Cooperative action support system and cooperative action support method

Abstract

To contribute to prediction and / or maximization of the achievement of an organization.SOLUTION: A system identifies a personal trait of each of multiple members belonging to an organization, from personal trait data that includes data representing the personal trait of each of the members, and predicts a cooperative action degree in the organization, based on the relationship between the personal traits of the members. The system visualizes information about the predicted cooperative action degree.SELECTED DRAWING: Figure 1

Description

[Technical Field]

[0001] The present invention generally relates to techniques for behavioral support. [Background technology]

[0002] The performance of an organization as a whole is influenced by the actions of each member of the organization. Patent Document 1 discloses a technology that quantitatively indicates the performance of individuals in an organization based on environmental factors. [Prior art documents] [Patent documents]

[0003] [Patent Document 1] Japanese Patent Publication No. 2023-038750 Summary of the Invention [Problem to be solved by the invention]

[0004] The technology disclosed in Patent Document 1 evaluates the characteristics of each member and the rest of the organization. This makes it possible to present the achievements of each member within the organization. However, it is difficult to contribute to predicting and / or maximizing the performance of the organization, including the member himself / herself. [Means for solving the problem]

[0005] A computer identifies the personal characteristics of each of multiple members belonging to an organization from personal characteristic data containing data representing the personal characteristics of each of the multiple members, predicts the degree of cooperative behavior in the organization based on the relationships between the personal characteristics of the multiple members, and visualizes information related to the predicted degree of cooperative behavior. [Effects of the Invention]

[0006] The present invention can contribute to predicting and / or maximizing organizational performance. [Brief explanation of the drawings]

[0007] [Figure 1] 1 shows an example of the overall configuration of a system according to an embodiment. [Figure 2] The data stored in the storage device and the functions of the computing device are shown. [Figure 3] 1 shows an example of the configuration of a person characteristic DB. [Figure 4] 10 shows an example of the configuration of an organization characteristic setting DB. [Figure 5] 1 shows an example of the configuration of a cooperative behavior degree DB. [Figure 6] 10 shows an example of the configuration of an output setting DB. [Figure 7] 10 shows an example of a flow of processing performed in an embodiment. [Figure 8] 10 shows an example of a member-oriented screen. [Figure 9] 10 shows an example of a screen for an administrator. DETAILED DESCRIPTION OF THE INVENTION

[0008] In the following description, an "interface apparatus" may refer to one or more interface devices, which may be at least one of the following: An I / O interface device is one or more I / O (Input / Output) interface devices. The I / O (Input / Output) interface devices are interface devices for at least one of an I / O device and a remote display computer. The I / O interface device for the display computer may be a communications interface device. The at least one I / O device may be a user interface device, for example, either an input device such as a keyboard and a pointing device, or an output device such as a display device. A communication interface apparatus that is one or more communication interface devices. The one or more communication interface devices may be one or more homogeneous communication interface devices (e.g., one or more NICs (Network Interface Cards)) or two or more heterogeneous communication interface devices (e.g., an NIC and an HBA (Host Bus Adapter)).

[0009] In the following description, "memory" refers to one or more memory devices, which are an example of one or more storage devices, and may typically be a primary storage device. At least one memory device in the memory may be a volatile memory device or a non-volatile memory device.

[0010] In the following description, a "persistent storage device" may refer to one or more persistent storage devices, which are an example of one or more storage devices. A persistent storage device may typically be a non-volatile storage device (e.g., an auxiliary storage device), and specifically may be, for example, a hard disk drive (HDD), a solid state drive (SSD), a non-volatile memory express (NVME) drive, or a storage class memory (SCM).

[0011] In the following description, the term "storage device" may refer to at least one of memory and persistent storage device.

[0012] Furthermore, in the following description, a "processor" may refer to one or more processor devices. The at least one processor device may typically be a microprocessor device such as a CPU (Central Processing Unit), but may also be another type of processor device such as a GPU (Graphics Processing Unit). The at least one processor device may be a single-core or multi-core. The at least one processor device may also be a processor core. The at least one processor device may also be a processor device in a broader sense, such as a circuit that is a collection of gate arrays written in a hardware description language that performs some or all of the processing (for example, an FPGA (Field-Programmable Gate Array), a CPLD (Complex Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit)).

[0013] In the following description, functions are sometimes described using the expression "yyy unit." However, the functions may be realized by one or more computer programs executed by a processor, by one or more hardware circuits (e.g., FPGAs or ASICs), or by a combination thereof. When a function is realized by a program executed by a processor, the specified processing is performed using a storage device and / or an interface device, etc., as appropriate, and therefore the function may be considered to be at least a part of the processor. Processing described using a function as the subject may be processing performed by a processor or a device having the processor. A program may be installed from a program source. The program source may be, for example, a program distribution computer or a computer-readable storage medium (e.g., a non-transitory storage medium). The description of each function is merely an example; multiple functions may be combined into one function, or one function may be divided into multiple functions.

[0014] In the following explanation, data that produces output in response to input may be described using expressions such as "xxxDB" ("DB" is an abbreviation for database), but the data may be of any structure (for example, structured data or unstructured data), or may be a model that outputs data in response to data input. Therefore, "xxxDB" can be referred to as "xxx data." In the following explanation, the configuration of each DB is an example, and one DB may be divided into two or more DBs, or all or part of two or more DBs may be one DB.

[0015] In the following description, when elements of the same type are described without distinction, common reference symbols are used, and when elements of the same type are described with distinction, reference symbols are used.

[0016] An embodiment will be described below. The "degree of cooperative behavior" of an organization means the degree of cooperative behavior that can be performed in the organization, but in the following embodiment, the "cooperative behavior rate" is used as the "degree of cooperative behavior." The "cooperative behavior rate" is the ratio of the number of members who perform cooperative behavior in the organization to the total number of members that make up the organization.

[0017] FIG. 1 shows an example of the overall configuration of a system according to an embodiment.

[0018] The collaborative action support system 100 communicates with the member devices 130 and the administrator device 180 via a communication network 170. The communication network 170 is, for example, the Internet, a wide area network (WAN), or a local area network (LAN). Either the member device 130 or the administrator device 180 may be absent.

[0019] The member device 130 is an information processing terminal of a member 101 belonging to an organization, for example, a computer such as a personal computer or a smartphone. The member device 130 has an input device 111 and a display device 112. The member device 130 may have one or more sensors that measure the actions of the member 101, for example, a camera 102 and a mouse. The mouse is an example of the input device 111. The display device 112 may be a touch panel.

[0020] The administrator device 180 is an information processing terminal of the administrator 151, for example, a computer such as a personal computer or a smartphone. The administrator device 180 has an input device 153 and a display device 152. The administrator 151 may or may not be a member of the organization. The administrator 151 may be a person who formulates measures to maximize the results of the organization.

[0021] The collaborative action support system 100 includes an interface device 113, a storage device 114, and a computing device 115 connected thereto.

[0022] The interface device 113 communicates with the member devices 130 and the administrator device 180 via a communication network 170 .

[0023] The storage device 114 stores a computer program executed by the arithmetic device 115 and data input / output by the arithmetic device 115. The storage device 114 stores a personal characteristic DB including data representing the personal characteristics of each of the multiple members 101 belonging to the organization.

[0024] The arithmetic device 115 is a processor, and executes a computer program to perform, for example, the following processes. That is, the arithmetic device 115 identifies the personal characteristics of each of multiple (typically all) members 101 belonging to an organization, predicts a cooperative behavior rate in the organization based on personal characteristic relationships, which are relationships between the personal characteristics of the multiple members, and visualizes information about the predicted cooperative behavior rate. The information about the cooperative behavior rate includes information for members (information visualized for the members 101) and / or information for administrators (information visualized for the administrator 151). This can contribute to predicting and / or maximizing the organization's performance. Specifically, for example, it is expected that the members 101 who view the member-oriented information displayed on the display device 112 of the member device 130 will take cooperative behavior, thereby maximizing the organization's performance. Furthermore, for example, it is expected that the administrator 151 who views the administrator-oriented information displayed on the display device 152 of the administrator device 180 will predict the organization's performance and encourage the members 101 to take cooperative behavior toward maximizing the organization's performance.

[0025] This embodiment will be described in detail below.

[0026] FIG. 2 shows the data stored in the storage device 114 and the functions of the processing device 115.

[0027] The storage device 114 stores a person characteristic DB 233, an organization characteristic setting DB 232, a cooperative behavior rate DB 231, an output setting DB 234, and an output DB 235. The person characteristic DB 233 includes data representing the person characteristics of each of multiple members belonging to an organization. The organization characteristic setting DB 232 includes data related to set organization characteristics. The cooperative behavior rate DB 231 includes data related to a predicted cooperative behavior rate. The output setting DB 234 includes data set for outputting inducement content, which is content that induces cooperative behavior. The output DB 235 includes data related to a predicted cooperative behavior rate.

[0028] The calculation device 115 executes the computer program to realize an input unit 210, a calculation unit 220, and an output unit 240. The input unit 210 has a behavior input unit 211, a person characteristic identification unit 213, and a setting input unit 212. The calculation unit 220 has a person characteristic estimation unit 222, a cooperative behavior rate prediction unit 223, a characteristic setting unit 225, an information setting unit 226, and an output determination unit 227.

[0029] Below, an example of the functions realized by the arithmetic unit 115 and the processing performed in this embodiment will be described.

[0030] The setting input unit 212 receives setting data (data to be set) from the administrator device 180. The setting data includes data related to organizational characteristics and data related to output content. The characteristic setting unit 225 creates or updates an organizational characteristic setting DB 232 including data related to organizational characteristics. The information setting unit 226 creates or updates an output setting DB 234 including data related to output content.

[0031] The setting data may include data representing the personal characteristics of each member. The characteristic setting unit 225 may create or update a personal characteristic DB 233 including data representing the personal characteristics of each member. This setting data may be input from the member device 130 instead of or in addition to the administrator device 180. For example, for at least one member 101, data representing the personal characteristics of that member may be input from the member 101 via the member device 130, and the data may be stored in the personal characteristic DB 233.

[0032] Furthermore, for example, for at least one member 101, the personal characteristics (e.g., at least psychological characteristics) of the member 101 may be identified using the technology disclosed in the prior applications filed by the same applicant as the present application (Patent Application No. 2023-014642, US18 / 378,475, and EP23203803.4), or may be identified based on answers to questions prepared for identifying the personal characteristics. Specifically, for example, the behavior input unit 211 receives behavioral data in response to questions (questions prepared for identifying personal characteristics and / or questions prepared for purposes other than identifying personal characteristics) from the member device 130. The behavioral data may include data on answers to the questions, or may include data on actions detected by a sensor such as a mouse or camera 102 (e.g., the “related actions” disclosed in the prior applications). The personal characteristic estimation unit 222 may estimate the personal characteristics of the member 101 from the behavioral data. Data representing the estimated personal characteristics is stored in the personal characteristic DB 233.

[0033] The personal characteristic identification unit 213 identifies the personal characteristics of each of the multiple members 101 belonging to the organization from the personal characteristic DB 233. The cooperative behavior rate prediction unit 223 identifies the characteristic settings of the organization from the organization characteristic setting DB 232, and predicts the cooperative behavior rate in the organization based on the personal characteristic relationships, which are the relationships between the personal characteristics of the multiple members of the organization, and the identified organization characteristic settings. The cooperative behavior rate prediction unit 223 stores data on the predicted cooperative behavior rate in the cooperative behavior rate DB 231.

[0034] The output determination unit 227 determines data on which visualized information related to the predicted cooperative behavior rate is based, based on data stored in the cooperative behavior rate DB 231 (i.e., data related to the predicted cooperative behavior rate). Specifically, for example, the output determination unit 227 may determine data on which the information for the administrator is based, based on the data stored in the cooperative behavior rate DB 231. The output determination unit 227 may also determine data on which the information for the members is based (e.g., data of incentive content for inducing cooperative behavior), based on the data stored in the cooperative behavior rate DB 231 and the data stored in the output setting DB 234. The output unit 240 causes the member devices 130 and / or the administrator device 180 to display information based on the data determined by the output determination unit 227, i.e., information related to the predicted cooperative behavior rate, via the interface device 113.

[0035] FIG. 3 shows an example of the configuration of the person characteristic DB 233.

[0036] The personal characteristic DB 233 may include data representing a member ID, psychological characteristics, prosociality, and Others for each member.

[0037] A psychological trait may be an example of a second-type personality trait. For example, a psychological trait may be composed of at least one of five components (the so-called Big Five), such as neuroticism, extraversion, openness, agreeableness, and conscientiousness. For each member, data on the psychological trait among the personality traits may include a value representing the magnitude (strength) of at least one of the five components. The value of each psychological trait component may be within a predetermined range from a maximum value to a minimum value.

[0038] Prosociality may be an example of a first-type personality trait. Prosociality may be composed of at least one of three components, such as social value orientation (SVO), conditional cooperation (CC), and unconditional cooperation (UC). For each member, data on prosociality among personality traits may include a value representing the magnitude (strength) of at least one of the three components. The value of each prosocial component may be in a range from a predetermined maximum value to a predetermined minimum value. Furthermore, the value range of the prosocial component may be the same as or different from the value range of the psychological characteristic component.

[0039] In this way, for each member, the personal characteristics include two or more types of personal characteristics (personal characteristic components) including first and second types of personal characteristics (personal characteristic components). That is, in this specification, "personal characteristics" may include other types of personal characteristics (personal characteristic components) such as personality in addition to psychological characteristics and / or prosociality. Personality characteristics may be a concept that includes personality characteristics. Personality characteristics may include psychological characteristics and / or prosociality.

[0040] A psychological trait, which is an example of a second-type personality trait, may be an example of a trait representing a way of thinking and / or a tendency related to communication (e.g., liking to communicate, wanting to interact with others, etc.) and / or planning (e.g., liking to follow a plan, having a strong sense of responsibility, etc.). One or more components of five components, such as neuroticism, extraversion, openness, agreeableness, and conscientiousness, may be an example of one or more second-type traits. A personality trait may be adopted as an example of a second-type personality trait instead of or in addition to a psychological trait. Furthermore, in this specification, a psychological trait may include a personality trait in a broad sense.

[0041] Prosociality, which is an example of a first-type personality trait, may be an example of a trait related to thoughts and / or tendencies related to cooperation (e.g., cooperativeness, adapting to others, altruism, wanting to do things for others, etc.). One or more of the three components, SVO, CC, and UC, may be an example of one or more first-type traits.

[0042] In the person characteristic DB 233, for each member, Others may include a list of organization IDs of organizations to which the member belongs.

[0043] FIG. 4 shows an example of the structure of the organization characteristic setting DB 232. As shown in FIG.

[0044] The organization characteristic setting DB 232 may include data representing an organization ID, a prediction method, and Others for each organization.

[0045] For each organization, the "prediction method" is a method for predicting the cooperative behavior rate of the organization, and may be composed of, for example, one or more characteristic combinations. A "characteristic combination" is a combination of a first type of characteristic and a second type of characteristic. Specifically, the person characteristic relationship (the relationship between the person characteristics of the multiple members 101) includes a relationship (one or more characteristic combinations) between one or more first type of characteristic and one or more second type of characteristic for each of the multiple members 101. The reason why the cooperative behavior rate is predicted from this perspective in this embodiment will be explained below.

[0046] According to the experiments conducted by the inventors of the present invention, the following results were obtained. The rate of cooperative behavior tends to vary relatively significantly depending on the prosociality of each member in an organization (in this embodiment, at least one of SVO, CC, and UC). Even if all members have high prosociality, the cooperative behavior rate tends not to reach the maximum value of “1.” Even if all members have low prosociality, the rate of cooperative behavior tends not to reach the minimum value of “0.” -Among multiple organizations with similar balance (variation) of prosociality among members, there are differences in the rate of cooperative behavior. The rate of cooperative behavior also tends to vary relatively significantly depending on at least one of the second-type traits of each member of an organization: agreeableness, extroversion, and conscientiousness.

[0047] Improving the accuracy of predicting rates of cooperative behavior contributes to predicting and / or maximizing organizational outcomes.

[0048] Therefore, a cooperative behavior support system 100 is constructed that predicts the rate of cooperative behavior based on a combination of a first type of trait, an example of which is prosociality, and a second type of trait, an example of which is at least one of agreeableness, extroversion, and conscientiousness. This improves the coefficient of determination, i.e., the prediction accuracy of the rate of cooperative behavior.

[0049] Specifically, the cooperative behavior rate prediction unit 223 calculates Cooperation (the cooperative behavior rate based on the combination) by calculating the following [Equation 1] for each combination of the first type characteristic and the second type characteristic specified as the "prediction method."

number

[0050] n is the number of members that make up the organization. X i is one of the psychological trait components (an example of the second type trait) for each member. X i The value that can be assigned to Y may be at least one of the values ​​of agreeableness, extroversion, and conscientiousness. i is a prosocial component (an example of a type 1 trait) for each member. Y i The value that can be substituted for Y may be at least one of SVO, CC, and UC. th is Y i The positive and negative thresholds of the prosocial component adopted in this study (for example, whether the person has a positive or negative influence on those around them, or a value determined based on the median of the scale, etc.)

[0051] According to the example shown in FIG. 4, for the organization with organization ID "GA", the combination of extraversion and SVO and the combination of conscientiousness and UC are used as the "prediction method". C1 is the Cooperation calculated using the combination of extraversion and SVO. C2 is the Cooperation calculated using the combination of conscientiousness and UC. X in Equation 1 i and Y i The values ​​substituted for Y are, for example, values ​​identified from the person characteristic DB 233. th is, for example, a value identified from the organization characteristic setting DB 232 (e.g., Others corresponding to the organization). The cooperative behavior rate prediction unit 223 calculates the cooperative behavior rate for the organization with organization ID "GA" based on C1 and C2 (e.g., the cooperative behavior rate is the average value of C1 and C2). Note that this example is a combination of extraversion and a prosocial component, but instead of or in addition to this combination, a combination of agreeableness and a prosocial component and / or a combination of conscientiousness and a prosocial component may be adopted.

[0052] In the organization characteristic setting DB 232, for each organization, Others contains a list of member IDs of members belonging to the organization, the ID of the administrator of the organization, and Y for each characteristic combination. th and a value of

[0053] FIG. 5 shows an example of the configuration of the cooperative behavior rate DB 231.

[0054] The cooperative behavior rate DB 231 may include data representing an organization ID, a prediction result, and "Others" for each organization. The "prediction result" includes the predicted cooperative behavior rate C W In addition to C W The prediction method may include the cooperative behavior rate that is an element of the above, that is, the cooperative behavior rate for each combination specified as the "prediction method."

[0055] FIG. 6 shows an example of the configuration of the output setting DB 234. As shown in FIG.

[0056] The output setting DB 234 may include data representing an output ID, output content, and Others for each output (for example, characteristic combination).

[0057] The "output content" may be, for example, the incentive content itself that may be included in the member-oriented information or data that is the basis of the incentive content. In this embodiment, the incentive content is an incentive message that is a message for inducing collaborative behavior.

[0058] The elicitation message is uniquely determined based on the relationship between the value of the prosocial component and the value of the psychological characteristic component. For example, the elicitation message is determined based on the identity of the psychological characteristic component and whether its value is high or low, and the identity of the prosocial component and whether its value is high or low. For example, for a combination of extraversion and SVO, the elicitation message is determined based on whether the extraversion value is high or low (e.g., whether the extraversion value is equal to or higher than a predetermined threshold) and whether the SVO value is high or low (e.g., whether the SVO value is equal to or higher than a predetermined threshold). More specifically, for example, if an individual has high extraversion and high SVO, the elicitation message may be a message that instructs the individual to encourage cooperative behavior from others. If an individual has high extraversion but low SVO, the elicitation message may be a message that instructs the individual to listen to the opinions of those around them. If an individual has low extraversion but high SVO, the elicitation message may be a message that instructs the individual to believe in themselves and not to be swayed by those around them. If the individual has low extroversion and low SVO, the prompting message may be one that instructs them to observe the behavior of those around them and encourage introspection.

[0059] In this way, for each of one or more members among the multiple members belonging to the organization, the output determination unit 227 identifies an inducement message that induces the member to engage in cooperative behavior based on the relationship between the level of the value of one or more prosocial components of the member and the level of the value of one or more psychological characteristic components. The "one or more members" may be all or some of the multiple members, and may be, for example, members whose psychological characteristics or cooperative behavior rate satisfy a predetermined condition, or members designated by the administrator 151. The "members whose cooperative behavior rate satisfies a predetermined condition" may be, for example, a characteristic combination of "X×(YY th )" value or "X × (YY th)" is less than the threshold, that is, the cooperative behavior rate predicting unit 223 predicts that the cooperative behavior rate predicting unit 223 has a low possibility of taking cooperative behavior.

[0060] The level of the prosocial component value can be considered as an indicator of the level of likelihood of prioritizing maximizing organizational performance. On the other hand, the level of the psychological characteristic component value can be considered as an indicator of the level of likelihood of prioritizing maximizing individual performance. Therefore, depending on the relationship (combination) between the level of the prosocial component value and the level of the psychological characteristic component value, it may be expected to contribute to maximizing organizational performance, or it may be expected to reduce organizational performance. According to this embodiment, an elicitation message is prepared and provided for each relationship (combination) between the level of the prosocial component value and the level of the psychological characteristic component value. As a result, cooperative behavior is supported for all members, regardless of the relationship (combination) between the level of the prosocial component value and the level of the psychological characteristic component value, and as a result, maximization of organizational performance can be expected.

[0061] An example of the flow of processing performed in this embodiment will be described below.

[0062] FIG. 7 shows an example of the flow of processing performed in this embodiment.

[0063] The person characteristic identification unit 213 identifies the organization ID of the organization and the member IDs of all members of the organization from the person characteristic DB 233 or the organization characteristic setting DB 232 (S701). Any method may be adopted as a method for identifying the organization ID and the member ID. For example, the member IDs input by each member in an online meeting may be the member IDs of all members. Alternatively, the organization ID may be input by the administrator or any member, and all member IDs linked to the organization ID may be identified from the person characteristic DB 233 or the organization characteristic setting DB 232.

[0064] The person characteristic identification unit 213 identifies one or more characteristic combinations from the "prediction method" corresponding to the organization ID identified in S701, and for each member ID identified in S701, identifies, for each of the one or more characteristic combinations, the value of the psychological characteristic component and the value of the prosocial component corresponding to the member ID from the person characteristic DB 233 (S702). For example, if the characteristic combination is a combination of extroversion and SVO, the value of extroversion and the value of SVO are identified from the person characteristic DB 233.

[0065] The cooperative behavior rate predicting unit 223 predicts the cooperative behavior rate, and the output determining unit 227 determines inducement content (S703). Data on the predicted cooperative behavior rate is stored in the cooperative behavior rate DB 231.

[0066] Specifically, the cooperative behavior rate prediction unit 223 calculates Cooperation for a combination of Extraversion and SVO by substituting the Extraversion values ​​of all members, the SVO values ​​of all members, and the SVO threshold into Equation 1 to calculate Equation 1. The cooperative behavior rate prediction unit 223 calculates Cooperation for each characteristic combination corresponding to the organization ID, and predicts (calculates) the cooperative behavior rate of the organization using all Cooperation corresponding to the organization ID.

[0067] Furthermore, the output determination unit 227 identifies, for each member, an invitation message for all or part of one or more characteristic combinations corresponding to the organization ID from the output setting DB 234. Specifically, for example, for a combination of extroversion and SVO, the output determination unit 227 identifies, from the output setting DB 234, an invitation message corresponding to a high value of extroversion and a high value of SVO of a member. Note that for each of the psychological characteristic component and the prosocial component, there may be values ​​that are neither high nor low, and in such cases, an invitation message need not be identified for a characteristic combination having such a component. In other words, it is not necessary to prepare an invitation message for each member for each of all characteristic combinations corresponding to the organization.

[0068] The output determination unit 227 determines information to be output based on the data on the cooperative behavior rate predicted in S703 and the invitation message for each member identified in S703, and visualizes the determined information (S704).

[0069] Specifically, for example, the output determination unit 227 generates member-directed information including an invitation message for a certain member, and displays the member-directed information on the member device 130 of the certain member. FIG. 8 shows an example of the member-directed information. Such member-directed information is displayed for each member. For each member, the content of the member-directed information depends on the invitation message identified according to the component value combination identified for the member (for each characteristic combination in the "prediction method" corresponding to the organization, the level of each value of the psychological characteristic component and the prosocial component that make up the characteristic combination). Furthermore, the number of invitation messages in the member-directed information does not necessarily have to be the same for all members.

[0070] Also, for example, the output determination unit 227 generates information for the administrator as information based on data on the predicted cooperative behavior rate of the organization, and causes the administrator information to be displayed on the administrator device 180 of the administrator of the organization. FIG. 9 shows an example of the information for the administrator. In FIG. 9, the cooperation score is a numerical value determined based on the cooperative behavior rate. For example, the cooperation score may be 100 times the cooperative behavior rate (for example, a cooperation score of "80" is 100 times the cooperative behavior rate of "0.8"). In FIG. 9, there are four stages for each member, and which stage each member belongs to is determined by the "X" of the member for each characteristic combination. i ×(Y i -Y th ) value.

[0071] Although one embodiment has been described above, this is merely an example for explaining the present invention, and the scope of the present invention is not limited to this embodiment. The present invention can be implemented in various other forms.

[0072] For example, the timing for predicting the rate of cooperative behavior of the organization and the timing for displaying the incentive content to the members of the organization may be any timing, such as during an organizational activity such as an online meeting, or when the results of the organization are required (for example, every month for a monthly project).

[0073] Furthermore, for the personal characteristics of each member, the values ​​of the psychological characteristic components and prosocial component may be either categorical or continuous values, and the value range (for example, maximum or minimum value) may not be defined.

[0074] Also, for example, as described above, the "output content" in the output setting DB 234 may be data that serves as the base for the inducement content, and "identifying" the inducement message may involve generating the inducement message based on the base data.

[0075] Furthermore, an "organization" may be defined as a different organization if it has only some different members, or may be defined as a different organization if it has completely the same members but different organizational goals.

[0076] Furthermore, for example, the cooperative behavior rate prediction unit 223 may predict the cooperative behavior rate of an organization using a machine learning-based or statistical-based model and psychological traits (values ​​of one or more psychological trait components) and prosociality (values ​​of one or more prosocial components) of each member. For example, the model may be a model trained using a training dataset that includes a combination of multiple component values ​​for each member of the organization and the organization's cooperative behavior rate.

[0077] The cooperative behavior rate prediction unit 223 predicts, for each of the multiple members belonging to the organization, the cooperative behavior rate from the relationship between the value of the prosocial component and the value of the psychological characteristic component for each characteristic combination in the "prediction method" corresponding to the organization (for example, "X × (YY thThe cooperative behavior rate prediction unit 223 may predict the cooperative behavior rate of the member based on the value of "(the probability that the member will take cooperative behavior for the organization)." The "cooperative behavior rate" of a member may be the probability that the member will take cooperative behavior for the organization. The cooperative behavior rate prediction unit 223 may estimate the aptitude of each member for the organization based on the relationship between the predicted cooperative behavior rate of each member and the predicted cooperative behavior rate of the organization (e.g., based on whether the cooperative behavior rate of the member is equal to or higher than the cooperative behavior rate of the organization). For example, the smaller the difference between the cooperative behavior rate of the member and the cooperative behavior rate of the organization, the higher the aptitude. Furthermore, the cooperative behavior rate prediction unit 223 may estimate the compatibility between the members based on the relationship between the personal characteristics of the members and the relationship between the predicted cooperative behavior rates between the members. The visualized information regarding the predicted cooperative behavior rate may include information representing the estimated aptitude of each member and the estimated compatibility between the members. As a result, the aptitude of the members for the organization and / or the compatibility between the members is expected to contribute more to maximizing the organizational results. For example, incentive content for a member may be specified (e.g., generated) based on the member's aptitude for the organization and / or the compatibility between the member and other members, instead of or in addition to the member's component value combination (the level of each value of the psychological characteristic component and the prosocial component in the characteristic combination), and such incentive content is expected to increase the success rate of inducing members to behave cooperatively. In this way, the cooperative behavior rate prediction unit 223 may estimate the aptitude of each member for the organization and / or the compatibility between members. [Explanation of symbols]

[0078] 100: Collaborative Action Support System

Claims

1. an interface device to which an input device and a display device are connected; A storage device; a computing device connected to the storage device; Equipped with the storage device stores personal characteristic data including data representing personal characteristics of each of a plurality of members belonging to an organization; the data representing the personal characteristics of each of the plurality of members is data input from the input device via the interface apparatus, or data estimated based on the input data; The computing device Identifying personal characteristics of each of the plurality of members belonging to the organization from the personal characteristic data; predicting a degree of cooperative behavior in the organization based on a personal characteristic relationship that is a relationship between the personal characteristics of the plurality of members; displaying information about the predicted degree of cooperative behavior on the display device via the interface device; Collaborative action support system.

2. For each of the plurality of members, the personal characteristics of the member include two or more types of personal characteristics; The two or more types of personality characteristics are: a first type of personality trait relating to collaboration-related attitudes and / or tendencies; Second-class personality traits related to psychological and / or personality traits; Including, the first type of person characteristic is comprised of one or more component first type characteristics; the second type of person characteristic is composed of one or more component second type characteristics; the person characteristic relationship includes a relationship between the one or more first type characteristics and the one or more second type characteristics for each of the plurality of members; The collaborative action support system according to claim 1 .

3. For each of the plurality of members, each of the one or more first type traits is a prosocial component; each of the one or more second type traits is one of agreeableness, extroversion, and conscientiousness; the person characteristic relationships include relationships between characteristic combinations of the plurality of members; For each of the plurality of members, the combination of properties comprises: A first combination of agreeableness and prosocial components, A second combination of extraversion and prosocial components, and A third combination of conscientiousness and prosocial components: including at least one of The collaborative action support system according to claim 2 .

4. For each of the plurality of members, each of the one or more first type characteristics is a value representing a magnitude of a prosocial component; each of the one or more second type traits is a value representing a magnitude of any one of agreeableness, extroversion, and conscientiousness; For each of the plurality of members, the first combination is a value calculated based on a value representing a magnitude of agreeableness and a value representing a magnitude of a prosocial component, the second combination is a value calculated based on a value representing a magnitude of extraversion and a value representing a magnitude of a prosocial component, the third combination is a value calculated based on a value representing a magnitude of conscientiousness and a value representing a magnitude of a prosocial component; The collaborative action support system according to claim 3 .

5. For each of one or more members of the plurality of members, the computing device determines inducement content that induces the member to engage in cooperative behavior based on a relationship between the magnitude of the one or more first-type characteristics of the member and the magnitude of the one or more second-type characteristics of the member; The information regarding the predicted degree of cooperative behavior includes the determined incentive content for the member. The collaborative action support system according to claim 2 .

6. each of the one or more first type traits is a prosocial component; each of the one or more second type traits is one of agreeableness, extroversion, and conscientiousness; The collaborative action support system according to claim 5 .

7. The computing device predicting a degree of cooperative behavior of each of the plurality of members based on a relationship between the one or more first characteristics and the one or more second characteristics; estimating the suitability of each member for the organization based on the relationship between the predicted degree of cooperative behavior of each member and the predicted degree of cooperative behavior of the organization; The information regarding the predicted degree of cooperative behavior includes information indicating an estimated aptitude of each member. The collaborative action support system according to claim 2 .

8. The computing device predicting a degree of cooperative behavior of each of the plurality of members based on a relationship between the one or more first characteristics and the one or more second characteristics; Estimating the compatibility between the members based on at least one of the relationship between personal characteristics between the members and the relationship between the degrees of cooperative behavior between the members; the information regarding the predicted degree of cooperative behavior includes information indicating an estimated compatibility between the members; The collaborative action support system according to claim 2 .

9. Identifying personal characteristics of each of a plurality of members belonging to the organization from personal characteristic data including data representing the personal characteristics of each of the plurality of members; predicting a degree of cooperative behavior in the organization based on a personal characteristic relationship that is a relationship between the personal characteristics of the plurality of members; visualizing information about the predicted degree of cooperative behavior; A method for supporting collaborative behavior using a computer.

10. Identifying personal characteristics of each of a plurality of members belonging to the organization from personal characteristic data including data representing the personal characteristics of each of the plurality of members; predicting a degree of cooperative behavior in the organization based on a personal characteristic relationship that is a relationship between the personal characteristics of the plurality of members; visualizing information about the predicted degree of cooperative behavior; A computer program that causes a computer to do something.

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