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Article

Improving the Effectiveness of Organisational Collaborative Innovation in Megaprojects: An Agent-Based Modelling Approach

by
Na Zhao
1,
Congcong Lei
1,
Hui Liu
2 and
Chunlin Wu
3,4,*
1
School of Traffic and Transportation Engineering, Changsha University of Science & Technology, Changsha 410114, China
2
School of Management Engineering, Zhengzhou University, Zhengzhou 450001, China
3
School of Economics and Management, Beihang University, Beijing 100191, China
4
Beijing Key Laboratory of Emergency Support Simulation Technologies for City Operations, Beihang University, Beijing 100191, China
*
Author to whom correspondence should be addressed.
Sustainability 2022, 14(15), 9070; https://doi.org/10.3390/su14159070
Submission received: 8 June 2022 / Revised: 17 July 2022 / Accepted: 18 July 2022 / Published: 25 July 2022
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Abstract

:
As the complexity, breadth of expertise and number of agents involved in megaprojects grow, collaborative innovation models become invaluable for helping to achieve sustainable project development. On this basis, the purpose of this study is to explore the innovation output mechanisms used for collaborative innovation in megaproject (CIMP) systems by the agent-based modelling (ABM) approach, and to promote the efficiency and effectiveness of organisational collaborative innovation through variable controls. A multi-agent simulation CIMP model was developed using the NetLogo tool. The model encompasses the behavioural factors and interaction rules that affect organisational CIMP. Four simulations were conducted, and the results showed that (1) the innovation environment, including policy environment, cultural climate, and engineering demand, has a positive effect on the output rate of CIMP; (2) a larger scale of innovative network organisation accelerates innovation output; (3) innovative organisations must avoid enforcing high standards for cooperation, communication, and recognition abilities when selecting partner organisations; (4) innovative organisations’ ability to absorb technology, information, and knowledge is positively related to output, while an increase in behavioural costs reduces the scale of innovative network organisations, thereby affecting their output. This study developed its CIMP theory from the perspective of organisational behaviour. The findings are expected to provide methodological and practical guidance for the selection of innovation agents, behavioural patterns, and for guaranteeing efficient innovation in collaborative megaproject organisations.

1. Introduction

Megaprojects are a category of construction projects with a large scale of investment and many stakeholders, and are related to scientific and technological progress, social development, and the interests of the population [1,2,3]. In recent years, with the advancement of infrastructure projects in fields such as transportation, energy, and water conservation, the demand for megaprojects has surged. For megaprojects, innovation is necessary for managing the project’s diverse needs, improving the quality of work, and achieving project sustainability [4,5]. As a core management object, the organisation of megaprojects is vital for achieving the realization of innovation targets. However, due to the complexity of social and organisational relationships [6], the field of innovation management for megaprojects often faces pressing problems, such as slow innovation efficiency and unremarkable innovation outcomes. To facilitate the successful operation of megaprojects, organisations need to innovate to solve complex construction problems and achieve efficient innovation outputs. Therefore, collaborative innovation in megaproject (CIMP) management is vital for promoting deep inter-organisational cooperation and achieving high-quality, efficient innovation, thereby increasing the likelihood of project success [7].
Unlike the traditional monolithic, linear management model [8], collaborative innovation systematically matches elements of various innovation agents to achieve optimal synchronisation in the collaborative process. The CIMP model refers to the process by which multiple innovation agents , such as owners, designers, constructors, research institutions, and government departments, collaborate on a project in order to optimise the integration of resources, while playing to their respective strengths to jointly complete innovation tasks [9,10]. Therefore, it is necessary to consider the elements of collaborative innovation’s impact in the context of megaprojects and how these factors affect the realisation of CIMP.
Innovation is often reflected singularly in engineering products, equipment, construction techniques, working methods, or holistically throughout an entire project [11]. Innovative activities in megaprojects are accomplished through the ad-hoc collaboration of multiple organisations. In addition to being influenced by external uncertainties, such as social, economic, and cultural factors [12,13], innovative activities in megaprojects are also highly reliant on the psychological perceptions, habits, communication styles and collaborative behaviours of the many parties involved [14]. Xie et al. [15] point out that megaproject stakeholders’ behavioural choices result from a combination of intra-organisational characteristics, inter-organisational relationships, and external contexts. Therefore, governments and suppliers use active political relationships to achieve oversight and control of the supply chain, with the behavioural goal of improving the efficiency of the agent’s work, while reflecting the market and company values. According to a statistical survey of actual cases, organisational fragmentation, unclear agent responsibilities, and uneven distribution of tasks are common in the management of megaprojects. The lack of awareness of the cultural backgrounds and innovation capacities of the participating organisations among managers [16], and the resulting behavioural and management failures, are major factors contributing to weak organisational relationships. Thus, the realisation of CIMP requires the involvement of multiple organisations working together to achieve innovation goals, and can also be influenced by factors such as cultural environment, behavioural choices, and innovation capacity.
Generally, researchers interpret the CIMP model as a comprehensive and integrated complex system consisting of multiple agents and elements. These factors are linked through the interactive behaviours of the agents, which then interact with and constrain one another. In this complex system, a single agent cannot complete all of the innovation activity alone, and the owner must build a close innovation network with the designer, contractor, and other agents to overcome the problem [17]. Lehtinen, Peltokorpi, and Artto [18] point out that consulting organisations, universities, and academic research units in multidisciplinary fields need to work with construction units to develop engineering innovation strategies and coordinate organisational members to achieve innovation goals. Chan, Liu, and Fellows [19] investigated the influence of leadership on the innovation atmosphere in construction companies using a questionnaire survey. Zhang et al. [20] analysed the network structure of the innovation process of construction projects from a network perspective and explored the complex relationships among stakeholders in the collaborative innovation management model. Xue et al. [21] used a social network method to study the collaborative relationships among organisations and explain the inner mechanisms of innovation in construction enterprises.
Recent research on megaprojects has highlighted inter-organisational collaborative relationships and collaboration strategies [22]. A study described how ecosystem captains create collaborative ecosystems for megaprojects by integrating innovation networks and fostering a culture of innovation [23]. Hetemi, Ordieres, and Nuur [24] explored knowledge transfer and utilisation between megaproject organisations from a process perspective, emphasising collaboration in an inter-organisational network environment. Xue et al. [25] provided insights into stakeholder relationship management in megaprojects by presenting a conceptual model and Partial Least Squares Structural Equation Modeling results that explained the differential impact of formal and informal relationships on the performance of megaprojects. Deng et al. [26] used structural equation modelling methods to analyse the role and impact of collaborative governance within and between organisations in megaprojects. This work provided favourable support for the development of CIMP theory. However, few studies have discussed the behavioural interactions between organisations in the CIMP process, and their differential impact on collaborative innovation outcomes. The bottleneck between organisations requires extensive evaluation predictions to reveal the impact of different contexts on CIMP, which should facilitate an organisation’s prioritization of enhanced decisions in the innovation process to achieve better collaborative innovation outcomes. Based on this, this study utilises the ABM approach to explain the complex interaction processes between agents. Innovation activities in megaprojects are controlled by agents and their attributes, resulting in complex and variable behavioural interactions [27]. As a complex system of multi-organisational collaboration, the effects of collaboration between organisations cannot be effectively captured without being studied via ABM. For example, Zhao and An [28] used multi-agent simulation models to reveal the collaborative elements, modes of action, and transmission of intentional information in the complex supply chain systems of megaprojects.
Along these lines, this article aims to propose and develop a CIMP agent simulation model that can simulate the impact of behavioural factors (e.g., policy environment, organisation scale, cooperation basis) on collaborative innovation outputs. The remainder of this article is organized as follows: Section 2 reviews the literature on CIMP. Section 3 presents the model and implements it programmatically through the NetLogo platform. Section 4 designs the simulation experiments and draws the results. Section 5 discusses the theoretical and practical implications of this study. Section 6 concludes this paper.

2. Literature Review

2.1. Collaborative Innovation in Megaprojects

Collaborative innovation is an organisational management model in which multiple innovation players form an innovation alliance or network to create new technologies and approaches in each environment [29]. Dubois’ research has shown that the decentralised and project-based nature of the construction industry hinders innovation effectiveness [30]. The collaborative efficiency of megaproject organisations determines the adoption of innovation management and technological innovation. For example, the Hong Kong–Zhuhai–Macao Bridge Technology Innovation and Tackling Group is comprised of technical professionals from several research units and universities in China [31]. On this basis, Dietrich et al. [32] describe the role of project collaboration quality and knowledge integration capabilities in collaborative projects, which can be used as attribute values for successful collaboration in megaproject organisations. When effective collaboration exists between parties, the effectiveness of the project’s innovation increases, representing a higher output of innovation outcomes. Researchers suggest that coordination between organisations is challenging and is influenced by complex interactions between participating organisations. In projects, the inputs and outputs of organisations can also be described as a production process, and performance evaluations are often used to measure the process of collaborative interactions between organisations. Performance certainty in megaprojects can be improved through increased collaboration and integration [33]. Furthermore, as engineering projects are the output of multiple ad-hoc organisations working together, each with its own agenda, strong collaborations and harmonious relationships between all parties guarantee successful innovation. Thus, researchers understand the CIMP process as a behavioural interaction between organisations and their innovation output.

2.2. Factors Influencing CIMP Behaviour

The interaction between organisations in the process of managing innovation in megaprojects is the basis of this study—a process requiring an innovative environment that includes both the built environment and the cultural context. Collaborative interactions between organisations arise from the formation of innovation network organisations and the establishment of inter-organisational relationships. The way resources are managed is key to producing innovative outcomes. Therefore, a review and identification of these aspects and their factor compositions was initiated. Table 1 presents the factors influencing CIMP behaviour in terms of innovation environment, innovation network organisation, inter-organisational relationships, and innovation resource management.

2.2.1. Innovation Environment

Recently, Patricia et al. [58] indicated that a new environment for implementing innovation emerges from the CIMP process. Nam and Tatum [34] highlighted the value of the innovation process’ demand side. For example, the needs of owners greatly influence the process of building innovation and help promote the active investment of project participants in innovation activities, including resources such as technology and capital [35]. Zhao [39] described support for innovation and resource availability as an innovation climate in which the megaprojects’ innovation shows special cultural characteristics, including selfless dedication, a spirit of unity, and the cooperation of many individuals and organisations [40]. Project culture plays a decisive role in cooperation between relevant participants [41]. Hampson and Manley [37], and Laberge and Yves [38], refer to the complexity of the environment in which megaprojects are built, the innovation policy regime established by the government, the overall innovation strategy, market demand, and market competition, all of which significantly impact the project innovation process. It is easy to see that the engineering needs, innovation culture, and policy environment of megaprojects can influence the collaborative innovation of organisations.

2.2.2. Innovation Network Organisations

Organisations in different areas of expertise form complex networks in the context of megaproject innovation behaviour [44]. From the network perspective, project participants are integrated organisations with temporary social relationships that form a network of project organisations. In Winch’s view of a project organisation as a temporary configuration of permanent organisations in a coalition, the scale of the network organisation affects the project output to varying degrees [42]. Tang et al. [43] analysed how project-based relationship networks evolve between organisations, and demonstrated that organisations can be coupled with each other through project collaboration. In this process, the innovative organisational structure of megaprojects is not static, rather, it evolves with collaborative forms of megaprojects, showing heterogeneous network characteristics [45]. Han et al. [46] used nationally-recognised megaproject data as a case basis to study the characteristics and evolution of the dynamic innovation collaboration network of projects according to industry background, finding that state-owned enterprises and universities are key innovation-driven organisations in the cooperative innovation network. The former occupy most of the innovation network, while the latter occupy the most influential positions determining a network’s evolution. With regard to the innovation network of megaprojects, the division of tasks undertaken by each participant and their organisational contributions to project innovation is different. Thus, the formation of innovation network organisations is influenced by the scale of the organisation, the collaboration form, and the positioning of roles.

2.2.3. Inter-Organisational Relationships

With megaprojects, innovation is built on effective collaboration between contractors, subcontractors, suppliers, engineers, and customers [49,50]. It has been found that the nature of inter-organisational relationships has a significant impact on project performance [51,59,60]. Complementary capabilities, open and transparent communication, and high levels of commitment to partnerships are identified by Amp and Huber [52] as success factors in developing and sustaining teamwork capabilities. The formation of inter-organisational relationships relies on the recognition of mutual strengths and effective communication between organisations to ensure the integration of resources and the realisation of innovation [53]. Dulaimi, Ling, and Bajracharya [47] investigated the innovation motivations of various participants in construction projects and found that innovation support between organisations promotes participation in the innovation process. In the CIMP process, the basis for inter-organisational cooperation, effective communication, and recognition of competencies are important means of resolving innovation conflicts and contradictions. When these conditions are met inter-organisational commitment to innovation collaboration is easily achieved, which facilitates the realisation of good innovation benefits.

2.2.4. Innovation Resources Management

Resource integration is an important process for CIMP [61], encompassing intensive interactions such as the transfer of highly integrated technologies and related expertise [55,56]. During project innovation, the organisation itself does not always have the necessary resources to meet engineering needs [54]. A typical example of project innovation is the construction of the Hong Kong–Zhuhai–Macao Bridge [17], during which, various innovation organisations in the project cycle are involved, sharing ideas and experiences and achieving technological convergence to overcome an unprecedented level of technological innovation difficulty. Companies are increasingly incorporating the knowledge of external partners into the innovation process [57], Ozorhon, Karatas, and Demirkesen [48] argue that effective knowledge management between companies and research institutions can bring the right ideas to a project and ensure that knowledge is properly diffused within the project team; hence, megaprojects must break down the silo effect between organisations in the innovation network [62], integrate the technologies, information, and knowledge that are mainly held by the innovators, and enhance the overall strength of the megaproject’s organisational innovation to generate value that individual organisations cannot realise alone in isolated models.

3. Methodology

This simulation study is based on the CIMP model, and ABM is used to capture the complex interactions between megaproject organisations and their impact on innovation outputs. This study was conducted in three main stages (Figure 1). First, the literature review identified the behaviour and influencing factors of CIMP organisations. Second, the simulation entities in megaproject innovation were modelled using NetLogo software, including scenario design, variable control, and simulation process design, to define, parameterise, and program the CIMP model. Third, a series of simulation experiments were conducted to demonstrate the collaborative interaction behaviours and innovative output processes of megaprojects.

3.1. Construction of the CIMP Basic Model

The behavioural factors described above are used to trigger collaboration in innovative organisations, thereby helping to understand and explain the impact of organisational innovation behaviour on innovation outcomes. This study uses the ABM approach to describe collaborative innovation interactions in megaproject organisations. ABM is a methodology for studying human behaviour and plays a critical role in sociological and management research [63]. Recently, ABM has been increasingly used to study the evolution of project networks and the interactive behaviour of organisations to represent individual elements or agents in a system, as well as individuals or groups [64]. Introducing comprehensive and systematic behavioural modelling, Du and El-Gafy [65] point out that ABM can be made available to address a range of difficult issues in construction projects and management, including organisational performance, project teamwork, and cross-cultural issues [66]. This study focuses on the complex series of evolutionary processes that occur in the model as a result of the constant interaction between different types of agents and the interaction between agents and their environment. ABM quantifies this process through the design of agent behaviour and interaction rules.
According to the idea of multi-agent modelling, CIMP agents can be regarded as resources, environments, and organisations. This study used the Wolf Sheep Predation model as a prototype to build the basic model for CIMP (see Figure 2). Wolf Sheep Predation is a classic multi-agent model of an ecosystem. Three agents—wolf, sheep, and grass—provide food to one another. These agents have a mutual fixed food chain relationship that includes predation, reproduction, and death. Each agent has specific properties, and some of these properties vary somewhat with their behaviour. Thus, within the complex environment of policy, demand, and culture, we designate that grass represents abundant technology, information, and knowledge resources, with sheep representing general organisations that are incapable of innovation, and wolves representing innovative organisations. Organisations, as mobile agents, make collaborative choices primarily through interactive behaviours. The cost of action represents the consumption of resources in the organisational interaction process. In this model, organisations can obtain the resources they need either from the grass or through collaboration between the innovating organisations (or between innovative organisations and general organisations), finally producing innovative outcomes.

3.2. Model Programming

The NetLogo platform was chosen as the simulation tool for this study. As a programming tool for agent-based simulation modelling, NetLogo has a broad library of software models covering the natural and social sciences, and can simulate natural and social phenomena. Scholars have used the NetLogo software tool to study the mechanisms of organisational relationships and resource sharing at work to achieve inter-organisational cooperation and efficient allocation of innovation resources, which, in turn, generates collaboration. Gao, Song, and Ding [67] developed an agent-based model of collaborative project network messaging in NetLogo to simulate the dynamic process of messaging and improve organisational communication performance. In conclusion, NetLogo software allows for features modelling, simulation run control, and real-time visualisation of a simulation’s process and results [68]; features that are fully aligned with the research topic of this study.

3.2.1. Parameter Settings

It is recognised above that organisations’ collaborative innovation behaviour is influenced by several factors. Owing to the number of factors and their potential combined impact, each factor cannot be studied individually. Agent-based modelling understands how agents interact and communicate with each other and how they move within the context of their environments; therefore, a range of parameters must be controlled for the creation and operation of the simulation model. Based on previous studies, the values of the parameters set for the relevant models were critical [69]. The CIMP simulation model was parameterised by modelling environment, general agent, innovation agent, and innovation outcome into four categories, these values were determined through expert advice and case studies (see Table 2).
The environmental category is seen as the behavioural space for CIMP organisations. Locatelli, Mancini, and Romano [70] believe that the degree of demand for innovation, the development of an innovation climate, and the level of government support are likely to influence megaprojects’ innovation management. An organisation, as an agent unit with behaviours, can act in a simulated environment. For megaprojects, the interaction of agents cannot be separated from technological integration, information sharing, and knowledge absorption. Therefore, the limits of technology maximum, information maximum, and knowledge maximum are considered, and the engineering demand value, innovation culture value, and policy environment value are set for the assessment of the innovation environment. We argue that the higher the value of the environmental attribute, the more likely it is that collaborative networks will form between innovation agents. On this basis, behaviour cost is defined as the amount of technology, information, and knowledge consumed by the agent during its actions. When the sum of technology, information, and knowledge is less than zero, the agent is judged to be dead (referring to death behaviour).
The attribute values of organisational agents include structural elements such as numerical scale and resource absorption capacity. Organisations cannot innovate in complete isolation but must reduce the cost of innovation and mitigate risk by exchanging knowledge, information, and resources with the outside world. Based on the basic attribute characteristics in the Wolf Sheep Predation model, the initial number and growth rate of general and innovative agents were first set (referencing reproductive behaviour). Then we define the absorptive capacity of agents (referencing predation behaviour): (1) The absorptive capacity of the general agent in the grass indicates that the general agent absorbs one unit of technology, information, and knowledge resources in one unit of movement, and transforms them into the innovative technology, information, and knowledge it needs in a certain proportion; (2) The absorptive capacity of the innovative agent in the grass indicates that the innovative agent absorbs technology, information, and knowledge resources from the grass; (3) The absorptive capacity of the sheep indicates that the innovation agent absorbs innovation technology, information, and knowledge from the general agent through collaboration with the general agent, and transforms it into the innovative technical information and knowledge it needs in a certain proportion; (4) The absorptive capacity of the wolf indicates that innovation agents collaborate to absorb innovative technologies, information, and knowledge, and transform them into innovation technology, information, and knowledge needed by oneself in a certain proportion.
The evaluation of the resource-matching effect between collaborative organisations and inter-organisational relationships was considered. The differences in technology, information, and knowledge were used to represent the range of level differences between two innovation agents that met randomly, and the overall strength difference was used to represent the range of differences in the total technical, information, and knowledge levels between the two innovation agents that met randomly. Preference coefficients for basic cooperation, recognition ability, and communication were set to indicate the degree of agreement between innovation agents when choosing a collaboration partner. Furthermore, an evaluation criterion was set to comprehensively evaluate the preference coefficients of innovation agents with the communication, cooperation, and recognition of other innovation agents to achieve the purpose of choosing an excellent partner by the innovation agent.
The purpose of the simulation was to observe how innovation outcomes changed in collaborative organisations when different factors acted individually or in multiple ways, by adjusting for environmental factors and various parameters. For this purpose, two parameters—innovation thresholds and innovation costs—were introduced to produce innovation outcomes. The total amount of resources acquired in the collaborative process of innovation agents needed to reach a specific value—the innovation threshold—otherwise collaborative innovation and the consumption of innovation costs (the technology, information, and knowledge paid for in the process) would not occur.

3.2.2. Running Rules

To better define the running process of the CIMP model, a flowchart of the simulation process was designed (see Figure 3). The simulation process is described as follows.
Initially, the model was set up to operate under the combined influence of the policy environment, cultural environment, and engineering needs. The core of the model run was then developed as agents interact randomly when an innovative agent meets a general agent, absorbing the superior resources to enhance its innovative power. When innovation agents meet, if the stock of a certain type of resource in technology, information, or knowledge for one party is greater than the single stock owned by the other innovation agent, or if the total number of innovation agents of both parties is similar, cooperation between innovation agents is considered to have started. Subsequently, the combined scores of cooperation basis, communication, and recognition ability between innovation agents are required to reach the collaboration criteria before collaboration between innovation agents is reached. Finally, the model operates on the basis that, when the resources stored by innovation agents reach a certain innovation threshold, the consumption of a certain amount of innovation costs can produce better innovation outcomes.

4. Simulations and Results

Four simulation experiments were set up based on the above model to analyse the influence of the innovation environment, innovation network organisation, inter-organisational relationships, and innovation resources on the outcome of organisational collaboration. The evolutionary process of the CIMP in complex situations is reflected through a series of parameter controls. Visualisation of the simulation experiment shows the number of general agents in blue, the number of innovative agents in red, and the number of innovation outcomes in black. Moreover, the effect of parameter changes is reflected in the numerical value of the innovation outcomes. In addition, because of the long collaboration cycle of megaprojects, the step size (Ticks) of the simulation experiment was set to 200 times, multiple repetitions of the experiment were conducted to reduce the random errors generated by individual experiments [71], and conclusions with realistic guidance were drawn from the results of multiple simulations.

4.1. Innovative Environment as Variables

To test the effectiveness of the collaboration of innovation agents under the degree of environmental differences, Simulation 1 uses the control variables method to ensure that other variables remain unchanged. To simplify the simulation experiment, the values of the three-parameter attributes of engineering demand, policy environment and innovation culture were uniformly set. The simulation results are shown in Figure 4. Three parameter values were set at 20: the outcome value was 119; at 40: the outcome value was 537; at 60: the outcome value was 865 and at 80: the outcome value was 1524.
Comparing Figure 4a–d, the number of general and innovative agents does not change much when the simulation results are stable, and the innovation outcome value increases from the initial 119 to 1524. It is obvious that, with increasing values of the policy environment, engineering demand and innovation culture attributes, the speed of the output of innovation outcomes is accelerated. A friendly innovation environment created by policy, culture, and engineering needs can effectively promote the output of innovation outcomes.

4.2. Innovation Network Organisational Power as a Variable

To test the influence of the power of innovation network organisation on the output of collaborative innovation outcomes, simulation 2 selected two aspects: innovation network organisation scale and innovation network organisation strength difference. The innovation network organisation scale can be characterised by the number of innovation agents and general agents, while the innovation network organisation capability differential contains four parameters: technology, information, knowledge and combined power differential. The simulation results obtained using the control variables method are shown in Figure 5 and Figure 6.
The innovation network organisation scale set the number of general agents to 120 and the number of innovation agents to 30 in a limited range, and the value of innovation outcomes was 417 (Figure 5a); when the simulation results were stable, the number of general agents was adjusted to 200, and the value of innovation outcomes increased to 978 (Figure 5b). In this sense, setting the number of innovation agents to 50 for the simulation, the innovation outcome value was 859 when the number of general agents was 120 (Figure 5c); when the number of general agents was set to 200, the innovation outcome grew rapidly to 1912 (Figure 5d).
Comparing Figure 5a–d, the scale of innovation networks is positively correlated with the output of innovation outcomes. Increasing the scale of organisations forming innovation networks implies a wider range of inter-organisational interactions and an increase in overall engineering innovation strength.
Innovation network organisational strength: within limits, setting the technology, information, and knowledge level difference values to 90, and the combined strength difference value to 20, the innovation outcome value was 561 when the simulation results were stable (Figure 6a), and after subsequently increasing the technology, information, and knowledge level difference values to 60, the innovation outcome value was 1075 (Figure 6b). In this sense, turning the combined power differential to 60 for the simulation, the innovation outcome value was 799 at a technology, information, and knowledge level differential of 90 (Figure 6c). When the technology, information, and knowledge level differential was 60, the innovation outcome value increased to 1391 (Figure 6d).
Figure 6 shows that reducing the gap in the strength of technological, information, and knowledge resources between agents in the innovation network organisation and expanding the gap in the comprehensive strength of innovation will enhance the effect of innovation realisation.
The expansion of the organisational scale of innovation networks, narrowing of the gap in the level of technology, information, and knowledge between agents, and widening of the combined strength gap all increase the output of collaborative innovation outcomes of organisations.

4.3. Inter-Organisational Evaluation Criteria as Variables

Simulation 3 clarified that a certain basis for cooperation, recognition, and communication between innovation agents existed to test the effect of the comprehensive evaluation criteria of inter-organisational relationships on collaborative innovation agents. At the same time, considering innovation individuals have a certain degree of preference when choosing a collaborative partner, the preference coefficients of the cooperation base, recognition ability, and communication of innovation agents were set at 3, 4, and 3, respectively. Finally, an objective score value that reflected the inter-organisational relationship through a comprehensive evaluation was obtained.
The other variables were kept constant using the control variable method. The inter-organisational relationship evaluation criteria were set to 20, 40, 60, and 80 for the simulations, which corresponded to the obtained innovation outcome values of 15,725, 10,915, 3732, and 1375, respectively, as in Figure 7a–d. According to Figure 7, the evaluation criteria for inter-organisational relationships are too high to facilitate the achievement of inter-organisational collaboration and the output of innovation outcomes. On the contrary, as the inter-organisational relationship evaluation criteria are lowered, the range of innovation collaboration partners that organisations can choose from expands, making it easier to form a broad collaborative innovation network, thus significantly increasing the value of the output of innovation outcomes. The organisation’s requirement for a collaborative base of innovation cooperation, recognition of capabilities, and level of communication with collaborators decreases, and the value of innovation outcomes increase significantly.

4.4. Behavioural Costs and Resource Absorption Capacity as Variables

To test the impact of resource absorption and consumption on collaborative innovation, Simulation 4 selects two variables—the absorption capacity and behavioural cost of innovation agents—from the perspective of innovation resource management.
The simulation used the control variable method, kept other variables constant, and set the ability of innovation agents to absorb technology, information, and knowledge to 0.1, 0.4, 0.7, and 1, and the results are shown in Figure 8.
With the value of absorptive capacity of innovation agents at 0.1, the number of innovation outputs is 2349 (Figure 8a); at 0.4, the number of innovation outcomes increases to 1314 (Figure 8b); at 0.7 and 1, the number of innovation outcomes is 1673 (Figure 8c) and 2753 (Figure 8d), respectively. The value of innovation outcomes grows rapidly as innovation agents’ ability to absorb technology, information, and knowledge increases.
Using the control variable method and keeping other variables constant, the simulation set the values of the behavioural cost attribute consumed by agents when acquiring innovation technology, information and knowledge to 2, 8, 14, and 20, respectively, and the results are shown in Figure 9.
When the behavioural cost is 2, the number of general agents continues to increase and eventually stays in the range of 1200, and the value of innovation outcome is 139 (Figure 9a); when it is 8, the number of general agents does not change significantly, and the value of innovation outcome rises rapidly to 1002 (Figure 9b); when it is 14, the number of general agents shows a significant downward trend, and the value of innovation outcome is 647 (Figure 9c); when it is 20, the number of general agents drops to 0, and the value of innovation outcome falls to 273 (Figure 9d).
Figure 9 shows that the value of the innovation outcome rises and then falls as the cost of behaviour increases. The innovation behaviour cost is too low, which is not conducive to the selection of the dominant innovation medium agent from among multiple general agents. When the cost of innovation behaviour is too high, the willingness of innovation agents to cooperate decreases, gradually decreasing the level of organisational participation, which, in turn, causes the formation of innovation network organisations to decrease in scale and the value of the innovation outcome to fall.
The ability of innovation agents to absorb technology, information, and knowledge is positively correlated with innovation output, whereas a negative correlation exists between behavioural costs and innovation output.
In summary, the evolutionary trend of intertwining, co-development, and change among agents fully reflects the degree of influence and relevance of each variable on inter-organisational collaboration behaviour. Its output can contribute to an overall improvement in innovation efficiency; in addition, the differences in the simulation process are normal and within an acceptable range owing to factors such as human subjective initiative.

5. Discussion

5.1. Theoretical Implications

The primary contribution of this study is the exploration of the specific implications of CIMP from an organizational–behavioural perspective. Organisational variability in modern social development systems can hinder innovation efficiency in megaprojects to varying degrees, especially in the decentralised construction industry. With the surge in demand for megaprojects, it is particularly important to manage innovation in megaproject organisations. The behaviour of collaboration between organisations becomes increasingly complex in describing and analysing megaprojects [72]. This study highlights the importance of collaborative organisational innovation in the context of megaprojects, suggesting that boundaries of collaborative organisational innovation be extended by correlating the innovation environment and resources, organisation of innovation networks, and inter-organisational relationships with collaborative organisational behaviour. The findings have important implications for further understanding the behavioural relationships between organisations. This study indicates that CIMPs are oriented towards engineering needs, with multiple innovation agents working together to form a network organisational structure and manage resources through adaptive collaborative behaviours to realise the innovation value of the project. This perspective fuels the complex emergence of engineering innovation and represents a new paradigm for the collaborative innovation management of megaprojects.
Secondly, this study focuses on the development of a CIMP simulation model to explain and analyse organisational interaction behaviour during megaproject innovations. Organisations have long been known as group terms with static properties. However, it must be emphasised that organisations are agents that generate behaviour dynamically [73]. Rather than pursuing the same specialist direction for all innovation organisations, the various innovation organisations in megaprojects must adopt complementary strengths in terms of knowledge structure, innovation capability, and technology level [74]. Therefore, the CIMP is a flexible model for multi-agent construction and behavioural change. Understood in this way, each innovation agent of a megaproject can change its behavioural rules autonomously by changing the environment and accumulating resources to make itself better in regard to collaborative behavioural selection, collaborative interaction development, and collaborative output. This study tests the effects of changes in the environment, innovation network organisational strength, inter-organisational relationship evaluation criteria, and resource absorption and consumption on the efficiency of collaborative innovation. In terms of overall effectiveness, the application of the CIMP model provides an important reference value for megaproject management.
Finally, this study refutes the limitation that traditional megaproject management can only randomly select collaborating organisations for innovation. In other words, this study illustrates how megaproject organisations can select the best innovation partners in a variety of ways, thereby strengthening the close cooperative relationship among innovation agents through organisational collaboration. At present, collaborative innovation alliances for megaprojects are still in the stage of gradual formation [75]; inter-organisational relationships lack continuity, and they only aim to accomplish task objectives, making it difficult to promote the formation of an innovation management system. We describe CIMP as a dynamic and coordinated structure of different participants’ innovation activities led by a common value proposition, and its innovation process is self-organising and can evolve in a feedback loop with the external environment [36]. The various participants in a megaproject exhibit the characteristics of a holistic network organisation structure, exerting collaboration effects [76]. Therefore, megaproject innovation is based on the cooperation of multiple innovation organisations, with each participant linked by contract and trust to take advantage of collaboration effects to facilitate the realisation of the CIMP, further providing a theoretical reference for improving the overall efficiency of collaborative innovation.

5.2. Practical Implications

This study provides a unique perspective that demonstrates the benefits of organisational collaboration in the management of megaproject innovation. From the perspective of collaboration management, this study can help organisations foster, co-ordinate, and implement innovative activities in megaprojects. CIMP can be seen as a management system formed by multiple organisations, under certain rules and procedures, to achieve engineering goals [77]. Action is now needed to promote cross-organisational collaborative innovation. Thus, we provide a holistic perspective that contains multiple facets to stimulate organisational collaboration and improve innovation efficiency.
On the one hand, when faced with complex innovation challenges and the uncertainty of innovation tasks, an interconnected, incentive-compatible, harmonious, civilised, and orderly internal and external environment for innovation is more likely to trigger the effects of organisational collaboration. Under the influence of market demand and the social economy, the development of megaproject innovation organisations should positively respond to national policies to formulate innovation strategies, strengthening the coordination of planning and coordination to promote and adhere to the steady development of various countries and industries. Additionally, it is important to fully mobilise the initiative of the organisation to form a cross-organisational innovation culture, thereby achieving the goal of a balanced development of the project innovation system.
On the other hand, the efficiency of CIMP depends on the development of the organisational power of the formed innovation network and the dynamic regulation of inter-organisational relations. An appropriate organisational model for innovation networks maximises organisational effectiveness [78]. The organisations involved in a megaproject can form a large-scale network alliance with superior innovation power by strengthening the team of innovative technical professionals and dividing the innovation tasks reasonably. In this process, cooperation between organisations is often negative due to the lack of cooperation base, communication, and cognitive ability, which requires organisations to reduce cooperation requirements without compromising task completion. This not only effectively enhances the cohesiveness, competitiveness, and innovation of the innovative network organisation, but it also has a positive effect on the overall achievement of the quality, cost, and schedule objectives of megaproject innovation.
In addition, the adoption of integrated innovation resource management is an important way to reduce the behavioural costs of organisations and improve the efficiency of CIMP. Facing increasingly complex and difficult megaproject innovation tasks, innovation organisations need to form cooperative alliances to promote the flow of innovation knowledge and breakthrough innovation technologies. The formed cooperative alliance can only achieve articulation and cross-collaboration of innovation activities if the innovation tasks are rapidly decomposed in a short period. Specifically, effective integration of innovative technology, innovation knowledge, and information of the participating organisations can promote the efficient utilisation of resources and optimise the collaborative innovation process. Therefore, establishing a management system with complementary resources and maximum benefits can help improve the overall innovation efficiency of heterogeneous agents.

5.3. Limitations and Future Research Directions

The CIMP model proposed in this study shows how megaproject organisations can engage in collaborative innovation output; however, it simplifies the behavioural characteristics and interaction rules of the organisation, which are mainly limited by model properties and conditions. This model can be improved in many different directions. For example, the dynamic introduction of organisational members and leaders who are differentiated from each other to create more collaboration needs to be considered. As the central figure in the project team, the project manager’s leadership behaviour has an impact on the behaviour and attitudes of team members. Its impact is not only in terms of individual performance but also in terms of the project team and the achievement of the project’s stated objectives [79]. In addition, more focus could be placed on multiple engineering phases, such as concept, project, design, construction, and operation, to precisely manage time and costs. Alternatively, researchers can optimise the process of forming the organisational structure of innovation networks in the CIMP model. In future work, more complex behavioural features will be considered, and the model will be revised to make it more realistic. Other simulation platforms could be used to conduct simulations and validate the simulation results obtained using the NetLogo tool, further improving the CIMP model and bringing it closer to construction engineering innovation practice. At the same time, the authors encourage organisational collaborative innovation processes involving case studies. Thus, despite these limitations, the authors are convinced that the findings could stimulate future research in the direction of organisational collaborative innovation behavioural interactions in megaprojects.

6. Conclusions

This study built a CIMP model that fits the management of megaproject innovation. Using the ABM approach, it analysed the behavioural factors affecting organisational collaborative innovation and the role relationships between them, focusing on the driving mechanisms for improving the effectiveness of collaborative innovation. The results show the different effects of the innovation environment, innovation network strength, inter-organisational relationship evaluation criteria, resource consumption, and absorption on collaborative innovation outcomes. In this context, adaptive managers should be trained with increasingly complex organisational collaboration and innovation management problems.
This study shows that the proposed model is an effective tool for understanding the innovation process in megaprojects and simulating collaborative interactions between organisations. Managers can predict trends in organisational interactions by adjusting the values of the model’s main attributes. This research is particularly important as an increasing number of megaprojects are under construction; therefore, the behavioural modelling and simulation analysis of CIMP organisations directly benefits not only researchers, but also government departments, owners, and other organisational institutions, helping them predict the impact of the behavioural dynamics of organisations on the efficiency of CIMP.

Author Contributions

Conceptualization, Methodology, N.Z., C.L. and C.W.; Methodology, Software, N.Z. and H.L.; Writing—original draft, N.Z. and C.L.; Writing—review and editing, Supervision, C.W. All authors have read and agreed to the published version of the manuscript.

Funding

This research was funded by NSFC, grant number 72171014 and 71801007, “Humanities and Social Science Foundation of Chinese Ministry of Education, grant number 18YJCZH188”, “the Fundamental Research Funds for the Central Universities, grant number YWF-21-BJ-W-225”, “General Scientific Research Project of Hunan Education Department, grant number 20C0041”, “Changsha Municipal Natural Science Foundation, grant number kq2014115” and “Changsha University of Science and Technology Postgraduate Research Innovation Project, grant number CX2021SS124.

Conflicts of Interest

The authors declare no conflict of interest.

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Figure 1. Overall research work plan.
Figure 1. Overall research work plan.
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Figure 2. The basic model for CIMP.
Figure 2. The basic model for CIMP.
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Figure 3. Flowchart of the simulation process.
Figure 3. Flowchart of the simulation process.
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Figure 4. The evolution map of model outputs in different engineering innovation environments. The engineering demand, policy environment and innovation culture are all at 20 in sub-figure (a), 40 at Sub-figure (b), 60 at Sub-figure (c), 80 at Sub-figure (d).
Figure 4. The evolution map of model outputs in different engineering innovation environments. The engineering demand, policy environment and innovation culture are all at 20 in sub-figure (a), 40 at Sub-figure (b), 60 at Sub-figure (c), 80 at Sub-figure (d).
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Figure 5. The evolution map of model outputs for changes in the number of innovative and general agents. The general agent and the innovative agent are respectively at 120 and 30 in sub-figure (a), 200 and 30 at sub-figure (b), 120 and 50 at sub-figure (c), 200 and 50 at sub-figure (d).
Figure 5. The evolution map of model outputs for changes in the number of innovative and general agents. The general agent and the innovative agent are respectively at 120 and 30 in sub-figure (a), 200 and 30 at sub-figure (b), 120 and 50 at sub-figure (c), 200 and 50 at sub-figure (d).
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Figure 6. The evolution map of the model output for changes in the resource strength gap and the overall strength gap. The technology, information, and knowledge level difference values and the combined strength difference value are respectively at 90 and 20 in sub-figure (a), 60 and 20 at sub-figure (b), 90 and 60 at sub-figure (c), 60 and 60 at sub-figure (d).
Figure 6. The evolution map of the model output for changes in the resource strength gap and the overall strength gap. The technology, information, and knowledge level difference values and the combined strength difference value are respectively at 90 and 20 in sub-figure (a), 60 and 20 at sub-figure (b), 90 and 60 at sub-figure (c), 60 and 60 at sub-figure (d).
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Figure 7. The evolution map of model outputs for changes in criteria for evaluating relationships between innovative organisations. The inter-organisational relationship evaluation criteria are at 20 in sub-figure (a), 40 at Sub-figure (b), 60 at Sub-figure (c), 80 at Sub-figure (d).
Figure 7. The evolution map of model outputs for changes in criteria for evaluating relationships between innovative organisations. The inter-organisational relationship evaluation criteria are at 20 in sub-figure (a), 40 at Sub-figure (b), 60 at Sub-figure (c), 80 at Sub-figure (d).
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Figure 8. The evolution map of the model output for changes in the information, knowledge, and resource absorptive capacity of innovation agents. The value of absorptive capacity of innovation agents are at 0.1 in sub-figure (a), 0.4 at Sub-figure (b), 0.7 at Sub-figure (c), 1 at Sub-figure (d).
Figure 8. The evolution map of the model output for changes in the information, knowledge, and resource absorptive capacity of innovation agents. The value of absorptive capacity of innovation agents are at 0.1 in sub-figure (a), 0.4 at Sub-figure (b), 0.7 at Sub-figure (c), 1 at Sub-figure (d).
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Figure 9. The evolution map of the model outputs for changes in behavioural costs. The behavioural cost are at 2 in sub-figure (a), 8 at Sub-figure (b), 14 at Sub-figure (c), 20 at Sub-figure (d).
Figure 9. The evolution map of the model outputs for changes in behavioural costs. The behavioural cost are at 2 in sub-figure (a), 8 at Sub-figure (b), 14 at Sub-figure (c), 20 at Sub-figure (d).
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Table
Table 1. Dominant factors of CIMP behavior.
Table 1. Dominant factors of CIMP behavior.
CategoryThe Dominant FactorLiterature Sources
Innovation environmentEngineering requirements[22,34,35,36]
Policy environment[37,38]
Innovation culture[39,40,41]
Innovation network organisationorganisation scale[42,43]
Form of collaboration[44,45]
Agent Positioning[3,46]
Inter-organisational relationshipCooperation basis[47,48]
Recognition ability[49,50]
Communication[51,52,53]
Innovation resource managementInformation sharing[17,54]
Technology integration[3,55,56]
Knowledge absorption[48,57]
Table
Table 2. Basic parameter setting of simulation models for CIMP.
Table 2. Basic parameter setting of simulation models for CIMP.
CategoryParameter NameCodeRangeSetpointAdjustable
Modelling environmentTechnical maximumMax-technology0–1010Not
Information maximumMax-information0–1010Not
Knowledge maximumMax-knowledge0–1010Not
Engineering demandDemand-value0–10080Yes
Policy environmentEnvironment-value0–10080Yes
Innovation cultureExperience & achievement-value0–10080Yes
Behavioral costsmetabolism0–206Yes
General agentInitial quantityInitial-number-sheep0–150150Yes
Growth rate (%)Sheep-produce0–20%4Yes
Absorptive capacity value in the grassSheep-gain0–10.2Yes
Innovation agentInitial quantityInitial-number-wolves0–25050Yes
Growth rate (%)Wolf-produce0–11Yes
Absorptive capacity value of grasswolf-gain0–10.3Yes
Absorptive capacity value of sheepWolf-gain-from-sheep0–10.4Yes
Absorptive capacity of wolvesWolf-gain-from-wolf0–10.5Yes
Technical level differenceValue-k10–100100Yes
Information level differenceValue-k20–100100Yes
Knowledge level differenceValue-k30–100100Yes
Overall strength differenceValue-k40–10050Yes
Cooperative basic preference coefficienta-contact0–103Not
Recognition ability preference coefficientb-recognition0–104Not
Communication preference coefficientc-relief0–103Not
Evaluation standardStandard-num0–2080Yes
Innovation outcomesInnovation thresholdInnovation-threshold0–200150Not
Innovation costInnovate-cost0–10040Not
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Zhao, N.; Lei, C.; Liu, H.; Wu, C. Improving the Effectiveness of Organisational Collaborative Innovation in Megaprojects: An Agent-Based Modelling Approach. Sustainability 2022, 14, 9070. https://doi.org/10.3390/su14159070

AMA Style

Zhao N, Lei C, Liu H, Wu C. Improving the Effectiveness of Organisational Collaborative Innovation in Megaprojects: An Agent-Based Modelling Approach. Sustainability. 2022; 14(15):9070. https://doi.org/10.3390/su14159070

Chicago/Turabian Style

Zhao, Na, Congcong Lei, Hui Liu, and Chunlin Wu. 2022. "Improving the Effectiveness of Organisational Collaborative Innovation in Megaprojects: An Agent-Based Modelling Approach" Sustainability 14, no. 15: 9070. https://doi.org/10.3390/su14159070

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