Evaluation support method and evaluation support system

Abstract

To facilitate identification of gap factors between two categories and quantitative evaluation of the relationships.SOLUTION: The evaluation support method includes: a presentation step (S1, S3) for presenting each of a plurality of evaluation targets belonging to each of two categories, to a subject; an acquisition step (S2, S4) for acquiring answers of the subject to a set of questions including at least one evaluation word related to each of the plurality of evaluation targets; and a model creation step (S7) for creating an association model including information indicating the degree of association between elements. The elements include at least one of an element indicating an impression of the evaluation target and an element indicating an attitude toward the evaluation target, and the association model has a hierarchical structure including two or more layers.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 gaps between categories of elements such as perception, impression, and emotion regarding an evaluation target. [Background technology]

[0002] A method for analyzing the relationship between elements such as the form of an external stimulus and the sensibility that humans feel toward that external stimulus is known. This method quantitatively links elements such as the form of an external stimulus 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 or experiences, which in turn evokes certain emotions."

[0003] For example, cited document 1 describes a device that estimates the emotional state of a subject when exposed to an auditory stimulus specified by sound data. [Prior art documents] [Patent documents]

[0004] [Patent Document 1] Japanese Patent Publication No. 2022-133570 Summary of the Invention [Problem to be solved by the invention]

[0005] When evaluating the differences (gaps) between the hierarchical perceptual and cognitive structures for multiple external stimuli, conventional methods required comparing hierarchical models that represent the perceptual and cognitive structures constructed individually for each external stimuli. This made it difficult to identify or quantitatively compare the differences between the hierarchical perceptual and cognitive structures for multiple external stimuli.

[0006] In the present invention, the data used to construct a hierarchical model is information indicating the results of a questionnaire administered to subjects exposed to external stimuli belonging to two categories. In the questionnaire, scores are obtained for multiple stimuli belonging to each category based on responses indicating the impressions the subjects had toward the evaluation targets when exposed to stimuli belonging to the two categories, as well as human sensibilities including attitudes such as emotions, cognition, and behavioral tendencies. In addition, in the present invention, a hierarchical model is constructed based on information indicating the difference (difference score) between the scores obtained when exposed to stimuli belonging to one category and those obtained when exposed to stimuli belonging to another category.

[0007] The method of the present invention aims to facilitate the identification of the causes of the gap between two categories and the quantitative evaluation of the relationship between them by constructing a hierarchical model that shows the gap between two categories as described above. [Means for solving the problem]

[0008] In order to solve the above-mentioned problems, an evaluation support method according to one embodiment of the present invention includes: a presentation step of presenting to a subject each of a plurality of evaluation objects belonging to two categories; an acquisition step of acquiring by a computer answers to a group of questions including, for each of the plurality of evaluation objects, a question statement related to each of the characteristics possessed by the plurality of evaluation objects and answer options to the question statement, wherein at least one of the question statement and the answer options includes at least one evaluation term related to each of the plurality of evaluation objects; and a model creation step of creating by a computer an association model including information indicating the degree of association between the elements based on the difference scores between the two categories of the answers and elements indicating factors or components common to the evaluation terms corresponding to the difference scores, wherein the elements include at least one of elements indicating an impression of the evaluation object and elements indicating an attitude toward the evaluation object, and the association model has a hierarchical structure with two or more layers (hierarchies).

[0009] In order to solve the above-mentioned problems, an evaluation support system according to one embodiment of the present invention includes an acquisition unit that acquires answers from the subject to a group of questions that includes, for each of a plurality of evaluation objects belonging to each of two categories, questions that include a question statement related to each of the characteristics of the plurality of evaluation objects and answer options to the question statement, and at least one of the question statement and the answer options includes at least one evaluation term related to each of the plurality of evaluation objects; and a model creation unit that creates an association model including information indicating the degree of association between the elements based on the difference scores between the two categories of the answers and elements that indicate factors or components common to the evaluation terms corresponding to the difference scores, wherein the elements include at least one of elements that indicate an impression of the evaluation object and elements that indicate an attitude toward the evaluation object, and the association model has a hierarchical structure with two or more layers.

[0010] 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]

[0011] According to one aspect of the present invention, it is possible to easily identify gap factors between two categories and quantitatively evaluate the relationship between them. [Brief explanation of the drawings]

[0012] [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

[0013] [Embodiment 1] Hereinafter, one embodiment of the present invention will be described in detail.

[0014] <Outline of evaluation support method> In this embodiment, a method for creating an association model using an evaluation target and a group of questions belonging to two categories to facilitate identification of gap factors between the two categories and quantitative evaluation of the relationship between them will be described.

[0015] 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.

[0016] In this embodiment, the "two categories" refer to categories in which the evaluation objects have different properties or attributes. For example, the two categories indicate the properties or attributes of the evaluation objects, and may include a category to which a group of products that are popular among the test subjects belong, and a category to which a group of products that are less popular than the group of products among the test subjects belong. "Presenting evaluation objects belonging to two categories" means presenting evaluation objects that belong to different, comparable categories. "Different categories" may also mean that the evaluation objects are different. "Presenting evaluation objects belonging to two categories" may mean presenting evaluation objects such as the following, for example: Popular and unpopular products New and existing products One tourist spot and another Multiple items purchased from different locations Furthermore, the two categories may be categories in which the conditions under which the evaluation objects are presented are different from each other. "Different categories" may mean that the evaluation objects presented are the same, but the conditions under which the evaluation objects are presented are different. For example, the two categories may include a situation in which an image of the evaluation object to be evaluated is shown to the subject, and a situation in which the actual evaluation object is shown to the subject and allowed to touch it. "Presenting each of the evaluation objects belonging to the two categories" may mean presenting the evaluation objects under different conditions, for example, as follows: Showing subjects a photo of a product and then letting them see and touch the actual product Before and after explaining the product being evaluated Before and after using the evaluation target - Using the evaluation target under different conditions Furthermore, the number of evaluation targets belonging to one category may be one or more. For example, when popular products and unpopular products are presented as evaluation targets, a group of evaluation targets including one product belonging to the category of popular products and a group of evaluation targets including multiple products belonging to the category of unpopular products may be presented to the test subject.

[0017] As used herein, an element refers to the difference (score difference) between categories of responses to evaluation terms related to a subject's impression or attitude toward the evaluation object, and is expressed by a term that indicates the element. An element refers to the score difference between categories of a subject's responses to a group of questions containing evaluation terms related to the evaluation object, or a factor or component derived by statistical analysis of the responses or score difference. It may be expressed by a term representing a layer in the association model or a term included in each layer. The elements used in the method according to the present disclosure may include at least one of an impression element, which is an element indicating an impression toward the evaluation object, and an attitude element, which indicates an attitude toward the evaluation object. Furthermore, the elements used in the method according to the present disclosure may include a feature element, which indicates a characteristic of the evaluation object. Furthermore, the attitude element used in the method according to the present disclosure may include multiple elements, including an element indicating emotions toward the evaluation object, an element indicating cognition toward the evaluation object, and an element indicating a behavioral tendency toward the evaluation object. Furthermore, impression elements may be divided into elements indicating impressions closer to the subject's emotions (higher-order impressions) and elements indicating impressions closer to the morphological characteristics of the evaluation object (lower-order impressions). In this case, the association model may be created with only a layer including lower-order impression elements and a layer including higher-order impression elements, without including elements such as attitude elements. The elements used in this specification are, for example, all numerical values ​​(difference scores that represent the difference between categories of answers to evaluation words), and are labeled with words that express each element.

[0018] The multiple characteristics of the evaluation object may be characteristics related to the five senses and other senses, and may be either measurement results related to the properties of the evaluation object or evaluation results obtained by evaluating the characteristics based on predetermined criteria. Each characteristic is represented by a corresponding term. For example, the characteristics 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 characteristics of the evaluation object may also be evaluation results indicating whether the characteristics of the evaluation object correspond to a predetermined number of levels or whether they are present or absent. Note that the characteristics are not limited to morphological characteristics of the evaluation object. For example, the characteristics 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. Furthermore, it is sufficient that the characteristics are possessed by evaluation objects belonging to at least one of the two categories.

[0019] Impression elements are elements that indicate the difference between two categories of impressions that a subject has of an evaluation object. Impression elements may include instinctive impressions based on the appearance and feel of the evaluation object, as well as elements that indicate impressions of its functions, such as ease of use and performance. Impression elements may also include elements that indicate introspective impressions linked to personal memories, personal creations, or associations. Impression elements may also 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 subject's formal characteristics (lower-order impressions). Attitude elements are composed of cognitive, emotional, and behavioral elements. Cognitive elements indicate the difference between two categories of the subject's cognition and judgment of the evaluation object. Emotional elements indicate the difference between two categories of the subject's feelings toward the evaluation object. Behavioral elements indicate the difference between two categories of the subject's behavioral tendencies toward the evaluation object.

[0020] The elements used in the method according to the present disclosure may further include other elements, for example, an element showing the difference (difference score) between two categories of answers to questions containing evaluation terms asking about financial situation, recommendations from others, strength of interest in the product, confidence in being able to use the product, etc.

[0021] Furthermore, the elements may include a value judgment element that indicates the subject's value judgment (change between two categories) for the evaluation object. The value judgment element is an element that indicates the subject's price perception for the evaluation object. The value judgment element is expressed by the subject's expected manufacturer's suggested retail price or retail price of the evaluation object based on the subject's response, or the subject's willingness to pay, which indicates how much the subject is willing to pay for the evaluation object. The elements may also include a purchase intention element. The purchase intention element is an element that indicates the purchase intention itself, indicating whether the subject will decide to purchase the evaluation object (change between two categories). Note that the value judgment element may be included in the cognitive element of the attitude element, or it may be included in the attitude element as a separate element from the cognitive element, or it may be independent as a separate element from the attitude element. Furthermore, the purchase intention element may be included in the behavioral element of the attitude element, or it may be included in the attitude element as a separate element from the behavioral element, or it may be independent as a separate element from the attitude element.

[0022] 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, S3), an acquisition step (S2, S4), and a model creation step (S7). The evaluation support method may be performed using a computer such as a general-purpose computer.

[0023] The presentation step is a step of presenting each of the evaluation objects belonging to two categories to a subject. The acquisition step is a step of presenting a set of questions related to the evaluation objects to multiple subjects and acquiring responses as a result. As shown in FIG. 1, in the evaluation support method according to the present disclosure, each of the evaluation objects belonging to a first category is presented to a subject, and then a set of questions related to the evaluation objects is presented to the same subject and responses are acquired as a result. Thereafter, each of the evaluation objects belonging to a second category is presented to the subject, and then a set of questions related to the evaluation objects is presented to the same subject and responses are acquired as a result. In the evaluation support method according to the present disclosure, the process from presenting the evaluation objects belonging to the first category to acquiring responses to the set of questions related to the evaluation objects belonging to the second category is repeatedly performed on multiple subjects. The order of presentation of the evaluation objects and acquisition of responses may be such that all evaluation objects are evaluated for each category, or that evaluations for the first and second categories are repeated for each evaluation object. The order in which the evaluation objects are presented is not important. The subject may be someone who regularly interacts with the evaluation objects. Alternatively, the subject may be someone who is desired to be exposed to the evaluation objects.

[0024] The question includes at least one evaluation term related to the evaluation target in at least one of the question sentence and answer options. The evaluation term is a term used to evaluate the evaluation target, and is selected in advance by the evaluator according to the evaluation target, and is related to the impression and attitude when interacting with the evaluation target, and is pre-categorized according to the nature of the term. For example, the evaluation term includes a term related to the impression of the evaluation target and a term related to the attitude toward the evaluation target. Furthermore, when the characteristics of evaluation targets belonging to two categories can be compared between the categories and the difference in characteristics can be quantified as a difference score, the evaluation term may include a term indicating the characteristics of the evaluation target.

[0025] The extraction process is a process of extracting elements to be used in the association model created in the subsequent model creation process from the answers acquired in the acquisition process. In the extraction process, the answers to the evaluation words included in the question group are subtracted between categories to find difference scores, and factors or components common to the evaluation words corresponding to the difference scores 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 words, depending on the words that represent each layer and element.

[0026] The model creation process is a process of creating an association model using the difference scores between categories of answers acquired in the acquisition process and elements extracted based on the answers or score differences, and acquiring information showing the relationships between elements.

[0027] FIG. 2 is a diagram illustrating an overview of an association model created by a 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 and the second information shown in the following (1) and (2). The first information (reference numeral 202 in FIG. 2) is related to impression elements, and the second information (reference numeral 205 in FIG. 2) is related to attitude elements including cognition, emotion, and behavioral tendencies. Among attitude elements, elements indicating cognition, emotion, and behavioral tendencies may be arranged in different hierarchies or in the same hierarchies. Furthermore, a hierarchical structure may be assumed within a single element. Furthermore, impression elements may be divided into elements indicating low-order impressions (low-order impression elements: reference numeral 203 in FIG. 2) and elements indicating high-order impressions (high-order impression elements: reference numeral 204 in FIG. 2), and a hierarchical structure from low-order impressions to high-order impressions may be shown. When impression elements are divided, the association model may be created using only the first information, or may further include the second information. The association model created by the method according to the first embodiment of the present disclosure has a hierarchical structure with two or more layers and may include information indicating at least one of impression elements and attitude elements. The elements included in the association model may be only impression elements, only attitude elements, or both impression elements and attitude elements. The association model may also include information indicating other elements.

[0028] (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 on the subject's attitude toward the subject based on characteristics or impressions The second information may include at least one of an emotional element, a cognitive element, and a behavioral element. FIG. 2 illustrates an example in which the second information included in the association model includes an emotional element and a cognitive element that is a higher-level element than the emotional element. The hierarchical structure within the attitude element of cognition, emotion, and behavioral tendency is not limited to the example shown in FIG. 2 and may be changed as appropriate depending on the situation assumed by the subject when answering the question and the analyst's judgment. For example, the hierarchical structure within the attitude element may be designed so that a layer indicating cognitive elements is placed higher than high-level impression elements, and a layer indicating emotional elements is placed higher than the cognitive elements.

[0029] The association model may also include information indicating each of the multiple characteristics of the evaluation target (reference numeral 206 in Figure 2). Furthermore, as third information other than the first, second, or characteristic elements, other information defined independently of the subject's sensibilities, or the environment or situation in which the subject finds himself, may also be included. 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.

[0030] 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.

[0031] Specifically, the association model includes information indicating the degree of association of the feature elements of the evaluation target included in the feature information with at least one of the impression elements included in the first information and the attitude elements included in the second information. Note that feature information is not required in the association model. The association model also includes information indicating the degree of association of the impression elements included in the first information with at least one of the attitude elements included in the second information. Furthermore, if the association model includes third information, the association model may also include information indicating the degree of association of the elements included in the third information with at least one of the impression elements and the attitude elements.

[0032] Conventionally, when comparing evaluations such as impressions and attitudes toward evaluation targets belonging to two categories, it was necessary to compare the evaluations of each of the evaluation targets belonging to each category.

[0033] An association model created using the model creation method of the present invention includes information indicating the degree of association between a difference in low-level impressions and a difference in high-level impressions, or between a difference in impressions and a difference in attitude, between two categories. Using this association model, it is possible to grasp the degree of association between differences between two categories without comparing the evaluations of multiple evaluation objects. Using this association model, analysts can grasp the degree of association between a difference in impression and a difference in attitude that a subject has when presented with an evaluation object belonging to one category and when presented with an evaluation object belonging to another category. Analysts can also grasp the degree of association between a difference in low-level impressions and a difference in high-level impressions evoked based on that difference in impression. An association model created using the model creation method of the present invention allows a viewer of the association model to more accurately and easily grasp the gap between multiple evaluation objects, the extent of the gap, and the characteristics or elements that cause the gap.

[0034] Furthermore, product designers can use the association model to understand what categories of products give consumers a favorable impression and lead to more favorable attitudes, which can be useful in product design. Therefore, the evaluation support method according to the present disclosure can also be applied to a product design method.

[0035] <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.

[0036] 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.

[0037] 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 calculates the difference scores between categories based on the acquired answers, and extracts the loadings and scores of factors or components representative of the evaluation words contained in each layer, or elements representative of the evaluation words contained in each layer. Details of the analysis performed by the analysis unit 212 will be described later.

[0038] 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.

[0039] The association model created by the model creation unit 213 includes first information and second information. The association model may also include feature information. The association model may also include third 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 between a feature element of the evaluation object and an impression element evoked by the evaluation object, or the degree of association between an impression element evoked by the evaluation object and an attitude element.

[0040] As described above, the model creation device 2 can create an association model that can analyze the degree of association between elements of the evaluation object based on the difference scores between categories of answers from subjects to a group of questions about the evaluation object. A person who checks the association model, such as a product designer, can analyze how the differences between categories of the characteristics of the evaluation object and the differences between categories of impressions evoked by the evaluation object relate to changes in consumer attitudes.

[0041] <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 by a person. For example, in the evaluation support method, the model creation step may be performed by a person who has acquired the model in the acquisition step and understood the analysis results in the analysis step.

[0042] First, in the evaluation support method, evaluation objects belonging to two categories are presented to a subject who will evaluate the evaluation objects (S1, S3: presentation process). In the presentation process, multiple evaluation objects may be presented as a group of evaluation objects belonging to one category. For example, in the presentation process, the subject may be asked to imagine a situation in which they are "choosing a cover for their smartphone," and photos of multiple smartphone covers, each with a different material and price, may be presented as a group of evaluation objects in one category. Also, in the presentation process, actual smartphone covers whose photos have already been shown may be presented as a group of evaluation objects in another category.

[0043] "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.

[0044] The evaluation object presented to the subject may be one or more. For example, the subject may be asked to imagine a situation in which he or she needs to compare evaluation objects, and multiple comparable evaluation objects may be presented, with each evaluation object belonging to two categories. Also, two conditions may be set regarding the item to be presented to the subject, and each of these conditions may be a category. The presentation order of the multiple evaluation objects presented in the presentation process may be different for each subject or may be the same. The presentation order of the multiple evaluation objects presented in the presentation process may be different for each category to which the evaluation objects belong or may be the same.

[0045] After each presentation step, a set of questions related to the evaluation object is presented to the subject, and answers are obtained. The presentation of the set of questions and the acquisition of answers are performed after a first category of evaluation object is presented, and answers related to that category of evaluation object are obtained. Then, a second category of evaluation object is presented, and answers related to that category of evaluation object are obtained. Alternatively, the procedure of randomly presenting evaluation objects and having the subject evaluate the first and second categories consecutively to obtain answers may be repeated. The set of questions includes multiple question pairs, each including a question statement related to the impression or attitude toward the evaluation object, and answer options for the question statement. Furthermore, the question includes at least one evaluation term related to the evaluation object in at least one of the question statement and answer options. Note that the method of presenting the set of questions is not particularly limited. For example, the set of questions may be displayed on a display or other presentation unit, printed on paper, or communicated to the subject orally.

[0046] The evaluation words included in the question group include (1) words related to the impression that the subject has of the evaluation target, and (2) words related to the subject's attitude, etc., according to the impression. Furthermore, when characteristic elements are collected by asking questions to respondents, the evaluation words included in the question group may include words related to the characteristics of the evaluation target. Note that the evaluation words included in the question group may include only either words related to impressions or words related to attitude, etc.

[0047] The evaluation words included in the question group may be common to each category. Note that, when the evaluation words included in the question group are common, the question sentences included in the question group may be different for each category. For example, if an image of the evaluation target is shown in the presentation step for the first category and the actual object of the evaluation target is shown and touched in the presentation step for the second category, the former does not allow the subject to recognize the actual characteristics of the evaluation target, such as its texture or temperature. Therefore, as a question sentence corresponding to the evaluation word related to the impression of "cold," the question sentence "Does it look cold?" may be presented after showing the image of the evaluation target, and "Is it cold?" may be presented after touching the actual object.

[0048] 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.

[0049] 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."

[0050] Words indicating emotions may be words such as "like," "dislike," "enhancing," "pleasant," and "awakening." Words indicating cognition may be words such as "good," "bad," "matches the image," and "reasonable price." Words may also be used to ask the subject about the manufacturer's suggested retail price or retail price of the evaluation item that the subject anticipates, or about the subject's willingness to pay, which indicates how much the subject is willing to pay for the evaluation item. In this case, the response method may be to have the subject answer a numerical value indicating the price of the evaluation item, or to present multiple price options and have the subject select one. Words indicating behavioral tendencies may be words such as "I want," "I don't want," "I want to buy," and "I want to go."

[0051] 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.

[0052] 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.

[0053] When multiple evaluation targets are presented to a subject as a group of evaluation targets belonging to one category, the subject may answer questions about each of the multiple evaluation targets, or may answer questions about the entire group of evaluation targets belonging to one category.

[0054] The acquisition unit 211 acquires answers to the results of presenting a set of questions related to evaluation targets belonging to each of two categories to a plurality of subjects after the presentation step corresponding to one category (S2, S4: 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.

[0055] The acquisition unit 211 identifies the 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 the properties of words, such as attitudes and impressions. Classifying evaluation terms into layers means classifying the evaluation terms into layers according to the properties of the words. Layers are used to represent each element in a hierarchical structure in the association model described below. In the association model, the layers may be assumed to be divided into two layers, for example, a layer including impression elements and a layer including attitude elements, or a four- or more-layered hierarchical structure may be assumed by further dividing attitude elements into layers including cognitive elements, emotional elements, and behavioral elements. Furthermore, the layer including impression elements may be further divided into two layers, a layer including higher-level impressions and a layer including lower-level impressions. In this case, a two-layered structure consisting of only a layer including lower-level impression elements and a layer including higher-level impression elements may be assumed, or a three- or more-layered structure may be assumed by adding a layer including attitude elements or at least one of their components. Note that a layer may include only attitude elements. In this case, a hierarchical structure including at least two elements from among cognitive elements, emotional elements, and behavioral elements may be assumed as layers in the association model.

[0056] 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 together with the response information to the analysis unit 212. Furthermore, when it is possible to measure the characteristics of each of the evaluation targets belonging to two categories and quantify them as a difference score, the acquisition unit 211 may acquire information indicating the characteristic elements and information indicating the words expressing the characteristic elements, and output these to the analysis unit 212. In this case, the layers are divided into a layer including impression elements, a layer including attitude elements, and a layer including characteristic elements.

[0057] The analysis unit 212 identifies evaluation points based on the answers to each question from the answer information. The analysis unit 212 also identifies the difference in evaluation points (score difference) between categories of evaluation words included in the question group.

[0058] When the analysis unit 212 acquires information indicating the difference scores between categories of answers for evaluation words classified by stratum, it extracts elements indicating factors or components common to the difference scores included in one stratum (S5: extraction step). The element extraction is performed, for example, using statistical analysis. The analysis unit 212 performs this extraction for each stratum. The word indicating the element may be one of the evaluation words included in the stratum, or a different word representing the evaluation words included in the stratum. Furthermore, one element may be extracted for one stratum, or multiple elements may be extracted for one stratum. The extraction step is performed only when many evaluation words are acquired and may be omitted if few evaluation words are acquired. The number of evaluation words may be appropriately determined by an analyst involved in creating the association model. For example, if the analyst determines that there are many evaluation words, the analysis unit 212 accepts instructions from the analyst and extracts elements common to the evaluation words included in one stratum. However, if it is determined that there are few evaluation words, the extraction step may be omitted.

[0059] If all of the evaluation terms contained in the question group or all of the characteristics of the evaluation target group were used directly to create an association model, the number of words (elements) would be too large, making it difficult to analyze the relationships between layers. By having the analysis unit 212 extract elements representing 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, "factor" refers to an element that influences the events and concepts expressed by the evaluation terms, and is extracted using factor analysis. Furthermore, "component" refers to an element expressed by a set of events and concepts expressed by the evaluation terms, and is integrated using principal component analysis.

[0060] 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, cluster analysis, quantification method 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.

[0061] The analysis unit 212 performs an analysis using information indicating the factors or components (elements), and quantitatively determines a loading that indicates the degree of influence that the evaluation words contained in the layer have on the factor or component that represents the layer, and a score that indicates the degree of influence that the evaluation target has on the factor or component (S6). The analysis unit 212 outputs information indicating the analysis results to the model creation unit 213.

[0062] For example, the analysis unit 212 performs factor analysis on the difference score data and extracts the factor loadings and factor scores of factors that represent the evaluation words included in each layer by performing a process of expressing the difference score data matrix as the product of a factor loading matrix and a factor score matrix.

[0063] Furthermore, for example, the analysis unit 212 may perform principal component analysis instead of factor analysis to determine principal component loadings and principal component 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, various cluster analyses (k-means or hierarchical clustering) or multidimensional scaling may be used as alternative methods to these to summarize multiple elements into a small number of representative elements.

[0064] Upon receiving the analysis results from the analysis unit 212, the model creation unit 213 creates an association model based on the analysis results (S7: model creation step). The analysis results from the analysis unit 212 include information such as loadings indicating the degree of influence between the evaluation terms and the common factors or principal components, obtained based on the difference scores between categories of answers obtained from the subjects, scores indicating the degree of influence between the evaluation target and the common factors or principal components, or representative values ​​(difference scores) of answers to multiple questions with similar trends in the difference scores between categories of answers from the subjects. Note that the analyst may assign appropriate terms to the common factors, principal components, or representative values. The model creation unit 213 obtains information indicating terms corresponding to the common factors, principal components, or representative values, for example, from the input device 1 operated by the analyst.

[0065] The model creation step can also be expressed as a step of creating an association model containing information indicating the degree of association between the extracted elements for each layer, expressed in words, based on the extracted elements (common factors, principal components, or representative values) and the difference scores of 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.

[0066] 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 higher-level elements.

[0067] For example, the model creation unit 213 identifies the degree of association between impression elements and attitude elements based on the analysis results by the analysis unit 212. Note that the linking of one element with another element performed by the model creation unit 213 is not limited to the above. For example, when impression elements are divided into low-order impression elements and high-order impression elements, the model creation unit 213 may identify the degree of association between the low-order impression elements and the attitude elements. When the information acquired by the acquisition unit 211 includes information indicating features, the model creation unit 213 may identify the degree of association between the feature elements and elements included in at least one of the layers indicating the impression elements and the attitude elements.

[0068] Based on the results of the above identification, the model creation unit 213 creates an association model including information indicating the degree of association between multiple elements. The association model created in this way includes (1) information indicating impression elements and (2) information indicating elements such as attitudes.

[0069] Furthermore, the information indicating impression elements may be divided into information indicating lower-order impression elements and information indicating higher-order impression elements. Furthermore, the association model may include information indicating feature elements. Furthermore, the association model includes information indicating the degree of association between elements included in a lower layer and elements included in a higher layer. For example, in the association model, the first information includes information indicating the degree of association between each impression element and an attitude element. Furthermore, the second information includes information indicating the degree of association between a certain element in the attitude element and another element. Note that, if the association model includes third information, the third information may include information indicating the degree of association between the first information and the second information.

[0070] 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 using a regression statistical method. Alternatively, particularly when using covariance structure analysis, the model creation unit 213 may specify the GFI (AGFI) and RMSEA values ​​in addition to the coefficient of determination of each element. The model creation unit 213 may adopt, as the association model for the evaluation target, a model in which the coefficient of determination and GFI are as large as possible within the range of 0 to 1 and the RMSEA is as small as possible.

[0071] After the model creation step, the model creation unit 213 outputs the created association model (S8). By checking the output association model, an analyst such as a product designer can quantitatively grasp the relationship between the gap between impression categories and the gap between attitude categories toward the evaluation target.

[0072] <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.

[0073] 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 each other in at least one of appearance, material, and price.

[0074] First, a group of evaluation objects belonging to one category is presented. In the first category, only visual information about the group of evaluation objects is provided. Specifically, photographs of the appearance of smartphone covers included in the group of evaluation objects are shown. After the presentation process, a group of questions is presented to the subject and their answers are obtained.

[0075] Next, a group of evaluation objects in another category is presented. In the second category, visual and tactile information about the group of evaluation objects is provided. Specifically, the subjects are shown and allowed to touch the actual smartphone cases included in the group of evaluation objects. After the presentation process, a set of questions is presented to the subjects, and their answers are obtained.

[0076] Table 1 shows the evaluation words included in the questions or answer options included in the question group used in this specific example.

[0077] [Table 1]

[0078] As shown in Table 1, the question group includes at least one evaluation term each indicating an impression and an attitude. Also, as shown in Table 1, the evaluation terms indicating impressions include evaluation terms indicating low-level impressions and evaluation terms indicating high-level impressions. As shown in Table 1, the evaluation terms indicating attitudes include evaluation terms indicating emotional / behavioral tendencies and evaluation terms indicating cognition. As shown in Table 1, the evaluation terms indicating cognition are words that indicate the expected price of the evaluation target, and may include multiple values ​​indicating the expected price. A question group may include only one of the evaluation terms indicating impressions and attitudes.

[0079] 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.

[0080] The analysis unit 212 identifies an evaluation score for 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 an evaluation score of 5 points to the question, and if the subject selects "dislike," the analysis unit 212 assigns an evaluation score of 1 point. The analysis unit 212 identifies the evaluation score for each evaluation target or for each category.

[0081] Next, the analysis unit 212 calculates differential scores, which are the differences in evaluation scores between categories, based on the identified evaluation scores. Furthermore, based on these differential scores, the analysis unit 212 extracts factors or components (elements) common to the evaluation words included in the group of questions, for each layer that classifies the properties of words. For example, statistical analysis such as factor analysis, principal component analysis, or various cluster analyses is performed to calculate loadings, scores, or representative values ​​corresponding to each factor or component. Examples of analysis results by the analysis unit 212 are shown in Tables 2 and 3. Table 2 shows an example of analysis results for each factor common to the higher-order impression elements among the impression elements. Table 3 shows an example of analysis results for each factor common to the lower-order impression elements among the impression elements. The analysis unit 212 may extract factors related to competence, activity, evaluability, functionality, etc. from the higher-order impression elements, taking into account the eigenvalues ​​and interpretability of each factor. In this specific example, five factors related to the higher-order impression elements and four factors related to the lower-order impression elements may be extracted.

[0082] Table 2 shows the factor loadings for each of the five factors on the higher-order impression components.

[0083] [Table 2]

[0084] Table 3 shows the factor loadings for the lower-order impression elements of each of the four factors.

[0085] [Table 3]

[0086] In this specific example, the analysis unit 212 may extract five factors as higher-order impression elements, namely, "It seemed protective and reassuring," "It seemed pretty and durable," "It felt solid and comfortable to wear," "It seemed easy to hold and match with other items," and "It felt luxurious and deep," using the magnitude of the factor loadings shown in Table 2 as the main criterion. Furthermore, the analysis unit 212 may extract four factors as lower-order impression elements, namely, "It was bright and eye-catching," "It was hard and linear," "It was thick and heavy," and "It was smooth and sleek," using the magnitude of the factor loadings shown in Table 3 as the main criterion. Here, the analysis unit 212 may add two lower-order impression elements, "It seemed scratch-resistant" and "It was shiny," to the extracted four factors without subjecting them to the subsequent factor analysis. These two elements were evaluation words that had small factor loadings on both elements in the preliminary factor analysis and could not be summarized in the four extracted factors. The analysis unit 212 may then analyze a total of six items. In this specific example, the analysis unit 212 extracted factors, but the step of extracting factors or components by the analysis unit 212 is not essential and may be omitted. For example, the analysis unit 212 may omit the extraction step for the emotion element and perform analysis on the three elements of "I felt excited," "I liked it," and "It was pleasant."

[0087] 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.

[0088] As shown in FIG. 4, the association model 401 includes a layer (402) including the difference between visual and visual-tactile low-level impressions (low-level impression elements), a layer (403) including high-level impression elements, and a layer (404) including emotional elements. The association model 401 may further include a layer (405) including the rate of change in the expected price as a layer including cognitive elements. The hierarchical structure shown in FIG. 4 is merely an example, and other hierarchical structures may be employed. For example, as shown in FIG. 4, the association model 401 may include layers including other cognitive elements and layers including behavioral tendency elements above the layer including the rate of change in the expected price. Alternatively, the cognitive elements may be in the lower layer, the emotional elements in the middle layer, and the behavioral elements in the upper layer. The association model may also include a layer including characteristic elements.

[0089] The association model shown in Figure 4 places the layer containing low-level impression elements at the bottom, and the layers containing high-level impression elements and emotional elements at the top, showing how the lower-level elements relate to the higher-level elements. 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 between the element at the start of the arrow and the element at the end of the arrow.

[0090] In Figure 4, arrows with positive values ​​indicate a positive correlation between elements. In the association model 401, as a lower-level element becomes larger, the related higher-level element also becomes larger. For example, there is a positive correlation between the lower-level impression element "seemed less vulnerable" and the element "seemed protective and reassuring." In this case, the more the subject's impression of the evaluation target being less vulnerable, the stronger the subject's impression of "seemed protective and reassuring" that the evaluation target has.

[0091] 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 increases, the associated higher-level element decreases. For example, there is a negative correlation between the low-level impression element "it was shiny" and the element "it had a luxurious and deep feel." In this case, the more shiny the evaluation target gives the subject, the smaller the impression of "it had a luxurious and deep feel."

[0092] The association model created by the method of the present invention includes information indicating the relationship between impression differences and attitude differences between two categories. For example, in the example shown in Figure 4, the subject's emotional element "I felt more excited (when touching it than when looking at the image)" is correlated with the higher-order impression elements "It seemed more protective and secure (when touching it than when looking at the image)," "It felt solid and comfortable to wear (when touching it than when looking at the image)," and two other higher-order impression elements, as well as the lower-order impression element "It felt bright and eye-catching (when touching it than when looking at the image)." Furthermore, in the example shown in Figure 4, the difference in the impression of the evaluation object "It felt solid and comfortable to wear (when touching it than when looking at the image)" has the greatest positive correlation with the subject's "elevated" emotion when the subject sees and touches the actual evaluation object, compared to when the subject only sees a photograph of the evaluation object. Therefore, it can be seen that the subject's impression of the evaluation object as being solid and comfortable to wear is more likely to cause a difference in the "elevated" emotion between the subject seeing an image of the evaluation object and the subject seeing and touching the actual object than other impressions. If the evaluation object gives the subject the impression of being heavy and comfortable to wear, when the subject sees and touches the actual object after seeing an image of the evaluation object, it is found that they are more likely to feel "more excited than when they saw the image."

[0093] On the other hand, it can be seen that the difference in the impression that "touching the evaluation object (rather than viewing an image) seemed more protective and reassuring" is negatively correlated with the difference in the subject's feeling of "elevated mood" when the subject sees and touches the actual evaluation object, compared to when the subject only views an image of the evaluation object. Therefore, if the evaluation object gives the subject a strong impression of being protective and reassuring, when the subject sees and touches the actual object after viewing an image of the evaluation object, they are more likely to feel "less excited than when viewing the image (or "more calm than when viewing only the image")."

[0094] [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.

[0095] 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 (S19) and a predicted value calculation step (S20) in addition to the evaluation support method according to the first embodiment. In Fig. 5, the processes of steps S11 to S18 are the same as the processes of steps S1 to S8 described in the first embodiment, and therefore their description 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.

[0096] 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.

[0097] 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 (S19). 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.

[0098] When new information is input, the prediction unit 214 inputs the new information into the association model (S19: input step). Subsequently, the prediction unit 214 calculates a predicted value of an element different from the element corresponding to the new information when the new information is input into the association model (S20: predicted value calculation step). For example, when a value indicating a new impression element is input, the prediction unit 214 calculates a predicted value of an attitude element related to the impression element. The prediction unit 214 causes the display unit 22 to present the predicted result indicating the calculated value (S21: presentation step). Note that the predicted value calculated by the prediction unit 214 is not limited to the value of the attitude element. For example, when a feature element is included in the association model, the prediction unit 214 may calculate a predicted value of an impression element related to the feature element. Alternatively, when impression elements are divided into low-order impression elements and high-order impression elements in the association model, and a value indicating a new low-order impression element is input, the prediction unit 214 may calculate a predicted value of a high-order impression element related to the low-order impression element. Furthermore, when a value indicating a new attitude element is input, the prediction unit 214 may calculate a predicted value of an impression element related to the attitude element (if the impression elements are divided into low-order impression elements and high-order impression elements, each element) or a feature element.

[0099] 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.

[0100] [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 (including a tablet terminal or smartphone) to function as each control block of the system (particularly each part included in the control unit 21, 21A).

[0101] In this case, the system includes a computer 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.

[0102] 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.

[0103] 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.

[0104] 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).

[0105] 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.

[0106] 〔summary〕 The evaluation support method according to a first aspect of the present invention includes a presentation step of presenting to a subject each of a plurality of evaluation objects belonging to two categories; an acquisition step of acquiring by a computer answers to a group of questions including, for each of the plurality of evaluation objects, a question statement related to each of the characteristics possessed by the plurality of evaluation objects and answer options to the question statement, wherein at least one of the question statement and the answer options includes at least one evaluation term related to each of the plurality of evaluation objects; and a model creation step of creating by a computer an association model including information indicating the degree of association between the elements based on the difference scores between the two categories of the answers and elements indicating factors or components common to the evaluation terms corresponding to the difference scores, wherein the elements include at least one of elements indicating an impression of the evaluation object and elements indicating an attitude toward the evaluation object, and the association model has a hierarchical structure with two or more layers.

[0107] In the evaluation support method according to a second aspect of the present invention, in the first aspect, the evaluation words included in the set of questions may be common to the two categories.

[0108] In the evaluation support method according to aspect 3 of the present invention, in the first or second aspect, the elements may further include elements indicating characteristics possessed by at least one of the evaluation targets in the two categories.

[0109] In the evaluation support method according to aspect 4 of the present invention, in any of aspects 1 to 3, the elements include elements indicating the attitude, and the elements indicating the attitude may include at least one of an element indicating the subject's feelings toward the evaluation object, an element indicating their cognition toward the evaluation object, and an element indicating their behavioral tendency toward the evaluation object.

[0110] In the evaluation support method of aspect 5 of the present invention, in any of aspects 1 to 4, the two categories may be (1) categories in which the properties or attributes of the evaluation objects are different from each other, or (2) categories in which the conditions under which the evaluation objects are presented are different from each other.

[0111] An evaluation support method according to aspect 6 of the present invention may be such that, in aspect 5, the two categories are categories in which the properties or attributes of the evaluation objects are different from each other, and include a category to which a group of products that are popular among the subjects belongs, and a category to which a group of products that are less popular than the group of products to which the subjects belong.

[0112] In the evaluation support method of aspect 7 of the present invention, in aspect 5, the two categories may be categories in which the conditions under which the evaluation object is presented are different from each other, and may include a situation in which an image of the evaluation object is shown to the subject, and a situation in which the actual evaluation object is shown to the subject and allowed to touch it.

[0113] The evaluation support method according to aspect 8 of the present invention, in any of aspects 1 to 7, may further include an input step in which a computer newly inputs information indicating at least one of the characteristics or elements of the evaluation target into the association model created in the model creation step, and a predicted value calculation step in which the computer calculates a predicted value of a specific element other than the input information based on the information input in the input step.

[0114] The evaluation support system according to a ninth aspect of the present invention includes an acquisition unit that acquires answers from the subject to a group of questions that includes, for each of a plurality of evaluation objects belonging to each of two categories, questions that include a question statement related to each of the characteristics of the plurality of evaluation objects and answer options to the question statement, and at least one of the question statement and the answer options includes at least one evaluation term related to each of the plurality of evaluation objects; and a model creation unit that creates an association model including information indicating the degree of association between the elements based on the difference scores between the two categories of the answers and elements that indicate factors or components common to the evaluation terms corresponding to the difference scores, wherein the elements include at least one of elements that indicate an impression of the evaluation object and elements that indicate an attitude toward the evaluation object, and the association model has a hierarchical structure with two or more layers. [Explanation of symbols]

[0115] 100, 100A evaluation support system 211 Acquisition Department 213 Model Creation Department 214 Prediction Department S1, S3, S11, S13 Presentation process S2, S4, S12, S14 acquisition process S7, S17 Model creation process S20 Prediction value calculation process

Claims

1. a presentation step of presenting to a subject each of a plurality of evaluation objects belonging to each of the two categories; an acquisition step in which a computer acquires answers from the subject to a group of questions including, for each of the plurality of evaluation targets, a question statement related to each of the characteristics of the plurality of evaluation targets and answer options to the question statement, wherein at least one of the question statement and the answer options includes at least one evaluation term related to each of the plurality of evaluation targets; a model creation step in which a computer creates an association model including information indicating the degree of association between the elements based on the difference score between the two categories of the answers and elements indicating factors or components common to the evaluation words corresponding to the difference score; Including, The elements include at least one of an element indicating an impression of the evaluation target and an element indicating an attitude toward the evaluation target, The association model has a hierarchical structure with two or more layers. Evaluation support methods.

2. The evaluation support method according to claim 1 , wherein the evaluation words included in the group of questions are common to the two categories.

3. The elements further include an element indicating a characteristic possessed by at least one of the evaluation subjects in the two categories. The evaluation support method according to claim 1 .

4. The elements include an element indicating the attitude, The evaluation support method according to claim 1, wherein the element indicating the attitude includes at least one of an element indicating the subject's feelings toward the evaluation object, an element indicating the subject's cognition toward the evaluation object, and an element indicating the subject's behavioral tendency toward the evaluation object.

5. 2. The evaluation support method according to claim 1, wherein the two categories are (1) categories in which the evaluation objects have different properties or attributes, or (2) categories in which the conditions under which the evaluation objects are presented are different.

6. The two categories are: The properties or attributes of the evaluation objects are in different categories, a category to which a group of products that are popular among the subjects belongs, and a category to which a group of products that are less popular than the group of products to which the subjects belong, The evaluation support method according to claim 5 .

7. The two categories are: The conditions under which the evaluation targets are presented are different from each other in the categories, A situation in which an image of the evaluation target is shown to the subject, and a situation in which an actual object of the evaluation target is shown to the subject and allowed to touch it, The evaluation support method according to claim 5 .

8. an input step in which a computer newly inputs information indicating at least one of the features of the evaluation target or the elements into the association model created in the model creation step; a predicted value calculation step in which the computer calculates a predicted value of a specific element other than the input information based on the information input in the input step, The evaluation support method according to claim 1 .

9. an acquisition unit that acquires answers from the subject to a group of questions that includes a question sentence related to each of the characteristics of a plurality of evaluation objects belonging to each of two categories that are presented to the subject, 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 each of the plurality of evaluation objects; a model creation unit that creates an association model including information indicating the degree of association between the elements based on the difference score between the two categories of the answer and elements indicating factors or components common to the evaluation words corresponding to the difference score; Including, The elements include at least one of an element indicating an impression of the evaluation target and an element indicating an attitude toward the evaluation target, The association model has a hierarchical structure with two or more layers. Evaluation support system.

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