WO2024106309A1 - Procédé d'inférence d'engagement, programme et système d'inférence d'engagement - Google Patents

Procédé d'inférence d'engagement, programme et système d'inférence d'engagement Download PDF

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WO2024106309A1
WO2024106309A1 PCT/JP2023/040446 JP2023040446W WO2024106309A1 WO 2024106309 A1 WO2024106309 A1 WO 2024106309A1 JP 2023040446 W JP2023040446 W JP 2023040446W WO 2024106309 A1 WO2024106309 A1 WO 2024106309A1
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worker
engagement
information
estimation
feature
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PCT/JP2023/040446
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English (en)
Japanese (ja)
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健一 入江
洋介 井澤
若正 清崎
拓磨 白井
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パナソニックＩｐマネジメント株式会社
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    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q10/00—Administration; Management
    • G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling

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  • the present disclosure relates generally to an engagement estimation method, program, and engagement estimation system, and more specifically to an engagement estimation method, program, and engagement estimation system that estimates a worker's engagement with their work.
  • engagement is defined in terms of the following two points: (1) commitment to the organization, specifically, affective commitment (emotional attachment to the organization) and continuance commitment (desire to remain with the organization) and (2) extra-role behavior (any behavior that enables the organization to function effectively).
  • Increased engagement can, for example, lead to increased productivity, sales, customer satisfaction, and worker retention within an organization.
  • Patent Document 1 Traditionally, engagement has been quantified by conducting surveys of workers and analyzing their responses (for example, Patent Document 1).
  • the present disclosure aims to provide an engagement estimation method, program, and engagement estimation system that can estimate engagement more objectively.
  • the engagement estimation method is an engagement estimation method in an engagement estimation system.
  • the engagement estimation method includes a first acquisition step, a second acquisition step, a feature determination step, and an estimation step.
  • a first acquisition unit acquires information about a worker.
  • the second acquisition step acquires the worker's engagement with work for a predetermined period of time.
  • the feature determination step determines at least one feature from the information about the worker based on the relationship between the information about the worker and the engagement for the predetermined period of time.
  • the estimation unit estimates the engagement of the worker at a time point other than the predetermined period of time based on the at least one feature.
  • the information about the worker includes biometric information about the worker, location information about the worker at the worker's place of work, and relationship information about the relationship between the worker and another worker at the workplace. The biometric information is measured by a biometric information measurement terminal.
  • a program according to one aspect of the present disclosure is a program for causing one or more processors of a computer system to execute the engagement estimation method.
  • the engagement estimation system includes a first acquisition unit, a second acquisition unit, a feature determination unit, and an estimation unit.
  • the first acquisition unit acquires information about a worker.
  • the second acquisition unit acquires the worker's engagement with work for a predetermined period of time.
  • the feature determination unit determines at least one feature from the information about the worker based on a relationship between the information about the worker and the engagement for the predetermined period of time.
  • the estimation unit estimates the engagement of the worker at a time other than the predetermined period of time based on the at least one feature.
  • the information about the worker includes biometric information about the worker, location information about the worker at the worker's place of work, and relationship information about a relationship between the worker and another worker at the workplace.
  • the biometric information is measured by a biometric information measurement terminal.
  • FIG. 1 is a block diagram of an engagement estimation system and related components according to one embodiment.
  • FIG. 2 is a graph illustrating a process in the engagement estimation system.
  • FIG. 3 is an explanatory diagram showing an engagement estimation process performed by the engagement estimation system.
  • FIG. 1 shows a schematic configuration of an engagement estimation system 1 of this embodiment.
  • the engagement estimation system 1 is used to estimate the engagement of workers.
  • “worker” refers to anyone who works in general. Unlike “worker” in the general sense, “worker” in the present disclosure refers not only to those who receive remuneration in exchange for labor, but also to those who work without remuneration.
  • the engagement estimation system 1 is used, for example, in companies, government offices, or organizations. In the following, as a representative example, a case where the engagement estimation system 1 is used in a company will be described.
  • the engagement estimation system 1 of this embodiment includes a first acquisition unit 21, a second acquisition unit 22, a feature determination unit 23, and an estimation unit 24.
  • the first acquisition unit 21 acquires worker information.
  • the second acquisition unit 22 acquires the worker's engagement with work for a specified period of time.
  • the feature determination unit 23 determines at least one feature from the worker information based on the relationship between the worker information and the engagement for the specified period of time.
  • the estimation unit 24 estimates the worker's engagement at a time point other than the specified period of time based on the at least one feature.
  • the worker information includes the worker's biometric information, the worker's location information at the worker's place of work, and relationship information regarding the relationship between the worker and another worker at the workplace.
  • the biometric information is measured by a biometric information measurement terminal 3.
  • the worker's biometric information, location information, and relationship information it is possible to estimate engagement more objectively compared to estimating engagement based solely on responses to a questionnaire. Furthermore, by using the worker's biometric information, location information, and relationship information, it is possible to reduce the number of questionnaire items or estimate engagement without conducting a questionnaire. This reduces the burden on people (workers, etc.) who respond to the questionnaire. Furthermore, by using the engagement estimation system 1, it is possible to increase the frequency with which engagement is estimated.
  • the engagement estimation method of this embodiment is an engagement estimation method in the engagement estimation system 1.
  • the engagement estimation method has a first acquisition step, a second acquisition step, a feature determination step, and an estimation step.
  • the first acquisition step acquires worker information.
  • the second acquisition step acquires the worker's engagement with work for a predetermined period.
  • the feature determination step determines at least one feature from the worker information based on the relationship between the worker information and the engagement for the predetermined period.
  • the estimation unit 24 estimates the worker's engagement at a time point other than the predetermined period based on the at least one feature.
  • the worker information includes the worker's biometric information, the worker's position information at the worker's place of work, and relationship information regarding the relationship between the worker and another worker at the workplace.
  • the biometric information is measured by the biometric information measurement terminal 3.
  • the engagement estimation method can also be embodied in a program.
  • the program of this embodiment is a program for causing one or more processors of a computer system to execute the engagement estimation method.
  • the program may be recorded on a non-transitory recording medium that can be read by the computer system.
  • the engagement estimation system 1 estimates the engagement of each of the multiple workers.
  • the engagement estimation system 1 is used together with, for example, a bio-information measuring terminal 3, a position measuring system 4, a data server 5, an operation terminal 6, an information processing server 7, a PC (personal computer) 8, an attendance management system 9, and an exercise measuring terminal 10.
  • the biological information measurement terminal 3 measures biological information of each of the multiple workers.
  • the biological information includes, for example, at least one of heart rate, blood pressure, skin temperature, sweat rate, and voice information.
  • a single biological information measurement terminal 3 may measure multiple types of biological information (for example, heart rate and blood pressure).
  • the bioinformation measuring terminal 3 is, for example, a wearable terminal worn by a worker.
  • the wearable terminal is equipped with, for example, an optical heart rate sensor, which measures the worker's heart rate and blood pressure.
  • the wearable terminal is also equipped with, for example, a temperature sensor, which measures the worker's skin temperature.
  • the wearable terminal is also equipped with, for example, a sweat sensor, which measures the amount of sweat produced by the worker.
  • the bio-information measuring terminal 3 captures an image of a worker for a certain period of time using a camera (such as a near-infrared camera) to generate image data, and measures the worker's heart rate based on the image data.
  • a camera such as a near-infrared camera
  • the bio-information measuring terminal 3 is, for example, a blood pressure monitor with an arm band, and measures the worker's blood pressure while the arm band is wrapped around the worker's arm.
  • the bioinformation measuring terminal 3 is provided with a microphone, for example, and converts the voice of the worker into audio information in the form of an electrical signal using the microphone.
  • the microphone may be provided in a wearable terminal.
  • the position measurement system 4 measures position information of each of the multiple workers.
  • the position information includes, for example, coordinate information of each of the multiple workers.
  • each of the multiple workers carries a mobile terminal such as a smartphone or a wearable terminal.
  • Multiple beacon devices are installed in the workplaces of the multiple workers (e.g., office buildings, stores, or factories).
  • Each of the multiple beacon devices transmits a beacon signal.
  • a mobile device carried by the worker measures the received signal strength of the beacon signal.
  • Information on the received signal strength is transmitted from the mobile device to the position measurement system 4.
  • the position measurement system 4 calculates the distance between the mobile device and each of the multiple beacon devices based on the received signal strength.
  • the position measurement system 4 measures the position information of the mobile device by three-point positioning based on the distance between the mobile device and each of the multiple beacon devices and the position information of each of the multiple beacon devices.
  • the position measurement system 4 transmits the position information of the mobile device to the engagement estimation system 1 as position information of the worker carrying the mobile device.
  • the mobile terminal e.g., a wearable terminal
  • receives the beacon signal may also serve as the biological information measurement terminal 3.
  • the data server 5 stores relationship information.
  • the relationship information is information about the relationships between the multiple workers in their workplaces (such as a company, a government office, or an organization). More specifically, the relationship information includes, for example, information about the hierarchical relationships between the multiple workers.
  • the information about the hierarchical relationships between the multiple workers is, for example, information about the job titles of each of the multiple workers.
  • the job title refers to a work position.
  • the job title is a position or a rank.
  • the relationship information also includes, for example, organizational information about the organization (department or division, etc.) to which each of the multiple workers belongs. The department or division is distinguished by the name, for example, XX Department, XX Section, or XX Center.
  • the relationship information also includes, for example, business information about the business (project, etc.) in which each of the multiple workers is involved.
  • the data server 5 also stores area information.
  • the area information includes, for example, map information of the workplaces of multiple workers.
  • the area information includes, for example, information on the location of each room and the purpose of each room.
  • the operation terminal 6 is, for example, a personal computer or a mobile terminal.
  • the mobile terminal is, for example, a mobile phone such as a smartphone, a wearable terminal, or a tablet terminal.
  • the operation terminal 6 generates the worker's work-related reporting information in response to human operation.
  • the worker himself/herself may operate the operation terminal 6, or another person may operate the operation terminal 6.
  • the operation terminal 6 includes, for example, a touch panel display, and displays survey items on the touch panel display. A person answers the survey items by operating the touch panel display. The operation terminal 6 then generates declaration information that includes the answers obtained from the person.
  • a person when asked a question in a questionnaire, a person selects an answer from among multiple options.
  • the multiple options are, for example, five options: “Agree,” “Somewhat agree,” “Can't say,” “Somewhat disagree,” and “Disagree.”
  • the questionnaire items include, for example, questions for estimating engagement for a specified period in the information processing server 7. Such questions are hereinafter referred to as “first questions”, and answers to the first questions are hereinafter referred to as “first answers”.
  • the first questions are, for example, questions asking about the worker's place of employment, the content of the work, and the worker's thoughts and feelings about their colleagues.
  • the questionnaire items also include, for example, questions for estimating engagement at a time other than the specified period using the engagement estimation system 1. Such questions are hereinafter referred to as “second questions,” and answers to the second questions are hereinafter referred to as “second answers.” At least one second question may be the same as the first question.
  • the information processing server 7 estimates the engagement of a worker for a predetermined period of time. More specifically, the information processing server 7 first acquires declaration information from the operation terminal 6. The declaration information includes at least one first response. Based on the at least one first response, the information processing server 7 estimates the engagement of a worker for a predetermined period of time.
  • the information processing server 7 can use a known method such as that disclosed in Patent Document 1 to estimate engagement. For example, a score for the first answer is determined based on which of multiple options is selected as the first answer. The information processing server 7 estimates the sum of the scores for each of the multiple first answers as the worker's engagement for a specified period of time.
  • PCs personal computers 8 for work. More specifically, each of the workers is assigned one or more PCs 8 by his/her workplace. Each of the workers uses the one or more PCs 8 assigned to him/her.
  • PC8 is equipped with a storage device that stores its own usage history.
  • the storage device is a hard disk drive (HDD) or a solid state drive (SSD), etc.
  • Software for acquiring the usage history of PC8 and storing it in the storage device may be installed in PC8.
  • PC8 is an example of a computer system that a worker uses for work.
  • a computer system includes one or more computers.
  • the computer system that a worker uses for work is not limited to PC8, and may be, for example, a mobile phone such as a smartphone, a tablet terminal, or a host computer.
  • the computer system that a worker uses for work may be, for example, a computer system for operating an object to be operated, such as a vehicle or a machine tool, and the usage history may include the operation history of the object to be operated.
  • the attendance management system 9 generates attendance information for each of a plurality of workers.
  • each of the plurality of workers carries a readable device such as a mobile terminal (such as a smartphone or wearable terminal) or an IC card, and holds the readable device over the reading device of the attendance management system 9 when arriving at and leaving work.
  • the attendance management system 9 then reads the identification information stored in the readable device from the readable device.
  • the attendance management system 9 generates attendance information for each of a plurality of workers, and the attendance information includes information on the arrival time and departure time of work.
  • the exercise measurement terminal 10 obtains an exercise index of a worker.
  • the exercise index indicates at least one of the quality and quantity of exercise.
  • the exercise index includes, for example, at least one of the amount of activity and the amount of movement.
  • the amount of activity is expressed, for example, in METs (Metabolic equivalents).
  • the amount of movement is, for example, the number of steps.
  • the exercise measuring terminal 10 is, for example, a wearable terminal.
  • the worker carries the wearable terminal.
  • the wearable terminal is equipped with, for example, a pedometer and measures the number of steps taken by the worker.
  • the wearable terminal also measures, for example, the worker's biometric information (heart rate, blood pressure, skin temperature, amount of sweat, etc.) as described above.
  • the wearable terminal calculates the worker's activity level based on the biometric information.
  • the engagement estimation system 1 includes a processing unit 2, a memory unit 11, and a communication unit 12.
  • the storage unit 11 is a storage device configured with a hard disk drive (HDD) or a solid state drive (SSD) or the like.
  • the storage unit 11 stores information.
  • the storage unit 11 stores information acquired from an external device, such as bioinformation acquired from the bioinformation measurement terminal 3, location information acquired from the location measurement system 4, and relationship information acquired from the data server 5.
  • the communication unit 12 includes a communication interface device.
  • the communication unit 12 is capable of communicating with external devices (e.g., the bioinformation measurement terminal 3, the position measurement system 4, and the data server 5) via the communication interface device.
  • external devices e.g., the bioinformation measurement terminal 3, the position measurement system 4, and the data server 5
  • “capable of communication” means that signals can be sent and received directly or indirectly via a network or a repeater, etc., using an appropriate communication method such as wired communication or wireless communication.
  • the processing unit 2 includes a computer system having one or more processors and a memory.
  • the functions of the processing unit 2 are realized by the processor of the computer system executing a program recorded in the memory of the computer system.
  • the program may be recorded in the memory, or may be provided via a telecommunications line such as the Internet, or may be recorded on a non-transitory recording medium such as a memory card and provided.
  • the processing unit 2 has a first acquisition unit 21, a second acquisition unit 22, a feature determination unit 23, an estimation unit 24, a presentation content generation unit 25, and a communication processing unit 26. Note that these merely indicate the functions realized by the processing unit 2, and do not necessarily indicate a concrete configuration.
  • the first acquisition unit 21 acquires worker information (information on each of a plurality of workers) via the communication unit 12.
  • the worker information includes the worker's bioinformation measured by the bioinformation measurement terminal 3, the worker's position information measured by the position measurement system 4, and the relationship information stored in the data server 5.
  • the worker information further includes reporting information generated by the operation terminal 6.
  • the reporting information is information about the work and is generated in response to operations on the operation terminal 6.
  • the reporting information is, for example, one or both of the first and second responses described above.
  • the worker information also includes the usage history of the computer system that the worker uses for work.
  • the computer system is PC8. That is, the worker information includes the usage history of PC8.
  • the worker information further includes the worker's attendance information.
  • the worker's attendance information is generated by the attendance management system 9.
  • the worker information further includes the worker's exercise index.
  • the worker's exercise index is generated by the exercise measurement terminal 10.
  • the second acquisition unit 22 acquires the engagement of a worker for a predetermined period of time estimated by the information processing server 7.
  • the feature determination unit 23 determines at least one feature from the worker information based on the relationship between the worker information and engagement for a predetermined period. For example, the feature determination unit 23 extracts multiple parameters from the worker information. The multiple parameters are, for example, the duration of a conversation between the worker and a specific person, the worker's activity level, and the worker's overtime hours.
  • a certain parameter may match certain information about a worker.
  • the information about the worker may be used as a parameter directly.
  • the overtime hours of a worker is an example of worker information.
  • the overtime hours of a worker may be used as a parameter.
  • a certain parameter may be found based on certain information about the worker. That is, the information about the worker may be processed to become the parameter.
  • the position information of the worker measured by the position measurement system 4 and the relationship information stored in the data server 5 are each an example of worker information.
  • the conversation time between the worker and a specific person may be found as a parameter. More specifically, the conversation time (face-to-face) between the worker and the supervisor can be found, for example, from the position information of the worker and the supervisor. That is, the time when the distance between the worker and the supervisor is within a specified distance (for example, one meter) can be determined as the conversation time between the worker and the supervisor. Also, whether or not a certain worker is a supervisor can be identified based on the relationship information.
  • the characteristic amount determining unit 23 may determine at least one characteristic amount for each worker. In other words, to determine at least one characteristic amount corresponding to a certain worker, information about the worker and the worker's engagement for a specified period of time may be referenced.
  • the feature determination unit 23 may determine at least one feature common to two or more workers. In other words, to determine at least one feature corresponding to two or more workers, information about each of the two or more workers and the engagement of each of the two or more workers for a specified period of time may be referenced.
  • the feature amount determination unit 23 determines, from among the multiple parameters, a parameter that has a strong correlation with engagement for a predetermined period as a feature amount.
  • the feature amount determination unit 23 calculates the strength of correlation, for example, by multiple regression analysis. That is, the feature amount determination unit 23 determines a multiple regression equation using engagement for a predetermined period as a response variable and multiple parameters for the predetermined period as multiple explanatory variables, and further determines a coefficient of determination of the multiple regression equation.
  • the coefficient of determination is a value between 0 and 1. The larger the coefficient of determination, the stronger the correlation.
  • the feature amount determination unit 23 determines, for example, multiple explanatory variables when the coefficient of determination is greater than a threshold value as multiple feature amounts.
  • the feature amount determination unit 23 may determine a simple regression equation instead of a multiple regression equation, and determine the strength of correlation (coefficient of determination) from the simple regression equation. In short, it is sufficient for the feature amount determination unit 23 to determine the correlation between at least one feature amount and engagement by regression analysis or the like.
  • Figure 2 shows an example of a simple regression equation (straight line L1) obtained by using engagement for a specified period as the dependent variable and one parameter for the specified period as the explanatory variable.
  • the explanatory variable is the number of conversations with a second-level supervisor (the direct supervisor's further direct supervisor).
  • the dependent variable and explanatory variable are each obtained for multiple time periods.
  • pairs of the dependent variable and explanatory variable for each period are plotted as points.
  • a simple regression equation is obtained based on these multiple points.
  • Figure 2 When determining at least one feature corresponding to one specific worker, Figure 2 will be a plot of data (pairs of objective variables and explanatory variables) for that one specific worker. When determining at least one feature common to two or more workers, Figure 2 will be a plot of data (pairs of objective variables and explanatory variables) for each of the two or more workers.
  • the feature determination unit 23 may determine at least one feature based on the difference between the engagement at the first time point and the engagement at the second time point. In other words, the feature determination unit 23 may determine at least one feature based on the amount of change in engagement. For example, the feature determination unit 23 may determine a multiple regression equation using multiple parameters as multiple explanatory variables and the amount of change in engagement as the objective variable, and may determine a parameter among the multiple parameters that has a strong correlation with the amount of change in engagement as the feature.
  • the feature determination unit 23 may set the explanatory variable as the difference between a predetermined parameter and a reference value.
  • the reference value may be, for example, the average value of the predetermined parameter in a specific period.
  • the predetermined parameter is set to the activity level of the worker in a predetermined period.
  • the reference value may be, for example, the average value of the activity level of the worker in the same period of the previous year to the predetermined period.
  • the reference value may be, for example, the average value of the activity level of the worker from a predetermined number of days (for example, six months) before the predetermined period to the predetermined period.
  • the estimation unit 24 executes an estimation step. That is, the estimation unit 24 estimates the engagement of the worker at a time point other than the predetermined period (the most recent time point, as an example here) based on at least one feature determined by the feature determination unit 23. For example, it is assumed that two parameters, the conversation time between the worker and the second-level supervisor and the overtime hours of the worker, are determined as feature amounts by the feature determination unit 23. In this case, the estimation unit 24 determines the most recent engagement reflecting the parameters of the previous month by substituting the above two parameters for the previous month into a multiple regression equation (determined by the feature determination unit 23) in which each of the above two parameters is an explanatory variable and the engagement is an objective variable.
  • the engagement may be calculated, for example, by substituting the average value of the parameters at the multiple points in time into the multiple regression equation.
  • multiple engagements may be calculated by substituting the parameters at the multiple points in time into the multiple regression equation.
  • a simple regression equation may be used instead of a multiple regression equation.
  • the presentation content generation unit 25 executes a presentation content generation step. That is, the presentation content generation unit 25 generates content to be presented to the viewer based on the estimation result of the estimation step by the estimation unit 24.
  • the content to be presented to the viewer includes, for example, a numerical value representing the engagement obtained in the estimation step.
  • the viewer may be the worker himself/herself who is the target of the engagement request, or may be another person (for example, the worker's supervisor).
  • the presentation content generation unit 25 generates, for example, a list of the engagement of each of a number of workers for each survey period (e.g., each month) as content to be presented to the viewer.
  • the presentation content generation unit 25 also generates, for example, a list of a worker's engagement for each survey period as content to be presented to the worker.
  • the communication processing unit 26 controls the transmission and reception of information by the communication unit 12.
  • the communication processing unit 26 executes the transmission step by controlling the communication unit 12.
  • the transmission step is a step of transmitting the content generated in the presentation content generation step to a terminal.
  • the terminal is, for example, a PC 8.
  • the terminal has a display that displays the received content. The viewer views the content generated in the presentation content generation step via the display of the terminal.
  • the content generated in the presentation content generation step is transmitted to the terminal at regular intervals.
  • the regular intervals are, for example, one week, two weeks, one month, or two months.
  • Worker information including biometric information, location information, and relationship information, is collected on a regular or irregular basis through measurements and questionnaires given to workers.
  • the worker information is then compiled on a monthly basis. For example, in Figure 3, worker information for January, worker information for February, etc. are compiled.
  • the information processing server 7 also estimates engagement based on the above-mentioned first response included in the declaration information.
  • a survey is conducted on workers once a month, and a first response is obtained as a response to the survey. Then, the information processing server 7 estimates engagement for each month based on the first response. For example, in FIG. 3, the information processing server 7 estimates engagement for January based on the first response in January, estimates engagement for February based on the first response in February, and estimates engagement for March based on the first response in March.
  • the engagement estimation system 1 begins to estimate engagement.
  • the engagement data estimated by the information processing server 7 is referred to as "reference engagement data.”
  • the engagement estimation system 1 first determines a multiple regression equation and feature quantities. For example, when estimating engagement for April (target period), the engagement estimation system 1 refers to worker information and reference engagement data for periods other than April (target period). In FIG. 3, the engagement estimation system 1 refers to worker information and reference engagement data for January to March. In this way, the engagement estimation system 1 determines a multiple regression equation and feature quantities. In more detail, the engagement estimation system 1 determines a multiple regression equation using engagement for January to March (predetermined period) as the objective variable and multiple parameters extracted from worker information for January to March (predetermined period) as multiple explanatory variables, and determines the multiple explanatory variables when the coefficient of determination of the multiple regression equation is greater than a threshold value as multiple feature quantities.
  • the engagement estimation system 1 estimates engagement for April (target period) from the multiple regression equation and the feature quantities. More specifically, the engagement estimation system 1 finds engagement for April (target period) by substituting the feature quantities obtained from the worker information for April (target period) into the multiple regression equation.
  • the engagement estimation system 1 generates content to be presented to the viewer based on the determined engagement, and transmits the content to the terminal (PC 8). For example, the engagement estimation system 1 generates content to be presented once a month, and transmits the content to the terminal.
  • the process of determining the multiple regression equation and feature quantities does not need to be executed every time the engagement estimation system 1 estimates engagement, but only needs to be executed the first time (i.e., when estimating engagement in April).
  • the process of determining (updating) the multiple regression equation and feature quantities may be executed every time a period longer than the engagement estimation interval (one month) by the engagement estimation system 1 elapses (e.g., every six months).
  • the burden is reduced, for example, because the frequency of conducting surveys to obtain a first response can be reduced.
  • the engagement estimation system 1 In order for the engagement estimation system 1 to estimate engagement for a target period (e.g., April), it is not necessary for the engagement estimation system 1 to collect information other than features from the worker's information for the target period. For example, assume that the conversation time between the worker and the second-level supervisor and the worker's overtime hours are determined as features by the feature determination unit 23. In this case, it is not necessary for the engagement estimation system 1 to collect information other than features from the worker's information for the target period (e.g., the worker's activity level).
  • the engagement estimation system 1 may determine the multiple regression equation and feature quantities by referring to worker information and reference engagement data for a period after April (target period) (e.g., May to June).
  • Feature A feature may be, for example, related to "job resources,””personalresources,” or “job demands” defined in the "job demands-resources model,” i.e., the "JD-R model.”
  • the feature may be related to empathy or a sense of satisfaction with at least one of the vision, mission, and philosophy of the group (company, etc.) to which the worker belongs for work.
  • the feature may be related to two or more of the above.
  • job resources refer to at least one of "support from others,” “relationships with others,” “job autonomy,” “coaching from colleagues,” “feedback from colleagues,” “diversity of relationships,” and “opportunities for career development.”
  • personal resources may relate to at least one of "optimism,” “resilience,” and “recovery status.”
  • job demands refers to at least one of the following: “quantitative workload,” “qualitative workload,” and “physical workload at work.”
  • the feature amount determining unit 23 extracts, for example, multiple parameters from the worker's information and determines at least one of them as a feature amount.
  • the parameters (feature amounts) are, for example, the conversation time between the worker and a specific person, the worker's activity level, and the worker's overtime hours.
  • the at least one feature amount determined by the feature amount determining unit 23 is not particularly limited. Therefore, the feature amount determining unit 23 may, for example, determine only the parameters extracted from the biometric information as the feature amount, or may determine only the parameters extracted from the declared information as the feature amount, or may determine both the parameters extracted from the biometric information and the parameters extracted from the declared information as the feature amount.
  • the conversation time between a worker and a specific person as a feature is related to the "work resources" of "support from the surroundings" and "relationships with the surroundings".
  • the (face-to-face) conversation time between a worker and a specific person can be obtained, for example, from the position information and voice information of each of a plurality of workers.
  • the conversation time between a worker and a specific person can be determined as the time when the distance between the worker and the specific person is within a predetermined distance (for example, 1 meter) and voice information is output from a microphone present around the place of conversation.
  • the microphone may be, for example, carried by the worker or installed near the worker's work place.
  • the duration of a (face-to-face) conversation between a worker and a specific person can also be calculated, for example, from only the location information of each of a number of workers.
  • the duration of a conversation can be calculated by conveniently determining that the worker and a specific person are in close proximity to each other as being for the purpose of conversation.
  • the duration of a conversation between a worker and a specific person can be determined as the time when the distance between them is within a specified distance.
  • conversation time between the worker and a specific person is not limited to face-to-face conversation time, but may also include non-face-to-face (e.g., online) conversation time.
  • Online conversation time is extracted, for example, from the usage history of PC8.
  • the amount of time a worker has face-to-face conversations with a specific person and the amount of time he or she has not face-to-face conversations may be required separately.
  • the conversation time between the worker and the specific person may be extracted from the declaration information input to the operation terminal 6.
  • the engagement estimation system 1 may obtain the conversation time between the worker and the specific person based on the declaration from the respondent (e.g., the worker).
  • the relationship information is information about the relationships between multiple workers in the workplace of the multiple workers.
  • the feature determination unit 23 may obtain the conversation time of multiple workers for each relationship based on the relationship information. That is, the feature determination unit 23 may obtain the conversation time between a worker and a worker in a specific position. For example, the feature determination unit 23 may obtain the conversation time between a worker and a superior, the conversation time between a worker and a subordinate, and the conversation time between workers of the same position.
  • the relationships may also be further subdivided. For example, the feature determination unit 23 may obtain the conversation time between a worker and a first-level superior (immediate superior) and the conversation time between a worker and a second-level superior (the superior of the immediate superior).
  • the feature determination unit 23 may obtain the conversation time between workers who belong to the same organization (department, etc.) and workers who belong to different organizations (departments, etc.). Also, for example, the feature determination unit 23 may determine the conversation time between workers who are in charge of the same work (project, etc.).
  • the number of conversations between a worker and a specific person may be obtained as a feature.
  • the number of face-to-face conversations can be obtained, for example, from the position information of each of a plurality of workers.
  • the number of times the distance between the worker and a specific person changes from more than a specified distance (for example, one meter) to within the specified distance can be regarded as the number of conversations between the worker and the specific person.
  • the feature quantities of a worker's overtime hours, the number of overtime hours, the number of night shift hours, and the number of holiday shift hours are related to the "work discretion" of the "work resources".
  • a worker's overtime hours, the number of overtime hours, the number of night shift hours, and the number of holiday shift hours are extracted, for example, from the attendance information output from the attendance management system 9, or the reported information input to the operation terminal 6.
  • a worker's overtime hours, the number of overtime hours, the number of night shift hours, and the number of holiday shift hours are determined, for example, from the start and end times of PC 8, which are extracted from the usage history of PC 8.
  • the mood at the end of the workday is related to the "work resources" of "coaching from colleagues” and “feedback from colleagues.”
  • the mood at the end of the workday is extracted, for example, from the reporting information entered into the operation terminal 6.
  • the number of communications within a department and the number of people with whom communication takes place within the department, as features, are related to the "diversity of human relationships" of "work resources.”
  • the number of face-to-face communications within a department and the number of people with whom communication takes place within the department can be determined, for example, from the location information of each of multiple workers, or from location information and voice information, or can be extracted from reporting information entered into the operation terminal 6.
  • the number of non-face-to-face (e.g., online) communications within a department and the number of people with whom communication takes place within the department can be extracted, for example, from the usage history of PC 8 or from reporting information entered into the operation terminal 6.
  • the number of spaces used and the number of times specialized tools were used, as features, are related to the "opportunities for career development" of the "work resources.”
  • the number of spaces used and the number of times specialized tools were used can be obtained, for example, from the location information of the worker, or extracted from the usage history of the PC 8 or the reporting information entered into the operation terminal 6.
  • the feature quantities of break time, time when no data was entered into the PC, and the number of times when no data was entered into the PC are related to the "resilience" of "individual resources.”
  • the break time, time when no data was entered into the PC, and the number of times when no data was entered into the PC are extracted, for example, from the usage history of the PC 8 or the reported information entered into the operation terminal 6.
  • the amount of movement within the office is related to the "resilience" of "personal resources.”
  • the amount of movement within the office can be calculated, for example, from the location information of the worker, or measured by a pedometer (movement measuring terminal 10) carried by the worker.
  • the feature quantities, ie, the PC operation time and the number of times the PC is operated before the start of work, after the end of work, late at night, and on holidays, relate to the "recovery status" of "personal resources.”
  • the PC operation time and the number of times the PC is operated before the start of work, after the end of work, late at night, and on holidays are extracted, for example, from the usage history of the PC 8, the attendance information output from the attendance management system 9, or the reporting information input to the operation terminal 6.
  • the work time interval (the length of time between the end of work on a certain day and the start of work on the following day) as a feature quantity is related to the "recovery status" of the "personal resources.”
  • the work time interval is extracted, for example, from the usage history of the PC 8, the attendance information output from the attendance management system 9, or the reporting information input to the operation terminal 6.
  • the volume of the worker's voice is related to the "recovery status" of the "personal resources.”
  • the volume of the worker's voice is measured, for example, by a microphone provided in the bio-information measurement terminal 3.
  • the feature quantity ie, the working time (the length of time from the time of entering the workplace to the time of leaving the workplace), is related to the "quantitative burden of work" of the "work demands.”
  • the working time can be obtained, for example, from the location information of the worker or the attendance information output from the attendance management system 9.
  • the amount of time spent using a PC after work hours is related to the "quantitative burden of work" of the "demands of work.”
  • the amount of time spent using a PC after work hours is extracted, for example, from the usage history of PC8.
  • the feature quantities relate to the "quantitative workload" of the "job demands."
  • the time spent at a rest area and the number of times the rest area is used can be determined, for example, from a combination of the worker's location information and area information related to the rest area, etc.
  • the area information is obtained, for example, from the data server 5.
  • the feature quantities ie, the time period during which there is no PC input between the start and end of work and the number of times (the number of times that no input continues for a specified period of time or more), relate to the "quantitative workload" of the "demand level of work.”
  • the time period during which there is no PC input between the start and end of work and the number of times are extracted, for example, from the usage history of PC8.
  • the amount of conversation in the workplace is related to the "demands of work” and the "quantitative burden of work.”
  • the amount of conversation in the workplace can be obtained, for example, from the output (audio information) of the microphone equipped in the bioinformation measurement terminal 3.
  • the number of keyboard operations per unit time and the amount of mouse cursor movement per unit time, which are characteristic quantities, are related to the "qualitative burden of work" of the "job demands.”
  • the number of keyboard operations per unit time and the amount of mouse cursor movement per unit time are extracted, for example, from the usage history of the PC 8.
  • PC operation time is related to the "quality of work burden" of the "job demands.”
  • PC operation time is extracted, for example, from the usage history of PC8.
  • the task time with a high qualitative burden is related to the "qualitative burden of work" of the "degree of work demands.”
  • the task time with a high qualitative burden can be obtained, for example, from bioinformation measured by the bioinformation measurement terminal 3.
  • the engagement estimation system 1 assumes that a state in which the heart rate, as bioinformation, is higher than a corresponding threshold value is a state of high qualitative burden, and obtains the accumulated time in the state of high qualitative burden as the task time with a high qualitative burden.
  • the amount of activity of a worker is related to the "physical burden of work" of the "job demands.”
  • the amount of activity of a worker is expressed, for example, in METs.
  • the amount of activity of a worker can be calculated, for example, from bio-information (heart rate, blood pressure, skin temperature, amount of sweat, etc.) measured by the bio-information measuring terminal 3.
  • the number of steps taken by a worker is related to the "physical burden of work" of the "job demands."
  • the number of steps taken by a worker can be determined, for example, from the worker's location information.
  • the number of steps taken by a worker can be measured, for example, by a pedometer (exercise measuring terminal 10) carried by the worker.
  • the maximum heart rate of a worker is related to the "physical burden of work" of the "job demands.”
  • the maximum heart rate of a worker is extracted, for example, from measurement data of heart rate as biometric information.
  • degree of Sympathy with Ideals A feature related to the degree of sympathy with ideals (sympathy or a sense of agreement with at least one of the vision, mission, and ideals of the group to which the worker belongs for work) is extracted, for example, from the declaration information input to the operation terminal 6.
  • the feature related to the degree of sympathy with ideals is, for example, an index indicating the extent to which the worker understands the meaning of the work.
  • the engagement estimation system 1 may be equipped with a display device that displays the information generated in the presentation content generation step.
  • the engagement estimation system 1 may include an operation unit that accepts operations for generating the declaration information, and may also serve as the operation terminal 6.
  • the feature determination unit 23 may refer to a correlation coefficient instead of the coefficient of determination to determine the strength of correlation between multiple parameters and engagement for a specified period of time.
  • the estimation unit 24 executes a regression step of acquiring a regression equation that expresses the relationship between at least one feature and engagement for a predetermined period of time.
  • the process of "acquiring a regression equation” may be a process in which the estimation unit 24 acquires a regression equation determined in a configuration external to the engagement estimation system 1 from the external configuration, or a process in which the estimation unit 24 acquires a regression equation stored in the memory unit 11 of the engagement estimation system 1.
  • the process of "acquiring a regression equation” may be a process of determining a regression equation.
  • the entity that executes the engagement estimation system 1 or the engagement estimation method in the present disclosure includes a computer system.
  • the computer system is mainly composed of a processor and a memory as hardware. At least a part of the functions of the entity that executes the engagement estimation system 1 or the engagement estimation method in the present disclosure is realized by the processor executing a program recorded in the memory of the computer system.
  • the program may be pre-recorded in the memory of the computer system, may be provided through a telecommunications line, or may be recorded and provided on a non-transitory recording medium such as a memory card, an optical disk, or a hard disk drive that can be read by the computer system.
  • the processor of the computer system is composed of one or more electronic circuits including a semiconductor integrated circuit (IC) or a large scale integrated circuit (LSI).
  • the integrated circuits such as ICs or LSIs referred to here are called different names depending on the degree of integration, and include integrated circuits called system LSIs, VLSIs (Very Large Scale Integration), or ULSIs (Ultra Large Scale Integration).
  • a field-programmable gate array (FPGA) that is programmed after the LSI is manufactured, or a logic device that allows the reconfiguration of the connection relationships within the LSI or the reconfiguration of the circuit partitions within the LSI, can also be used as a processor.
  • Multiple electronic circuits may be integrated into one chip, or may be distributed across multiple chips.
  • the computer system referred to here includes a microcontroller having one or more processors and one or more memories.
  • the microcontroller is also composed of one or more electronic circuits including a semiconductor integrated circuit or a large-scale integrated circuit.
  • the engagement estimation system 1 it is not essential for the engagement estimation system 1 that multiple functions are consolidated into one device, and multiple components of the engagement estimation system 1 may be distributed across multiple devices. Furthermore, at least some of the functions of the engagement estimation system 1 may be realized by a server or a cloud (cloud computing), etc.
  • multiple functions distributed across multiple devices may be consolidated into one device.
  • at least two of the data server 5, the information processing server 7, and the engagement estimation system 1 may be consolidated into one device.
  • the PC 8 may also function as the operation terminal 6.
  • the bio-information measuring terminal 3 may also function as the exercise measuring terminal 10.
  • At least a portion of the functions of the engagement estimation system 1 may be realized by a computational model generated by machine learning.
  • a feature determination step of determining at least one feature may be realized by the computational model.
  • an estimation step of estimating worker engagement based on at least one feature may be realized by the computational model.
  • the engagement estimation method is an engagement estimation method in an engagement estimation system (1).
  • the engagement estimation method includes a first acquisition step, a second acquisition step, a feature determination step, and an estimation step.
  • a first acquisition unit (21) acquires worker information.
  • a second acquisition unit (22) acquires the worker's engagement with work for a predetermined period.
  • a feature determination unit (23) determines at least one feature from the worker information based on the relationship between the worker information and the engagement for the predetermined period.
  • an estimation unit (24) estimates the worker's engagement at a time point other than the predetermined period based on the at least one feature.
  • the worker information includes the worker's biometric information, the worker's location information at the worker's place of work, and relationship information regarding the relationship between the worker and another worker at the workplace.
  • the biometric information is measured by a biometric information measurement terminal (3).
  • engagement can be estimated more objectively by using workers' biometric information, location information, and relationship information.
  • the worker information further includes work-related reporting information.
  • the reporting information is generated in response to operations on the operation terminal (6).
  • the accuracy of engagement estimation can be improved by using the declared information.
  • the declared information includes a response to a question for estimating engagement during a specified period.
  • the accuracy of estimating engagement can be improved by using answers to questions.
  • the declaration information includes a first answer to a first question for estimating engagement in a specified period of time, and a second answer to a second question for estimating engagement at a time other than the specified period of time.
  • the accuracy of estimating engagement can be improved by using answers to questions.
  • the worker information further includes the usage history of the computer system (PC8) used by the worker for work.
  • the worker information further includes the worker's attendance information.
  • the accuracy of estimating engagement can be improved by using attendance information.
  • At least one feature quantity is related to at least one of the following: "job resources,” “personal resources,” and “job demands” defined in the "job demands-resources model,” i.e., the "JD-R model,” and the worker's empathy and understanding for at least one of the vision, mission, and philosophy of the group to which the worker belongs for work.
  • the "job resources” relate to at least one of “support from those around,” “relationships with those around,” “work discretion,” “coaching from colleagues,” “feedback from colleagues,” “diversity of relationships,” and “opportunities for career development.”
  • the "personal resources” relate to at least one of "optimism,” “resilience,” and “recovery status.”
  • job demands relate to at least one of "quantitative workload,” “qualitative workload,” and "physical workload at work.”
  • the engagement estimation method is any one of the first to eighth aspects, and further includes a presentation content generation step.
  • the presentation content generation step content to be presented to the viewer is generated based on the estimation result of the estimation step.
  • the engagement estimation method is the same as the ninth aspect, but further includes a transmission step.
  • the transmission step the content generated in the presentation content generation step is transmitted to the terminal.
  • the transmission step the content generated in the presentation content generation step is transmitted to the terminal at regular intervals.
  • the engagement estimation method is any one of the first to eleventh aspects, and further includes a regression step in which the estimation unit (24) acquires a regression equation that represents the relationship between at least one feature and engagement for a predetermined period of time.
  • the estimation step engagement at a time point other than the predetermined period is estimated based on the at least one feature and the regression equation.
  • the feature determination step at least one feature is determined based on the coefficient of determination of a regression equation that represents the relationship between the worker's information and engagement over a predetermined period of time.
  • At least one feature can be determined using the coefficient of determination.
  • Configurations other than the first aspect are not essential to the engagement estimation method and may be omitted as appropriate.
  • the program according to the fourteenth aspect is a program for causing one or more processors of a computer system to execute the engagement estimation method according to any one of the first to thirteenth aspects.
  • engagement can be estimated more objectively by using workers' biometric information, location information, and relationship information.
  • an engagement estimation system (1) includes a first acquisition unit (21), a second acquisition unit (22), a feature determination unit (23), and an estimation unit (24).
  • the first acquisition unit (21) acquires worker information.
  • the second acquisition unit (22) acquires the worker's engagement with work for a predetermined period of time.
  • the feature determination unit (23) determines at least one feature from the worker information based on the relationship between the worker information and the engagement for the predetermined period of time.
  • the estimation unit (24) estimates the worker's engagement at a time point other than the predetermined period of time based on the at least one feature.
  • the worker information includes the worker's biometric information, the worker's location information at the worker's place of work, and relationship information regarding the relationship between the worker and another worker at the workplace.
  • the biometric information is measured by a biometric information measurement terminal (3).
  • engagement can be estimated more objectively by using workers' biometric information, location information, and relationship information.
  • various configurations (including modified examples) of the engagement estimation system (1) according to the embodiment can be embodied in an engagement estimation method, a (computer) program, or a non-transitory recording medium having a program recorded thereon.

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Abstract

L'objectif de la présente divulgation est de déduire un engagement de manière plus objective. Ce procédé d'inférence d'engagement comprend une étape de détermination de quantité de caractéristiques et une étape d'inférence. L'étape de détermination de quantité de caractéristiques consiste à déterminer, d'après une relation entre des informations concernant un travailleur et un engagement pendant une période prescrite, au moins une quantité de caractéristiques à partir des informations du travailleur. L'étape d'inférence est destinée à déduire, d'après la ou les quantités de caractéristiques, l'engagement du travailleur à un instant différent de la période prescrite. Les informations du travailleur comprennent les informations biologiques du travailleur, les informations de position du travailleur sur son lieu de travail, ainsi que des informations sur la relation entre le travailleur et un autre travailleur sur un site de travail.
PCT/JP2023/040446 2022-11-15 2023-11-09 Procédé d'inférence d'engagement, programme et système d'inférence d'engagement WO2024106309A1 (fr)

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Citations (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
WO2013191054A1 (fr) * 2012-06-19 2013-12-27 日本電気株式会社 Dispositif de gestion de motivation, procédé de gestion de motivation, et support d'enregistrement lisible par ordinateur
JP2022021444A (ja) * 2020-07-22 2022-02-03 株式会社リンクアンドモチベーション スコア予測装置、スコア予測方法、およびプログラム

Patent Citations (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
WO2013191054A1 (fr) * 2012-06-19 2013-12-27 日本電気株式会社 Dispositif de gestion de motivation, procédé de gestion de motivation, et support d'enregistrement lisible par ordinateur
JP2022021444A (ja) * 2020-07-22 2022-02-03 株式会社リンクアンドモチベーション スコア予測装置、スコア予測方法、およびプログラム

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