Colleagues or Friends? Comparing Communication and Advice Networks for Building Information Modeling (BIM) Implementation in Construction Projects

Abstract

We examine how formal task-oriented communication and informal knowledge-oriented advice networks for building information modeling (BIM) implementation in construction projects exhibit different macrostructural characteristics and microformation mechanisms. The results show that while the two networks have some shared connections, individuals are more densely connected and centrally embedded within triads in the communication network than in the advice network. The results further show that the formation of these networks is differently driven by BIM implementation behaviors and individual demographic characteristics, suggesting that managers need to contingently design measures to facilitate task coordination and knowledge exchange for innovation implementation among heterogeneous participants in temporary projects.

Keywords

construction projects building information modeling (BIM)social network analysis communication relationships advice relationships innovation resistance behaviors social exchange

Introduction

As a disruptive technological innovation that has great potential to improve the efficiency and effectiveness of facility delivery processes (Cao et al., 2015; Sacks et al., 2018), building information modeling (BIM) has been increasingly adopted in different types of construction projects worldwide (Construction Industry Council [CIC], 2021; National Building Specification [NBS], 2020). Despite its great potential and increasing rate of adoption, however, the benefits derived from BIM implementation in practice have been significantly lower than expected in many projects (Ma et al., 2020; Wang et al., 2020). An important reason for this lies in the insufficient awareness and ineffective management of the complex interpersonal relationships that are reshaped or newly induced by multidisciplinary BIM implementation practices in construction projects (Dossick et al., 2010; Li et al., 2021). These BIM-related relationships include not only task-oriented communication relationships but also knowledge-oriented advice relationships. Communication relationships are generally formally developed with the aim of leveraging BIM to streamline design and construction processes and thus could play central roles in improving project performance in the short term (Marlow et al., 2018). In comparison, advice relationships are largely informally generated from the intrinsic needs of project participants to overcome knowledge barriers constraining the use of complex BIM technology; thus, they could act as critical conduits to develop individual competence and organizational capability in the long term (Argote, 2013).

Despite the importance of these relationships, extant literature has paid little attention to characterizing their different macrostructures and microformation mechanisms. Drawing on the social network perspective, a considerable amount of research in the construction domain has been devoted to investigating the structures of interpersonal ties, such as information exchange relationships (Chinowsky et al., 2008; Pirzadeh & Lingard, 2017), communication relationships (Lingard et al., 2019; Ryynänen et al., 2013), and knowledge exchange relationships (Schröpfer et al., 2017; Terhorst et al., 2018), in traditional project contexts. While recent years have witnessed a growing interest in exploring specific interpersonal relationships within the context of BIM implementation (Al Hattab & Hamzeh, 2018; Du et al., 2020; Park & Lee, 2017), this line of research in interpersonal relationships for BIM implementation is still in its infancy and primarily focusing on investigating the structural characteristics of BIM-enabled design communication ties. With the implementation of BIM as a systemic innovation, however, project participants not only have the duties to formally share BIM-related information with colleagues to collaboratively perform related project tasks; they also have the need to seek informal help from companions with closer connections to overcome knowledge barriers constraining their BIM implementation activities. As such, project participants tend to form not only design communication relationships but also other complex ties with colleagues or friends. Further investigations on how and why such different types of BIM-related relationships are differently formed within multidisciplinary construction projects could provide significant insights into how BIM-induced organizational and process changes can be more effectively managed to facilitate the advancement of BIM and related innovations in temporary project organizations.

Drawing on the network modeling method of the exponential random graph model (ERGM) and the network data of a BIM-based construction project, this study aims to explore how the formal task-oriented communication network and the informal knowledge-oriented advice network for BIM implementation in construction projects exhibit different macrostructural characteristics and how the formation of these networks is differently driven by microformation mechanisms. Building on social exchange theory, and taking into account the characteristics of BIM-related communication and advice networks in construction projects, this study specifically examines how the network formation processes are driven by the actor (effects based on behaviors/characteristics of nodes) and dyadic (homophily/similarity effects based on behaviors/characteristics of pairs of nodes) covariate effects related to BIM implementation behaviors (i.e., resistance behaviors). This study also controls for the effects of individual demographic characteristics (e.g., participant type and position level) and network structures (e.g., preferential attachment and triadic closure). The remainder of this article will first outline the theoretical background and propose the research hypotheses on the roles of these effects, and then use the network data to empirically validate or extend the hypotheses.

Theoretical Background and Research Hypotheses

Network Perspective on Interpersonal Relationships in Construction Projects

Construction projects, which generally rely on multiple heterogeneous actors with different disciplines, interests, and norms to collaboratively execute unique and interdependent activities, are temporary and heterogeneous in nature (Pauget & Wald, 2013). These inherent characteristics have led to increasing attention of researchers to the complex relationships between different types of project participating organizations and individuals, which makes the network perspective an important theoretical lens used in project management research (Adami & Verschoore, 2018; Braun, 2018; Cao et al., 2017; Guo et al., 2020; Steen et al., 2018; Takahashi et al., 2018; Xu & Lu, 2023). The network perspective moves the focus away from autonomous individuals to social relationships within which they are embedded (Borgatti et al., 2009; Zaheer et al., 2010). It holds the potential to provide a deepened understanding of what the structure of relationships is, how actors form relationships, and how the relationships impact outcome variables such as project performance (Pryke et al., 2018; Wang et al., 2018). Extant network studies in the construction domain have characterized various types of interpersonal relationships such as information exchange relationships (Chinowsky et al., 2008; Pirzadeh & Lingard, 2017; Williams et al., 2015), communication relationships (Lingard et al., 2019; Ryynänen et al., 2013), and knowledge exchange relationships (Herrera et al., 2020; Schröpfer et al., 2017; Terhorst et al., 2018). Taking into account the substantial impacts of BIM implementation on interpersonal relationships (Merschbrock et al., 2018; Sacks et al., 2018) and leveraging the advantage of recent developments of network modeling methods (Lusher et al., 2013; Kim et al., 2016), this study focuses on comparing the macrostructural characteristics and the microformation mechanisms of the following two types of emerging relationships for BIM implementation in construction projects: formal task-oriented communication relationships and informal knowledge-oriented advice relationships. Delving into these two specific relationship types is not only because of their distinct structures in project BIM implementation processes but also because of their substantial impacts on the effectiveness of collaborative BIM implementation efforts and the success of project practices. While communication as a bidirectional process for task coordination and information sharing can flow through both formal and informal channels (Henderson & Clark, 1990), this study focuses on formal task-oriented communication relationships, which are largely formally developed for task coordination and collaboration (Scott & Judge, 2009; Tsai & Compeau, 2021) associated with BIM implementation processes in construction projects. In contrast, advice relationships largely go beyond formal task-oriented communication ties and not only offer necessary information and support for problem-solving activities but also enhance knowledge exchange (Agneessens & Wittek, 2012; Argote, 2013; Erdogan et al., 2020; Nebus, 2006; Tröster et al., 2019). This type of relationship is unidirectional in nature and largely informally generated from individual intrinsic needs to overcome knowledge barriers (Soda & Zaheer, 2012) constraining the use of BIM in construction projects.

As the collaborative implementation of BIM in construction projects is generally accompanied by substantial changes in project processes, redistributions of responsibilities and risks, as well as restructuring of capability requirements (Cao et al., 2015; Dossick et al., 2010; Forsythe et al., 2015; Sacks et al., 2018; Whyte, 2019), individuals may exhibit negative responses, such as resistance behaviors, toward the revolutions associated with BIM implementation (Cao et al., 2022; Wang et al., 2020). Drawing on social exchange theory (SET) and taking into account the potential complex associations between BIM-related implementation behaviors and relationships, this study examines how the formation of interpersonal communication and advice relationships for BIM implementation is differently impacted by the actor (effects based on behaviors/characteristics of nodes) and dyadic (homophily/similarity effects based on behaviors/characteristics of pairs of nodes) covariate effects related to individual resistance to BIM implementation. This study also controls for the covariate effects of related individual demographic characteristics (e.g., BIM experience, project participant type, project position level) and the endogenous structural effects (e.g., reciprocity, preferential attachment, triadic closure) on the formation of interpersonal communication and advice relationships for BIM implementation. The research model for the formation mechanisms of communication and advice ties for BIM implementation is depicted in Figure 1. SET assumes that self-interested actors interact with others to accomplish their goals (Lawler & Thye, 1999). Based on cost-benefit analyses, actors behave in ways to maximize the benefits and minimize the costs (Blau, 1964; Molm, 1990). SET has been widely used to investigate the formation mechanisms of interpersonal relationships such as friendship (Bingham et al., 2014; Klein et al., 2004), communication (Lyu et al., 2020), and advice-seeking relationships (Agneessens & Wittek, 2012; Brennecke & Rank, 2016). From the perspective of SET, individuals consciously decide whether to form relationships with others by evaluating the potential benefits against the costs associated with relationship formation (Blau, 1964).

Figure 1.

Research model.

In the context of BIM implementation in construction projects, the formation of communication and advice relationships for BIM implementation can directly contribute to increased individual performance, since these relationships convey relevant information and knowledge that are essential for the collaborative implementation of BIM (Oraee et al., 2019). Additionally, since the implementation process of BIM, such as collaboration procedures, is often enacted by project owners or project managers (Sacks et al., 2018), forming formal communication relationships for task coordination and collaboration associated with BIM implementation shows conformity to organizational expectations, which will bring rewards such as prestige and promotion to individuals (Pennings, 1970). While there are clear benefits to communication and advice-seeking, individuals also need to bear potential costs. Communication and advice-seeking costs in temporary and multidisciplinary construction projects can be exacerbated by divergent organizational cultures among different participants as well as inconsistent cognition toward project goals and the implementation of innovations such as BIM. Apart from these costs, as to advice-seeking, costs may also be derived from interpersonal risks for advice seekers by admitting having less expertise than advice givers and the increased obligation to pay back a favor as reciprocation in the future (Nebus, 2006; Tröster et al., 2019). Drawing on SET, this study proposes that resistance behaviors of project participants to BIM implementation can be closely associated with their perceptions of the benefits and costs of communication and advice-seeking for BIM implementation; thus, resistance behaviors could influence the formation of these relationships.

The Actor Covariate Effect of Resistance Behaviors to BIM Implementation

Individuals with lower levels of resistance to BIM implementation are more likely to have higher levels of perceived value for BIM implementation (Wang et al., 2020). Perceived value refers to the perceived benefits of BIM-related change, which evaluates whether the benefits derived from changing from the status quo to the new situation (i.e., the implementation of BIM) are worth the costs (Kim & Kankanhalli, 2009). Therefore, individuals with lower levels of resistance to BIM implementation are more apt to hold stronger beliefs in the benefits of communication for BIM implementation, which should outweigh the costs. To enhance their task performance and show conformity to organizational expectations, they would be more likely to communicate with other project participants to obtain necessary information and resources related to BIM implementation. Regarding advice-seeking relationships, individuals with lower levels of resistance to BIM implementation could also hold stronger beliefs in the benefits of asking advice from others during the technology implementation process. However, they might simultaneously experience substantial advice-seeking costs, as asking for advice tends to signal incompetence and may lead to a loss of professional prestige from a social status perspective (Agneessens & Wittek, 2012; Blau, 1955; Borgatti & Cross, 2003). Individuals with more positive attitudes toward innovation implementation tend to care more about others’ opinions on their own implementation behaviors and thus hold stronger intentions to increase their associated prestige (Yiu et al., 2007). As a result, individuals with lower levels of resistance to BIM implementation tend to experience the higher prestige costs of advice-seeking, which could further constrain their willingness to ask for advice for BIM implementation from other project participants. Therefore, the impacts of resistance behaviors on the formation of advice-seeking ties would be weaker than the formation of communication ties for BIM implementation. Based on the above discussions, the following hypothesis is proposed:

H1: While individuals with lower levels of resistance to BIM implementation are more likely to form communication ties for BIM implementation with others in construction projects, the corresponding effect is less substantial for the formation of advice-seeking ties.

Network studies in other domains suggest that an individual’s choice of from whom to ask advice is influenced by their perception of the potential advice provider’s willingness, capabilities, and the costs needed to help (Nebus, 2006). Specifically, individuals tend to ask for advice from those who are perceived as willing and convenient to offer valuable information and knowledge and are not very costly to seek advice from (Borgatti & Cross, 2003; Nebus, 2006). As such, individuals behaving more positively and cooperatively in related domains would be regarded more as desirable advice givers (Erdogan et al., 2020). Within the context of BIM implementation in construction projects, the level of resistance to BIM implementation can act as a significant indicator of cooperative or uncooperative intent. Individuals who exhibit lower levels of resistance to BIM implementation are more likely to be regarded as more cooperative and more willing to give valuable advice. As they generally have relatively high levels of recognition on the value of BIM-related changes in the project (Wang et al., 2020), interactions with them may produce satisfactory outcomes for advice seekers. Moreover, since individuals with more positive attitudes toward innovation implementation tend to care more about others’ opinions on their own implementation behaviors (Yiu et al., 2007)—and thus hold stronger intentions to increase their prestige through giving advice to others (Agneessens & Wittek, 2012; Borgatti & Cross, 2003)—they tend to be more attractive and less costly to seek advice from. As such, individuals with lower levels of resistance to BIM implementation are more likely to be targeted as desirable advice givers and thus would be approached by advice seekers more frequently. These discussions lead to the following hypothesis:

H2: Individuals with lower levels of resistance to BIM implementation are more likely to attract advice-seeking ties for BIM implementation from others in construction projects.

The Dyadic Covariate Effect of Resistance Behaviors to BIM Implementation

Extant studies in other domains suggest that individuals may experience more benefits than costs from interacting with others who have similar attributes in terms of demographic characteristics, attitudes, and behaviors (Bingham et al., 2014; Klein et al., 2004; McPherson et al., 2001). This mechanism can be explained by the similarity-attraction and social-categorization paradigms. The former paradigm argues that similarity in attributes can increase interpersonal attraction and reduce discomfort occurring from divergent cognitions (Makela et al., 2007; Tsui & O’Reilly III, 1989). The latter paradigm posits that individuals define their social identities by forming distinct social groups based on attributes, which can lead individuals to perceive out-group others as less cooperative and trustworthy than the in-group others (Makela et al., 2007; Williams & O’Reilly III, 1998). In this research context, resistance behaviors of project participants signal their attitudes and intentions toward BIM implementation and provide a basis for social attraction and categorization. On the one hand, individuals are more attracted to those who share similar levels of resistance to BIM implementation because these similar others tend to have a shared understanding of what they know; additionally, interacting with these similar others could help to reduce discomfort occurring from the lack of common ground (e.g., beliefs, capabilities) for BIM implementation. Thus, it would be easier and less costly to form ties with those with similar resistance behaviors. On the other hand, under the logic of social categorization, information is perceived as less credible if it comes from those with dissimilar behaviors. As such, interacting with others with dissimilar levels of BIM-associated resistance might increase subjective uncertainty and impede self-enhancement.

Compared with that of advice ties, however, the formation of communication ties for BIM implementation would be less likely to be driven by the similarity-attraction and social-categorization paradigms associated with resistance behaviors, since communication relationships are generally more formally formed for task coordination and collaboration and more substantially impacted by external process and organizational requirements (Scott & Judge, 2009) than informal advice relationships. As such, while individuals with lower levels of resistance to BIM implementation tend to be more active in forming communication relationships—due to the formal process and organizational requirements in construction projects—they might communicate with not only those with higher levels of resistance but also those with lower levels of resistance to BIM implementation. By contrast, advice ties are generally less impacted by external requirements but are largely informally impacted by individuals’ intrinsic needs and willingness to gain external support and overcome knowledge barriers (Nebus, 2006). Compared with the formation of communication ties for BIM implementation in construction projects, therefore, the formation of advice ties tends to be more significantly impacted by the similarity-attraction and self-categorization paradigms associated with BIM-related resistance behaviors. These discussions lead to the following hypothesis:

H3: While individuals with more similar levels of resistance to BIM implementation are more likely to form advice ties for BIM implementation with one another in construction projects, the corresponding effect is less substantial for the formation of communication ties.

Research Method

Data Collection Procedure

The network data for the present study were collected from a complex BIM-based construction project in Shanghai, China. As planned to be one of the world’s largest indoor ski resorts with a total construction area of 227,000 square meters, this project involved multiple design and construction disciplines such as mechanical, electrical, and plumbing (MEP) engineering; steel structural engineering; curtain wall engineering; and theme packaging engineering. These complexities motivated the project owner to formulate BIM implementation guidelines from the outset and request the use of BIM throughout the project life cycle. This project was divided into two districts, namely, the north district and the south district. The south district was in the final stage of construction and the north district had just started construction when this study began. Considering the impacts of sample size on the feasibility of complex network data collection (Borgatti & Cross, 2003), this study focused the sampling frame on all the project participants who have been involved in the construction of the MEP engineering part in the north district of the project due to its inherent complexity for design-construction coordination and heightened needs for collaborative BIM implementation (Sacks et al., 2018). This includes those from the project owner, the designer, the general contractor, the subcontractor, and the BIM consultant.

A mix of methods, including questionnaire surveys, interviews, field observations, and document analysis, were used to collect and triangulate the data for network analysis. First, in June 2020, an interview was conducted with the project manager, who is affiliated with the project owner, to get an overview of the status of the project and to identify the participants involved in the implementation of BIM in the MEP engineering part in the north district. The boundary of the analyzed networks was thus exogenously specified based on the research objective (Butts, 2008), and the identified roster of network nodes was further verified by project documents. A total of 37 participants were then identified as targets for subsequent questionnaire surveys. These participants were from multiple organizations, including the project owner (11 participants), the designer (13 participants), the general contractor (10 participants), and the BIM consultant (three participants). Second, in July 2020, a pilot survey was conducted with 10 participants in the roster to assess the comprehensiveness of the roster and identify unclear expressions in the questionnaire. Based on the feedback, seven additional participants were included in the roster (four from the subcontractor and three from the construction supervisor). Third, in August 2020, when MEP models of the basement were initially completed and project teams had formed relatively stable communication and advice relationships for BIM implementation, all participants on the roster were invited to complete the questionnaire. To enhance response rates, each respondent obtained an announcement note from the project manager, a small gift, and a formal commitment guaranteeing the confidentiality and anonymity of the responses. While all the respondents completed the questionnaire, 34 responses were found to be complete and valid. Two days later, the research team conducted another round of surveys with the remaining 10 respondents, which resulted in a valid response rate of 100%. The research team also attended related project meetings for data verification.

Measures

The relationship ties among the targeted respondents were measured with the roster-based sociometric technique, which has been widely accepted and used in the management and organization literature (Sykes & Venkatesh, 2017; Tröster et al., 2019). Specifically, each respondent was provided with a randomly sorted list of names and asked to mark the frequency of the indicated relationship with the other 43 participants on the roster (scores ranging from 1 = “less than once a month,” 2 = “once a month,” 3 = “once a week,” 4 = “once a day,” to 5 = “many times a day”) (Sykes & Venkatesh 2017). To diminish the potential measurement error related to the fixed roster instrument, respondents were given the option to add the names of participants with whom they communicated and sought advice from, in addition to those listed in the roster.

Following previous studies (Brass, 1984; Perry-Smith, 2006; Tröster et al., 2019), communication ties were measured with the question, “In this project, with whom do you talk about work-related topics for BIM implementation (through scheduled meetings, work reporting, or task allocations) and how often do you communicate with them?” Advice ties were measured with the question, “In this project, to whom do you go for advice/help when you have difficulties in implementing BIM and how often do you ask them for advice/help?” The responses to these questions resulted in two adjacency matrices of size 44 × 44, which were further converted into binary adjacency matrices by setting a cut-off value. Following previous research (Hanneman & Riddle, 2005), the average frequency of the two matrices (1.84 and 1.59, respectively) was used as the cut-off value and, hence, 1 was entered in the cell X_ij if i reported communication or advice ties with j with scores greater than 1; otherwise, 0 was entered. Regarding the undirected and symmetric communication ties, the reciprocation rate is 59%. Following Brass (1984), the project manager affiliated with the project owner, who led BIM implementation in this project, was further interviewed to eliminate these discrepancies.

The measurement items for the level of resistance (LRE) to BIM implementation were adapted from Kim and Kankanhalli (2009) and adjusted to fit the context of BIM implementation in construction projects. These items were validated by Wang et al. (2020) and Cao et al. (2022). A seven-point Likert scale ranging from “strongly disagree” to “strongly agree” was used to measure the items. Due to their potential impacts on interpersonal relationships, BIM experience, participant type, and project position level were further controlled to isolate the variation caused by related characteristics. Specifically, BIM experience was operationalized as an ordinal variable (1 = less than 3 years; 2 = 3–5 years; 3 = more than 5 years), whereas participant type (0 = other types of participants; 1 = project owner) and position level (0 = project engineer; 1 = project manager) were operationalized as dummy variables.

Statistical Analysis Methods

The following network indicators were first calculated to descriptively analyze the macrostructural characteristics of the communication and advice networks for BIM implementation:

Fragmentation, which refers to the proportion of pairs of nodes that are unconnected from each other (Borgatti, 2006);

Average node degree, which measures the average number of ties each node has;

Network density, which refers to the ratio of the total number of present ties to the total number of possible ties;

Average distance, which refers to the average length of the shortest paths among reachable pairs of nodes;

Clustering coefficient, which refers to the average density of each node’s open neighborhood (i.e., the neighborhoods connected to the focal node). This study specifically calculated the weighted clustering coefficient since it gives weight to the number of a node’s neighborhood and therefore can more precisely characterize the cohesion of a network (Hanneman & Riddle, 2005); and

Freeman’s graph centralization, which describes the nodes as a whole and reflects the degree of inequality in a network as a percentage of that of a perfect star network of the same size (Freeman, 1979).

Based on these descriptive analyses, the quadratic assignment procedure (QAP) correlation analysis was then conducted to identify the extent to which the communication network is associated with the probability of the advice network being formed. This algorithm first computes correlation coefficients between the corresponding cells of the two matrices. Next, it randomly permutes the rows and the columns of one matrix and recomputes the association measures. The procedure repeats the second step hundreds of times and recomputes the correlation measures at each iteration. Specifically, the Jaccard coefficient—which measures the proportion of the co-occurrence of ties for the two networks when ties are observed in at least one of the networks (Giuliani, 2008)—was calculated in this study with the tool of UCINET 6 with 5,000 random permutations (Borgatti et al., 2002).

The method of ERGM was then used to test the proposed hypotheses on the formation mechanisms of project communication and advice networks for BIM implementation. ERGM is a class of parametric models used for understanding how and why network ties form (Lusher et al., 2013). In contrast to traditional regression models, ERGM models network ties as being interdependent, which not only offers advantages to model a variety of endogenous network structures but also allows the estimation of the effects of exogenous factors such as actor and dyadic covariate effects. The ERGM model can be specified as the following form, which estimates the likelihood that a network with n nodes exhibits specific configurations as compared to random networks with the same number of nodes:

Pr (X = x | Y = y) = \frac{1}{κ} \exp (θ^{T} z (x) + θ_{a}^{T} z_{a} (x, y))

where X denotes all possible networks with n nodes and x denotes a realization. Y denotes an array of attribute variables and y is the actual realization.

z (x)

are statistics for endogenous network covariates, and

θ

are parameters corresponding to these effects.

z_{a} (x, y)

are statistics for exogenous effects, and

θ_{a}

are the parameters corresponding to the statistics

z_{a} (x, y)

. The normalizing quantity

κ

ensures that the sum of the equation is a proper probability distribution. This study applied the Markov chain Monte Carlo maximum likelihood (MCMC-MLE) estimations using the ergm package of R (Hunter et al., 2008). The covariate-related and structural effects investigated in the ERGM models are shown in Table 1.

Table 1.

Summary of the Effects Included in the ERGM Models

Parameter	Definition	Statistics
Actor Covariate Effects
LRE (nodecov)BIM experience (nodecov)Participant type (nodefactor)Position level (nodefactor)	Tendency for actors with higher values of a specific attribute to form communication ties	$\sum x_{i j} (y_{i} + y_{j})$
LRE seeker (nodeocov)BIM experience seeker (nodeocov)Participant type seeker (nodeofactor)Position level seeker (nodeofactor)	Tendency for actors with higher values of a specific attribute to form advice-seeking ties	$\sum x_{i j} y_{i}$
LRE giver (nodeicov)BIM experience giver (nodeicov)Participant type giver (nodeifactor)Position level giver (nodeifactor)	Tendency for actors with higher values of a specific attribute to attract advice-seeking ties from others	$\sum x_{i j} y_{j}$
Dyadic Covariate Effects
LRE dissimilarity (absdiff)BIM experience dissimilarity (absdiff)	Tendency for network ties to occur between actors who are similar in a specific aribute	$\sum x_{i j} \| y_{i} - y_{j} \|$
Participant type match (nodematch) Position level match (nodematch)		$\sum x_{i j} y_{i} y_{j}$
Network Structural Effects
EdgesReciprocity (mutual)	Baseline tendency for a tie to formTendency toward reciprocity in getting advice	$\sum x_{i j}$ $\sum x_{i j} x_{j i}$
Preferential attachment (gwdegree)Preferential attachment (gwidegree)	Tendency toward variation in the degree to which actors communicate with others	$\sum_{k = 2}^{n - 1} (- 1)^{k} \frac{S_{k}}{λ^{k - 2}}$
	Tendency for variation in the degree to which actors are nominated as providers of advice	$\sum_{k = 2}^{n - 1} (- 1)^{k} \frac{S_{k_i n}}{λ^{k - 2}}$
Triadic closure (gwesp)	Tendency for triadic closure in communication or advice networks	$λ \sum_{i < j} x_{i j} {1 - {(1 - \frac{1}{λ})}^{{\tilde{L}}_{2 i j}}}$ $λ \sum_{i < j} x_{i j} {1 - {(1 - \frac{1}{λ})}^{{\tilde{L}}_{T 2 i j}}}$
Cyclic closure (gwesp.ITP)	Tendency for cyclic closure in advice network	$λ \sum_{i < j} x_{i j} {1 - {(1 - \frac{1}{λ})}^{{\tilde{L}}_{C 2 i j}}}$

Note: A darker gray circle indicates actor with a specific attribute, a larger size of circle is used to represent a larger score on a specific attribute, lines without arrows indicate communication ties, and lines with arrows represent get-advice ties. $x_{i j}$ $=$ network tie between actors i and j; y $=$ attribute of actors i and j; $λ$ $=$ a fixed dampening constant; $S_{k} = \sum_{1 \leq i \leq n} (\begin{matrix} x_{i +} \\ k \end{matrix})$ , where $x_{i +}$ is the degree of actor i, k represents the size of the star (for instance an edge is a 1-star, a two-path is a 2-star); $S_{k_i n}$ refers to the in-degree of actors in the advice network (Snijders et al., 2006); $\tilde{L} (*)_{2 i j} =$ the number of indirect paths of length two between actors i and j.

Data Analysis Results

Results of Descriptive Network Analysis

Table 2 summarizes the descriptive statistics and correlations for the investigated variables. The composite reliability of LRE is 0.94, which exceeds the recommended threshold of 0.70 (Fornell & Larcker, 1981). The communication and advice networks for BIM implementation were descriptively analyzed with UCINET 6 and visualized in Figure 2 with Gephi 0.9.2. The gray level of the nodes in Figure 2 differentiates participant types, the shape of the nodes indicates position levels, and the size of the nodes represents degree centrality. While undirected ties in Figure 2a represent symmetric communication relationships between i and j, the directed ties in Figure 2b represent asymmetric advice-giving relationships from i to j. It is evident from Figure 2 that, while the communication network is denser than the advice network, there are some nodes having large numbers of communication and advice ties and thus occupying central positions in the two networks. Further analyses on the distributions of communication centrality and advice centrality, as exhibited in Figure 3, also suggest that while the average node degree in the communication network is substantially larger than that in the advice network, there are some individuals with relatively high levels of both communication and advice centralities.

Figure 2.

Visualizations of communication and advice networks for BIM implementation.

Figure 3.

Distributions of node degrees of communication and advice-giving.

Table 2.

Descriptive Statistics, Correlation Matrix, and Measurement Reliability

Construct		Mean	SD^a	1	2	3	4	5	6
1	BIM experience	1.70	0.82
2	Participant type	0.25	0.44	0.15
3	Position level	0.25	0.44	0.21	0.15
4	LRE	2.66	1.26	−0.39**	−0.50**	−0.00	(0.94)^b
5	No. of communication ties	9.23	6.91	0.27	0.54***	0.13	−0.52***
6	No. of advice-seeking ties	3.43	3.51	0.32**	−0.07	−0.24	−0.16	0.40**
7	No. of advice-giving ties	3.43	2.88	0.61***	−0.05	0.26	−0.35*	0.47**	0.44**

Notes: *** p < 0.001, ** p < 0.01, * p < 0.05 (two-tailed).

Standard deviation.

Composite reliability for LRE.

To quantitatively compare the characteristics of the two networks, a set of network structure statistics was further calculated and is reported in Table 3. The values of the fragmentation are 0 and 0.217 for the communication network and the advice network, respectively, indicating that while all pairs of individuals in the project are reachable through communication ties, more than 20% of the pairs of individuals in the project are disconnected through advice ties. The values of the average node degree and network density also indicate that individuals are more densely connected in the communication network than in the advice network. Another noteworthy result shown in Table 3 is that the value of the clustering coefficient of the communication network is almost 1.25 times that of the advice network, suggesting that the triadic structures can be more often observed in the communication network than in the advice network. The Freeman’s graph centralization of the communication network is more than twice that of the advice network. This result indicates that, compared with the advice network, the communication network is more substantially organized around certain star individuals. The additional QAP correlation analysis result shows that the Jaccard coefficient for the two networks is 0.317, indicating that only 31.7% of the communication and advice ties overlap with each other. Taken together, the descriptive network analysis results provide clear evidence that the task-oriented communication network and the knowledge-oriented advice network for BIM implementation generally exhibit distinctly different structures, suggesting that colleagues are not always friends during project BIM implementation processes.

Table 3.

Network Structure Statistics

	Communication Network	Advice Network^a
Fragmentation	0.000	0.217
Average node degree	9.227	5.273
Network density	0.215	0.123
Average distance	2.047	2.397
Clustering coefficient	0.469	0.375
Freeman’s graph centralization	0.482	0.237

Note: ^aAll indicators for the advice network are calculated based on symmetrized data.

Results of Exponential Random Graph Model

Table 4 presents further ERGM results on the formation mechanisms of the communication and advice networks for BIM implementation. The estimate for the actor covariate effect of LRE on the communication network is significant and negative (β = -0.151, p < 0.01), whereas the estimate for the actor covariate effect of LRE seeker on the advice network is non-significant (β = 0.131, p < 0.10). This suggests that while individuals with lower levels of resistance to BIM implementation are more likely to form communication ties for BIM implementation with others, the corresponding effect is less substantial for the formation of advice-seeking ties. Therefore, H1 is supported. The negative coefficient for the actor covariate effect of LRE giver on the advice network (β = -0.213, p < 0.05) suggests that individuals with lower levels of resistance to BIM implementation are more likely to act as providers of advice and help for BIM implementation. This provides support for H2. The dyadic covariate effect of LRE dissimilarity is more significant for the advice network (β = -0.152, p < 0.05) than the communication network (β = 0.148, p < 0.10). This indicates that, while individuals with more similar levels of resistance to BIM implementation are more likely to form advice ties for BIM implementation with one another, the corresponding effect is less substantial for the formation of communication ties, confirming H3.

Table 4.

Predicting the Communication Network and Advice Network Formation: Results of ERGM

Variable	Communication Network	Advice Network
Resistance Behaviors
LRE	−0.151 (0.050)**	Seeker: 0.131 (0.072)
LRE	−0.151 (0.050)**	Giver: -0.213 (0.109)*
LRE dissimilarity	0.148 (0.084)	−0.153 (0.075)*
Controls: Demographic Characteristics
BIM experience	0.126 (0.052)*	Seeker: -0.076 (0.096)
BIM experience	0.126 (0.052)*	Giver: 0.356 (0.145)*
Participant type: Project owner	1.219 (0.230)***	Seeker: 0.364 (0.185)*
Participant type: Project owner	1.219 (0.230)***	Giver: -0.293 (0.271)
Position level: Project manager	0.159 (0.141)	Seeker: -0.861 (0.242)***
Position level: Project manager	0.159 (0.141)	Giver: 1.204 (0.308)***
BIM experience dissimilarity	−0.350 (0.095)***	−0.288 (0.088)**
Participant type match	1.541 (0.255)***	0.616 (0.137)***
Position level match	0.211 (0.220)	0.236 (0.228)
Controls: Network Structural Characteristics
Edges	−9.450 (0.809)***	−5.471 (0.522)***
Reciprocity		2.067 (0.416)***
Preferential attachment	3.823 (0.813)***	3.003 (0.757)***
Triadic closure	2.849 (0.313)***	1.155 (0.128)***
Cyclic closure		−0.168 (0.133)

Note: Standard errors are reported in parentheses; ***p < 0.001, **p < 0.01, *p < 0.05 (two-tailed).

As to the effects of individual demographic characteristics and network structural properties on the formation of communication ties, the results reveal that BIM experience and participant type have significant positive relationships with the formation of communication ties. Moreover, communication ties are significantly more frequently observed among individuals with similar BIM experience and of the same participant type while not significantly related to the match of project position levels. The coefficient of edges is a constant term in ERGM models. Its significant negative effect suggests that the communication network is relatively sparse as compared with random graphs. The preferential attachment effect is significant and positive, suggesting that there exists a small number of super-connected star nodes in the communication network. The positive coefficient of triadic closure suggests that communication ties tend to occur in transitive structures.

As to the effects of individual demographic characteristics and network structural properties on the formation of advice ties, the results illustrate that while individuals from the project owner and with lower project position levels tend to seek advice for BIM implementation from others more frequently, individuals with higher levels of BIM experience and project position tend to more frequently act as advice givers. It is also illustrated that advice ties are significantly more likely to be observed among individuals with similar BIM experience and of the same project participant type. The positive reciprocity effect indicates that the advice network exhibits significant reciprocated seeking and giving relationships. The positive preferential attachment effect indicates that individuals are more likely to seek advice from those who are more frequently nominated as advice givers by others. The significant positive effect of triadic closure and the insignificant effect of cyclic closure provide evidence that there is a tendency toward status hierarchy in the advice network for BIM implementation.

Discussions, Implications, and Future Research

Discussions

In empirically examining the formal task-oriented communication network and the informal knowledge-oriented advice network for BIM implementation in construction projects, this study compares not only how the two types of networks exhibit different macrostructural characteristics but also how the formation of the networks is differently driven by a set of microactor and dyadic covariate effects. The results of descriptive network analyses provide evidence that individuals are more densely connected, more highly centralized, and more closely embedded within triadic structures in the communication network than in the advice network. The results also indicate that colleagues are not always friends during project BIM implementation practices—the communication and advice networks overlap with each other with a relatively low Jaccard coefficient of 0.317. Regarding the mechanisms underpinning network formation, the ERGM results provide evidence that while individuals with lower levels of resistance to BIM implementation are more likely to form communication ties for BIM implementation with other project participants, this effect is less substantial for the formation of advice-seeking ties. The ERGM results also provide evidence that individuals with lower levels of resistance to BIM implementation are more likely to attract advice-seeking ties for BIM implementation from others. These results collectively corroborate the argument that status considerations generally play an important role in the individual evaluation of benefits against costs associated with advice-seeking and thus impact the formation of advice relationships (Blau, 1955), whereas these considerations are less likely to influence the formation of communication relationships. Additionally, the results show that the advice network is more significantly organized among individuals with similar levels of resistance behaviors to BIM implementation than the communication network. This result tends to suggest that compared with choosing with whom to seek advice, project participants are less capable of autonomously choosing with whom to communicate based on individual preferences driven by the similarity-attraction and social categorization paradigms associated with resistance behaviors. This could be due to the fact that communication relationships are more substantially impacted by external processes and organizational requirements (Scott & Judge, 2009) than informal advice relationships. Taken together, the results suggest that while individuals tend to engage in social interactions in ways to maximize the benefits and minimize the costs (Blau, 1964), the formation mechanisms of interpersonal relationships for innovation implementation in temporary project organizations could also be contingent on the types of relationships differently impacted by external requirements, such as work process, and internal needs such as status considerations.

Apart from resistance behaviors, this study also explores the impacts of related individual demographic characteristics on the formation of the communication and advice networks for BIM implementation. BIM experience is found to be positively associated with the formation of both BIM-related communication and advice-giving ties. It is also found that both communication and advice relationships tend to be formed among individuals with similar levels of BIM experience. This result is consistent with previous studies suggesting that similar experiences among individuals could provide commonality and thus make interpersonal interactions less costly (Bingham et al., 2014; Brennecke, 2020). Another noteworthy finding is that, compared with those from other disciplines, individuals from the owner organization are more frequently involved in communication and advice-seeking relationships. Also of note is the significant dyadic covariate effect of participant type match for both communication and advice networks. Taken together, these results provide clear evidence that while the leverage of BIM benefits in construction projects needs close interdisciplinary collaborations (Sacks et al., 2018), the in-depth BIM-related interactions are still limited to specific disciplines, and the expected interdisciplinary collaborations for BIM implementation are still substantially constrained. As for the effects of individual position level, it is revealed that project managers are more likely to be sought for advice, whereas project engineers have a greater tendency to seek advice.

This study also examines how the formation of the two networks is associated with related structural effects. In line with the results of descriptive network analysis and related covariate effects, the significant structural effects of preferential attachment and triadic closure provide further evidence that the communication network for BIM implementation is prominently developed around a small number of super-connected star actors and exhibits a distinct clustering structure. Regarding the structural effects for the advice network, the significant preferential attachment effect is consistent with the actor covariate effects of resistance behaviors, BIM experience, and position level, providing further evidence that a small number of proactive, experienced, and high-status actors generally play central roles in providing knowledge and support for BIM implementation. Another noteworthy finding is that the advice network is characterized by strong tendencies toward reciprocity and triadic closure but tendencies against cyclic closure. These results, along with the significant actor covariate effect of position level, provide clear evidence that the advice network for BIM implementation exhibits distinct asymmetric hierarchical and clustering structure governed by high-status individuals, which further strengthens the argument that status considerations seem to play an important role in the formation of advice relationships (Blau, 1955).

Implications

As an exploratory effort to empirically compare the formal task-oriented communication network and the informal knowledge-oriented advice network for BIM implementation in construction projects, this study contributes to the growing body of network literature in the construction domain by characterizing how different types of emerging relationship networks in the implementation context of disruptive technologies exhibit different macrostructural characteristics and how the formation of these networks is differently driven by microcovariate and structural effects. The findings provide clear evidence that colleagues are not always friends during project BIM implementation practices—the communication and advice networks overlap with each other with a relatively low Jaccard coefficient, with the former network being more densely connected, more highly centralized, and more closely embedded within triadic structures than the latter network. Through drawing on social exchange theory (Blau, 1964) and taking advantage of recent developments in network modeling methods (Kim et al., 2016), this study further explores how the formation of the two networks is driven by a collection of microactor and dyadic covariate effects while controlling for related endogenous structural effects, which has been largely underexplored in the management literature in the construction domain. By providing evidence that the formation of the two networks is differently attributed to the effects of resistance behaviors, individual demographic characteristics, and endogenous network structures, this study contributes to a deepened understanding of how individuals formally and informally interact with one another for innovation implementation in multidisciplinary construction projects.

This study also provides valuable insights for practitioners into how the complexity of intraproject relationships could be more effectively managed to facilitate the advancement of BIM and related innovative technologies in temporary project organizations. First, the empirical results provide clear evidence that both the communication and advice networks for BIM implementation in construction projects distinctly exhibit the structural tendency of centralization around super-connected star nodes. This structural tendency is significantly related to the actor covariate effects of individual demographic characteristics and behaviors (e.g., the negative effect of resistance behaviors for communication and advice-giving ties, the positive effect of BIM experience for communication and advice-giving ties, the positive effect of participant type for communication and advice-seeking ties). To deepen interpersonal interactions in these networks and thus facilitate the collaborative implementation of disruptive technologies like BIM in construction projects, therefore, specific intervention strategies need to be designed to leverage the critical roles of those individuals with specific characteristics and behaviors in fostering related communication and advice relationships. Second, while the leverage of BIM benefits in construction projects needs close interdisciplinary collaborations (Sacks et al., 2018), the empirical results reveal that the communication and advice networks for BIM implementation are both significantly related to the dyadic covariate effects of participant type match and BIM experience similarity, suggesting that disciplinary boundaries and experience have caused substantial separations in BIM-related interactions. As such, it would be significant to design related policies to specifically foster interactions among participants with different disciplinary and experience contexts. Third, the empirical results on the covariate effects of resistance behaviors provide evidence for the differentiated role of individual resistance to BIM implementation in shaping related communication and advice relationships. As such, it would also be significant to contingently design specific resistance-alleviation measures, such as offering BIM-related technical trainings and providing innovation incentives (Cao et al., 2022; Wang et al., 2020), to facilitate effective task coordination, information sharing, and knowledge exchange for BIM implementation among project participants.

Limitations and Future Research Directions

The findings of this study should be interpreted with the following limitations in mind. First, as an exploratory effort to explore the macrostructural characteristics and the microformation mechanisms of formal and informal interpersonal relationships for BIM implementation in construction projects, this study limits its scope to the comparisons of the formal task-oriented communication relationships and the informal knowledge-oriented advice relationships. Delving into these two specific relationship types is not only because of their distinct structures in BIM implementation processes but also because of their substantial impacts on the effectiveness of collaborative BIM implementation efforts. Considering the multidimensionality and complexity of interpersonal relationships in multidisciplinary construction projects (Chinowsky & Taylor, 2012), a natural extension of the present study would be to incorporate other types of formal and informal relationships in the analysis to provide a more comprehensive understanding of how individuals with heterogeneous demographic characteristics and behaviors interact with one another in different types of project contexts. Second, this empirical study was conducted based on relationship and behavior data in a complex BIM-based project in the Chinese mainland. While the process of “hypothesis proposing–hypothesis testing–results discussion” is strictly followed to organize the theoretical argumentation and discuss the findings from a general perspective, the specific market and cultural contexts in the Chinese construction industry might limit the generalizability of the empirical findings, especially those related to the formation mechanisms for the informal knowledge-oriented advice networks to other contexts. Future research could conduct related empirical investigations in other regions to further validate the applicability of related empirical findings in different market and cultural contexts.

Conclusion

While the complexity of formal and informal relationships within multidisciplinary construction projects has substantially increased with the implementation of disruptive innovations such as BIM, little is understood about the macrostructures and microformation mechanisms of these emerging relationships. This study empirically examines how the formal task-oriented communication network and the informal knowledge-oriented advice network for BIM implementation in construction projects exhibit different macrostructural characteristics and how the formation of these networks is differently driven by a set of microactor and dyadic covariate effects. The empirical results based on the network data from a BIM-based construction project in China provide evidence that, although the two networks overlap with each other with a relatively low Jaccard coefficient of 0.317, individuals are more densely connected, more highly centralized, and more closely embedded within triadic structures in the communication network than in the advice network. Drawing on social exchange theory and exponential random graph models, this study further analyzes the tie formation mechanisms of the two types of networks. The ERGM results reveal that while individuals with lower levels of resistance to BIM implementation and higher levels of BIM experience generally play more active roles in forming communication ties than forming advice-seeking ties, the advice network is more significantly organized among individuals with similar levels of resistance behaviors than the communication network. Although the leverage of BIM benefits in construction projects needs close interdisciplinary collaborations, the communication and advice network for BIM implementation both exhibit a significant intra-disciplinary characteristic. The two networks are also significantly impacted by the dyadic covariate effect of BIM experience similarity as well as the structural effects of preferential attachment and triadic closure. This study represents an exploratory effort to explore the macrostructural characteristics and the microformation mechanisms of formal and informal interpersonal relationships for BIM implementation in construction projects. The findings contribute to a deepened understanding of how individuals with different demographic and behavioral characteristics formally and informally interact with one another to collaboratively implement disruptive technologies across disciplinary boundaries in temporary project organizations. Furthermore, the results provide insights into how the complexity of intraproject relationships can be more effectively managed to facilitate the advancement of BIM and related innovations in temporary project organizations.

Footnotes

Acknowledgments

This work is supported by the National Natural Science Foundation of China (Grant No. 71802150, 72271186) and the Fundamental Research Funds for the Central Universities in China (Grant No. 12002150055).

Declaration of Conflicting Interests

The authors declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

ORCID iD

Shiting Shao

Correction (October 2023):

Article has been updated to correct full form of BIM in Title and Abstract.

Author Biographies

Dongping Cao is an associate professor in the Department of Construction Management and Real Estate, School of Economics and Management, Tongji University. His research interests cover digital innovation in construction, project organizing, and project networks. His research has been funded by the National Natural Science Foundation of China and has appeared in several journals in the construction and project management domain, including Automation in Construction, International Journal of Project Management, Journal of Construction Engineering and Management, and Journal of Management in Engineering. He is the coeditor of the book Research Handbook on Complex Project Organizing (Edward Elgar). He has received several academic awards, such as the 2018 Best Journal Paper Award of the Journal of Computing in Civil Engineering. He can be contacted at dongping.cao@tongji.edu.cn

Shiting Shao is a doctoral candidate in Construction Engineering and Management in the Department of Construction Management and Real Estate, School of Economics and Management, Tongji University. Her expertise is specifically in the theory and application of statistical methods for social networks, including stochastic actor-oriented model (SAOM), exponential random graph modeling (ERGM), and multiple regression quadratic assignment procedure (MRQAP), in project organization and innovation studies. She can be contacted at shiting_shao@tongji.edu.cn

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