Task allocation device

Abstract

A task allocation device according to one embodiment of the present invention includes: a collection unit for collecting evaluation scores on items which are related to a personality that represents a character of a person, and collecting evaluation scores on items which indicate characteristics of a prescribed type of task and are related to a personality that represents a character of a person suitable for the task; a conversion unit for converting the collected evaluation scores so as to minimize the differences in the same person or the same task resulting from variations in items regarding the personality and to equalize the variance of the differences resulting from the variations in the individual person and the individual task; a calculation unit for expressing the characteristics of the person and the task as vectors having the converted evaluation scores as elements, and, regarding a combination of the person and the task to be performed by the person, calculating the distance between the vectors as a prediction value of the degree of motivation that the person has toward performing the task; and an allocation processing unit for obtaining an allocation, to the person, of the task to be performed by said person for which the calculated prediction value is maximized.

Description

Task Allocation Device

[0001] TECHNICAL FIELD An embodiment of the present invention relates to a task allocation device.

[0002] It is believed that by allocating tasks in accordance with the motivation of individuals, in this case employees, which is the willingness to carry out various tasks, it is possible to maximize the total amount of motivation for tasks that employees within an organization have.

[0003] In a conventional technique using a career interest scale (see, for example, Non-Patent Document 1), the characteristics of employees and tasks are quantitatively expressed using RIASEC-type descriptive variables related to job content, thereby achieving a match between employees and tasks that maximizes the employee's motivation to perform the task (sometimes referred to as task motivation). RIASEC-type descriptive variables related to job content are quantified degrees of fit to linguistic descriptions, such as, for example, R-type jobs are jobs such as mechanics, C-type jobs are jobs such as clerical work, and I-type jobs are jobs such as research and investigation.

[0004] For example, by using a questionnaire, it is possible to quantify characteristics in the form of an employee being most motivated for R-type work, being second most motivated for C-type work, and being second most motivated for I-type work.

[0005] In many conventional techniques, a person's characteristics are expressed using an RCI code. At the same time, the characteristics can be quantified by saying that a certain task contains the most C-type aspects, the next most I-type aspects, and the next most R-type aspects. In this case, the characteristics of the task are represented by a CRI code. The employee's motivation for the task is predicted based on the match between the employee and the task code. Descriptive variables are arranged in a circular fashion in RIASEC order, and the closer the worker and the job code are to each other, the higher the employee's motivation for the task is predicted to be. For example, a perfect match is given a score of 3, a close match is given a score of 2, a close match is given a score of 1, and all other matches are given a score of 0. A possible method is to weight the codes from highest to lowest, with 3, 2, and 1, respectively. In the example above, for the combination of employees and tasks, the predicted motivation value is calculated as "2 x 3 = 6" for the RC combination, "2 x 2 = 4" for the CR combination, and "3 x 1 = 3" for the I-I combination, for a total of 13 points. Using this motivation value as an index, by matching employees and tasks so as to maximize this index, task allocation can be achieved that increases the motivation of employees in the organization.

[0006] Nye, CD, Su, R., Rounds, J., & Drasgow, F. (2012). Vocational interests and performance: A quantitative summary of over 60 years of research., Perspectives on Psychological Science,7(4), 384-403.

[0007] If it were possible to quantitatively express employee motivation and task characteristics using descriptive variables related to employee personality that express the individuality that characterizes employees, rather than the descriptive variables related to job content mentioned above, it would be possible to predict motivation values ​​with even greater accuracy, potentially realizing a match between employees and tasks that can maximize the work motivation of personnel within an organization.

[0008] Descriptive variables related to an employee's personality are, for example, personality traits such as the Big Five personality, which quantify the degree to which the employee conforms to verbal descriptions such as whether they are open to new experiences or whether they are socially outgoing.

[0009] Unlike descriptions of job content, descriptions of personality are linguistic descriptions that broadly include behavioral patterns in everyday life in general, not just professional life, and therefore may be descriptions that reflect employees' motivations by taking into account behavior in a wider range of situations.

[0010] Therefore, quantifying both the personality of the employee and the personality of the person best suited to the task and combining these will enable more accurate prediction of motivation values, and matching employees with tasks in a way that maximizes this value may lead to task allocation that maximizes the motivation of personnel within an organization.

[0011] However, there is no established method for predicting motivation values ​​when using descriptive variables related to employees' personalities.

[0012] This invention has been made in light of the above circumstances, and its purpose is to provide a task allocation device that can appropriately allocate tasks to be performed by individuals using the personalities of the individuals performing the tasks.

[0013] A task allocation device according to one aspect of the present invention includes a collection unit that collects first rating points, which are rating points for each item related to a person's personality, which is their individuality, and collects second rating points, which are rating points for each item related to a person's personality, which indicates the characteristics of a predetermined type of task and is suited to performing the task; a conversion unit that converts each of the rating points collected by the collection unit to minimize differences due to differences in the personality items for the same person or the same task and to make equivalent the variance of differences due to differences between individual people or differences between individual tasks; a calculation unit that expresses the characteristics of the person and the task as a vector whose elements are the rating points converted by the conversion unit, and calculates the closeness of the distance between the vectors for a combination of the person and a task to be performed by the person as a predicted value of the person's level of motivation for performing the task; and an allocation processing unit that determines the allocation of tasks to be performed by the person to the person such that the predicted value calculated by the calculation unit is maximized.

[0014] According to the present invention, it is possible to appropriately allocate tasks to be performed by a person using the personality of the person who will perform the task.

[0015] Fig. 1 is a diagram showing an application example of a task allocation device according to an embodiment of the present invention. Fig. 2 is a flowchart showing an example of a procedure of processing operations by the task allocation device. Fig. 3 is a block diagram showing an example of the hardware configuration of a task allocation device according to an embodiment of the present invention.

[0016] An embodiment of the present invention will be described below. In this embodiment, descriptive variables related to the personality of employees are used to quantitatively represent the characteristics of employees and the characteristics of tasks, and a predicted value of the employee's motivation for tasks is calculated by combining the two, and tasks are assigned to employees in a way that maximizes this motivation.

[0017] The process involves first collecting responses to personality statements that describe employee characteristics and responses to a questionnaire that describes personality statements that describe task characteristics.

[0018] Next, the raw response data is transformed to minimize within-sample differences and equalize the variance of between-sample differences for each descriptive variable related to employee personality. Next, employee characteristics and task characteristics are considered as vectors quantitatively represented by each descriptive variable, and the proximity between the two is calculated as a predictor of motivation. Finally, the predicted value of motivation is used as an index value, and tasks are assigned to maximize this index value.

[0019] 1 is a diagram illustrating an example of application of a task allocation device according to an embodiment of the present invention. As shown in FIG. 1, the task allocation device 100 according to this embodiment includes a data collection unit 10, a conversion processing unit 20, a calculation processing unit 30, and an allocation processing unit 40.

[0020] 2 is a flowchart showing an example of the procedure of the processing operation of the task allocation device. The data collection unit 10 obtains raw data of the descriptive variables related to the employee's personality by using a questionnaire regarding the employee's personality and the personality of the person who is best suited to the task.

[0021] The data collection unit 10 asks each employee to respond to personality statements that describe the employee's characteristics, such as questions like "Are you open to new experiences?" or "Are you a socially outgoing person?", asking the employee to rate the extent to which the statements apply to them on a 7-point scale, and collects the responses in response to these requests (S10).

[0022] Furthermore, the data collection unit 10 requests managers or employees who are familiar with the tasks to respond to personality descriptions that describe the characteristics of each task, such as questions like "Do people suited to this task have a personality that is open to new experiences?" or "Do people suited to this task have a socially outgoing personality?", asking how well they fit the descriptions, using a 7-point rating or the like, and collects the responses in response to these requests (S11).

[0023] The conversion processing unit 20 performs a conversion on the collected raw data to minimize the within-sample difference and to equalize the variance of each descriptive variable for the between-sample difference for each descriptive variable related to personality (S20). The variance of the raw data is composed of both the between-sample difference and the within-sample difference. A sample refers to an individual employee or an individual task. Therefore, the between-sample difference is a difference related to an individual employee and an individual task, i.e., a difference due to differences between individual employees or differences between individual tasks. On the other hand, the above-mentioned within-sample difference is a difference linked to differences in the personality-related descriptive items within an individual employee or individual task. Therefore, in S20, the conversion processing unit 20 converts the average value of each personality-related descriptive item to 0 and calculates a z-score, which is a score that converts the standard deviation of each personality-related descriptive item to 1, thereby achieving the above conversion.

[0024] The number of samples is N, and the rating score of the jth personality question item in the ith sample is a i,j Then, the z-score of the jth personality question item in the ith sample can be calculated by the following (1).

[0025]

[0026] This transformation is based on the principle that employee motivation and task characteristics are quantitatively expressed, and a predicted value for motivation is calculated by combining the two, and that in order to maximize this predicted value, inter-sample differences become important, while intra-sample differences become unimportant.

[0027] In this embodiment, it has been found through experiments that when personality-related descriptors are used to describe the personalities of employees and the personalities of people who are best suited to tasks, the pattern of the profile of within-sample scores correlates with motivation.

[0028] That is, in this embodiment, it has been found that, regardless of which employee or task is selected, motivation is high when a certain rating pattern is present within the sample. This correlation should be removed because it does not explain the fluctuations in motivation values ​​depending on the combination of employee and task. Therefore, the above transformation is necessary.

[0029] Next, the calculation processing unit 30 regards the motivation, which is a characteristic of the employee, and the characteristics of the task as vectors quantitatively expressed by each descriptive variable after conversion by the conversion processing unit 20, and calculates the Euclidean distance, which is the closeness between the two, for all combinations of employee and task as a predicted value of the employee's motivation for the task (S30).

[0030] Finally, the allocation processing unit 40 uses the predicted employee motivation value as an index and allocates tasks to maximize the index value (S40). Here, motivation values ​​are calculated for all combinations of employees and tasks, and matching of employees and tasks that maximizes the total value is proposed. In this process, a solver that can quickly solve optimization problems may be used.

[0031] 3 is a block diagram showing an example of the hardware configuration of a task allocation device according to an embodiment of the present invention. In the example shown in FIG. 3, the task allocation device 100 according to the embodiment is configured, for example, by a server computer or a personal computer, and has a hardware processor 111A such as a CPU (Central Processing Unit). A program memory 111B, a data memory 112, an input / output interface 113, and a communication interface 114 are connected to this hardware processor 111A via a bus 115.

[0032] The communication interface 114 includes, for example, one or more wireless communication interface units, and enables transmission and reception of information to and from a communication network. As the wireless interface, for example, an interface that adopts a low-power wireless data communication standard such as a wireless LAN (Local Area Network) is used.

[0033] An input device 500 and an output device 600, which are attached to the task assignment device 100 and used by a user or the like, are connected to the input / output interface 113. The input / output interface 113 can acquire operation data input by a user or the like through the input device 500, such as a keyboard, touch panel, touchpad, or mouse, and can output output data to an output device 600, which can include a display device using a liquid crystal or organic electroluminescence (EL) display, for display. The input device 500 and the output device 600 may be devices built into the task assignment device 100, or may be input devices and output devices of other information terminals that can communicate with the task assignment device 100 via a network.

[0034] The program memory 111B is a non-transitory tangible storage medium that is a combination of a non-volatile memory that can be written to and read from at any time, such as a hard disk drive (HDD) or a solid state drive (SSD), and a non-volatile memory such as a read only memory (ROM), and can store programs necessary to execute various control processes, etc., according to one embodiment.

[0035] The data memory 112 is a tangible storage medium that is, for example, a combination of the above-mentioned nonvolatile memory and a volatile memory such as RAM (Random Access Memory), and can be used to store various data or information acquired and created during various processing steps.

[0036] The task allocation device 100 according to one embodiment of the present invention can be configured as a data processing device having the units shown in FIG. 1 as software processing function units.

[0037] The information storage unit used as a work memory or the like by each unit of the task allocation device 100 may be configured using the data memory 112 shown in Fig. 3. However, these configured storage areas are not essential components within the task allocation device 100, and may be areas provided in, for example, an external storage medium such as a USB (Universal Serial Bus) memory, or a storage device such as a database server located in the cloud.

[0038] The processing function units in each of the above units can be realized by reading and executing a program stored in the program memory 111B by the hardware processor 111A. Note that some or all of these processing function units may be realized in various other forms, including integrated circuits such as an application specific integrated circuit (ASIC) or a field-programmable gate array (FPGA).

[0039] The methods described in each embodiment can be stored as a program (software means) that can be executed by a computer on a recording medium such as a magnetic disk (floppy disk, hard disk, etc.), optical disk (CD-ROM, DVD, MO, etc.), or semiconductor memory (ROM, RAM, flash memory, etc.), and can also be distributed by transmitting it via a communication medium. The program stored on the medium also includes a configuration program that configures the software means (including not only execution programs but also tables or data structures) that the computer executes. The computer that realizes this device reads the program stored on the recording medium and, in some cases, configures the software means using the configuration program, and executes the above-mentioned processing by controlling the operation of this software means. The term "recording medium" as used herein is not limited to a storage medium for distribution, but also includes a storage medium such as a magnetic disk or semiconductor memory installed inside the computer or in a device connected via a network.

[0040] The present invention is not limited to the above-described embodiments, and various modifications can be made in the implementation stage without departing from the spirit of the invention. Furthermore, the embodiments may be implemented in appropriate combinations, in which case the combined effects can be obtained. Furthermore, the above-described embodiments include various inventions, and various inventions can be extracted by combining selected elements from the disclosed elements. For example, if the problem can be solved and the desired effect can be obtained even if some elements are deleted from all elements shown in the embodiments, the configuration from which these elements are deleted can be extracted as an invention.

[0041] 100... Task allocation device 10... Data collection unit 20... Conversion processing unit 30... Calculation processing unit 40... Allocation processing unit

Claims

1. A task allocation device comprising: a collection unit that collects first rating points, which are rating points for each item related to a person's personality, which is their individuality, and collects second rating points, which are rating points for each item related to a person's personality, which is their individuality and indicates the characteristics of a predetermined type of task and is suitable for performing the task; a conversion unit that converts each of the rating points collected by the collection unit to minimize differences due to differences in the personality items for the same person or the same task and to make equivalent the variance of differences due to differences between individual people or differences between individual tasks; a calculation unit that expresses the characteristics of the person and the task as a vector whose elements are the rating points converted by the conversion unit, and calculates the closeness of the distance between the vectors for a combination of the person and the task to be performed by the person as a predicted value of the person's degree of motivation for performing the task; and an allocation processing unit that determines the allocation of tasks to be performed by the person to the person such that the predicted value calculated by the calculation unit is maximized.

2. The task allocation device of claim 1, wherein the conversion unit performs the conversion by calculating a score in which the average value of each sample of each rating score collected by the collection unit is set to 0 and the standard deviation of each sample of each rating score collected by the collection unit is set to 1.

3. The task allocation device according to claim 1, wherein the collection unit collects, as the first evaluation score, responses to a questionnaire that asks the person about the degree to which their personality matches a predetermined type of personality.

4. The task allocation device of claim 1, wherein the collection unit collects responses to a questionnaire asking about the degree to which a person's personality suited to performing a specified type of task matches the personality of the specified type as the second evaluation score.

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