a group of questions is presented to the subject, and responses are obtained.

[0075] Table 1 shows the evaluation words included in the questions or answer options included in the question group used in this specific example.

[0076] [Table 1]

[0077] As shown in Table 1, the question group includes at least one evaluation term each indicating an impression, an attitude (here, an emotional element), and a value judgment. Also, as shown in Table 1, the evaluation terms indicating impressions include evaluation terms indicating low-order impressions and evaluation terms indicating high-order impressions. As shown in Table 1, the evaluation terms indicating value judgments are words indicating the expected price of the evaluation target, and may include multiple values ​​indicating the expected price.

[0078] The acquisition unit 211 acquires answers input by, for example, the questioner or the subject using the input device 1 (acquisition process), and outputs information indicating the acquired answers and information indicating the evaluation words included in the question group to each part of the control unit 21.

[0079] Furthermore, the analysis unit 212 identifies the evaluation score of each question included in the question group. For example, if the subject selects "like" in response to the question "Do you like the evaluation target," the analysis unit 212 assigns the evaluation score to the question as 5 points, and if the subject selects "dislike," the analysis unit 212 assigns 1 point.

[0080] The analysis unit 212 performs statistical analysis such as factor analysis, principal component analysis, or various cluster analyses based on information indicating the evaluation scores of the test subject's responses and information indicating the characteristics of the evaluation target, and calculates loadings, scores, or representative values ​​corresponding to each factor or component. Examples of the analysis results by the analysis unit 212 are shown in Tables 2 and 3. Table 2 shows an example of the analysis results (factor loadings) for each factor included in the higher-order impression elements among the impression elements. Table 3 shows an example of the analysis results (factor loadings) for each factor included in the lower-order impression elements among the impression elements.

[0081] [Table 2]

[0082] [Table 3]

[0083] The analysis unit 212 extracts factors or components based on the evaluation words included in the group of questions. For example, the analysis unit 212 extracts factors or components common to the evaluation words included in the group of questions for each layer that classifies the properties of words. For example, the analysis unit 212 extracts at least one characteristic element, one impression element, and one attitude element based on the characteristics of the evaluation target and the evaluation words included in the group of questions. As an example, the analysis unit 212 extracts factors related to competence, activity, evaluability, and functionality from the higher-order impression elements, taking into account eigenvalues ​​and the interpretability of factors. For example, the analysis unit 212 may extract four factors related to higher-order impression elements and three factors related to lower-order impression elements from the impression elements through factor analysis.

[0084] Specifically, the analysis unit 212 may extract four factors as high-order impression elements, namely, "protective and unlikely to come off," "youthful and pretty," "luxurious and substantial," and "harmonizes with other items," using the magnitude of the factor loadings shown in Table 2 as the main criterion for judgment. Furthermore, the analysis unit 212 may extract three factors as low-order impression elements, namely, "shiny and smooth," "hard and rough," and "thick and heavy," using the factor loadings shown in Table 3 as the main criterion for judgment. In this specific example, the analysis unit 212 extracts factors, but the process of the analysis unit 212 extracting factors or components is not essential and may be omitted. For example, the analysis unit 212 may omit the extraction process for emotion elements and analyze only one element, "like."

[0085] The model creation unit 213 performs statistical modeling or other modeling based on the analysis results of the analysis unit 212, and determines a value indicating the degree of association between each element and a higher-level element. The model creation unit 213 creates an association model 401 based on the analysis results. Figure 4 is a diagram showing an example of the association model 401 created by the model creation unit 213.

[0086] As shown in FIG. 4, the association model 401 has a layer including feature elements (reference numeral 402), a layer including low-level impression elements (reference numeral 403), a layer including high-level impression elements (reference numeral 404), a layer including emotional elements (reference numeral 405), and a layer including value judgment elements (reference numeral 406). Note that the hierarchical structure shown in FIG. 4 is merely an example, and other hierarchical structures may be adopted. For example, the association model may include layers indicating cognitive elements and elements indicating behavioral tendencies. Alternatively, cognitive elements may be in lower layers, emotional elements in middle layers, and behavioral elements in upper layers. Furthermore, the association model may not have a layer including feature elements. Furthermore, the association model may have a layer including fourth information.

[0087] The association model shown in Figure 4 shows how the lower-level elements relate to the higher-level elements, with the layer containing characteristic elements at the bottom and the layer containing value judgment elements at the top. The numbers near the arrows indicate the degree of association from each element to another element. The larger the absolute value of the listed number, the stronger the relationship from the element at the start of the arrow to the element at the end of the arrow.

[0088] In Figure 4, arrows with positive values ​​indicate that there is a positive correlation between elements. In the association model 401, as the lower-level element increases, the related higher-level element also increases. For example, there is a positive correlation between the lower-level impression element of "thick and heavy" and the element of "protective and unlikely to come off." In this case, the thicker and heavier the evaluation object gives the subject, the stronger the subject's impression of "protective and unlikely to come off."

[0089] Additionally, arrows with negative values ​​in Figure 4 indicate that there is a negative correlation between elements. In the association model 401, as a lower-level element becomes larger, the related higher-level element becomes smaller. For example, there is a negative correlation between the low-level impression element of "shiny and smooth" and the element of "luxury and profound feeling." In this case, the more the evaluation object gives the subject the impression of being shiny and smooth, the smaller the impression of "luxury and profound feeling."

[0090] Furthermore, the model creation unit 213 extracts elements related to the value judgment elements from the elements included in the association model, evaluates the degree of association of each element with the value judgment elements, and includes information indicating the results in the association model. Specifically, in the association model shown in Figure 4, it can be seen that the impression element of "luxury and stately feeling" and the emotional element of "like" are related to "expected price," which is a value judgment element. The model creation unit 213 evaluates the degree of relationship of each element with the value judgment elements and identifies the relationship of each element with the expected price.

[0091] [Table 4]

[0092] Table 4 shows the correlation between human emotions and impressions and price, calculated from the model in Figure 4, assuming a smartphone case priced at 1,000 yen. The proportion of the product price that can be explained by the model in Figure 4 is 20% (the coefficient of determination for "expected price" R 2 : 0.20), so for a 1,000 yen product, the model can interpret 200 yen worth. Furthermore, Table 4 shows the sum of the relevance of elements directly and indirectly linked to "expected price" based on the model. Looking at this, we can see that the impression of "luxury and solidity" has a stronger correlation with expected price than the emotion of "liking." We can also see that among the lower-level impression elements, the absence of a "shiny or smooth" impression has the strongest correlation with price perception.

[0093] Furthermore, Table 4 shows that the difference in the strength of the relationship between one element and another element in value judgments can be evaluated using a common measure, namely price. For example, the high-level impression element of "luxury and solidity" is several dozen times more strongly related to expected price than elements such as "protective and durable" and "youthful and pretty." These results show that a sense of luxury can increase the value consumers perceive in a smartphone case more than impressions such as its protectiveness and prettyness.

[0094] As described above, the association model created by the method of the present invention makes it possible to evaluate the relationship between each of the elements, such as characteristics, impressions, and attitudes toward the evaluation object and the price perception toward the evaluation object using a unified scale, namely, price. Furthermore, by using the association model, it is possible to compare elements included in different segments using a common scale, namely, price.

[0095] [Embodiment 2] Other embodiments of the present invention will be described below. For ease of explanation, the same reference numerals will be used to designate components having the same functions as those described in the above embodiment, and the description thereof will not be repeated.

[0096] Fig. 5 is a flowchart showing an example of the flow of the evaluation support method according to the second embodiment. As shown in Fig. 5, the evaluation support method according to the second embodiment further includes a new information input step (S18, S20) and a predicted value calculation step (S19, S21) in addition to the evaluation support method according to the first embodiment. In Fig. 5, the processing of steps S11 to S17 is the same as the processing of steps S1 to S7 described in the first embodiment, and therefore a description thereof will be omitted here. The input step is a step of newly inputting information indicating at least one of the features and elements of the evaluation target into the association model created in the model creation step. The predicted value calculation step is a step of calculating a predicted value of a specific element other than the information input in the input step, based on the information input in the input step.

[0097] Fig. 6 is a block diagram showing the configuration of an evaluation support system 100A according to embodiment 2. As shown in Fig. 6, the evaluation support system 100A includes an input device 1 and a model creating device 2A. The model creating device 2A includes a control unit 21A having a prediction unit 214.

[0098] The flow of the evaluation support method according to the second embodiment will be described below with reference to FIGS. 5 and 6. After the model creation unit 213 creates an association model and displays it on the display unit 22, the prediction unit 214 acquires newly input information (S18). The new information is input, for example, by an analyst who performs analysis using the association model, using the input device 1. The new information may be a numerical value obtained by changing a value indicating an element included in the association model. Note that, when the association model includes a feature element, the new information input to the prediction unit 214 may be a value indicating the feature element. Furthermore, when the association model includes a feature element, the prediction unit 214 may calculate a predicted value of the feature element based on the value indicating the new element.

[0099] When new feature information is input (YES in S18), the prediction unit 214 calculates a predicted value of the third information (value judgment) when the new feature information is input to the association model (S19: prediction step). For example, the prediction unit 214 predicts the value of the third information after the new feature information is input. The prediction unit 214 causes the display unit 22 to present a prediction result indicating the predicted degree of influence (S22). Note that the prediction unit 214 may calculate a predicted value of the first information and a predicted value of the second information due to the first information influenced by the new feature information, based on the new feature information.

[0100] If no new feature information has been input (NO in S18), the prediction unit 214 determines whether new third information has been input (S20). The new third information is input, for example, by an analyst who performs analysis using the association model, using the input device 1. The new third information may be the value of a value judgment element included in the association model.

[0101] When new third information is input (YES in S20), the prediction unit 214 inputs the new third information into the association model and calculates a predicted value of the feature information (S21: prediction step). For example, the prediction unit 214 inputs the new third information and calculates a predicted value of each feature element included in the feature information. The prediction unit 214 causes the display unit 22 to present a prediction result indicating the calculated predicted value (S22). Note that the prediction unit 214 may calculate a predicted value of the second information and a predicted value of the first information due to the second information influenced by the new third information, based on the new third information.

[0102] As described above, the evaluation support method according to the second embodiment uses the created association model and new information to predict how other elements will change when the value of an element included in the association model changes.

[0103] [Software implementation example] The functions of the evaluation support system 100, 100A (hereinafter referred to as the "system") can be realized by a program that causes a computer to function as the system, and a program that causes a computer to function as each control block of the system (particularly each part included in the control unit 21, 21A).

[0104] In this case, the system includes a computer (including a tablet terminal or a smartphone) having at least one control device (e.g., a processor) and at least one storage device (e.g., a memory) as hardware for executing the program. The control device and storage device execute the program, thereby realizing the functions described in each of the above embodiments.

[0105] The program may be non-transitory and may be recorded on one or more computer-readable recording media. The recording media may or may not be included in the device. In the latter case, the program may be supplied to the device via any wired or wireless transmission medium.

[0106] Furthermore, some or all of the functions of the control blocks can be realized by logic circuits. For example, an integrated circuit in which a logic circuit that functions as each of the control blocks is formed is also included in the scope of the present invention. In addition, the functions of the control blocks can also be realized by, for example, a quantum computer.

[0107] Furthermore, each process described in each of the above embodiments may be executed by AI (Artificial Intelligence). In this case, the AI ​​may run on the control device or on another device (for example, an edge computer or a cloud server).

[0108] The present invention is not limited to the above-described embodiments, and various modifications are possible within the scope of the claims. Embodiments obtained by appropriately combining the technical means disclosed in different embodiments are also included in the technical scope of the present invention.

[0109] 〔summary〕 The evaluation support method according to a first aspect of the present invention includes a presentation step of presenting an evaluation target having a plurality of characteristics to a subject; an acquisition step of acquiring, by a computer, answers from the subject to a group of questions including a question sentence related to each of the plurality of characteristics and answer options to the question sentence, wherein at least one of the question sentence and the answer options includes at least one evaluation term related to the evaluation target; and a step of acquiring, by a computer, answers from the subject to a group of questions including each of the plurality of characteristics, words indicating factors or components common to the evaluation terms included in the group of questions, and the answers from the subject. The method includes a model creation process for creating an association model including (1) first information regarding the impression the subject receives from the evaluation object based on each of the plurality of features, (2) second information regarding the subject's attitude toward the evaluation object according to the impression, and (3) third information regarding the subject's price perception toward the evaluation object according to the impression or the attitude, and an evaluation process for evaluating the degree to which at least one of the impression and the attitude is related to the price perception based on the created association model, wherein the evaluation words include words related to the impression, words related to the attitude, and words related to the price perception.

[0110] In the evaluation support method according to aspect 2 of the present invention, in aspect 1, the degree to which at least one of the impression and the attitude is related to the price perception may be expressed by a numerical value indicating the price perception of the evaluation object.

[0111] In the evaluation support method according to aspect 3 of the present invention, in the first or second aspect, the association model may further include information indicating each of the plurality of features.

[0112] The evaluation support system according to a fourth aspect of the present invention includes an acquisition unit that acquires answers from the subject to a group of questions that includes a question sentence related to each of a plurality of characteristics of an evaluation target having the plurality of characteristics and answer options to the question sentence, and at least one of the question sentence and the answer options includes at least one evaluation term related to the evaluation target; and an acquisition unit that acquires answers from the subject to a group of questions that includes a question sentence related to each of the plurality of characteristics and answer options to the question sentence, and at least one of the question sentence and the answer options includes at least one evaluation term related to the evaluation target, and and a model creation unit that creates an association model including (1) first information regarding the impression that the subject has of the evaluation object, (2) second information regarding the subject's attitude toward the evaluation object according to the impression, and (3) third information regarding the subject's price perception toward the evaluation object according to the impression or the attitude, wherein the model creation unit evaluates the degree to which at least one of the impression and the attitude is related to the price perception based on the created association model, and the evaluation words include words related to the impression, words related to the attitude, and words related to the price perception. [Explanation of symbols]

[0113] 100, 100A evaluation support system 211 Acquisition Department 213 Model Creation Department 214 Prediction Department S1, S11 Presentation process S2, S12 acquisition process S5, S15 Model creation process S6, S16 evaluation process S19, S21 Prediction process

Claims

1. a presentation step of presenting an evaluation target having a plurality of features to a subject; an acquisition step in which a computer acquires answers from the subject to a group of questions including a question sentence related to each of the plurality of characteristics of the evaluation target and answer options to the question sentence, wherein at least one of the question sentence and the answer options includes at least one evaluation term related to the evaluation target; a model creation process in which a computer creates an association model based on each of the plurality of characteristics, words indicating factors or components common to the evaluation words included in the group of questions, and the answers of the subject, including: (1) first information on the impression the subject receives from the evaluation object based on each of the plurality of characteristics; (2) second information on the subject's attitude toward the evaluation object according to the impression; and (3) third information on the subject's price perception toward the evaluation object according to the impression or the attitude; an evaluation step of evaluating a degree to which at least one of the impression and the attitude is related to the price perception based on the created association model; Including, The evaluation words include words related to the impression, words related to the attitude, and words related to the price perception. Evaluation support methods.

2. 2. The evaluation support method according to claim 1, wherein the degree to which at least one of the impression and the attitude is related to the price perception is expressed by a numerical value indicating the price perception of the evaluation object.

3. The evaluation support method according to claim 1 , wherein the association model further includes information indicating each of the plurality of features.

4. an acquisition unit that acquires answers from the subject to a group of questions that includes a question sentence related to each of a plurality of characteristics of an evaluation target that is presented to the subject and an answer option to the question sentence, wherein at least one of the question sentence and the answer option includes at least one evaluation term related to the evaluation target; and a model creation unit that creates an association model based on each of the plurality of features, words indicating factors or components common to the evaluation words included in the group of questions, and the answers of the subject, including: (1) first information on the impression the subject receives from the evaluation object based on each of the plurality of features; (2) second information on the subject's attitude toward the evaluation object according to the impression; and (3) third information on the subject's price perception toward the evaluation object according to the impression or the attitude, the model creation unit evaluates a degree to which at least one of the impression and the attitude is related to the price perception based on the created association model; The evaluation words include words related to the impression, words related to the attitude, and words related to the price perception. Evaluation support system.

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