Evaluation system for influence of enterprise digital transformation on employees

Abstract

The invention belongs to the technical field of enterprise management, and particularly relates to a system for evaluating the influence of enterprise digital transformation on employees, and the method comprises the steps: obtaining an enterprise digital transformation grade, and behavior data, psychological state data and work efficiency data of employees in the period of using social media under the corresponding grade; wherein the behavior data comprises the use frequency and the interaction type, and according to the enterprise digital transformation level, the employee behavior data, the psychological state data and the work performance data, determining an evaluation result of the influence of the enterprise digital transformation on the employees; according to the invention, comprehensive and scientific digital transformation influence evaluation can be provided for enterprises. By comprehensively considering multiple dimensions such as the digital transformation level, the employee social media use behavior, the psychological state and the work efficiency, the enterprise can more accurately understand the actual influence of the digital transformation on the employee level. Therefore, enterprises can find problems in time and adjust strategies, and digital transformation can be ensured to better serve development of employees and enterprises.

Description

Technical Field

[0001] The present invention belongs to the technical field of enterprise management, and specifically relates to an evaluation system for the impact of enterprise digital transformation on employees. Background Art

[0002] At present, with the deepening of digital transformation of enterprises, a variety of digital transformation assessment tools and technologies have emerged in the market. These tools and technologies mainly focus on evaluating the technical implementation level of digital transformation of enterprises, such as the maturity of digital transformation, the depth of technology application, and the degree of business process optimization. They provide enterprises with feedback and suggestions on the effects of technology implementation by collecting and analyzing the relevant data of digital transformation of enterprises. These existing technologies have helped enterprises understand the progress and results of digital transformation to a certain extent, and provided an important reference for enterprise decision-making.

[0003] However, existing digital transformation assessment tools and technologies also have some obvious shortcomings. First, most of them focus on the technical level and ignore the impact of digital transformation on individual employees. This makes it difficult for companies to fully understand the actual impact of digital transformation on employees, and thus cannot formulate a more humane transformation strategy. Secondly, the assessment models of existing technologies are often relatively simple and it is difficult to accurately reflect the complex relationship between digital transformation and employee impact. This leads to certain deviations in the assessment results and fails to provide accurate and scientific decision-making support for enterprises. Therefore, future technologies need to pay more attention to employee-level impact assessments and build more accurate and comprehensive assessment models to help companies better cope with the challenges brought about by digital transformation. Summary of the invention

[0004] The purpose of the present invention is to provide an evaluation system for the impact of enterprise digital transformation on employees, 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] The present invention proposes an evaluation system for the impact of enterprise digital transformation on employees, including a data acquisition module, a data analysis module and an evaluation output module; wherein:

[0007] The data acquisition module is used to obtain the enterprise digital transformation level, the behavior data, psychological state data, and work efficiency data of employees during the use of social media at the corresponding level; wherein the behavior data includes the frequency of use and the type of interaction;

[0008] The data analysis module is used to determine the evaluation results of the impact of the enterprise's digital transformation on employees based on the enterprise's digital transformation level, employee behavior data, psychological state data, and work efficiency data;

[0009] The evaluation output module is used to determine the interval corresponding to the evaluation result according to the preset level interval.

[0010] Furthermore, the data acquisition module includes:

[0011] The first acquisition unit is used to obtain the maturity of the enterprise's digital transformation, the depth of technology application and the degree of business process optimization, and determine the digital transformation level of the corresponding enterprise based on the set level standards;

[0012] The second acquisition unit is used to obtain the frequency of use and interaction type of employees during the use of social media at the corresponding level, as well as the psychological satisfaction and stress index of employees;

[0013] The third acquisition unit is used to obtain the work efficiency, task completion and creativity data of the targeted tasks within the set period of the employees at the corresponding level.

[0014] Furthermore, the data analysis module includes:

[0015] A model building unit is used to establish an evaluation model of the impact of digital transformation level, employee social media usage behavior data and psychological state data on employee work effectiveness based on a structural equation model under set construction rules;

[0016] A first calculation unit is used to call the evaluation model to calculate the evaluation result of the impact of digital transformation on employees' social media usage behavior data and psychological state data;

[0017] The second calculation unit is used to call the evaluation model and calculate the evaluation results of the impact of the enterprise's digital transformation on employees in combination with a preset parameter estimation algorithm.

[0018] Furthermore, in the model building unit, building the structural equation model under the set building rules includes the following steps:

[0019] Define latent variables: digital transformation level, social media behavior data, employee psychological state data, and employee work effectiveness as latent variables;

[0020] Set observation variables: use frequency and interaction type as observation variables of social media behavior, satisfaction and stress index as observation variables of employee psychological state, work efficiency, task completion quality, and innovation ability as observation variables of work effectiveness;

[0021] Construct a path diagram: determine the causal relationship and correlation between the latent variables, including the first path of the impact of digital transformation level on social media behavior data and employee psychological state data, and the second path of the impact of social media behavior and employee psychological state on employee work effectiveness;

[0022] Apply statistical software for model fitting and parameter estimation to obtain the coefficients of the first path and the second path.

[0023] Further, in the second calculation unit, the preset parameter estimation algorithm is weighted summation, which is used to calculate the comprehensive evaluation index of the impact of digital transformation on employees according to the output result of the structural equation model. The formula is as follows:

[0024]

[0025] Where, A is the comprehensive evaluation index, n is the number of evaluation paths, wi is the weight of the coefficient of the i-th evaluation path, B is the standard evaluation value, and the standardized evaluation value represents the value of the coefficient of the i-th evaluation path after being standardized.

[0026] Further, the preset grade intervals in the evaluation output module include:

[0027] Excellent impact area: Comprehensive evaluation index ≥ x1, indicating that digital transformation has a very positive impact on employees, and employees perform excellently in aspects such as social media usage behavior, psychological state, and work efficiency;

[0028] Good impact area: x2 ≤ Comprehensive evaluation index < x1, indicating that digital transformation has a positive impact on employees, and employees perform well overall, but there is still room for improvement;

[0029] General impact area: x3 ≤ Comprehensive evaluation index < x2, indicating that digital transformation has a general impact on employees, and employees show certain fluctuations or deficiencies in some aspects, and enterprises need to pay attention and take measures to improve;

[0030] Lower impact area: x4 ≤ Comprehensive evaluation index < x3, indicating that digital transformation has a relatively limited impact on employees, and employees have many problems in aspects such as social media usage behavior, psychological state, and work efficiency. Enterprises need to deeply analyze the reasons and formulate corresponding improvement measures;

[0031] Negative impact area: Comprehensive evaluation index < x4, indicating that digital transformation has a negative impact on employees, and employees show a significant decline in aspects such as social media usage behavior, psychological state, and work efficiency. Enterprises need to immediately take measures for intervention and adjustment;

[0032] Where, x1, x2, x3, and x4 are set based on historical data or expert evaluation.

[0033] The beneficial effects of the present invention are as follows:

[0034] 1. The present invention can provide enterprises with a comprehensive and scientific digital transformation impact assessment. By comprehensively considering multiple dimensions such as the digital transformation level, employee social media usage behavior, psychological state, and work efficiency, enterprises can more accurately understand the actual impact of digital transformation at the employee level. This helps companies to identify problems and adjust strategies in a timely manner to ensure that digital transformation can better serve the development of employees and enterprises. At the same time, the evaluation system can also serve as an important monitoring tool in the process of digital transformation of enterprises, helping enterprises to continuously track and optimize the transformation effects.

[0035] 2. The present invention helps to improve employees' work experience and satisfaction. By deeply analyzing the impact of digital transformation on employees, companies can more accurately meet employees' needs and expectations, thereby improving their work enthusiasm and loyalty. For example, companies can provide employees with more personalized training and development opportunities based on the evaluation results, or optimize work processes to reduce employee stress and burden. These measures not only help improve employees' work efficiency, but also enhance employees' sense of belonging and identity with the company, thereby promoting the long-term and stable development of the company. BRIEF DESCRIPTION OF THE DRAWINGS

[0036] Figure 1 A system architecture diagram of a system for evaluating the impact of enterprise digital transformation on employees provided in an embodiment of the present application.

[0037] Figure 2 A schematic diagram of the structure of a data acquisition module in a system for evaluating the impact of enterprise digital transformation on employees provided in an embodiment of the present application.

[0038] Figure 3 A schematic diagram of the structure of a data analysis module in a system for evaluating the impact of enterprise digital transformation on employees provided in an embodiment of the present application.

[0039] Figure 4 An example of a path map in a system for evaluating the impact of enterprise digital transformation on employees provided in an embodiment of the present application. DETAILED DESCRIPTION

[0040] 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.

[0041] Example 1

[0042] like Figure 1 As shown, this embodiment proposes an evaluation system for the impact of enterprise digital transformation on employees, including a data acquisition module, a data analysis module and an evaluation output module; wherein:

[0043] The data acquisition module is used to obtain the enterprise digital transformation level, the behavioral data, psychological state data, and work efficiency data of employees during the use of social media at the corresponding level; among which, the behavioral data includes the frequency of use and the type of interaction;

[0044] The data analysis module is used to determine the evaluation results of the impact of the enterprise's digital transformation on employees based on the enterprise's digital transformation level, employee behavior data, psychological state data, and work efficiency data;

[0045] The evaluation output module is used to determine the interval corresponding to the evaluation result according to the preset level interval.

[0046] Further preferably, Figure 2 As shown, the data acquisition module includes:

[0047] The first acquisition unit is used to obtain the maturity of the enterprise's digital transformation, the depth of technology application and the degree of business process optimization, and determine the digital transformation level of the corresponding enterprise based on the set level standards; this can be achieved by collecting relevant reports, data analysis or expert evaluation of the enterprise. Based on this information, the set level standards are used to determine the digital transformation level of the enterprise.

[0048] The second acquisition unit is used to obtain the usage frequency and interaction type of employees at the corresponding level during the use of social media, as well as the employees' psychological satisfaction and stress index. The specific method of obtaining the psychological satisfaction and stress index can be obtained through questionnaires, psychological assessment tools, etc.; for the usage frequency and interaction type of employees during the use of social media, the API interface can be used to obtain relevant data from the social media platform.

[0049] The third acquisition unit is used to obtain the work efficiency, task completion and creativity data of the targeted tasks within the set period of the employee at the corresponding level. In order to obtain the work efficiency, task completion and creativity data of the employee within the set period, it can be integrated with the enterprise's HR system or task management system to automatically extract relevant data.

[0050] Further preferably, Figure 3 As shown, the data analysis module includes:

[0051] A model building unit is used to establish an evaluation model of the impact of digital transformation level, employee social media usage behavior data and psychological state data on employee work effectiveness based on a structural equation model under set construction rules;

[0052] The first calculation unit is used to call the evaluation model to calculate the evaluation results of the impact of digital transformation on employees' social media usage behavior data and psychological state data;

[0053] The second calculation unit is used to call the evaluation model and calculate the evaluation results of the impact of the enterprise's digital transformation on employees in combination with a preset parameter estimation algorithm.

[0054] Further preferably, in the model building unit, building a structural equation model under set building rules includes the following steps:

[0055] Define latent variables: digital transformation level, social media behavior data, employee psychological state data, and employee work effectiveness as latent variables;

[0056] Set observation variables: use frequency and interaction type as observation variables of social media behavior, satisfaction and stress index as observation variables of employee psychological state, work efficiency, task completion quality, and innovation ability as observation variables of work effectiveness;

[0057] Construct path diagrams: determine the causal relationships and correlations between latent variables, including the first path of the impact of digital transformation level on social media behavior data and employee psychological state data, and the second path of the impact of social media behavior and employee psychological state on employee work effectiveness;

[0058] Statistical software (such as AMOS and Mplus) was used to perform model fitting and parameter estimation to obtain the coefficients of the first path and the second path.

[0059] More specifically, this embodiment uses a specific algorithm (such as maximum likelihood estimation) to estimate the coefficients of each path. These coefficients reflect the magnitude of the direct and indirect effects between latent variables and between latent variables and observed variables. Among them:

[0060] The first path coefficient reflects the direct impact of the digital transformation level on employees’ social media behavior data and psychological state data. These coefficients can help companies understand to what extent digital transformation has changed employees’ behavior patterns and psychological states on social media.

[0061] Second path coefficient: reveals the joint impact of social media behavior data and employee psychological state data on employee work effectiveness. These coefficients show how employees' behavior and psychological state on social media work together to affect their work effectiveness, including work efficiency, task completion quality, and innovation ability.

[0062] Through the analysis of these coefficients, enterprises can have a deeper understanding of the impact of digital transformation on employees, evaluate the effects of different digital transformation strategies, and formulate more scientific and reasonable employee management and incentive policies accordingly. At the same time, these coefficients can also be used as one of the important indicators to evaluate the success of digital transformation projects.

[0063] As an example, combined Figure 4 ,In the path diagram, we first identify the digital transformation level, social media behavior data, employee psychological state data, and employee work effectiveness as four core latent variables.,These latent variables are connected through specific paths,,reflecting the causal relationship and correlation between them.

[0064] The first path: Starting from the level of digital transformation, it points to social media behavior data and employee psychological state data. This path indicates that the level of digital transformation directly affects employees' behavior on social media (such as frequency of use, interaction type) and their psychological state (such as satisfaction, stress index).

[0065] The second path: After the social media behavior data and employee psychological state data are combined, they point to employee work effectiveness. This path reveals how social media behavior and psychological state work together to affect employee work effectiveness, including work efficiency, task completion quality, and innovation ability.

[0066] Description of key nodes and flow directions:

[0067] Digital transformation level: As a starting point, it reflects the maturity and depth of the enterprise's digital transformation. The status of this node will directly affect the downstream social media behavior and psychological state.

[0068] Social media behavior data: This node captures the behavioral characteristics of employees on social media, such as usage frequency and interaction type. These behavioral data are one of the important indicators for evaluating the impact of digital transformation on employees.

[0069] Employee psychological state data: This node reflects the psychological feelings of employees during the digital transformation process, including satisfaction and stress index. The quality of psychological state is directly related to the work efficiency and commitment of employees.

[0070] Employee work effectiveness: As a terminal node, it summarizes the influence of social media behavior and psychological state, reflecting the overall work performance of employees in the context of digital transformation.

[0071] Through the above path map example and related explanations, we can clearly see the internal connection and impact path between the digital transformation level, social media behavior data, employee psychological state data and employee work efficiency. This helps companies to more comprehensively evaluate the impact of digital transformation on employees and formulate corresponding strategies to optimize employees' work experience and improve overall work efficiency.

[0072] Further preferably, in the second calculation unit, the preset parameter estimation algorithm is a weighted summation, which is used to calculate a comprehensive evaluation index of the impact of the comprehensive digital transformation on employees according to the output results of the structural equation model, and the formula is as follows:

[0073]

[0074] Among them, A is the comprehensive evaluation index, n is the number of evaluation paths, wi is the weight of the coefficient of the i-th evaluation path, B is the standard evaluation value, and the standardized evaluation value represents the value of the coefficient of the i-th evaluation path after being standardized.

[0075] Further preferably, the preset grade intervals in the evaluation output module include:

[0076] Excellent influence area: Comprehensive evaluation index ≥ x1, indicating that the digital transformation has had a very positive impact on employees, and employees perform excellently in aspects such as social media usage behavior, psychological state, and work efficiency;

[0077] Good influence area: x2 ≤ comprehensive evaluation index < x1, indicating that the digital transformation has had a positive impact on employees, and employees perform well overall, but there is still room for improvement;

[0078] General influence area: x3 ≤ comprehensive evaluation index < x2, indicating that the digital transformation has a general impact on employees, and employees show certain fluctuations or deficiencies in some aspects, and the enterprise needs to pay attention and take measures to improve;

[0079] Lower influence area: x4 ≤ comprehensive evaluation index < x3, indicating that the digital transformation has a relatively limited impact on employees, and employees have many problems in aspects such as social media usage behavior, psychological state, and work efficiency. The enterprise needs to deeply analyze the reasons and formulate corresponding improvement measures;

[0080] Negative influence area: Comprehensive evaluation index < x4, indicating that the digital transformation has had a negative impact on employees, and employees have shown a significant decline in aspects such as social media usage behavior, psychological state, and work efficiency. The enterprise needs to immediately take measures for intervention and adjustment;

[0081] Among them, x1, x2, x3, and x4 are set based on historical data or expert evaluation.

[0082] It can be understood that the evaluation output module determines the grade interval where the impact of the enterprise's digital transformation on employees is located according to the comprehensive evaluation index and outputs the corresponding evaluation results. This module quantitatively evaluates the impact of the enterprise's digital transformation on employees through preset grade intervals (such as excellent, good, general, lower, negative), helping the enterprise comprehensively understand the performance and affected degree of employees during the digital transformation process. The evaluation results of different grade intervals can provide important references for the enterprise to formulate targeted improvement strategies, thereby optimizing the work experience of employees and improving the overall work efficiency.

[0083] 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.

[0084] 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.

[0085] 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.

[0086] 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 system for evaluating the impact of enterprise digital transformation on employees, characterized in that: It includes a data acquisition module, a data analysis module and an evaluation output module; wherein: The data acquisition module is used to obtain the enterprise digital transformation level, the behavior data, psychological state data, and work efficiency data of employees during the use of social media at the corresponding level; wherein the behavior data includes the frequency of use and the type of interaction; The data analysis module is used to determine the evaluation results of the impact of the enterprise's digital transformation on employees based on the enterprise's digital transformation level, employee behavior data, psychological state data, and work efficiency data; The evaluation output module is used to determine the interval corresponding to the evaluation result according to the preset level interval.

2. The evaluation system for the impact of enterprise digital transformation on employees according to claim 1 is characterized by: The data acquisition module comprises: The first acquisition unit is used to obtain the maturity of the enterprise's digital transformation, the depth of technology application and the degree of business process optimization, and determine the digital transformation level of the corresponding enterprise based on the set level standards; The second acquisition unit is used to obtain the frequency of use and interaction type of employees during the use of social media at the corresponding level, as well as the psychological satisfaction and stress index of employees; The third acquisition unit is used to obtain the work efficiency, task completion and creativity data of the targeted tasks within the set period of the employees at the corresponding level.

3. The system for evaluating the impact of enterprise digital transformation on employees according to claim 2, characterized in that: The data analysis module includes: A model building unit is used to establish an evaluation model of the impact of digital transformation level, employee social media usage behavior data and psychological state data on employee work effectiveness based on a structural equation model under set construction rules; A first calculation unit is used to call the evaluation model to calculate the evaluation result of the impact of digital transformation on employees' social media usage behavior data and psychological state data; The second calculation unit is used to call the evaluation model and calculate the evaluation results of the impact of the enterprise's digital transformation on employees in combination with a preset parameter estimation algorithm.

4. The system for evaluating the impact of enterprise digital transformation on employees according to claim 3, characterized in that: In the model building unit, building the structural equation model under the set building rules includes the following steps: Define latent variables: digital transformation level, social media behavior data, employee psychological state data, and employee work effectiveness as latent variables; Set observation variables: use frequency and interaction type as observation variables of social media behavior, satisfaction and stress index as observation variables of employee psychological state, work efficiency, task completion quality, and innovation ability as observation variables of work effectiveness; Construct a path diagram: determine the causal relationship and correlation between the latent variables, including the first path of the impact of digital transformation level on social media behavior data and employee psychological state data, and the second path of the impact of social media behavior and employee psychological state on employee work effectiveness; Statistical software was used to perform model fitting and parameter estimation to obtain the coefficients of the first path and the second path.

5. The system for evaluating the impact of enterprise digital transformation on employees according to claim 4, characterized in that: In the second calculation unit, the preset parameter estimation algorithm is weighted summation, which is used to calculate the comprehensive evaluation index of the impact of digital transformation on employees according to the output results of the structural equation model, and the formula is as follows: Among them, A is the comprehensive evaluation index, n is the number of evaluation paths, wi is the weight of the coefficient of the i-th evaluation path, and B is the standard evaluation value. The standardized evaluation value represents the value of the coefficient of the i-th evaluation path after standardization.

6. The system for evaluating the impact of enterprise digital transformation on employees according to claim 5, characterized in that: The preset level intervals in the said evaluation output module include: Excellent impact area: Comprehensive evaluation index ≥ x1, indicating that digital transformation has had an extremely positive impact on employees, and employees perform excellently in aspects such as social media usage behavior, psychological state, and work efficiency; Good impact area: x2 ≤ Comprehensive evaluation index < x1, indicating that digital transformation has had a positive impact on employees, and employees perform well overall, but there is still room for improvement; Average impact area: x3 ≤ Comprehensive evaluation index < x2, indicating that digital transformation has an average impact on employees, and employees show certain fluctuations or deficiencies in some aspects, and enterprises need to pay attention and take measures to improve; Lower impact area: x4 ≤ Comprehensive evaluation index < x3, indicating that digital transformation has a relatively limited impact on employees, and there are many problems in aspects such as social media usage behavior, psychological state, and work efficiency of employees. Enterprises need to deeply analyze the reasons and formulate corresponding improvement measures; Negative impact area: Comprehensive evaluation index < x4, indicating that digital transformation has had a negative impact on employees, and there is a significant decline in aspects such as social media usage behavior, psychological state, and work efficiency of employees. Enterprises need to immediately take measures for intervention and adjustment; Among them, x1, x2, x3, and x4 are set based on historical data or expert evaluation.

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