Micromechanisms of tacit knowledge transfer in construction projects from the perspective of interpersonal brain synchronization

Xiaotong GUO; Shuailong ZHANG; Fangyuan TIAN; Hanliang FU; Mengmeng WANG

doi:10.16511/j.cnki.qhdxxb.2024.22.041

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Journal of Tsinghua University(Science and Technology) ›› 2025, Vol. 65 ›› Issue (1) : 62-70. DOI: 10.16511/j.cnki.qhdxxb.2024.22.041

Special Section: Construction Management

Micromechanisms of tacit knowledge transfer in construction projects from the perspective of interpersonal brain synchronization

Xiaotong GUO ¹^,² ,
Shuailong ZHANG ¹^,² ,
Fangyuan TIAN ³^,⁴ ,
Hanliang FU ¹^,² ,
Mengmeng WANG ¹^,²^,*

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Abstract

Objective: Efficient transfer of tacit knowledge is crucial for enhancing resilience and promoting collaborative innovation among construction project teams. Although most studies focus on knowledge transfer between organizations or between organizations and individuals, they often rely on subjective reports to explore influencing factors and mechanisms. Given the inherent nature of tacit knowledge, its transfer process involves complex cognitive interactions and integration among parties, which traditional monitoring methods fail to fully capture. Therefore, this study aims to reveal the micro-level mechanisms of tacit knowledge transfer between individuals in construction projects from a cognitive perspective. Methods: This study categorizes tacit knowledge of construction projects into cognitive and technical types. The study involved 96 healthy university students divided into 48 pairs. Each pair included one sender with engineering knowledge and one receiver without. These pairs were randomly assigned to either a cognitive or technical group, each containing 24 pairs, to perform tacit knowledge transfer tasks relevant to construction projects. Brain oxygenation data were monitored using near-infrared equipment during the transfer process. A general linear model was used to calculate β-values for each channel in both groups based on the modified Beer-Lambert law, representing brain region activation. Hyperscanning technology extracted the preprocessed oxyhemoglobin time series for both the sender and receiver. Wavelet transform coherence was employed to model interpersonal brain synchronization (IBS) activities. The study then examined the relationship between IBS during the transfer and the performance of knowledge transfer measured by the efficiency of knowledge utilization and the degree of knowledge internalization. Results: The results indicated the following: (1) Both senders and receivers showed significant brain activation during tacit knowledge transfer. Receivers in the cognitive group exhibited higher brain activation than those in the technical group, indicating that transferring cognitive tacit knowledge demands greater neural engagement. (2) Significant IBS was observed between senders and receivers during the transfer tasks. The cognitive group exhibited lower IBS levels compared to the technical group, implying that the complexity of cognitive tacit knowledge might reduce immediate neural synchrony despite increased brain activation. (3) A strong causal relationship was found between IBS levels and the dual-dimensional performance of tacit knowledge transfer in terms of both knowledge utilization and internalization by the receiver. Higher IBS levels were associated with better transfer performance, highlighting the importance of neural synchrony in successful tacit knowledge transfer. Conclusions: This study sheds light on the cognitive processes involved in tacit knowledge transfer within construction project teams, highlighting differences in cognitive processes across different types of tacit knowledge. These findings underscore the critical role of IBS in predicting and enhancing knowledge utilization and internalization by the receiver, providing a reliable indicator for successful tacit knowledge transfer. These insights contribute to a deeper understanding of how cognitive interactions enhance project team efficiency, paving the way for improved strategies to enhance teamwork and innovation in construction projects.

Key words

construction projects / tacit knowledge transfer / interpersonal brain synchronization / functional near-infrared spectroscopy / hyperscanning

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Xiaotong GUO , Shuailong ZHANG , Fangyuan TIAN , et al . Micromechanisms of tacit knowledge transfer in construction projects from the perspective of interpersonal brain synchronization[J]. Journal of Tsinghua University(Science and Technology). 2025, 65(1): 62-70 https://doi.org/10.16511/j.cnki.qhdxxb.2024.22.041

References

List( Publishing order | Descend order by publishing year | Descend order by cited within ) Chart analysis

1	HOLZMANN V . A meta-analysis of brokering knowledge in project management[J]. International Journal of Project Management, 2013, 31 (1): 2- 13. https://doi.org/10.1016/j.ijproman.2012.05.002 Cited in this article [1]

2	李忆, 司有和. 知识管理战略、组织能力与绩效的关系实证研究[J]. 南开管理评论, 2009, 12 (6): 69- 76. LI Y , SI Y H . An empirical study on the relationship among knowledge management strategies, organizational capabilities and performance[J]. Nankai Business Review, 2009, 12 (6): 69- 76. Cited in this article [1]

3	NONAKA I . A dynamic theory of organizational knowledge creation[J]. Organization Science, 1994, 5 (1): 14- 37. https://doi.org/10.1287/orsc.5.1.14 Cited in this article [1]

4	SERENKO A , BONTIS N . Understanding counterproductive knowledge behavior: Antecedents and consequences of intra-organizational knowledge hiding[J]. Journal of Knowledge Management, 2016, 20 (6): 1199- 1224. https://doi.org/10.1108/JKM-05-2016-0203 Cited in this article [1]

5	AMBROSINI V , BOWMAN C . Tacit knowledge: Some suggestions for operationalization[J]. Journal of Management Studies, 2001, 38 (6): 811- 829. https://doi.org/10.1111/1467-6486.00260 Cited in this article [2]

6	ZHOU Q W , DENG X P , WANG G , et al. Linking elements to outcomes of knowledge transfer in the project environment: Current review and future direction[J]. Frontiers of Engineering Management, 2022, 9 (2): 221- 238. https://doi.org/10.1007/s42524-022-0195-3 Cited in this article [1]

7	董佳敏, 刘人境, 严杰, 等. 知识分享意愿和隐性知识对组织学习绩效的交互影响[J]. 管理评论, 2021, 33 (2): 153- 163. DONG J M , LIU R J , YAN J , et al. Interactive effects of knowledge sharing intention and tacit knowledge on organizational learning performance[J]. Management Review, 2021, 33 (2): 153- 163. Cited in this article [1]

MCTIERNAN

, MUSGRAVE

, COOPER

. Conceptualising trust as a mediator of pro-environmental tacit knowledge transfer in small and medium sized tourism enterprises[J]. Journal of Sustainable Tourism, 2023, 31 (4): 1014- 1031.

https://doi.org/10.1080/09669582.2021.1942479

Cited in this article [1]

9	GOROVAIA N , PAJIĆ M , WINDSPERGER J . Performance of knowledge transfer mechanisms: The case of franchising networks[J]. Industrial Marketing Management, 2023, 115, 539- 549. https://doi.org/10.1016/j.indmarman.2023.09.019 Cited in this article [1]

10	李浩, 马庆国, 董欣. 神经组织学: 概念解析、理论发展和研究展望[J]. 管理世界, 2016 (8): 164- 173. LI H , MA Q G , DONG X . Neurohistology: Conceptual analysis, theoretical development, and research prospects[J]. Management World, 2016 (8): 164- 173. Cited in this article [1]

11	TAN S H J , WONG J N , TEO W P . Is neuroimaging ready for the classroom? A systematic review of hyperscanning studies in learning[J]. NeuroImage, 2023, 281, 120367. https://doi.org/10.1016/j.neuroimage.2023.120367 Cited in this article [1]

12	SCHOLKMANN F , HOLPER L , WOLF U , et al. A new methodical approach in neuroscience: Assessing inter-personal brain coupling using functional near-infrared imaging (fNIRI) hyperscanning[J]. Frontiers in Human Neuroscience, 2013, 7, 813. Cited in this article [1]

13	NGUYEN M , CHANG A , MICCICHE E , et al. Teacher-student neural coupling during teaching and learning[J]. Social Cognitive and Affective Neuroscience, 2022, 17 (4): 367- 376. https://doi.org/10.1093/scan/nsab103 Cited in this article [1]

14	KELSEN B A , SUMICH A , KASABOV N , et al. What has social neuroscience learned from hyperscanning studies of spoken communication? A systematic review[J]. Neuroscience & Biobehavioral Reviews, 2022, 132, 1249- 1262. Cited in this article [1]

15	REDCAY E , SCHILBACH L . Using second-person neuroscience to elucidate the mechanisms of social interaction[J]. Nature Reviews Neuroscience, 2019, 20 (8): 495- 505. https://doi.org/10.1038/s41583-019-0179-4 Cited in this article [1]

16	STEPHENS G J , SILBERT L J , HASSON U . Speaker-listener neural coupling underlies successful communication[J]. Proceedings of the National Academy of Sciences of the United States of America, 2010, 107 (32): 14425- 14430. Cited in this article [2]

17	SCHILBACH L , TIMMERMANS B , REDDY V , et al. Toward a second-person neuroscience[J]. Behavioral and Brain Sciences, 2013, 36 (4): 393- 414. https://doi.org/10.1017/S0140525X12000660 Cited in this article [2]

18	HAGEN E H , BRYANT G A . Music and dance as a coalition signaling system[J]. Human Nature, 2003, 14 (1): 21- 51. https://doi.org/10.1007/s12110-003-1015-z Cited in this article [1]

朱怡, 胡谊. 师生互动中组块化反馈促进长时学习迁移: 行为和近红外超扫描研究[J]. 心理学报, 2024, 56 (5): 555- 576.

ZHU

, HU

. Chunking feedback in instructor-learner interaction facilities long-term learning transfer: Behavioral and fNIRS hyperscanning studies[J]. Acta Psychologica Sinica, 2024, 56 (5): 555- 576.

Cited in this article [1]

20	SUN B H , XIAO W L , FENG X D , et al. Behavioral and brain synchronization differences between expert and novice teachers when collaborating with students[J]. Brain and Cognition, 2020, 139, 105513. https://doi.org/10.1016/j.bandc.2019.105513 Cited in this article [1]

21	LIU W D , BRANIGAN H P , ZHENG L F , et al. Shared neural representations of syntax during online dyadic communication[J]. NeuroImage, 2019, 198, 63- 72. https://doi.org/10.1016/j.neuroimage.2019.05.035 Cited in this article [1]

BEVILACQUA

, DAVIDESCO

, WAN

, et al. Brain-to-brain synchrony and learning outcomes vary by student-teacher dynamics: Evidence from a real-world classroom electroencephalography study[J]. Journal of Cognitive Neuroscience, 2019, 31 (3): 401- 411.

https://doi.org/10.1162/jocn_a_01274

Cited in this article [1]

23	ZHU Y , LEONG V , HOU Y Y , et al. Instructor-learner neural synchronization during elaborated feedback predicts learning transfer[J]. Journal of Educational Psychology, 2022, 114 (6): 1427- 1441. https://doi.org/10.1037/edu0000707 Cited in this article [1]

24	BALLIET D , LI N P , MACFARLAN S J , et al. Sex differences in cooperation: A meta-analytic review of social dilemmas[J]. Psychological Bulletin, 2011, 137 (6): 881- 909. https://doi.org/10.1037/a0025354 Cited in this article [1]

25	VIGNEAU M , BEAUCOUSIN V , HERVÉ P Y , et al. Meta-analyzing left hemisphere language areas: Phonology, semantics, and sentence processing[J]. NeuroImage, 2006, 30 (4): 1414- 1432. https://doi.org/10.1016/j.neuroimage.2005.11.002 Cited in this article [1]

26	LIU J Q , ZHANG R Q , GENG B B , et al. Interplay between prior knowledge and communication mode on teaching effectiveness: Interpersonal neural synchronization as a neural marker[J]. NeuroImage, 2019, 193, 93- 102. https://doi.org/10.1016/j.neuroimage.2019.03.004 Cited in this article [1]

27	DELPY D T , COPE M , VAN DER ZEE P , et al. Estimation of optical pathlength through tissue from direct time of flight measurement[J]. Physics in Medicine & Biology, 1988, 33 (12): 1433. Cited in this article [1]

28	HOSHI Y . Functional near-infrared spectroscopy: Current status and future prospects[J]. Journal of Biomedical Optics, 2007, 12 (6): 062106. https://doi.org/10.1117/1.2804911 Cited in this article [1]

29	VON LÜHMANN A , ORTEGA-MARTINEZ A , BOAS D A , et al. Using the general linear model to improve performance in fNIRS single trial analysis and classification: A perspective[J]. Frontiers in Human Neuroscience, 2020, 14, 30. https://doi.org/10.3389/fnhum.2020.00030 Cited in this article [1]

30	GRINSTED A , MOORE J C , JEVREJEVA S . Application of the cross wavelet transform and wavelet coherence to geophysical time series[J]. Nonlinear Processes in Geophysics, 2004, 11 (5-6): 561- 566. Cited in this article [1]

31	JIANG J , DAI B H , PENG D L , et al. Neural synchronization during face-to-face communication[J]. The Journal of Neuroscience, 2012, 32 (45): 16064- 16069. https://doi.org/10.1523/JNEUROSCI.2926-12.2012 Cited in this article [1]

32	成晓君, 刘美焕, 潘亚峰. 责任共担促进新手的互动决策获益: 超扫描研究[J]. 心理学报, 2022, 54 (11): 1391- 1402. CHENG X J , LIU M H , PAN Y F . Shared responsibility promotes the benefit of interactive decision-making in novices: A hyperscanning study[J]. Acta Psychologica Sinica, 2022, 54 (11): 1391- 1402. Cited in this article [1]

33	王福兴, 黄宇, 张洋, 等. 提示对学习者自我生成教学过程和学习效果的作用[J]. 心理学报, 2024, 56 (4): 469- 481. WANG F X , HUANG Y , ZHANG Y , et al. Prompts affect learning-by-teaching process and learning outcome in multimedia learning[J]. Acta Psychologica Sinica, 2024, 56 (4): 469- 481. Cited in this article [1]

34	OKAMOTO M , DAN H R K , SHIMIZU K , et al. Multimodal assessment of cortical activation during apple peeling by NIRS and fMRI[J]. NeuroImage, 2004, 21 (4): 1275- 1288. https://doi.org/10.1016/j.neuroimage.2003.12.003 Cited in this article [1]

35	PAN Y F , NOVEMBRE G , SONG B , et al. Interpersonal synchronization of inferior frontal cortices tracks social interactive learning of a song[J]. NeuroImage, 2018, 183, 280- 290. https://doi.org/10.1016/j.neuroimage.2018.08.005 Cited in this article [1]