Dynamic Effects of Management Support on Knowledge-Based Competitiveness in Construction Companies

Abstract

The construction industry faces challenges like project complexity, labor intensity, and dynamic changes, which demand effective knowledge management practices to attain competitiveness. Successful knowledge management implementation relies on management support, specifically budget allocations, to achieve knowledge-based competitiveness. This study aims to examine the dynamic effects of management support on competitiveness enhancement over time and suggest short- and long-term strategies for knowledge management practices. The study develops a system dynamics model comprising five key factors: knowledge storage, knowledge acquisition, knowledge dissemination, knowledge responsiveness, and knowledge utilization to simulate the effects of management support on knowledge management implementation and knowledge-based competitiveness over time. The simulation results reveal the fluctuations of the competitiveness scores through knowledge management implementation, which is decided by management. It is suggested that the efforts should be focused on improving the activities under the knowledge utilization and dissemination factors to raise the scores in a short period. To achieve sustainable knowledge-based competitiveness development, management must commit to enhancing human- and technology-related activities under the knowledge storage, acquisition, and responsiveness factors and provide adequate financial support to invest in knowledge management-related systems. Such activities as skill training, data warehouse systems, and stakeholder interconnection are crucial to maintaining knowledge management performance. This study provides valuable insights into the strategic planning of knowledge management practices through budget allocation from management to achieve long-term competitiveness.

1. Introduction

The construction industry plays a crucial role in the economy worldwide. WorldBank [1] stated that the industry had an added value of USD 27.81 trillion, representing 26.5% of the world’s GDP, with a growth rate of 1.9% annually [2]. The total number of workers in the European Union increased by 19% from 2017 to 2022. The growth offers extensive employment opportunities and economic stability. Nevertheless, specific industry characteristics, including the uniqueness of projects, labor intensity, high budget, and location dependence, result in several risks that may affect cost, productivity, and project performance. With the increasing demand for urban development under smart standards and rapid advancements in science and technology, new approaches are being introduced to address these challenges. For instance, Peráček and Kaššaj [3] highlighted that legal easement plays a critical role in facilitating the development of infrastructure, digital connectivity, and environmental protection, which are core components of smart cities. Building information modeling (BIM) is a modern tool that significantly enhances the execution of projects governed by such legal easements [4].

Many construction companies are increasingly required to adapt to various complex requirements to navigate these challenges effectively. Knowledge management (KM) is a tool to mitigate the challenges and attain competitive advantages for companies. Marinho and Couto [5] mentioned that KM positively affects company performance and competitiveness. Sharing information and exchanging work experience may improve performance, where lessons learned from completed projects and work meetings help improve project outcomes, minimize risks, enhance safety, and effectively manage project schedules. Kokkaew et al. [6] agreed that KM positively impacts performance. They mentioned that facilitating knowledge sharing among team members and increasing knowledge applications help companies achieve time, cost, and quality performance. Dang et al. [7] stated that KM capabilities positively influence market development through long-term relationships with existing and new clients. KM cultivates a learning culture that enhances the performance of design firms [8]. It mitigates losses of valuable information and improves team relationships through knowledge sharing.

Despite KM’s insights on construction competitiveness, there remains a significant gap in how the attention of top management toward KM implementation enhances competitiveness in the long term. The strategic allocation of executive attention can significantly impact firm performance across different operational domains. Krishnakumar, Kishore, and Suresh [9] studied the effects of executive attention on business performance through e-transaction and e-customer relationship management. They concluded that senior executives should focus on these processes to enhance the firm’s performance. Eklund, Raj, and Eggers [10] stated that managerial attention significantly impacts the organization’s adaptability in a dynamic environment. Zhou, Wang, and Wang [11] utilized the text mining approach to analyze risk disclosures and found that decentralized risk attention of management improves the company’s profitability and solvency in the consumer goods sector. In contrast, concentrated attention proves more effective in the financial and healthcare sectors.

As the construction industry is dynamic and involves many changes, management decisions on KM implementation highly affect competitiveness. It is crucial to understand how changes in management attention impact long-term knowledge-based competitiveness (KBC) so that effective plans can be initiated. Various studies have examined the dynamic effects of management attention on project performance. For example, Molwus, Erdogan, and Ogunlana [12] used structural equation modeling to examine dynamic interactions between critical success factors for stakeholder management and project success in the construction industry. They concluded that strong stakeholder engagement improves project success. Ekanayake et al. [13] used the fuzzy synthetic evaluation to assess supply chain vulnerability levels in Hong Kong’s construction industry. They pinpointed the need for top management to pay attention to these issues. Chinda [14] developed a system dynamics (SD) model to examine changes in management attention on ergonomics culture through effective policy, people, resources, and process improvement.

The SD modeling approach is suitable for understanding changes in dynamic environments. Many researchers used it in the area of construction management. For example, Ansari et al. [15] used SD modeling to predict the dynamic changes in claims and their impacts on construction performance regarding scheduling, sustainability, and quality. Guan, Liu, and Shen [16] developed an evaluation index system through an SD model to assess construction performance regarding investment, design, construction, operation, and environment. Ajayi and Chinda [17] attempted to improve the project schedule in the pre-construction and construction phases, utilizing an SD model. They concluded that minimizing design errors during pre-construction significantly reduces project delays.

To explore management’s dynamic attention on KM implementation and KBC enhancement (i.e., the gap of previous studies), this study aims to develop an SD model to better understand the interrelationships among management support, specifically in budget allocation, KM implementation, and KBC enhancement. The research questions are set as follows.

• Does management support influence KM implementation and KBC enhancement, specifically through budget allocation?
• How can KBC be enhanced through KM implementation in the short and long term?

Based on the above research questions, two hypotheses were developed:

Hypothesis 1.

Management support, through strategic budget allocation for KM implementation, affects KBC scores in the construction industry.

Hypothesis 2

Short- and long-term strategies for KBC enhancement could be set through KM implementation focusing on human- and technology-related activities.

The study is expected to employ the SD modeling technique to plan for KBC enhancement in a dynamic environment. It explores interrelationships among key KM factors, management support, and KBC while outlining short- and long-term competitiveness improvement strategies. Short-term strategies emphasize leveraging the human-driven elements of KM, such as focusing on knowledge dissemination and utilization. In contrast, long-term strategies advocate investments in knowledge storage, knowledge acquisition, and knowledge responsiveness to adapt to dynamic market conditions. This study underscores the critical role of management support in sustaining competitiveness, providing actionable recommendations for KM planning in a dynamic environment.

2. Literature Review

KM is used to promote competitiveness and achieve sustainable growth for companies under intense market competition [18,19,20]. Investment in knowledge yields great benefits as it drives innovation, reduces cost, improves quality, and avoids delays [20,21]. Chen and Fong [22] stated that KM raises business performance, specifically financial management, customer relations, and internal processes. Integrating KM in work processes can lead to sustainable competitive enhancement. Kokkaew et al. [6] added that KM mediates between human resource management and organizational performance. The positive relationships exist between human resource management and KM, and KM and organizational performance. Enhancing human resource management improves KM practices, which, in turn, raises the organization’s performance. Tabejamaat et al. [23] mentioned that KM infrastructures significantly improve employee satisfaction, leading to high individual productivity and organizational performance. Its capabilities foster innovation, resulting in performance improvement [24]. Organizations enhance decision-making and optimize their operations [25]. Effective KM assists in improving project performance and emphasizing the importance of a collaborative environment [26].

Various KM-related studies have been conducted in the construction industry. Specifically, the use of KM to enhance construction competitiveness, also known as KBC, has received much attention in recent years. It refers to how construction companies apply KM to reuse and share project-specific knowledge to reduce costs and improve competitiveness [27]. It emphasizes using intellectual capital, strategies, and practices to compete with competitors [28]. Wang et al. [29] utilized KM, specifically inter-organizational collaborations, to transform intellectual liabilities into assets, thus enhancing relational capital, driving innovation, and enhancing KBC. Khoa and Chinda [30] utilized key KM factors to boost competitiveness in Vietnamese construction companies. It was found that key KM factors, namely knowledge acquisition (KA), dissemination (KD), responsiveness (KR), storage (KS), and utilization (KU), assist in enhancing KBC directly and indirectly. Direct relationships between KM factors and KBC are found through the KU and KR factors. On the other hand, the KS, KA, and KD factors indirectly link KBC through the KU and KR factors.

KBC enhancement relies on KM improvement, specifically in human- and technology-related areas [31,32]. Wang and Meng [33] stated that training employees with KM-related tools enhances their skills and reduces the chances of reoccurrences. Debs and Hubbard [34] commented that US construction companies must improve the collection, storage, and sharing processes of lessons learned to transform information into knowledge effectively. Eken et al. [35] added that a web-based lesson-learned system like Learning in Construction Tool (LinCTool) could enhance organizational learning in construction companies by collecting, storing, and disseminating lessons learned across multiple projects. It is a web application compatible with most web browsers and mobile devices. All calculations are performed on the server side and used as a database to store information. Altaie and Dishar [36] mentioned that integrating AI technologies in KM implementation leads to fast and effective decision-making. Yan et al. [37] added that data mining in energy, safety management, building occupancy, and material performance is an important tool for knowledge discovery in the construction industry. Mandičák, Mésároš, and Tkác [38] mentioned that building information modeling and knowledge technology can improve efficiency in construction projects by providing insights for planning, designing, and managing buildings and infrastructure.

A summary of KM-related studies is in

Table 1. Summary of previous KM-related studies.

Author	Methodology	Contribution
Ahmad and An [21]	Qualitative study	Developed a practical KM model focusing on the effectiveness of tacit/explicit KM.
Abuezhayeh [25]	Questionnaires, qualitative interviews, hypothesis testing	Proposed a conceptual framework, a tool to enable improved decision-making, for KM-BPM integration, emphasizing strategic and operational alignment.
Albooyeh and Yaghmaie [20]	Fuzzy analytic hierarchy process	Introduced fuzzy decision models for comparative KM evaluations across organizations.
Altaie and Dishar [36]	Surveys, statistical analysis	Enhanced construction project success in Iraq by improving the KM process through AI applications.
Chen and Fong [22]	SD modeling	Provided a comprehensive and dynamic KM evaluation framework focusing on long-term strategic alignment.
Debs and Hubbard [34]	Qualitative interviews, surveys	Identified gaps in knowledge capture, highlighted the need for systematic protocols and recommended improvements for reuse.
Eken et al. [35]	Interview with construction experts.	Introduced an effective digital tool for lessons learned management, enhancing organizational learning via technology.
Fong and Kwok [18]	Empirical surveys, statistical analysis	Explained how cultural type influences KM, with practical implications for fostering sharing in construction firms, increasing the chances of success.
Idrees et al. [24]	Survey; structural equation modeling	Quantified the impact of KM on performance and clarified the mediation role of innovation in construction organizations.
Jia et al. [26]	Empirical survey, component-based structural equation modeling	Provided understanding of how knowledge shared in virtual spaces could be leveraged for improving management performance in construction project teams
Khoa and Chinda [30]	Survey; structural equation modeling	Offered empirical evidence linking KM to competitiveness, recommended actions to enhance KM in construction, and developed a validated self-assessment tool for construction firms.
Kokkaew et al. [6]	Survey, structural equation modeling	Offered guidance for human resource managers in construction companies.
Leal, Cunha and Couto [27]	Comprehensive literature review	Guided organizations in fostering effective KS practices.
Mandičák, Mésároš and Tkác [38]	Case studies, surveys	Supported BIM’s strategic role in management and highlighted practical ways to enhance KM.
Nobert Leo Raja, Dhamodharn and Janardhanan [28]	Literature review, group interview	Proposed a model for ICT-enabled KM in project-based industries.
Perotti et al. [31]	Literature synthesis, conceptual analysis	Introduced the concept of knowledge sabotage.
Tabejamaat et al. [23]	Quantitative survey, structural equation modeling	Showed the relationships between KM infrastructure, job satisfaction, and productivity in the construction industry.
Wang and Meng [33]	Literature review, survey	Provided a framework for integrating BIM into KM in construction.
Wang et al. [29]	Quantitative survey, structural equation modeling	Linked social structures with KM and innovation.
Yan et al. [37]	Systematic literature review, content analysis, bibliometric mapping	Identified research gaps and suggested future directions to enhance KM.
Yap and Lock [32]	Questionnaire survey, statistical analysis	Highlighted the importance of soft factors in KM and provided policy recommendations for SMEs.

.

3. Methodology

3.1. Research Flow

The research flow of this study is in Figure 1. It is designed to address the study’s objectives. It starts with a literature review analyzing KBC and management’s role in construction, then identifies gaps in the problem statement. The research aim and objectives are stated. The conceptual framework is then developed to link KM factors, management support, and KBC. Data collection provides the foundation for constructing a causal loop diagram, illustrating dynamic interrelationships. This informs system dynamics model development and simulations, which predict the impact of management support on KBC. Scenario analysis evaluates short- and long-term strategies, while the discussion and conclusion synthesize findings and offer recommendations. This approach highlights the strategic integration of KM and management support to enhance construction sector competitiveness.

3.2. Conceptual Framework of KBC Enhancement

Based on the literature review, KM varies from person to person and company to company. Identifying key KM factors and their interrelationships is crucial so managers can plan for KBC improvement effectively. Khoa and Chinda [30] proposed five key KM factors (i.e., KU, KD, KR, KS, and KA) to enhance KBC in terms of time, profit, innovation, productivity, client satisfaction, and quality perspectives. They utilized the structural equation modeling approach to examine relationships between KM and KBC factors and found that the KS factor influences the KA and KD factors directly and the KU and KR factors indirectly. In contrast, the KU and KR factors directly impact construction’s competitiveness (CP), leading to KBC improvement. The performance of CP is fed back to the KS factor to store, analyze, and summarize lessons learned for future projects [30].

The KM implementation is mostly controlled and decided by management. It is restricted by management decisions regarding budget allocation in human- and technology-related improvement. Le and Le [39] mentioned that transformational leadership drives innovation by empowering employees to participate actively in KM practices. Zhang et al. [40] added that innovative processes of KM implementation, such as training programs, forums for knowledge exchanges, and recognition systems, contribute to KBC enhancement. Mohamad and Mat Zin [41] pinpointed that management must strategically invest in technological solutions to support KM implementation. Tools, such as KM-related databases, collaborative platforms, and information systems, enhance KM practices by promoting information sharing and integrating valuable insights into daily operations. Technological investments are crucial for maintaining a competitive advantage by ensuring timely and practical knowledge integration [5].

Depending on KBC’s performance, management support for KM implementation may fluctuate, i.e., increase and decrease. Tasks that require high attention are usually allocated a high budget. Once those tasks reach the set targets, management attention is withdrawn, and the budget is reduced or shifted to other areas of improvement (see Figure 2). Therefore, this study develops a conceptual model of KBC enhancement through management support on KM implementation, focusing on KM factors from Khoa and Chinda [30] (see Figure 3 and

Table 2. KM and CP factors and their associated items (adapted from Khoa and Chinda [30]).

Budget Perspective	Factor		Associated Item				Total Score
	Name	Maximum Score	Name	Definition	Initial Score	Maximum Score
Technology-related	KS	241	KS1	Accessing the control system	5.9	41.0	241
			KS2	Automating data validation	6.2	43.4
			KS3	Applying a data warehouse platform	5.6	38.6
			KS4	Digitally archiving system	4.6	31.3
			KS5	Storing lessons learned in a database	6.2	43.4
			KS6	Having an automated storage workflow	6.2	43.4
Human-related	KA	101	KA1	Gathering client insights through direct feedback and interactions	2.7	14.1	539
			KA2	Collecting market intelligence from competitors via observation and analysis	2.7	14.1
			KA3	Learning from employee performance reviews and KPI evaluations	2.7	14.1
			KA4	Interpreting financial reports to inform managerial decision-making	2.9	15.2
			KA5	Conducting market research using interviews, surveys, and fieldwork	3.1	16.2
			KA6	Sharing printed knowledge materials with project teams and stakeholders	2.4	12.1
			KA7	Applying industry benchmarking results through collaborative discussion and analysis	2.9	15.2
	KD	180	KD1	Facilitating interactive dialogue and feedback sessions between teams	8.9	43.2
			KD2	Conducting one-on-one mentorship and knowledge transfer programs	9.4	45.0
			KD3	Sharing product and process insights through team workshops and training	9.4	45.0
			KD4	Coordinating cross-departmental meetings for market trend updates	9.8	46.8
	KR	126	KR1	Adapting services based on client feedback and requirements	4.4	21.4
			KR2	Addressing client concerns during technological transitions	4.7	22.7
			KR3	Developing strategic responses to competitive market actions	4.2	20.2
			KR4	Implementing solutions based on employee feedback and suggestions	3.9	18.9
			KR5	Adjusting business strategies to align with market development	4.9	23.9
			KR6	Applying lessons learned from past project experiences	3.9	18.9
	KU	132	KU1	Implementing process improvement based on organizational experience	5.6	26.4
			KU2	Developing competitive strategies through market insights	5.2	25.1
			KU3	Guiding strategic planning with collective organizational wisdom	5.8	27.7
			KU4	Applying team expertise to resolve operational challenges	5.2	25.1
			KU5	Enhancing business operations through best practices	5.8	27.7
-	CP	220	CP1	Time	5.7	30.8	220
			CP2	Cost	5.4	28.6
			CP3	Quality	5.7	30.8
			CP4	Productivity	5.7	30.8
			CP5	Employee’s satisfaction	5.7	30.8
			CP6	Client’s satisfaction	6.2	33.0
			CP7	Innovation	6.6	35.2
Total KBC score					186.2	1000	1000

). The key KM and CP factors are associated with several attributes that can help achieve KBC enhancement in the long term. For example, the KS factor is associated with six attributes (KS1-KS6), focusing mainly on technology-related activities, such as developing data warehousing systems and automating storage workflow. On the other hand, the KD factor focuses mainly on human-related activities (KD1-KD4) to support the dissemination of knowledge and feedback among team members to enhance the KBC. The CP factor comprises seven associated items to explain the construction competitiveness: time (CP1), cost (CP2), quality (CP3), productivity (CP4), employees’ satisfaction (CP5), clients’ satisfaction (CP6), and innovation (CP7).

The maximum scores of KM attributes in Table 2 are calculated using the important weights in Figure 3 and the maximum scores of KM factors. To explain, the KS factor has a maximum score of 241 points (derived from Khoa and Chinda [28]) that are distributed to six associated items (KS1–KS6). With the important weight of KS1 of 0.17 (see Figure 3), this attribute has a maximum score of 0.17 × 241 = 41 points (see Table 2). The initial scores of the attributes in Table 2 derive from Khoa and Chinda [28] and reflect the initial KBC score of 186.2 points. It is noted that the total scores of technology- and human-related perceptions in Table 2 are adapted from Khoa and Chinda [28] by considering the focused activities of the five KM factors.

The five KM factors influence each other directly and indirectly. For example, the KS factor directly influences KA and KD factors with weights of 0.45 and 0.47, respectively (see Figure 3). This factor also indirectly influences the KR, KU, and CP factors through the KD and KA factors. For instance, the KS factor indirectly influences the KU factor through the KD factor with a weight of 0.35 (i.e., 0.47 × 0.74). The five KM factors directly (through the KR and KU factors) and indirectly influence the CP factor. These raise the KBC, and the improvement is fed back to management to decide on further KM improvement. The budget allocated to KM factors and their attributes depends on their performance, important weights, and KBC achievement (see Figure 3).

3.3. Scoring System of the Conceptual Framework

Based on Table 2, the KBC improvement depends on implementing KM factors and their associated variables controlled by management decisions on budget allocations. The maximum KBC score of 1000 points is separated into 780 points for the five KM factors and 220 points for the CP factor [42]. The 780 points are further distributed to five KM factors (see Table 2). For example, 241 points are allocated for the KS factor, which is distributed to six associated variables based on their importance weights (see Figure 3). Among the 780 points of KM factors, 539 points are achieved through the budget allocated for human-related improvement (i.e., for the KA, KD, KR, and KU factors), and 241 points are from the technology-related improvement (i.e., the KS factor), see Table 2.

The KM and CP factors interrelate with KBC, creating feedback loops for continuous improvement. This improvement is achieved by management support through budget allocation to human- and technology-related activities. To explain, the gaps in maximum scores between the two perspectives (i.e., human and technology) are compared in each simulation to adjust the portions of the budget allocation. In addition, management may withdraw its attention from KBC enhancement once the KBC score reaches a high standard level, representing over 95% of performance (i.e., 950 of 1000 points) [14]. The attention withdrawal is reflected by less budget allocated for KM implementation. This, in turn, may reduce the KM, CP, and ultimately KBC scores. In case the KBC score drops below the acceptable level, which is less than 400 points in this study, the management team may take prompt actions to enhance the KBC level and pay more attention to KM improvement [14]. This continues to raise the KBC score until it reaches the target level. Then, management may withdraw the KM’s attention, and the KBC score may start to drop again, creating cycle oscillations (see Figure 2). Nevertheless, with continuous improvement, the KBC is expected to reach its maximum score (i.e., 1000 points) in a very long period.

3.4. SD Modeling Approach

SD modeling is an approach to comprehending the dynamic behaviors of complex systems through simulation [43]. It provides a deep understanding of dynamic interactions in the systems, resulting in better decision-making. Liu et al. [44] stated that SD modeling benefits the construction industry through improved sustainability, project planning, performance, site and resource management, and KBC. It represents social and technical activities that interrelate and generate causal relationships through stocks, flows, converters, and connectors (see Figure 4) [45]. This approach has been utilized in various construction-related studies. For instance, Mak, Wang, and Tsang [45] employed an SD modeling to analyze construction and demolition waste management. They concluded that the SD modeling method effectively explores what-if scenarios for the environmental complexities. Sea-Lim et al. [46] utilized an SD model to examine the feasibility of steel waste recycling in the Thai construction industry. The results revealed that the reverse logistics program requires high upfront costs, resulting in eight years to achieve the minimum acceptable return on investment. Khan, Flanagan, and Lu [47] examined the complexity of information in construction projects, particularly for small- and medium-sized enterprises. Chen and Fong [22] used an SD simulation to predict KM performance drivers and outcomes over time. They concluded that companies should adopt an evolutionary perspective when implementing KM in response to the needs of the evolving market. Duryan and Smyth [48] explored the interactions between the service design and KM in infrastructure projects. The results help streamline processes, improve collaboration, and ensure efficient delivery of complex projects.

SD modeling is suitable for this study as it deals with dynamic changes in management attention and budget allocation on KM implementation and KBC enhancement. Secondary and primary data are collected to develop equations for the SD model. The model is developed and simulated to examine the dynamic results over time. The developed model is validated using sensitivity, policy, or scenario analyses to ensure its use in actual practices.

3.5. The SD Model of KBC Enhancement

In this study, the SD model is developed to examine the effects of management decisions on KBC enhancement using the Ithink software version 9.1.3 (see Figure 5). Similar software includes Vensim, Powersim, and Dynamo [49]. However, the Ithink software has great interface capabilities and displays the model in graphical depictions, making it a good tool for model development [49].

The KBC score is the sum of five KM and CP scores (see Equation (1)). Each KM factor is improved through its associated variables (which depend on budget allocations) and the influences achieved from the other KM factors. For example, the “KS_score” is maximum at 241 points, see Equation (2). The “KS_stock” is increased or decreased depending on the “KS_inflow” and “KS_outflow”, see Equations (3)–(5). The inflow is based on the improvement of KS variables (KS_IS), the influence the CP factor has on the KS factor (KS_IF) (see Figure 3), and management perception of the KBC performance (i.e., MAC).

$$\mathrm{K}\mathrm{B}\mathrm{C}=\mathrm{K}\mathrm{S}\_\mathrm{s}\mathrm{c}\mathrm{o}\mathrm{r}\mathrm{e}+\mathrm{K}\mathrm{A}\_\mathrm{s}\mathrm{c}\mathrm{o}\mathrm{r}\mathrm{e}+\mathrm{K}\mathrm{D}\_\mathrm{s}\mathrm{c}\mathrm{o}\mathrm{r}\mathrm{e}+\mathrm{K}\mathrm{R}\_\mathrm{s}\mathrm{c}\mathrm{o}\mathrm{r}\mathrm{e}+\mathrm{K}\mathrm{U}\_\mathrm{s}\mathrm{c}\mathrm{o}\mathrm{r}\mathrm{e}+\mathrm{C}\mathrm{C}\_\mathrm{s}\mathrm{c}\mathrm{o}\mathrm{r}\mathrm{e}$$

(1)

$$\mathrm{K}\mathrm{S}\_\mathrm{s}\mathrm{c}\mathrm{o}\mathrm{r}\mathrm{e}=\mathrm{M}\mathrm{i}\mathrm{n}(241,\mathrm{K}\mathrm{S}\_\mathrm{s}\mathrm{t}\mathrm{o}\mathrm{c}\mathrm{k})$$

(2)

$$\mathrm{K}\mathrm{S}\_\mathrm{s}\mathrm{t}\mathrm{o}\mathrm{c}\mathrm{k}\left(\mathrm{t}\right)=\mathrm{K}\mathrm{S}\_\mathrm{s}\mathrm{t}\mathrm{o}\mathrm{c}\mathrm{k}(\mathrm{t}-\mathrm{d}\mathrm{t})+(\mathrm{K}\mathrm{S}\_\mathrm{i}\mathrm{n}\mathrm{f}\mathrm{l}\mathrm{o}\mathrm{w}-\mathrm{K}\mathrm{S}\_\mathrm{o}\mathrm{u}\mathrm{t}\mathrm{f}\mathrm{l}\mathrm{o}\mathrm{w})\times \mathrm{d}\mathrm{t}$$

(3)

$$\mathrm{K}\mathrm{S}\_\mathrm{i}\mathrm{n}\mathrm{f}\mathrm{l}\mathrm{o}\mathrm{w}=\mathrm{I}\mathrm{f}(\mathrm{K}\mathrm{S}\_\mathrm{s}\mathrm{t}\mathrm{o}\mathrm{c}\mathrm{k}<241)\mathrm{T}\mathrm{h}\mathrm{e}\mathrm{n}\left[\right(\mathrm{K}\mathrm{S}\_\mathrm{I}\mathrm{S}+\mathrm{K}\mathrm{S}\_\mathrm{I}\mathrm{F})\times \mathrm{M}\mathrm{A}\mathrm{C}]\mathrm{E}\mathrm{l}\mathrm{s}\mathrm{e}0$$

(4)

$$\mathrm{K}\mathrm{S}\_\mathrm{o}\mathrm{u}\mathrm{t}\mathrm{f}\mathrm{l}\mathrm{o}\mathrm{w}=\mathrm{I}\mathrm{f}(\mathrm{M}\mathrm{A}\mathrm{C}=1)\mathrm{T}\mathrm{h}\mathrm{e}\mathrm{n}0\mathrm{E}\mathrm{l}\mathrm{s}\mathrm{e}\left[\mathrm{M}\mathrm{a}\mathrm{x}\right(0.31\times \mathrm{K}\mathrm{S}\_\mathrm{s}\mathrm{c}\mathrm{o}\mathrm{r}\mathrm{e},0.31\times \mathrm{I}\mathrm{n}\mathrm{i}\mathrm{t}\mathrm{i}\mathrm{a}\mathrm{l}\_\mathrm{K}\mathrm{S}\left)\right]$$

(5)

The “KS_IS” score depends on the gap between its current and maximum scores. This gap is reduced by implementing its six associated variables, which are dictated by their importance weights (see Figure 3 and Table 2) and budget allocation, see Equations (6)–(9). The “gapIT” in Equation (6) represents the gap of technology-related score that could be improved through the budget allocated to enhance the KS factor (see Table 2). In contrast, the “gapPP” in Equation (8) refers to the gap of human-related score that could be improved through the budget allocated for the KA, KD, KR, and KU factors.

$$\mathrm{K}\mathrm{S}\_\mathrm{I}\mathrm{S}=\left[\right(\mathrm{g}\mathrm{a}\mathrm{p}\mathrm{I}\mathrm{T}/\mathrm{g}\mathrm{a}\mathrm{p}\mathrm{K}\mathrm{B}\mathrm{C})\times \mathrm{g}\mathrm{a}\mathrm{p}\mathrm{K}\mathrm{S}\mathrm{I}\mathrm{T}]\times \mathrm{i}\mathrm{n}\mathrm{i}\mathrm{t}\mathrm{i}\mathrm{a}\mathrm{l}\_\mathrm{K}\mathrm{S}$$

(6)

$$\mathrm{g}\mathrm{a}\mathrm{p}\mathrm{I}\mathrm{T}=\mathrm{g}\mathrm{a}\mathrm{p}\mathrm{K}\mathrm{S}1+\mathrm{g}\mathrm{a}\mathrm{p}\mathrm{K}\mathrm{S}2+\mathrm{g}\mathrm{a}\mathrm{p}\mathrm{K}\mathrm{S}3+\mathrm{g}\mathrm{a}\mathrm{p}\mathrm{K}\mathrm{S}4+\mathrm{g}\mathrm{a}\mathrm{p}\mathrm{K}\mathrm{S}5+\mathrm{g}\mathrm{a}\mathrm{p}\mathrm{K}\mathrm{S}6$$

(7)

$$\mathrm{g}\mathrm{a}\mathrm{p}\mathrm{K}\mathrm{B}\mathrm{C}=\mathrm{g}\mathrm{a}\mathrm{p}\mathrm{P}\mathrm{P}+\mathrm{g}\mathrm{a}\mathrm{p}\mathrm{I}\mathrm{T}+\mathrm{g}\mathrm{a}\mathrm{p}\mathrm{C}\mathrm{P}$$

(8)

$$\mathrm{g}\mathrm{a}\mathrm{p}\mathrm{K}\mathrm{S}\mathrm{I}\mathrm{T}=\left(\mathrm{g}\mathrm{a}\mathrm{p}\mathrm{K}\mathrm{S}1\times 0.17\right)+\left(\mathrm{g}\mathrm{a}\mathrm{p}\mathrm{K}\mathrm{S}2\times 0.18\right)+\left(\mathrm{g}\mathrm{a}\mathrm{p}\mathrm{K}\mathrm{S}3\times 0.16\right)+(\mathrm{g}\mathrm{a}\mathrm{p}\mathrm{K}\mathrm{S}4\times 0.13)+(\mathrm{g}\mathrm{a}\mathrm{p}\mathrm{K}\mathrm{S}5\times 0.18)+(\mathrm{g}\mathrm{a}\mathrm{p}\mathrm{K}\mathrm{S}6\times 0.18)$$

(9)

The KS factor is influenced by the “CP” factor by 48% (see Figure 3). To explain, when the CP score increases by 1 point, the KS_IF score increases by 0.48 points, see Equation (10).

$$\mathrm{K}\mathrm{S}\_\mathrm{I}\mathrm{F}=0.48\times \mathrm{C}\mathrm{P}\_\mathrm{s}\mathrm{c}\mathrm{o}\mathrm{r}\mathrm{e}$$

(10)

The “MAC” gives values of 0 or 1, where 1 proves management’s attention to enhance KBC to reach the high standard level, and 0 reflects management’s attention withdrawal (see Equation (11)). The outflow of “KS stock” (i.e., KS_outflow) occurs when management withdraws its KBC attention, i.e., when the “MAC” is zero (see Equation (5)).

$$\begin{gathered} \mathrm{M}\mathrm{A}\mathrm{C}=\mathrm{I}\mathrm{f}\left[\left(\mathrm{K}\mathrm{B}\mathrm{C}\_\mathrm{i}\mathrm{n}\mathrm{d}\mathrm{e}\mathrm{x}<\mathrm{U}\mathrm{p}\mathrm{p}\mathrm{e}\mathrm{r}\_\mathrm{b}\mathrm{o}\mathrm{u}\mathrm{n}\mathrm{d}\right)\mathrm{A}\mathrm{n}\mathrm{d}\left(\mathrm{s}\mathrm{l}\mathrm{o}\mathrm{p}\mathrm{e}\ge 0\right)\right]\mathrm{O}\mathrm{r}\left[\right(\mathrm{s}\mathrm{l}\mathrm{o}\mathrm{p}\mathrm{e}<0)\mathrm{A}\mathrm{n}\mathrm{d} \\ (\mathrm{K}\mathrm{B}\mathrm{C}\_\mathrm{i}\mathrm{n}\mathrm{d}\mathrm{e}\mathrm{x}<\mathrm{L}\mathrm{o}\mathrm{w}\mathrm{e}\mathrm{r}\_\mathrm{b}\mathrm{o}\mathrm{u}\mathrm{n}\mathrm{d})] \mathrm{T}\mathrm{h}\mathrm{e}\mathrm{n} 1 \mathrm{E}\mathrm{l}\mathrm{s}\mathrm{e} 0 \end{gathered}$$

(11)

4. Results

The SD model of KBC enhancement is developed to analyze complex, time-based relationships between management support, KM factors, and KBC in the dynamic context. The model is simulated to examine the effects of management support through budget allocation on KBC scores in the long term.

4.1. Simulation Results

The SD model of KBC enhancement is simulated, and the results are in Figure 6. Initially, the KBC score was 186.2 points (see Table 2), and management put effort into enhancing the KBC score by allocating a budget to improve KM implementation. Once the KBC score reaches its desired level (i.e., 950 points), management attention is withdrawn and shifts to other projects. This gradually reduces the KBC score until it is lower than the average industry level (i.e., 400 points) [14]. This urges management to focus on KM improvement to raise the KBC score and reach its satisfaction level. Once the satisfaction level is reached, the attention is re-withdrawn, and the cycle repeats. Nevertheless, with more experience in KM implementation, KBC’s performance is expected to improve, raising the lower and upper bounds and reaching the maximum score of 1000 points in a very long time.

A closer examination of the KBC score reveals that the KU and KD factors highly contribute to the KBC score (see Figure 7). In contrast, the KS, KR, and KA factors take a long time to achieve high performance. This may be because many activities subject to these factors are time-consuming and require a high investment. For example, to store and apply lessons learned from previous projects (i.e., related to the KS5, KR6, and KA4 variables), the company needs to collect data from past projects, which are primarily in hard copies, and input them into the database prior to performing further analyses (i.e., KS2 and KS6 variables). The data warehousing program requires high investment and specific training for practical use (i.e., KS3 variable).

When management withdraws its attention from KBC enhancement, less of a budget is allocated, resulting in decreased KBC scores. This, in turn, lowers customers’ and employees’ satisfaction. The cut-off budget in human- and technology-related activities, such as no training provided, no incentives, and no technology upgrading, reduces the KM performance, specifically in the KD, KU, and KS factors. The KS factor, particularly, is highly affected by the cut-off budget, as technology changes rapidly and the budget for software upgrading is required to maintain efficiency. The decline of the KBC score continues until it is lower than the acceptable level. Then, this urges management to take prompt actions to improve KBC, and the cycle repeats.

4.2. Scenario Analysis

4.2.1. Budget Allocation Scenario

As the KBC score relies on the budget allocated for KM implementation, further analysis is performed by increasing and decreasing the budget to reflect management support for KBC enhancement. The results in Figure 8 show consistent behaviors of the developed SD model, proving its validity to be used in actual practices [50]. It is clear that when a bigger budget is invested in KM implementation, the KBC score matures earlier, i.e., in year 18, which is six years earlier than that with a smaller budget. Nevertheless, when the budget is removed to other projects (i.e., attention withdrawal occurs), the KBC score decreases expeditiously and is lower than the industry’s level within six years (see Figure 8). This proves the importance of management support in achieving sustainable KBC development. Figure 8 shows that, despite the attention withdrawal, the KBC improves through time and experience. The reduction of KBC scores is slower in later cycles (i.e., six years in the first cycle, five years in the second cycle, and four years in the third cycle). The score is reversed to increase at higher scores (i.e., 390 points in the first cycle, 465 points in the second cycle, and 544 points in the third cycle).

4.2.2. Short- and Long-Term Plan Scenario

The simulation results in Figure 7 highlight the implications of KM factors to enhance KBC. The KU and KD factors highly contribute to the KBC score in the early stages. The short-term plan is then performed by expediting the implementation of these two factors compared with the other three factors (i.e., the KS, KA, and KR factors). The plan addresses immediate project challenges, specifically human-related activities, to improve operational efficiency and responsiveness. Extra efforts may be added to the KU and KD activities, such as leveraging team expertise through daily stand-up meetings to brainstorm and solve problems, organizing workshops and training sessions, and collaborating feedback mechanisms to support the rapid dissemination of knowledge, foster teamwork, reduce error, and boost employees’ and clients’ satisfaction. These low-cost activities with the basic tools and human-centered initiatives are highly effective at enhancing the KBC score in the early stages.

The results in Figure 9 reveal that by focusing on the KU and KD factors, the KBC score reaches its desired level earlier than the baseline results. However, only focusing on these two factors may not assist in achieving high performance in the long term (i.e., it takes longer to achieve the desired KBC score in the later cycles).

The results in Figure 7 also show that KS, KA, and KR factors take a long time to achieve their maximum scores, as many activities require experience and investment. The long-term plan is then performed by putting more effort into enhancing these three factors. The plan focuses on establishing sustainable knowledge systems that drive resilience and innovation. The budget is invested in human- and technology-related activities for sustainable improvement. Investment in digital storage systems, such as lessons-learned databases and automated workflows, is required to preserve accessible knowledge, reduce repetitive errors, and enhance productivity and quality [26]. Market research and competitive analysis should be initiated to foster innovation, adapt to changes in client requirements, and adjust to the dynamic market [51]. Figure 9 shows that the long-term plan is not highly effective in the early stages (i.e., early cycles) as it takes time to achieve the desired KBC score. Nevertheless, with more experience in KM implementation, this plan will bring better results in later cycles.

Successful KM implementation and sustainable KBC enhancement depend on the continuous support of management. As the construction market is highly dynamic, implementing KM to achieve KBC should be included in the company’s short- and long-term policies with strong support from management.

5. Discussion

This study demonstrates the critical role of management attention, budget allocation, and KM practices in shaping KBC in the construction industry. Five KM factors are considered in the SD model development to plan for KBC enhancement in the long term. The developed SD model reveals the inherent oscillations in KBC performance. With management support for KM implementation, the improvement in KBC scores exists. On the contrary, a decline in KBC scores occurs when the budget is redirected to other projects. Such oscillation patterns reaffirm findings in the literature, emphasizing the fluctuating nature of organizational competitiveness in the resource-sensitive environment [20,30]. To achieve sustainable KBC enhancement, continuous support from management is crucial.

The study results reveal that improving KBC scores in the early stages depends mainly on the KU and KD factors. A short-term improvement plan could be established focusing on these two factors. Management may support human-related activities using existing resources, such as regular online meetings with clients to achieve prompt responses and a relationship program initiative to share lessons learned among stakeholders. These activities require moral support and attention from management and less budget. By focusing on this short-term plan, the companies could accelerate the KBC scores in a short period. Nevertheless, the activities under the KS, KA, and KR factors must be gradually implemented to achieve sustainable development. They require immense moral and financial support from management. Storing and analyzing data from past projects requires employees’ time, which may interrupt their routine work. Efficient tools, such as Google BigQuery, IBM Db2 warehouses, and software systems, require a vast investment and training for full utilization. This is consistent with Yan et al. [37], who stated that creating and managing digital archives and automating workflows require substantial early-stage investments in infrastructure and training. Management should commit to these improvements to achieve sustainable KBC development.

6. Conclusions

This study develops the SD model of KBC enhancement to examine the dynamic effects of management support on KM implementation and KBC in the long term. The results answer the two research questions and confirm the hypotheses.

• Management support, through budget allocation, has an important role in KBC enhancement (Hypothesis 1).
• Short- and long-term strategies to enhance KBC can be set through KM implementation, focusing on human-centered activities (i.e., activities in the KU, KD, KA, and KR factors) and technology-driven activities (i.e., activities in the KS factor) (Hypothesis 2).

This study contributes to the body of knowledge. It highlights the inherent oscillations in KBC performance from dynamic changes in management support. KBC’s performance may decline when management attention shifts away from KM initiatives. While investments in KM-related activities can improve the KBC scores, sustaining the implementation requires continuous financial support, iterative learning, and alignment of KM efforts with organizational goals. The findings also underscore the pivotal role of technology-driven solutions, such as automated storage systems and digital repositories, in enhancing KS activities while emphasizing the importance of fostering a collaborative knowledge-sharing culture through KD and KU initiatives. For companies with a smaller budget, KBC scores may be improved using existing tools and simple online applications to raise performance, specifically in the KD and KU factors. Nevertheless, investment in high-technology tools and systems is necessary for long-term success.

This study has some limitations and suggestions for future studies. The study focuses on KBC enhancement through management support for KM implementation in the construction industry. Future studies may apply the developed SD model in similar industries for comparison and validation. The important weights of factors and their attributes may be adjusted to suit different working environments and utilized in the model development and gap (or score) reduction. Additionally, while SD modeling effectively illustrates the impacts of management attention on KBC, its simulation results may not fully capture real-world complexities. Empirical research using case studies may provide deeper insights. Furthermore, this study neglects cultural and behavioral factors in KM adoption. Exploring how organizational culture and employee behaviors affect KM effectiveness could add value to the construction industry.