JP7459006B2 - Factor analysis device, factor analysis method, and factor analysis program - Google Patents

Description

The present invention relates to an apparatus, method, and program for determining intervention content that promotes a behavioral change stage.

BACKGROUND ART Conventionally, measures have been proposed to assist users in autonomously performing countermeasure actions for promoting the user's behavioral change stage, for example, actions for maintaining or promoting health.
For example, it is possible to present a message to the user (for example, see Patent Document 1), to present physical activity according to the user's attributes (for example, see Patent Document 2), or to display an average situation and each user's individual situation. Intervention methods have been proposed, such as providing services according to the difference between the two (for example, see Patent Document 3).

JP2019-139320A Japanese Patent Application Publication No. 2018-161239 Patent No. 5321713

In conventional methods, once a user's attributes are identified based on questionnaire data or observation data, intervention content is selected from among pre-prepared intervention candidates based on knowledge obtained through trial and error. Selected.
However, effective intervention contents vary depending on the target behavior and user attributes, so if a different behavior is targeted, the intervention contents will differ even if the attributes are the same. Therefore, the knowledge was not utilized, and it was necessary to reexamine the rules for deciding the intervention content through trial and error.

SUMMARY OF THE INVENTION An object of the present invention is to provide a factor analysis device, a factor analysis method, and a factor analysis program that can analyze factors that should be intervened to promote or inhibit a user's desired behavior in order to make the user perform the desired behavior. do.

The factor analysis device according to the present invention includes a setting section that presets a plurality of candidate factors that inhibit or promote the execution of countermeasure actions by a user, and questionnaire items regarding the strength of the factors, and a setting section that presets the attributes of the user. an attribute determination unit that determines based on the attribute classification rules determined by the user; a score calculation unit that calculates a score for each of the plurality of factors by linking the user's attributes to the user's attributes based on the answers to the questionnaire items; The method further includes a factor selection unit that ranks the plurality of factors for each attribute based on the score, and selects a high-ranking factor as a factor that should be intervened in order to promote the countermeasure action.

The attribute determination unit may determine the attributes of the user based on responses to a questionnaire to the user.

The attribute determination unit may determine the attribute of the user based on historical observation data regarding the behavior of the user.

The score calculation unit may calculate scores for each of a plurality of axes including the strength of dissatisfaction and the strength of desire for improvement for each of the plurality of factors.

The factor selection unit may rank the factors based on an evaluation value that is the sum of the average scores of the multiple axes associated with each attribute.

The setting unit presets an intervention plan for each of the factor candidates, and the factor analysis device includes an intervention plan presenting unit that presents the intervention plan corresponding to the factor selected by the factor selection unit. You may prepare.

The factor analysis device includes an intervention plan evaluation unit that evaluates an intervention effect resulting from the implementation of the intervention plan, and the setting unit is configured to set the implemented intervention plan to: It may be excluded from the settings.

The setting unit may exclude a factor corresponding to the implemented intervention plan from the setting contents when the evaluation by the intervention plan evaluation unit is less than a predetermined value.

The factor analysis method according to the present invention includes a setting step of presetting a plurality of candidate factors that inhibit or promote the execution of countermeasure actions by a user, and questionnaire items regarding the strength of the factors; and a setting step of presetting the attributes of the user. a score calculation step of calculating a score for each of the plurality of factors by linking it to the user's attributes based on the answers to the questionnaire items; A computer executes a factor selection step of ranking the plurality of factors by the score for each attribute, and selecting the higher-ranking factors as factors that should be intervened to promote the countermeasure action.

The factor analysis program of the present invention is for causing a computer to function as the factor analysis device.

The present invention makes it possible to analyze factors that promote or hinder a desired behavior and require intervention in order to encourage the user to perform the behavior.

It is a diagram showing the functional configuration of a factor analysis device in an embodiment. FIG. 3 is a diagram illustrating a setting example of factor candidates in the embodiment. FIG. 3 is a diagram illustrating questionnaire items in the embodiment. It is a figure which illustrates the classification rule based on the answer of the questionnaire in embodiment. FIG. 3 is a diagram illustrating the results of calculating scores for each factor candidate on two axes in the embodiment. It is a figure showing the flow of a factor analysis method in an embodiment.

An example of an embodiment of the present invention will be described below.
In the factor analysis method of this embodiment, when the user's attributes, desired countermeasure behavior, and candidates for factors that promote or inhibit this behavior are set as input, each attribute is selected from among the candidate factors based on the questionnaire results. The factors that require intervention are identified for the users. This helps determine appropriate intervention content for users of each attribute.

Here, the targeted countermeasure behavior is one that raises the behavior change stage, and the behavior change stage is, for example, a step-by-step process to promote health, such as an indifference period, interest period, preparation period, implementation period, and maintenance period. There are several possible stages, such as a gradual stage from a state of indifference to the implementation of security measures to a state of continuous implementation of security measures.
In addition, user attributes are classified based on predetermined scales indicating user characteristics, such as gender, age, behavioral change stage, and personality.

FIG. 1 is a diagram showing the functional configuration of a factor analysis device 1 according to this embodiment.
The factor analysis device 1 is an information processing device (computer) such as a server device or a personal computer, and includes a control unit 10, a storage unit 20, as well as various data input/output devices and communication devices.

The control unit 10 is a part that controls the entire factor analysis device 1, and realizes each function in this embodiment by appropriately reading and executing various programs stored in the storage unit 20. The control unit 10 may be a CPU.

The storage unit 20 is a storage area for various programs and various data for making the hardware group function as the factor analysis device 1, and may be a ROM, RAM, flash memory, hard disk drive (HDD), or the like.
Specifically, the storage unit 20 stores setting information such as factor candidates, questionnaire items, attribute classification rules, etc., which will be described later, in addition to a program (factor analysis program) for causing the control unit 10 to execute each function of the present embodiment. Remember.

The control unit 10 includes a setting unit 11, an attribute determination unit 12, a score calculation unit 13, a factor selection unit 14, an intervention plan presentation unit 15, and an intervention plan evaluation unit 16.

Based on the input from the administrator, the setting unit 11 selects a plurality of candidate factors that inhibit or promote a user's execution of a certain countermeasure action, questionnaire items for the user regarding the strength of these factors, and user information. A classification rule for classifying the attributes of is set in advance.
Specifically, for example, various information is set as shown in (1) to (4) below.

(1) Targeted countermeasure actions:
One countermeasure action that the user is encouraged to take is set according to the intervention plan described below. In this embodiment, it is assumed that among various security measures such as using security software and upgrading the OS, the action of "not reusing passwords" is set as an example of the countermeasure action.

(2) Candidate factors:
A plurality of candidates for factors that inhibit or promote the implementation of countermeasure actions are set.
FIG. 2 is a diagram showing an example of setting factor candidates in this embodiment.

In this embodiment, candidate factors that inhibit the behavior of "not reusing passwords" are factors that the user recognizes, such as "it's a hassle," "I can't do it within my ability," and "I can't feel the effect." Candidates for inhibiting factors are set.

In addition, one or more intervention plans may be preset for each candidate factor. For example, intervention plans may be set such as "Show a simple method of countermeasures" for the factor "It's troublesome," "Show a method that even beginners can use" for the factor "It is beyond my capabilities," and "Show evidence of the effectiveness of the countermeasures" for the factor "I cannot feel the effect."

(3) Questionnaire items:
Items for asking the user about the attribute classification and the strength of the factor are set.
FIG. 3 is a diagram illustrating questionnaire items in this embodiment.

In this example, question items for determining the knowledge level regarding ICT are set in question X (1, 2, . . . ) for classifying attributes. For each question, the user selects "correct" or "incorrect" as an answer.

In addition, questions Y-1 and Y-2 to inquire about the strength of the factors that inhibit countermeasure actions are questions about the strength of feeling the inhibiting factors (degree of dissatisfaction) and the strength of the desire to eliminate dissatisfaction, respectively. This is a question regarding (improvement requests).

For example, in response to the inhibiting factor of ``not reusing passwords,'' which is a hindrance to the action of ``not reusing passwords,'' we asked questions such as ``Not reusing passwords is a hassle,'' and ``Not reusing passwords.'' ``I want to eliminate the trouble of not doing things.'' Each question has response options such as ``I don't agree at all,'' ``I don't agree,'' ``I agree,'' and ``I strongly agree.'' It will be done.
Note that questions Y-1 and Y-2 for inquiring about the strength of these factors may be automatically generated by applying character strings set as candidate countermeasure actions and factors to a predetermined template. good.

(4) Attribute classification rules:
Effective intervention plans vary depending on the user's characteristics, so rules are set for classifying users by attribute.
FIG. 4 is a diagram illustrating classification rules based on answers to a questionnaire (test) in this embodiment.

In this example, a rule is set in which the user's knowledge level regarding ICT is classified into high/low according to the correct answer rate (score) for N questions (test items).

The attribute determining unit 12 determines user attributes based on attribute classification rules set in advance by the setting unit 11. The attribute determining unit 12 may determine the user's attributes based on, for example, answers to a questionnaire given to the user, or based on historical observation data regarding the user's behavior.

Specifically, for example, a person may be classified into one of the behavioral change stages based on conditions based on questionnaire items, or, as mentioned above, knowledge level may be classified by comparing test scores with a threshold. Good too.

When classifying attributes based on observation data, for example, by observing the update status of the OS, users who have not updated the OS for a predetermined number of days or more are classified into the non-updating group, and the others are classified into the updating group. Further, for example, by observing the user's Web access history, it is possible to classify the user into a plurality of attributes related to the usage status of the Internet (frequency, type of access destination, etc.).

The score calculation unit 13 calculates scores for each of a plurality of factors based on the answers to the questionnaire items in association with user attributes.
At this time, the score calculation unit 13 may calculate scores for each of a plurality of axes, including the strength of dissatisfaction and the strength of desire for improvement, for each of the plurality of factors. In the above example of questionnaire items (Figure 3), the score on the x-axis is calculated according to the answer to question Y-1 for listening to hindrance factors, and the score on the y-axis is calculated according to the answer to question Y-2. Calculated.

FIG. 5 is a diagram illustrating the results of calculating the score for each factor candidate on two axes in this embodiment.
In this example, based on survey responses obtained from multiple users classified into certain attributes, the average scores for each axis for each factor are calculated and plotted on a two-axis graph of dissatisfaction and requests for improvement.

The factor selection unit 14 ranks a plurality of factors by score for each attribute, and selects the top factors as factors that should be intervened in to promote countermeasure behavior.
Here, the factor selection unit 14 ranks the plurality of factors, for example, based on an evaluation value that is the sum of the average values of the scores of each of the plurality of axes linked to the attributes. In the example of FIG. 5, the evaluation value is the degree of dissatisfaction x + the degree y of request for improvement, and the factor candidates with strong dissatisfaction and request are ranked high. Note that the calculation formula for the evaluation value is not limited to this.

The factor selection unit 14 selects a factor to be intervened in based on the following scale (1) or (2), for example, from the plurality of factors ranked in this way.

(1) Relative scale:
The factor selection unit 14 extracts the factor with the highest evaluation value. In the example of FIG. 5, the factor of rank 1 is selected.
(2) Absolute scale:
The factor selection unit 14 extracts factors whose evaluation values are greater than or equal to a predetermined threshold. In the example of FIG. 5, when the threshold value is set to 10, factors of rank 1 and rank 2 are selected.

This selection method is based on the unmet needs theory, which states that by intervening in the area where dissatisfaction or request for improvement is strongest, user satisfaction and utilization rate will be increased, and two axes are set as indicators.
In this embodiment, an example is shown in which the score for each factor is calculated on two axes, but it may be calculated on one axis or on three or more axes.

The intervention plan presenting unit 15 presents an intervention plan corresponding to the factor selected by the factor selecting unit 14 .
For example, in the example of FIG. 2, when the factor "it is troublesome" is selected, the corresponding intervention plan "show a simple countermeasure" is presented.

The intervention plan evaluation unit 16 evaluates the intervention effect of implementing the intervention plan.
Existing methods can be applied as the evaluation method. For example, the intervention plan evaluation unit 16 may conduct a post-questionnaire for each of the intervention group and the control group and perform a test for significant differences to determine whether or not there is a significant difference in behavioral change.
Furthermore, the intervention plan assessment unit 16 may assess the degree of intervention effectiveness by measuring the rate at which the user's behavior has changed based on the observation data.

Based on this evaluation result, the setting unit 11 may exclude the implemented intervention plan from the settings set by the setting unit 11 if the evaluation by the intervention plan evaluation unit 16 is less than a predetermined value.
Alternatively, if the evaluation by the intervention plan evaluation section 16 is less than a predetermined value, the setting section 11 may exclude the factor corresponding to the implemented intervention plan from the settings made by the setting section 11.

FIG. 6 is a diagram showing the flow of the factor analysis method in this embodiment.
In the analysis phase (steps S1 to S4), factors to be intervened are selected for each attribute set according to the countermeasure action.
In the intervention phase (steps S11 to S16), user attributes are identified and an intervention plan is put into practice.

In step S1, the factor analysis device 1 obtains answers from the user based on the set questionnaire items. Furthermore, the factor analysis device 1 collects observation data regarding the user's behavior history, as necessary.

In step S2, the factor analysis device 1 classifies the attributes of each user based on the answers to the questionnaire or observation data and the attribute classification rules.

In step S3, the factor analysis device 1 totals the scores of each factor candidate for each attribute, and selects and stores a factor to be intervened based on a predetermined evaluation value.

In step S4, the factor analysis device 1 determines the intervention content according to the selected factor for each attribute. Alternatively, the factor analysis device 1 receives and stores input of intervention details corresponding to the presented factors.
As a result, appropriate intervention details are determined for each attribute for the set countermeasure action.

In step S11, the factor analysis device 1 conducts a questionnaire similar to that in the analysis phase (only the question items related to attribute classification) to the user who wants to actually perform the intervention, and obtains the answers. Alternatively, the factor analysis device 1 collects observation data regarding the user's behavior history.

In step S12, the factor analysis device 1 classifies the attributes of each user based on the questionnaire responses or observation data and the attribute classification rules.

In step S13, the factor analysis device 1 extracts and presents the intervention content determined in step S4 according to the attributes classified in step S12.

In step S14, the presented intervention content is implemented for the user.
In step S15, the factor analysis device 1 evaluates the effectiveness of the implemented intervention content.

In step S16, the factor analysis device 1 deletes the less effective intervention plan or factor candidate from the set database according to the evaluation result of the intervention effect.

According to this embodiment, the factor analysis device 1 preliminarily sets up a plurality of candidate factors that hinder or promote the execution of countermeasure actions by the user, and questionnaire items related to the strength of the factors, and calculates a score for each of the plurality of factors based on the answers to the questionnaire items and the attributes of the user.The factor analysis device 1 then ranks the plurality of factors for each attribute by the scores, and selects the top factors as factors in which intervention should be made to promote the execution of countermeasure actions.
Therefore, the factor analysis device 1 can automatically analyze factors that promote or hinder a desired behavior and require intervention in order to allow the user to perform the behavior. As a result, it becomes possible to determine an appropriate intervention plan according to the selected factors.

In addition, this can promote the practice of various behaviors, such as health maintenance measures or security measures. Contribute to Goal 9: “Ensure healthy lives and promote well-being for all people” and Goal 9: “Build resilient infrastructure, promote sustainable industrialization and foster innovation.” becomes possible.

Furthermore, the factor analysis device 1 determines the attributes of a user based on responses to a questionnaire given to the user, or based on observation data of the user's behavior history.
This allows the factor analysis device 1 to appropriately determine the attributes of each user based on a predetermined classification rule.

The factor analysis device 1 calculates scores for each of a plurality of axes, including the strength of dissatisfaction and the strength of desire for improvement, for each of the plurality of factors.
Thereby, the factor analysis device 1 can improve the reliability of ranking of factors and determine an appropriate intervention plan using evaluation values based on a plurality of indicators.
For example, the factor analysis device 1 ranks multiple factors based on an evaluation value that is the sum of the average scores of multiple axes linked to this attribute for each attribute, and determines which factors should be intervened in. Factors can be easily selected.

The factor analysis device 1 preliminarily sets an intervention plan for each candidate factor, and presents the intervention plan corresponding to the selected factor.
This allows the factor analysis device 1 to efficiently present appropriate intervention plans based on the pre-settings.

The factor analysis device 1 evaluates the intervention effect of implementing the intervention plan, and if the evaluation is less than a predetermined value, excludes the implemented intervention plan or the factor corresponding to the intervention plan from the setting contents.
Thereby, the factor analysis device 1 can optimize the factor candidates or intervention plans set in advance by narrowing them down to those that actually have an intervention effect.

Although the embodiments of the present invention have been described above, the present invention is not limited to the embodiments described above. Further, the effects described in the embodiments described above are merely a list of the most preferable effects resulting from the present invention, and the effects according to the present invention are not limited to those described in the embodiments.

The factor analysis method by the factor analysis device 1 is realized by software. When realized by software, a program constituting this software is installed in an information processing device (computer). Further, these programs may be recorded on removable media such as CD-ROMs and distributed to users, or may be distributed by being downloaded to users' computers via a network. Furthermore, these programs may be provided to the user's computer as a web service via a network without being downloaded.

REFERENCE SIGNS LIST 1 Factor analysis device 10 Control unit 11 Setting unit 12 Attribute determination unit 13 Score calculation unit 14 Factor selection unit 15 Intervention plan presentation unit 16 Intervention plan evaluation unit 20 Storage unit

Claims (10)

a setting unit that presets candidates for a plurality of factors that inhibit or promote the execution of countermeasure actions by the user, and questionnaire items regarding the strength of the factors;
an attribute determination unit that determines the user's attributes based on preset attribute classification rules;
a score calculation unit that calculates a score for each of the plurality of factors based on the answers to the questionnaire items in association with the attributes of the user;
A factor analysis device comprising: a factor selection unit that ranks the plurality of factors by the score for each of the attributes, and selects a high-ranking factor as a factor that should be intervened in order to promote the countermeasure action. The factor analysis device according to claim 1, wherein the attribute determining unit determines the attribute of the user based on answers to a questionnaire for the user. The factor analysis device according to claim 1, wherein the attribute determining unit determines the attribute of the user based on historical observation data regarding the user's behavior. The factor analysis according to any one of claims 1 to 3, wherein the score calculation unit calculates scores for each of a plurality of axes including the strength of dissatisfaction and the strength of desire for improvement for each of the plurality of factors. Device. The factor analysis device according to claim 4, wherein the factor selection unit ranks the multiple factors based on an evaluation value that is the sum of the average scores of the multiple axes associated with each attribute. The setting unit presets an intervention plan for each of the candidate factors,
The factor analysis device according to any one of claims 1 to 5, further comprising an intervention plan presenting section that presents the intervention plan corresponding to the factor selected by the factor selecting section. comprising an intervention plan evaluation unit that evaluates the intervention effect by implementing the intervention plan,
The factor analysis device according to claim 6, wherein the setting unit excludes the implemented intervention plan from the setting contents if the evaluation by the intervention plan evaluation unit is less than a predetermined value. 8. The factor analysis device according to claim 7, wherein the setting section excludes a factor corresponding to the implemented intervention plan from the setting contents when the evaluation by the intervention plan evaluation section is less than a predetermined value. A setting step of setting in advance a plurality of candidate factors that hinder or promote the execution of countermeasure actions by a user and questionnaire items regarding the strength of the candidate factors;
An attribute determination step of determining an attribute of the user based on a preset attribute classification rule;
a score calculation step of calculating a score for each of the plurality of factors based on the answers to the questionnaire items and linking the answers to the questionnaire items to attributes of the user;
a factor selection step of ranking the multiple factors for each attribute according to the score, and selecting a top-ranked factor as a factor in which intervention should be made to promote the countermeasure action, the factor analysis method being executed by a computer. A factor analysis program for causing a computer to function as the factor analysis device according to any one of claims 1 to 8.

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