Acta Psychologica Sinica ›› 2026, Vol. 58 ›› Issue (9): 1900-1918.doi: 10.3724/SP.J.1041.2026.1900
• Column on the Stress, Resilience, and Health • Previous Articles
PENG Shuai1, ZHANG Jian1, YIN Kui1(
), BU Xing2
Published:2026-09-25
Online:2026-07-29
Contact:
YIN Kui, E-mail: bluesky7198@163.com
PENG Shuai, ZHANG Jian, YIN Kui, BU Xing. (2026). Reverse mentoring relationship: A structural theory and empirical examination in the context of Chinese organizations. Acta Psychologica Sinica, 58(9), 1900-1918.
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URL: https://journal.psych.ac.cn/acps/EN/10.3724/SP.J.1041.2026.1900
| No. | Gender | Age | Education | Position | Industry | Role in Traditional Mentoring Relationship | Presence of Reverse Mentoring Phenomenon | Duration of Reverse Mentoring Relationship | Type of Reverse Mentoring Relationship | Form of Address for the Other Party |
|---|---|---|---|---|---|---|---|---|---|---|
| 1 | Male | 32 | Bachelor | Operations Supervisor | Internet | Mentor | Present | 3 years | Formal relationship | Young mentor |
| 2 | Female | 39 | Bachelor | Administrative Supervisor | Manufacturing | Mentor | Present | 2.5 years | Formal relationship | Protégé |
| 3 | Male | 54 | Bachelor | Senior Teacher | Higher education | Mentor | Present | 4 years | Informal relationship | Young mentor |
| 4 | Male | 49 | Associate | Engineer | Manufacturing | Mentor | Present | 3 years | Formal relationship | Protégé |
| 5 | Male | 51 | Associate | Department Manager | Wholesale | Mentor | Present | 3 years | Formal relationship | Young protégé |
| 6 | Female | 30 | Bachelor | Engineer | Internet | Mentor | Present | 5 years | Informal relationship | Protégé |
| 7 | Male | 39 | Bachelor | Operations | Internet | Mentor | Present | 2 years | Informal relationship | Young mentor |
| 8 | Male | 40 | Master | Manager | Internet | Mentor | Present | 3 years | Formal relationship | Protégé |
| 9 | Female | 27 | Bachelor | Engineer | Internet | Mentor | Present | 0.5 years | Formal relationship | Young mentor |
| 10 | Male | 35 | Master | Project Manager | Finance | Mentor | Present | 3.5 years | Informal relationship | Protégé |
| 11 | Female | 35 | Master | Marketing Planning Director | E-commerce | Mentor | Present | 2 years | Formal relationship | Protégé |
| 12 | Female | 36 | Bachelor | Hotel Manager | Services | Mentor | Present | 4 years | Informal relationship | Young mentor |
| 13 | Male | 42 | Master | Product Manager | Internet | Mentor | Present | 3 years | Informal relationship | Young mentor |
| 14 | Male | 33 | Master | Programmer | Internet | Mentor | Present | 2 years | Informal relationship | Protégé |
| 15 | Male | 28 | Bachelor | Finance Supervisor | Manufacturing | Protégé | Present | 2 years | Formal relationship | Mentor |
| 16 | Female | 29 | Master | Legal Affairs Specialist | Insurance | Protégé | Present | 1.5 years | Formal relationship | Mentor |
| 17 | Female | 28 | Associate | Human Resources | Internet | Protégé | Present | 3 years | Informal relationship | Mentor |
| 18 | Female | 28 | Bachelor | Public Relations | Construction | Protégé | Present | 2 years | Informal relationship | Mentor |
| 19 | Female | 30 | Master | Teacher | Education | Protégé | Present | 3 years | Formal relationship | Mentor |
| 20 | Female | 23 | Bachelor | Teacher | Education | Protégé | Present | 0.5 years | Informal relationship | Mentor |
| 21 | Female | 25 | Bachelor | Social Worker | Community services | Protégé | Present | 1 year | Informal relationship | Mentor |
| 22 | Male | 32 | Bachelor | Engineer | Chemical industry | Protégé | Present | 2 years | Formal relationship | Mentor |
| 23 | Male | 32 | Master | Manager | Internet | Protégé | Present | 2.5 years | Formal relationship | Mentor |
| 24 | Female | 32 | Master | Technical R&D | Manufacturing | Protégé | Present | 2 years | Informal relationship | Mentor |
| 25 | Female | 30 | Bachelor | Office Clerk | Government agency/public institution | Protégé | Present | 1 year | Informal relationship | Mentor |
| 26 | Male | 29 | Bachelor | Manager | Services | Protégé | Present | 2 years | Formal relationship | Mentor |
| 27 | Male | 28 | Master | Engineer | Internet | Protégé | Present | 0.5 years | Formal relationship | Mentor |
| 28 | Female | 29 | Bachelor | Accountant | Manufacturing | Protégé | Present | 3 years | Formal relationship | Mentor |
Table 1 Background Information of Interviewees (n = 28)
| No. | Gender | Age | Education | Position | Industry | Role in Traditional Mentoring Relationship | Presence of Reverse Mentoring Phenomenon | Duration of Reverse Mentoring Relationship | Type of Reverse Mentoring Relationship | Form of Address for the Other Party |
|---|---|---|---|---|---|---|---|---|---|---|
| 1 | Male | 32 | Bachelor | Operations Supervisor | Internet | Mentor | Present | 3 years | Formal relationship | Young mentor |
| 2 | Female | 39 | Bachelor | Administrative Supervisor | Manufacturing | Mentor | Present | 2.5 years | Formal relationship | Protégé |
| 3 | Male | 54 | Bachelor | Senior Teacher | Higher education | Mentor | Present | 4 years | Informal relationship | Young mentor |
| 4 | Male | 49 | Associate | Engineer | Manufacturing | Mentor | Present | 3 years | Formal relationship | Protégé |
| 5 | Male | 51 | Associate | Department Manager | Wholesale | Mentor | Present | 3 years | Formal relationship | Young protégé |
| 6 | Female | 30 | Bachelor | Engineer | Internet | Mentor | Present | 5 years | Informal relationship | Protégé |
| 7 | Male | 39 | Bachelor | Operations | Internet | Mentor | Present | 2 years | Informal relationship | Young mentor |
| 8 | Male | 40 | Master | Manager | Internet | Mentor | Present | 3 years | Formal relationship | Protégé |
| 9 | Female | 27 | Bachelor | Engineer | Internet | Mentor | Present | 0.5 years | Formal relationship | Young mentor |
| 10 | Male | 35 | Master | Project Manager | Finance | Mentor | Present | 3.5 years | Informal relationship | Protégé |
| 11 | Female | 35 | Master | Marketing Planning Director | E-commerce | Mentor | Present | 2 years | Formal relationship | Protégé |
| 12 | Female | 36 | Bachelor | Hotel Manager | Services | Mentor | Present | 4 years | Informal relationship | Young mentor |
| 13 | Male | 42 | Master | Product Manager | Internet | Mentor | Present | 3 years | Informal relationship | Young mentor |
| 14 | Male | 33 | Master | Programmer | Internet | Mentor | Present | 2 years | Informal relationship | Protégé |
| 15 | Male | 28 | Bachelor | Finance Supervisor | Manufacturing | Protégé | Present | 2 years | Formal relationship | Mentor |
| 16 | Female | 29 | Master | Legal Affairs Specialist | Insurance | Protégé | Present | 1.5 years | Formal relationship | Mentor |
| 17 | Female | 28 | Associate | Human Resources | Internet | Protégé | Present | 3 years | Informal relationship | Mentor |
| 18 | Female | 28 | Bachelor | Public Relations | Construction | Protégé | Present | 2 years | Informal relationship | Mentor |
| 19 | Female | 30 | Master | Teacher | Education | Protégé | Present | 3 years | Formal relationship | Mentor |
| 20 | Female | 23 | Bachelor | Teacher | Education | Protégé | Present | 0.5 years | Informal relationship | Mentor |
| 21 | Female | 25 | Bachelor | Social Worker | Community services | Protégé | Present | 1 year | Informal relationship | Mentor |
| 22 | Male | 32 | Bachelor | Engineer | Chemical industry | Protégé | Present | 2 years | Formal relationship | Mentor |
| 23 | Male | 32 | Master | Manager | Internet | Protégé | Present | 2.5 years | Formal relationship | Mentor |
| 24 | Female | 32 | Master | Technical R&D | Manufacturing | Protégé | Present | 2 years | Informal relationship | Mentor |
| 25 | Female | 30 | Bachelor | Office Clerk | Government agency/public institution | Protégé | Present | 1 year | Informal relationship | Mentor |
| 26 | Male | 29 | Bachelor | Manager | Services | Protégé | Present | 2 years | Formal relationship | Mentor |
| 27 | Male | 28 | Master | Engineer | Internet | Protégé | Present | 0.5 years | Formal relationship | Mentor |
| 28 | Female | 29 | Bachelor | Accountant | Manufacturing | Protégé | Present | 3 years | Formal relationship | Mentor |
| Raw Materials | Open Coding |
|---|---|
| Initial Codes | |
| In the use of the Internet, especially AI-based intelligent technologies, younger employees share their experience because they are able to understand the current level of AI technological development and its operating principles more quickly. [1] This helps older employees like us better master the skills needed to use AI technologies and thereby improve overall work efficiency. [2] In addition, regarding some new legal issues and policy changes, new employees also collect information and integrate information channels, thereby providing older employees with certain documentary support for writing work reports. [3] It is worth emphasizing that, compared with me, my young mentor has a more open mindset. [4] Many of his innovative ideas help me simplify and innovate certain work processes, enabling me to better adapt to new changes in the company’s operating procedures and improve work efficiency. [5] At the same time, I believe that this reverse mentoring relationship involves not only the learning of technologies and knowledge, but also spiritual and emotional exchanges between us, [6] as well as influences on our outlooks on life and values. [7] Therefore, the reverse mentoring relationship between my young mentor and me is, overall, a relationship that combines mentorship and friendship. We often share problems we encounter and freely express our confusion, [8] after which we provide answers and comfort to each other. [9] Compared with my young mentor, I still have relatively rich life and work experience. [10] Therefore, he is very willing to listen to some of my suggestions at work. [11]... | [1] Sharing experience in using AI technologies |
| [2] Improving work efficiency through AI technology application | |
| [3] Sharing new work-related policies | |
| [4] Broadening thinking | |
| [5] Guiding adaptation to change | |
| [6] Emotional influence | |
| [7] Collision of values | |
| [8] Freely expressing confusion | |
| [9] Support and encouragement | |
| [10] Recognizing the value of experience | |
| [11] Respecting senior employees’ opinions... |
Table 2 Examples of Initial Coding (Mentors)
| Raw Materials | Open Coding |
|---|---|
| Initial Codes | |
| In the use of the Internet, especially AI-based intelligent technologies, younger employees share their experience because they are able to understand the current level of AI technological development and its operating principles more quickly. [1] This helps older employees like us better master the skills needed to use AI technologies and thereby improve overall work efficiency. [2] In addition, regarding some new legal issues and policy changes, new employees also collect information and integrate information channels, thereby providing older employees with certain documentary support for writing work reports. [3] It is worth emphasizing that, compared with me, my young mentor has a more open mindset. [4] Many of his innovative ideas help me simplify and innovate certain work processes, enabling me to better adapt to new changes in the company’s operating procedures and improve work efficiency. [5] At the same time, I believe that this reverse mentoring relationship involves not only the learning of technologies and knowledge, but also spiritual and emotional exchanges between us, [6] as well as influences on our outlooks on life and values. [7] Therefore, the reverse mentoring relationship between my young mentor and me is, overall, a relationship that combines mentorship and friendship. We often share problems we encounter and freely express our confusion, [8] after which we provide answers and comfort to each other. [9] Compared with my young mentor, I still have relatively rich life and work experience. [10] Therefore, he is very willing to listen to some of my suggestions at work. [11]... | [1] Sharing experience in using AI technologies |
| [2] Improving work efficiency through AI technology application | |
| [3] Sharing new work-related policies | |
| [4] Broadening thinking | |
| [5] Guiding adaptation to change | |
| [6] Emotional influence | |
| [7] Collision of values | |
| [8] Freely expressing confusion | |
| [9] Support and encouragement | |
| [10] Recognizing the value of experience | |
| [11] Respecting senior employees’ opinions... |
| Raw Materials | Open Coding |
|---|---|
| Initial Codes | |
| Recent university graduates like me can quickly accept and skillfully use the latest science and technologies. From time to time, we explain the use of new technologies to older employees who have worked in the company for many years, helping them better adapt to their current work. [397] Older employees mainly rely on their own experience, whereas young colleagues who have just entered the workplace can fully apply their own skills, transmit new concepts and knowledge to older employees, and better optimize work plans. [398] In my department, what impressed me most was a newcomer who guided an employee who had actually worked in the company for six or seven years. When the older employee was solving a problem, the newcomer provided some new methods and used different ways of thinking to address the problem. [399] In fact, my mentor is very experienced, and almost no difficulty can defeat him. [400] However, the times are changing very rapidly, and mentors may encounter problems that are difficult to solve. I am very willing to use current advanced technologies and knowledge [401] to come up with ideas that others may not think of, but that are novel and innovative, and to provide him with new ways of thinking. [402] Even if he may be standing still, the injection of fresh blood [403] can inspire older employees to adopt new ideas and methods to solve problems. [404] At the same time, in my daily work, I am very willing to receive suggestions from older employees, [405] because they all have rich work experience. [406]... | [397] Guidance on new technologies |
| [398] Sharing new industry knowledge and ideas | |
| [399] Flexible thinking guiding multi-perspective analysis and problem solving | |
| [400] Recognizing older employees’ job competence | |
| [401] Providing technical guidance for problem solving and work tasks | |
| [402] Using divergent thinking to broaden perspectives | |
| [403] Inspiring enthusiasm | |
| [404] Solving problems with innovative ideas | |
| [405] Recognizing older employees’ suggestions | |
| [406] Acknowledging the value of older employees’ experience... |
Table 3 Examples of Initial Coding (Protégés)
| Raw Materials | Open Coding |
|---|---|
| Initial Codes | |
| Recent university graduates like me can quickly accept and skillfully use the latest science and technologies. From time to time, we explain the use of new technologies to older employees who have worked in the company for many years, helping them better adapt to their current work. [397] Older employees mainly rely on their own experience, whereas young colleagues who have just entered the workplace can fully apply their own skills, transmit new concepts and knowledge to older employees, and better optimize work plans. [398] In my department, what impressed me most was a newcomer who guided an employee who had actually worked in the company for six or seven years. When the older employee was solving a problem, the newcomer provided some new methods and used different ways of thinking to address the problem. [399] In fact, my mentor is very experienced, and almost no difficulty can defeat him. [400] However, the times are changing very rapidly, and mentors may encounter problems that are difficult to solve. I am very willing to use current advanced technologies and knowledge [401] to come up with ideas that others may not think of, but that are novel and innovative, and to provide him with new ways of thinking. [402] Even if he may be standing still, the injection of fresh blood [403] can inspire older employees to adopt new ideas and methods to solve problems. [404] At the same time, in my daily work, I am very willing to receive suggestions from older employees, [405] because they all have rich work experience. [406]... | [397] Guidance on new technologies |
| [398] Sharing new industry knowledge and ideas | |
| [399] Flexible thinking guiding multi-perspective analysis and problem solving | |
| [400] Recognizing older employees’ job competence | |
| [401] Providing technical guidance for problem solving and work tasks | |
| [402] Using divergent thinking to broaden perspectives | |
| [403] Inspiring enthusiasm | |
| [404] Solving problems with innovative ideas | |
| [405] Recognizing older employees’ suggestions | |
| [406] Acknowledging the value of older employees’ experience... |
| Main Category | Subcategory |
|---|---|
| Technical Support | Transmitting the latest AI applications and operating skills |
| Sharing emerging technological developments and innovative ideas within the industry | |
| Providing methods for effectively improving mastery of AI | |
| Discussing how to apply the latest technologies in actual work | |
| Solving specific problems encountered when using new technologies | |
| Using AI tools and knowledge to improve work efficiency | |
| Mental Inspiration | Guiding adaptation to and integration into different work cultures and values |
| Providing support and encouragement when challenges at work lead to confusion | |
| Guiding multi-perspective analysis and problem solving through flexible thinking | |
| Inspiring work enthusiasm and motivation for progress | |
| Actively understanding others’ ideas and encouraging the expression of genuine views | |
| Encouraging independent thinking and problem solving based on one’s own judgment | |
| Competence Recognition | Building confidence in growth and progress at work |
| Affirming professional competence and judgment at work | |
| Enabling free expression of ideas at work |
Table 4 Results of Axial and Selective Coding
| Main Category | Subcategory |
|---|---|
| Technical Support | Transmitting the latest AI applications and operating skills |
| Sharing emerging technological developments and innovative ideas within the industry | |
| Providing methods for effectively improving mastery of AI | |
| Discussing how to apply the latest technologies in actual work | |
| Solving specific problems encountered when using new technologies | |
| Using AI tools and knowledge to improve work efficiency | |
| Mental Inspiration | Guiding adaptation to and integration into different work cultures and values |
| Providing support and encouragement when challenges at work lead to confusion | |
| Guiding multi-perspective analysis and problem solving through flexible thinking | |
| Inspiring work enthusiasm and motivation for progress | |
| Actively understanding others’ ideas and encouraging the expression of genuine views | |
| Encouraging independent thinking and problem solving based on one’s own judgment | |
| Competence Recognition | Building confidence in growth and progress at work |
| Affirming professional competence and judgment at work | |
| Enabling free expression of ideas at work |
| Core Dimension | Definition |
|---|---|
| Technical Support | Emphasizes younger mentors’ provision of relevant knowledge and application guidance concerning emerging technologies to older protégés, with the aim of helping older protégés understand and master the latest technological developments. |
| Mental Inspiration | Emphasizes younger mentors’ adjustment of older protégés’ mindsets and guidance of their thinking and cognition, with the aim of helping older protégés transform traditional or fixed mental models and develop a positive mindset and innovative capability. |
| Competence Recognition | Refers to younger mentors’ positive affirmation of older protégés’ work competence and recognition of their existing authority, thereby enhancing older protégés’ confidence, sense of dignity, and willingness to express themselves, and enabling them to participate more fully in interactions and realize their potential. |
Table 5 Definitions of Core Dimensions
| Core Dimension | Definition |
|---|---|
| Technical Support | Emphasizes younger mentors’ provision of relevant knowledge and application guidance concerning emerging technologies to older protégés, with the aim of helping older protégés understand and master the latest technological developments. |
| Mental Inspiration | Emphasizes younger mentors’ adjustment of older protégés’ mindsets and guidance of their thinking and cognition, with the aim of helping older protégés transform traditional or fixed mental models and develop a positive mindset and innovative capability. |
| Competence Recognition | Refers to younger mentors’ positive affirmation of older protégés’ work competence and recognition of their existing authority, thereby enhancing older protégés’ confidence, sense of dignity, and willingness to express themselves, and enabling them to participate more fully in interactions and realize their potential. |
| Item | Technical Support | Mental Inspiration | Competence Recognition | α if Item Deleted |
|---|---|---|---|---|
| At work, younger mentors actively transmit the latest AI applications and operating skills to older protégés. | 0.84 | 0.07 | 0.26 | 0.88 |
| At work, younger mentors regularly share emerging technological developments and innovative ideas within the industry with older protégés. | 0.68 | 0.40 | ?0.01 | 0.88 |
| At work, younger mentors provide older protégés with methods that can effectively improve their mastery of AI. | 0.86 | 0.07 | 0.23 | 0.88 |
| At work, younger mentors discuss with older protégés how to apply the latest technologies in actual work | 0.76 | 0.08 | 0.21 | 0.88 |
| At work, younger mentors guide older protégés in solving specific problems encountered when using new technologies. | 0.81 | 0.25 | ?0.05 | 0.88 |
| At work, younger mentors use AI tools and knowledge to improve older protégés’ work efficiency. | 0.85 | 0.09 | 0.23 | 0.88 |
| At work, younger mentors guide older protégés to adapt to and integrate into different work cultures and values. | 0.31 | 0.66 | ?0.12 | 0.89 |
| At work, when older protégés feel confused after encountering challenges, younger mentors provide them with support and encouragement. | ?0.04 | 0.56 | 0.40 | 0.89 |
| At work, younger mentors’ flexible thinking guides older protégés to analyze and solve problems from multiple perspectives. | 0.45 | 0.58 | 0.07 | 0.89 |
| At work, younger mentors’ work enthusiasm inspires older protégés to keep making progress. | 0.03 | 0.74 | 0.20 | 0.89 |
| At work, younger mentors actively understand older protégés’ ideas and encourage them to express their genuine views. | 0.20 | 0.60 | 0.41 | 0.89 |
| At work, younger mentors encourage older protégés to think independently and solve problems based on their own judgment. | 0.13 | 0.65 | 0.22 | 0.89 |
| At work, younger mentors have confidence in older protégés’ ability to achieve growth and progress. | 0.04 | 0.47 | 0.59 | 0.89 |
| At work, younger mentors believe in older protégés’ professional competence and judgment. | 0.29 | 0.05 | 0.75 | 0.89 |
| At work, older protégés can freely express their own ideas to their mentors. | 0.30 | 0.34 | 0.58 | 0.89 |
| Rotated eigenvalue | 4.36 | 2.99 | 1.90 | |
| Variance explained (%) (total = 61.68%) | 29.09 | 19.94 | 12.65 |
Table 6 Exploratory Factor Analysis Results (n = 200)
| Item | Technical Support | Mental Inspiration | Competence Recognition | α if Item Deleted |
|---|---|---|---|---|
| At work, younger mentors actively transmit the latest AI applications and operating skills to older protégés. | 0.84 | 0.07 | 0.26 | 0.88 |
| At work, younger mentors regularly share emerging technological developments and innovative ideas within the industry with older protégés. | 0.68 | 0.40 | ?0.01 | 0.88 |
| At work, younger mentors provide older protégés with methods that can effectively improve their mastery of AI. | 0.86 | 0.07 | 0.23 | 0.88 |
| At work, younger mentors discuss with older protégés how to apply the latest technologies in actual work | 0.76 | 0.08 | 0.21 | 0.88 |
| At work, younger mentors guide older protégés in solving specific problems encountered when using new technologies. | 0.81 | 0.25 | ?0.05 | 0.88 |
| At work, younger mentors use AI tools and knowledge to improve older protégés’ work efficiency. | 0.85 | 0.09 | 0.23 | 0.88 |
| At work, younger mentors guide older protégés to adapt to and integrate into different work cultures and values. | 0.31 | 0.66 | ?0.12 | 0.89 |
| At work, when older protégés feel confused after encountering challenges, younger mentors provide them with support and encouragement. | ?0.04 | 0.56 | 0.40 | 0.89 |
| At work, younger mentors’ flexible thinking guides older protégés to analyze and solve problems from multiple perspectives. | 0.45 | 0.58 | 0.07 | 0.89 |
| At work, younger mentors’ work enthusiasm inspires older protégés to keep making progress. | 0.03 | 0.74 | 0.20 | 0.89 |
| At work, younger mentors actively understand older protégés’ ideas and encourage them to express their genuine views. | 0.20 | 0.60 | 0.41 | 0.89 |
| At work, younger mentors encourage older protégés to think independently and solve problems based on their own judgment. | 0.13 | 0.65 | 0.22 | 0.89 |
| At work, younger mentors have confidence in older protégés’ ability to achieve growth and progress. | 0.04 | 0.47 | 0.59 | 0.89 |
| At work, younger mentors believe in older protégés’ professional competence and judgment. | 0.29 | 0.05 | 0.75 | 0.89 |
| At work, older protégés can freely express their own ideas to their mentors. | 0.30 | 0.34 | 0.58 | 0.89 |
| Rotated eigenvalue | 4.36 | 2.99 | 1.90 | |
| Variance explained (%) (total = 61.68%) | 29.09 | 19.94 | 12.65 |
| Model | χ2 | df | χ2/df | IFI | CFI | TLI | RMSEA | SRMR |
|---|---|---|---|---|---|---|---|---|
| Three-factor model | 210.69*** | 85 | 2.48 | 0.95 | 0.95 | 0.94 | 0.055 | 0.047 |
| Two-factor model | 320.94*** | 89 | 3.61 | 0.90 | 0.89 | 0.87 | 0.081 | 0.066 |
| One-factor model | 754.07*** | 90 | 8.38 | 0.75 | 0.75 | 0.71 | 0.123 | 0.106 |
| Second-order three-factor model | 232.14*** | 86 | 2.70 | 0.95 | 0.95 | 0.93 | 0.059 | 0.047 |
Table 7 Confirmatory Factor Analysis Results (n = 492)
| Model | χ2 | df | χ2/df | IFI | CFI | TLI | RMSEA | SRMR |
|---|---|---|---|---|---|---|---|---|
| Three-factor model | 210.69*** | 85 | 2.48 | 0.95 | 0.95 | 0.94 | 0.055 | 0.047 |
| Two-factor model | 320.94*** | 89 | 3.61 | 0.90 | 0.89 | 0.87 | 0.081 | 0.066 |
| One-factor model | 754.07*** | 90 | 8.38 | 0.75 | 0.75 | 0.71 | 0.123 | 0.106 |
| Second-order three-factor model | 232.14*** | 86 | 2.70 | 0.95 | 0.95 | 0.93 | 0.059 | 0.047 |
| Model | χ2 | df | CFI | RMSEA [90%CI] | ΔCFI | ΔRMSEA |
|---|---|---|---|---|---|---|
| Gender | ||||||
| M0 | 545.65 | 322 | 0.92 | 0.078 [0.07, 0.09] | ||
| M1 | 559.93 | 338 | 0.92 | 0.076 [0.07, 0.09] | 0.001 | 0.002 |
| M2 | 578.20 | 354 | 0.92 | 0.075 [0.06, 0.09] | 0.001 | 0.001 |
| Role | ||||||
| M0 | 536.32 | 327 | 0.92 | 0.075 [0.06, 0.09] | ||
| M1 | 552.75 | 343 | 0.92 | 0.073 [0.06, 0.09] | 0.000 | 0.002 |
| M2 | 573.66 | 359 | 0.92 | 0.073 [0.06, 0.08] | 0.003 | 0.000 |
| Relationship type | ||||||
| M0 | 553.57 | 327 | 0.91 | 0.078 [0.07, 0.09] | ||
| M1 | 576.07 | 343 | 0.91 | 0.077 [0.07, 0.09] | 0.003 | 0.001 |
| M2 | 592.54 | 359 | 0.91 | 0.076 [0.07, 0.09] | 0.000 | 0.001 |
| Age | ||||||
| M0 | 571.99 | 311 | 0.91 | 0.076 [0.08, 0.10] | ||
| M1 | 589.53 | 327 | 0.91 | 0.079 [0.07, 0.10] | 0.001 | 0.002 |
| M2 | 606.19 | 343 | 0.91 | 0.080 [0.07, 0.09] | 0.000 | 0.001 |
| Education | ||||||
| M0 | 550.04 | 324 | 0.92 | 0.078 [0.07, 0.09] | ||
| M1 | 580.97 | 340 | 0.91 | 0.079 [0.07, 0.09] | 0.006 | 0.001 |
| M2 | 597.46 | 356 | 0.91 | 0.077 [0.07, 0.09] | 0.000 | 0.001 |
| Position level | ||||||
| M0 | 540.80 | 323 | 0.92 | 0.077 [0.07, 0.09] | ||
| M1 | 575.98 | 339 | 0.91 | 0.078 [0.07, 0.09] | 0.007 | 0.001 |
| M2 | 598.40 | 335 | 0.91 | 0.078 [0.07, 0.09] | 0.003 | 0.000 |
| Organizational ownership | ||||||
| M0 | 541.80 | 325 | 0.92 | 0.078 [0.06, 0.08] | ||
| M1 | 552.75 | 343 | 0.91 | 0.078 [0.07, 0.09] | 0.001 | 0.000 |
| M2 | 576.07 | 343 | 0.91 | 0.077 [0.07, 0.09] | 0.003 | 0.001 |
Table 8 Measurement Invariance Test Results for the Measurement Model (n = 492)
| Model | χ2 | df | CFI | RMSEA [90%CI] | ΔCFI | ΔRMSEA |
|---|---|---|---|---|---|---|
| Gender | ||||||
| M0 | 545.65 | 322 | 0.92 | 0.078 [0.07, 0.09] | ||
| M1 | 559.93 | 338 | 0.92 | 0.076 [0.07, 0.09] | 0.001 | 0.002 |
| M2 | 578.20 | 354 | 0.92 | 0.075 [0.06, 0.09] | 0.001 | 0.001 |
| Role | ||||||
| M0 | 536.32 | 327 | 0.92 | 0.075 [0.06, 0.09] | ||
| M1 | 552.75 | 343 | 0.92 | 0.073 [0.06, 0.09] | 0.000 | 0.002 |
| M2 | 573.66 | 359 | 0.92 | 0.073 [0.06, 0.08] | 0.003 | 0.000 |
| Relationship type | ||||||
| M0 | 553.57 | 327 | 0.91 | 0.078 [0.07, 0.09] | ||
| M1 | 576.07 | 343 | 0.91 | 0.077 [0.07, 0.09] | 0.003 | 0.001 |
| M2 | 592.54 | 359 | 0.91 | 0.076 [0.07, 0.09] | 0.000 | 0.001 |
| Age | ||||||
| M0 | 571.99 | 311 | 0.91 | 0.076 [0.08, 0.10] | ||
| M1 | 589.53 | 327 | 0.91 | 0.079 [0.07, 0.10] | 0.001 | 0.002 |
| M2 | 606.19 | 343 | 0.91 | 0.080 [0.07, 0.09] | 0.000 | 0.001 |
| Education | ||||||
| M0 | 550.04 | 324 | 0.92 | 0.078 [0.07, 0.09] | ||
| M1 | 580.97 | 340 | 0.91 | 0.079 [0.07, 0.09] | 0.006 | 0.001 |
| M2 | 597.46 | 356 | 0.91 | 0.077 [0.07, 0.09] | 0.000 | 0.001 |
| Position level | ||||||
| M0 | 540.80 | 323 | 0.92 | 0.077 [0.07, 0.09] | ||
| M1 | 575.98 | 339 | 0.91 | 0.078 [0.07, 0.09] | 0.007 | 0.001 |
| M2 | 598.40 | 335 | 0.91 | 0.078 [0.07, 0.09] | 0.003 | 0.000 |
| Organizational ownership | ||||||
| M0 | 541.80 | 325 | 0.92 | 0.078 [0.06, 0.08] | ||
| M1 | 552.75 | 343 | 0.91 | 0.078 [0.07, 0.09] | 0.001 | 0.000 |
| M2 | 576.07 | 343 | 0.91 | 0.077 [0.07, 0.09] | 0.003 | 0.001 |
| Variable | 1 | 2 | 3 | 4 | 5 |
|---|---|---|---|---|---|
| Reverse mentoring relationship (Chen, | 0.70 | ||||
| Reverse mentoring relationship in the Chinese context | 0.44*** | 0.91 | |||
| Technical support | 0.21** | 0.83** | 0.80 | ||
| Mental inspiration | 0.48** | 0.87** | 0.55** | 0.73 | |
| Competence recognition | 0.44** | 0.67** | 0.28** | 0.64** | 0.65 |
Table 9 Pearson Correlations and Square Roots of AVE (n = 249)
| Variable | 1 | 2 | 3 | 4 | 5 |
|---|---|---|---|---|---|
| Reverse mentoring relationship (Chen, | 0.70 | ||||
| Reverse mentoring relationship in the Chinese context | 0.44*** | 0.91 | |||
| Technical support | 0.21** | 0.83** | 0.80 | ||
| Mental inspiration | 0.48** | 0.87** | 0.55** | 0.73 | |
| Competence recognition | 0.44** | 0.67** | 0.28** | 0.64** | 0.65 |
| Respondent | Model | χ2 | df | χ2/df | CFI | TLI | RMSEA | SRMR |
|---|---|---|---|---|---|---|---|---|
| Older protégés (Sample 1) | Three-factor model | 732.91 | 402 | 1.82 | 0.95 | 0.94 | 0.059 | 0.043 |
| Two-factor model | 1484.78 | 404 | 3.68 | 0.83 | 0.81 | 0.106 | 0.129 | |
| One-factor model | 3206.28 | 405 | 7.92 | 0.55 | 0.52 | 0.170 | 0.173 | |
| Younger mentors (Sample 2) | Three-factor model | 280.03 | 146 | 1.92 | 0.98 | 0.98 | 0.050 | 0.035 |
| Two-factor model | 1181.75 | 149 | 7.93 | 0.84 | 0.82 | 0.137 | 0.100 | |
| One-factor model | 3462.12 | 152 | 22.78 | 0.49 | 0.43 | 0.242 | 0.221 |
Table 10 Confirmatory Factor Analysis Results
| Respondent | Model | χ2 | df | χ2/df | CFI | TLI | RMSEA | SRMR |
|---|---|---|---|---|---|---|---|---|
| Older protégés (Sample 1) | Three-factor model | 732.91 | 402 | 1.82 | 0.95 | 0.94 | 0.059 | 0.043 |
| Two-factor model | 1484.78 | 404 | 3.68 | 0.83 | 0.81 | 0.106 | 0.129 | |
| One-factor model | 3206.28 | 405 | 7.92 | 0.55 | 0.52 | 0.170 | 0.173 | |
| Younger mentors (Sample 2) | Three-factor model | 280.03 | 146 | 1.92 | 0.98 | 0.98 | 0.050 | 0.035 |
| Two-factor model | 1181.75 | 149 | 7.93 | 0.84 | 0.82 | 0.137 | 0.100 | |
| One-factor model | 3462.12 | 152 | 22.78 | 0.49 | 0.43 | 0.242 | 0.221 |
| Variable | M | SD | 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 |
|---|---|---|---|---|---|---|---|---|---|---|---|
| Gender | 3.68 | 1.23 | 1 | ||||||||
| Age | 49.89 | 1.25 | 0.62** | 1 | |||||||
| Education | 3.62 | 1.20 | 0.46** | 0.60** | 1 | ||||||
| Organizational ownership | 3.80 | 1.14 | 0.33** | 0.34** | 0.33** | 1 | |||||
| Position level | 3.81 | 1.13 | 0.33** | 0.36** | 0.30** | 0.58** | 1 | ||||
| Relationship type | 3.78 | 1.16 | 0.38** | 0.31** | 0.27** | 0.61** | 0.65** | 1 | |||
| Reverse mentoring relationship | 3.63 | 0.94 | 0.25** | 0.23** | 0.20** | 0.32** | 0.36** | 0.33** | 1 | ||
| Self-efficacy | 3.78 | 0.89 | 0.40** | 0.45** | 0.39** | 0.43** | 0.46** | 0.44** | 0.45** | 1 | |
| Task performance | 3.60 | 0.81 | 0.27** | 0.21** | 0.28** | 0.16* | 0.18** | 0.12 | 0.38** | 0.17** | 1 |
Table 11 Descriptive Statistics and Correlation Analysis Results for Sample 1 (n = 240)
| Variable | M | SD | 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 |
|---|---|---|---|---|---|---|---|---|---|---|---|
| Gender | 3.68 | 1.23 | 1 | ||||||||
| Age | 49.89 | 1.25 | 0.62** | 1 | |||||||
| Education | 3.62 | 1.20 | 0.46** | 0.60** | 1 | ||||||
| Organizational ownership | 3.80 | 1.14 | 0.33** | 0.34** | 0.33** | 1 | |||||
| Position level | 3.81 | 1.13 | 0.33** | 0.36** | 0.30** | 0.58** | 1 | ||||
| Relationship type | 3.78 | 1.16 | 0.38** | 0.31** | 0.27** | 0.61** | 0.65** | 1 | |||
| Reverse mentoring relationship | 3.63 | 0.94 | 0.25** | 0.23** | 0.20** | 0.32** | 0.36** | 0.33** | 1 | ||
| Self-efficacy | 3.78 | 0.89 | 0.40** | 0.45** | 0.39** | 0.43** | 0.46** | 0.44** | 0.45** | 1 | |
| Task performance | 3.60 | 0.81 | 0.27** | 0.21** | 0.28** | 0.16* | 0.18** | 0.12 | 0.38** | 0.17** | 1 |
| Variable | M | SD | 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 |
|---|---|---|---|---|---|---|---|---|---|---|---|
| Gender | 1.37 | 0.48 | 1 | ||||||||
| Age | 28.32 | 7.48 | 0.08 | 1 | |||||||
| Education | 3.13 | 0.57 | ?0.05 | ?0.06 | 1 | ||||||
| Organizational ownership | 3.36 | 1.14 | 0.07 | 0.01 | ?0.05 | 1 | |||||
| Position level | 1.49 | 0.73 | ?0.10 | 0.34** | 0.32** | 0.11 | 1 | ||||
| Relationship type | 1.42 | 0.49 | 0.13* | ?0.19** | ?0.05 | ?0.11 | ?0.21** | 1 | |||
| Reverse mentoring relationship | 3.66 | 0.84 | 0.07 | 0.06 | ?0.10 | 0.09 | ?0.11 | 0.05 | 1 | ||
| Perceived insider status | 3.68 | 0.95 | 0.06 | ?0.04 | ?0.10 | ?0.07 | ?0.12* | 0.09 | 0.18** | 1 | |
| Turnover intention | 2.44 | 0.96 | ?0.05 | ?0.02 | 0.10 | ?0.05 | 0.07 | ?0.06 | ?0.50** | ?0.40** | 1 |
Table 12 Descriptive Statistics and Correlation Analysis Results for Sample 2 (n = 292)
| Variable | M | SD | 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 |
|---|---|---|---|---|---|---|---|---|---|---|---|
| Gender | 1.37 | 0.48 | 1 | ||||||||
| Age | 28.32 | 7.48 | 0.08 | 1 | |||||||
| Education | 3.13 | 0.57 | ?0.05 | ?0.06 | 1 | ||||||
| Organizational ownership | 3.36 | 1.14 | 0.07 | 0.01 | ?0.05 | 1 | |||||
| Position level | 1.49 | 0.73 | ?0.10 | 0.34** | 0.32** | 0.11 | 1 | ||||
| Relationship type | 1.42 | 0.49 | 0.13* | ?0.19** | ?0.05 | ?0.11 | ?0.21** | 1 | |||
| Reverse mentoring relationship | 3.66 | 0.84 | 0.07 | 0.06 | ?0.10 | 0.09 | ?0.11 | 0.05 | 1 | ||
| Perceived insider status | 3.68 | 0.95 | 0.06 | ?0.04 | ?0.10 | ?0.07 | ?0.12* | 0.09 | 0.18** | 1 | |
| Turnover intention | 2.44 | 0.96 | ?0.05 | ?0.02 | 0.10 | ?0.05 | 0.07 | ?0.06 | ?0.50** | ?0.40** | 1 |
| Path | Estimate | S.E. | Est./S.E. | p | 95% Confidence Interval |
|---|---|---|---|---|---|
| Reverse mentoring relationship → older protégés’ task performance | 0.33 | 0.06 | 5.67 | 0.000 | [0.22, 0.44] |
| Reverse mentoring relationship → older protégés’ self-efficacy | 0.43 | 0.06 | 7.83 | 0.000 | [0.32, 0.54] |
| older protégés’ self-efficacy → older protégés’ task performance | 0.17 | 0.05 | 6.67 | 0.006 | [0.13, 0.20] |
Table 13 Hypothesis Testing Results for Sample 1
| Path | Estimate | S.E. | Est./S.E. | p | 95% Confidence Interval |
|---|---|---|---|---|---|
| Reverse mentoring relationship → older protégés’ task performance | 0.33 | 0.06 | 5.67 | 0.000 | [0.22, 0.44] |
| Reverse mentoring relationship → older protégés’ self-efficacy | 0.43 | 0.06 | 7.83 | 0.000 | [0.32, 0.54] |
| older protégés’ self-efficacy → older protégés’ task performance | 0.17 | 0.05 | 6.67 | 0.006 | [0.13, 0.20] |
| Path | Estimate | S.E. | Est./S.E. | p | 95% Confidence Interval |
|---|---|---|---|---|---|
| Reverse mentoring relationship → younger mentors’ turnover intention | ?0.51 | 0.06 | ?9.20 | 0.000 | [?0.61, ?0.40] |
| Reverse mentoring relationship → younger mentors’ perceived insider status | 0.20 | 0.07 | 3.16 | 0.002 | [0.08, 0.33] |
| younger mentors’ perceived insider status → younger mentors’ turnover intention | ?0.33 | 0.05 | ?6.67 | 0.000 | [?0.42, ?0.23] |
Table 14 Hypothesis Testing Results for Sample 2
| Path | Estimate | S.E. | Est./S.E. | p | 95% Confidence Interval |
|---|---|---|---|---|---|
| Reverse mentoring relationship → younger mentors’ turnover intention | ?0.51 | 0.06 | ?9.20 | 0.000 | [?0.61, ?0.40] |
| Reverse mentoring relationship → younger mentors’ perceived insider status | 0.20 | 0.07 | 3.16 | 0.002 | [0.08, 0.33] |
| younger mentors’ perceived insider status → younger mentors’ turnover intention | ?0.33 | 0.05 | ?6.67 | 0.000 | [?0.42, ?0.23] |
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