JP6129393B1 - Competency target setting support device, support method, and support program - Google Patents

Abstract

[PROBLEMS] To help each employee voluntarily set appropriate competency goals. A management server 1 includes a competency target setting DB 10 that stores qualitative data related to target setting of a behavioral characteristic of an evaluated person, an MBODB 21 that stores quantitative data related to MBO acquisition points according to business goals, and a competency. A quantification unit 18 that obtains quantitative data based on the qualitative data stored in the goal setting DB 10 and a person who makes a bad goal based on the quantitative data obtained by the quantification unit make a good goal setting. A distribution unit 22 that distributes to a person, and an evaluated person database 23 that stores information on the evaluated person who sets a bad target and is distributed by the distribution unit 22. The quantification unit obtains a difference value between the competency acquisition rate and the MBO acquisition rate, and the allocating unit 22 assigns each evaluated person a bad target based on the difference value or a value taking this difference into account. Assign to those who set good goals. [Selection] Figure 2

Description

  The present invention relates to a competency target setting support apparatus, a support method, and a support program for supporting a target setting (competency target setting) of behavior characteristics by an evaluated person. More specifically, the present invention relates to a device having a function of determining whether or not the competency target setting performed by the evaluator is good and bad, and assists the evaluator to voluntarily set a better target. Can be used in consulting work.

  In order for a company to grow, it is important for each employee to have a goal linked to the management philosophy and to achieve it steadily. For this reason, in recent years, after having set appropriate “competency goals” for employees themselves, that is, “targets of desirable behavioral characteristics that produce results,” management guidance has been given to carry out daily work in accordance with those goals. This is important in corporate personnel management.

  Conventionally, various inventions have been proposed for personnel evaluation systems used in the personnel department of companies (for example, Patent Document 1 and Patent Document 2). It has been common practice to evaluate employee behavior and results objectively and fairly using such personnel evaluation systems.

  For example, according to the personnel evaluation system disclosed in Patent Document 1, it is said that the objectivity for evaluation can be improved, and the behavior that needs to be changed for the evaluated person can be more objectively shown. In this system, when evaluating whether an evaluated person is acting in accordance with preset expected behavior, multiple evaluators are selected by the input / output terminal of the evaluated person to be evaluated. . In this evaluation system, an evaluation execution notification mail requesting the evaluation to the selected evaluator is delivered, and feedback information is created based on the evaluation result input from the evaluator's input / output terminal. Display on the evaluator's input / output terminal. In this way, since the evaluated person is evaluated by the boss and a plurality of evaluators other than the boss, the objectivity for the evaluation is improved.

  Further, according to the personnel evaluation system disclosed in Patent Document 2, it is supposed that evaluator assignment can be performed with simple condition setting. In this system, the evaluator assignment processing unit includes evaluator assignment setting information stored in the evaluator assignment setting information storage area of the personnel evaluation system database, personnel information stored in the personnel information storage area of the personnel management system database, An appropriate evaluator is selected based on the organization information stored in the organization information storage area, and the evaluator is assigned to each evaluated person to update the evaluation information.

JP 2006-185080 A JP 2007-323352 A

  However, all of the conventional personnel evaluation systems evaluate ex-postly whether or not the person being evaluated is taking action in accordance with the expected behavior that has been set in advance. ) Did not have a mechanism for evaluating whether or not the goal was set appropriately. For example, if the evaluator's competency goal setting is ambiguous, judgment in business activities will be reduced and consistent actions will not be taken, resulting in poor productivity. In addition, if the competency goal setting is ambiguous, the evaluation of the person being evaluated will also deteriorate, leading to a decrease in the willingness and morale of the person being evaluated. On the other hand, if the competency goal setting of the evaluator is concrete and clear, he / she can take appropriate actions every day without hesitation in accordance with the goal, thus improving productivity and The evaluation of the surroundings is improved. As a result, it is possible to improve the motivation and morale of the person being evaluated.

  In this way, ex-post evaluation of employee behavior is important, but it can be said that in future corporate activities, it is more important to encourage employees to set appropriate competency targets. Therefore, an object of the present invention is to provide a technique that can objectively evaluate the competency goal setting of an employee and assist each employee to voluntarily set an appropriate competency goal.

  As a result of earnestly examining the means for solving the problems of the conventional invention, the inventor of the present invention quantitatively evaluates the competency target setting to obtain the competency acquisition rate of each evaluated person, MBO (Management By Objectives) acquisition rate is calculated from the ratio of MBO acquisition points to the maximum value, and each evaluated person sets a good standard based on the difference value between these competency acquisition rates and MBO acquisition rates. It was found that it is possible to objectively evaluate the good or bad goal setting of the person being evaluated by judging whether the person is a person who sets a target or a bad target. Establishing such objective evaluation criteria can help each employee to set appropriate competency goals voluntarily. The inventor has conceived that the problems of the prior art can be solved based on the above knowledge, and has completed the present invention. If it demonstrates concretely, this invention has the following structures and processes.

A first aspect of the present invention relates to a competency target setting support apparatus. The apparatus according to the present invention includes a competency target setting DB, an MBODB, a quantification unit, a distribution unit, and an evaluated person DB. “DB” means a database.
The competency goal setting DB stores qualitative data related to goal setting of behavior characteristics for each person to be evaluated. The qualitative data related to the goal setting of behavior characteristics is, for example, text data in which the evaluated person describes the goals related to his / her behavior characteristics.
The MBODB stores quantitative data relating to MBO acquisition points corresponding to business goals for each person to be evaluated. The business target is a preset target numerical value such as target sales. The MBO acquisition point is a numerical value obtained by scoring how much the business performance (eg, actual numerical value such as actual sales) satisfies the business goal, for example. For example, if the business target is 100%, the MBO score is 6 if the business performance is 120% or more, 5 if 100 to 120%, 4 if 80 to 100%, etc. Can be scored. Further, the MBO acquisition points may be simply the ratio of the business performance to the business goal.
The quantification unit obtains quantitative data based on the qualitative data stored in the competency target setting DB. More specifically, the quantification unit first scores the qualitative data stored in the competency target setting DB based on a predetermined standard, and obtains competency score of each person to be evaluated. In addition, the quantification unit obtains the competency acquisition rate of each evaluated person from the ratio of the competence acquiring points of each evaluated person to the maximum score. Further, the quantification unit obtains the MBO acquisition rate of each evaluated person from the ratio of the MBO acquisition points to the maximum score for the quantitative data stored in the MBODB. Thereafter, the quantification unit obtains a difference value between the competency acquisition rate and the MBO acquisition rate. At least, the quantification unit quantifies the qualitative data regarding the target setting of the behavioral characteristics of the evaluated person in this way.
Based on the quantitative data obtained by the quantification unit, the distribution unit assigns each person to be evaluated to a person who sets a bad target and a person who sets a good target. Specifically, the allocating unit allocates each person to be evaluated to a person who sets a bad target and a person who sets a good target based on the difference value obtained by the quantifying unit or a value taking this into consideration. The value added with the difference value here may be, for example, a value obtained by performing four arithmetic operations on another value for the difference value, or a value obtained by scoring the difference value with a predetermined criterion, It may be a value obtained by performing another four arithmetic operations on the scored value. In any case, the distribution unit distributes each person to be evaluated in consideration of the difference value obtained by the quantification unit.
The to-be-evaluated DB stores information on the to-be-evaluated person who sets at least a bad target assigned by the allocating unit. The evaluated person DB may store information related to the evaluated person who sets a good target in addition to the evaluated person who sets a bad target.

  As in the above configuration, the present invention determines whether the target setting of the evaluated person's behavior characteristic is good or bad in consideration of the difference value between the competency acquisition rate and the MBO acquisition rate obtained by the quantification unit. To do. When the difference value between the competency acquisition rate and the MBO acquisition rate is large, it means that the evaluation of the behavioral characteristics and the evaluation subject's MBO acquisition points are greatly different from each other. For example, if the MBO acquisition rate is low while the competence acquisition rate is high, the target setting of the behavior characteristic is vague and abstract, or the actual performance does not meet the target. It cannot be said that the goal setting and appropriate self-analysis are made. Therefore, if a more appropriate goal can be set, there can be room for further improvement in performance. In such a case, it can be determined that the person to be evaluated is a person who sets a bad target and is included in the management guidance target so that a more appropriate target can be set. In addition, by using objective data such as the MBO acquisition rate at the time of distribution of the evaluated persons, fairness and objectivity can be ensured when performing this evaluation.

  It is preferable that the competency target setting support apparatus of the present invention further includes a correction unit. The correction unit corrects the qualitative data regarding the target setting of the behavior characteristic input from the user terminal handled by the evaluator. Specifically, the correction unit extracts an evaluator who sets a bad target from the evaluator DB, and when the qualitative data regarding the behavior characteristics is input by the evaluator who sets a bad target, Correct the qualitative data.

  As shown in the above configuration, by setting the text data (qualitative data) related to the target setting to the target person who is determined to set a bad target, the target setting method for the target person Can be taught. As a result, the ability of the person being evaluated can be improved, and support can be provided so that appropriate target setting can be performed voluntarily in the future.

  The competency goal setting support device of the present invention preferably further includes an NG word DB that stores NG words related to behavior characteristic goal setting. At this time, the correction unit refers to the NG word DB, and notifies the user terminal when the NG word is included in the text constituting the qualitative data regarding the target setting of the behavior characteristic.

  As described above, the apparatus according to the present invention includes the NG word DB, so that the person to be evaluated can be instructed not to use an inappropriate word for setting the target of behavioral characteristics. As a result, the person being evaluated is careful not to use ambiguous words that are not suitable for goal setting.

  The competency goal setting support apparatus of the present invention preferably further includes a goal setting example DB and a good / bad discrimination model generation unit. The goal setting example DB stores at least a part of the qualitative data stored in the competency goal setting DB in association with information regarding the quality of goal setting of behavioral characteristics. The good / bad discrimination model generation unit generates a good / bad discrimination model (specifically, an AI model) in which information stored in the target setting example DB is learned as training data. In this case, the quantification unit scores the qualitative data stored in the competency target setting DB using the good / bad discrimination model when obtaining the competence acquisition points of each evaluated person.

  As in the above configuration, for example, the qualitative data stored in the competency target setting DB is manually determined to be good or bad, and the target setting example DB is constructed. By quantifying qualitative data using a good / bad discrimination model learned based on this goal setting example DB, it is possible to automatically evaluate qualitative data and to increase its objectivity. it can. Moreover, the accuracy of evaluation can be increased by accumulating data.

  The competency target setting support device of the present invention preferably further includes a correction model generation unit. The correction model generation unit generates a correction model (specifically, an AI model) obtained by learning, as training data, information related to the target setting of the behavioral characteristics, which is regarded as good, from the information stored in the target setting example DB. To do. In this case, the correction unit corrects the qualitative data regarding the target setting of the behavior characteristic input from the user terminal handled by the evaluator using the correction model.

  As in the above configuration, the correction model is learned based on qualitative data that has been discriminated between good and bad by hand, for example, so this can be used to correct the qualitative data of the person being evaluated. The correction process can be performed automatically with high accuracy.

The second aspect of the present invention relates to a competency target setting support method. The method according to the present invention is basically executed by the competency target setting support device according to the first aspect described above. That is, the method according to the present invention includes a quantification process for obtaining quantitative data based on qualitative data stored in the competency target setting DB, and each evaluated object based on the quantitative data obtained in the quantification process. A distribution step of assigning a person to a person who sets a bad target and a person who sets a good target.
In the quantification step, qualitative data stored in the competency target setting DB is scored based on a predetermined standard to obtain competency score of each person to be evaluated, and the competence score of each person to be evaluated is obtained. From the ratio to the maximum score, the process of obtaining the competence acquisition rate of each evaluated person and the quantitative data stored in the MBODB, the MBO acquisition rate of each evaluated person is determined from the ratio of the MBO acquisition point to the maximum score. And a step of obtaining a difference value between the competency acquisition rate and the MBO acquisition rate.
The distribution step includes a step of allocating each person to be evaluated to a person who sets a bad target and a person who sets a good target based on the difference value obtained in the quantification step or a value taking this into consideration.

  A third aspect of the present invention relates to a program for causing a computer to function as the competency target setting support apparatus according to the first aspect described above. The program of the present invention may be downloaded and installed on a computer via the Internet, or may be installed on a computer via a recording medium.

  According to the present invention, it is possible to objectively evaluate an employee's competency goal setting and assist each employee in voluntarily setting an appropriate competency goal.

FIG. 1 is a conceptual diagram showing an example of the overall configuration of a personnel evaluation system. FIG. 2 is a block diagram showing a functional configuration of a personnel evaluation system, particularly a competency target setting device (management server). FIG. 3 is a flowchart showing an example of a process flow for registering data in the target setting example DB. FIG. 4 is a flowchart showing the flow of processing for registering data in the NG word DB. FIG. 5 is a flowchart showing an example of the flow of processing performed by the good / bad discrimination model generation unit. FIG. 6 is a flowchart illustrating an example of a flow of processing performed by the automatic correction model generation unit. FIG. 7 is a flowchart showing an example of the flow of processing performed by the quantification unit. FIG. 8 is a flowchart showing an example of the flow of processing performed by the distribution unit. FIG. 9 is a flowchart showing an example of the flow of processing performed by the correction unit.

  Hereinafter, embodiments for carrying out the present invention will be described with reference to the drawings. This invention is not limited to the form demonstrated below, The thing suitably changed in the range obvious to those skilled in the art from the following forms is also included.

  FIG. 1 shows the configuration of the entire personnel evaluation system. As shown in FIG. 1, the personnel evaluation system 100 is configured by connecting a management server 1, an administrator terminal 2, and a user terminal 3 to each other through a network. Examples of networks are the Internet or an intranet for the company. In the present embodiment, the management server 1 corresponds to a competency target setting support device according to the present invention. The function of the management server 1 may be realized by a single server computer, or may be realized by a plurality of server computers connected to each other via a network. . The administrator terminal 2 is a computer terminal operated by an administrator who operates this system. The administrator terminal 2 can register new information in the management server 1 or update information stored in the management server 1. The user terminal 3 is a computer terminal operated by an evaluator (evaluator: for example, a supervisor) or an evaluated person (evaluator: for example, a subordinate) in this system. In this system, it is assumed that a plurality of administrator terminals 2 and user terminals 3 exist.

  The management server 1, the administrator terminal 2, and the user terminal 3 can be realized by a commonly used computer. An example of the management server 1 is a server type computer as described above. Examples of the administrator terminal 2 and the user terminal 3 are desktop computers, laptop computers, and other portable information terminals such as smartphones and tablet terminals. Each of the devices 1 to 3 basically includes a control arithmetic device, a storage device, an input device, and an output device. The control arithmetic device executes predetermined arithmetic processing according to a program stored in the storage device based on information input from the input device, and writes and reads out the arithmetic result to / from the storage device as appropriate. The output device can be controlled. An example of the control arithmetic device is a processor such as a CPU or a GPU. The storage function of the storage device can be realized by a non-volatile memory such as HDD and SDD, and the memory function of the storage device can be realized by a volatile memory such as RAM and DRAM. Examples of the input device are operation modules such as a mouse and a keyboard, and a communication module for receiving information via a network. Examples of the output device are a display, a speaker, and a communication module for transmitting information via a network. The functions of the devices 1 to 3 are realized by such a hardware configuration.

  FIG. 2 is a block diagram mainly showing an example of the functional configuration of the management server 1 (competency target setting support device). As shown in FIG. 2, the management server 1 includes a competency target setting DB 10, a target setting example DB 11, an NG word DB 12, a good / bad discrimination model generation unit 13, a frequent word DB 14, a good / bad discrimination model 15, and an automatic correction model generation unit. 16, automatic correction model 17, quantification unit 18, evaluated person monitoring result DB 19, evaluator monitoring result DB 20, MBODB 21, distribution unit 22, bad target setting evaluated person DB 23, good target setting evaluated person DB 24, And a correction unit 25. Various databases are realized by a storage device included in the management server 1. Further, the good / bad discrimination model generation unit 13, the automatic correction model generation unit 16, the quantification unit 18, the distribution unit 22, and the correction unit 25 are realized by a processor included in the management server 1. Hereinafter, the functional configuration of these management servers 1 will be described in detail with reference to the flowcharts shown in FIGS.

  FIG. 3 to FIG. 6 show the flow of processing related to advance preparation for evaluating the competency target setting (target setting of behavior characteristics) of the person to be evaluated. 7 and 8 show the flow of processing for evaluating the competency target setting of the person to be evaluated. Further, FIG. 9 shows a flow of processing for performing automatic correction of the target setting in order to support the evaluation target's competency target setting. In each figure of the present application, a normal arrow indicates a data flow, and an arrow with a double tip indicates a process flow.

  FIG. 3 shows an example of a process for registering data in the target setting example DB 11. The competency goal setting DB 10 stores qualitative data relating to competency goal setting created by the person to be evaluated for each person to be evaluated. For example, the person being evaluated describes his competency goals in text. For this reason, the text data created by the person to be evaluated is stored in the competency target setting DB 10 as qualitative data regarding the competency target setting. Basically, the management server 1 performs various arithmetic processes using information stored in the competency target setting DB 10 as a data source. As the amount of information stored in the competency target setting DB 10 increases, the accuracy of the arithmetic processing increases, so it is preferable to add information here as appropriate.

  Information stored in the competency target setting DB 10 can be read out by the administrator terminal 2. In the administrator terminal 2, text data is read from the competency target setting DB 10, an identifier is assigned according to the quality of the competency target setting created by each person to be evaluated, and processing for distributing the competency target settings is performed ( Step S1-1). For example, an administrator who operates the administrator terminal 2 may browse the contents of the competency target setting, determine whether the contents are good or bad, and perform identifier assignment. This work can also be automated using a good / bad discrimination model (AI model) described later. In the example shown in FIG. 3, the identifiers assigned to the competency target setting are classified into four levels: “very good: A”, “good: B”, “bad: C”, and “very bad: D”. ing. However, the type and number of identifiers can be changed as needed.

  The management server 1 registers the competency target setting text data in the target setting example DB 11 in association with the identifier distributed by the administrator terminal 2 (step S1-2). For this reason, in the goal setting example DB 11, text data for competency goal setting and identifiers (A to D) relating to good and bad are stored in association with each other. Further, in the target setting example DB 11, information (ID information) relating to the evaluated person who created the target setting may also be associated with each text data. The target setting example DB 11 in which information is registered in this manner is used to generate a good / bad discrimination model (FIG. 5), an automatic correction model generation process (FIG. 6), and a competency target setting automatic correction process (FIG. 5). 9).

  FIG. 4 shows an example of a process for registering data in the NG word DB 12. The NG word DB 12 stores NG words that are not preferably used for setting competency targets. More specifically, the administrator terminal 2 reads text data from the competency target setting DB 10 and extracts NG words from the text data (step S2-1). For example, the administrator who operates the administrator terminal 2 browses the contents of the competency target setting, and extracts words that are not suitable for the competency target setting as NG words. The management server 1 registers the NG word extracted by the administrator terminal 2 in the NG word DB 12 (step S2-2). In this way, NG words are stored in the NG word DB 12 as needed. The NG word DB 12 in which the information is stored is used in the automatic correction process (FIG. 9) for setting the competency target.

  FIG. 5 shows an example of processing performed by the good / bad discrimination model generation unit 13. As shown in FIG. 5, the good / bad discrimination model generation unit 13 first acquires a predetermined number of frequent words from the competency target setting DB 10 and registers them in the frequent word DB 14 (step S3-1). Specifically, the good / bad discrimination model generation unit 13 decomposes the text data stored in the competency target setting DB 10 into words, obtains the total number of appearances of each word, extracts words having a large number of appearances, and extracts them. The registered word is registered in the frequent word DB 14. For example, the good / bad discrimination model generation unit 13 may extract the top 10,000 words. In this way, frequently used words in the competency target setting are accumulated in the frequent word DB 14.

  Next, the good / bad discrimination model generation unit 13 acquires target setting example data from the target setting example DB 11 and performs morphological analysis (step S3-2). Morphological analysis uses grammatical rules and part-of-speech dictionaries of the target language as an information source, and divides sentences written in natural language into columns of morphemes (the smallest unit that has meaning in the language) and discriminates each part of speech. It is processing to do. As described above, each target setting example data is stored in the target setting example DB 11 in association with an identifier corresponding to the quality of the contents. The good / bad discrimination model generation unit 13 associates the identifier stored in the target setting example DB 11 with the result of the morphological analysis. For this reason, a good or bad evaluation regarding competency target setting is given to the morpheme. The good / bad discrimination model generation unit 13 stores such a morphological analysis result in a memory or the like as a temporary file.

  Next, the good / bad discrimination model generation unit 13 collates the morpheme analysis result created in step S3-2 with the frequent words registered in the frequent word DB 14, and among the morpheme analysis results, data that does not match the frequent words. A process of masking (morpheme) as <unknown> (unknown) is performed (step S3-3). Thereby, unique noise data that is not used in normal competency target setting is removed from the morphological analysis result. The good / bad discrimination model generation unit 13 stores the masked morpheme analysis result created here in a memory or the like as a temporary file.

  Subsequently, the good / bad discrimination model generation unit 13 generates an AI model in which the masked morphological analysis result created in step S3-3 is learned as training data, that is, the good / bad discrimination model 15 (step S3-4). . Specifically, the good / bad discrimination model generation unit 13 constructs a multilayer perceptron network (a so-called neural network) using the masked morpheme analysis results as training data, and performs network learning, thereby obtaining the good / bad discrimination model 15. Generate. A method for creating an AI model using a multilayer perceptron network is known. The good / bad discrimination model 15 generated in this way basically has a tendency that the competency target setting including any morpheme or combination of morphemes tends to be judged good or bad. Learning what is there. Specifically, in the example described here, the good / bad discrimination model 15 learns, as training data, four-stage identifiers (A to D) related to good / bad regarding the goal setting registered in the goal setting example DB 11. Therefore, if this good / bad discrimination model 15 is used, it is possible to automatically classify the contents of text data relating to competency target setting into four stages. The good / bad discrimination model 15 generated in this way can be used in the quantification process of the competency target setting (FIG. 7). As described above, the good / bad discrimination model 15 with advanced learning can also be used in the process of registering data in the target setting example DB 11 (FIG. 3).

  FIG. 6 shows an example of processing performed by the automatic correction model generation unit 16. As shown in FIG. 6, the automatic correction model generation unit 16 acquires a predetermined number of frequently occurring words from the competency target setting DB 10 and registers them in the frequently occurring word DB 14 (step S4-1). Note that step S4-1 is the same as step S3-1 described above. For this reason, when the data registration to the frequent word DB 14 has already been completed, the automatic correction model generating unit 16 can omit the process of step S4-1.

  Next, the automatic correction model generation unit 16 acquires target setting example data stored in association with identifiers (A and B) that the contents are good among the target setting example data stored in the target setting example DB 11. Then, morphological analysis is performed (step S4-2). That is, the automatic correction model generation unit 16 uses only competency target setting data with good contents. The automatic correction model generation unit 16 associates words extracted by performing morphological analysis with their appearance order. Specifically, the automatic correction model generation unit 16 analyzes what word is used after a certain word in setting competency targets with good contents. In this way, the context in which words appear is associated. The automatic correction model generation unit 16 stores the morphological analysis result as a temporary file in a memory or the like.

  Next, the automatic correction model generation unit 16 collates the morpheme analysis result created in step S4-2 with the frequent words registered in the frequent word DB 14, and among the morpheme analysis results, data that does not match the frequent words ( A process of masking the morpheme) as <unknown> (unknown) is performed (step S4-3). Thereby, unique noise data that is not used in normal competency target setting is removed from the morphological analysis result. The automatic correction model generation unit 16 stores the masked morpheme analysis result created here in a memory or the like as a temporary file.

  Subsequently, the automatic correction model generation unit 16 generates an AI model, that is, an automatic correction model 17 in which the masked morpheme analysis result created in step S4-3 is learned as training data (step S4-4). Specifically, the automatic correction model generation unit 16 generates an automatic correction model 17 by constructing a recurrent network based on the masked morpheme analysis results and performing network learning. A method for creating an AI model using a recurrent network is known. The automatic correction model 17 generated in this way basically has a tendency of what kind of word is frequently used in setting a competency goal with good contents, and what kind of word follows a certain word. Learning the tendency to determine that it is appropriate as a competency goal setting. Therefore, the automatic correction model 17 can be used for correction of competency target setting created by the evaluator. Therefore, the automatic correction model 17 generated in this way can be used in the automatic correction process (FIG. 9) for setting competency targets.

  The above is the process in the preparation stage. Subsequently, a process for evaluating the competency target setting actually created by the person to be evaluated using the database and the AI model created by the above process will be described below. FIG. 7 and FIG. 8 show an example of processing for assigning whether the competency target setting of the evaluator is good or bad. In particular, FIG. 7 shows the process of the quantification unit 18, and FIG. 8 shows the process of the distribution unit 22.

  As shown in FIG. 7, the quantification unit 18 uses the good / bad discrimination model 15 created in the process of FIG. 5 to qualitative data (text) Data) is evaluated (step S5-1). As described above, the good / bad discrimination model 15 learns whether the competency target setting is good or bad based on the identifiers (A to D) registered in the goal setting example DB 11. Is used, the qualitative data of competency goal setting stored in the competency goal setting DB 10 is “very good: A”, “good: B”, “bad: C”, and “very bad: D Can be classified into four stages. Therefore, the quantification unit 18 scores each of the qualitative data for setting the competency target according to the four-stage evaluation classified by the good / bad discrimination model 15. In the example shown in FIG. 7, 1.2 points, 0.9 points, 0.6 points, and 0.4 points in the order of “very good”, “good”, “bad”, and “very bad”, respectively. Points are given. The scoring method can be adjusted as appropriate.

  Further, the quantification unit 18 reads out the monitoring result of the evaluated person who is the evaluation target from the evaluated person monitoring result DB 19 (step S5-2). In the evaluated person monitoring result DB 19, evaluations of the evaluated person's ability are scored and stored. For example, in the evaluated person monitoring result DB 19, evaluations regarding items such as business manners, self-development, thoroughness, and teamwork are finely scored. The quantification unit 18 reads the score of the person to be evaluated from the person-to-be-evaluated monitoring result DB 19 and scores it according to a predetermined standard. In the example shown in FIG. 7, the evaluation results of the evaluator are “3 points or more”, “2.5 to 3 points”, “2 to 2.5 points”, and “less than 2 points” in order of 1.6. Points, 1.2 points, 0.8 points, and 0.4 points are given. Note that the evaluation items and evaluation criteria in the evaluation target monitoring result DB 19 can be appropriately adjusted for each company. Moreover, what is necessary is just to adjust the reference | standard of score provision in step S5-2 according to the content of the to-be-evaluated person monitoring result DB19. Further, in the present invention, this step S5-2 is an optional step and can be omitted.

  Moreover, the quantification part 18 reads the monitoring result of the evaluator who evaluated the to-be-evaluated person from evaluator monitoring result DB20 (step S5-3). In the evaluator monitoring result DB 20, evaluations of the evaluator's ability are scored and stored. For example, in the evaluator monitoring result DB 20, evaluations regarding items such as the evaluator's support ability, the accuracy of advice, and the ability to communicate with subordinates are finely scored. The quantification unit 18 reads out the evaluator's score from the evaluator monitoring result DB 20 and scores it according to a predetermined standard. In the example shown in FIG. 7, the evaluator monitoring results are 0.4 points in the order of “3 points or more”, “2.5 to 3 points”, “2 to 2.5 points”, and “less than 2 points”. , 0.3, 0.2, and 0.1 points are given. Note that the evaluation items and evaluation criteria in the evaluator monitoring result DB 20 can be adjusted as appropriate for each company. Moreover, what is necessary is just to adjust the reference | standard of score provision in step S5-3 according to the content of evaluator monitoring result DB20. Further, in the present invention, this step S5-3 is an optional step and can be omitted.

  Next, the quantification unit 18 refers to the MBODB 21 and acquires the MBO score of the person to be evaluated and its maximum score (step S5-4). MBO means Management By Objectives. The MBODB 21 stores quantitative data related to MBO acquisition points corresponding to business goals for each person to be evaluated. The business target is a preset target numerical value such as target sales. The MBO acquisition point is a numerical value obtained by scoring how much the business performance (eg, actual numerical value such as actual sales) satisfies the business goal, for example. For example, if the business target is 100%, the MBO score is 6 if the business performance is 120% or more, 5 if 100 to 120%, 4 if 80 to 100%, etc. Can be scored. Further, the MBO acquisition points may be simply the ratio of the business performance to the business goal. The business target and the MBO acquisition point may be stored in the MBODB 21 for each predetermined item. For example, in the MBODB 21, a business target, a business performance, and an MBO acquisition point may be registered for each item such as an order budget achievement rate, a new customer acquisition rate, the number of business improvement cases, and a cost reduction rate. Further, in the MBODB 21, weighting may be performed for each item. As described above, the quantification unit 18 can acquire the MBO acquisition score and the maximum score for each person to be evaluated by referring to the MBODB 21.

  Next, the quantification unit 18 obtains a competency acquisition rate and an MBO acquisition rate (step S5-5). The competence acquisition rate is based on a ratio in which the score of the evaluated person (competency acquisition point) obtained in steps S5-1 to S5-3 is a numerator and the maximum point obtained in steps S5-1 to S5-3 is a denominator Desired. In the example shown in FIG. 7, the maximum point of competency is 3.2 points (1.2 + 1.6 + 0.4). Further, the MBO acquisition rate is obtained from a ratio in which the MBO acquisition point of the evaluated person acquired in step S5-4 is a numerator and the maximum MBO acquisition point is a denominator. Competency acquisition rate and MBO acquisition rate are 100% respectively maximum.

  Next, the quantification unit 18 performs an operation [competency acquisition rate−MBO acquisition rate] that subtracts the MBO acquisition rate from the competency acquisition rate (step S5-5). Further, the quantification unit 18 gives a score on a predetermined basis according to the difference value obtained here. In the example shown in FIG. 7, when the difference value is less than 30%, 0.8 points, when the difference value is 30 to 40%, 0.6 points, and when the difference value is 40 to 50% 0.4 points. As described above, the difference value obtained by subtracting the MBO acquisition rate from the competency acquisition rate is set such that the smaller the value, the higher the score. When the difference value between the competency acquisition rate and the MBO acquisition rate is large, it means that the evaluation of the behavioral characteristics and the evaluation result of the evaluationee are greatly different from each other. For example, if the MBO acquisition rate is low while the competence acquisition rate is high, the target setting of the behavior characteristic is vague and abstract, or the actual performance does not meet the target. It cannot be said that the goal setting and appropriate self-analysis are made. Therefore, in this case, the score is low. On the other hand, when the difference value between the competency acquisition rate and the MBO acquisition rate is small, it can be said that the target setting of the behavior characteristics is clear and concrete, and the actual performance is also in accordance with the target setting. Therefore, in this case, the score is high.

  By performing the above steps S5-1 to S5-6, the quantification unit 18 quantifies the qualitative data stored in the competency target setting DB 10. In the embodiment described here, the quantification unit 18 determines the value [a] obtained in step S5-1, the value [b] obtained in step S5-2, the value [c] obtained in step S5-3, And the value [d] calculated | required by step S5-6 is output to the distribution part 22. FIG.

  Subsequently, as shown in FIG. 8, the distribution unit 22 first obtains a total value of the values [a] [b] [c] [d] calculated by the quantification unit 18 for each person to be evaluated. (Step 5-7). In the example shown in FIGS. 7 and 8, the maximum total value obtained here is 4.0 points (1.2 + 1.6 + 0.4 + 0.8).

  Next, the allocating unit 22 determines whether or not the total value obtained in step S5-7 is less than 2 points (step S5-8). As described above, in this example, the maximum of the total value is 4.0 points, so half the value is set as the threshold value. Thus, the threshold value in step S5-8 may be a half value of the maximum of the total value. If the total value is less than 2 points, the process proceeds to step S5-9. If the total value is 2 points or more, the process proceeds to step S5-10.

  When the total value is less than two points, the allocating unit 22 registers information about the evaluated person (ID information or the like) in the evaluated person DB 23 with a bad target setting (step S5-9). In this evaluated person DB 23, evaluated persons who tend to set a bad target are recorded in a list. On the other hand, when the total value is 2 or more, the allocating unit 22 registers information on the evaluated person in the evaluated person DB 24 with good target setting (step S5-10). In this evaluated person DB 24, the evaluated persons who tend to perform good target setting are recorded in a list.

  As described above, the allocating unit 22 performs a process of allocating the person to be evaluated to one that performs bad target setting and one that performs good target setting based on the quantitative data obtained by the quantifying unit 18. The evaluated target DB 23 with a poor target setting in which information is registered in this way is used in an automatic correction process (FIG. 9) described later. In addition, in the process so far, the evaluated person DB 23 with a bad target setting is recorded in the list of evaluated persons who tend to perform bad target setting. For this reason, by performing the processing so far, it is possible to evaluate the content of the competency target setting of the evaluated person and to find the evaluated person who tends to set a bad target. It should be noted that the evaluated person DB 24 with good target setting can also be used effectively. However, in the present invention, the evaluated target DB 24 having a good target setting is an optional element and can be omitted.

  In the present invention, the value [d] obtained in S5-6 is one of the features. Therefore, in the present invention, the quantification unit 18 may output only the value [d] obtained in S5-6 to the distribution unit 22. In that case, the distribution unit 22 may perform a distribution process related to the quality of the evaluated person based only on the value [d].

  Next, with reference to FIG. 9, an automatic correction process for setting competency targets by the correction unit 25 will be described. As shown in FIG. 9, the correction unit 25 first extracts a list of evaluated persons registered in the evaluated target DB 23 with a bad target setting (step S6-1). As described above, the automatic correction process is performed only for the evaluator registered in the evaluator DB 23 with a bad target setting.

  Next, the correction unit 25 is a competency registered as very good (identifier A) in the target setting example DB 11 on the display screen of the user terminal 3 operated by the evaluator extracted in step S6-1. A target setting example is displayed (step S6-2). As a result, the evaluated person can create his or her own goal setting while referring to a very good example of competency goal setting.

  The target setting input data input to the user terminal 3 by the evaluator is transmitted from the user terminal 3 to the management server 1 as needed. When the correction unit 25 of the management server 1 receives the target setting input data from the user terminal 3, it uses the automatic correction model 17 generated by the process shown in FIG. The word or sentence to be input next is calculated from the sentence and transmitted to the user terminal 3 (step S6-3). That is, as described above, the automatic correction model 17 has a tendency of what kind of word is frequently used in setting a competency target with good contents, and what kind of word is placed after a certain word to set the competency target. I am learning the tendency to be appropriate. Therefore, if the automatic correction model 17 is used, a word or sentence to be input next to the word can be determined based on the word data input from the user terminal 3 in real time. Therefore, the correction unit 25 returns the recommended word and sentence data determined in this way to the user terminal 3 in real time. On the display screen of the user terminal 3, recommended word and sentence data transmitted from the management server 1 are displayed as candidates for the next input. Thereby, it is possible to provide appropriate advice to an evaluated person who is not good at inputting competency target settings.

  In addition, when the correction unit 25 of the management server 1 receives the target setting input data from the user terminal 3, the correction server 25 refers to the NG word DB 12 created in FIG. 4 and selects a word that matches the NG word in the input data. If found, the result is returned to the user terminal 3 (step S6-4). For example, when the correction unit 25 finds an NG word in the input data from the user terminal 3, the correction unit 25 generates a control signal for highlighting the NG word and transmits the control signal to the user terminal 3. As a result, the NG word becomes conspicuous on the display screen of the user terminal 3. Thereby, it is possible to urge the person to be evaluated to correct the NG word.

  Thereafter, the correction unit 25 determines whether or not the input of the competency target setting by the user terminal 3 has been completed (step S6-5). When the input is completed, the automatic correction process ends. On the other hand, if the input continues, the process returns to step S6-3 again to continue the automatic correction.

  Although ex-post evaluation of employee behavior is important, it can be said that in future corporate activities, it is more important to encourage employees to set appropriate competency targets. In this respect, according to the present invention, it is possible to objectively evaluate the competency goal setting of an employee and assist each employee in voluntarily setting an appropriate competency goal.

  As mentioned above, in this specification, in order to express the content of this invention, embodiment of this invention was described, referring drawings. However, the present invention is not limited to the above-described embodiments, but includes modifications and improvements obvious to those skilled in the art based on the matters described in the present specification.

  The present invention relates to an apparatus having a function of discriminating whether a competency target set by an evaluated person is good or bad. Therefore, the present invention can be suitably used in a consulting operation that supports the evaluated person so that better target setting can be voluntarily performed.

1 ... Management server (Competency target setting support device)
2 ... Administrator terminal 3 ... User terminal 10 ... Competency target setting DB
11 ... Target setting example DB
12 ... NG Word DB
13 ... Good / bad discrimination model generation unit 14 ... Frequent word DB
15 ... Good / bad discrimination model 16 ... Automatic correction model generator 17 ... Automatic correction model 18 ... Quantification unit 19 ... Evaluator monitoring result DB
20 ... Evaluator monitoring result DB
21 ... MBODB
22 ... distribution part 23 ... evaluated person DB (bad evaluated person)
24 ... Evaluator DB (good evaluator)
25 ... Correction section 100 ... Personnel evaluation system

Claims (7)

A competency goal setting database for storing qualitative data on goal setting of behavioral characteristics for each person to be evaluated;
An MBO database for storing quantitative data on MBO acquisition points according to business goals for each person to be evaluated;
A quantification unit for obtaining quantitative data based on the qualitative data stored in the competency goal setting database;
Based on the quantitative data obtained by the quantification unit, a distribution unit that distributes each person to be evaluated to a person who sets a bad target and a person who sets a good target;
An evaluator database for storing information on an evaluator who sets at least a bad target assigned by the distribution unit, and
The quantification unit
Scoring the qualitative data stored in the competency goal setting database based on a predetermined standard to obtain competency score of each person to be evaluated;
Calculating the competence acquisition rate of each evaluated person from the ratio of each evaluated person's competency acquisition point to the maximum score;
Obtaining the MBO acquisition rate of each evaluated person from the ratio of the MBO acquisition points to the maximum points for the quantitative data stored in the MBO database;
Obtaining a difference value between the competency acquisition rate and the MBO acquisition rate;
The distribution unit is
A competency target setting support apparatus that distributes each person to be evaluated to a person who sets a bad target and a person who sets a good target based on the difference value obtained by the quantification unit or a value taking this into consideration. A correction unit that corrects qualitative data regarding goal setting of behavioral characteristics input from the user terminal handled by the evaluator;
The correction unit extracts an evaluator who sets a bad target from the evaluator database, and qualitative data regarding the behavior characteristic is input by the evaluator who sets the bad target, The competency target setting support apparatus according to claim 1, wherein correction of the qualitative data is performed. An NG word database for storing NG words related to goal setting of behavioral characteristics;
The correction unit refers to the NG word database and notifies the user terminal when an NG word is included in a sentence constituting qualitative data related to the input behavior characteristic target setting. Item 3. The competency target setting support device according to Item 2. A goal setting example database for storing at least a portion of the qualitative data stored in the competency goal setting database in association with information on the quality of goal setting of behavioral characteristics;
A good / bad discrimination model generation unit that generates a good / bad discrimination model that learns the information stored in the target setting example database as training data;
The quantification unit uses the good / bad discrimination model to score the qualitative data stored in the competency target setting database when obtaining competency score of each person to be evaluated. Competency target setting support device described in 1. A goal setting example database for storing at least a portion of the qualitative data stored in the competency goal setting database in association with information on the quality of goal setting of behavioral characteristics;
A correction model generation unit that generates a correction model in which information about the target setting of the behavioral characteristics that are considered good among the information stored in the target setting example database is learned as training data;
The competency target setting support apparatus according to claim 1, further comprising a correction unit that corrects qualitative data regarding target setting of behavioral characteristics input from a user terminal handled by the evaluator using the correction model. A competency goal setting database for storing qualitative data on goal setting of behavioral characteristics for each person to be evaluated;
Competency target setting support method executed by a device including an MBO database that stores quantitative data related to MBO acquisition points corresponding to business goals for each evaluated person,
A quantification step for obtaining quantitative data based on the qualitative data stored in the competency goal setting database;
A distribution step of allocating each person to be evaluated to a person who sets a bad target and a person who sets a good target based on the quantitative data obtained in the quantification step,
The quantification step includes
Scoring the qualitative data stored in the competency goal setting database based on a predetermined standard to obtain competency score of each person to be evaluated;
Calculating the competence acquisition rate of each evaluated person from the ratio of each evaluated person's competency acquisition point to the maximum score;
Obtaining the MBO acquisition rate of each evaluated person from the ratio of the MBO acquisition points to the maximum points for the quantitative data stored in the MBO database;
Obtaining a difference value between the competency acquisition rate and the MBO acquisition rate,
The distribution step includes
A competency target setting support method including a step of assigning each person to be evaluated to a person who sets a bad target and a person who sets a good target based on the difference value obtained in the quantification step or a value taking this into consideration. A competency goal setting database for storing qualitative data on goal setting of behavioral characteristics for each person to be evaluated;
An MBO database for storing quantitative data on MBO acquisition points according to business goals for each person to be evaluated;
A quantification unit for obtaining quantitative data based on the qualitative data stored in the competency goal setting database;
Based on the quantitative data obtained by the quantification unit, a distribution unit that distributes each person to be evaluated to a person who sets a bad target and a person who sets a good target;
An evaluator database for storing information on an evaluator who sets at least a bad target assigned by the distribution unit, and
The quantification unit
Scoring the qualitative data stored in the competency goal setting database based on a predetermined standard to obtain competency score of each person to be evaluated;
Calculating the competence acquisition rate of each evaluated person from the ratio of each evaluated person's competency acquisition point to the maximum score;
Obtaining the MBO acquisition rate of each evaluated person from the ratio of the MBO acquisition points to the maximum points for the quantitative data stored in the MBO database;
Obtaining a difference value between the competency acquisition rate and the MBO acquisition rate;
The distribution unit is
Based on the difference value obtained by the quantification unit or a value that takes this into account, the computer functions as a competency target setting support device that assigns each evaluated person to a person who sets a bad target and a person who sets a good target Program for.

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