Knowledge Transfer Characteristics of Construction Workers Based on Social Network Analysis
Abstract
:1. Introduction
- To analyze the group characteristics of construction workers and their influence on knowledge transfer;
- To explore the knowledge transfer characteristics and paths of construction workers and the influence of workers with different roles on knowledge transfer networks;
- To provide a useful reference to help decision-makers to formulate effective policies and measures to improve the knowledge and skills of construction workers.
2. Group Characteristics and Types of Knowledge
2.1. Regional Concentration
2.2. Group Closure
2.3. Frequent Migration
2.4. Types of Knowledge
3. Methodology
- The language was easy for the construction workers to understand. Limited by their educational levels and knowledge, construction workers tend to refuse to answer questions that they do not understand or answer them indiscriminately, which results in the invalidity of questionnaires. Therefore, using easy-to-understand language for questionnaires could enhance the validity of the results.
- The questionnaire was simple and the number of items was small. Most construction workers work long hours and highly labor-intensive jobs, and therefore, they are unwilling to spend too much time on other things. If there were too many items on the questionnaire, it would have occupied the working and rest time of the workers and they would have easily become tired of completing them, thus affecting the authenticity of the results of the survey.
4. Data Research and Analysis
4.1. Data Survey and Basic Information
4.2. Verification of the Effects of Social Networks on Knowledge Transfer
5. Social Network Analysis of Knowledge Transfer among Workers
5.1. Building the Social Network Model of Knowledge Transfer
5.2. Network Density Analysis
5.3. Network Centrality Analysis
5.4. Structural Hole Analysis
6. Results and Discussion
- Team leaders are the core of the whole team, are closely connected with other members and have a great influence on others in terms of knowledge transfer. At the same time, team leaders are also the most important hubs of knowledge transfer within whole networks. Team leaders are authoritative in knowledge transfer and the transfer paths are short and the transfer speed is fast; therefore, their knowledge and skills are easily spread to whole networks. This means that they play great guiding roles in the promotion of the knowledge and skills of the other members. On the contrary, if the knowledge or skills of team leaders is defective, this has significant negative effects on the whole team. Therefore, the government and enterprises should give full support to the core role of team leaders and provide stable working environments, development spaces and sustainable incomes for them. At the same time, through training from team leaders and the core members of teams, the levels of knowledge and skills of the workers could be improved. This method could not only reduce the costs of training, but also the mobility of workers.
- In this study, C1, C3, C18 and C9 were all structural holes, but their connections were quite different. From Figure 2, it can clearly be seen that the network composed of L1, C1, C2, C3, C16, C17 and C18 was denser than the network composed of the other members and that the connections between these seven members were more frequent, indicating that there were smaller knowledge transfer groups within the team. Due to the large gaps in knowledge and skills among the workers, one-way flows of knowledge occurred easily within the group. Common workers form internal, small groups according to the intimacy of their relationships, which creates barriers to knowledge transfer within networks. Thus, it is necessary to pay attention to the training and guidance of workers who are on the edges of networks and cannot acquire new knowledge and skills in a timely manner. According to the above, managers should pair workers with active personalities who are good at communication and workers who are on the edge of the network so that the former can improve the knowledge and skills of the latter. At the same time, technical instructors should strengthen the help and supervision provided for workers at network edges and find and solve their problems with work processes in a timely manner to ensure their work quality and efficiency.
- In this study, the technical instructors T1 and T2 did not fully participate to in the knowledge transfer within the network. From the results of our network analysis, the OC and IC values of T1 and T2 were low, indicating that they had little knowledge-based interaction with the other workers. The BC values of T1 and T2 were very small, especially the BC value of T1 at 0.0, which meant that T1 and T2 could hardly play the role of a “bridge” in knowledge transfer. Compared to education and knowledge levels, workers pay more attention to work experience and intimacy; therefore, if highly educated technical instructors lack practical experience and cannot form close relationships with the workers, then they cannot be the “bridge” role in the knowledge transfer among the team, and ultimately, only make few contributions to improvements in the team’s knowledge and skills. Technical instructors can only play guiding roles for a few people, but not for the promotion of the overall knowledge and skills of the network. Therefore, when selecting technical instructors, enterprises should not only pay attention to educational background and skill level, but also to close relationships with other workers.
- Most previous studies have focused on the transfer of safety knowledge among construction workers. This research focused on the transfer characteristics and transfer rules of comprehensive, professional knowledge among construction workers and provided targeted suggestions to improve knowledge transfer efficiency and the professional skills of construction workers in order to lessen the learning and efficiency weaknesses of traditional construction workers in the professional and intelligent development of the construction industry.
- Due to the limitations of environments and methods, many research studies on the learning abilities and learning efficiency of construction workers have used volunteers to replace construction workers as experimental objects [45,46,47,48]. This study used a construction worker group to conduct research and obtained objective data that could accurately reflect the characteristics of knowledge transfer among construction workers, thereby providing a scientific basis for formulating strategies to improve the knowledge and skills of construction workers.
- From this study, we found that traditional construction workers were still most affected by blood and geographical relationships in terms of knowledge transfer and that different roles within the worker group had different characteristics in the process of knowledge transfer. Therefore, adopting unified training or technical guidance is not an effective way to improve the professional skills of construction workers.
7. Conclusions
- Compared to other groups, the willingness of construction workers to transfer knowledge is very low. Most individuals do not learn from each other well and the exchange of knowledge and skills is not sufficient. They only focus on the current work and lack the motivation to improve their skills and work efficiency.
- Team leaders, based on blood and geographical relationships, are the cores of teams and have greater impacts on knowledge transfer among construction workers than technical instructors without blood and geographical relationships, even if they have a higher level of education.
- It is very difficult for expatriate technical instructors with high education levels but no blood or geographical relationships with workers to establish knowledge authority among the workers.
- Construction worker groups lack internal knowledge transmission networks with close contacts and frequent interactions. There are big gaps between the workers at the center of a networks and those at the edges in terms of knowledge transfer channels and knowledge transfer efficiency, which means that one-way flows of knowledge occur easily within the group.
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Acknowledgments
Conflicts of Interest
References
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Q1 | Please select your age: under 30 ( ); 30–40 ( ); 40–50 ( ); over 50 ( ). |
Q2 | Please select your length of service: less than 5 years ( ); 6–10 years ( ); 11–20 years ( ); more than 20 years ( ). |
Q3 | Please select your education background: primary school and below ( ); junior middle school or senior middle school ( ); junior college ( ); bachelor’s degree or above ( ). |
Q4 | Please select your position: group leader ( ); technical instructor ( ); common worker ( ). |
Q5 | I have close contact with my team members. |
Q6 | I have kinships or geographical relationships with closely connected workers on my team. |
Q7 | I cooperate with members of my team and learn from them to complete tasks. |
Q8 | My life problems and work problems can be solved by my team and I seldom turn to people outside my team. |
Q9 | Communicating with my team members helps me to master and understand all aspects of the required information and knowledge. |
Q10 | I am very satisfied with the form and effect of communication among my team members. |
Member Code | Who to Ask for Help |
---|---|
L1 | |
T1 | |
T2 | |
… | |
Tn | |
C1 | |
C2 | |
… | |
Cn |
Age | Service Years | Educational Levels | ||||||||
---|---|---|---|---|---|---|---|---|---|---|
≤30 years | 31–40 years | 41–50 years | >50 years | ≤5 years | 6–10 years | 11–20 years | >20 years | Primary school or below | Junior or senior high school | Junior college |
9 | 15 | 138 | 26 | 87 | 112 | 72 | 17 | 63 | 217 | 8 |
Items | Cronbach’s Alpha | CITC | Cronbach’s Alpha If the Item Deleted | Mean Value |
---|---|---|---|---|
Q5 | 0.714 | 0.499 | 0.655 | 3.74 |
Q6 | 0.504 | 0.651 | 3.88 | |
Q7 | 0.527 | 0.637 | 3.94 |
Items | Cronbach’s Alpha | CITC | Cronbach’s Alpha If the Item Deleted | Mean Value |
---|---|---|---|---|
Q8 | 0.829 | 0.674 | 0.778 | 3.79 |
Q9 | 0.696 | 0.754 | 3.99 | |
Q10 | 0.696 | 0.755 | 3.81 |
Node | OC | IC | CC | BC |
---|---|---|---|---|
L1 | 23.810 | 95.238 | 95.455 | 69.571 |
T1 | 4.762 | 14.286 | 52.500 | 0.000 |
T2 | 4.762 | 9.524 | 52.500 | 0.095 |
C1 | 14.286 | 33.333 | 60.000 | 1.222 |
C2 | 14.286 | 28.571 | 58.333 | 0.429 |
C3 | 4.762 | 38.095 | 60.000 | 1.222 |
C4 | 4.762 | 9.524 | 53.846 | 1.452 |
C5 | 4.762 | 4.762 | 52.500 | 1.452 |
C6 | 4.762 | 19.048 | 55.263 | 0.667 |
C7 | 19.048 | 9.524 | 53.846 | 0.159 |
C8 | 23.810 | 9.524 | 56.757 | 1.159 |
C9 | 19.048 | 9.524 | 58.333 | 5.167 |
C10 | 28.571 | 0.000 | 58.333 | 1.810 |
C11 | 19.048 | 4.762 | 55.263 | 0.952 |
C12 | 14.286 | 9.524 | 52.500 | 0.000 |
C13 | 14.286 | 4.762 | 41.176 | 1.190 |
C14 | 4.762 | 0.000 | 50.000 | 0.000 |
C15 | 4.762 | 4.762 | 52.500 | 1.452 |
C16 | 23.810 | 4.762 | 55.263 | 0.000 |
C17 | 23.810 | 4.762 | 55.263 | 0.000 |
C18 | 33.333 | 9.524 | 58.333 | 1.048 |
C19 | 14.286 | 0.000 | 52.500 | 0.000 |
Node | ES | EF | CT |
---|---|---|---|
L1 | 17.380 | 0.869 | 0.152 |
T1 | 1.750 | 0.583 | 0.575 |
T2 | 2.167 | 0.722 | 0.422 |
C1 | 4.500 | 0.563 | 0.383 |
C2 | 3.556 | 0.508 | 0.420 |
C3 | 4.444 | 0.556 | 0.378 |
C4 | 1.833 | 0.611 | 0.487 |
C5 | 2.000 | 1.000 | 0.500 |
C6 | 2.100 | 0.525 | 0.484 |
C7 | 2.250 | 0.563 | 0.639 |
C8 | 3.286 | 0.657 | 0.525 |
C9 | 4.667 | 0.778 | 0.304 |
C10 | 3.833 | 0.639 | 0.343 |
C11 | 3.000 | 0.600 | 0.440 |
C12 | 1.600 | 0.533 | 0.671 |
C13 | 3.500 | 0.875 | 0.321 |
C14 | 1.000 | 1.000 | 1.000 |
C15 | 2.000 | 1.000 | 0.500 |
C16 | 2.233 | 0.467 | 0.462 |
C17 | 2.233 | 0.467 | 0.462 |
C18 | 4.389 | 0.627 | 0.390 |
C19 | 1.333 | 0.444 | 0.637 |
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Cao, X.; Qin, P.; Zhang, P. Knowledge Transfer Characteristics of Construction Workers Based on Social Network Analysis. Buildings 2022, 12, 1876. https://doi.org/10.3390/buildings12111876
Cao X, Qin P, Zhang P. Knowledge Transfer Characteristics of Construction Workers Based on Social Network Analysis. Buildings. 2022; 12(11):1876. https://doi.org/10.3390/buildings12111876
Chicago/Turabian StyleCao, Xinying, Peicheng Qin, and Ping Zhang. 2022. "Knowledge Transfer Characteristics of Construction Workers Based on Social Network Analysis" Buildings 12, no. 11: 1876. https://doi.org/10.3390/buildings12111876