Enterprise health assessment system based on fsqca analysis

Abstract

The present application belongs to the technical field of enterprise management, and specifically relates to an enterprise health assessment system based on fsQCA analysis, which realizes comprehensive and in-depth assessment of the health status of an enterprise through the organic combination of three modules of data collection, data analysis and evaluation. The data collection module can collect multi-dimensional data including employee personal characteristics, organizational environment, digital transformation characteristics, etc. The data analysis module uses the fuzzy set qualitative comparative analysis method to construct a relationship model between the condition variable and the result variable, accurately identifies the factors affecting the health of the enterprise and their configuration path. The evaluation module further generates a detailed evaluation report containing the excitation path, inhibition path and improvement suggestions according to the analysis results. The system not only provides accurate and objective evaluation results for enterprises, but also effectively guides enterprises to make strategic planning and operational management adjustments in a timely manner, and promotes the healthy and sustainable development of enterprises.

Description

Technical Field

[0001] This invention belongs to the field of enterprise management technology, specifically relating to an enterprise health assessment system based on fsQCA analysis. Background Technology

[0002] In current corporate management practices, corporate health assessment has become an important tool for measuring a company's operational status and guiding its strategic planning. Traditional corporate health assessment methods often rely on subjective judgment or simple financial indicators, making it difficult to comprehensively and deeply reflect the true health status of a company. With the deepening of digital transformation, corporate health assessment also faces new challenges and opportunities.

[0003] In recent years, fuzzy set qualitative comparative analysis (fsQCA), as an emerging research method, has been gradually applied to the field of corporate health assessment. By constructing a relationship model between conditional and outcome variables, fsQCA can systematically analyze the factors leading to changes in corporate health status and their configuration paths. However, currently, there are no corporate health assessment systems based on fsQCA analysis on the market, which poses a certain obstacle to the digital transformation of corporate health assessment. Although fsQCA has shown great potential in the field of corporate health assessment, existing technologies still have many shortcomings in practical applications. First, traditional assessment methods lack scientific data collection and analysis tools, making it difficult to guarantee the accuracy and objectivity of assessment results. Second, existing assessment systems often focus only on single-dimensional assessment indicators, ignoring the complexity and diversity of corporate health status, making it difficult to effectively apply to corporate strategic planning and operational management. Summary of the Invention

[0004] The purpose of this invention is to provide an enterprise health assessment system based on fsQCA analysis to solve the problems mentioned in the background art.

[0005] The present invention achieves the above objectives through the following technical solutions:

[0006] This invention proposes an enterprise health assessment system based on fsQCA analysis, comprising a data acquisition module, a data analysis module, and an assessment module:

[0007] The data acquisition module is used to collect a multidimensional dataset including employee personal characteristic data, organizational environment, digital transformation characteristics, and employee innovation level according to a preset data collection framework; wherein, the employee innovation level is based on the set project information and innovation evaluation standards at each stage, and the innovation evaluation value is calculated through a preset evaluation model.

[0008] The data analysis module is used to construct conditional variables and outcome variables based on the multidimensional dataset, and to identify configuration paths that lead to an increase or decrease in employee innovation through fuzzy set operations and truth table analysis.

[0009] The assessment module is used to draw assessment charts based on the configuration path to show whether the innovation capabilities of digital transformation units and employees are enhanced or weakened, and to conduct enterprise health assessments in conjunction with the assessment charts.

[0010] Furthermore, the data acquisition module includes a first acquisition unit and a second acquisition unit;

[0011] The first data collection unit is used to collect employee personal characteristic data, organizational environment, and digital transformation characteristics;

[0012] The second acquisition unit is pre-set with an innovation evaluation benchmark library and an evaluation sub-unit. The innovation evaluation benchmark library contains innovation evaluation standards and benchmark data set according to different projects and project stages. The evaluation sub-unit has a built-in evaluation model based on the innovation evaluation benchmark library. The evaluation model includes the following formula: Innovation evaluation value = Σ(X*(Z / Y));

[0013] Where X is the weight of the i-th evaluation index, Z is the data collected in real time by the first acquisition unit, and Y is the baseline data for the corresponding stage.

[0014] Furthermore, the personal characteristic data includes employees' age, gender, educational background, and work experience information; the organizational environment includes the company's organizational structure, cultural atmosphere, and management system information; and the digital transformation characteristics include the company's level of digital transformation.

[0015] Furthermore, the data analysis module includes a variable construction unit and a fuzzy set operation unit;

[0016] The variable construction unit is used to construct condition variables from the employee personal characteristics data, organizational environment and digital transformation characteristics in the multidimensional dataset, and to construct the result variables from the innovation evaluation value;

[0017] The fuzzy set operation unit is used to construct a relationship model between condition variables and outcome variables through fuzzy set operations. Its expression is defined as: R = f(Condition_1, Condition_2, ..., Condition_i), where R represents the outcome variable, Condition_i represents the i-th condition variable, and f is the membership function set in fsQCA analysis. Then, based on the outcome variable values ​​under each combination of conditions, the configuration path is determined through the truth table. In the configuration path, the path that enhances innovation is set as the stimulating path, and the path that weakens innovation is set as the inhibiting path.

[0018] Furthermore, the evaluation module includes a chart drawing unit and a graded evaluation unit;

[0019] The chart drawing unit is used to draw radar charts to show the performance of employees’ innovation under different combinations of conditions. Each dimension represents an evaluation indicator, and the radius or area of ​​the chart indicates the strength of innovation.

[0020] The grading assessment unit is used to conduct a comprehensive assessment based on a preset grading assessment system and the data in the chart to determine the enterprise's health level.

[0021] Furthermore, the preset hierarchical evaluation system includes:

[0022] Grade A Health: This corresponds to the presence of 60% or more activation paths in the chart results, with the average influence of these activation paths exceeding 0.7.

[0023] Grade B health: Corresponds to 50%-60% of the activation pathways in the chart results, with the average influence of the activation pathways between 0.5 and 0.7; meanwhile, the inhibition pathways do not exceed 20%, and their average influence is less than 0.3;

[0024] Grade C health: Corresponds to the coexistence of activating and inhibiting pathways, with activating pathways accounting for 40%-50% and an average influence value of less than 0.5; inhibiting pathways account for 10%-20% and an average influence value of more than 0.4.

[0025] Grade D health: Corresponds to more than 60% of the inhibition paths in the chart results, with an average influence value of more than 0.5, and less than 10% of the activation paths, with an average influence value of less than 0.2.

[0026] Furthermore, the assessment system also includes a report generation module, which automatically generates an enterprise health assessment report containing activation pathways, inhibition pathways, and improvement suggestions based on the fsQCA analysis results.

[0027] The beneficial effects of this invention are as follows:

[0028] 1. In this invention, the data acquisition module can comprehensively collect multidimensional datasets, including employee personal characteristics, organizational environment, digital transformation characteristics, and employee innovation levels, according to a preset data collection framework. The data analysis module then constructs a relationship model between conditional and outcome variables based on these datasets, and identifies the factors leading to changes in the company's health status and their configuration paths through fuzzy set operations and truth table analysis. This method ensures the accuracy and objectivity of the evaluation results.

[0029] 2. This invention not only focuses on single-dimensional evaluation indicators, but also comprehensively considers the influence of multiple factors such as individual employee characteristics, organizational environment, and digital transformation characteristics. This makes the evaluation results more comprehensive and in-depth in reflecting the true health status of the enterprise.

[0030] 3. The evaluation module in this invention can automatically generate an enterprise health assessment report containing activation paths, inhibition paths, and improvement suggestions based on the analysis results. This automated report generation function greatly improves the efficiency and quality of the evaluation work, enabling the evaluation results to be applied to the enterprise's strategic planning and operational management in a timely and effective manner. Attached Figure Description

[0031] Figure 1 This is a system architecture diagram of the evaluation system in this invention.

[0032] Figure 2 This is another system architecture diagram of the evaluation system in this invention.

[0033] Figure 3 This is a structural diagram of the data acquisition module in this invention.

[0034] Figure 4 This is a structural diagram of the data analysis module in this invention.

[0035] Figure 5 This is a structural diagram of the evaluation module in this invention. Detailed Implementation

[0036] The present application will now be described in further detail with reference to the accompanying drawings. It should be noted that the following specific embodiments are only used to further illustrate the present application and should not be construed as limiting the scope of protection of the present application. Those skilled in the art can make some non-essential improvements and adjustments to the present application based on the above application content.

[0037] Example 1

[0038] like Figure 1-2 As shown, this embodiment proposes an enterprise health assessment system based on fsQCA analysis, including a data acquisition module, a data analysis module, and an assessment module:

[0039] The data acquisition module is used to collect multidimensional datasets including employee personal characteristics data, organizational environment, digital transformation characteristics, and employee innovation level according to a preset data collection framework (preset data collection framework: defines the scope, format, frequency, etc. of data collection to ensure the comprehensiveness and consistency of data). Among them, the employee innovation level is based on the set project information and innovation evaluation standards at each stage, and the innovation evaluation value is calculated through a preset evaluation model.

[0040] Further optimized data includes: personal characteristics such as employees' age, gender, education background, and work experience; organizational environment such as the company's organizational structure, culture, and management systems; and digital transformation characteristics such as the company's digital transformation level (the company's digital transformation level can be determined by collecting relevant reports, data analysis, or expert evaluations. Based on this information, a set level standard is used to determine the company's digital transformation level).

[0041] The data analysis module is used to construct conditional and outcome variables based on multidimensional datasets, and to identify configuration paths that lead to increased or decreased employee creativity through fuzzy set operations and truth table analysis.

[0042] The assessment module is used to generate assessment charts based on the configuration path to show whether the innovation capabilities of digital transformation units and employees have increased or decreased, and to conduct enterprise health assessments in conjunction with the assessment charts.

[0043] like Figure 3 As shown, in a further preferred embodiment, the data acquisition module includes a first acquisition unit and a second acquisition unit;

[0044] The first data collection unit is used to collect employee personal characteristics data, organizational environment, and digital transformation characteristics.

[0045] The second data acquisition unit is pre-set with an innovation evaluation benchmark library and an evaluation sub-unit. The innovation evaluation benchmark library contains innovation evaluation standards and benchmark data set according to different projects and project stages. The evaluation sub-unit has an evaluation model based on the innovation evaluation benchmark library. The evaluation model includes the following formula: Innovation evaluation value = Σ(X*(Z / Y)).

[0046] Where X is the weight of the i-th evaluation index, Z is the data collected in real time by the first acquisition unit, and Y is the baseline data for the corresponding stage.

[0047] like Figure 4 As shown, in a further preferred embodiment, the data analysis module includes a variable construction unit and a fuzzy set operation unit;

[0048] The variable building unit is used to construct condition variables from employee personal characteristics data, organizational environment and digital transformation characteristics in the multidimensional dataset, and to construct outcome variables from innovation assessment values.

[0049] The fuzzy set operation unit is used to construct a relationship model between condition variables and outcome variables through fuzzy set operations. Its expression is defined as: R = f(Condition_1, Condition_2, ..., Condition_i), where R represents the outcome variable, Condition_i represents the i-th condition variable, and f is the membership function set in fsQCA analysis. Then, based on the outcome variable values ​​under each combination of conditions, the configuration path is determined through the truth table. In the configuration path, the path that enhances innovation is set as the stimulating path, and the path that weakens innovation is set as the inhibiting path.

[0050] Understandably, in the data analysis module, the method for determining whether a path is an activation or inhibition pathway based on the results of fsQCA analysis is as follows:

[0051] Fuzzy set operations: A relationship model between condition variables and outcome variables is constructed using fuzzy set operations. Based on the membership function (f) in the relationship model, the value of the outcome variable is calculated for each combination of conditions.

[0052] Truth table analysis: Based on the results of fuzzy set operations, a truth table is constructed, listing all combinations of conditions and their corresponding innovation evaluation values.

[0053] Path recognition:

[0054] Inspiration Path: In the truth table, identify the combination of conditions that leads to a significant increase in the innovation assessment value (i.e., exceeding the preset threshold or being significantly better than other combinations). These combinations are the inspiration paths.

[0055] Suppression Paths: Conversely, identify the combinations of conditions that lead to a significant decrease in the innovation assessment value (i.e., below a preset threshold or significantly worse than other combinations). These combinations are called suppression paths.

[0056] More specifically, the data analysis module includes: variable construction: constructing conditional and outcome variables from the various data points in the multidimensional dataset; fuzzy set operations: constructing a relationship model between conditional and outcome variables through fuzzy logic processing, and calculating the values ​​of outcome variables under various combinations of conditions; and truth table analysis: constructing a truth table based on the results of fuzzy set operations to identify configuration paths that lead to increased or decreased employee creativity.

[0057] like Figure 5 As shown, in a further preferred embodiment, the evaluation module includes a chart drawing unit and a graded evaluation unit;

[0058] The chart drawing unit is used to draw radar charts to show the performance of employees’ innovation under different combinations of conditions. Each dimension represents an evaluation indicator, and the radius or area of ​​the chart indicates the strength of innovation.

[0059] The grading assessment unit is used to conduct a comprehensive assessment based on a preset grading assessment system and the data in the charts to determine the health level of the enterprise.

[0060] A further preferred, pre-defined tiered evaluation system includes:

[0061] Grade A Health: The corresponding chart results show that 60% or more of the activation paths exist, and the average influence of the activation paths exceeds 0.7;

[0062] Grade B health: This corresponds to 50%-60% of the activation pathways in the chart results, with an average influence of 0.5 to 0.7; meanwhile, the inhibition pathways do not exceed 20%, and their average influence is below 0.3.

[0063] Grade C health: Corresponds to the coexistence of activating and inhibiting pathways, with activating pathways accounting for 40%-50% and an average influence value of less than 0.5; inhibiting pathways account for 10%-20% and an average influence value of more than 0.4.

[0064] Grade D health: This corresponds to more than 60% of the inhibition paths in the chart results, with an average influence value of more than 0.5 for the inhibition paths, and less than 10% of the activation paths, with an average influence value of less than 0.2 for the activation paths.

[0065] More specifically, the assessment module includes charting: using visualization tools such as radar charts to show employee innovation performance under different combinations of conditions. Tiered assessment: based on a pre-set tiered assessment system, a comprehensive evaluation is conducted using chart data to determine the company's health level.

[0066] Accordingly, in the evaluation module, during implementation, a radar chart is first drawn to visually display the innovation performance under each evaluation indicator. Then, based on the tiered evaluation system, the ratio and average influence of the stimulating and inhibiting paths are calculated. Finally, the overall health of the enterprise is comprehensively evaluated, and a corresponding level is determined.

[0067] Furthermore, the assessment system also includes a report generation module, which automatically generates an enterprise health assessment report containing activation pathways, inhibition pathways, and improvement suggestions based on the fsQCA analysis results.

[0068] According to the above embodiments of the present invention, this assessment system integrates multiple modules such as data collection, data analysis, assessment, and report generation to achieve a comprehensive assessment of employee innovation and overall corporate health. The system has a pre-defined data collection framework to ensure the comprehensiveness and consistency of the data, and accurately identifies the configuration paths that stimulate and inhibit employee innovation through fuzzy set operations and truth table analysis. The assessment module uses visualization tools to display the assessment results, and combined with a tiered assessment system, provides enterprises with an intuitive and accurate assessment of their health. The report generation module automatically summarizes the analysis results and outputs an assessment report containing improvement suggestions, helping enterprises optimize management and promote innovation. The system features smooth interaction, a user-friendly interface, and supports regular data updates, ensuring the timeliness and accuracy of the assessment. It is an important tool for enterprises to enhance competitiveness and achieve sustainable development.

[0069] It will be apparent to those skilled in the art that the embodiments of the present invention are not limited to the details of the exemplary embodiments described above, and that the embodiments of the present invention can be implemented in other specific forms without departing from the spirit or essential characteristics of the embodiments of the present invention. Therefore, the embodiments should be considered exemplary and non-limiting in all respects, and the scope of the embodiments of the present invention is defined by the appended claims rather than the foregoing description. Therefore, all variations falling within the meaning and scope of equivalents of the claims are intended to be encompassed within the embodiments of the present invention. No reference numerals in the claims should be construed as limiting the scope of the claims. Furthermore, it is clear that the word "comprising" does not exclude other units or steps, and the singular does not exclude the plural. Multiple units, modules, or devices recited in the system, apparatus, or terminal claims may also be implemented by the same unit, module, or device through software or hardware. The terms "first," "second," etc., are used to indicate names and do not indicate any particular order.

[0070] The above embodiments are only used to illustrate the technical solutions of this application, and are not intended to limit them. Although this application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of this application.

Claims

1. A system for enterprise health assessment based on fsQCA analysis, characterized in that, It includes a data acquisition module, a data analysis module, and an evaluation module: The data acquisition module is used to collect a multidimensional dataset including employee personal characteristic data, organizational environment, digital transformation characteristics, and employee innovation level according to a preset data collection framework; wherein, the employee innovation level is based on the set project information and innovation evaluation standards at each stage, and the innovation evaluation value is calculated through a preset evaluation model. The data acquisition module includes a first acquisition unit and a second acquisition unit; The first data collection unit is used to collect employee personal characteristic data, organizational environment, and digital transformation characteristics; The second acquisition unit is pre-set with an innovation evaluation benchmark library and an evaluation sub-unit. The innovation evaluation benchmark library contains innovation evaluation standards and benchmark data set according to different projects and project stages. The evaluation sub-unit has a built-in evaluation model based on the innovation evaluation benchmark library. The evaluation model includes the following formula: Innovation evaluation value = Σ(X*(Z / Y)). Where X is the weight of the i-th evaluation index, Z is the data collected in real time by the first collection unit, and Y is the baseline data for the corresponding stage. The personal characteristics data include employees' age, gender, educational background, and work experience; the organizational environment includes the company's organizational structure, culture, and management system; and the digital transformation characteristics include the company's level of digital transformation. The data analysis module is used to construct conditional variables and outcome variables based on the multidimensional dataset, and to identify configuration paths that lead to an increase or decrease in employee innovation through fuzzy set operations and truth table analysis. The data analysis module includes a variable construction unit and a fuzzy set operation unit; The variable construction unit is used to construct condition variables from the employee personal characteristics data, organizational environment and digital transformation characteristics in the multidimensional dataset, and to construct the result variables from the innovation evaluation value; The fuzzy set operation unit is used to construct a relationship model between condition variables and outcome variables through fuzzy set operations. Its expression is defined as: R = f(Condition_1, Condition_2, ..., Condition_i), where R represents the outcome variable, Condition_i represents the i-th condition variable, and f is the membership function set in fsQCA analysis. Then, based on the outcome variable values ​​under each combination of conditions, the configuration path is determined through the truth table. In the configuration path, the path that enhances innovation is set as the stimulation path, and the path that weakens innovation is set as the inhibition path. The assessment module is used to draw assessment charts based on the configuration path to show whether the innovation capabilities of digital transformation units and employees are enhanced or weakened, and to conduct enterprise health assessments in conjunction with the assessment charts.

2. The system for enterprise health assessment based on fsQCA analysis as claimed in claim 1 wherein: The evaluation module includes a chart drawing unit and a graded evaluation unit; The chart drawing unit is used to draw radar charts to show the performance of employees’ innovation under different combinations of conditions. Each dimension represents an evaluation indicator, and the radius or area of ​​the chart indicates the strength of innovation. The grading assessment unit is used to conduct a comprehensive assessment based on a preset grading assessment system and the data in the chart to determine the enterprise's health level.

3. The system for enterprise health assessment based on fsQCA analysis as claimed in claim 2 wherein: The pre-defined hierarchical evaluation system includes: Grade A Health: This corresponds to the presence of 60% or more activation paths shown in the chart results, with the average influence of these activation paths exceeding 0.

7. Grade B Health: Corresponds to 50%-60% of the activation paths in the chart results, with the average influence of the activation paths between 0.5 and 0.7; meanwhile, the inhibition paths do not exceed 20%, and their average influence is below 0.3; Grade C health: Corresponds to the coexistence of activation and inhibition pathways, with activation pathways accounting for 40%-50% and an average influence value of less than 0.5; inhibition pathways account for 10%-20% and an average influence value of more than 0.

4. Grade D health: Corresponds to more than 60% of the inhibition paths in the chart results, with an average influence of more than 0.5, and less than 10% of the activation paths, with an average influence of less than 0.

2.

4. The system for enterprise health assessment based on fsQCA analysis as claimed in claim 3 wherein: The assessment system also includes a report generation module, which automatically generates an enterprise health assessment report containing activation pathways, inhibition pathways, and improvement suggestions based on the fsQCA analysis results.

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