Information providing device, method, and program

Abstract

[PROBLEM] To provide an information providing device, method, and program that make it possible to visually ascertain, for each evaluation attribute, the degree of contribution of an evaluation factor with respect to an employee's satisfaction or intention to continue. [SOLUTION] This information providing device comprises: a means for acquiring evaluation data about employee's satisfaction or intention to continue; a multivariate analysis means for calculating a contribution degree of an explanatory variable with respect to an objective variable, where the employee's satisfaction or intention to continue is used as the objective variable and a plurality of evaluation items in evaluation data are used as explanatory variables; and a means for causing an information terminal to display the results of the analysis by the multivariate analysis means. The evaluation data includes: a level 1 evaluation item corresponding to the objective variable; level 2 evaluation items that explain the objective variable; and level 3 evaluation items that explain the level 2 evaluation items. The multivariate analysis means executes: a first multivariate analysis using the evaluation values of the level 2 evaluation items as intermediate objective variables and using the level 3 evaluation items as explanatory variables; and a second multivariate analysis using the level 1 evaluation item as an objective variable and using the level 2 evaluation items as explanatory variables.

Description

Information providing device, method and program

[0001] The present invention relates to an information providing device, method, and program for providing information that contributes to employee satisfaction or intention to continue working for an employee.

[0002] Employee satisfaction surveys and attitude surveys have traditionally been conducted by conducting employee questionnaires. For example, Patent Document 1 discloses an evaluation device for workplace attitude assessment, which conducts a survey categorized by employee motivation and the degree to which employees meet work expectations. The program according to the present invention is computer-executable software that can be stored on computer-readable storage media such as CD-ROMs, DVDs, USB memory, HDDs, and solid-state drives (SSDs). The employees referred to in the present invention include not only those employed in tertiary industries such as commerce, finance, and services, but also those employed in primary industries such as agriculture, dairy farming, livestock farming, forestry, and fishing, and secondary industries such as manufacturing and construction. Furthermore, employees include not only those working at physical locations (real locations), but also those working at virtual locations, telecommuters, and non-regular employees.

[0003] Patent No. 6438614

[0004] With conventional methods, survey results are categorized and compiled, but it is not possible to determine which factors contribute to employee satisfaction or the extent to which they intend to continue working.

[0005] An object of the present invention is to provide an information providing device, method, and program that make it possible to easily grasp the degree of contribution of evaluation factors to an employee's satisfaction or intention to continue working for the company.

[0006] The inventor has invented an information providing device and program that makes it possible to easily grasp effective measures for improving employee satisfaction or intent to continue by performing a factor analysis of evaluation elements that are expected to be effective in improving employee satisfaction or intent to continue, collecting a statistically significant number of employee evaluations, and displaying the results of the statistical analysis.

[0007] [1] An information providing device comprising: survey data acquisition means for acquiring evaluation data including evaluation values ​​for evaluation items related to employee satisfaction or intent to continue working, the evaluation data including evaluation values ​​for multiple evaluation items related to employee satisfaction or intent to continue working; multivariate analysis means for performing multivariate analysis based on the evaluation data using the evaluation values ​​of the multiple evaluation items in the evaluation data as a dependent variable and the evaluation values ​​of the multiple evaluation items as a dependent variable, thereby calculating the degree of contribution of the explanatory variables to the dependent variable; and analysis result output means for displaying the analysis results by the multivariate analysis means on an information terminal capable of communicating via a network, wherein the evaluation data includes level 1 evaluation items corresponding to the dependent variable, level 2 evaluation items that explain the dependent variable, and level 3 evaluation items that explain the level 2 evaluation items, and the multivariate analysis means performs a first multivariate analysis using the evaluation values ​​of the level 2 evaluation items as an intermediate dependent variable and the level 3 evaluation items as explanatory variables, and a second multivariate analysis using the level 1 evaluation items as a dependent variable and the level 2 evaluation items as explanatory variables. [2] An information providing device comprising: survey data acquisition means for acquiring evaluation data including evaluation values ​​for evaluation items related to employee satisfaction or intent to continue working, the evaluation data including evaluation values ​​for multiple evaluation items related to employee satisfaction or intent to continue working; multivariate analysis means for performing multivariate analysis based on the evaluation data using the evaluation value of employee satisfaction or intent to continue working as a dependent variable and the evaluation values ​​for the multiple evaluation items in the evaluation data as multiple explanatory variables to calculate the contribution of the explanatory variables to the dependent variable; and analysis result output means for displaying the analysis results by the multivariate analysis means on an information terminal capable of communicating via a network, wherein the analysis result output means displays the multiple explanatory variables on the information terminal using graphics of sizes corresponding to the contributions. [3] The information providing device of [1], wherein the analysis result output means displays the multiple explanatory variables on the information terminal using graphics of sizes corresponding to the contributions. [4] The information providing device of [2] or [3], wherein the analysis result output means displays the graphics on the information terminal in a radial pattern at equal intervals.[5] The information providing device according to [2] or [3], further comprising a correlation analysis means for calculating the correlation between the plurality of explanatory variables, and wherein the analysis result output means causes the information terminal to display a line connecting the pair of explanatory variables, the line having a thickness and / or a type corresponding to the strength of the correlation. [6] The information providing device according to [2] or [3], further comprising a correlation analysis means for calculating the correlation between the plurality of explanatory variables, and wherein the analysis result output means causes the information terminal to display the graphic at the vertices of a regular polygon, and causes the information terminal to display a line connecting the pair of explanatory variables, the line having a thickness and / or a type corresponding to the strength of the correlation. [7] The information providing device according to any of [2] to [6], wherein the graphic is a circle, a polygon, or an illustration. [8] The information providing device according to any of [2] to [7], wherein the information terminal does not display a line connecting the pair of graphics if the correlation between the pair of graphics is less than a predetermined value. [9] The information providing device according to any one of [2] to [8], further comprising a reference value setting means for setting a reference value for each of the plurality of explanatory variables, wherein the analysis result output means displays the explanatory variables in different colors depending on the difference from the reference value.

[10] The information providing device according to [9], wherein the reference value setting means has a function for calculating an average value of the ratings calculated for each of the plurality of explanatory variables in the same or similar industry or business type, and setting the average value as the reference value.

[11] The information providing device according to [9] or

[10] , wherein the analysis result output means colors the explanatory variables in a first color when the explanatory variables have values ​​higher than the reference value, and colors the explanatory variables in a second color different from the first color when the explanatory variables have values ​​lower than the reference value.

[0008]

[12] An information provision method in which a computer executes the following steps: a step of acquiring evaluation data including evaluation values ​​for evaluation items related to employee satisfaction or intent to continue working, the evaluation data including evaluation values ​​for multiple evaluation items related to employee satisfaction or intent to continue working; a multivariate analysis step of performing multivariate analysis based on the evaluation data using the evaluation value of employee satisfaction or intent to continue working as a dependent variable and the evaluation values ​​of the multiple evaluation items in the evaluation data as multiple explanatory variables to calculate the contribution of the explanatory variables to the dependent variable; and an analysis result output step of displaying the analysis results of the multivariate analysis step on an information terminal capable of communicating via a network, wherein the evaluation data includes level 1 evaluation items corresponding to the dependent variable, level 2 evaluation items that explain the dependent variable, and level 3 evaluation items that explain the level 2 evaluation items.

[13] A program that causes a computer to execute the following: survey data acquisition means for acquiring evaluation data including evaluation values ​​for evaluation items related to employee satisfaction or intent to continue working, the evaluation data including evaluation values ​​for multiple evaluation items related to employee satisfaction or intent to continue working; multivariate analysis means for performing multivariate analysis based on the evaluation data using the evaluation value of employee satisfaction or intent to continue working as a dependent variable and the evaluation values ​​of the multiple evaluation items in the evaluation data as multiple explanatory variables, thereby calculating the contribution of the explanatory variables to the dependent variable; and analysis result output means for displaying the analysis results by the multivariate analysis means on an information terminal that can communicate via a network, wherein the evaluation data includes level 1 evaluation items corresponding to the dependent variable, level 2 evaluation items that explain the dependent variable, and level 3 evaluation items that explain the level 2 evaluation items.

[0009] According to the present invention, it is possible to easily grasp the degree of contribution of evaluation factors to an employee's satisfaction or intention to continue working for the company.

[0010] 1 is a configuration diagram of an information provision system according to a first embodiment. FIG. 2 is a diagram explaining the function of an information provision device according to the first embodiment. FIG. 3 is a diagram explaining an example of a survey form (overview) according to the present embodiment. FIG. 4 is a diagram explaining details of the survey form of FIG. 3. FIG. 5 is a diagram explaining a multivariate analysis means according to the first embodiment. FIG. 6 is a diagram showing analysis results indicating the contribution of each explanatory variable to employee "satisfaction" in the survey results related to the survey form of FIG. 4. FIG. 7 is a diagram showing correlation coefficients between each explanatory variable of FIG. 6. FIG. 8 is a diagram in which the contribution of each explanatory variable of FIG. 6 and the correlation coefficient of each explanatory variable are graphically displayed in a radial pattern by an analysis result output means according to the first embodiment. FIG. 9 is a diagram in which the contribution of each explanatory variable of FIG. 9 and the correlation coefficient of each explanatory variable are graphically displayed in a radial pattern by an analysis result output means according to the first embodiment.

[0011] The information provision system 1 of the first embodiment allows employees to easily understand evaluation factors that affect their satisfaction or intention to continue working by displaying them numerically or graphically, and has the configuration described below.

[0012] Fig. 1 is a configuration diagram of an information provision system according to this embodiment. As shown in Fig. 1, the information provision system 1 according to this embodiment is composed of an information provision device 10 functioning as a server, a management information terminal 20 functioning as a management client, an information terminal 30 used by survey respondents, a survey data collection device 40, and the Internet 50. Note that, although the example shown in Fig. 1 illustrates one each of the information provision device 10, management information terminal 20, and survey data collection device 40, the configuration is not limited to this, and two or more of each may be used.

[0013] The information providing device 10 is, for example, a PC server, and has a calculation unit 11, a memory unit 12 that stores an analysis program 121, and a communication unit 13 that enables communication with the information terminal 30 and the survey data collection device 40.

[0014] The management information terminal 20 is an information terminal, such as a personal computer, for inputting data into the information providing device 10 and the survey data collection device 40 and for executing the analysis program 121 and the survey data collection program 141. By executing the analysis program 121 (described later) from the management information terminal 20, the user can output various analysis results (for example, FIGS. 6 to 11) on the display device of the management information terminal 20.

[0015] The information terminal 30 is an information terminal used by the survey respondents and is used to input survey data into the database 122. In the example of Fig. 1, the information terminal 30 is a smartphone, but is not limited to this and can be configured as any information terminal that runs a web browser, such as a personal computer or tablet.

[0016] The survey data collection device 40 is, for example, a PC server, and includes a calculation unit 41, a storage unit 42 that stores a database 122 and an input program 123, and a communication unit 13 that enables communication with the information providing device 10 and the information terminal 30. The input program 123 includes a survey data input means that provides the database 122 with a web page that enables survey data to be input from the information terminal 30. In this embodiment, the survey data is acquired by inputting the survey data in the form of responses to a questionnaire using a web browser on the information terminal 30 using the input program 123, but it may also be configured to process survey data collected by other means, such as paper media, and upload it to the database 122 from the management information terminal 20.

[0017] 2, the calculation unit 11 of the information providing device 10 executes the analysis program 121 stored in the storage unit 12 to realize (1) a survey data acquisition means, (2) a multivariate analysis means, and (3) an analysis result output means. Each of these means will be described below.

[0018] (1) Survey Data Acquisition Means The survey data acquisition means is a means for storing survey data to be used in the multivariate analysis means in the storage unit 12. In this embodiment, a screen for inputting a query for acquiring survey data from the database 122 is provided to the management information terminal 20. The survey data in this embodiment consists of survey data regarding employee satisfaction.

[0019] The questionnaire survey in this embodiment is an employee survey with predetermined evaluation items and is composed of questions suitable for multivariate analysis. FIG. 3 is a diagram showing an overview of the questionnaire for the questionnaire survey in this embodiment, and FIG. 4 is a diagram showing details of FIG. 3. At the beginning of the questionnaire shown in FIG. 4, for example, there is an explanatory statement stating that the responses will not be provided to the company in a form that identifies individuals. However, the questionnaire may also be conducted in a form that identifies individuals. Level 1 questions consist of satisfaction and intention to continue. The former questions ask about satisfaction with the company, and the latter questions about whether the respondent wants to continue working there. Level 1 questions serve as the objective variable for the multiple regression analysis described below. Levels 1 to 3 questions are all multi-level evaluations. Level 2 questions are factors that affect the objective variable, Level 1 questions. For example, questions about contributions ask about self-perceptions of contributions to the company or department. Level 3 questions are explanatory variables when performing multiple regression analysis or correlation analysis using Level 2 questions as the dependent variable. For example, the question about excellent superiors (C-2) asks whether there is a superior you would like to emulate. Here, level 2 questions and level 3 questions have a relationship of superordinate and subordinate concepts. For example, the subordinate concepts of "job content" in subcategory A of level 2 questions are "clarifying goals" and "sense of accomplishment" in subcategory A of level 3 questions. However, not all subcategories of level 2 questions have level 3 questions.

[0020] Furthermore, a fit evaluation may be included as another question equivalent to a level 2 question. The fit question may be, for example, a multi-level evaluation of a question such as, "Do you feel this company is a good fit for you?" For ease of explanation, the analysis results shown in Figures 6 to 10 (described below) do not include a fit evaluation. In this embodiment, fit refers to an individual's preference for a target, such as a preference structure of Individual A expressed as a variable, such as Individual A preferring option B over option A, or utility B being greater than utility A. By including fit, which takes into account the respondent's preferences, in the evaluation items, the multivariate analysis means (described below) may be provided with a function that enables output of the correlation between fit and clustered respondent attributes.

[0021] (2) Multivariate Analysis Means The multivariate analysis means of this embodiment uses multiple regression analysis to analyze how each evaluation item influences the satisfaction and continuation intent scores (hereinafter referred to as "ES scores"). Here, regression analysis is a statistical method that clarifies the relationship between numerical values ​​indicating results (dependent variables) and numerical values ​​that are factors (explanatory variables). Multiple regression analysis is a statistical method that, when explaining dependent variables, quantifies which explanatory variables influence the results to what extent, and makes future predictions. By quantifying the magnitude of the influence of the explanatory variables using multiple regression analysis, it becomes easier to plan improvement methods. It also makes it possible to predict future changes in the dependent variables that accompany expected changes in the explanatory variables.

[0022] FIG. 5 is a diagram illustrating the multivariate analysis method according to this embodiment. The multivariate analysis method according to this embodiment performs (a) multiple regression analysis of levels 2 and 3, (b) simple correlation of level 3 evaluation factors, (c) multiple regression analysis of levels 1 and 2, and (d) simple correlation of level 2 evaluation factors. In this embodiment, a hierarchical multiple regression analysis can be performed to enhance the explanatory power of the influence (contribution) of level 2 evaluation factors on employee satisfaction or intent to continue. In other words, when a multiple regression analysis is performed using level 3 evaluation factors as explanatory variables and the evaluation values ​​of level 1 evaluation items as target variables, it may be difficult to accurately determine which evaluation factor is dominant. However, this embodiment solves this problem by including level 2 evaluation factors, which are intermediate concepts.

[0023] Analysis (a) is a first multiple regression analysis using the level 2 evaluation items as the objective variables (intermediate objective variables) and the level 3 evaluation items as the explanatory variables. This analysis allows us to understand the level 3 evaluation items (evaluation factors) that influence the level 2 evaluation items (intermediate objective variables). For example, in Figure 3, the influence (contribution) of each of goal clarification (A-1) and contribution evaluation (A-2) on the job content evaluation (A) can be quantified. Analysis (b) quantifies the correlation between level 3 evaluation factors. For example, if a high correlation is found between goal clarification (A-1) and contribution evaluation (A-2) using simple correlation analysis, it can be seen that an increase in one evaluation will also increase the other evaluation.

[0024] Analysis (c) is a second multiple regression analysis using the level 1 evaluation items as the dependent variable and the level 2 evaluation items as the explanatory variables. This analysis allows us to understand the level 2 evaluation items (evaluation factors) that influence the level 1 evaluation items. For example, we can quantify the impact (contribution) of each of the following evaluations on satisfaction or intent to continue: evaluation of job content (A), evaluation of work environment and systems (B), evaluation of communication with others (C), evaluation of company growth and stability (D), evaluation of career advancement (E), and compensation (cost-effectiveness) (F) (see Figures 6 and 9). Analysis (d) calculates correlation coefficients that quantify the correlations between level 2 evaluation factors. Figure 7 is a table showing correlation coefficients quantified using simple correlation analysis to quantify the correlations between the level 2 evaluation factors shown in Figure 6. For example, Figure 7 shows a high correlation of .669 between evaluation of job content (A) and evaluation of career advancement (E), indicating that an increase in one evaluation also increases the other evaluation.

[0025] FIG. 6 shows an example of the results of calculating the contribution of each explanatory variable by multiple regression analysis when the objective variable is set to "satisfaction" in the survey results related to the questionnaire in FIG. 4. Here, VIF (Variance Inflation Factor) is an index used to evaluate multicollinearity in multiple regression analysis. Multicollinearity means that there is a high correlation between explanatory variables, and if the VIF value is large, it is considered better to exclude that variable from the analysis. Here, the coefficient of determination (R ２ ) is an index that shows how closely the predicted value of the dependent variable obtained by multiple regression analysis matches the actual value of the dependent variable. Generally, the closer the coefficient of determination is to 1, the higher the explanatory power. In the example shown in Figure 6, it can be seen that "job content" has the highest contribution to satisfaction, followed by "company growth potential and stability." In Figure 6, the VIF of each explanatory variable is not a large value, which shows that there is no problem with multicollinearity. Also, in Figure 6, the coefficient of determination R ２ Since the value is large at .703, it can be seen that this analysis has high explanatory power.

[0026] The multivariate analysis means according to this embodiment also has a function for calculating the difference in evaluation from a comparative score (for example, the average score for each evaluation item in an employee satisfaction survey in the same or similar industry or business type; hereinafter referred to as a "standard score"). As shown in Fig. 6, by looking at the difference in evaluation from the standard score calculated for each explanatory variable, it is possible to grasp the strengths and weaknesses of one's own company compared to other companies.

[0027] (3) Analysis Result Output Means The analysis result output means of this embodiment not only outputs the analysis results in a table format as shown in FIGS. 6 to 7 and 9, but also graphically displays the contribution of explanatory variables to the objective variable (ES score), allowing for visual understanding (see FIGS. 8 and 10). In this embodiment, each explanatory variable is graphically displayed as a circle whose size is proportional to its contribution to the ES score. Furthermore, in this embodiment, when the contribution is below a certain level, the circle is drawn with a predetermined minimum radius, and the size is not set to be less than the minimum radius. Note that the size of the circle does not necessarily have to be proportional to the contribution value, and it may be emphasized (for example, the top two explanatory variables may be displayed larger by multiplying their contribution values ​​by a coefficient).

[0028] The analysis result output means has a function of coloring explanatory variables with ratings higher than the standard rating in a first color and explanatory variables with ratings lower than the standard rating in a second color. In this embodiment, the average score of the respondents' evaluation values ​​is calculated for each explanatory variable, and explanatory variables with an average rating higher than the standard rating are colored green (diagonal lines), and explanatory variables with an average rating lower than the standard rating are colored red (dots). Here, in this embodiment, the greater the difference between the average rating of the explanatory variables and the standard rating, the darker the color is drawn.

[0029] In addition, in this embodiment, explanatory variables having a specific correlation coefficient are connected and displayed by a correlation display line. That is, the analysis result output means can display the correlation display line connecting pairs of figures corresponding to explanatory variables with a line thickness and / or type that corresponds to the value of the correlation coefficient (strength of association). Any rule can be set for the method of displaying the correlation display line. For example, the correlation display line may be displayed by rounding off the least significant digit to the nearest digit and displaying the top few pairs that are 0.4 or greater. Furthermore, the correlation display line in this embodiment employs the following rules: when rounded off to the least significant digit, a double line is displayed for a value that is 0.7 or greater; a thick solid line is displayed for a value less than 0.7 to 0.6 or greater; a solid line of normal thickness is displayed for a value less than 0.6 to 0.5 or greater; a thin dotted line is displayed for a value less than 0.5 to 0.4 or greater; and a value less than 0.4 is not displayed.

[0030] Figure 8 is a diagram in which the contribution of each explanatory variable and the correlation coefficient of each explanatory variable are graphically displayed in a radial pattern by the analysis result output means when the objective variable shown in Figure 6 is satisfaction. In Figure 8, the diagonal lines represent the first color, green, and the dots represent the second color, red. In Figure 8, "Remuneration (Cost Performance)" and "Work Environment and Systems" are colored red, so it can be seen at a glance that the average rating is particularly lower than the reference rating.

[0031] In addition, in FIG. 8 , each explanatory variable is depicted as a circle of a size corresponding to the numerical value of its contribution to satisfaction. From the size of the circles in FIG. 8 , it can be seen that "job content" has the strongest influence on satisfaction, followed by "company growth potential and stability." While the present embodiment places the circles corresponding to each explanatory variable at the vertices of a regular hexagon, the drawing method is not limited to this. The explanatory variables may be placed at the vertices of any regular polygon depending on the number of explanatory variables, or may be placed at equal intervals around a circle. The arrangement order of the figures representing the explanatory variables may be changed by dragging.

[0032] Furthermore, in Figure 8, the degree of correlation between explanatory variables can be visually grasped from the thickness and type of the correlation display lines connecting the explanatory variables. For example, it can be seen that "career advancement" has a strong influence on "job content," which is connected by a solid correlation display line. In this way, by graphically displaying the correlation coefficients of each explanatory variable using correlation display lines of different thicknesses and types, it is possible to easily grasp the factors that affect employee satisfaction. Note that the number of explanatory variables connected by correlation display lines is not limited to the number shown in the example, and any number of correlation display lines can be displayed.

[0033] In this embodiment, explanatory variables are represented by circles whose size is proportional to the degree of contribution. However, the figure representing the degree of contribution is not limited to this and any figure can be used, such as a polygon or an illustration of food or a character. The arrangement order of the figures representing explanatory variables may be changed by dragging. A function may be added to arrange the explanatory variables in ascending or descending order by degree of contribution, or by ascending or descending order by score.

[0034] FIG. 9 is a diagram showing the analysis results showing the contribution of each explanatory variable (evaluation factor) at level 2 when the objective variable is set to "intention to continue." In the example shown in FIG. 9, it can be seen that "job content" has the highest contribution to intention to continue, followed by "career advancement." Furthermore, since the VIFs of each explanatory variable are not large, it can be seen that there is no problem with multicollinearity. Furthermore, in FIG. 9, the coefficient of determination R ２ Since the score is .668, it can be seen that this analysis has high explanatory power. Furthermore, in FIG. 9, similar to FIG. 6, the evaluation difference from the standard score for each explanatory variable is also calculated.

[0035] Figure 10 is a diagram in which the contribution of each explanatory variable in Figure 9 and the correlation coefficient of each explanatory variable are graphically displayed radially (at the vertices of a regular hexagon) by the analysis result output means. In Figure 10, the diagonal lines represent the first color, green, and the dots represent the second color, red. In Figure 10, only "Remuneration (Cost-Effectiveness)" and "Work Environment and Systems" are colored red, so it is immediately clear that these are the only items with ratings lower than the standard score (e.g., the average score for the same industry or different industries). Furthermore, the dark coloring of "Intention to Continue," "Job Content," "Career Advancement," and "Communication with Others" indicates that these are significantly higher than the standard score. Similar to Figure 8, the two explanatory variables are connected by a correlation line of a thickness and type corresponding to the value of the correlation coefficient.

[0036] As described above, the information providing system 1 according to this embodiment graphically visualizes and provides the analysis results obtained by multivariate analysis of dependent variables and explanatory variables (evaluation factors), making it possible to instantly grasp dependent variables for efficient improvement efforts. Furthermore, by graphically visualizing and providing explanatory variables with high correlation coefficients, it becomes possible to easily predict the impact of improving the explanatory variables.

[0037] While the preferred embodiments of the present invention have been described above, the technical scope of the present invention is not limited to the above-described embodiments. Various modifications and improvements can be made to the above-described embodiments, and such modifications and improvements are also included in the technical scope of the present invention.

[0038] For example, in the above-described embodiment, a configuration was illustrated in which multiple regression analysis, a type of multivariate analysis, was used to determine the contribution of each evaluation factor to the ES score. However, the present invention is not limited to this configuration, and other methods such as principal component analysis, factor analysis, correspondence analysis, multidimensional scaling, cluster analysis, path analysis, analysis of variance, conjoint analysis, regression analysis, and logistic regression analysis may also be used. In addition to the above-described embodiment, the information providing device 10 can also be configured to collect condition information such as respondent attributes and workplace location, and analyze the evaluation factors that contribute to the ES score taking this condition information into account. Furthermore, analysis by respondent attributes is also possible by adding graphic attributes and psychographic attributes to the questionnaire.

[0039] In the above-described embodiment, an example has been described in which the explanatory power of the influence (contribution) of evaluation factors on ES scores is improved by performing hierarchical multiple regression analysis, but the technical concept of the present invention is also applicable to multivariate analysis using non-hierarchical evaluation factors.

[0040] REFERENCE SIGNS LIST 1... Information provision system 10... Information provision device 20... Management information terminal 30... Information terminal 40... Survey data collection device 50... Internet

Claims

1. An information providing device comprising: a survey data acquisition means for acquiring evaluation data including evaluation values ​​for evaluation items related to employee satisfaction or intent to continue, the evaluation data including evaluation values ​​for multiple evaluation items related to employee satisfaction or intent to continue; a multivariate analysis means for performing a multivariate analysis based on the evaluation data using the evaluation value of employee satisfaction or intent to continue as a response variable and the evaluation values ​​of the multiple evaluation items in the evaluation data as a response variable, thereby calculating the contribution of the explanatory variables to the response variable; and an analysis result output means for displaying the analysis results by the multivariate analysis means on an information terminal capable of communicating via a network, wherein the evaluation data includes level 1 evaluation items corresponding to the response variable, level 2 evaluation items explaining the response variable, and level 3 evaluation items explaining the level 2 evaluation items, and the multivariate analysis means executes a first multivariate analysis using the evaluation values ​​of the level 2 evaluation items as an intermediate response variable and the level 3 evaluation items as explanatory variables, and a second multivariate analysis using the level 1 evaluation items as response variables and the level 2 evaluation items as explanatory variables.

2. An information providing device comprising: a survey data acquisition means for acquiring evaluation data including evaluation values ​​for evaluation items related to employee satisfaction or intention to continue, the evaluation data including evaluation values ​​for multiple evaluation items related to employee satisfaction or intention to continue; a multivariate analysis means for performing multivariate analysis based on the evaluation data using the evaluation value of employee satisfaction or intention to continue as a dependent variable and the evaluation values ​​of the multiple evaluation items in the evaluation data as a multiple explanatory variables, thereby calculating the contribution of the explanatory variables to the dependent variable; and an analysis result output means for displaying the analysis results by the multivariate analysis means on an information terminal capable of communicating via a network, wherein the analysis result output means displays the multiple explanatory variables on the information terminal in a figure of a size corresponding to the contribution degree.

3. The information providing device according to claim 1, characterized in that said analysis result output means causes said plurality of explanatory variables to be displayed on said information terminal in the form of a graphic having a size corresponding to said degree of contribution.

4. The information providing device according to claim 2 or 3, wherein said analysis result output means displays said figures on said information terminal in a radial arrangement at equal intervals.

5. The information providing device according to claim 2 or 3, further comprising a correlation analysis means for calculating the correlation between the plurality of explanatory variables, and wherein the analysis result output means causes the information terminal to display lines connecting pairs of the explanatory variables, the lines having thickness and / or type corresponding to the strength of the correlation.

6. An information providing device as described in claim 2 or 3, further comprising a correlation analysis means for calculating the correlation between the multiple explanatory variables, and wherein the analysis result output means causes the figures to be arranged at the vertices of a regular polygon and displayed on the information terminal, and also causes lines connecting pairs of explanatory variables to be displayed on the information terminal with a thickness and / or type according to the strength of the correlation.

7. The information providing device according to claim 2 or 3, wherein the graphic form is a circle, a polygon or an illustration.

8. The information providing device according to claim 2 or 3, characterized in that, when the correlation between said pair of graphics is less than a predetermined value, the line connecting said pair of graphics is not displayed on said information terminal.

9. An information providing device as described in claim 2 or 3, further comprising a standard value setting means for setting standard values ​​for each of the plurality of explanatory variables, and wherein the analysis result output means displays the explanatory variables in different colors depending on their differences from the standard values.

10. The information providing device according to claim 9, characterized in that the standard value setting means has a function of calculating an average value of the ratings calculated for each of the multiple explanatory variables in the same or similar industry or business type, and setting this average value as the standard value.

11. The information providing device according to claim 9, characterized in that the analysis result output means colors the explanatory variable in a first color when the explanatory variable is higher than the reference value, and colors the explanatory variable in a second color different from the first color when the explanatory variable is lower than the reference value.

12. A method for providing information, the method comprising: a step of acquiring evaluation data including evaluation values ​​of evaluation items related to employee satisfaction or intention to continue working, the evaluation data including evaluation values ​​of multiple evaluation items related to employee satisfaction or intention to continue working; a multivariate analysis step of performing a multivariate analysis using the evaluation value of employee satisfaction or intention to continue working based on the evaluation data as a response variable and the evaluation values ​​of the multiple evaluation items in the evaluation data as a response variable, thereby calculating the contribution of the explanatory variables to the response variable; and an analysis result output step of displaying the analysis results in the multivariate analysis step on an information terminal capable of communicating via a network, wherein the evaluation data includes level 1 evaluation items corresponding to the response variable, level 2 evaluation items explaining the response variable, and level 3 evaluation items explaining the level 2 evaluation items, the multivariate analysis step comprising: a first multivariate analysis using the evaluation values ​​of the level 2 evaluation items as an intermediate response variable and the level 3 evaluation items as explanatory variables; and a second multivariate analysis using the level 1 evaluation items as response variables and the level 2 evaluation items as explanatory variables.

13. A program causing a computer to execute: a survey data acquisition means for acquiring evaluation data including evaluation values ​​for evaluation items related to employee satisfaction or intent to continue, the evaluation data including evaluation values ​​for multiple evaluation items related to employee satisfaction or intent to continue; a multivariate analysis means for performing a multivariate analysis based on the evaluation data using the evaluation value of employee satisfaction or intent to continue as a response variable and the evaluation values ​​of the multiple evaluation items in the evaluation data as a multiple explanatory variables, thereby calculating the contribution of the explanatory variables to the response variable; and an analysis result output means for displaying the analysis results by the multivariate analysis means on an information terminal capable of communicating via a network, wherein the evaluation data includes level 1 evaluation items corresponding to the response variable, level 2 evaluation items explaining the response variable, and level 3 evaluation items explaining the level 2 evaluation items, the multivariate analysis means executing a first multivariate analysis using the evaluation values ​​of the level 2 evaluation items as an intermediate response variable and the level 3 evaluation items as explanatory variables, and a second multivariate analysis using the level 1 evaluation items as response variables and the level 2 evaluation items as explanatory variables.

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