Exploring cognitive process of construction engineering tacit knowledge transfer based on interpersonal brain synchronization

Abstract

The efficient transfer of tacit knowledge is crucial for enhancing teamwork resilience and promoting collaborative innovation in construction projects. This study used functional near-infrared spectroscopy (fNIRS) hyperscanning technology to measure interpersonal brain synchronization (IBS) during the transfer of different classifications of tacit knowledge. This study explored the relationships among types of tacit knowledge, IBS during the transfer process, and the performance of tacit knowledge transfer. Finally, the role of knowledge behavioral characteristics in the process of tacit knowledge transfer was revealed. The results show that i) there is a significant IBS between the sender and receiver during the transfer task, with the IBS level of the cognitive tacit knowledge group being significantly lower than that of the technical tacit knowledge group. ii) There is a significant causal relationship between the IBS level of the transferring subjects and transfer performance, and the type of tacit knowledge influences transfer performance through IBS. iii) The tacit knowledge learning willingness of the receiver and the tacit knowledge sharing willingness of the sender moderate the relationship between the classification of tacit knowledge and the IBS level, and the absorptive capacity of the receiver moderates the relationship between the IBS level and tacit knowledge transfer performance. This study identifies the transfer mechanism of engineering tacit knowledge and provides a reliable predictor for the performance of tacit knowledge transfer with strong hysteresis.

Graphical abstract

Keywords

construction tacit knowledge transfer / interpersonal brain synchronization (IBS) / functional near-infrared spectroscopy (fNIRS) hyperscanning / knowledge behavior characteristics / transfer performance

1 Introduction

Under the continuous advancement of emerging technologies and innovative ideas, the construction industry is transforming into a technology-intensive and knowledge-intensive industry, in which knowledge management plays an increasingly important role. Knowledge is generally categorized into two classifications: explicit knowledge, which can be coded and easily expressed in writing, and tacit knowledge, which is difficult to code, composite, and highly private (Alavi et al., 2024). Scholars generally agree that explicit knowledge accounts for only 10% of a project team, whereas tacit knowledge constitutes 90%, forming the enormous base of the project’s “knowledge iceberg” (Kucharska and Erickson, 2023). In construction engineering, tacit knowledge can be categorized into cognitive tacit knowledge and technical tacit knowledge according to the characteristics of the knowledge content and degree of abstraction. Technical tacit knowledge includes operational skills and work experience. For example, an experienced construction professional can determine the correct ratio of cement to water by observing the color and consistency of the mixture and make quick adjustments to ensure the quality of the concrete or use years of practical experience to ensure a wall’s alignment and stability. Cognitive tacit knowledge, on the other hand, encompasses intuitive judgment and problem-solving ability. For example, project managers can make effective decisions on unexpected schedules, costs, and other conflicts based on long-term experience (Ambrosini and Bowman, 2001). The core of tacit knowledge management is the transfer of tacit knowledge. This process involves the dissemination of knowledge from the sender to the receiver within a specific environment, facilitating the accumulation of valuable knowledge systems by individuals or organizations (Li et al., 2019). During a project, the transfer of tacit knowledge improves individual knowledge updating and promotes teamwork. After project completion, tacit knowledge is transferred to other projects with engineers, promoting the overall development of the construction engineering industry (Sun and Anumba, 2019). However, as the field of construction engineering is dominated by mostly onsite construction operations, there is much technical experience that cannot be learned from books, and it is often difficult to express this experience graphically in reasonable language, hindering the transfer and promotion of the utilization of such tacit knowledge. In addition, for an internal project, the construction project has a one-time and temporary nature; that is, once the project where the employees of a construction enterprise are located is completed, the project organization will be dissolved on its own, and the tacit knowledge possessed by the individual members of the project will not be preserved in its entirety; thus valuable tacit knowledge is constantly lost (Wang et al., 2025).

Current research on tacit knowledge transfer focuses on three main aspects. First, research has focused on interorganizational tacit knowledge transfer and tacit knowledge transfer between organizations and individuals. Interorganizational knowledge transfer is the process of transferring tacit knowledge from the source organization team to the recipient organization team, which can help the team transfer the valuable knowledge and experience gained in construction to other organizations and avoid the loss of knowledge due to team dissolution. The greater the similarity between organizations is, the greater the effect of knowledge transfer (Tian et al., 2025). In addition, a cultural climate that promotes knowledge sharing is an important factor in the success of interorganizational knowledge transfer (Zhou et al., 2022a). In addition to tacit knowledge transfer across organizations, some research has focused on knowledge transfer between organizations and individuals. Organizations transfer specific know-how, work experience, or project management skills to individual members in various ways. Transfer methods include formal training, job coaching, mentorship, and the accumulation of project experience (Duryan et al., 2020). Second, research has been conducted on the performance of tacit knowledge transfer. The performance of tacit knowledge transfer in the construction industry is the degree of the knowledge sharing effect and practical application that occurs after the transfer of tacit knowledge from the knowledge sender to the receiver (Chi et al., 2021). Researchers usually assess the performance of tacit knowledge transfer through a variety of methods, including the improvement in the innovation ability of the construction project team and the growth in overall organizational benefits (Qiao et al., 2025). Research on the performance of tacit knowledge transfer also addresses how to optimize the transfer process. For example, some studies have proposed the establishment of better knowledge-sharing platforms and mechanisms to enhance knowledge exchange and interaction within and outside the organization, which in turn increase the success rate and benefits (Maravilhas and Martins, 2019). Third, research has focused on the influencing factors of tacit knowledge transfer. Such research is reflected in three main aspects: the factors of the transferring subject and content, including the willingness and ability to transfer of personnel in the construction industry, as well as the implicit, embedded and complex nature of knowledge, which affects whether knowledge can be effectively conveyed and absorbed (Liu et al., 2024). The strength of the relationship between transferring subjects and differences in knowledge and culture also have important impacts on the transfer process (Li et al., 2019). Finally, transfer media and environmental factors, such as the media abundance of transfer methods, the sophistication of transfer platforms, organizational systems and the cultural climate, play key roles in the effectiveness of construction engineering tacit knowledge transfer (Xu et al., 2024).

Although some studies have been conducted in the field of construction engineering, three aspects have not yet been considered. First, most studies discuss the organizational level and organization‒individual transfer, whereas explorations of the interaction of individuals, who are the carriers of knowledge, in real online transfer tasks are lacking in existing research. Second, most existing studies on tacit knowledge transfer performance consider direct measurement in terms of recipients’ problem-solving ability and homogenized task performance. However, tacit knowledge transfer performance has a lag, and performance measures are often not accurately revealed until later, when receivers are faced with the same problem. Few studies have taken this feature into account and considered the role of the transferring subject’s knowledge behavioral characteristics in the whole transfer process. Third, previous studies have not specifically categorized tacit knowledge, focusing only on the results of transfer, using subjective reports to explore the influencing factors of tacit knowledge transfer, and not revealing the mechanisms of the process. However, tacit knowledge is characterized by embeddedness and complexity, and transfer is mainly in the form of face-to-face communication (Gorovaia et al., 2023), the process of which involves the interaction and integration of the cognitive levels of both subjects involved in the transfer, and the monitoring and portrayal of which, by traditional means, are not very comprehensive (Sørensen et al., 2010). In recent years, the paradigm of research exploring individual activities through neurophysiological and behavioral experiments has gradually developed (Pan et al., 2018). The basic process of tacit knowledge transfer involves the dynamic and continuous exchange of information between subjects. Single-brain metrics reflect only the processing of information by a single subject, which is limited in revealing the interactive process of tacit knowledge transfer (Tan et al., 2023). Multiplayer synchronized interactive scanning (HYPERSCANNING) has emerged. Hyperscanning refers to a multiplayer brain monitoring technique based on simultaneous neuroimaging recordings of people jointly accomplishing a certain cognitive activity (Quaresima and Ferrari, 2019).

To fill the gaps in research noted above, this study aims to carry out the following: first, the process of engineering tacit knowledge transfer is quantitatively measured at the individual level. Hyperscanning technology is used to measure the interpersonal brain synchronization (IBS) of transferring subjects under different types of tacit knowledge, and then, the relationship between IBS and transfer performance is explored. Second, the process of engineering tacit knowledge transfer is divided into two stages to explore the mechanism of the impact of individuals’ knowledge behavioral characteristics on tacit knowledge transfer. In this study, the first section introduces a theoretical framework for engineering tacit knowledge transfer and outlines the research hypotheses. The second section details the research design, including the selection of participants, the experimental procedure, and the data collection methods. The third section presents the experimental results. The fourth section provides a discussion of the findings and offers a forward-looking perspective. Finally, the conclusion summarizes the key points of the study. This study reveals the cognitive process of engineering tacit knowledge transfer, provides a reliable predictor for the performance of tacit knowledge transfer with strong hysteresis, explores the mechanism of the impact of transferring subjects’ knowledge behavioral characteristics on the transfer process, and is highly important for enhancing the resilience of teamwork in construction projects and promoting collaborative innovation.

2 Theoretical framework

2.1 Cognitive load theory

In construction engineering projects, tacit knowledge is categorized into two distinct types: technical tacit knowledge and cognitive tacit knowledge. Technical tacit knowledge encompasses concrete operational skills and technical proficiencies that can be readily acquired through observational learning and repetitive practice. In contrast, cognitive tacit knowledge involves abstract reasoning, intuitive judgment, and complex problem-solving processes, hence imposing a greater cognitive load during its acquisition. According to cognitive load theory, the human brain has a limited capacity to process information, and when cognitive resources are overloaded, learning and performance may be hindered (Paas and van Merriënboer, 2020). Based on cognitive load theory and the principles of automaticity in skill acquisition, it can be inferred that the transfer of technical tacit knowledge consumes fewer cognitive resources, resulting in more efficient and higher-quality transfer outcomes compared to cognitive tacit knowledge. Consequently, we hypothesize that in the context of construction engineering projects, the performance of tacit knowledge transfer for technical tacit knowledge will be significantly greater than that for cognitive tacit knowledge. Therefore, we propose Hypothesis 1:

H1: Different classifications of tacit knowledge in construction engineering significantly affect tacit knowledge transfer performance.

2.2 Classification of tacit knowledge, IBS and transfer performance

IBS between the transferring subjects indicates effective knowledge sending and receiving (Nguyen et al., 2022). It is widely accepted that brain synchronization is a key mechanism through which interactants achieve agreement at the behavioral, emotional, and cognitive levels. It reflects the degree of dynamic cognitive and neural coherence in complex social interactions and is fundamental for successful communication and social interaction (Kelsen et al., 2022). For example, some scholars have reported that when communication is successful, there is spatiotemporal coupling between the brain activity of the sender and the receiver (Stephens et al., 2010); other researchers, using functional near-infrared spectroscopy (fNIRS) hyperscanning to monitor the teaching process, have discovered that strong synchronization between the teacher’s and students’ brain activities occurs only when students truly understand the teacher’s intentions (Schilbach et al., 2013). During the transfer process, the type of tacit knowledge significantly impacts the level of IBS between transferring subjects. Technical tacit knowledge, with its lower tacitness, can be more readily transferred and understood through direct interactions such as demonstrations and hands-on activities, thus facilitating a greater degree of IBS between the sender and receiver. Conversely, cognitive tacit knowledge involves more complex and abstract concepts, making it more difficult for the sender to convey and for the receiver to comprehend. Effective transfer of this type of knowledge typically requires more extended, high-quality interactions, higher levels of knowledge behavioral characteristics, and deeper communication (Ambrosini and Bowman, 2001). Owing to its inherent complexity, the sender may find it challenging to articulate, while the receiver may need additional time and effort to internalize this knowledge, potentially resulting in lower IBS levels during transfer. On this basis, the following hypothesis is proposed:

H2: Different classifications of tacit knowledge in construction engineering significantly affect IBS during transfer.

Coalitional signaling theory proposes that cognitive synchronization produces a coalition signal that enables group members to coordinate and collaborate effectively, hence enhancing task performance (Hagen and Bryant, 2018). The higher the intensity of IBS between the sender and the receiver during the transfer task is, the more efficient the information transfer between the corresponding brain regions, and the better the sharing and integration effect on cognitive resources. This efficient information transfer and cognitive integration enables the receiver to learn and recall tacit knowledge faster, thus enhancing its efficiency of knowledge utilization during the transfer process (Zheng et al., 2018). IBS not only reflects team performance in real-time interactions, such as cooperative performance and information comprehension (Sun et al., 2020), but also predicts individual knowledge accumulation during interactions. For example, the level of IBS in teacher‒student interactions can predict students’ classroom participation (Bevilacqua et al., 2019) and academic performance (Zhu et al., 2022). Thus, a high level of IBS means that the receiver not only acquires knowledge more quickly but also deeply internalizes that knowledge in subsequent reflection and application. Additionally, the process of knowledge transfer is a two-way interaction between the sender and the receiver. Not only does IBS impact the receiver’s knowledge transfer performance, but it is also closely related to an enhancement in the sender’s knowledge transfer capabilities. When the sender and receiver exhibit higher levels of IBS, the sender can better consolidate his or her own knowledge during the transfer process, improve the decoding of tacit knowledge in subsequent transfers, and quickly identify the receiver’s emotions. These effects create a more favorable transfer environment and directly improve the sender’s ability in future knowledge transfer (Sun et al., 2023). Therefore, in tacit knowledge transfer within construction engineering projects, activities characterized by higher levels of IBS lead to higher-quality interactions and feedback mechanisms. These activities effectively promote the integration, internalization, and updating of tacit knowledge in the cognitive memory systems of both receivers and senders, thus fully enhancing tacit knowledge transfer performance. The absorption of tacit knowledge takes time to be expressed, and the IBS signal is a manifestation of this process. In summary, the following hypotheses are proposed:

H3: IBS is a mediator in the relationship between the classification of tacit knowledge and the degree of the receiver’s knowledge internalization.

H4: IBS is a mediator in the relationship between the classification of tacit knowledge and the sender’s enhancement of knowledge transfer capability.

2.3 The socialization-externalization-combination-internalization model

2.3.1 Externalization phase

Nonaka proposed the SECI (socialization-externalization-combination-internalization) model to represent the process of tacit knowledge transfer (Nonaka, 1994). The knowledge behavioral characteristics of subjects encompass specific individual characteristics that collectively influence the effectiveness of tacit knowledge transfer in construction engineering of the SECI model (Zhou et al., 2023). These characteristics shape how individuals interact, process, and apply knowledge, including their willingness for knowledge sharing and learning, as well as their capacities for knowledge absorption and reflection. In the externalization stage, individuals transfer their own tacit knowledge to others by externalizing it in a certain way, and the receiver’s tacit knowledge learning willingness and the sender’s tacit knowledge sharing willingness play important roles in this stage (Rahimi and Fathi, 2024). The receiver’s learning willingness refers to the willingness and motivation to actively acquire and absorb knowledge in the process of transferring tacit knowledge (Ngah et al., 2022). Receivers with a high learning willingness will participate more actively in the process of transferring knowledge, which can promote the in-depth processing of complex cognitive dimensions of tacit knowledge and maintain sustained attention and commitment, hence enhancing and maintaining IBS with the sender. The sender’s sharing willingness refers to the willingness and motivation of knowledge holders to externalize and transfer their tacit knowledge to the receiver. Senders with a high sharing willingness will explain complex cognitive dimensions of tacit knowledge in more detail, externalize tacit knowledge in multiple ways, and pay more attention to the receiver’s feedback to adjust the knowledge transfer in a timely manner. This process is usually accompanied by emotional investment, which can enhance emotional resonance with the receiver and thus improve the degree of IBS between the transferring subjects. The stronger the willingness of the transferring subjects to learn and share, the more efficient the cooperation is, which significantly improves the degree of IBS through enhanced interaction and feedback mechanisms. Hypotheses 5 and 6 are proposed as follows:

H5: The receiver’s tacit knowledge learning willingness moderates the relationship between the classification of tacit knowledge and the level of IBS.

H6: The sender’s tacit knowledge sharing willingness moderates the relationship between the classification of tacit knowledge and the level of IBS.

2.3.2 Internalization phase

The internalization stage involves digesting and absorbing preacquired knowledge to form new tacit knowledge. The absorption of tacit knowledge takes time to be expressed, and the IBS signal is a manifestation of this process. The performance of the receiver’s tacit knowledge transfer also depends on his or her absorptive capacity. It has been found that absorptive capacity is critical for project-based organizations to cope with the transfer of complex knowledge in project environments and that the stronger the absorptive capacity is, the greater the knowledge performance (Wang and Wang, 2022). The receiver’s absorptive capacity reflects his or her ability to recognize, acquire, assimilate, and apply external knowledge. Receivers with high absorptive capacity can acquire and understand tacit knowledge faster and more accurately, efficiently utilizing this knowledge in specific operational skills or strategies (Singh et al., 2020). Receivers with high absorptive capacity can understand the tacit knowledge deeply transmitted by the sender, effectively transforming it into practical skills and strategies. Receivers with low absorptive capacity face challenges in assimilating and understanding tacit knowledge. Even in high-IBS situations, receivers struggle with deep understanding and assimilation, leading to lower levels of internalization. On this basis, the following hypothesis is proposed:

H7: The receiver’s absorptive capacity moderates the relationship between IBS and his or her degree of knowledge internalization.

Reflective ability refers to an individual’s capacity to engage in deep thinking and to evaluate the process and outcomes of an activity or event after experiencing it. It includes reflecting on one’s own behaviors and decisions, as well as analyzing feedback from the interaction process to improve future behaviors and strategies (Raković et al., 2022). A sender with high reflective ability can make better use of the cognitive and emotional support provided by IBS. By reflecting after the knowledge transfer task, a sender can continuously adjust and optimize the knowledge transfer method, thus improving the clarity and accuracy of the transfer and enhancing his or her tacit knowledge transfer ability. H8 is proposed as follows:

H8: The sender’s reflective capacity moderates the relationship between IBS and his or her enhancement in knowledge transfer capability.

In summary, the theoretical model of this paper is shown in Fig. 1.

2.4 Research workflow

Based on the previous literature and relevant theories, this study establishes an analytical framework to explain the mechanism of construction engineering tacit knowledge transfer. As shown in Fig. 2, the research framework consists of five parts: the research background and objectives, the theoretical foundation, data collection, data analysis, and the conclusions and discussion.

3 Experimental design

3.1 Experimental participants

This study aimed to precisely explore the micromechanisms of tacit knowledge transfer between two transferring subjects. Therefore, it used a two-person experiment rather than a multiple-person experiment to better control the variables and reduce the interference of group dynamics. That is, the between-subjects design helps to reduce the interference between the experimental group and the control group and to avoid confounding conditions within the group, thus ensuring the internal validity of the experimental results. Age and gender, which are important influencing factors in tacit knowledge transfer and hyperscanning studies (Balliet et al., 2011), were used as control variables in this study to ensure the consistency of the experimental sample. In addition, considering the high proportion of male employees in engineering practice, the participants were all male. To ensure that there was a significant difference in knowledge potential between transferring subjects, the sender was a student in construction engineering with some engineering knowledge, whereas the receiver had no engineering knowledge at all (Zhou et al., 2022b). The design simulated a real-world situation in which experts transfer complex tacit knowledge to novices. The participants were strangers to each other, and interpersonal interference was excluded.

G*Power 3.1 was used to calculate the required sample size, with an effect size set to 0.9, a classical value reported in studies examining the impact of different categories on IBS in the field of social interaction (Sun et al., 2021). Additionally, the alpha level was set to 0.05, and the beta level was set to 0.80. According to this analysis, the required sample size was determined to be N = 42. Ultimately, we recruited 48 pairs of healthy male college students (aged 21.0 ± 2.3 years) from Xi’an University of Architecture and Technology, none of whom had a history of neurological or psychiatric disorders. For the senders, participants were initially recruited randomly for standardized training on tacit knowledge in the technical and cognitive groups according to 1.5 times the sample requirement (72 participants), and 48 sender participants were identified after screening for the degree of execution of the transfer task (transfer competence and procedural consistency), with 24 each in the technical group and the cognitive group. The 48 receivers were then randomly paired with senders to perform the tacit knowledge transfer task in the field of construction engineering projects. College students typically possess strong learning and cognitive abilities. While they may lack practical work experience, they are capable of effectively completing tasks in a laboratory environment and providing valuable data. The experimental location was the Laboratory of Neuromanagement in Engineering at Xi’an University of Architecture and Technology, where silence and low light levels were ensured during the experiments to ensure that the external environment had minimal impact on both the participants and the equipment. The study obtained ethics approval from the Research Review Board of the Laboratory of the Neuromanagement in Engineering, Xi’an University of Architecture and Technology. The participants signed informed consent forms prior to participation and received compensation of 50 RMB upon completion of the experiment.

3.2 Experimental equipment

In this study, two identical near-infrared optical brain imaging systems (Photon Cap, Cortivision, Poland) were employed to simultaneously record brain activity of both the sender and the receiver during tacit knowledge transfer. Each system was equipped with emitters, detectors and an amplifier designed to measure brain hemodynamics. The emitters transmitted near-infrared light at wavelengths of 760 nm and 850 nm, whereas the detectors captured the absorption changes caused by blood oxygenation levels. The amplifier transferred the collected data to the experimental programming software (E-Prime 3.0) and finally displayed the data from both parties on a screen. The entire process was connected by a local area network (LAN). The optical setup included specialized accessories such as light-emitting caps (optode caps) to house the emitters and detectors, pressure caps to ensure optimal contact with the scalp, and light-blocking caps to minimize external light interference during data acquisition (Fig. 3).

The transmitters direct light through the scalp and skull, targeting the cerebral cortex, while the detectors measure absorption changes, which reflect the relative changes in the concentration of oxyhemoglobin (HbO) and deoxyhemoglobin (HbR). As indicated by the literature, the left hemisphere plays a dominant role in knowledge transfer functions (Vigneau et al., 2006). Therefore, in this study, the measurement regions were focused on the left prefrontal cortex, the left inferior frontal gyrus, the left temporal lobe, and the left temporoparietal junction, all of which are associated with learning, memory, and expression (Liu et al., 2019). The locations of the optical sensors and channels are depicted in Fig. 4. Both the receiver and sender participants were monitored using the same channels (CH1-22), with a sampling rate of 6 Hz and a transmitter-to-detector spacing of approximately 3 cm.

3.3 Measurement of the IBS level

The fNIRS data were preprocessed by the Homer3 and NIRS_KIT toolboxes in MATLAB 2019. First, raw data, consisting of multichannel light intensity signals, were converted to optical density (OD) values. Channels with low signal quality were removed. Next, motion artifacts caused by participant movement were corrected, and a 0.01–0.1 Hz bandpass filter was applied to reduce physiologic noise. Finally, the OD data were converted into HbO and HbR concentrations using the modified Beer–Lambert law (Delpy et al., 1988), as shown in the equations below:

(1)\mathrm{\Delta }OD(\lambda )={\epsilon }_{\mathrm{H}\mathrm{b}\mathrm{O}}(\lambda )\times \mathrm{\Delta }{C}_{\mathrm{H}\mathrm{b}\mathrm{O}}\times DPF\times L+{\epsilon }_{\mathrm{H}\mathrm{b}\mathrm{R}}(\lambda )\times \mathrm{\Delta }{C}_{\mathrm{H}\mathrm{b}\mathrm{R}}\times DPF\times L,

where ∆OD(λ) is the change in optical density at wavelength λ, defined as the logarithm of the ratio of incident to transmitted light intensity. ε_HbO(λ) and ε_HbR(λ) are the molar absorption coefficients of HbO and HbR at wavelength λ. ∆C_HbO and ∆C_HbR represent the changes in the concentrations of HbO and HbR, respectively. DPF is the differential path length factor, which accounts for the increased path length of light due to scattering in tissue. L is the geometric path length of the light through the tissue.

Previous studies have indicated that HbO is the most sensitive indicator of changes in regional cerebral blood flow (Hoshi, 2007); therefore, this study focused on the HbO concentration. The preprocessed HbO time series for the same channel in both the sender and receiver were then extracted, and the computational modeling of IBS activity was conducted using wavelet transform coherence (WTC) (Grinsted et al., 2004), following the equations provided below:

(2)

In this model, t denotes the time point, s denotes the wavelet scale, <·> represents the smoothing process in time and scale, W denotes the successive wavelet transforms, and i and j represent the sender’s and receiver’s HbO time series, respectively. The two-dimensional WTC matrix (time × frequency) was computed. This frequency band effectively reveals the presence of significantly increased IBS in task-related regions. The task-related IBS value is obtained by subtracting the IBS value of the resting phase from that of the tacit knowledge transfer phase in the selected frequency band (Eq. (3)).

(3){WTC}_{\mathrm{t}\mathrm{a}\mathrm{s}\mathrm{k}-\mathrm{r}\mathrm{e}\mathrm{l}\mathrm{a}\mathrm{t}\mathrm{e}\mathrm{d}}={WTC}_{\mathrm{t}\mathrm{a}\mathrm{s}\mathrm{k}}-{WTC}_{\mathrm{r}\mathrm{e}\mathrm{s}\mathrm{t}},

where {WTC}_{\mathrm{t}\mathrm{a}\mathrm{s}\mathrm{k}} and {WTC}_{\mathrm{r}\mathrm{e}\mathrm{s}\mathrm{t}} are the IBS in the task and resting phases, respectively. This difference effectively excludes basal IBS in the resting phase and extracts task-related IBS increases. The subsequent statistical analysis is based on {WTC}_{\mathrm{t}\mathrm{a}\mathrm{s}\mathrm{k}-\mathrm{r}\mathrm{e}\mathrm{l}\mathrm{a}\mathrm{t}\mathrm{e}\mathrm{d}}.

Given the relatively small sample size (n = 24 per group), the Shapiro‒Wilk test was used as the primary method for assessing normality, supplemented by the Kolmogorov‒Smirnov test for validation. The results revealed that all variables in the technical group had p values greater than 0.05, indicating conformity with the assumption of a normal distribution. Additionally, in the cognitive group, except for Channel 15 (W = 0.875, p = 0.007), the remaining variables did not significantly deviate from normality. The Kolmogorov‒Smirnov test yielded consistent conclusions. Considering the small sample size and generally high p values, the data were approximately normally distributed overall, satisfying the normality assumption required for subsequent parametric analyses.

3.4 Measurement of Tacit Knowledge Transfer Performance

For the receiver, the performance of tacit knowledge transfer was assessed in terms of the degree of knowledge internalization. This dimension aligns with the foundational aspects of effectiveness in knowledge transfer performance research (Pérez-Nordtvedt et al., 2008). The degree of knowledge internalization was measured by test completion quality, which collectively assesses the receiver’s ability to grasp and apply the tacit knowledge involved in the transfer task. For the sender, a five-point Likert scale was used to self-assess the increase in knowledge transfer capability.

The receiver’s degree of knowledge internalization: For the technical group, the assessment focused on the number of techniques employed and the completeness of the building model construction, with each contributing 50% to the total score. The number of techniques was five, with each technique being worth 20 points. The model completeness score was determined based on the steps completed, with each completed step earning 25 points. In the cognitive group, the test involved solving a complex project management problem similar to the transfer task, which was expressed through a flowchart of solution ideas. The test consisted of 20 fill-in-the-blank items, with each being worth 5 points.

The sender’s enhancement in knowledge transfer capability: This indicator is assessed through two main dimensions, namely, the ability to design instruction and the ability to teach effectively. For this purpose, this study used a revised and proven scale suitable for assessing the effectiveness and accuracy of knowledge transfer in the teaching and learning process (Gao et al., 2024). Specifically, instructional design competence measures the logical structure and task design rationality of the sender in the knowledge transfer process. Effective teaching competency measures the sender’s communication and modeling effectiveness in the transfer task and is scored on the clarity of expression, interactivity, and novelty.

To facilitate the statistical analysis in the subsequent sections, the degree of knowledge internalization and the enhancement of knowledge transfer capability were standardized, see Eq. (4). Furthermore, the Shapiro‒Wilk test results revealed that all the performance variables followed a normal distribution, supporting the use of parametric tests.

(4)X=\frac{X_i-X_{\mathrm{m}\mathrm{e}\mathrm{a}\mathrm{n}}}{X_{\mathrm{S}\mathrm{t}\mathrm{d}}}.

3.5 Measurement of knowledge behavioral characteristics

This study measured the sender’s tacit knowledge sharing willingness and reflective capacity (Bock and Lee, 2005) as well as the receiver’s tacit knowledge learning willingness and absorptive capacity using self-reported scales (Zahra and George, 2002). The knowledge learning willingness scale was adapted from the knowledge sharing willingness scale, and the other scales were adapted from well-established scales. Each item was rated on a 5-point Likert scale ranging from 1 (strongly disagree) to 5 (strongly agree). The total score for all the items was summed to produce a composite score, with higher scores indicating stronger knowledge behavioral characteristics. Since most participants were not fluent in English, the Chinese versions of the scales were employed. Those scoring above the mean were classified into the high trait group, and those below into the low trait group.

To assess the internal consistency and construct validity of the scale, both Cronbach’s alpha was used, and the Kaiser-Meyer-Olkin (KMO) test was conducted. The Cronbach’s alpha values for the sender’s tacit knowledge sharing willingness and reflective capacity, the receiver’s tacit knowledge learning willingness and absorptive capacity, and the sender’s enhancement of knowledge transfer capability were 0.810, 0.810, 0.781, 0.861, and 0.858, respectively. These values suggest strong internal consistency across all five scales, as they exceed the generally accepted threshold of 0.7, indicating that the items within each scale reliably measure the same underlying construct. Additionally, the KMO coefficients were 0.704, 0.777, 0.714, 0.862, and 0.752, respectively, all of which are above the minimum recommended value of 0.6. The combination of high Cronbach’s alpha values and adequate KMO coefficients provides strong evidence of both the reliability and validity of the scales, supporting their use in measuring the respective constructs in this study. These results align with those of prior studies, confirming the robustness of the scales in capturing the knowledge behavioral characteristics of tacit knowledge transfer.

To increase the validity of the self-reported scales, this study incorporated observable behavioral indicators during the tacit knowledge transfer process. For example, the sender’s sharing willingness was evaluated through the frequency of voluntary demonstrations, unsolicited explanations, and the use of gesture-based or physical expressions—behaviors that signal the sender’s active involvement and intent to convey knowledge in a detailed manner. The receiver’s learning willingness was assessed by measuring the sustained gaze on the instructor or instructional material, nodding responses, and leaning forward, all of which reflect cognitive engagement and motivation to learn. The sender’s reflective capacity was indicated by the frequency of self-corrections (rephrasing or reordering information), visual scanning of receiver reactions, and the use of analogies or examples to enhance understanding. For absorptive capacity, we examined observable reactions to key “problem points” in instruction, such as delayed realization, relief-like facial expressions upon clarification, and note-taking or mimicking of demonstrated actions, behaviors that reveal internalization of knowledge. All video materials were independently coded by three trained raters using a back-to-back method to ensure interrater reliability. After the experiment, follow-up interviews were conducted with the participants to verify and adjust whether the observed behaviors were consistent with their internal experience, further supporting the interpretive validity of the behavioral indicators.

3.6 Experimental procedure

First, the participants provided their demographic information and completed scales assessing their characteristics: senders completed scales measuring tacit knowledge sharing willingness and reflective capacity; receivers completed scales measuring learning willingness and absorptive ability. After the task flow and rules were understood, the experiment commenced. Both the technical and cognitive groups followed the same basic procedures: the resting state, tacit knowledge transfer phase, independent thinking phase, and testing phase (Fig. 5). The entire experiment lasted approximately 20−30 min in total, and the participants’ cognitive neural data were recorded by the fNIRS. The entire process was recorded with a digital video camera to perform retrospective checks and to record behavioral data such as the application of the receiver’s tacit knowledge items.

Step I (resting state): Prior to the formal task, both the sender and the receiver sat quietly in a comfortable environment for 3 min. They were instructed to remain still and avoid conscious thought to ensure reliable baseline data. No external stimuli were present during this phase.

Step II (tacit knowledge transfer phase): The sender was responsible for delivering the tacit knowledge content. To ensure that the task design for both the technical and cognitive groups was consistent and scientifically aligned with the needs and challenges of real engineering projects, three professors and five postgraduate students specializing in construction engineering and knowledge management were invited to discuss, revise, and evaluate the test materials. A comparison of task content between the technical group and the cognitive group is shown in Table 1. The sender was allowed to use limited gestures and eye contact but not external aids. This phase lasted for 5 min. The receiver sat face-to-face with the sender, listening in silence and observing the sender’s verbal and nonverbal cues. Although passive in speech, the receiver was encouraged to maintain attention, and natural responses such as nodding or facial expressions were allowed. The receiver’s behaviors (e.g., leaning forward, attention level) were recorded as part of objective behavioral indicators.

Step III (independent thinking phase): Knowledge senders were asked to reflect for 3 min after the transfer task, reviewing and summarizing the whole transfer process, especially the difficulties in transferring tacit knowledge and their own performance during the transfer process. At the same time, the receiver was also asked to perform 3 min of independent reflection to absorb and internalize the learned tacit knowledge. At this stage, the receiver and the sender were in different rooms to avoid external and human interference.

Step IV (testing phase): For senders, a scale on the degree of improvement in knowledge transfer skills was requested. The content of the scale was adapted from previous research and covered the sender’s self-assessment of both improvement in instructional design skills and improvement in effective teaching (Gao et al., 2024). For the receiver, the test content was different from that of the knowledge transfer task but at the same level of difficulty to avoid duplication of the learning effect with the content of the transfer task. The results of the test were used to assess the performance of the recipient’s tacit knowledge transfer.

4 Results

4.1 Assessment of tacit knowledge transfer performance

4.1.1 Performance of the receiver in tacit knowledge transfer

In this study, the receiver’s performance was assessed based on the degree of knowledge internalization. The receiver’s degree of knowledge internalization was measured by the actual scores obtained on the post-transfer assessment. In technical tacit knowledge transfer tasks, the average score was 81.04, with a standard deviation of 12.04. In cognitive tacit knowledge transfer tasks, the average score was 65.00, with a standard deviation of 11.51. Independent samples t-tests were performed on the degree of knowledge internalization between the technical and cognitive groups. The results indicate observable differences in the receiver’s performance between the technical and cognitive tacit knowledge transfer tasks [t(46) = 4.71, p < 0.00]. Specifically, the receiver’s performance in technical tasks—both in terms of efficiency and knowledge internalization—was greater than that in cognitive tasks (Fig. 6(a)).

4.1.2 Performance of the sender in tacit knowledge transfer

The sender’s performance was evaluated based on the enhancement in his knowledge transfer capability. This metric reflects how much the sender improved in his ability to effectively transfer tacit knowledge. In technical tacit knowledge transfer tasks, the sender’s average self-reported score for improvement in knowledge transfer capability was 67.17, with a standard deviation of 5.45. In cognitive tacit knowledge transfer tasks, the sender’s average score was slightly higher at 67.04, with a standard deviation of 5.98. Independent samples t-tests were performed on the enhancement in the senders’ knowledge transfer capability between the technical and cognitive groups. The results indicate that there is no significant difference in the performance of senders in technical and cognitive tacit knowledge transfer tasks [t(46) = 0.07, p = 0.94]. These findings suggest that sender’s enhancement of knowledge transfer capability was relatively consistent across both types of tasks (Fig. 6(b)). In summary, Hypothesis 1 is partially supported.

4.2 IBS in tacit knowledge transfer

4.2.1 IBS during the transfer of different classifications of tacit knowledge

One-sample t-tests were conducted on the IBS values for both the technical and cognitive groups. Hotspot maps effectively illustrate the distribution of t values across different brain regions and channels (Fig. 7(a)). During technical tacit knowledge transfer, the IBS values in the postcentral gyrus were significantly greater than those in other regions [channel 19: t(23)=5.353, p=0.000], whereas the IBS values in the left temporoparietal junction were relatively low [channel 22: t(23)= 0.786, p=0.440]. In contrast, when cognitive tacit knowledge transfer was performed, the IBS values in the left prefrontal cortex were significantly greater than those in other regions [channel 10: t(23)= 3.218, p=0.004], whereas the left dorsolateral prefrontal cortex had relatively low IBS values [channel 13: t(23)= 1.061, p=0.300]. Furthermore, in this study, independent samples t-tests were performed on the channels with significantly increased IBS values between the technical and cognitive groups. The IBS values for channel 19, located in the left temporoparietal junction, exhibited a significant between-condition effect [t(46) =2.093, p = 0.042], indicating that the IBS effect was significantly greater in the technical group than in the cognitive group during tacit knowledge transfer (Fig. 7(b)). As a result, H2 is supported.

4.2.2 Predictive effects of IBS on transfer performance

Pearson correlation analysis was conducted to explore the correlations and causal relationships between the IBS levels in the corresponding brain regions of the sender and receiver during tacit knowledge transfer and the performance of tacit knowledge transfer. The IBS values at channels 13 (r = 0.393, p = 0.00), 14 (r = 0.583, p < 0.00), 16 (r = 0.338, p = 0.01), 19 (r = 0.554, p < 0.00), and 20 (r = 0.292, p = 0.04) were significantly and positively correlated with the receiver’s degree of knowledge internalization. Channel 13 is located in the left dorsolateral prefrontal cortex; channel 14 is located in the left inferior frontal gyrus; and channels 16, 19, and 20 are located in the left temporoparietal junction. Furthermore, the IBS values of channels 17 and 19 were significantly positively correlated with the sender’s enhancement of knowledge transfer capability (r = 0.300, p = 0.04; r = 0.509, p < 0.00), with both channels being located in the left temporoparietal junction (Fig. 7(c)).

4.2.3 Mediating effect of IBS on the relationship between tacit knowledge classification and transfer performance

To further explain the potential mechanisms of tacit knowledge transfer in construction engineering, the SPSS PROCESS macro developed by Hayes was employed to analyze the mediating role of IBS in the relationship between tacit knowledge classification and transfer performance. Model 4 was selected and tested at a 95% confidence interval using the bootstrap method with a sample size of 5000. The path coefficients between the three variables are shown in Fig. 8.

According to Table 2, the bootstrap 95% confidence interval for the mediating effect of tacit knowledge classification on the relationship between IBS and the receiver’s degree of knowledge internalization does not include zero, indicating that tacit knowledge classification not only has a direct effect on the degree of knowledge internalization but also exerts a mediating effect through IBS (at CH19) on the degree of knowledge internalization. The direct effect (0.88) and the mediating effect (0.25) account for 77.88% and 22.12% of the total effect (1.13), respectively.

For sender’s enhancement of knowledge transfer capability, we applied the same procedure. The path coefficients between the three variables are shown in Fig. 9.

According to Table 3, the mediating effect of IBS (at CH19) did not include zero within the 95% bootstrap confidence interval, whereas the direct effect of IBS (at CH19) on the enhancement in knowledge transfer capability included zero within the 95% bootstrap confidence interval. This finding indicates that IBS plays a fully mediating role in the relationship between the tacit knowledge type and the sender’s enhancement of knowledge transfer capability. Hypotheses 3 and 4 are supported.

4.3 The mechanism of the impact of individual knowledge characteristics on tacit knowledge transfer

4.3.1 Sharing willingness and learning willingness in the externalization phase

To explore the differences in IBS during technical and cognitive tacit knowledge transfer among individuals with varying levels of the sender’s tacit knowledge sharing willingness and the receiver’s tacit knowledge learning willingness, we examined the moderating effect of these factors on the relationship between tacit knowledge classification and IBS. Based on the mediating effect, we used PROCESS Model 7 to test the moderating effect. To avoid multicollinearity, all predictor variables were standardized. The interaction term between tacit knowledge classification and the receiver’s learning willingness significantly predicted IBS (B = −1.41, SE = 0.53, P = 0.01, 95% CI = [−2.48, −0.35]), indicating the presence of a moderated mediation effect. Simple slope analysis revealed (Fig. 10(a)) that for receivers with higher levels of learning willingness, the positive effect of tacit knowledge classification on IBS was not significant (B = −0.04, P = 0.91, 95% CI = [−0.76, 0.68]), whereas for receivers with lower levels of learning willingness, the positive effect was significant (B = 1.37, P = 0.00, 95% CI = [0.59, 2.15]). To validate the self-reported grouping of learning willingness, we created a composite behavioral score by summing the observed aggregating gaze duration, nodding frequency, and forward-leaning occurrences for each participant. An independent samples t-test revealed that individuals in the high learning willingness group scored significantly higher on this composite behavior index than did those in the low learning willingness group (t = −5.121, p < 0.05), supporting the alignment between subjective ratings and observable behaviors. H5 is supported.

For the sender’s sharing willingness, the same procedure was applied. The interaction term between tacit knowledge classification and the sender’s sharing willingness significantly predicted IBS (B = −1.18, SE = 0.55, P = 0.03, 95% CI = [−2.28, −0.08]), indicating the presence of a moderated mediation effect. Simple slope analysis revealed (Fig. 9(b)) that for senders with higher levels of sharing willingness, the positive effect of tacit knowledge classification on IBS was not significant (B = −0.05, P = 0.91, 95% CI = [−0.86, 0.76]), whereas for senders with lower levels of sharing willingness, the positive effect was significant (B = 1.13, P = 0.00, 95% CI = [0.39, 1.88]). Similarly, sharing willingness, a composite behavioral score, was constructed by frequencies of voluntary demonstrations, unsolicited elaborations, and gesture-based expressions. The high sharing willingness group presented significantly higher composite behavior scores than did the low sharing willingness group (t = −10.49, p < 0.05), further confirming the construct validity of the scale grouping. H6 is supported.

4.3.2 Absorptive and reflective capacities in the internalization stage

To investigate the differences in performance changes under varying levels of IBS for individuals with different levels of the sender’s reflective capacity and the receiver’s absorptive capacity, we explored the moderating effect of absorptive and reflective capacities on the relationship between IBS and tacit knowledge transfer performance. Similarly, based on the mediating effect, we employed PROCESS Model 14 to test the moderating effect in the latter part of the mediating effect. The interaction term between IBS and the receiver’s absorptive capacity significantly predicted the receiver’s degree of knowledge internalization (B = 0.57, SE = 0.24, P = 0.02, 95% CI = [0.10, 1.04]), indicating a significant moderating effect of the receiver’s absorptive capacity and the presence of a moderated mediation effect. Simple slope analysis (Fig. 10(c)) revealed that for receivers with higher absorptive capacity, IBS had a significant positive effect on the receiver’s degree of knowledge internalization (B = 0.57, P = 0.00, 95% CI = [0.32, 0.81]), whereas for receivers with lower absorptive capacity, the positive effect of IBS on the receiver’s degree of knowledge internalization was not significant (B = −0.001, P = 0.99, 95% CI = [−0.42, −0.41]), indicating that the receiver’s absorptive capacity positively moderated the effect of IBS on the receiver’s degree of knowledge internalization. For absorptive capacity, the composite behavior score included note-taking, mimicking gestures, and giving expressive feedback. Independent samples t-tests revealed a significantly higher behavioral engagement score in the high absorptive capacity group (t = −2.266, p < 0.05), confirming the validity of scale-based classifications. Hypothesis 7 is supported.

For the sender’s reflective capacity, the interaction term between IBS and the sender’s reflective capacity did not significantly predict the sender’s enhancement of knowledge transfer capability(B = −0.29, SE = 0.26, P = 0.28, 95% CI = [−0.81, 0.24]). Hypothesis 8 is not supported.

5 Discussion

Tacit knowledge transfer is a complex and dynamic interpersonal interaction process. This study employs a multiplayer hyperscanning paradigm to explain the micromechanisms of the transfer of different classifications of tacit knowledge of construction projects at the cognitive level. The results revealed significant IBS between the sender and receiver of tacit knowledge during the transfer task, aligning with the cooperative interaction hypothesis, which posits that interactions in cooperative tasks lead to synchronization of brain activities. The level of IBS during the transfer of tacit knowledge in the technical category was significantly higher than that in the cognitive category. This finding suggests that tacit cognitive knowledge is more challenging to articulate and express by the sender and to assimilate and digest by the receiver. Additionally, this finding supports previous research indicating that stronger synchronization in brain activities between both subjects occurs only when the receiver genuinely comprehends the sender’s intent (Stephens et al., 2010).

Consistent with the IBS observed in teacher‒student interactions (Pan et al., 2018), the results of this study indicate that in the context of tacit knowledge transfer within construction projects, the receiver’s transfer performance after completing the task is closely correlated with the level of IBS during tacit knowledge transfer. The brain‒brain coupling activities of the transferring subjects serve as a reliable predictor of learning outcomes. Through further mediating effect analysis, IBS not only serves as an immediate response in the knowledge transfer process but also plays a mediating role in how the type of tacit knowledge influences transfer performance. Different types of tacit knowledge elicit varying levels of interbrain coordination during the transfer process, and this difference further impacts the depth of understanding by the receiver and the sender’s enhancement of knowledge transfer capability. As a mediating variable, IBS effectively captures the key link in the “knowledge attributes–interactive coordination–performance outcomes” dynamic chain, highlighting the bridging role of neural mechanisms in knowledge transfer.

The findings suggest that subject characteristics play important moderating roles in the tacit knowledge transfer process. First, in the externalization stage, senders with a high sharing willingness are able to explain complex cognitive dimensions of tacit knowledge in more detail and externalize this knowledge through multiple methods. This process aids in transforming tacit knowledge into explicit knowledge and facilitates neural resonance with the receiver, hence improving IBS. Similarly, receivers with a high learning willingness engage more actively in the knowledge transfer process, further enhancing interaction and IBS with the sender. Notably, observational data revealed that individuals with a higher learning willingness exhibited more pronounced forward-leaning behavior and maintained longer gaze durations on the sender or instructional materials, both of which are indicators of focused attention and cognitive engagement during the task. Consequently, a higher willingness to share by the sender and a higher willingness to learn by the receiver lead to more efficient cooperation between the two subjects in the knowledge transfer process and a higher degree of IBS.

In the internalization stage, the receiver’s absorptive capacity plays a significant moderating role in the relationship between IBS and the receiver’s degree of knowledge internalization. Receivers with high absorptive capacity exhibit stronger cognitive resource management skills, enabling them to process and integrate externalized tacit knowledge more efficiently, thus reducing their cognitive load and improving their degree of knowledge internalization. Although the sender’s reflective capacity did not show a significant moderating effect on the relationship between IBS and the sender’s enhancement of knowledge transfer capability, this result may be due to limitations of the self-reported measure or the short duration of interaction, which may not allow reflective strategies to fully take effect. However, behavioral observations revealed that senders rated with higher reflective capacity displayed more frequent eye-scanning behavior, such as actively observing the receiver’s facial expressions and engagement cues, indicating an ongoing effort to monitor understanding and adjust their explanations in real time. This finding suggests that while the reflective trait may not immediately lead to measurable improvements in self-perceived transfer outcomes, it nevertheless manifests in real-time adaptive behaviors during the interaction.

In construction projects, tacit knowledge transfer is a dynamic interaction influenced by both the sender and receiver, requiring active participation and real-time feedback. Managers should establish flexible adjustment mechanisms to optimize transfer strategies based on project progress and learning feedback, for example, focusing on theoretical explanations and case simulations in early stages and increasing hands-on training as the project advances. Transfer methods should also match the type of knowledge: cognitive tacit knowledge (e.g., quality control, project planning) is best shared through building information modeling (BIM), scenario simulations, or case-based teaching, whereas skill-based tacit knowledge (e.g., concrete pouring, formwork installation) requires onsite demonstration and repeated practice. Given the long-term nature of tacit knowledge absorption, managers should implement continuous tracking and evaluation, such as quality inspections, skill assessments, or experience-sharing sessions, to adjust strategies in a timely manner. IBS can serve as an objective indicator to monitor interaction quality and support dynamic optimization. To enhance effectiveness, individual capabilities should be strengthened. Senders can be motivated through performance rewards or recognition to actively share knowledge and improve expression clarity, for example, by summarizing past project issues for training. Receivers should be encouraged to learn through promotion opportunities, certifications, and targeted training, including mentoring and field practice. Finally, knowledge platforms (such as technical databases, online training systems, and experience-sharing forums) can increase flexibility in access and foster collaboration, supporting the successful implementation of construction projects.

6 Conclusions

By using the fNIRS-based hyperscanning method, we observed significant IBS between the sender and receiver during tacit knowledge transfer in construction projects. Notably, the level of synchronization during cognitive tacit knowledge transfer was significantly lower than that during technical tacit knowledge transfer. Furthermore, IBS not only serves as a dynamic indicator that positively predicts transfer performance but also mediates the relationship between tacit knowledge types and transfer performance. This study also revealed that knowledge behavioral characteristics (such as learning willingness, sharing willingness, and absorptive capacity) moderate the relationships between tacit knowledge classification and IBS and between IBS and tacit knowledge transfer performance. This study reveals the mechanisms of tacit knowledge transfer in the construction engineering field, and opens the “black box” of the cognitive neural mechanisms that trigger such transfer for the first time. The SECI model innovatively introduces the concept of IBS to divide the tacit knowledge transfer process into two stages: externalization and internalization. A dual-participant “sender‒receiver” experimental design is employed, with individual characteristics as moderating variables. This approach expands the research paradigm, significantly enhances ecological validity, and provides new theoretical perspectives and methodological paths for future studies. At the practical level, the findings contribute to improving the efficiency of tacit knowledge flow, strengthening team collaboration, and optimizing human‒machine system coordination, thus supporting the intelligent transformation and high-quality development of the construction engineering industry.

However, this study has some limitations. First, fNIRS can detect only cortical activity and cannot capture signals from deeper brain regions. Given that tacit knowledge transfer is a complex process involving various types of brain activity, relying solely on cortical signals may not provide a comprehensive understanding of the interactions between knowledge senders and receivers. Second, to simulate the male-dominated nature of the current construction industry and to control for gender-related effects on IBS, this study recruited only male participants. However, this approach also limits the generalizability of the findings to mixed-gender or all-female construction teams. Gender may influence communication styles, cognitive strategies, and social sensitivity during tacit knowledge transfer. Future studies should consider involving participants actively engaged in engineering work and expanding sample diversity to increase the external validity of the results. These improvements would deepen our understanding of the micromechanisms of tacit knowledge transfer and provide stronger scientific support for optimizing knowledge management and teamwork.

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