Method and system for evaluating influence of AI training on work based on fsQCA analysis

Abstract

The invention belongs to the technical field of organizational behavioristics and human resource management, and particularly relates to a method and system for evaluating the influence of AI training on work based on fsQCA analysis. The conditions at least comprise AI training, organization support, leader support, colleague support, task complexity, task connectivity and task ambiguity; a fuzzy set qualitative comparative analysis method is applied, the sufficiency influence of multiple condition combinations on improvement of the work input degree is analyzed, at least one set of optimal condition combination is determined, and each set of optimal condition combination comprises at least one of the conditions; and taking AI training as a work resource and constructing a JD-R model, and determining the comprehensive influence of the AI training on work according to the optimal condition combination and the JD-R model. By combining the fuzzy set qualitative comparative analysis method and the JD-R model, the interaction among the conditions and the comprehensive influence of the conditions on the work input degree can be understood more deeply.

Description

Technical Field

[0001] The present invention belongs to the technical field of organizational behavior and human resource management, and specifically relates to an evaluation method and system for the impact of AI training on work based on fsQCA analysis. Background Art

[0002] With the rapid development of artificial intelligence (AI) technology, its application in the field of corporate training is becoming more and more extensive, aiming to improve employees' work skills and efficiency. However, although AI training has shown great potential, the evaluation of its actual impact on work still faces many challenges. In existing technologies, the evaluation of the effectiveness of AI training mostly relies on traditional questionnaires or performance evaluation methods. These methods often have problems such as strong subjectivity, one-sided data collection, and insufficient analysis depth. It is difficult to fully and accurately reflect the deep-seated impact of AI training on work engagement and the overall work environment.

[0003] Specifically, traditional evaluation methods ignore the complex interactions between multiple factors and fail to effectively capture the combined impact of multi-dimensional factors such as AI training, organizational support, leadership style, and colleague interaction on work engagement. In addition, due to the lack of scientific and rigorous data analysis methods in the evaluation process, such as the application of advanced methods such as fuzzy set qualitative comparative analysis (fsQCA), the evaluation results often fail to provide actionable improvement suggestions. Therefore, when evaluating the impact of AI training on work, existing technologies have defects such as a single evaluation dimension, insufficient analysis depth, and weak results guidance. Summary of the invention

[0004] The purpose of the present invention is to provide a method and system for evaluating the impact of AI training on work based on fsQCA analysis, so as to solve the problems raised in the background technology.

[0005] The present invention achieves the above-mentioned purpose through the following technical solutions:

[0006] In a first aspect, the present invention proposes a method for evaluating the impact of AI training on work based on fsQCA analysis, comprising:

[0007] Identify multiple conditions that affect employee work engagement, including at least AI training, organizational support, leadership support, peer support, task complexity, task connectivity, and task ambiguity;

[0008] Applying the fuzzy set qualitative comparative analysis method, analyzing the effects of multiple combinations of the above conditions on the adequacy of improving work engagement, and determining at least one set of optimal condition combinations, each of which contains at least one of the above conditions;

[0009] AI training is used as a work resource and a JD-R model is constructed, and the comprehensive impact of AI training on work is determined based on the optimal condition combination and the JD-R model.

[0010] Furthermore, the identifying of multiple conditions that affect employee work engagement includes: collecting an indicator set of the multiple conditions and work engagement of employees receiving AI training, converting each condition in the indicator set into a fuzzy set according to a set membership function, making the condition a conditional variable in the fuzzy set, and making work engagement a fuzzy set result variable.

[0011] Furthermore, the work engagement includes three dimensions: vitality, dedication and focus, which are specifically quantified using a four-vector scale.

[0012] Furthermore, before converting the indicator set into a fuzzy set, the reliability of the indicator set is evaluated by using Cronbach's α coefficient, and the correlation between each condition in the indicator set and employee engagement is analyzed by using Pearson correlation coefficient, and the reliability of the indicator set is verified based on the analysis results of the reliability and the correlation.

[0013] Furthermore, the optimal one or more sets of condition combinations include any of the following:

[0014] (1) the presence of task complexity and task connectivity, and at least one of organizational support, leadership support, or colleague support;

[0015] (2) Combination of AI training with organizational or leadership support, where task complexity exists and task ambiguity does not exist;

[0016] and (3) the presence of task complexity or task connectedness and at least one of organizational support, leadership support, or coworker support in the absence of task ambiguity.

[0017] Furthermore, the fuzzy set qualitative comparative analysis method is applied to analyze the effects of multiple combinations of conditions on the adequacy of improving work engagement, and determine the optimal set or sets of conditions, including:

[0018] The fuzzy sets are subjected to subset association analysis by fsQCA software to identify the condition combinations that lead to changes in employee work engagement, and the best one or more condition combinations among the several condition combinations are determined according to pre-set optimal combination judgment rules.

[0019] Furthermore, the optimal combination judgment rule is: the overall consistency of each set of condition combinations exceeds 0.9, and the overall coverage of all determined condition combinations is at least 0.7.

[0020] Furthermore, the AI ​​training is used as a work resource and a JD-R model is constructed, and the comprehensive impact of the AI ​​training on the work is determined according to the optimal condition combination and the JD-R model, including:

[0021] A JD-R model is constructed by taking task complexity, task connectivity and task ambiguity as job requirements, and AI training, organizational support, leadership support and colleague support as job resources. The JD-R model is used to verify the impact of the optimal combination of conditions on job engagement.

[0022] In a second aspect, the present invention proposes an evaluation system for the impact of AI training on work based on fsQCA analysis, which is applied to perform any of the above-mentioned evaluation methods, and the system includes:

[0023] A data collection module for identifying multiple conditions that affect employee work engagement, the conditions including at least AI training, organizational support, leadership support, peer support, task complexity, task connectivity, and task ambiguity;

[0024] A comparative analysis module, for applying a fuzzy set qualitative comparative analysis method to analyze the sufficiency effects of a plurality of combinations of the conditions on improving work engagement, and determining at least one set of optimal condition combinations, each set of optimal condition combinations including at least one of the above conditions;

[0025] The model verification module uses AI training as a work resource and constructs a JD-R model, and determines the comprehensive impact of AI training on work based on the optimal condition combination and the JD-R model.

[0026] The beneficial effects of the present invention are:

[0027] By combining the fuzzy set qualitative comparative analysis method and the JD-R model, this paper can provide a deeper understanding of the interaction between various conditions and their comprehensive impact on work engagement. This not only helps organizations optimize AI training strategies, but also provides strong support for improving employee work engagement and overall work performance. BRIEF DESCRIPTION OF THE DRAWINGS

[0028] Figure 1 Schematic diagram of the evaluation method of the present invention.

[0029] Figure 2 It is a system framework diagram of the evaluation method in the present invention. DETAILED DESCRIPTION

[0030] The present application is further described in detail below in conjunction with the accompanying drawings. It is necessary to point out here that the following specific implementation methods are only used to further illustrate the present application and cannot be understood as limiting the scope of protection of the present application. Technical personnel in this field can make some non-essential improvements and adjustments to the present application based on the above application content.

[0031] Example 1

[0032] like Figure 1-2 As shown, this embodiment proposes a method for evaluating the impact of AI training on work based on fsQCA analysis, including the following steps:

[0033] S1. Identify multiple conditions that affect employee work engagement, including at least AI training, organizational support, leadership support, colleague support, task complexity, task connectivity, and task ambiguity;

[0034] S2. Apply fuzzy set qualitative comparative analysis (fsQCA analysis) to analyze the effects of multiple combinations of conditions on the adequacy of improving work engagement and determine at least one set of optimal condition combinations, each of which contains at least one of the above conditions;

[0035] S3. Use AI training as a work resource and build a JD-R model. Determine the comprehensive impact of AI training on work based on the optimal combination of conditions and the JD-R model.

[0036] In this embodiment, the core of the evaluation method is to identify multiple conditions that affect employee work engagement and use fuzzy set qualitative comparative analysis (fsQCA) to evaluate how these conditions affect work engagement. In particular, this application considers AI training as an important condition and explores its interaction with other conditions (such as organizational support, leadership support, etc.).

[0037] Further preferably, identifying multiple conditions that affect employee work engagement includes: collecting an indicator set of multiple conditions and work engagement of employees receiving AI training, converting each condition in the indicator set into a fuzzy set according to a set membership function, making the condition a condition variable in the fuzzy set, and making work engagement a fuzzy set result variable. These data can be obtained through questionnaires, interviews or observations. Among them, the collected indicator set will be converted into a fuzzy set according to the set membership function for subsequent qualitative comparative analysis.

[0038] Step S1 includes defining and measuring the definition of each conditional variable, using the corresponding scale for evaluation, defining and measuring work engagement, using the corresponding scale for evaluation from the three dimensions of vitality, dedication and focus, collecting the relevant data of the above variables through questionnaires or existing databases to obtain an indicator set, and converting the collected questionnaire data into fuzzy set membership scores.

[0039] Step S2 is specifically fsQCA analysis: using fuzzy set qualitative comparative analysis (fsQCA) technology, the configuration of each variable under different conditions is compared to identify the necessary and sufficient conditions that affect employee work engagement.

[0040] Further preferably, work engagement includes three dimensions: vitality, dedication and focus, which are specifically quantified using a four-dimensional scale. Work engagement is a multi-dimensional concept, and this method uses three dimensions: vitality, dedication and focus to quantify. By using a four-dimensional scale, the degree of employee engagement at work can be more accurately measured.

[0041] Preferably, before converting the indicator set into a fuzzy set, the reliability of the indicator set is evaluated by using Cronbach's α coefficient, and the correlation between each condition in the indicator set and employee engagement is analyzed by using Pearson correlation coefficient, and the reliability of the indicator set is verified based on the analysis results of reliability and correlation. The internal consistency of the indicator set and the degree of correlation between each condition and employee engagement can be verified, thereby ensuring the reliability of the data.

[0042] Further preferably, the optimal set or multiple sets of condition combinations include any of the following:

[0043] (1) the presence of task complexity and task connectivity, and at least one of organizational support, leadership support, or colleague support;

[0044] (2) Combination of AI training with organizational or leadership support, where task complexity exists and task ambiguity does not exist;

[0045] and (3) the presence of task complexity or task connectedness and at least one of organizational support, leadership support, or coworker support in the absence of task ambiguity.

[0046] Further preferably, the fuzzy set qualitative comparative analysis method is applied to analyze the effects of multiple combinations of conditions on the adequacy of improving work engagement, and determine the optimal set or sets of combinations of conditions, including:

[0047] The fsQCA software is used to perform subset association analysis on fuzzy sets to identify the combination of conditions that lead to changes in employee work engagement, and the optimal set or sets of condition combinations among several condition combinations are determined based on the pre-set optimal combination judgment rules.

[0048] Further preferably, the optimal combination judgment rule is: the overall consistency of each set of condition combinations exceeds 0.9, and the overall coverage of all determined condition combinations is at least 0.7.

[0049] This embodiment uses fsQCA software to perform subset association analysis to identify those condition combinations that lead to changes in employee work engagement. By setting the optimal combination judgment rule (such as overall consistency exceeding 0.9 and overall coverage at least 0.7), the most influential combination can be screened out from many condition combinations.

[0050] Further preferably, AI training is used as a work resource and a JD-R model is constructed, and the comprehensive impact of AI training on work is determined based on the optimal condition combination and the JD-R model, including:

[0051] The JD-R model is constructed by taking task complexity, task connectivity and task ambiguity as job requirements, and AI training, organizational support, leadership support and colleague support as job resources. The JD-R model is used to verify the impact of the optimal combination of conditions on job engagement.

[0052] It is understandable that this embodiment regards AI training as a work resource and incorporates it into the JD-R model together with other work requirements (such as task complexity, task connectivity, etc.) and work resources (such as organizational support, leadership support, etc.). Through this model, the comprehensive impact of AI training on employee work can be more comprehensively evaluated.

[0053] Based on the same inventive concept, this embodiment proposes an evaluation system for the impact of AI training on work based on fsQCA analysis, which is applied to execute the above evaluation method. The system includes:

[0054] A data collection module for identifying multiple conditions that affect employee work engagement, including at least AI training, organizational support, leadership support, peer support, task complexity, task connectivity, and task ambiguity;

[0055] A comparative analysis module, for applying a fuzzy set qualitative comparative analysis method, analyzing the effects of multiple condition combinations on the adequacy of improving work engagement, and determining at least one set of optimal condition combinations, each of which contains at least one of the above conditions;

[0056] The model verification module regards AI training as a work resource and builds a JD-R model, and determines the comprehensive impact of AI training on work based on the optimal combination of conditions and the JD-R model.

[0057] According to the above embodiment, the evaluation system can fully evaluate the impact of AI training on employee work. By combining the fuzzy set qualitative comparative analysis method and the JD-R model, it is possible to have a deeper understanding of the interaction between various conditions and their comprehensive impact on work engagement. This not only helps organizations optimize AI training strategies, but also provides strong support for improving employee work engagement and overall work performance.

[0058] Those of ordinary skill in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of this application.

[0059] In addition, each functional module in each embodiment of the present application may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit.

[0060] If the functions are implemented in the form of software function modules and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present application can be essentially or partly embodied in the form of a software product that contributes to the prior art. The computer software product is stored in a storage medium and includes several instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the method described in the embodiment of the present invention. The aforementioned storage medium includes: various media that can store program codes, such as a USB flash drive, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk.

[0061] The above embodiments are only used to illustrate the technical solutions of the present application, rather than to limit it. Although the present application has been described in detail with reference to the aforementioned embodiments, a person skilled in the art should understand that the technical solutions described in the aforementioned embodiments can still be modified, or some of the technical features can be replaced by equivalents. However, these modifications or replacements 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 the present application.

Claims

1. A method for evaluating the impact of AI training on work based on fsQCA analysis, characterized in that: include: Identify multiple conditions that affect employee work engagement, including at least AI training, organizational support, leadership support, peer support, task complexity, task connectivity, and task ambiguity; Applying the fuzzy set qualitative comparative analysis method, analyzing the effects of multiple combinations of the above conditions on the adequacy of improving work engagement, and determining at least one set of optimal condition combinations, each of which contains at least one of the above conditions; AI training is used as a work resource and a JD-R model is constructed, and the comprehensive impact of AI training on work is determined based on the optimal condition combination and the JD-R model.

2. The method for evaluating the impact of AI training on work based on fsQCA analysis according to claim 1 is characterized in that: The identifying of multiple conditions that affect employee work engagement includes: collecting an indicator set of the multiple conditions and work engagement of employees who have received AI training, converting each condition in the indicator set into a fuzzy set according to a set membership function, making the condition a conditional variable in the fuzzy set, and making the work engagement a fuzzy set result variable.

3. The method for evaluating the impact of AI training on work based on fsQCA analysis according to claim 2 is characterized in that: The work engagement includes three dimensions: vitality, dedication and focus, which are quantified using a four-vector scale.

4. The method for evaluating the impact of AI training on work based on fsQCA analysis according to claim 2 is characterized in that: Before converting the indicator set into a fuzzy set, the reliability of the indicator set is evaluated by using Cronbach's α coefficient, and the correlation between each condition in the indicator set and employee engagement is analyzed by using Pearson correlation coefficient, and the reliability of the indicator set is verified based on the analysis results of the reliability and the correlation.

5. The method for evaluating the impact of AI training on work based on fsQCA analysis according to claim 3 is characterized by: The optimal set or multiple sets of condition combinations include any of the following: (1) the presence of task complexity and task connectivity, and at least one of organizational support, leadership support, or colleague support; (2) Combination of AI training with organizational or leadership support, where task complexity exists and task ambiguity does not exist; and (3) the presence of task complexity or task connectedness and at least one of organizational support, leadership support, or coworker support in the absence of task ambiguity.

6. The method for evaluating the impact of AI training on work based on fsQCA analysis according to claim 5 is characterized by: The fuzzy set qualitative comparative analysis method is used to analyze the effects of various combinations of conditions on the adequacy of improving work engagement, and determine the optimal set or sets of conditions, including: The fuzzy sets are subjected to subset association analysis by fsQCA software to identify the condition combinations that lead to changes in employee work engagement, and the best one or more condition combinations among the several condition combinations are determined according to pre-set optimal combination judgment rules.

7. The method for evaluating the impact of AI training on work based on fsQCA analysis according to claim 6 is characterized by: The optimal combination judgment rule is: the overall consistency of each set of condition combinations exceeds 0.9, and the overall coverage of all determined condition combinations is at least 0.

7.

8. The method for evaluating the impact of AI training on work based on fsQCA analysis according to claim 1 is characterized in that: The using of AI training as a work resource and constructing a JD-R model, and determining the comprehensive impact of AI training on work according to the optimal condition combination and the JD-R model, include: A JD-R model is constructed by taking task complexity, task connectivity and task ambiguity as job requirements, and AI training, organizational support, leadership support and colleague support as job resources. The JD-R model is used to verify the impact of the optimal combination of conditions on job engagement.

9. An evaluation system for the impact of AI training on work based on fsQCA analysis, characterized in that: Applied to executing the evaluation method according to any one of claims 1 to 8, the system comprises: A data collection module for identifying multiple conditions that affect employee work engagement, the conditions including at least AI training, organizational support, leadership support, peer support, task complexity, task connectivity, and task ambiguity; A comparative analysis module, used for applying a fuzzy set qualitative comparative analysis method to analyze the sufficiency effects of a plurality of the condition combinations on improving work engagement, and determining at least one set of optimal condition combinations, each set of optimal condition combinations including at least one of the above conditions; The model verification module uses AI training as a work resource and constructs a JD-R model, and determines the comprehensive impact of AI training on work based on the optimal condition combination and the JD-R model.

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