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Article

Configurational Path to Collaborative Innovation in Large and Complex Construction Projects

by
Xiaoyan Chen
1,
Ting Wang
2,*,
Yan Liu
3 and
Zixin Dou
4
1
School of Urban Economics and Management, Beijing University of Civil Engineering and Architecture, Beijing 100044, China
2
College of Urban Construction and Safety Engineering, Shanghai Institute of Technology, Shanghai 201418, China
3
School of Management and Engineering, Nanjing University, Nanjing 210093, China
4
School of Business, Guangzhou University, Guangzhou 510006, China
*
Author to whom correspondence should be addressed.
Buildings 2024, 14(1), 117; https://doi.org/10.3390/buildings14010117
Submission received: 16 November 2023 / Revised: 7 December 2023 / Accepted: 11 December 2023 / Published: 2 January 2024
(This article belongs to the Section Construction Management, and Computers & Digitization)
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Abstract

:
Collaborative innovation contributes to sustainable development in many industries. Although there is a growing literature on collaborative innovation, it is still unclear whether and how configurations of drivers affect collaborative innovation in large and complex construction projects (hereafter megaprojects). This research adopts fuzzy-set qualitative comparative analysis (fsQCA) to explore multiple conjunctural causations of collaborative innovation in megaprojects. The findings show that four equifinal solutions can influence collaborative innovation in megaprojects, i.e.: “system innovation-oriented” (configuration 1), “radical innovation-oriented” (configuration 2), “second-tier supplier oriented” (configuration 3), and “modular or architectural innovation-oriented” (configuration 4). This study contributes to explaining the mechanisms regarding how configurations of drivers contribute to collaborative innovation in megaprojects. It also contributes to the development of collaborative innovation research in a construction project context. The research findings provide construction project managers with four useful suggestions for promoting collaborative innovation in megaprojects.

1. Introduction

Megaprojects require huge investments (often in the region of billions of USD), a long implementation period, and substantial technological and managerial innovations to achieve sustainability goals [1]. Megaprojects are distinguished from other projects by the interaction and interdependency of project elements and by a high level of uncertainty resulting from a shortage of clarity and agreement concerning project goals and methods for achieving these goals. They are not only representatives of technological breakthroughs, public accomplishments, and economic development but also are commemorative and typical achievements that surpass the significance of general projects [1]. The planning and implementation of megaprojects require various stakeholders to share heterogeneous knowledge and resources and collaborate to generate innovative products or services required for the project [2]. These organisations would share or create knowledge on novel technologies, advanced techniques, and effective management skills when implementing innovations.
Collaborative innovation is a key element to achieving megaproject sustainability goals. First, collaborative innovation can help align the interests of different stakeholders and enable them to work together towards the common goal of delivering the megaproject successfully, even promoting long partnerships. In addition, megaprojects often require innovative solutions to overcome huge challenges, such as technological breakthroughs, severe environmental protection, and social and economic sustainability. Collaborative innovation can bring together stakeholders’ diverse perspectives, skills, expertise, and actions to generate new ideas and approaches that may not be possible with a single organisation. What is more, megaprojects may involve multiple cultures and social groups, leading to differences in laws, regulations, and even behaviours and requiring deeper and tight collaboration strategies that can address cultural and social differences and achieve sustainability.
Existing studies indicated that collaborative innovation is complicated and difficult work [3]. It requires the full integration of information, goals, performances and the integration of actions. It also requires deeper and more interactions. Overall, collaborative innovation in megaprojects is an elaborate task that involves not only searching and processing relevant information but also integrating actions of different stakeholders around collaborative innovation.
Despite the importance and complexity of collaborative innovation to megaproject success, it is still under-researched in the project management literature [4]. First, existing studies have mainly made an effort to scrutinise antecedents/drivers and possible results of collaborative innovation at different levels, such as the national, industry, and organisation levels, neglecting the analysis on the megaproject level. Megaprojects are conducted by many permanent firms in a temporary project organisation. The research findings of permanent organisations can not be fully used in a megaproject context. In addition, the conclusions derived from existing research are inconsistent and even conflicted, which requires further exploration. For example, research [5] found that competition has a positive promoting effect on collaborative innovation of small and medium-sized enterprises, but its promoting effect is not significant. Research [6] showed that a stronger competitive environment could contribute more to the development of collaborative innovation. What is more, existing research mainly adopts variance-based methods, for instance, regression analysis (RA)/structural equation modelling (SEM). Those analysis techniques rely on a symmetrical approach and test conditions in competing contexts for that it mainly evaluates the net effect between conditions but removes possible asymmetric relationships (i.e., low and high values of the same antecedent within a data set can have the same impact) between variables [7]. It can not provide useful information about the interaction among both inputs and outputs and ignores the “combined effect” of several related factors. These linear analyses cannot provide true outcomes of behaviour due to the complexity of any decision-making process [8]. In megaprojects, collaborative innovation is a complicated process that could be achieved through equivalent pathways composed of various configurations of drivers. Thus, it is necessary to analyse the impact of complex configurations of drivers on collaborative innovation from a holistic and asymmetric perspective. Based on analysis, this study aims to answer the following question:
RQ: What configurations of drivers will promote collaborative innovation in megaprojects?
To answer the question, we first reviewed peer-reviewed journal papers to select the drivers. Then, fuzzy-set qualitative comparative analysis (fsQCA) was employed to conduct a detailed analysis of the collected data. The exploration of the causal “recipes” on collaborative innovation fills the research gap of how configurations of drivers conduce to collaborative innovation and is conducive to a deep comprehension of collaborative innovation, which will lead to high project performance.

2. Literature Review

2.1. Collaborative Innovation

Megaprojects’ collaborative innovation demonstrates two main attributes. On the one hand, it requires adaptability in innovation management to deal with the complexity and achieve sustainability [9]. On the other hand, collaboration among stakeholders asks for appropriate management rules to cope with differences and conflicts across organisations [10]. Collaborative innovation in projects depends on the characteristics of the project or organisation involved [11]. Compared with other projects, collaborative innovation is a voluntary organisational behaviour that is formed by self-compliance and profit-seeking [12].
Existing research explores the variety of organisations included in collaborative innovation and the relationships between the organisations. Paper [2] discussed the collaboration among main suppliers, users, universities, institutions, competitors, etc., toward innovation in megaprojects. Paper [13] argues that innovation champions in collaborative networks can be held by temporary and permanent owners of infrastructure departments, as well as members of the supply chain, thus, they pose a significant effect on improving innovation and learning abilities.
Regarding relationships between organisations, research [14] suggested that in collaborative innovation, only a few organisations could make actual contributions, while most organisations are latent or peripheral actors, and their commitments are minimal. Paper [15] described both collaboration and competition between organisations. Also, many negotiations and interactions between various organisations that may have diverse norms, values and goals would occur during the collaborative innovation process [16]. Three ties could transfer inter-organisational interactions: resource flows, information flows, and flows of mutual expectations.

2.2. Drivers of Collaborative Innovation

Existing research shows that external factors such as project requirements [7], competition [17], external changes [18], and rewards [19]; internal factors such as knowledge and learning [20] and project performance improvement [21] are common to lead to collaborative innovation. In the current research, we explore the relationships of these drivers with the collaborative innovation in megaprojects to deepen megaproject managers’ understanding of the combinations of drivers on project collaboration and to provide managerial suggestions on managing stakeholder collaboration.
Project requirements propel organisations to actively take part in innovation activities and further facilitate collaboration among each other. Many research points out that requirements of digital transformation in megaprojects facilitate collaboration among stakeholders as it provides a platform and lays the foundation for project stakeholders to process or exchange information. Besides, it is strenuous to integrate all the resources, organize all construction activities and make miscellaneous innovations by just a stakeholder [22]. What is more, numerous megaprojects are established in the severe natural environment or even impact the natural environment. Considering the complexity, uniqueness, and social responsibility of megaprojects, understanding the relationship between these project requirements and collaboration helps stakeholders process a great quantity of diverse information streaming in different but connected construction activities to improve project performance.
Competition in construction industries is getting tougher as a result of globalisation; therefore, many organisations choose to form a partnership with their competitors to combine resources and create new knowledge [23]. Competitors are likely to occupy analogous knowledge/information that might decrease ambiguity and increase absorptive capacity [24]. Collaboration with competitors facilitates the share and transfer of valuable knowledge/information and the absorption of complementary knowledge. Overall, understanding the impact of competition on collaboration helps different stakeholders to keep a competitive and collaborative relationship to create value together [25]. It can also help practitioners balance the relationship between competition and collaboration.
External changes from the natural or the human aspects will have a tremendous influence on an organisation. To diminish the negative effect, many organisations take the initiative to build partnerships and collaborate with others. By this method, they can achieve risk sharing or transferring and improve their potential values. There is no doubt that innovation often brings losses or even failures; thus, collaborative innovation is a good choice because of its integration into information, goals, performance, and even actions. By investigating the effects of external changes on collaboration, policymakers or managers could develop appropriate guidance policies or management strategies at the right time to direct stakeholders’ behaviours.
Rewards are positive feedback for a person or organisation’s behaviour. Existing research demonstrates the significant relationship between rewards and organisations’ interaction behaviour [26]. Receiving a reward is usually regarded as positive competence feedback that indicates achievement and stimulates intrinsic motivation, participation, and effort for certain behaviours. Also, considerable empirical research investigates the positive effect of rewards given by managers on participants’ collaborative relationships [27]. In megaprojects, the contributing effects of rewards have been stressed in project performance, project alliance and collaboration, project partnerships, and increased creative effort.
Knowledge and learning can be regarded as a motivation that promotes collaborative innovation because it is a key element in developing knowledge. Based on innovation and knowledge management theory, collaborative innovation means that heterogeneous organisations come to an agreement on making innovations by sharing, transferring, acquiring, and creating knowledge [28]. By investigating the effect of knowledge and learning on project collaboration, organisations in megaprojects could seize the opportunities to learn valuable skills, for instance, collective management skills along with effective information communication skills, which significantly affect their management capability improvement.
Project performance means that the project can be achieved on time, within budget, and on high quality. It needs to achieve value for money. Due to the division of project stages and disciplines in megaprojects, different organisations need to engage in the projects according to project arrangements that make collaboration necessary rules. Investigating the effect of project performance on project collaboration can also help managers break the organisation’s boundaries to increase project quality and safety and even reduce risks [29]. It also facilitates the formation of dynamic and robust project governance strategies to deal with rapidly changing project ecology.
Considering this background, we use a configurational model to show the complex relationship between different drivers and collaborative innovation, as shown in Figure 1. We suggest that the combination of project requirements, competition, external changes, rewards, knowledge, and learning and project performance improvement can lead to collaborative innovation in megaprojects.

3. Research Method

3.1. Research Design

This study includes three main stages. The first stage was to identify measures. Based on the literature, we initially identified 6 drivers and 26 scales that impacted collaborative innovation in megaprojects, as shown in Table 1. This set of drivers and scales was validated by three experts separately (see Section 3.2).
The second stage was data collection. We designed a questionnaire survey and conducted a pilot survey with ten Chinese megaproject experts in June 2021 to check the reliability and validity. After the interviews, the formal survey (as shown in Appendix A) was distributed to more than 100 Chinese megaproject experts between July 2021 and October 2021 and received 56 questionnaires. The third stage was data analysis using the fsQCA. The overall research design is shown in Figure 2.

3.2. Measures

We adopt different multi-item scales to measure the drivers proposed in Section 2.2. These measures are derived from existing studies where necessary changes are made to meet our research context. Specifically, we evaluate “knowledge and learning” by four questions based on studies [30,31]. We measure “project requirements” by four questions adapted from [32,33]. We evaluate “project performance improvement” using five items based on [34]. We assess “rewards” with three questions drawn from papers [35,36]. We measure “competition” according to the measures modified from [4,37]. We assess “external changes” by five items according to research [38] and “collaborative innovation” using five questions derived from research [39].
We then invited 2 senior managers and 1 professor to judge the selection of drivers and measures to improve their suitability. The criteria for the selection are the working experiences and achievements. All 3 experts have more than 20 years of experience in megaprojects and have been honoured at the national/provincial level for their contributions to megaproject practices or theoretical research. No conflicts occurred regarding their suggestions. They advised improving measurement items in “project requirements”, i.e., changing “The tasks in megaprojects include exploring organisational and technological innovation collaboratively” to “The tasks in megaprojects include exploring organisational, financial, contract, and technological innovation collaboratively”. Also, the measurement item “to improve operability” in driver-“project performance improvement” changes to “to improve user satisfaction during project operation” to evaluate the influence of megaprojects from a broad view. We also improved the language according to their suggestions.

3.3. Questionnaire Design and Data Collection

To obtain the configurations of conditions that can explain the outcome, fsQCA adopts diverse kinds of data, for instance, the Likert-scale data, clickstreams, and multimodal data, provided that the investigators can convert them into fuzzy sets [40]. As there is less public qualitative and quantitative data showing drivers of collaborative innovation in megaprojects, we adopt questionnaire surveys to gather Likert-scale quantitative data. The questionnaire survey is a method to gain useful information for megaproject and project research which has been widely used in existing research. The questionnaire design included two steps.
First, based on the results of measures identified in Section 3.2, we designed a questionnaire survey by referring to well-established scales and reviewing the results of published literature. Following [41], we formed a Likert-scale survey in which 1 indicated “absolutely disagree” while 7 meant “absolutely agree”. The questionnaire is composed of different sections and contents. The first section attempts to learn basic information about the megaprojects selected by respondents (for instance, the name, the investment, and the location of the selected megaproject). For the second section, respondents have to reveal the extent of drivers which motivated organisations/persons to be involved in collaborative innovation according to the actual situation of the selected megaproject. The third section gathers respondents’ personal information for the analysis, for instance, gender, academic and educational history, work experience, etc. The survey was drafted in English and interpreted into Chinese by an investigator. Another researcher then translated the Chinese version back into English to verify the consistency. To reduce the misinterpretation, all researchers discussed and dealt with the conflicts.
Second, we conducted an anonymous pilot questionnaire survey with ten experts who were top managers in megaprojects and had abundant experience (more than 15 years) in megaproject innovation and collaboration. During this stage, we contacted ten experts via Wechat/phone calls and explained our purpose. We then sent them an online pilot questionnaire and asked them to answer questions based on their experience. Based on the data received, we checked the reliability and validity according to statistical analysis of data collected (e.g., Cronbach’s α values and Kaiser-Meyer-Olkin (KMO) result) and adjusted. We combined the “monetary benefits” and “financial compensations” into a measurement item, “monetary rewards”, as their scores are the same. The formal questionnaire was formed based on pilot survey results.
Then, we conducted a formal questionnaire survey during July 2021 and October 2021. Firstly, we employed an online questionnaire website and e-mail to invite 10 experts who provided their opinions in the pilot test to fill out the improved questionnaire. They are also suggested to recommend their colleagues or friends who have extensive experience in megaprojects. We employed a combined sampling method, containing both the purposive and snowball sampling, to get enough samples and guarantee the multiplicity. It is declared at the beginning of the questionnaire that only those who have been involved in megaproject innovation can complete the questionnaire to ensure all respondents had experience in megaproject’s collaborative innovation. By purposive and snowball sampling, we distributed 104 questionnaires and finally received 56 questionnaires. We cleaned the collected data by removing the answers with a completion time of shorter than 200 s because the quality of the answers could not be assured. Indeed, we calculated the length of time spent in the pilot and formal questionnaires and found that most of the answers were taken more than 230 s, and the average time was 4 min. We also cleaned the data by checking the first section, especially the name and investment of the megaproject selected. These megaprojects, which have an investment between 1 million and 6 million and have a significant effect on social and economic development, are chosen as well. After cleaning, 48 surveys were obtained to look into the research question. The number of samples can meet the requirement of fsQCA, which is suitable for small and intermediate-size samples. Also, the literature indicates that 10 to 50 samples are sufficient [42].
Among all experts, as shown in Table 2, more than 81% have a bachelor’s degree or above. Additionally, 60% or more respondents worked in megaprojects for at least 10 years. Thus, these experts can provide suitable and valuable data for our research.

3.4. Data Analysis Method

This study uses fsQCA, which is based on the configurational theory, to analyse data. fsQCA has been widely used in social science and other disciplines to indicate the “asymmetrical relationship” rather than the “symmetrical relationship” in reality. It combines both Boolean algebra and fuzzy set theory to interpret complex relationships between variables. It also builds on configurational thinking, which allows for equifinality and punctuated equilibrium rather than situational equifinality context and Quasi-stationary equilibrium (e.g., contingency theory) [43]. fsQCA is particularly useful in cases where the relationships between variables are not linear or direct and where there are multiple possible configurations of factors that lead to a particular outcome. It enables the use of fuzzy scales (continuous scales) instead of dichotomous scales (binary scales) for both conditions and results [44].
As an innovative method, fsQCA moves beyond RA, factor analysis, SEM, etc., to indicate the “asymmetrical relationship” among variables. RA, factor analysis, and SEM assume that the relationships between variables are linear, and the goal is to reduce the complexity of a set of variables to limited factors that can interpret most of the information of the variance in the data.
fsQCA is increasingly used to study various research domains, including sustainability, supply chain management, risk management, and innovation management. For example, ref. [45] examined how open innovation ecosystem modes lead to product innovation, combining both grounded theory and fsQCA. Ref. [46] examined the combined effects of supply chain traceability and supply chain transparency on financial performance. Overall, fsQCA is a useful method for exploring complex causal relationships in different research areas, particularly when the data do not show linear relationships and the variables are categorical or binary.

4. Results

4.1. Data Reliability and Validity

Before conducting fsQCA, we first analysed data to ensure reliability and validity. Based on the analysis, we can find that reliability (Cronbach’s α values) is 0.936, reaching the baseline requirement [47]. Additionally, the Kaiser-Meyer-Olkin (KMO) result is 0.718, demonstrating that the data meet the standard of validity. The CR results are much larger than the baseline, and the AVE results are smaller than the basic requirement of 0.50 [48]. Above all, the basic requirement and standard of reliability and validity are met and assured.
We also conduct Harman’s one-factor test [49] which has been widely adopted in existing studies to evaluate the extent of common method bias (CMB). Herman’s one-factor test is a statistical procedure where the examined conditions will be put into a factor analysis. It has been widely used in project management research [50]. Thus, we calculate the unrotated factor solution to obtain how many factors can explain the variance. Harman’s single-factor test produced a seven-factor solution where the first factor accounts for below 40% of the variance (37.595% of 80.98%), indicating that CMB was unlikely to be a major issue.

4.2. Calibration

Because the causal conditions are evaluated by multiple questions, the average values are first computed to carry out fsQCA. Based on the procedures, we need to calibrate the data before detailed calculation, which requires us to transform data into a degree of set membership according to three different thresholds, including the full membership, full non-membership, and the crossover point. Following [42], we adopt both empirical and theoretical points to avoid subjectivity. The minimum values of uncalibrated data in this study are quite high (2–4 on the Likert scale), and the mean values were above 5, as shown in Table 2. Thus, to prevent insignificant outcomes that could result in a solution that contains all conditions selected as necessary [51], this study adopts percentiles to calibrate data [45]. Following [40], the 95th percentile represents the full membership, and the 50th percentile demonstrates the crossover point. What is more, the 5th percentile indicates the full non-membership. Based on the above, calibration results are presented in Table 3.

4.3. Necessary Conditions Analysis

We need to calculate the necessity of six casual conditions prior to establishing a truth table [52]. If a condition always exists (does not exist) when the result exists (does not exist), then the condition is considered a necessary condition [53].
Table 4 shows that the consistency and coverage results are smaller than the baseline of 0.9 [42], meaning that any separate condition variables could not interpret the outcome variable. In summary, no one particular condition (i.e., drivers) is necessary for the outcome (i.e., collaborative innovation). In this study, collaborative innovation in megaprojects is formed by various combinations of drivers. Thus, further sufficient analyses are needed.

4.4. Sufficient Conditions Analysis

Based on the analysis above, a truth table is preliminarily constructed to list the distribution of the whole potential sample cases to explain collaborative innovation [42]. The truth table analysis forms a truth table of 26 rows. Each row demonstrates a possible configuration. We use two criteria, frequency value and consistency threshold [42], to further exclude less important configurations of drivers and distil a truth table. The frequency threshold value is set to 2 (e.g., [45]) and the consistency threshold value is configured as 0.8 (e.g., [54]). After employing two different thresholds and based on the Quine–McCluskey algorithm principle, fsQCA produces three potential solutions that can explain the results, including the complex solutions, the parsimonious solutions, and the intermediate solutions. Complex solutions demonstrate every potential combination of conditions based on the traditional logic calculation principle [40]. The parsimonious solution is a simplified version of a complex solution, and it identifies the most significant conditions that must not be missed in any solution [40]. The intermediate solution derives from counterfactual analysis and embodies both the parsimonious and (part of the) complex solutions [42]. In this study, we used the intermediate solutions to clarify the calculated configurations (Table 5) while we employed the parsimonious solutions to separate the core conditions from the periphery conditions [45]. After calculation, four configurational paths are obtained by the fsQCA 30 analytical tool.
The core condition cannot be excluded from any solution and usually appears in parsimonious and intermediate solutions [40]. The periphery condition is less important and usually appears only in the intermediate solution [40]. The “do not care” implies that a certain result might or might not exist, indicating that it is not effective in a certain configuration [40].
From Table 4, we can find that four equifinal pathways are sufficient for collaborative innovation; in other words, these four equifinal pathways promote collaborative innovation between organisations in megaprojects. To clearly state the four pathways, we label them as “system innovation-oriented” (configuration 1), “radical innovation-oriented” (configuration 2), “second-tier supplier oriented” (configuration 3), and “modular or architectural innovation-oriented” (configuration 4), respectively. The raw coverage results are from 0.273 to 0.473, while the consistency results are from 0.880 to 0.991. We can also find that overall solution consistency results are 0.885, showing that the degree to which these four pathways guaranteed the proper results is high. Above all, the four equifinal pathways contribute to collaborative innovation in megaprojects.
We also analysed the robustness of the results of fsQCA by separately adjusting the consistency threshold. We adjust the consistency threshold from 0.8 to 0.85, and the results are the same as those in Table 5. The result shows that the robustness is achieved.

5. Discussion and Conclusions

5.1. Discussion

This research investigates which configurations of drivers can lead to collaborative innovation in a specific background, i.e., megaprojects. The existing literature considers a single factor in collaborative innovation [55] or, if considering more than one factor, ignores the asymmetric relationships among drivers and the nonlinear relationships [56]. We overcame this limitation using the fsQCA to explore collaborative innovation in megaprojects, where organisations and various innovation activities coincide but are not reciprocally restricted [57].
The result indicates that four equifinal pathways will promote collaborative innovation in megaprojects. The result also verifies the configurational theory that different equivalent pathways could lead to the same outputs [43].
Configuration 1 includes four core conditions and one peripheral condition that contribute to collaborative innovation. These four core conditions involve both the internal drivers, such as project requirements, improving performance as well as learning, and external drivers, i.e., external changes. A peripheral condition is an external change. A similar example of this configuration might be the system innovation in megaprojects (e.g., the introduction of Industry 4.0 and lean thinking into the Hong Kong–Zhuhai–Macau megaproject), in which the organisations have to explicitly integrate and share knowledge on new technologies and heterogeneous resources to avoid failure, reduce uncertainty and challenges, and improve productivity. This configuration can be explained by the fact that megaprojects are greatly complex, requiring different stakeholders to balance the collaboration and competition relationships to solve the external changes along with further facilitating megaproject performance [58]. Additionally, the client is often considered a champion in innovation activities and also the integrator of the project society, which facilitates tight collaboration in megaprojects and organisations and promotes the environment where knowledge and information are shared, and collaboration and competition relationships are balanced [59]. This configuration is theoretically explained, referring to resource dependence theory, i.e., the organisation’s social and organisational resources. Each organisation in megaprojects has a unique pool of tangible and intangible resources, which help them show their distinct identity and competitiveness. For example, design consultants participate in various types of projects (e.g., highways, railways, long bridges) to learn and apply the latest technologies and knowledge to support the organisations’ development. The result also indicates the cost reduction of organisations that depend on external resource providers to manage uncertainty. Because of the resource limitation, each organisation hinges on external resource providers to obtain complimentary resources, reduce both primary uncertainties from the expected or unexpected changes (e.g., changes in client’s requirements, regulation changes, the natural environment changes) and increase their possibilities to obtain rewards [16]. Based on the analysis above, we call configuration 1 a “system innovation-oriented” pathway.
Configuration 2 contains three different conditions, as presented in Table 4, ranging from requirements and improving the performance of the project to the organisation’s learning motivation. This result may be interpreted considering radical innovations in megaprojects where few organisations can independently bear the huge challenges and deliver the project (e.g., deep undersea island-tunnel construction in a long bridge) [60]. In such circumstances, hardly any single stakeholder can innovate on their own. They are faced with huge challenges from megaprojects, sharing knowledge and resources to achieve the project objectives within schedule, quality, and budget restriction work as the most salient forces to facilitate collaborative innovation. For example, to solve the world-class construction problems of building the immersed tunnel project within a limited time and budget, HZMB Authority invites three famous Chinese construction enterprises, including China Communications Construction, China Railway Construction Corporation Limited, and China Railway Group Limited, to bid and encourage them to integrate domestic resources for industrial M&A to solve the construction problems. After careful preparation, China Communications Construction integrates domestic and international enterprises and collaborates with COWI, AECOM, etc., to seek, learn, and even create new construction technologies to finish the project. Based on the analysis above, we call configuration 2 a “radical innovation-oriented” pathway.
Configuration 3 takes gaining rewards, huge competition in the market, and the organisation’s learning goal into account to explain their positive effects on collaborative innovation. This result may be illustrated by considering a second-tier supplier that provides material, equipment, and information based on the requirements of the first-tier supplier [61]. The second-tier supplier has no direct connection and relationships with clients/owners, and often, the first-tier suppliers work as the systems integrator [61]. In this condition, the second-tier supplier captures the opportunities of collaboration with the first-tier supplier to accumulate experiences, knowledge, and social resources, obtain monetary or non-monetary rewards and further increase its competitiveness for its long-term survival [62]. Based on the analysis above, we call configuration 3 a “second-tier supplier-oriented” pathway. The “second-tier supplier oriented” pathway indicates that as a rational economic agent [62], organisations depend on external resources to improve competitiveness and achieve survival [63].
Configuration 4 is composed of four variables concerning the internal drivers, including the requirements and performance enhancement of the megaproject, along with the external drivers containing changes from the environment and humans and positive rewards. This result indicates that external drivers can also pose a huge influence on collaborative innovation [4]. This finding is distinctive from existing findings that argue that collaborative innovation is primarily dominated by internal motivations and that external factors play hardly any role [64]. This is because megaprojects are proposed and launched by the government and have a strong political orientation [65]. Meanwhile, with the project implementation, stakeholders must establish collaborative innovation relationships with the aim of carrying out construction work smoothly and jointly addressing construction problems. This result may be understood by considering modular or architectural innovations. Modular or architectural innovation typically involves just minor modifications within a concept or component, with less restricted influence on relevant components or systems. Based on the analysis above, we call configuration 4 a “modular or architectural innovations oriented” pathway.

5.2. Conclusions

Collaborative innovation is considered a salient innovation paradigm that facilitates an increasing number of different organisations to share sustainable sources of competitive advantage from external resource providers. Nevertheless, much research cannot provide useful findings on whether and how the configurations of drivers impact collaborative innovation. Based on fsQCA of the survey collected from Chinese megaproject experts, we find that four equivalent configurations can facilitate collaborative innovation, i.e., “system innovation-oriented” (configuration 1), “radical innovation-oriented” (configuration 2), “second-tier supplier oriented” (configuration 3), and “modular or architectural innovation-oriented” (configuration 4).
The research findings could promote both theoretical advancement and managerial implications.
From a theoretical perspective, the research advances collaborative innovation research in megaprojects using the configurational theory. Although drivers are regarded as vital determinants that motivate organisations to engage in collaborative innovation, and despite existing literature finding that collaborative innovation can facilitate project performance, the mechanisms regarding how configurations of drivers contribute to collaborative innovation have not been deeply explored. In this context, this study suggests appropriate configurations of drivers that impact collaborative innovation in megaprojects, as various drivers jointly produce stimulating effects for organisations. This study extends existing research by providing four appropriate configurations of drivers for promoting collaborative innovation in megaprojects. It also reveals that different configurations of drivers can achieve different types of innovation in megaprojects; for example, the combination of knowledge and learning, rewards, and competition can contribute to the formation of “second-tier supplier oriented” collaborative innovation. Future research can investigate the relationships between different configurations of drivers and the type of innovation in megaprojects.
For the managerial implication, the four paths can provide managers or organisations with a better understanding of how to promote collaborative innovation through strategies that appropriately combine different drivers. Understanding the drivers that impact collaborative innovation can help managers improve innovation performance and even further facilitate project performance. Each driver has an important role in promoting collaborative innovation [4], and it is rarely the case that managers use a certain driver solely in practice. Thus, managers must reflect on how to efficiently combine the various drivers based on realistic practice to achieve better collaborative innovation.
In megaprojects, clients can adopt diverse configurations of drivers based on the innovation types. Clients should select potential suppliers carefully for those radical innovations, provide detailed requirements on the form of collaborative innovation and the performance indicators in the contract, and build a knowledge management system to promote collaborative innovation. The project-based organisations (e.g., main contractors, designers, consultants) could grasp the opportunities to learn and codify the most advanced technologies and knowledge and improve their competitiveness with almost no transaction costs. Clients are supposed to integrate and organize organisations to be involved in different collaborative innovation activities for those system innovations by putting detailed collaborative innovation requirements, knowledge management requirements, and project performance improvement requirements. The project-based organisations should follow instructions and adapt quickly to external changes. Second-tier suppliers need to respond to market changes and development opportunities in megaproject’s collaborative innovation to facilitate themself getting valuable rewards, learn knowledge, and further improve their competitiveness.
This study has two main limitations. First, the data were cross-sectional rather than longitudinal. However, collaborative innovation is a dynamic process in a megaproject context. Thus, more studies should be conducted to extend the generalization of the findings in the current study, for example, by using different research methods, such as combining multiple case studies and the system dynamics approach, to replicate this study and verify the findings obtained from it. Second, this study is focused on government-sponsored megaprojects in China. Generalising the findings to other types of projects and other countries needs further research.

Author Contributions

Conceptualization, X.C.; methodology, Y.L.; software, X.C.; validation, T.W.; writing—original draft preparation, X.C.; writing—review and editing, T.W., Y.L. and Z.D.; funding acquisition, X.C., T.W. and Y.L. All authors have read and agreed to the published version of the manuscript.

Funding

This research was funded by Shanghai Sailing Program (23YF1446400), Fundamental Research Funds for Beijing University of Civil Engineering and Architecture (X23017), National Natural Science Foundation of China (72201125 and 72301178), Shanghai Youth Soft Science Research Project (23692117700), Scientific Research Foundation for the Talented Young Scholars of Shanghai Institute of Technology (YJ2022-21).

Data Availability Statement

The data presented in this study are available on request from the corresponding author. The data are not publicly available due to privacy.

Conflicts of Interest

The authors declare no conflict of interest.

Appendix A

Questionnaire survey
Dear Sir/Madam,
This questionnaire survey aims to investigate the effect of drivers in promoting collaborative innovation in megaprojects. Megaprojects are large-scale and complex projects that involve large investments, extreme complexity and uncertainty, multiple stakeholders and require substantial innovations during their planning, design, construction, and delivery stages.
Please answer the questions based on your innovation experience in a specific megaproject. If you have no such experience, please ignore it. Thank you for your time!
Section 1: basic information of the megaproject
1. The name of a recent megaproject you participated
2. The investment amount of the megaproject (RMB)
3. Which stakeholders do you belong to
□ Owner
□ Client
□ Designer
□ Contractors
□ Suppliers
□ Consultants
□ NGOs
□ Operators
□ Others
4. Your position in the megaproject
Section 2: drivers for collaborative innovation
The table demonstrates drivers that motivate stakeholders to engage in collaborative innovation. Please choose whether you agree with the drivers or not (1—absolutely disagree, 7—absolutely agree).
1234567
By engaging in group thinking, communication, informal encounters, etc., to accumulate and learn experience
By engaging in brainstorming sessions, formal reviews, de-brief meetings, lessons learned, and/or post-mortem meetings to articulate knowledge
By engaging in project plan/audit, milestones, case writing, etc., to codify knowledge and information
By visiting other projects, inter-communication, and networking to transfer, absorb, and use knowledge
The megaproject always requires trying out new approaches to difficult problems
Introducing new ideas into the organisations is part of the project goals
The tasks in megaprojects include exploring organisational, financial, contract, and technological innovation collaboratively
We need to be innovative to fulfil our tasks and clients’ requirements
To achieve the project on schedule or ahead of time
To reduce construction costs
To improve construction quality
To ensure safety
To improve user satisfaction during project operation
To gain monetary rewards (e.g., financial compensations, monetary benefits)
To obtain industry/national honour
To gain non-monetary rewards from the project (e.g., feedback and recognition from clients)
To build a good image and reputation
To achieve increased value creation (e.g., the first-mover advantage, increased market share, new market opportunities)
To express the firm’s ability and creativity
To improve other participants and clients’ satisfaction
In response to peer competition
In response to construction technology changes
In response to regulation changes (taxes, international, etc.)
In response to information and communication technology changes
In response to natural environmental changes
In response to the evolution of the construction industry
By engaging in group thinking, communication, informal encounters, etc., to accumulate and learn experience
By engaging in brainstorming sessions, formal reviews, de-brief meetings, lessons learned, and/or post-mortem meetings to articulate knowledge
By engaging in project plan/audit, milestones, case writing, etc., to codify knowledge and information
By visiting other projects, inter-communication, and networking to transfer, absorb, and use knowledge
The megaproject always requires trying out new approaches to difficult problems
Section 3: personal information
1. Gender
□ Male
□ Female
2. Educational background
□ College degree or below
□ Bachelor degree
□ Master degree
□ Doctor degree
3. Working experience
□ <5
□ 5–10 years
□ 10–20 years
□ >20 years
4. Working experience in megaprojects
□ <5
□ 5–10 years
□ 10–20 years
□ >20 years
If you have any suggestions for this survey, please provide your comments and suggestions. Thank you!
If you want to know the result of our research, please leave your contact information E-mail: we will send you when we finish.
Thank you very much for your support!

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Buildings 14 00117 g001
Figure 1. Configuration model.
Figure 1. Configuration model.
Buildings 14 00117 g001
Buildings 14 00117 g002
Figure 2. Research design.
Figure 2. Research design.
Buildings 14 00117 g002
Table 1. Drivers and scales of collaborative innovation.
Table 1. Drivers and scales of collaborative innovation.
DriversScales
Knowledge and learningBy engaging in group thinking, communication, informal encounters, etc., to accumulate and learn experience
By engaging in brainstorming sessions, formal reviews, de-brief meetings, lessons learned, and/or post-mortem meetings to articulate knowledge
By engaging in project plan/audit, milestones, case writing, etc., to codify knowledge and information
By visiting other projects, inter-communication, and networking to transfer, absorb, and use the knowledge
Project requirementsThe megaproject always requires trying out new approaches to difficult problems
Introducing new ideas into the organisations is part of the project goals
The tasks in megaprojects include exploring organisational and technological innovation collaboratively
We need to be innovative to fulfil our tasks and clients’ requirements
Project performance improvementTo achieve the project on schedule or ahead of time
To reduce construction costs
To improve construction quality
To ensure safety
To improve operability
RewardsTo gain monetary rewards (e.g., financial compensations, monetary benefits)
To obtain industry/national honour
To gain non-monetary rewards from the project (e.g., feedback and recognition from clients)
CompetitionTo build a good image and reputation
To achieve increased value creation (e.g., the first-mover advantage, increased market share, new market opportunities)
To express the firm’s ability and creativity
To improve other participants and clients’ satisfaction
In response to peer competition
External changesIn response to construction technology changes
In response to regulation changes (taxes, international, etc.)
In response to information and communication technology changes
In response to natural environmental changes
In response to the evolution of the construction industry
Table 2. Experts’ Demographic Information.
Table 2. Experts’ Demographic Information.
NumberPercentage
Educational backgroundBelow bachelor918.75%
Bachelor2041.67%
Master1327.08%
PhD612.50%
Working experience in megaprojects (years)<5510.42%
5–101429.17%
10–202245.83%
>20714.58%
PositionsProject manager918.75%
Site manager1837.50%
Engineer714.58%
Consultant manager1020.84%
Cost management Manager48.33%
Types of stakeholdersClients714.58%
Consultants1225.00%
Contractors1633.33%
Designers1327.09%
Table 3. Calibration results.
Table 3. Calibration results.
ConditionsMeanSDCalibration Values
Percentile 5MedianPercentile 95
Knowledge and learning5.810.784.116.007.00
Project requirements5.900.853.616.007.00
Project performance improvement6.000.564.896.007.00
Rewards5.320.523.455.337.00
Competition5.570.683.235.757.00
External changes5.410.613.245.506.67
Collaborative innovation5.670.703.655.806.82
Table 4. Necessary condition analysis.
Table 4. Necessary condition analysis.
Collaborative Innovation~Collaborative Innovation
ConsistencyCoverageConsistencyCoverage
Knowledge and learning0.7390.7860.6500.593
~Knowledge and learning0.6180.6740.7660.715
Project requirements0.7660.7280.7170.583
~Project requirements0.5620.6980.6660.709
Project performance improvement0.7190.7600.6810.616
~Project performance improvement0.6360.6990.7350.692
Rewards0.7060.7330.6620.588
~Rewards0.6030.6760.6990.671
Competition0.7440.7770.6770.606
~Competition0.6220.6920.7500.715
External changes0.7880.7990.7040.611
~External changes0.6160.7080.7690.756
Notes: ~ indicates the absence of the antecedent.
Table 5. Results of fsQCA.
Table 5. Results of fsQCA.
Configuration 1Configuration 2Configuration 3Configuration 4
Knowledge and learning⬤●⬤⊗
Project requirements⬤⬤⊗⬤
Project performance improvement⬤⬤⊗⬤
Rewards ⊗●●
Competition⬤⊗⬤⊗
External changes●⊗Buildings 14 00117 i001●
Consistency0.8800.9710.9910.965
Raw coverage0.4730.2980.2910.273
Unique coverage0.1450.0210.0570.042
Overall solution consistency0.885
Overall solution coverage0.601
Notes: ⬤ means the existence of a core condition, ● means the existence of a peripheral condition; Buildings 14 00117 i001 means a shortage of a core condition; ⊗ means a shortage of a peripheral condition; and blank spaces mean a “don’t care”.
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Chen, X.; Wang, T.; Liu, Y.; Dou, Z. Configurational Path to Collaborative Innovation in Large and Complex Construction Projects. Buildings 2024, 14, 117. https://doi.org/10.3390/buildings14010117

AMA Style

Chen X, Wang T, Liu Y, Dou Z. Configurational Path to Collaborative Innovation in Large and Complex Construction Projects. Buildings. 2024; 14(1):117. https://doi.org/10.3390/buildings14010117

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Chen, Xiaoyan, Ting Wang, Yan Liu, and Zixin Dou. 2024. "Configurational Path to Collaborative Innovation in Large and Complex Construction Projects" Buildings 14, no. 1: 117. https://doi.org/10.3390/buildings14010117

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