Provision device, provision method, and program

Abstract

The purpose of the present disclosure is to increase acceptance of users with respect to a promotion target.　The present disclosure pertains to a provision device that performs provision for promoting a promotion target, the provision device comprising: an identification unit that, by using a technology acceptance model, performs a covariance structure analysis on acceptance-related information including answers of users to a questionnaire related to acceptance factors and performed on the promotion target, outputs an analysis result indicating the degree to which the plurality of acceptance factors affect the acceptance of users, and identifies, from among the plurality of acceptance factors, a prescribed acceptance factor that is an influence source exhibiting the maximum score of causal effects in the analysis result; and a selection unit, within an intervention-related information management unit in which the acceptance factor, an intervention target that is a target to be intervened by the promotion activity of the promotion target, and an intervention method at the time of intervention with respect to the intervention target are managed in association with one another, that selects a prescribed intervention target and a prescribed intervention method corresponding to the prescribed acceptance factor.

Description

Providing device, providing method, and program

[0001] The present disclosure relates to a technology for promoting a target of promotion, such as an information system, to each user by utilizing the estimation results of a technology acceptance model.

[0002] In order to introduce and spread newly emerging technologies (including services and information systems), models have been proposed to analyze factors that influence people's acceptance, and methods have been used to analyze the models by collecting actual user data.

[0003] For example, the Technology Acceptance Model (TAM) is widely used as a machine learning model to analyze the factors that influence people's acceptance of information systems. This model was proposed as a model of human behavior when using information systems, such as new digital therapeutic applications for smartphones. It lists four factors that influence an individual's use of a given technology, such as an information system: "perceived usefulness," "perceived ease of use," "attitude toward using," and "behavioral intention to use" (see Figure 6).

[0004] Each acceptance factor can be observed by conducting a survey of users, and the extent to which each acceptance factor has an impact can be determined by analyzing the survey data using methods such as covariance structure analysis (Non-Patent Document 1). This makes it possible to analyze the acceptance factors for introducing or popularizing health promotion applications, etc.

[0005] Davis, FD (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS quarterly, 319-340. <https: / / www.researchgate.net / publication / 200085965_Perceived_Usefulness_Perceived_Ease_of_Use_and_User_Acceptance_of_Information_Technology>

[0006] However, the method of Non-Patent Document 1 merely estimates the acceptance factors, and does not know how to promote the target of the information system, etc. to each user based on these acceptance factors, making it difficult to increase the acceptance of each user.

[0007] The present disclosure has been made in consideration of the above circumstances, and aims to increase the acceptability of each user for the target of dissemination.

[0008] In order to solve the above problem, the disclosure of claim 1 is a provision device that makes a provision to popularize a target for promotion, and uses a technology acceptance model to perform covariance structure analysis of acceptance-related information including responses to a questionnaire of each user regarding acceptance factors administered to the target for promotion, thereby outputting an analysis result indicating the extent to which each of a plurality of acceptance factors influences the acceptance of each user, and the provision device has: an identification unit that identifies a predetermined acceptance factor among the plurality of acceptance factors that is the source of influence with the highest causal score in the analysis result; an intervention-related information management unit that associates and manages the acceptance factor, an intervention target that is an object to be intervened in the promotion activity of the target for promotion, and an intervention method for intervening in the intervention target, and the selection unit that selects a predetermined intervention target and a predetermined intervention method corresponding to the predetermined acceptance factor; and an output unit that outputs information indicating at least the predetermined intervention target and the predetermined intervention method.

[0009] As described above, the present disclosure has the effect of increasing the acceptability of each user for the target of dissemination.

[0010] 7 is a configuration diagram of a provision system according to an embodiment. FIG. 8 is an electrical hardware configuration diagram of a provision device according to an embodiment. FIG. 9 is a conceptual diagram of an acceptance-related information management table. FIG. 10 is a conceptual diagram of an intervention-related information management table. FIG. 11 is a diagram showing an example of a questionnaire showing multiple questions for each acceptance factor. FIG. 12 is a diagram showing a base TAM. FIG. 13 is a diagram showing a technology acceptance model according to an embodiment. FIG. 14 is a diagram showing analysis results when a covariance structure analysis is performed using the technology acceptance model shown in FIG. 7. FIG. 15 is a diagram showing an example of a prompt in estimation using a generation AI by an identification unit. FIG. 16 is a diagram showing an example of a prompt in estimation using a generation AI by a selection unit. A flowchart showing identification processing executed by an identification unit. A flowchart showing selection processing executed by a selection unit.

[0011] Hereinafter, embodiments of the present invention will be described with reference to the drawings. Note that the present invention is not limited to the embodiments shown below, and various modifications are possible within the scope of the technical concept of the present invention. Since the drawings are intended to conceptually explain the present invention, dimensions, ratios, or numbers may be exaggerated or simplified as necessary to facilitate understanding.

[0012] [Outline of the Providing Device] The functional configuration of the providing device 10 will be described using Fig. 1. Fig. 1 is a configuration diagram of a providing system according to an embodiment. In Fig. 1, the providing device 10, the user terminal 30, and the popularizer terminal 50 are configured by one or more computers. Of the providing device 10, the user terminal 30, and the popularizer terminal 50, at least the providing device 10 is a component of the providing system.

[0013] The providing device 10 provides a promotion target, such as a digital therapeutic application, for promotion. To this end, the providing device 10 identifies a predetermined acceptance factor that most influences user acceptance among multiple acceptance factors based on acceptance-related information, including responses to a questionnaire about acceptance factors administered to each user for the promotion target. The providing device 10 then provides predetermined intervention-related information to the popularizer terminal 50 of the popularizer who is promoting the promotion target, including information on "acceptance factors" that can be identified by the identification unit 12 described below, "intervention targets" that are targets for intervention in promotion activities to increase each user's acceptance, and specific "intervention methods" for intervening in the intervention targets. The providing device 10 can also be referred to as a proposing device that proposes intervention-related information. The providing device 10 may also provide information on intervention targets and intervention methods without providing the predetermined acceptance factors.

[0014] The user terminal 30 is operated by a specific user and transmits data of responses to a questionnaire entered by the specific user to the providing device 10. The responses include the user ID of the user who sent the responses. The user ID is an example of user identification information for identifying the user.

[0015] In addition, an application that detects details related to the user's exercise, sleep, etc. can be installed on the user terminal 30, which can then transmit data on the detection results (reception-related information) to the providing device 10. This application can also transmit to the providing device 10 detection result data (reception-related information) acquired from a wearable device worn by the user.

[0016] The popularizer terminal 50 is operated by a popularizer who is trying to popularize a newly emerging technology (including services, information systems, etc.), and receives data of specified intervention-related information sent from the providing device 10 and displays the specified intervention-related information, etc.

[0017] [Hardware Configuration] Next, the electrical hardware configuration of the providing device 10 will be described with reference to Fig. 2. Fig. 2 is a diagram showing the electrical hardware configuration of the providing device and database server according to the embodiment.

[0018] As shown in Figure 2, the providing device 10 has a drive device 1000, an auxiliary storage device 1002, a memory device 1003, a processor 1004, an interface device 1005, a display device 1006, an input device 1007, an output device 1008, etc., which are all interconnected by a bus 1010.

[0019] The program that realizes the processing on the computer is provided by a recording medium 1001, such as a CD-ROM or a memory card. When the recording medium 1001 storing the program is set in the drive device 1000, the program is installed from the recording medium 1001 to the auxiliary storage device 1002 via the drive device 1000. However, the program does not necessarily have to be installed from the recording medium 1001, but may be downloaded from another computer via the communication network 100. The auxiliary storage device 1002 stores the installed program as well as necessary files, data, etc.

[0020] When an instruction to start a program is received, the memory device 1003 reads the program from the auxiliary storage device 1002 and stores it. The processor 1004 realizes functions related to the device in accordance with the program stored in the memory device 1003. The processor 1004 may include not only a CPU (Central Processing Unit) but also a GPU (Graphics Processing Unit).

[0021] The interface device 1005 is used as an interface for connecting to a communication network, etc. The display device 1006 displays a GUI (Graphical User Interface) or the like according to a program. The input device 1007 is composed of a keyboard, mouse, buttons, a touch panel, etc., and is used to input various operation instructions. The output device 1008 outputs the calculation results to an external device such as a printer or an external display.

[0022] The user terminal 30 and the spreader terminal 50 have the same hardware configuration as the providing device 10, and therefore a description thereof will be omitted.

[0023] [Functional Configuration of Providing Device] Next, the functional configuration of the providing device 10 will be described with reference to FIG.

[0024] As shown in Fig. 1, the providing device 10 has an acquiring unit 11, an identifying unit 12, a selecting unit 13, and an output unit 14. Each of these units has a function realized by an instruction from a processor 1004 (described later in Fig. 2) based on a program. A storage unit 20 is realized by an auxiliary storage device 1002, a memory device 1003, or a recording medium 1001. In this storage unit 20, a reception-related information management unit 21 and an intervention-related information management unit 22 are constructed as DBs (databases).

[0025] The acquisition unit 11 of the providing device 10 is communicably connected to a user terminal 30 via a communication network such as the Internet or a LAN (Local Area Network). The output unit 14 is communicably connected to a spreader terminal 50 via the communication network.

[0026] Furthermore, at least one of the reception-related information management unit 21 and the intervention-related information management unit 22 may be managed by an external device (such as a database server) outside the providing device. In this case, the identification unit 12 can access the external device to search for the reception-related information management unit 21, and the selection unit 13 can access the external device to search for the intervention-related information management unit 22.

[0027] <Each Management Unit> (Reception-related Information Management Unit) The reception-related information management unit 21 manages reception-related information relating to the reception of each user (people) for a dissemination target, such as a newly emerged technology, service, or information system. Specifically, the reception-related information management unit 21 is configured by a reception-related information management table as shown in Fig. 3. Fig. 3 is a conceptual diagram of the reception-related information management table.

[0028] As shown in Figure 3, the acceptance-related information indicates, for each user ID used to identify a user, the user's answers to multiple questions regarding each acceptance factor handled in the technology acceptance model M1 (described below). This acceptance-related information is information (data) acquired as responses to a questionnaire from each user's user terminal 30. (Intervention-related information management unit) The intervention-related information management unit 22 manages intervention-related information indicating, depending on the acceptance factor, who (the intervention target) should be given what intervention method to increase acceptance among general users. Specifically, the intervention-related information management unit 22 is configured by an intervention-related information management table as shown in Figure 4. Figure 4 is a conceptual diagram of the intervention-related information management table.

[0029] As shown in Figure 4, the intervention-related information is information (data) that associates information on "acceptance factors" that can be identified by the identification unit, "intervention targets" that are targets for intervention in the dissemination activities of the dissemination target in order to increase the acceptability of each user, and specific "intervention methods" for intervening in these intervention targets.

[0030] Even if the acceptance factors are the same, there are multiple different intervention targets, and intervention methods by experts etc. are indicated for each of the multiple different intervention targets.

[0031] <Functional Configuration> (Acquisition Unit) The acquisition unit 11 acquires data of responses (acceptance-related information) to a questionnaire such as that shown in FIG. 5 from each user terminal 30. FIG. 5 is a diagram showing an example of a questionnaire showing multiple questions for each acceptance factor. Generally, there are four acceptance factors: perceived usefulness, perceived ease of use, attitude toward using, and behavioral intention to use. In this embodiment, social influence is also added as a new acceptance factor.

[0032] In addition, information from the usage history of an information system and data from user sensors, etc., showing that the system can be used without any errors or problems can also be used as an indication of the information system's "perceived ease of use."

[0033] (Identification Unit) The identification unit 12 has a technology acceptance model M1 that includes social influences as an acceptance factor. The "technology acceptance model" applies the Theory of Reasoned Action (TRA), a user (human) behavior model that has been widely studied in the field of social psychology, and is proposed as a behavior model of users (humans) who use newly emerging technologies, services, information systems, etc., and is widely used as a model for analyzing factors (acceptance factors) that influence users' acceptance of technologies, etc. In this embodiment, the technology acceptance model M1 uses the TAM shown in Non-Patent Document 1 as an example, but is not limited to this. The technology acceptance model M1 may also be TAM2 or UTAUT (Unified Theory of Acceptance and Use of Technology).

[0034] Figure 6 shows the base TAM. Figure 7 shows the technology acceptance model of this embodiment. The technology acceptance model M1 of this embodiment is a model in which the acceptance factor of "social influence" is added to the external variables of the TAM model shown in Figure 6. Note that in Figures 7 and 8, the horizontal direction in Figure 6 is used to draw the diagram, but the vertical direction in Figures 7 and 8 is used to draw the diagram, because the arrows and numerical values ​​make the diagrams complicated.

[0035] The identification unit 12 also reads out each piece of reception-related information from the reception-related information management unit 21. Then, the identification unit 12 performs Structural Equation Modeling (SEM) on the reception-related information using (according to) the technology acceptance model M1, and outputs the analysis results shown in FIG.

[0036] Figure 8 shows the results of a covariance structure analysis performed using the technology acceptance model shown in Figure 7. "Covariance structure analysis" is an analysis that uses a numerical value called covariance to model the relationship between multiple interrelated elements and the degree of that relationship. Furthermore, the "analysis results" show the extent to which each of multiple acceptance factors (here, five acceptance factors) influences each user's acceptance based on acceptance-related information, as determined by covariance structure analysis. In Figure 8, the edge values ​​indicate standardized coefficients, and a larger value can be considered to indicate a larger influence (greater degree of influence).

[0037] Furthermore, the identification unit 12 identifies a predetermined acceptance factor that is the influence source of the maximum causal score in the analysis result from among the multiple acceptance factors. In Fig. 8, since the maximum causal score of "0.915" is the influence of "social influence," "social influence" is identified as the acceptance factor that most influences user acceptance.

[0038] Note that because the detection results of the user's sensor, etc., are not always complete, some of the reception-related information may be missing or the number of pieces of reception-related information may be insufficient. Therefore, if there are missing pieces of reception-related information, the identification unit 12 estimates and complements the missing information using generative artificial intelligence (AI) such as large language models (LLMs). If there are insufficient pieces of reception-related information, the identification unit 12 complements them using a synthetic minority over-sampling technique (SMOTE) library (see Reference 1). The identification unit 12 stores the complemented reception-related information in the reception-related information management unit 21 and uses it in the current and subsequent identification processes (see FIG. 11). <Reference 1> SMOTE: Synthetic Minority Over-sampling Technique (https: / / jair.org / index.php / jair / article / view / 10302) Note that FIG. 9 illustrates an example of a prompt used by the identification unit 12 in estimation using a generative AI. FIG. 9 illustrates an example of a prompt used by the identification unit 12 in estimation using a generative AI.

[0039] (Selection unit) The selection unit 13 searches the intervention-related information management unit 22 using the predetermined acceptance factor identified by the identification unit 12 as a search key, thereby reading out all intervention-related information including intervention targets and intervention methods corresponding to the predetermined acceptance factor. Furthermore, the selection unit 13 randomly selects predetermined intervention-related information from all the read intervention-related information. The predetermined intervention-related information may be one or more. Furthermore, the selection unit 13 may select the predetermined intervention-related information according to some estimation model or score.

[0040] The identification unit 12 may find that some of the intervention-related information it searches for and retrieves is missing, or that the number of pieces of intervention-related information it searches for and retrieves is insufficient. Therefore, if there is a missing piece of information, the selection unit 13, like the identification unit 12, estimates and complements the missing information using a generation AI such as LLM. If the lack of information is due to UI / UX issues, the selection unit 13 complements the information based on existing design guidelines (see References 2 and 3). The selection unit 13 stores the complemented intervention-related information in the intervention-related information management unit 22 and uses it for the current and subsequent selection processes (see Figure 12). Reference 2: Human Interface Guidelines (HIG) (https: / / developer.apple.com / jp / design / ) Reference 3: Usability-related standards (ISO 9241, ISO 13407) (https: / / u-site.jp / usability / standards) Figure 10 shows an example of a prompt displayed when the selection unit 13 uses a generation AI to make an estimation. FIG. 10 is a diagram showing an example of a prompt in estimation by the selection unit using a generation AI.

[0041] (Output unit) The output unit 14 outputs the specified intervention-related information (specified acceptance factors, specified intervention targets, specified intervention methods) selected by the selection unit 13 to the popularizer terminal 50, thereby providing the popularizer with information to increase user acceptance.

[0042] The output destination may be not only the popularizer terminal 50 but also the display device 1006 of the providing device 10, or may be an external device such as a printer or an external display. The output unit 14 may output information indicating at least the predetermined intervention target and the predetermined intervention method out of the intervention-related information (predetermined acceptance factors, predetermined intervention target, predetermined intervention method).

[0043] [Processing or Operation of the Embodiment] Next, processing or operation relating to the providing method of the present embodiment will be described with reference to FIGS. 11 and 12. FIG.

[0044] <Processing of Identification Unit> First, the identification process executed by the identification unit 12 will be described with reference to Fig. 11. Fig. 11 is a flowchart showing the identification process executed by the identification unit. Note that, before process S11, the acquisition unit 11 acquires questionnaire response data from the user terminal 30 of each user, and stores and manages reception-related information including the responses in the reception-related information management unit 21.

[0045] S11: The specification unit 12 reads out each piece of reception-related information from the reception-related information management unit 21.

[0046] S12: The specification unit 12 determines whether each piece of reception-related information is missing or whether the number of pieces of reception-related information is less than a predetermined number. The predetermined number is, for example, one piece of reception-related information without missing information.

[0047] S13: In process S12, if there is a missing piece of reception-related information or the number is less than a predetermined number (YES), the identification unit 12 complements the missing reception-related information and stores it in the reception-related information management unit 21, or performs complementation to generate at least one piece of reception-related information without a missing piece of information and stores it in the reception-related information management unit 21.

[0048] S14: In process S12, if there are no missing data and the number is equal to or greater than a predetermined number (NO), and after process S13, the identification unit 12 performs covariance structure analysis of the acceptance-related information using (according to) the technology acceptance model M1, and outputs the analysis results as shown in FIG. 8.

[0049] S15: The identification unit 12 identifies a specific acceptance factor (here, "social influence") from among multiple acceptance factors (here, five acceptance factors) that is the source of influence with the highest causal score in the analysis results of process S14.

[0050] S16 : The identification unit 12 outputs the identified predetermined acceptance factor to the selection unit 13 .

[0051] This concludes the description of the specific processing.

[0052] <Processing of Selector> Next, the selection processing executed by the selector 13 will be described with reference to Fig. 12. Fig. 12 is a flowchart showing the selection processing executed by the selector.

[0053] S21: The selection unit 13 searches for each intervention-related information managed by the intervention-related information management unit 22 based on the predetermined acceptance factor acquired in step S16.

[0054] S22: The selection unit 13 determines whether the intervention-related information to be searched for is missing or whether the number of pieces of intervention-related information is less than a predetermined number. The predetermined number is, for example, one piece of intervention-related information without missing pieces.

[0055] S23: In process S22, if there is a missing piece or the number is less than a predetermined number (YES), the selection unit 13 complements the missing intervention-related information and stores it in the intervention-related information management unit 22, or performs complementation to generate at least one piece of intervention-related information without a missing piece and stores it in the intervention-related information management unit 22.

[0056] S24: In process S22, if there are no missing data and the number is greater than or equal to a predetermined number (NO), and after process S23, the selection unit 13 selects and reads out from the intervention-related information management unit 22 the specified intervention-related information corresponding to the specified acceptance factor.

[0057] S25: The selection unit 13 outputs the selected predetermined intervention-related information to the output unit 14.

[0058] This concludes the explanation of the selection process.

[0059] Thereafter, the output unit 14 outputs the predetermined intervention-related information selected by the selection unit 13 to the popularizer terminal 50 or the like, thereby providing the popularizer with information to increase user acceptance.

[0060] [Major Effects of the Embodiments] As described above, according to the embodiments, the identification unit 12 uses the technology acceptance model M1 to perform covariance structure analysis on acceptance-related information, including questionnaire responses, to identify predetermined acceptance factors that most influence each user's acceptance. Furthermore, the selection unit 13 selects predetermined intervention-related information that corresponds to the predetermined acceptance factors and indicates a specific intervention target and intervention method. The output unit 14 then outputs the predetermined intervention-related information to the disseminator terminal 50, etc. As a result, the providing device 10 provides information on how to disseminate an information system or other dissemination target to each user based on the predetermined acceptance factors, thereby achieving the effect of increasing each user's acceptance of the dissemination target, such as a digital therapeutic application.

[0061] [Supplementary Note] The present invention is not limited to the above-described embodiment, and may have the following configurations or processes (operations).

[0062] (1) The providing device 10 can be realized by a computer and a program, but this program can also be recorded on a (non-transitory) recording medium or provided via a communication network.

[0063] (2) The processor 1004 as hardware may be a single processor or multiple processors.

[0064] REFERENCE SIGNS LIST 10 Providing device 11 Acquisition unit 12 Identification unit 13 Selection unit 14 Output unit 20 Storage unit 21 Reception-related information management unit 22 Intervention-related information management unit 30 User terminal 50 Disseminator terminal

Claims

1. A provision device that provides information to promote a target for promotion, comprising: an identification unit that uses a technology acceptance model to perform covariance structure analysis of acceptance-related information including responses to a questionnaire administered to each user regarding acceptance factors for the target, thereby outputting analysis results indicating the degree to which each of a plurality of acceptance factors influences the acceptance of each user, and identifies a predetermined acceptance factor from among the plurality of acceptance factors that is the source of influence with the highest causal score in the analysis results; a selection unit that selects a predetermined intervention target and a predetermined intervention method corresponding to the predetermined acceptance factor in an intervention-related information management unit that associates and manages the acceptance factor, an intervention target that is an object to be intervened in the promotion activities of the target for promotion, and an intervention method for intervening in the intervention target; and an output unit that outputs information indicating at least the predetermined intervention target and the predetermined intervention method.

2. The providing device according to claim 1, wherein the technology acceptance model is a model in which an acceptance factor, which is a social influence, is added to the external variables of the Technology Acceptance Model (TAM) model.

3. A provision method executed by a provision device that provides a provision for the promotion of a target for promotion, comprising: a specification process that uses a technology acceptance model to perform covariance structure analysis of acceptance-related information including each user's responses to a questionnaire regarding acceptance factors administered to the target for promotion, outputting analysis results indicating the extent to which each of a plurality of acceptance factors influences the acceptance of each user, and specifies a predetermined acceptance factor among the plurality of acceptance factors that is the source of influence with the highest causal score in the analysis results; a selection process that selects a predetermined intervention target and a predetermined intervention method corresponding to the predetermined acceptance factor in an intervention-related information management unit that associates and manages the acceptance factor, an intervention target that is an object to be intervened in the promotion activities of the target for promotion, and an intervention method for intervening in the intervention target; and an output process that outputs information indicating at least the predetermined intervention target and the predetermined intervention method.

4. A program for causing a computer to execute the method according to claim 3.

Images