JP2020135543A - Estimation device, estimation method, program, and recording medium for engagement - Google Patents

Abstract

To provide a novel system that makes it possible to estimate engagement while a questionnaire is made essential.SOLUTION: An engagement estimation device according to the present invention includes an information acquisition part for information from a database, an input part, a similar network extraction part, a similar property extraction part, and a calculation part. The database includes property information, network information, and engagement information, group by group. The similar network extraction part selects a group similar to a feature quantity of network information of an object group input from the input part, and extracts engagement information X thereof. The similar property extraction part selects a group similar to property information of the object group, and extracts engagement information Y thereof. The calculation part calculates, based upon X and Y, an average of engagement values as to an item of an engagement factor, and estimates the average value as an engagement value Z of the object group.SELECTED DRAWING: Figure 1

Description

The present invention relates to an estimation device, an estimation method, a program, and a recording medium for estimating engagement in a group such as a company.

In an organization such as a company, it is extremely important to understand the engagement of the organization as information for improving the performance of the organization. Therefore, in the above-mentioned organization, for example, data on human resources is grasped, regular questionnaires are conducted to employees, and engagement is grasped.

However, grasping engagement based on a questionnaire involves, for example, the subjectivity of the person in charge of summarizing the results of the questionnaire. In addition, even if the questionnaire is conducted regularly, it is difficult to estimate the transition of engagement after the questionnaire until the next questionnaire result is obtained, that is, it is difficult to estimate the engagement in real time.

Therefore, an object of the present invention is to provide a new system that enables estimation of engagement without requiring a questionnaire.

In order to achieve the above object, the engagement estimation device of the present invention is used.
Includes information acquisition unit, input unit, similar network extraction unit, similar attribute extraction unit, and engagement calculation unit.
The information acquisition unit
For each group, the group information is acquired from the database in which the group information is associated and stored.
The group information includes attribute information, network information, and engagement information.
In the attribute information, the item of the attribute and the attribute value are associated with each other.
The network information is information indicating a network between the members of the group based on the log information of the communication application that sends and receives messages within the group.
The engagement information is associated with the item of the engagement factor, the engagement value, and the significance probability of the engagement value with respect to the number of transmissions and receptions of the communication application.
The input unit is
For the target group, enter the attribute information and the network information,
The extraction unit of the similar network
Based on the group information of each group acquired from the database, a group showing the network information of the feature amount similar to the feature amount of the network information of the target group is selected, and the engagement information X of the selected group is extracted.
The extraction unit of the similar attribute
Based on the group information of each group acquired from the database, a group showing attribute information similar to the attribute information of the target group is selected, and the engagement information Y of the selected group is extracted.
The engagement calculation unit
Based on the engagement information X and the engagement information Y, the average of the engagement values for the items of the engagement factor is calculated, and the average value is estimated as the engagement value Z of the target group.

The method of estimating engagement of the present invention
Includes information acquisition process, input process, similar network extraction process, similar attribute extraction process, and engagement calculation process.
The information acquisition process is
For each group, the group information is acquired from the database in which the group information is associated and stored.
The group information includes attribute information, network information, and engagement information.
In the attribute information, the item of the attribute and the attribute value are associated with each other.
The network information is information indicating a network between the members of the group based on the log information of the communication application that sends and receives messages within the group.
The engagement information is associated with the item of the engagement factor, the engagement value, and the significance probability of the engagement value with respect to the number of transmissions and receptions of the communication application.
The input process is
For the target group, enter the attribute information and the network information,
The extraction step of the similar network is
Based on the group information of each group acquired from the database, a group showing the network information of the feature amount similar to the feature amount of the network information of the target group is selected, and the engagement information X of the selected group is extracted.
The step of extracting similar attributes is
Based on the group information of each group acquired from the database, a group showing attribute information similar to the attribute information of the target group is selected, and the engagement information Y of the selected group is extracted.
The engagement calculation step is
Based on the engagement information X and the engagement information Y, the average of the engagement values for the items of the engagement factor is calculated, and the average value is estimated as the engagement value Z of the target group.

The program of the present invention is characterized in that the computer executes the method of estimating the engagement of the present invention.

The recording medium of the present invention is a computer-readable recording medium on which the program of the present invention is recorded.

According to the present invention, by using the log information of a communication application that sends and receives messages between members in a group, for example, the engagement of the group can be estimated regardless of the presence or absence of questionnaire information. In addition, since log information is used, it is possible to estimate real-time engagement, for example.

FIG. 1 is a block diagram showing an example of the estimation device of the first embodiment. FIG. 2 is a block diagram showing an example of the hardware configuration of the estimation device of the first embodiment. FIG. 3 is an example of a flowchart showing the procedure of the estimation method in the first embodiment. FIG. 4 is a table showing an example of group information in the database according to the first embodiment. FIG. 5 is a table showing an example of the input information of the target group Z in the first embodiment. FIG. 6 is a schematic diagram showing an outline of selection of similar groups based on network information in the first embodiment. FIG. 7 shows a calculation formula for calculating the value of the engagement factor of the target group by the weighted average in the first embodiment.

In the present invention, the type of "communication application" is not particularly limited, and is an application capable of transmitting and receiving messages between members belonging to a group. The type of the message is not particularly limited. The format of the message is not particularly limited, and may be a sentence, a word, an illustration representing the content, a combination thereof, or the like.

In the present invention, the type of the communication application is not limited as described above, but above all, since it is an estimation of engagement that affects the performance of the group as described above, a thank-you message is sent and received between the members in the group. Communication apps that do are preferred. Hereinafter, the communication application that sends and receives the thank-you message is also referred to as a "thank-you application". For the appreciation application, for example, Japanese Patent Application Laid-Open No. 2018-147494 can be referred to. Hereinafter, in the embodiment of the present invention, a thank-you application will be described as an example of the communication application, but the present invention is not limited to this example.

In the present invention, the unit of the "group" is not particularly limited and may be a unit including a plurality of constituent members. The group may be, for example, an organizational unit such as a company, a union, a school, or an administration, or a unit such as a sports team or a club.

An embodiment of the present invention will be described. The present invention is not limited to the following embodiments. In each of the following figures, the same parts are designated by the same reference numerals. Further, the explanations of the respective embodiments can be referred to each other's explanations unless otherwise specified. Further, the configurations of the respective embodiments can be combined unless otherwise specified.

[Embodiment 1]
FIG. 1 is a block diagram showing a configuration of an example of the estimation device 10 of the present embodiment. As shown in FIG. 1A, the estimation device 10 includes, for example, an information acquisition unit 11, an input unit 12, a similar network extraction unit 14, a similar attribute extraction unit 15, and an engagement calculation unit 16. The estimation device 10 may further include, for example, a feature amount extraction unit 13. The estimation device 10 is also referred to as an estimation system, for example. The estimation device 10 may be, for example, one device including the above-mentioned parts, or a device in which the above-mentioned parts can be connected via a communication network. The communication network is, for example, the same as the examples described later.

The estimation device 10 can be connected to the database 30 via the communication network 20 as shown in FIG. 1B in order to acquire various information from the database, for example. The estimation device 10 may acquire information from the database 30 each time, or has a storage unit (for example, the storage device 107 in FIG. 2), which is stored in the database 30 in the storage unit. You may memorize the information. Further, since the information and the like estimated by the estimation device 10 are output to the user who uses the estimation device 10, for example, as shown in FIG. 1B, for example, the user's terminal 40 and the communication network. It is possible to connect via 20. In the present embodiment, the user is a user of the estimation device 10, and specifically, means an administrator who estimates engagement in a group or an organization higher than the group. Further, in the present embodiment, the user who uses the communication application is referred to as a member constituting the group.

The communication line network 20 is not particularly limited, and a known communication line network can be used, and may be wired or wireless. Examples of the communication line network include an Internet line, a telephone line, a LAN (Local Area Network), WiFi (Wireless Fidelity), and the like.

First, the information stored in the database 30 will be described. In the database 30, group information is described in group units, for example, in association with group identification information. The number of groups stored in database 30 is not limited. The group identification information is, for example, information that identifies the group, such as an ID number and a group name. The group information includes attribute information, network information, and engagement information.

In the attribute information, the item of the attribute and the attribute value are associated with each other. The item is not particularly limited, and is, for example, information that is characteristic of the group. Examples of the items include the number of organizations, the ratio of men and women, the average length of service, the turnover rate, and the like, and may be one or two or more. The unit of the attribute value is not particularly limited and is set according to the type of the item. The unit of the organization scale is, for example, the number of people, the number of stores, and the like.

The network information is information indicating a network among the members of the group based on the log information of the communication application. The network information may include, for example, network features in advance.

The engagement information is associated with the item of the engagement factor, the engagement value, and the significance probability of the engagement value with respect to the number of transmissions and receptions of the communication application. The above items are not particularly limited, and examples thereof include a sense of fulfillment of work, teamwork, trust in colleagues, trust in groups, trust in organizations, teamwork, convincing work, and convincing evaluation. It may be one or two or more. The engagement value is not particularly limited and can be expressed by an arbitrary relative value. As a specific example, a relative value having a maximum value of 1 can be given, and the engagement value can also be expressed by a rate. The significance probability is the significance probability of the engagement value with respect to the number of transmissions and receptions of the communication application in the group, and examples thereof include a p value calculated from the correlation between the number of transmissions and receptions and the engagement value.

The database 30 may further include log information of the communication application (for example, the appreciation application) as the group information. The log information may be stored as, for example, the attribute information. The log information includes, for example, a message reception history and a message transmission history, which are associated with identification information of a member who has sent / received a message. The reception history and the transmission history include, for example, a receiver, a sender, and a time (transmission / reception time) thereof. The log information may be, for example, the number of messages sent and received in a predetermined period in the entire group, and the predetermined period may be, for example, daily, weekly, or monthly. The member identification information is, for example, information that identifies the user, such as an ID number, a name, a nickname, and a transmission / reception address.

In the estimation device 10, the information acquisition unit 11 acquires various stored group information from the database 30.

In the estimation device 10, the input unit 12 inputs attribute information and the network information for the target group for which engagement is estimated. Specifically, for example, the information is input by the user via the input unit 12.

The extraction unit 14 of the similar network selects a group showing network information of a feature amount similar to the feature amount of the network information of the target group based on the group information of each group acquired from the database 30, and the selected group. Engagement information X is extracted.

The estimation device 10 may further include, for example, a feature amount extraction unit 13. The feature amount extraction unit 13 extracts the feature amount of the network information based on the network information. The extracted feature amount can be used as information in the similar network extraction unit 14, for example. The type of the feature amount is not particularly limited, and examples thereof include a feature amount in general network analysis, and specific examples thereof include mediation centrality and order centrality in a network. In general, in a network connecting points with a line, the mediation centrality can be expressed by a larger numerical value as the number of paths (lines) passing through the points increases. In general, the degree centrality can be expressed by a larger numerical value as the number of lines connected to other nodes increases in a network in which points are connected by lines.

The similar attribute extraction unit 15 selects a group showing attribute information similar to the attribute information of the target group based on the group information of each group acquired from the database 30, and extracts the engagement information Y of the selected group. To do.

The engagement calculation unit 16 calculates the average of the engagement values for the items of the engagement factor based on the engagement information X and the engagement information Y, and estimates the average value as the engagement value Z of the target group.

The estimation device 10 may further include, for example, an output unit, and the output unit outputs an estimation result. For example, when the estimation device 10 has a display, the output unit may output to the display to present the estimation result to the user, or output to the user's terminal to estimate to the user. Results may be presented.

Next, FIG. 2 illustrates a block diagram of the hardware configuration of the estimation device 10. The estimation device 10 includes, for example, a CPU (central processing unit) 101, a memory 102, a bus 103, an input device 104, a display 105, a communication device 106, a storage device 107, and the like. Each part of the estimation device 10 is connected to each other by an interface (I / F) via a bus 103.

The CPU 101 is a processor that controls the entire estimation device 10, and is not limited to the CPU, and may be another processor. In the estimation device 10, for example, the program of the present invention and other programs are executed by the CPU 101, and various information is read and written.

The estimation device 10 can be connected to the communication network by, for example, the communication device 106 connected to the bus 103, and can also be connected to an external device via the communication network. The external device is not particularly limited, and is, for example, the database 30, the terminal 40, or the like as described above. The terminal 40 is, for example, a terminal or the like, and the terminal is, for example, a PC (personal computer), a tablet, a smartphone, a mobile phone, or the like. The connection method between the estimation device 10 and the external device is not particularly limited, and may be, for example, a wired connection or a wireless connection. The wired connection may be, for example, a cord connection or a cable connection for using the communication network. The wireless connection may be, for example, a connection using a communication network or a connection using wireless communication. The communication line network is not particularly limited, and for example, a known communication line network can be used, which is the same as described above.

The memory 102 includes, for example, a main memory, and the main memory is also referred to as a main storage device. When the CPU 101 performs processing, for example, the memory 102 reads various operation programs 108 such as the program of the present invention stored in the auxiliary storage device described later, and the CPU 101 receives data from the memory 102. , Program 108 is executed. The main memory is, for example, a RAM (random access memory). The memory 102 further includes, for example, a ROM (read-only memory).

The storage device 107 is also referred to as a so-called auxiliary storage device with respect to the main memory (main storage device), for example. The storage device 107 includes, for example, a storage medium and a drive for reading and writing to the storage medium. The storage medium is not particularly limited, and may be an internal type or an external type, and examples thereof include HD (hard disk), CD-ROM, CD-R, CD-RW, MO, DVD, flash memory, and memory card. The drive is not particularly limited. The storage device 107 may be, for example, a hard disk drive (HDD) in which a storage medium and a drive are integrated. For example, the storage device 107 stores the program 108 as described above, and as described above, when the CPU 101 is executed, the memory 102 reads the operation program 108 from the storage device 107. Further, the storage device 107 may include, for example, the storage unit, and may store the information stored in the database 30 as described above.

The estimation device 10 may further include, for example, an input device 104 and a display 105. The input device 104 is, for example, a scanner, a touch panel, a keyboard, or the like. Examples of the display 105 include an LED display and a liquid crystal display.

Next, the estimation method of the present embodiment will be described with reference to the flowchart of an example of FIG. 3 and an example of group information. The estimation method of the present embodiment includes an information acquisition step, an input step, a similar network extraction step, a similar attribute extraction step, and an engagement calculation step, and may further include, for example, a feature amount calculation step. The estimation method of this embodiment can be carried out using, for example, the estimation device 10 shown in FIGS. 1 and 2. The estimation method of the present embodiment is not limited to the use of the estimation device 10.

In the database 30, for example, group information as shown in FIG. 1 is stored for each group. Group 1-5 is illustrated in FIG. 4, but is not limited thereto. In the network information column of FIG. 4, "Data1" is network information based on the log information of group 1 (identification information ID = 1), and is further associated with log information as shown in the balloon column. It may be remembered. The log information is, for example, an ID of a message transmitting member, an ID of a receiving member, a transmission / reception date, and the like. The network information may include, for example, the feature amount in advance, or may be extracted from the network information, for example, in the feature amount extraction step described later.

In the input step, the attribute information A and the network information A are input for the target group Z (S101). The input step can be executed, for example, by the input unit 12 of the estimation device 10. Examples of the input information of the target group Z include attribute information and network information as illustrated in FIG. The network information Z is network information based on the log information of the group Z. The network information may include, for example, the feature amount in advance, or may be extracted from the network information, for example, in the feature amount extraction step described later.

When the feature amount is not included in the network information, the feature amount Z of the network information is extracted based on the network information of the target group A by the feature amount extraction step (S102). The feature amount extraction step can be executed, for example, by the feature amount extraction unit 13 of the estimation device 10.

On the other hand, the group information of each stored group is acquired from the database 30 (S103). This information acquisition step can be executed, for example, by the information acquisition unit 11 of the estimation device 10. Then, when the network information in the group information does not include the feature amount, similarly, for each group, the feature amount of the network information is extracted based on the network information (S104). The feature amount extraction step can be executed, for example, by the feature amount extraction unit 13 of the estimation device 10.

Then, based on the group information of each group acquired from the database 30, a group X showing the network information of the feature amount similar to the feature amount Z of the network information of the target group Z is selected (S105), and the selected group X is selected. Engagement information X is extracted (S106). These steps can be performed, for example, by the similar network extraction unit 14 of the estimation device 10.

Here, FIG. 6 shows an outline of selection of group X and extraction of engagement information X. As shown in FIG. 6, for each of the mediation centrality and the order centrality based on the network information of the target group Z, the ranking from the top and the ranking from the worst are arranged, and the difference between them is shown. In this case, the behavior of the difference between the 1st and 3rd places regarding the mediation centrality (0.5 → 0.39 → 0.34) and the behavior of the difference between the 1st and 3rd places of the order stop centrality (0.54 → 0.34 →). 0.47 → 0.34) is the feature amount of the network information of the group Z. Similarly, by extracting and comparing the feature amounts of each group of the database 30, group 4 similar to the feature amount of the target group Z can be selected as the group Y. Then, the engagement information Y can be extracted from the group information of the group 4.

Next, as the extraction step of the similar attribute, the group Y showing the attribute information similar to the attribute information of the target group Z is selected based on the group information of each group acquired from the database 30 (S107), and the selection is made. The engagement information Y of the group Y is extracted (S108). These steps can be performed, for example, by the extraction unit 15 having similar attributes of the estimation device 10.

Here, FIG. 7 shows an outline of selection of group Y and extraction of engagement information Y. As shown in FIG. 4, when the attribute information of the group has three items of scale, gender ratio, and average length of service, a similar group Y is selected for each item. That is, as shown in FIG. 7, as for the group similar to the attribute information of the target group Z with respect to the item "scale" of the attribute information, group 1 is selected, the engagement information is extracted, and with respect to the item "gender ratio". Group 2 is selected for the group similar to the attribute information of the target group Z, the engagement information is extracted, and group 3 is selected for the group similar to the attribute information of the target group Z with respect to the item "average years of service". , The engagement information is extracted.

More specifically, the selection of the group of each item may be performed as follows. In the case of the item "scale", first, a group in which the number of people of the scale is similar to the target group Z is selected. In this case, similar conditions can be set as appropriate. For example, as the first step, a group having a range of ± 50 with respect to the number of people in the target group Z is extracted, and the number of people close to the number of people in the target group Z. The top n groups (eg, 3) of are selected as similar groups. If there is no group with a range of ± 50 people, or if there are less than n groups, for example, a group with a range of ± 100 people is further extracted, and the number of people close to the number of people in the target group Z is higher. Select this group as a similar group. Next, for the selected n groups, multiple engagement factors (eg, work fulfillment, teamwork, trust in colleagues, trust in the group, trust in the organization, teamwork, job conviction, evaluation). Of the groups with the largest number of factors whose engagement value is equal to or greater than an arbitrary value (for example, 0.4 or more) and p is equal to or greater than an arbitrary value (for example, 0.01 or less). , Finally, select as a group for the item "scale". The group can be selected in the same manner for the item "male / female ratio" and the item "average years of service".

Next, as the engagement calculation step, the average of the engagement values for the items of the engagement factor is calculated based on the engagement information X and the engagement information Y (S109), and the average value is the engagement value Z of the target group. Is estimated (S110). These steps can be performed, for example, by the engagement calculation unit 16 of the estimation device 10. Then, the estimation result is output (S111), and the process ends (END). The output step can be executed, for example, by the output unit of the estimation device 10.

The average value may be, for example, a simply calculated average value or a weighted average value that takes an average value after weighting. An example of a weighted average will be described below.

As shown in FIG. 7 (A), the engagement information X of the group 4 is extracted based on the feature amount of the network information according to (S106), and the item "scale" is extracted based on the attribute information according to (S108). Engagement information Y1 of group 1, engagement information Y2 of group 2 regarding item "gender ratio", and engagement information Y3 of group 3 regarding item 3 "average years of service" are extracted. Each of the engagement information includes an engagement value and a significance probability (p value). When the significance probability is low, it is preferable that the engagement value of the engagement item having a low significance probability has a small influence on the estimated value. Therefore, the engagement information Y1, Y2, and Y3 obtained based on the attribute information are set, for example, larger as the significance probability is relatively smaller, and smaller as the significance probability is relatively larger. Weight. Then, it is preferable to obtain a weighted average value from the weighted engagement value and estimate this as the engagement value Z of the target group Z.

For the calculation of the engagement value Z by the weighted average, for example, the formula of FIG. 7B can be exemplified. Based on this formula, when the value of each engagement factor is calculated by the weighted average from FIG. 7 (A), as shown in FIG. 7 (C), "teamwork" is calculated as "0.19", and others. The factor of, that is, "feeling of fulfillment of work" is calculated as "0.36", and "trust in colleagues" is calculated as "0.39". These are presumed to be the engagement values of the engagement factors of subject group Z.

[Embodiment 2]
The program of the present embodiment is a program capable of executing the estimation method of the present invention on a computer. Alternatively, the program of this embodiment may be recorded on, for example, a computer-readable recording medium. The recording medium is not particularly limited, and examples thereof include the storage medium described above.

Although the present invention has been described above with reference to the embodiments, the present invention is not limited to the above embodiments. Various changes that can be understood by those skilled in the art can be made within the scope of the present invention in terms of the structure and details of the present invention.

<Additional notes>
Some or all of the above embodiments and examples may be described as, but not limited to, the following appendices.
(Appendix 1)
Includes information acquisition unit, input unit, similar network extraction unit, similar attribute extraction unit, and engagement calculation unit.
The information acquisition unit
For each group, the group information is acquired from the database in which the group information is associated and stored.
The group information includes attribute information, network information, and engagement information.
In the attribute information, the item of the attribute and the attribute value are associated with each other.
The network information is information indicating a network between the members of the group based on the log information of the communication application that sends and receives messages within the group.
The engagement information is associated with the item of the engagement factor, the engagement value, and the significance probability of the engagement value with respect to the number of transmissions and receptions of the communication application.
The input unit is
For the target group, enter the attribute information and the network information,
The extraction unit of the similar network
Based on the group information of each group acquired from the database, a group showing the network information of the feature amount similar to the feature amount of the network information of the target group is selected, and the engagement information X of the selected group is extracted.
The extraction unit of the similar attribute
Based on the group information of each group acquired from the database, a group showing attribute information similar to the attribute information of the target group is selected, and the engagement information Y of the selected group is extracted.
The engagement calculation unit
An engagement estimation device, characterized in that an average of engagement values is calculated for an item of the engagement factor based on the engagement information X and the engagement information Y, and the average value is estimated as the engagement value Z of the target group.
(Appendix 2)
Furthermore, it includes a feature amount extraction unit.
The estimation device according to Appendix 1, wherein the feature amount extraction unit extracts the feature amount of the network information based on the network information.
(Appendix 3)
The attribute information includes a plurality of items.
The extraction unit of the similar attribute
The estimation device according to Appendix 1 or 2, wherein a group showing attribute information similar to the attribute information of the target group is selected for each item of the attribute information, and the engagement information Y of each selected group is extracted.
(Appendix 4)
The engagement calculation unit weights the engagement value of the extracted engagement information Y according to its superiority probability, and estimates the weighted average value as the engagement value Z of the target group.
The estimation device according to any one of Supplementary note 1 to 3, wherein the weighting weight is set larger as the significance probability is relatively smaller, and is set smaller as the significance probability is relatively larger.
(Appendix 5)
The estimation device according to any one of Supplementary note 1 to 4, wherein the attribute information includes log information of the communication application.
(Appendix 6)
The estimation device according to any one of Appendix 1 to 5, wherein the item of the attribute information is at least one selected from the group consisting of scale, gender ratio, average years of service, and turnover rate.
(Appendix 7)
The engagement factor item was selected from the group consisting of work fulfillment, teamwork, trust in colleagues, trust in the group, trust in the organization, teamwork, convincing work, and convincing evaluation. The estimation device according to any one of Appendix 1 to 6, which is at least one.
(Appendix 8)
Includes information acquisition process, input process, similar network extraction process, similar attribute extraction process, and engagement calculation process.
The information acquisition process is
For each group, the group information is acquired from the stored database in which the group information is associated with each other.
The group information includes attribute information, network information, and engagement information.
In the attribute information, the item of the attribute and the attribute value are associated with each other.
The network information is information indicating a network between the members of the group based on the log information of the communication application that sends and receives messages within the group.
The engagement information is associated with the item of the engagement factor, the engagement value, and the significance probability of the engagement value with respect to the number of transmissions and receptions of the communication application.
The input process is
For the target group, enter the attribute information and the network information,
The extraction step of the similar network is
Based on the group information of each group acquired from the database, a group showing the network information of the feature amount similar to the feature amount of the network information of the target group is selected, and the engagement information X of the selected group is extracted.
The step of extracting similar attributes is
Based on the group information of each group acquired from the database, a group showing attribute information similar to the attribute information of the target group is selected, and the engagement information Y of the selected group is extracted.
The engagement calculation step is
An engagement estimation method comprising calculating an average of engagement values for the items of the engagement factor based on the engagement information X and the engagement information Y, and estimating the average value as the engagement value Z of the target group.
(Appendix 9)
In addition, it includes a feature extraction step.
The estimation method according to Appendix 8, wherein the feature amount extraction step extracts the feature amount of the network information based on the network information.
(Appendix 10)
The attribute information includes a plurality of items.
The step of extracting similar attributes is
The estimation method according to Appendix 8 or 9, wherein a group showing attribute information similar to the attribute information of the target group is selected for each item of the attribute information, and the engagement information Y of each selected group is extracted.
(Appendix 11)
In the engagement calculation step, the extracted engagement information Y is weighted according to its superiority probability, and the weighted average value is estimated as the engagement value Z of the target group.
The estimation method according to any one of Appendix 8 to 10, wherein the weighting is set larger as the significance probability is relatively smaller, and is set smaller as the significance probability is relatively larger.
(Appendix 12)
The estimation method according to any one of Appendix 8 to 11, wherein the attribute information includes log information of the communication application.
(Appendix 13)
The estimation method according to any one of Appendix 8 to 12, wherein the item of the attribute information is at least one selected from the group consisting of scale, human sex ratio, average length of service, and turnover rate.
(Appendix 14)
The engagement factor item was selected from the group consisting of work fulfillment, teamwork, trust in colleagues, trust in the group, trust in the organization, teamwork, convincing work, and convincing evaluation. The estimation method according to any one of Appendix 8 to 13, which is at least one.
(Appendix 15)
A program comprising causing a computer to execute the estimation method according to any one of Supplementary note 8 to 14.
(Appendix 16)
A computer-readable recording medium on which the program according to Appendix 15 is recorded.

According to the present invention, by using the log information of a communication application that sends and receives messages between members in a group, for example, the engagement of the group can be estimated regardless of the presence or absence of questionnaire information. In addition, since log information is used, it is possible to estimate real-time engagement, for example.

10 Estimator 11 Information acquisition unit 12 Input unit 13 Feature amount extraction unit 14 Similar network extraction unit 15 Similar attribute extraction unit 16 Engagement calculation unit

Claims (10)

Includes information acquisition unit, input unit, similar network extraction unit, similar attribute extraction unit, and engagement calculation unit.
The information acquisition unit
For each group, the group information is acquired from the database in which the group information is associated and stored.
The group information includes attribute information, network information, and engagement information.
In the attribute information, the item of the attribute and the attribute value are associated with each other.
The network information is information indicating a network between the members of the group based on the log information of the communication application that sends and receives messages within the group.
The engagement information is associated with the item of the engagement factor, the engagement value, and the significance probability of the engagement value with respect to the number of transmissions and receptions of the communication application.
The input unit is
For the target group, enter the attribute information and the network information,
The extraction unit of the similar network
Based on the group information of each group acquired from the database, a group showing the network information of the feature amount similar to the feature amount of the network information of the target group is selected, and the engagement information X of the selected group is extracted.
The extraction unit of the similar attribute
Based on the group information of each group acquired from the database, a group showing attribute information similar to the attribute information of the target group is selected, and the engagement information Y of the selected group is extracted.
The engagement calculation unit
An engagement estimation device, characterized in that an average of engagement values is calculated for an item of the engagement factor based on the engagement information X and the engagement information Y, and the average value is estimated as the engagement value Z of the target group. Furthermore, it includes a feature amount extraction unit.
The estimation device according to claim 1, wherein the feature amount extraction unit extracts the feature amount of the network information based on the network information. The attribute information includes a plurality of items.
The extraction unit of the similar attribute
The estimation device according to claim 1 or 2, wherein a group showing attribute information similar to the attribute information of the target group is selected for each item of the attribute information, and the engagement information Y of each selected group is extracted. The engagement calculation unit weights the engagement value of the extracted engagement information Y according to its superiority probability, and estimates the weighted average value as the engagement value Z of the target group.
The estimation device according to any one of claims 1 to 3, wherein the weighting weight is set larger as the significance probability is relatively smaller, and is set smaller as the significance probability is relatively larger. The estimation device according to any one of claims 1 to 4, wherein the attribute information includes log information of the communication application. The estimation device according to any one of claims 1 to 5, wherein the item of the attribute information is at least one selected from the group consisting of scale, gender ratio, average years of service, and turnover rate. The engagement factor item was selected from the group consisting of work fulfillment, teamwork, trust in colleagues, trust in the group, trust in the organization, teamwork, convincing work, and convincing evaluation. The estimation device according to any one of claims 1 to 6, which is at least one. Includes information acquisition process, input process, similar network extraction process, similar attribute extraction process, and engagement calculation process.
The information acquisition process is
For each group, the group information is acquired from the database in which the group information is associated and stored.
The group information includes attribute information, network information, and engagement information.
In the attribute information, the item of the attribute and the attribute value are associated with each other.
The network information is information indicating a network between the members of the group based on the log information of the communication application that sends and receives messages within the group.
The engagement information is associated with the item of the engagement factor, the engagement value, and the significance probability of the engagement value with respect to the number of transmissions and receptions of the communication application.
The input process is
For the target group, enter the attribute information and the network information,
The extraction step of the similar network is
Based on the group information of each group acquired from the database, a group showing the network information of the feature amount similar to the feature amount of the network information of the target group is selected, and the engagement information X of the selected group is extracted.
The step of extracting similar attributes is
Based on the group information of each group acquired from the database, a group showing attribute information similar to the attribute information of the target group is selected, and the engagement information Y of the selected group is extracted.
The engagement calculation step is
An engagement estimation method comprising calculating an average of engagement values for the items of the engagement factor based on the engagement information X and the engagement information Y, and estimating the average value as the engagement value Z of the target group. A program comprising causing a computer to execute the estimation method according to claim 8. A computer-readable recording medium on which the program according to claim 9 is recorded.

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