Aligning misaligned systemic innovations
probing inter-firm effects development in project networks
Antti O. Maunula, Magenta Advisory Oy, Helsinki, Finland
John E. Taylor, The Charles E. Via Jr. Department of Civil and Environmental Engineering, Virginia Tech, Blacksburg, VA, USA
Riitta Smeds, Simlab, Department of Industrial Engineering and Management, Aalto University School of Science, Espoo, Finland
Implementing systemic innovations in a project network can significantly improve its performance; however, implementing systemic innovations is difficult if project network structures misalign to the innovation. Little research has examined how project network structures can align to systemic innovations. To address this research gap, we studied an advanced building information modeling tool implemented in a Finnish design and development project network. We found that misaligned innovations are aligned through a process of task sequence alignment, knowledge base alignment, and work allocation alignment. Our findings are important; they suggest that implementing systemic innovations in project networks is a multistage inter-firm process.
KEYWORDS: alignment; building information modeling; innovation; interorganizational process alignment; project networks; systemic innovation; technology implementation
Interorganizational networks composed of multiple firms are replacing vertically organized hierarchical firms as focal economic actors (Dyer & Singh, 1998). Firms in interorganizational networks collaborate to execute work for mutual gain, and much of the collaborative work is organized in projects (Manning, 2008; Whitley, 2006). The interorganizational network assembled to execute a project has been called the project network (Boland, Lyytinen, & Yoo, 2007; Hellgren & Stjernberg, 1995; Taylor & Levitt, 2007; Windeler & Sydow, 2001). Competitive advantage and firm performance are driven, in part, by successful implementation of innovations (Tushman & Nelson, 1990). Implementing new innovations in a project network may require that its organizations change their inter-firm processes. Such innovations can be any innovation in the spectrum, ranging from incremental to radical innovations, and they can be technological or product innovations. In short, implementing any innovation that impacts inter-firm processes may require those processes to realign. We define inter-firm process as the sequence of value-creating activities that takes place across two or more organizations. Designing a building is a typical inter-firm process because it involves a sequence of design activities by multiple specialist firms, such as architectural design and structural design firms. (By contrast, we refer to the sequence of value-creating activities that takes place within one organization as intra-firm process.)
Innovations that impact inter-firm processes have been described as systemic innovations (Taylor & Levitt, 2004). Researchers have shown that implementing systemic innovation in project networks is difficult (Hartmann, Fischer, & Haymaker, 2009; Whyte, Bouchlaghem, & Thorpe, 2002) and that the difficulties arise because systemic innovations are often misaligned with extant project network structures (Taylor & Levitt, 2007). Under such structural misalignment, successful systemic innovation implementation requires that the project network align its structure to the innovation by adapting existing inter-firm processes. We refer to the alignment process as inter-firm effects development process; however, there is little research examining the inter-firm effects development processes in project networks implementing systemic innovation. In this paper, we examine the specific case of Building Information Modeling (BIM) tool innovation implementation in the context of the design and development of a new university building to explore the inter-firm process development through which systemic innovation is implemented in a project network. Thus, the unit of analysis in this study is the inter-firm effects development process, and we studied it empirically with the BIM tool implementation case. We use a theory-building qualitative case study method with embedded cases (Glaser & Strauss, 1967; Yin, 2003) to induce a propositional theoretical model of inter-firm effects development process following innovation misalignment in project networks. The model addresses the relationships between theoretical constructs as well as the underlying theoretical mechanisms behind the relationships (Eisenhardt & Graebner, 2007).
Innovation Misalignment and Inter-Firm Effects in Project Networks
Innovations—novel technologies, products, and processes—drive competitive advantage of firms (Tushman & Nelson, 1990). More specifically, the ability to integrate knowledge and implement innovations forms an important competitive capability of firms (Zahra & George, 2002), inter-firm networks (Lane & Lubatkin, 1998), and projects (Bresnen, Edelman, Newell, Scarbrough, & Swan, 2003; Dietrich, Eskerod, Dalcher, & Sandhawalia, 2010). Integrating knowledge is particularly critical for innovations whose successful implementation requires that existing knowledge and expertise are linked across “knowledge boundaries” in novel ways (Carlile, 2002, p. 442). Implementing such cross-boundary innovations is important because they are hard to copy by competitors and thus have important competitive consequences. In the project network context, such cross-boundary innovations have been labeled as unbounded (Harty, 2005) or systemic (Taylor & Levitt, 2004) innovations. In this paper, we refer to innovations whose implementation requires knowledge integration by the network of firms assembled to complete the project as systemic innovations.
Implementing systemic innovations in a project network is important: Properly implemented systemic innovation enables the network to share knowledge among its firms (Anumba, Bouchlaghem, Whyte, & Duke, 2000), which is likely to have a positive effect on project network learning and performance (Calantone, Cavusgil, & Zhao, 2002). We understand project network performance here primarily as operational performance (i.e., productivity) of the project network. In turn, when a project network's operational performance increases, the financial performance (e.g., profitability, return on investment) of its firms can also increase. Despite the important link between project network performance and systemic innovation implementation, project networks often find it hard to implement systemic innovations successfully. Based on a survey of 42 managers of British construction firms, Whyte and colleagues (Whyte, Bouchlaghem, Thorpe, & McCaffer, 1999) concluded that construction project networks typically find it difficult to implement innovations that span organizational boundaries. A more recent study examined a data set of 26 cases of BIM innovation implementation in project networks and found that the project networks applied the innovation only for a small number of the possible application areas, thus diluting the potential benefits of the innovation (Hartmann, Gao, & Fischer, 2008). Dubois and Gadde (2002) speculated that innovation difficulties in project networks occur because the networks are loosely coupled systems whose decentralized structures hamper interaction and knowledge exchange across firm boundaries. Supporting this speculation, Whyte and colleagues (2002) identified that implementing a 3D CAD innovation in a multiunit construction firm was slow due to cross-boundary coordination problems. Similarly, Bresnen and colleagues (Bresnen, Goussevskaia, & Swan, 2005) identified that implementing new management practices in project networks was slow due to misalignment between the new management practices and existing project practices. Others have identified that lack of individuals’ expertise and time to learn new technologies may hamper systemic innovation implementation (Whyte & Bouchlaghem, 2002). Taken together, these findings suggest that implementing systemic innovations in project networks is difficult because these innovations tend not to align well to the project networks’ existing processes, routines, and structures.
More recent research focused this argument by positing that when a new systemic innovation is misaligned to a project network's work allocation, the project network finds it difficult to implement the innovation. Examining systemic innovation implementation in multiple construction project networks, researchers found that, in addition to single firms changing their intra-firm processes, the affected firms in the project network have to change their inter-firm processes to successfully implement the innovation (Taylor & Levitt, 2007). A similar argument was presented by Leonardi (2007), who argued that in implementing technological innovations, “users must be allowed the freedom to adjust both their own practices of appropriation and patterns of interaction” (p. 829). The argument that the project network should align its inter-firm processes to the innovation is sensible, given that the alternative, aligning the innovation to the project network (i.e., forcing the innovation conform to extant processes), is often difficult (Hartmann et al., 2009). This argument implies that if a project network implementing systemic innovation can change its inter-firm processes, it can align itself to the innovation and subsequently the innovation can be successfully implemented. Taylor and Levitt (2007, p. 31) referred to the process by which a project network changes its inter-firm processes following systemic innovation misalignment as “inter-firm effects” development. We draw from Taylor and Levitt (2007) and define inter-firm effects development as a sequence of value-creating activities through which a project network involving multiple organizations changes its inter-firm processes. Thus, inter-firm effects development refers to the project network-level development of new inter-firm practices that are driven by the implementation of systemic innovation.
The concept of inter-firm effects development is useful because it suggests that understanding systemic innovation implementation requires understanding how networks adjust the inter-firm work allocation after adopting a systemic innovation initially misaligned to the project network's structure. However, although it appears that inter-firm effects play an important role in systemic innovation implementation, there seems to be relatively little research on how inter-firm effects develop in project networks implementing misaligned systemic innovations. Although researchers have acknowledged the importance of inter-firm processes in project networks when innovations are created (Boland et al., 2007) and implemented (Dubois & Gadde, 2002), few studies have examined the specific issue of inter-firm process development following systemic innovation implementation. This lack of research is problematic: If implementing systemic innovation can provide competitive advantage for project networks and if inter-firm effects development is a determinant of systemic innovation implementation performance, we should understand better how inter-firm effects develop following systemic innovation implementation. In this research, we extend earlier research on systemic innovation misalignment in project networks to explore the process by which inter-firm effects develop following systemic innovation misalignment in project networks.
Research Setting and Methods
We employed a qualitative grounded theory building approach (Glaser & Strauss, 1967) with a multiple case study design and embedded cases (Eisenhardt, 1989; Yin, 2003). We collected empirical data regarding BIM implementation to explore and induce theoretical propositions concerning the process of inter-firm effects development following innovation misalignment. In line with the grounded theory building approach, we used a process research design, which enabled us to explore how and why the process of inter-firm effects development unfolds (Langley, 1999). We understand processes as sequences of events unfolding over time and leading to outcomes (Langley, 1999). We collected process data that enabled us to describe and explain the events and outcomes of inter-firm effects development in project networks.
BIM Technology Misalignment in A/E/C Project Networks
Building Information Modeling (BIM) tools can be defined as a collection of information and communication technologies aimed at integrating a complex project network in the architecture, engineering, and construction (A/E/C) industry with the help of visual representations that help explicate the interdependencies between the project network's specialists (Dossick & Neff, 2010; Taylor & Bernstein, 2009). In general, BIM includes the use of multiple computer applications by different specialists within an A/E/C project network for creating and sharing information about a facility during its design and construction. Although BIM has the potential to accelerate the design and construction processes and thus increase project network performance, using BIM successfully requires some amount of mutual adaptation for the specialist firms involved. For example, in a study of 26 A/E/C organizations, Taylor (2007) found the implementation of BIM to require considerable adjustment in the interfaces between interdependent organizations. The study showed that technological inter-operability, inter-firm structures (e.g., partnerships), contractual issues, and change management all affect the successful implementation of BIM. In a similar study of the Terminal 5 design and construction project at Heathrow Airport, Harty (2005) identified that successful implementation of BIM requires “focusing on the actual processes by which existing positions and expectations interact with novel technologies and new ways of working” (p. 521). Due to its nature as a novel technology integrating existing components across boundaries while requiring mutual inter-firm adjustment by the implementing project firms, BIM is a relevant example of a systemic innovation in the context of project networks.
The use of BIM is expected to bring major productivity improvements to the industry, but the successful implementation of BIM has been difficult (Hartmann et al., 2008; Harty, 2005). One reason inhibiting the implementation of BIM has been the lack of inter-operability of tools used by different specialist designers (Szykman, Fenves, Keirouz, & Shooter, 2001). Researchers have also recognized that BIM impacts the work processes of multiple organizations, thus leading to inter-firm effects (Harty, 2005; Taylor, 2007; Taylor & Levitt, 2007). Because implementing BIM successfully requires aligning inter-firm processes by the different specialists involved, BIM implementation offers an ideal setting for our study of inter-firm effects development following misaligned innovation adoption in project networks.
We probe the process of inter-firm effects development in considerable detail in the context of a detailed case study of BIM implementation in which we investigate how A/E/C firms in a project network aligned their inter-firm work processes. We chose the case study research design because it allows investigators to understand complex social phenomena based on real-life events (Yin, 2003). The rationale behind choosing a case study was to examine an extreme case (Yin)—an advanced pilot project adopting BIM called “Aurora”—to observe inter-firm effects development following systemic innovation implementation. The case study approach enabled us to analyze the implementation of BIM in a complex project network environment, where multiple specialists were impacted by the new technology and were prepared to share their experiences with the researchers over a series of individual and group interviews.
The Aurora Project
We studied a new university building construction project built for a State University in Finland (end user). We call the project “Aurora.” Aurora was an advanced pilot project for the use of BIM and interoperable software solutions in the A/E/C domain. The Aurora building was developed by a property management organization that manages and develops properties owned by the Finnish state (hereinafter referred to as the building owner). The building owner had been actively driving the implementation of BIM technologies in Finland. The design and construction process was conducted between 2003 and 2006. The use of BIM on this project was unprecedented in scope and depth because integrated BIM technologies were used across project stakeholders and across project phases.
The Aurora project was a fixed-price incentive contract based on an architecture competition. In addition to the building owner, the Aurora project network included the following specialists: architect tasked with architectural design, cost estimator tasked with estimating the project costs, general contractor tasked with the overall construction of the building, MEP (mechanical/electrical/plumbing) designer tasked with designing the MEP systems, MEP contractor tasked with constructing the MEP systems, project consultant tasked with managing the project, structural engineer tasked with completing the structural design, and technical consultant tasked with helping the network with the interoperability of BIM technologies. Each of these specialists was a distinct company. The key design specialists (i.e., architect, MEP designer, and structural engineer) developed BIM models and the models were used by all specialists during design and construction. We present an example of a BIM model used in Aurora in Figure 1.
The needs and objectives phase started by analyzing the needs and objectives of the end user. The purpose was to find out what kinds of spatial needs the end user had and define the spatial requirements for the spaces. As an end result, a preliminary spatial program was completed. The preliminary spatial program described the areas of different types of spaces needed. After the creation of the preliminary spatial program, the architect started to fit the spatial program to the site. The fitting was done using a spatial group model, which was the first building information model used in the project. The creation of the spatial group model also marked the beginning of the conceptual design phase. The spatial group model made by the architect was shared with the building owner and the end user to get comments on the division of different spaces. The discussions also helped to further specify the preliminary spatial program that was made in the end of the needs and objectives phase.
Based on the spatial program, the cost estimator estimated the building costs. The cost estimator used the areas and volumes of the building, defined in the spatial program as the basis of the estimate. Simultaneously, the architect continued by developing the spatial group model into a spatial model and further into a preliminary building element model. These models were used to analyze different alternatives for the building. At this stage of the conceptual design, the participating firms in the project network executed energy simulations to evaluate the energy efficiency of the building. The energy simulations showed that the energy consumption of the building was not optimal. As a consequence, the building structure was changed to better achieve energy targets that had been laid out for the building.
Upon completion of the conceptual design, the improved spatial program and structure of the building were discussed with the building owner and the end user. The end user made a preliminary lease with the building owner, and the building owner made the decision to invest in the building. The conceptual design phase had some unique features that the building owner had not experienced in previous new building projects. For example, the building owner investigated the benefits of doing more design work through modeling before making the investment decision and conducted energy simulations to optimize the energy consumption of the building.
At the beginning of the early design phase, the project consultant was tasked with managing and coordinating the rest of the building project. The first task of the project consultant was to create a project plan. In the early design phase, the basic design solutions were developed further. The architect continued to work on the preliminary building element model that was already started in the conceptual design phase. The preliminary building element model was developed into a building element model as the level of detail increased during the early design phase.
During the early design phase, the MEP designer started to define targets for ambient conditions in the building, which were based on the end user's wishes. The information from the end user enabled the MEP designer to identify requirements for the MEP systems needed. In the end of the early design phase, the MEP designer created a preliminary model for the MEP systems in the building. The structural engineer then began defining structural types. According to the designers (i.e., architect, MEP designer, and structural engineer), the collaboration between them was highly interactive during the early design phase; hence, the designers consulted one another frequently, and for this reason, the work flow between designers was difficult to describe by the interviewees. Nevertheless, a number of collaborations where knowledge from multiple specialists was integrated could be distinguished. One such collaboration was the designing of an energy use model by the building owner, the architect, and the MEP designer. Another important collaboration was the designing of a spatial reservation model by the architect and the MEP designer. The spatial reservation model helped to illustrate the spaces needed by the MEP systems. A third important collaboration was the designing of a structural model by the architect, MEP designer, and structural engineer. A fourth important collaboration was the designing of an MEP systems model by the MEP designer, the structural engineer, and the general contractor.
In the Aurora project, contract tendering (seeking for bids for construction) started during the early design phase. According to the BIM guidelines of the building owner, contract tendering would normally take place after the detailed design phase in a separate project phase. Due to the pioneering nature of the Aurora project, cost estimates were taken twice during the early design phase. At both times, these estimates were calculated using BIM and using traditional calculation methods, because the specialists did not fully trust the model-based estimates.
In the detailed design phase, the accuracy of information in the models was increased by developing them further. The architect developed the building element model, the MEP designer developed system models, and the structural engineer further developed the building element model. The final lease was signed between the end user and the owner, and at the end of the detailed design, the building owner made the decision to start construction.
During the construction of the building the designers made supplementary and alteration designs to the models when they were needed. For the general contractor, the most useful model was a 4D model by the structural engineer, which was used on the construction site daily. The general contractor was able to use the structural engineer's 4D model to schedule construction. Another structural engineer's model was used in manufacturing the foundation reinforcement bars. The manufacturer of this reinforcement steel was able to directly use the information from the model to run the equipment that produced the steel reinforcing materials. When the construction of the building was complete, the building was examined and approved, and finally, commissioned to the owner and the end user.
Data Collection and Analysis
We used an embedded case study strategy with the four identified collaborations as cases of inter-firm effects development embedded in the higher-level case of the Aurora project (Yin, 2003). Case studies typically involve multiple data collection and analysis methods to increase the validity of the results (Eisenhardt, 1989). We used interviews, focus groups, and review of project documentation as data collection methods. The project documentation included project schedules, records from the meetings of the design team, and records from the building site meetings. We used the project documentation to get an overall view of the construction process prior to the interviews and to identify important milestones and phases of the project. To increase the quality of the interview data, we also used the project documentation data during the interviews to help the interviewees to recall events within the project.
We conducted 11 interviews, including single person (N = 4) and small group (N = 7) interviews. The small group interviews included 2 to 4 interviewees. All interviews were open-ended and lasted from 60 to 170 minutes each; overall, 22 individuals participated in the interviews. When selecting the interviewees, our objective was to choose individuals who were the most involved in BIM or affected by it, and also to get a good representation of the whole project network. The project network specialists represented in the interviews were the building owner, end user, project consultant, cost estimator, architect, structural engineer, and the general contractor. To gain a sufficient understanding of the specific BIM technology involved in the project, we also interviewed a design software provider. During the interviews, we discussed with the interviewees themes such as: their roles in the Aurora project, what tasks they were responsible for, and how the collaboration with specialists from other firms in Aurora worked. We asked them questions such as: “In the beginning, who decides what should be done?”; “How is the end user involved in the beginning?”; “What kind of interaction across firms was required?”; and “Who was estimating costs at this stage?” The interviews were digitally recorded and transcribed, leading to 200 pages of transcribed text. To enhance the quality of the interview data, we used multiple (3–4) investigators in the interviews (Eisenhardt, 1989). One investigator always made sure that all themes of theoretical interest were covered during the interview, another investigator concentrated on asking additional questions, and the third and fourth investigators took notes during the interview.
In addition to the interviews, we arranged a full-day focus group workshop to collect further data and also to validate the data obtained in the interviews. The workshop was arranged as a facilitated group discussion with a focus on examining inter-firm processes (Smeds & Alvesalo, 2003). The workshop included 23 individuals from the specialist firms that had been involved in the Aurora project. In the workshop, these individuals gathered for one work day (6 hours) to discuss inter-firm challenges and their resolutions related to BIM implementation during the Aurora project. The workshop was a useful data collection tool because it enabled the specialists to describe design and construction events by immediately building on other specialists’ accounts of the same events, thus increasing the accuracy of the event descriptions. Like the interviews, the workshop was digitally recorded and transcribed into text, leading to 40 pages of text; thus, the primary data set for our data analysis in this paper consists of 240 pages of transcribed text based on the interviews and the focus group workshop. These 240 pages consist of the project network's designers’ first-hand descriptions about inter-firm effects development following BIM implementation into the Aurora project network.
The first objective of our data analysis was to identify and describe the key collaborative process stages through which the project network implemented BIM, and use these stages as embedded cases of inter-firm effects development from which to build theory. To attain this objective, we constructed a visual process chart based on the individual interviews and project documentation. The process chart depicted the entire design and construction process from bidding to the completion of the construction. The process chart depicted the key specialists involved, the key design and construction stages required, and information flows between and within specializations. With the process chart we were able to isolate the four distinct design process stages in which the specialists collaborated using BIM: (1) designing energy use model, (2) designing spatial reservations model, (3) designing structural model, and (4) designing MEP systems model. In the further analysis we treat these stages as cases from which to build theory using literal replication logic (Eisenhardt, 1989).
During the designing energy use model case, the architect designed a preliminary building element model and used it jointly with the MEP designer to design an energy use model that illustrated the final building's energy use. The energy use model was based on computer simulations. The specialists referred to these simulations as “energy simulations.” Based on the energy use model, the building owner made the final investment decision. During the subsequent designing spatial reservations model case, the architect's preliminary building element model was used by the project consultant to make a project plan and by the MEP designer to design a MEP requirements model. Based on the MEP requirements model, the architect then designed a spatial reservations model, an early model of the building depicting spaces required for MEP systems. In the ensuing designing structural model case, the architect's spatial reservations model was used by the MEP designer to design a preliminary MEP systems model and by the structural engineer to design a structural model as well as a 4D model to be used in construction. Using these models, the cost estimator provided an initial cost estimate and updated the estimate as the project advanced. In the designing MEP systems model case, the MEP designer finished the MEP systems model design, and the structural engineer submitted the structural model to the general contractor to be used to guide construction.
Describing and Explaining Inter-Firm Effects
The second objective of our data analysis was to describe and explain the inter-firm effects development in the identified cases and compare patterns in the findings across cases to build theory on inter-firm effects development, following innovation misalignment in project networks (Eisenhardt, 1989). To describe the inter-firm effects development in the four cases, we performed a line-by-line analysis of the data and selected relevant quotes by the interviewees and the focus group participants (Strauss & Corbin, 1990). We selected all quotes relating to inter-firm effects development in the identified cases. We included quotes in which the specialists described an inter-firm challenge that was resolved during the project. We also included quotes describing inter-firm effects that were identified as problems but could not be solved during the project. In addition, we also selected quotes describing within-firm (i.e., intra-firm) effects of BIM implementation, because we soon realized that many inter-firm effects depended on how intra-firm effects developed in the project network.
Using the aforementioned criteria, we eventually selected 276 quotes from the interviews and the focus group workshop relating to inter-firm effects development in the identified four cases. We then coded the 276 quotes using open and axial coding procedures (Strauss & Corbin, 1990). We first labeled and then categorized the quotes into four higher-level thematic categories: requirements, inter-firm process change, inter-firm process logic, and inter-firm process standardization. We understood these four categories as theoretical constructs. Informed by our research focus on inter-firm effects development, we focused our subsequent analytical efforts on the inter-firm process change construct. Within the inter-firm process change construct we identified four subconstructs: (1) work, (2) data, (3) communication, and (4) specialist roles. Switching to a higher-level constant comparative analysis mode, we then compared each quote with the emerging constructs and sub-constructs. Glaser and Strauss (1967) call this higher-level constant comparative analysis “integrating categories and their properties” (p. 108). We then grouped these four subconstructs into three inter-firm effects: (1) task sequence alignment, (2) knowledge base alignment, and (3) work allocation alignment. We then selected and coded all quotes in which the interviewees talked specifically about how these inter-firm effects developed in the Aurora project. We identified 93 such Aurora-specific process quotes. Using these 93 quotes describing four cases of inter-firm effects development, we were able to discover a general process through which the inter-firm effects developed following misaligned BIM implementation in the Aurora project.
To explain why the inter-firm effects developed in the four cases as they did, we coded the 93 quotes one more time. In this coding round, our focus was on trying to understand why what the quote described had happened (or not happened). We coded the quotes with analytical labels, such as “resequencing led to knowledge integration,” “lack of resequencing led to lack of knowledge integration,” “new work tasks led to keeping knowledge current,” and “lack of new work tasks led to lack of keeping knowledge current.” This final coding, with its focus on explaining why something had happened (or not happened) in the Aurora project, enabled us to build a firmly grounded process theory of inter-firm effects development following systemic innovation misalignment in project networks.
Inter-Firm Effects Following Systemic Innovation Misalignment
Task Sequence Alignment
We refer to the process through which a project network changes the work task sequence as task sequence alignment. The way in which work tasks were initially sequenced among the different specialists in the Aurora project network created difficulties in implementing BIM. Accordingly, to successfully implement BIM, the project network had to align the misaligned work task sequence to the work task sequence assumed by BIM. The need to change how existing work tasks were sequenced between firms in the Aurora project network was described by interviewees from all specializations.
As an example, the general contractor's and the MEP design consultant's work tasks were misaligned to the work task sequence assumed by BIM: the BIM technology assumed that the general contractor would enter MEP-related drawings into the model at an earlier stage. However, the general contractor's modeling tool, designed to align chiefly to the general contractor's own work tasks, did not allow the general contractor to produce the drawings in a compatible format at the earlier stage assumed by BIM. The project network solved this task sequence misalignment by resequencing work tasks. The MEP designer described the resequencing as follows:
This quote suggests that a possible solution to an initially misaligned work task sequence in a project network is resequencing, or changing the sequence of work tasks. In this example, resequencing resulted in a new work task sequence in which the knowledge outputs of one specialist's work tasks are knowledge inputs to another specialist's work tasks. Resequencing work tasks into a sequential task sequence was useful because it enabled the integration of knowledge from multiple firms into BIM (“and then the MEP designer integrates the changes,” as the MEP designer put it in the above quote). If the general contractor's work tasks were not resequenced to take place before the MEP designer's work task, knowledge could not have been integrated into the BIM model as required.
Overall, members from multiple specializations emphasized the benefits of resequencing (i.e., architect, building owner, cost estimator, general contractor, MEP designer, and structural engineer). Extant literature on project networks suggests that the way in which tasks are sequenced in projects has implications for project performance (Dossick & Schunk, 2007; Jin & Levitt, 1996) and more specifically that work task alignment impacts systemic innovation implementation in project networks (Taylor & Levitt, 2007). Based on these studies as well as on our findings, we argue that task sequence alignment is an important inter-firm effect in project networks implementing systemic innovation.
Knowledge Base Alignment
Although a central purpose of BIM technology is to enable knowledge sharing across firms in a project network (Halfawy & Froese, 2005), actually sharing knowledge across firms with BIM is often difficult (Fox & Hietanen, 2007). Corroborating this observation, we found that sharing knowledge across the firms using BIM was initially difficult in the Aurora project. Our data suggest that the knowledge-sharing difficulties occurred because existing knowledge bases among the specialist firms were misaligned; however, we observed that when the firms in the Aurora project network were able to align their knowledge bases, they were able to share knowledge, thus enhancing BIM implementation. Aligning knowledge bases successfully in the project network occurred by integrating knowledge and keeping knowledge current.
Knowledge in project networks is typically distributed across firms in the network (Newell, Goussevskaia, Swan, Bresnen, & Obembe, 2008). Achieving the benefits of BIM requires that the distributed knowledge, or at least some of it, is integrated into BIM (Halfawy & Froese, 2005); however, we discovered that integrating knowledge into BIM was difficult in the Aurora project. We identified specific knowledge integration problems, such as the difficulty of merging project data created using different formats and linking requirements to models. For example, the designers produced both 2D paper drawings and 3D digital models, thus creating problems for firms using the knowledge embedded in these formats. Despite the knowledge integration challenges, in many cases the Aurora project network was able to integrate knowledge successfully into BIM. For example, the MEP designer described how spatial objectives for the building were integrated into the MEP systems model:
This MEP designer's comment illustrates how existing knowledge about spatial objectives was integrated with existing knowledge about the MEP system, creating new integrated knowledge about “spatial requirements for the MEP system.” In this case, by integrating knowledge the project network was able to use BIM in ways that enabled the network to advance beyond “the more traditional process,” as the MEP designer put it. This was possible because the knowledge integration enabled the MEP designer to appropriately design the MEP system as part of the entire building from the start. In a traditional design process, such an early design of the MEP model would not be usable in later stages of the project. In the Aurora project, the early MEP system was usable at later stages of the project; hence, successful knowledge integration facilitated the subsequent design processes. We observed knowledge integration related quotes from multiple specialists (architect, building owner, cost estimator, general contractor, MEP designer, and project consultant). Supporting our findings, knowledge integration (i.e., the process of combining disparate bits of specialist knowledge into a collective knowledge product) has been identified as a key part of project work in earlier research (Dietrich et al., 2010).
Keeping Knowledge Current
Firms in construction project networks produce large amounts of project data and other knowledge related to engineering and project management issues. Keeping the knowledge entered into BIM current is a key requirement for successfully implementing BIM to a project network (Halfawy & Froese, 2005), because outdated knowledge may discourage individuals from adopting technological innovations in project networks (Javernick-Will & Levitt, 2010). We observed that the firms in the Aurora project network faced a challenge of keeping knowledge in the model current. The firms pondered, for example, how often models should be shared between firms and how often cost estimates should be provided. For example, the architect pointed out that “models were updated too infrequently in the project database,” and the cost estimator commented that “the model has not been up to date vis-à-vis the most current plans.” Similarly, the structural engineer explicated both the importance and difficulty of keeping knowledge current:
The structural engineer's comment shows that it would be beneficial to the project network to keep knowledge current so that the designers could “have all information available all the time.” However, the Aurora project network did not achieve such a level of keeping knowledge current. One reason leading to this problem was lack of sufficient computing power: Working on a model simultaneously with architects slowed down the structural engineer's work. As a result, the structural engineering designers decided that they would not even attempt to keep the knowledge current in BIM; instead, they would deliberately keep working on their own models and enter them only periodically to BIM. However, the structural engineer acknowledged that it would be beneficial if both specialists—the architect and the structural engineer—could work simultaneously on the model and “observe other designers’ designs simultaneously.” This view was echoed by the architect, who on one occasion, hoped that “eventually we would get to the point where we could get rid of the [periodical] integration of the models and could work on the same model.”
We observed quotes related to keeping knowledge current from multiple specialists (architect, building owner, MEP designer, and structural engineer), and others have identified that keeping knowledge current is important for project networks adopting innovations (Dossick & Schunk, 2007; Javernick-Will & Levitt, 2010). Overall, our data as well as extant project network literature suggest that integrating existing knowledge and keeping knowledge current form the important stage of knowledge base alignment in project networks. In project networks, sharing knowledge across specialists is often difficult, because existing knowledge bases among the specialists are misaligned, often due to deeply rooted professional differences (Fox & Hietanen, 2007). Therefore, aligning the specialists’ knowledge bases can contribute to systemic innovation implementation in project networks by better enabling knowledge sharing (Anumba et al., 2000).
Work Allocation Alignment
We observed that the BIM innovation in the Aurora project was initially misaligned with the project network's existing work task allocation: The BIM innovation did not align with the way in which work was allocated to different specialists in the project network. However, we also observed that the initially misaligned innovation became aligned to work allocation through changing work tasks, creating new work tasks, and changing specialist roles.
Changing Work Tasks
We observed that the specialists in the Aurora project network had to learn how to conduct some of their existing work tasks differently than before they adopted BIM. The different ways of conducting work tasks included altering the scope of an existing work task (e.g., architect expanding the scope of an existing modeling task to include massing modeling), changing the duration of an existing traditional work task (e.g., structural engineer extending the duration of an existing design task), and using technology in a novel way when conducting an existing work task (e.g., MEP designer using spatial model in BIM when designing MEP systems). We refer to the processes by which the specialists change their work tasks simply as changing work tasks. The following quote by the architect illustrates how implementing BIM meant changing work tasks in the project network:
This quote by the architect demonstrates how, following BIM adoption, the architect had to change some of his work tasks by expanding the scope of design work being conducted (“it requires us to do additional design work”). In this example, the architect's role was changed to include additional portions of design work. Changing the architect's work task helped the project network to implement BIM, because the changed work task resulted in an expanded architect's model that other specialists could easily understand and accept.
We observed quotes explicating the importance of changing work tasks from multiple specialists (architect, building owner, MEP designer, and structural engineer). These findings support the argument that changing work tasks may be an important factor in how organizations and project networks operate and adopt new innovations (Bailey, Leonardi, & Chong, 2010; Chen, Partington, & Qiang, 2009; DeSanctis & Poole, 1994).
Creating New Work Tasks
We discovered that the specialists in the Aurora project network created new work tasks when implementing BIM. We define new work task as a work task that did not exist before the project. New work tasks can be conducted by one specialist or jointly by specialists from multiple specializations. For example, the Aurora project network created the new work task of conducting energy simulations to examine the future energy use of the building. Conducting energy simulations was a task that no specialist in the project network had previously conducted. Energy simulations in the Aurora project were jointly conducted by the architect and the MEP designer. The following architect's comment illustrates the new work task of energy simulations:
This comment by the architect illustrates how the creation of the energy simulations work task helped the project network to align to BIM by helping the specialists to make “right decisions” and “get the knowledge flowing.” We observed quotes relating to creating new work tasks from multiple specialists (architect, building owner, cost estimator, general contractor, MEP designer, and structural engineer). As others have observed that technology adoption in project networks can lead the networks’ specialists to create new work tasks (Leonardi & Bailey, 2008), we are confident that the construct of creating new work tasks has internal and external validity.
Changing Specialist Roles
The specialists in the Aurora project network occasionally changed their traditional specialist roles when implementing BIM. Changing a specialist role means either that a specialist in a project network becomes responsible for a work task that is new to that specialist, or that a specialist no longer is responsible for a work task previously allocated to that specialist. For example, in the Aurora project both the architect's and the MEP designer's specialist roles in modeling the building's spaces were changed. The changing of specialist roles occurred because BIM enabled the architect to do some of the modeling that had previously been the MEP designer's responsibility. As a result, the MEP designer's new specialist role did not include modeling the building's spaces as extensively as before. The following architect's comment illustrates the changing of the specialist roles:
This quote illustrates how both the architect's and the MEP designer's specialist roles were changed: The architect's role was changed to include more spatial modeling, and, accordingly, the MEP designer's role was changed to include less spatial modeling.
We observed quotes relating to changing specialist roles from multiple specialists (architect, building owner, general contractor, MEP designer, and structural engineer); thus, we feel confident that the identified construct of changing specialist roles exhibits sufficient internal validity. Also, as others have documented similar specialist role changes in contexts such as product development engineering (Hong, Vonderembse, Doll, & Nahm, 2005) and movie production project networks (Bechky, 2006), we feel confident that the identified construct of changing specialist roles has sufficient external validity.
Overall, our data and extant literature suggest that changing work tasks, creating new work tasks, and changing specialist roles form the important stage of work allocation alignment in project networks. Furthermore, drawing from extant literature on innovation implementation in project networks (Taylor & Levitt, 2007), we argue that work allocation alignment is an important inter-firm effect in project networks implementing systemic innovation.
Toward a Process Model of Inter-Firm Effects Development in Project Networks
In addition to identifying the three inter-firm effects of task sequence alignment, knowledge base alignment, and work allocation alignment, we analyzed the data further to discover how and why the three identified effects developed following the adoption of the misaligned BIM innovation. The data analysis provides evidence for a process model of inter-firm effects development in which misaligned systemic innovation becomes aligned to the project network structure through task sequence alignment, knowledge base alignment, and work allocation alignment.
We observed that adopting BIM impacted the existing task sequence of the project network's specialists. The task sequence assumed by BIM often differed from the existing task sequence of the Aurora project network. This misalignment hindered the innovation implementation in the project network and required the project network to align its existing work task sequence. For example, the structural engineer commented on the importance of aligning the project network's existing task sequence to the task sequence assumed by BIM:
The structural engineer's comment that the specialists “do not have knowledge inputs” due to misaligned task sequence illustrates how BIM assumed that the project network has a task sequence that produces knowledge, which can readily be used as “knowledge inputs” in subsequent tasks. In fact, however, the Aurora project's existing task sequence did not permit such knowledge production; the existing task sequence did not produce readily usable knowledge for the specialists. Instead, based on their existing knowledge requirements, the specialists had first to align the task sequence by designing a novel task sequence that would produce usable knowledge. We observed task sequence alignment as a requirement to attain usable knowledge in comments by other interviewees. For example, the cost estimator commented:
This cost estimator's comment illustrates how aligning the task sequence by resequencing work tasks (i.e., the work task sequence should have been “tightened up”) would have resulted in alleviating the problem of lacking knowledge. We observed 37 quotes, specifically suggesting that the adoption of BIM initially impacts the task sequence alignment of the project network. We observed that, in all four identified cases, the BIM implementation initially impacted the task sequence alignment. These observations suggest that if implementing misaligned systemic innovation results in the project network aligning its task sequence, the project network can begin aligning its structure to the innovation and achieve the performance-enhancing benefits of the innovation. We formalize this discussion in the following proposition:
We observed that when task sequences became aligned, the specialists in the Aurora project network were better able to align their knowledge bases. The following architect's comment on the relationship between task sequence alignment and knowledge base alignment illustrates this observation:
This quote by the architect suggests that when the specialists’ task sequences were aligned—in this case by assembling the design team at a “very early stage”—it then became possible to integrate knowledge from the specialists and thus align their knowledge bases. In this instance, the disparate knowledge was integrated from the design team's specialists into the spatial reservations model. One explanation for this observation is that because much of organizational knowledge is embedded in specialized work practices (Orlikowski, 2002), aligning work tasks to occur concurrently in cross-specialist teams enables the specialists to learn others’ knowledge through participating in their practices; thus, through gaining understanding of the knowledge of others, the specialists are able to integrate knowledge across the specialist boundary. In our data, this explanatory mechanism was perhaps most apparent in situations where the specialists assembled concurrent design teams to integrate knowledge into BIM (e.g., assembling a design team to integrate knowledge into spatial reservations model). In such concurrent design teams, the specialists could observe and, to an extent, participate in the work practices of other specialists. This explanatory mechanism is supported by knowledge management research positing that assembling designers in concurrent project teams, where knowledge can be exchanged face-to-face across boundaries, facilitates cross-boundary knowledge flows (Alin, Taylor, & Smeds, 2011). However, we also observed that task alignment in the Aurora project network enhanced knowledge task alignment even when tasks were aligned to occur not concurrently but sequentially. For example, the project network resequenced tasks to occur sequentially in order to integrate knowledge. The following quote by MEP designer illustrates how the project network resequenced tasks to occur sequentially, thus enhancing knowledge integration:
This quote illustrates how the MEP designer was able to begin designing the MEP systems model after the architect had designed the architect's model. This example suggests that sequential task sequence alignment enhances knowledge base alignment because tasks produce knowledge usable by other tasks: Once the specialists resequence their tasks into an appropriate sequential sequence, the knowledge output produced by each task can be used as input for a subsequent task. As a result, knowledge can be integrated and/or kept current. We observed 22 quotes in which the specialists described task alignment leading to knowledge base alignment in the Aurora project. Furthermore, we observed 32 quotes describing how the lack of task alignment in the project network inhibited knowledge base alignment; thus, a total of 54 quotes suggests that task sequence alignment helps aligning firms’ knowledge bases in project networks implementing misaligned innovations. All identified cases support this argument: We observed that task sequence alignment enhanced aligning the specialist firms’ knowledge bases in all four cases.
Researchers have suggested that organizational knowledge is distributed across firms (Nissen & Levitt, 2004; Orlikowski, 2002;) and embedded in work task sequences (Argote & Ingram, 2000). Regarding organizational knowledge development, work task sequence is important because, when occurring concurrently, work tasks can provide mutual learning opportunities (Nissen & Levitt, 2004), and, when occurring sequentially, work tasks can provide required knowledge inputs to subsequent work tasks (Thompson, 1967). These arguments, combined with our empirical findings, lead us to conclude that task sequence alignment is beneficial to project networks implementing misaligned systemic innovation, because task sequence alignment can enable firms in the project network to better align their distributed knowledge bases to the knowledge base distribution assumed by the innovation. We formalize this conclusion in the following proposition:
We observed that when knowledge bases were aligned, the specialists in the Aurora project network were better able to align the network's work allocation to the work allocation assumed by BIM. The following quote by the architect illustrates how knowledge base alignment facilitated work allocation alignment. In this quote, the architect describes how integrating knowledge from the disparate specialists to a BIM model leads the project network to change the architect's role:
This architect's comment illustrates how aligning knowledge bases led to a change in the architect's specialist role. The architect's specialist role was expanded toward the MEP designer's specialist role as the architect began to include air conditioning requirements in the architectural model. That the architect was able to include air conditioning requirements was enabled by the fact that knowledge bases became aligned as knowledge was integrated to BIM. We observed 34 quotes in which the specialists described knowledge base alignment leading to work allocation alignment in the Aurora project network. In addition, we observed eight quotes describing how lack of knowledge base alignment inhibited work allocation alignment in the project network. Thus, a total of 42 quotes suggest that knowledge base alignment enhances work allocation alignment in project networks implementing misaligned systemic innovations. We observed that in all four identified cases, knowledge base alignment had a positive effect on work allocation alignment. Accounts in extant literature suggest that aligning disparate knowledge bases may give rise to changing work tasks (Bailey et al., 2010; Chen et al., 2009), new work tasks (Leonardi & Bailey, 2008), and specialist role change (Hong et al., 2005). These arguments corroborate our findings and lead us to propose:
The way in which an organization responds to changes in its environment—such as systemic innovation—depends, at least partially, on the structure of the organization (Damanpour & Gopalakrishnan, 1998). Specifying the relationship of organizational structure and systemic innovation implementation in the context of project networks, Taylor and Levitt (2007) argued that work allocation forms a key structure of project networks implementing systemic innovations. These authors further argued that “innovations that align with the allocation of work will circumvent the difficulties associated with implementing innovations across project networks” (p. 31). Hence, work allocation alignment can align the project network structure to the systemic innovation being implemented. Finally, if project network structure determines how easily a project network can implement systemic innovations and if successful implementation of systemic innovations can lead to competitive advantage and increased project network performance (Calantone et al., 2002), aligning project network's work allocation to systemic innovation can have a positive effect on project network performance. More formally:
In sum, we have found evidence for a theoretical model of inter-firm effects development following systemic innovation misalignment. The evidence is based on four cases of inter-firm effects development that were embedded in a broader case of a construction project. We summarize the case-based evidence in Table 1, which shows the specialists and stages involved in each of the four embedded cases. Table 1 also demonstrates that the propositions P1, P2, and P3 were supported by the empirical case data by providing exemplary quotes from each case and for each proposition. The theoretical model suggests that if systemic innovation misalignment leads to task sequence alignment, the project network can begin aligning its structure to the innovation (P1). Then, task sequence alignment leads to knowledge base alignment (P2), and knowledge base alignment leads to work allocation alignment (P3). Subsequently, because work allocation alignment aligns the project network structure to the systemic innovation being implemented, it has a positive effect on project network performance (P4). We summarize these propositions in a theoretical model of inter-firm effects development following implementation of misaligned systemic innovations in project networks in Figure 2.
Validation and Limitations
We used a case study method with embedded cases to investigate inter-firm process alignment resulting from the implementation of a single misaligned boundary spanning technology. Our focus enabled us to analyze inter-firm process alignment in depth and reveal a process model of inter-firm effects development. Because we used multiple data collection methods (interviews, a focus group workshop, and project documentation) and the identified constructs and their relationships were supported by multiple informants as well as accounts in extant literature, our model has achieved a measure of sufficient internal and external validity (Eisenhardt, 1989; Langley, 1999). However, the developed theoretical model of inter-firm process alignment has limited empirical generalizability: We do not wish to make the argument that all systemic innovation implementation cases in project networks would follow the linear path suggested by our model. For example, the studied industry—architecture, engineering, and construction—is characterized by a high degree of reciprocally interdependent work across organizations, and as such the inter-firm effects development may be different in that industry compared with others. Future research should test the model with other misaligned boundary spanning technologies and across project networks. Although our data strongly support the emerging model and the direction of the relationships, it would be useful to explore whether the process we identified might unfold differently in different contexts and under different conditions. Although extant literature does provide theoretical rationale for our model, we speculate that in different industries and/or different contexts, some relationships between the model's constructs might be reversed (e.g., knowledge base alignment could lead to task sequence alignment). Thus, further testing of how well the model explains and predicts systemic innovation alignment in different contexts is needed. In addition, the building owner in our research was a large government agency with considerable resources. The building owner actively encouraged companies to adopt the technology, which may have made the project network partner firms more accepting of the difficulty of cross-boundary innovation implementation. Future research should examine the role of the owner in the inter-firm effects development process model.
This research drew from previous work on systemic innovation implementation in project networks (Boland et al., 2007; Hartmann et al., 2008; Harty, 2005; Taylor & Levitt, 2004, 2007). Previous findings from the implementation of BIM demonstrate that when new systemic, boundary-spanning innovations misalign to the current network structure of project networks, innovation implementation is impeded (Taylor & Levitt, 2007). We extend this research in important ways. First, data from our study of an advanced BIM implementation project suggest a set of inter-firm process change constructs relating to task sequence alignment, knowledge base alignment, and work allocation alignment that should be understood and addressed to successfully adapt work processes to implement new technologies across a project network. By developing these constructs using empirical data, we have answered the call in recent project management literature to more closely examine the actual practices of interorganizational projects (Blomquist, Hällgren, Nilsson, & Söderholm, 2010; Bresnen et al., 2003). Moreover, we found evidence for an inter-firm process alignment model in which task sequence alignment leads to knowledge base alignment and to subsequent work allocation alignment. This inter-firm process alignment model gives us a better understanding of how and why project networks respond to new systemic innovations misaligned to the existing network structure. These inter-firm process change constructs, and their relationships specify how the important inter-firm effects develop when misaligned systemic innovations are implemented in project networks (Taylor & Levitt, 2007). However, the developed process model does not explain how institutional factors (e.g., standards, norms) might influence the inter-firm innovation alignment process. Researchers have recently begun to suggest that institutional level factors in innovation implementation may be more important than previously believed (Leonardi & Barley, 2010; Mahalingam & Levitt, 2007), and therefore future research should explore how such factors may influence inter-firm effects development and innovation implementation in project networks.
The findings of this research have important implications for firms seeking to adapt to organizational boundary spanning technological changes. The model of inter-firm effects development helps project networks to understand the critical elements (i.e., task sequence alignment, knowledge base alignment, and work allocation alignment) that need to unfold to successfully implement technologies that misalign with existing project network structures. Being able to prepare for inter-firm task sequence alignment, knowledge base alignment, and ensuing work allocation alignment may help project networks to realize the benefits of these new technologies more quickly. Researchers have described interorganizational networks as the new focal actors in economic activity; therefore, project network level adaptation to new technologies may provide a rich source of competitive advantage to networks of firms assembled to complete projects.
This article is based on research funded by the Finnish Foundation for Economic Education, the Finnish Funding Agency for Technology and Innovation (ECPIP and ISIS research projects) and the participating firms in the A/E/C industry. Any opinions, findings, conclusions, omissions, or recommendations expressed in this article are those of the authors and do not necessarily reflect the views of the funders, participating firms, or individuals.
Alin, P., Taylor, J. E., & Smeds, R. (2011). Knowledge transformation in project networks: A speech act level cross-boundary analysis. Project Management Journal, 42(4), 58–75.
Anumba, C.J., Bouchlaghem, N.M., Whyte, J., & Duke, A. (2000). Perspectives on an integrated construction project model. International Journal of Cooperative Information Systems, 9(3), 283.
Argote, L., & Ingram, P. (2000). Knowledge transfer: A basis for competitive advantage in firms. Organizational Behavior and Human Decision Processes, 82(1), 150–169.
Bailey, D. E., Leonardi, P. M., & Chong, J. (2010). Minding the gaps: Understanding technology interdependence and coordination in knowledge work. Organization Science, 21(3), 713–730.
Bechky, B. (2006). Gaffers, gofers, and grips: Role-based coordination in temporary organizations. Organization Science, 17(1), 3–21.
Blomquist, T., Hällgren, M., Nilsson, A., & Söderholm, A. (2010). Project-as-practice: In search of project management research that matters. Project Management Journal, 41(1), 5–16.
Boland, R. J., Jr., Lyytinen, K., & Yoo, Y. (2007). Wakes of innovation in project networks: The case digital 3-D representations in architecture, engineering, and construction. Organization Science, 18(4), 631–647.
Bresnen, M., Edelman, L., Newell, S., Scarbrough, H., & Swan, J. (2003). Social practices and the management of knowledge in project environments. International Journal of Project Management, 21(3), 157–166.
Bresnen, M., Goussevskaia, A., & Swan, J. (2005). Organizational routines, situated learning and processes of change in project-based organizations. Project Management Journal, 36(3), 27–41.
Calantone, R. J., Cavusgil, S. T., & Zhao, Y. (2002). Learning orientation, firm innovation capability, and firm performance. Industrial Marketing Management, 31(6), 515–524.
Carlile, P. R. (2002). A pragmatic view of knowledge and boundaries: Boundary objects in new product development. Organization Science, 13(4), 442–455.
Chen, P., Partington, D., & Qiang, M. (2009). Cross-cultural understanding of construction project managers’ conceptions of their work. Journal of Construction Engineering & Management, 135(6), 477–487.
Damanpour, F., & Gopalakrishnan, S. (1998). Theories of organizational structure and innovation adoption: The role of environmental change. Journal of Engineering and Technology Management, 15(1), 1–24.
DeSanctis, G., & Poole, M. S. (1994). Capturing the complexity in advanced technology use: Adaptive structuration theory. Organization Science, 5(2), 121–147.
Dietrich, P., Eskerod, P., Dalcher, D., & Sandhawalia, B. (2010). The dynamics of collaboration in multipartner projects. Project Management Journal, 41(4), 59–78.
Dossick, C. S., & Neff, G. (2010). Organizational divisions in BIM-enabled commercial construction. Journal of Construction Engineering and Management, 136(4), 459–467
Dossick, C. S., & Schunk, T. K. (2007). Subcontractor schedule control method. Journal of Construction Engineering & Management, 133(3), 262–265.
Dubois, A., & Gadde, L. (2002). The construction industry as a loosely coupled system: Implications for productivity and innovation. Construction Management & Economics, 20(7), 621.
Dyer, J. H., & Singh, H. (1998). The relational view: Cooperative strategy and sources of interorganizational competitive advantage. Academy of Management Review, 23(4), 660–679.
Eisenhardt, K. M. (1989). Building theories from case-study research. Academy of Management Review, 14(4), 532–550.
Eisenhardt, K. M., & Graebner, M. E. (2007). Theory building from cases: Opportunities and challenges. Academy of Management Journal, 50(1), 25–32.
Fox, S., & Hietanen, J. (2007). Interorganizational use of building information models: Potential for automational, informational and transformational effects. Construction Management and Economics, 25, 289–296.
Glaser, B. G., & Strauss, A.M. (1967). The discovery of grounded theory: Strategies for qualitative research. New Brunswick, NJ: Aldine Transaction.
Halfawy, M., & Froese, T. (2005). Building integrated architecture/engineering/construction systems using smart objects: Methodology and implementation. Journal of Computing in Civil Engineering, 19(2), 172–181.
Hartmann, T., Fischer, M., & Haymaker, J. (2009). Implementing information systems with project teams using ethnographic–action research. Advanced Engineering Informatics, 23(1), 57–67.
Hartmann, T., Gao, J., & Fischer, M. (2008). Areas of application for 3D and 4D models on construction projects. Journal of Construction Engineering & Management, 134(10), 776–785.
Harty, C. (2005). Innovation in construction: A sociology of technology approach. Building Research & Information, 33(6), 512–522.
Hellgren, B., & Stjernberg, T. (1995). Design and implementation in major investments: A project network approach. Scandinavian Journal of Management, 11(4), 377–394.
Hong, P., Vonderembse, M. A., Doll, W. J., & Nahm, A. Y. (2005). Role change of design engineers in product development. Journal of Operations Management, 24(1), 63–79.
Javernick-Will, A., & Levitt, R. E. (2010). Mobilizing institutional knowledge for international projects. Journal of Construction Engineering and Management, 136(4), 430–441.
Jin, Y., & Levitt, R. E. (1996). The virtual design team: A computational model of project organizations. Computational & Mathematical Organization Theory, 2(3), 171–196.
Lane, P. J., & Lubatkin, M. (1998). Relative absorptive capacity and interorganizational learning. Strategic Management Journal, 19(5), 461–477.
Langley, A. (1999). Strategies for theorizing from process data. Academy of Management Review, 24(4), 691–710.
Leonardi, P. M. (2007). Activating the informational capabilities of information technology for organizational change. Organization Science, 18(5), 813–831.
Leonardi, P. M., & Bailey, D. E. (2008). Transformational technologies and the creation of new work practices: Making implicit knowledge explicit in task-based offshoring. MIS Quarterly, 32(2), 411–436.
Leonardi, P. M., & Barley, S. R. (2010). What's under construction here? Social action, materiality, and power in constructivist studies of technology and organizing. The Academy of Management Annals, 4(1), 1–51.
Mahalingam, A., & Levitt, R. E. (2007). Institutional theory as a framework for analyzing conflicts on global projects. Journal of Construction Engineering & Management, 133(7), 517–528.
Manning, S. (2008). Embedding projects in multiple contexts: A structuration perspective. International Journal of Project Management, 26(1), 30–37.
Newell, S., Goussevskaia, A., Swan, J., Bresnen, M., & Obembe, A. (2008). Interdependencies in complex project ecologies: The case of biomedical innovation. Long Range Planning, 41(1), 33–54.
Nissen, M. E., & Levitt, R. E. (2004). Agent-based modeling of knowledge dynamics. Knowledge Management Research & Practice, 2(3), 169–183.
Orlikowski, W. J. (2002). Knowing in practice: Enacting a collective capability in distributed organizing. Organization Science, 13(3), 249–273.
Smeds, R., & Alvesalo, J. (2003). Global business process development in a virtual community of practice. Production Planning & Control, 14(4), 361–371.
Strauss, A. L., & Corbin, J. (1990). Basics of qualitative research: Grounded theory procedures and techniques. Newbury Park, CA: Sage Publications.
Szykman, S., Fenves, S. J., Keirouz, W., & Shooter, S.B. (2001). A foundation for interoperability in next-generation product development systems. Computer-Aided Design, 33(7), 545–559.
Taylor, J., & Levitt, R. (2004). Understanding and managing systemic innovation in project-based industries. In D. Slevin, D. Cleland, & J. Pinto (Eds.), Innovations: Project Management Research (pp. 83–99). Newtown Square, PA: Project Management Institute.
Taylor, J. E. (2007). Antecedents of successful three-dimensional computer-aided design implementation in design and construction networks. Journal of Construction Engineering & Management, 133(12), 993–1002.
Taylor, J. E., & Bernstein, P. G. (2009). Paradigm trajectories of building information modeling practice in project networks. Journal of Management in Engineering, 25(2), 69–76.
Taylor, J. E., & Levitt, R. (2007). Innovation alignment and project network dynamics: An integrative model for change. Project Management Journal, 38(3), 22–35.
Thompson, J. D. (1967). Organizations in action: Social science bases of administrative theory. New Brunswick, NJ: Transaction Publishers.
Tushman, M. L., & Nelson, R. R. (1990). Introduction: Technology, organizations, and innovation. Administrative Science Quarterly, 35(1), 1–8.
Whitley, R. (2006). Project-based firms: New organizational form or variations on a theme? Industrial & Corporate Change, 15(1), 77–99.
Whyte, J., & Bouchlaghem, D. (2002). Implementation of VR systems: A comparison between the early adoption of CAD and current uptake of VR. Construction Innovation, 2(1), 3–13.
Whyte, J., Bouchlaghem, N., Thorpe, A., & McCaffer, R. (1999). A survey of CAD and virtual reality within the house building industry. Engineering Construction & Architectural Management, 6(4), 371–379.
Whyte, J., Bouchlaghem, D., & Thorpe, T. (2002). IT implementation in the construction organization. Engineering Construction & Architectural Management, 9(5), 371–377.
Windeler, A., & Sydow, J. (2001). Project networks and changing industry practices: Collaborative content production in the German television industry. Organization Studies, 22(6), 1035–1060.
Yin, R. K. (2003). Case study research: Design and methods. Thousand Oaks, CA: Sage.
Zahra, S. A., & George, G. (2002). Absorptive capacity: A review, reconceptualization, and extension. The Academy of Management Review, 27(2), 185–203.
Pauli Alin, DSc, is a postdoctoral researcher at SimLab, Department of Industrial Engineering & Management at Aalto University, Finland. He researches how work is coordinated across firms in engineering project networks for innovation. His current research program focuses on coordination of work in construction engineering and design projects where engineers coordinate work in virtual 3-D workspaces. His research has been funded by the Finnish Funding Agency for Technology and Innovation (TEKES), the Finnish Foundation for Economic Education, and the Marcus Wallenberg Foundation. He is the founder and past CEO of a management consultancy firm, and he has experience in consulting organizations for project-based industries. He received his doctorate from Aalto University in 2010.
Antti O. Maunula, MSc (Tech), is a management consultant at Magenta Advisory in Helsinki, Finland, a leading management consultancy focusing on digital business. Mr. Maunula is interested in digital transformation, which is fundamentally changing the lives of people and businesses alike. Prior to joining the management consulting industry he was a researcher at SimLab, Department of Industrial Engineering & Management at Aalto University, Finland. His research focused on the implications that Building Information Modeling (BIM) has on the interorganizational processes of construction engineering and design projects.
John E. Taylor, PhD, is an associate professor in the Charles E. Via Jr. Department of Civil and Environmental Engineering at Virginia Tech where he is director of the Civil Engineering Network Dynamics Lab. His research explores how firms in networks adapt to system-level perturbations such as the trending increase in global outsourcing of complex engineering services, the implementation of integrated information systems, and the virtualization of project work teams executing complex work in distributed project networks. His research has received funding from the National Science Foundation, the Alfred P. Sloan Foundation, the Construction Industry Institute and the Earth Institute, among others. In 2011, he was awarded the National Science Foundation CAREER Award for his network dynamics research. He is the founder of two technology startups serving project-based industries and has five years of experience working as a project manager. He received his PhD from Stanford University in 2006.
Riitta Smeds, DSc (Tech), is a professor in the Aalto University School of Science's Department of Industrial Engineering and Management. She researches and teaches about innovations in networked business and service processes. She is the founding director of the Enterprise Simulation Laboratory SimLab, an experimental interactive environment for researching, teaching, and developing interorganizational collaboration and innovation.
Project Management Journal, Vol. 44, No. 1, 77–93
© 2013 by the Project Management Institute