Journal of Management in Engineering

The infrastructure development of emerging economies forms new market opportunities with great potential for multinational firms. However, international contractors are faced with extraordinary institutional challenges when investing in emerging countries. Extant studies of international contractors’ institutional pressures in emerging economies mainly focus on the awareness of, impacts of, and learning about the institutional pressures; few studies have investigated how to cope proactively with the institutional challenges. This study investigated how international contractors should deal with the institutional challenges in emerging countries. We developed a contingency model of strategic responses to institutional challenges, evaluated the model, and applied the model to some common institutional challenges in emerging countries. A case study provided empirical evaluation and exploratory findings. A new pattern of the response to institutional challenges, which is psychological in nature, was discovered in the case study.

Abstract

Facility managers are increasingly tasked with being the champions of change in their organizations, which may include activities such as space management revisions, construction delivery practices, maintenance programs, technology implementations, and others. While much of the previous organization change management research has focused on how facility managers enable the adoption of planned change events (i.e., new technology), this study identifies the successful change strategies used in responding to unforeseen circumstances or events. The researchers evaluated these unplanned changes through the lens of COVID-19, given its nearly universal impact on facility management as a profession. The research team collected survey data from nearly 900 organizations in more than 60 countries, and conducted detailed follow-up interviews with 28 organizations. Twenty-two parameters were grouped into three categories: organizational characteristics, organizational change management practices, and change adoption measures. The research team used three random forest models and three logistic regression models to further examine the relationship between the data and the change adoption measures. The results indicate that three organizational change practices were particularly important to overall adoption success and achievement: appointment of a change agent, support of leaders, and the timeframe for implementing the change. The analysis also suggests that the type of unplanned change initiative has an impact on its long-term adoption throughout the organization. Organizations should consider using a formal change management approach in order to successfully implement unplanned change initiatives. This paper highlights the importance of an effective change, the active involvement and support of senior leaders, and the timeline of implementing the change—even if it is unforeseen.

Practical Applications

The COVID-19 pandemic has put many facility management professionals at the helm of directly implementing numerous organizational changes. Today’s facility manager must provide solutions for a variety of unprecedented (and unforeseen) changes related to space planning, building operations, social distancing, hybrid working, and others. This article provides a number of key findings that assist facility practitioners in dealing with the unexpected, namely: A formal change management approach can help facility owners respond to a variety of unforeseen circumstances. There is value in laying out a schedule, identifying key resources, and explaining the benefit or impact of a change to building occupants. An effective change agent (who is seen as a peer by those who will be affected by a change) is an important asset to help facility management ensure that the change sticks. For example, the keen facility manager would identify employees who have positive experiences with hybrid working and ask them to explain the benefits of a hybrid work schedule to their colleagues (instead of someone from management trying to push the change).

Contracts are the main governance mechanism regulating construction participants’ behaviors and safeguarding project performance. Besides traditional subjective measurement via survey, machine learning has been utilized as a more objective method to measure contract complexity in the field of construction management. However, little is known about whether misalignment between these two measurements exists and whether their impact on project performance differs. To answer these questions, we collected 202 pairs of construction contracts and questionnaires. Construction contracts were analyzed by machine learning to obtain the objective measurement of contract complexity, whereas questionnaires provided the subjective measurement. Through a multifunctional perspective of contracts, we found that the two measures are positively correlated with each other for contractual coordination and adaptation but nonsignificant for control. Regarding the ongoing debate on the relationship between contract complexity and trust, we selected trust as another antecedent of construction project performance. The results showed that trust is only positively related to the subjective measurement of contract complexity, which has a direct impact on project performance. In contrast, the objective measurement strengthens the positive effect of trust on project performance. Theoretically, our study contributes to construction contract research by highlighting that different measures cannot be used interchangeably and that scholars should be aware of the measurement issue when conducting and assessing relevant research. Practically, construction project managers are provided with guidance on performance improvement through perspectives from both objective design and subjective perceptions of contracts.

Off-site construction (OSC) has been drawing attention as a solution to the internal and external problems that the construction industry is facing. This study identified and analyzed the critical risk factors and activities to manage during OSC project execution, including production, transportation, assembly, and integration. Twelve critical risk factors and 50 management activities were derived from the existing literature. OSC experts scored each attribute’s importance and performance, and reliability was tested with Cronbach’s alpha. Results represented by an importance and performance analysis (IPA) matrix showed low performance and high importance of OSC projects. Poor human resources management had the highest importance, whereas lifting error had the highest performance. Production planning error ranked lowest in both importance and performance. Hence, OSC projects need to improve performance further, and intensive management should be used to minimize the performance–importance imbalance.

Over the last decades, numerous automated compliance checking (ACC) systems have been developed. However, ACC is still not broadly used in the architecture, engineering, and construction (AEC) industry today; little is known as to how ACC can be better accepted by the end users. This paper reports on a multicase study to learn valuable lessons from recent attempts to adopt ACC systems worldwide. Firstly, 18 semistructured interviews were conducted with 20 experts from eight countries, and supplementary data (e.g., documents, product information, and literature) related to each case were collected. Secondly, the interview and supplementary data were then coded to develop prominent themes. Thirdly, through a cross-case analysis, the 12 most determining variables that could influence the ACC adoption were identified. Three path models that explain the interrelationships between these variables and 10 propositions that can guide future ACC adoption were deduced. The results indicate that the government should play an important role to facilitate ACC adoption through funding, policies, and incentives. This study also provides valuable information to software vendors for delivering ACC systems that meet the needs of the industry, and for innovation managers in the industry to develop appropriate adoption plans for the ACC technology.

Collusive bidding is regarded as one of the construction sector’s most socially harmful, anticompetitive, and unsustainable practices. In this scheme, bidders are accustomed to varying their collusive team scale (CTS) with external environment (EE), and it consequently poses an overwhelming challenge for antitrust authorities to detect them. This study conducts an empirical analysis of the impacts of external environmental factors (EEFs) on the CTS from multiple perspectives with an aim to aid antitrust authorities in chasing collusive bidders’ behaviors in responding to the fluctuation of EE. The data were extracted from multiple sources, including 577 collusive bidding cases, statistical yearbooks of cities, governmental statistical bulletins, Baidu Maps, and enterprise information platforms in China. Although the impacts of EEFs on CTS are verified, it is found that the stronger the economic environment, the smaller the CTS; the more the industrial competition intensity and geographical advantages, the larger the CTS. The study also demonstrates the heterogeneous influence of competition intensity on the CTS among different project types. These research findings provide a stepping stone for noncollusion studies and favor antitrust authorities to formulate effective governance strategies.

With recent advances in information and communication technologies, trust and collaboration have become imperative in the construction industry. The increasing exchange of digital information and assets without much regard to contractual requirements has resulted in various data corruption, privacy, and security issues escalating to claims and disputes. Hence, there is a need to institute reforms in the existing contracting system to achieve a more efficient, collaborative, and transparent system. However, little research has investigated the digitalization of the contracting process, which continues to be traditional. This study is one of the first to develop a digital and automated contract management system leveraging blockchain technology and smart contracts. Design science research methodology was adopted for the research work. The proposed system aims to achieve the following: (1) a decentralized permissioned network of stakeholders with well-defined roles and approval workflow, (2) automated contract execution based on predefined conditions using smart contracts, and (3) transaction history integrity with time stamps via a blockchain-based secured data structure. To exemplify these points, a use case is presented for implementing blockchain-based contract management in a construction megaproject. A conceptual model was developed illustrating the decentralized network of the key project stakeholders based in a Hyperledger Fabric blockchain environment. The roles and access controls of the stakeholders within the network are defined using the responsibility assignment [responsible, accountable, consulted, and informed (RACI) matrix]. A computable and automated smart contract clause is formulated for the price variation clause used in construction contracts. A prototype of the proposed system was developed and demonstrated to industry experts for evaluation and validation. Positive feedback from the experts concerning the system characteristics and user-friendliness of the prototype showed that it possesses the potential to improve trust and transparency in the existing system. Whereas the decentralized stakeholder network would add trust and accountability to the system, the underlying blockchain data structure would enhance security and traceability by providing an immutable and real-time record of the transactions. The study provides a workable solution that serves as a valuable reference for future research in the field of contract management across multiple sectors. Although the system is shown to be applicable and feasible in this study, it may not be economically viable. It could be optimized in the future through cost–benefit analysis for different projects.

The construction industry compromises the sustainability of the built environment because of its adverse impacts on the environment, economy, and society. In response, green building (GB) projects advocate for targeted sustainability goals while some particular risks in GB projects could cause several unfavorable outcomes. This study aims to assess the life cycle risks of GB projects and emphasize the role of stakeholders in the management of corresponding risks in a four-step analysis procedure. In the first two steps, a comprehensive literature review and focus-group discussion were employed to construct the decision-making framework suitable to the GB life cycle. A fuzzy analytical hierarchy process (AHP) was used to prioritize the identified risks based on expert opinions. The fuzzy technique for order preference by similarity to ideal solution (TOPSIS) was used to associate stakeholders’ role with the management of GB risks that might arise throughout GB projects. Findings indicate that lack of experienced green construction staff, limited availability and reliability of green subcontractors and suppliers for the specific requirements of GB, and inflation of green materials’ prices are ranked as the most significant risks. A generic life cycle risk management framework for green buildings was developed to analyze the most significant risks and corresponding stakeholders. By addressing the gap in the literature, this study is expected to engage practitioners and GB project stakeholders for more effective and robust risk management frameworks.

Construction sites are considered high-risk working environments. Safety risk analysis of construction sites is one of the important aspects of construction safety management. However, existing construction safety risk analysis methods typically face overreliance on subjective experience and cannot reflect the real-time risk level of construction projects. Crucial information about construction accidents implies a complex semantic network that can obtain objective quantitative data and then serve the quantitative analysis of construction safety risk. Therefore, a knowledge-graph-improved dynamic risk analysis method for behavior-based safety (BBS) management on construction sites is proposed. Specifically, this study quantifies the risks and consequences of unsafe behaviors in construction by conducting graph topology analysis based on historical accident data, improves the grey clustering model, and calculates the construction site risk. Thus, the proposed method combines expert experience and objective historical data to give more objective and realistic results. The results of the application of this method on one project show that the improved dynamic analysis method can utilize the historical accident data to achieve a more reasonable overall risk grading for BBS on construction sites; at the same time, the method can determine real-time key BBS indicators; further, the construction safety management measures for key BBS indicators can effectively reduce current BBS risks of a construction project.

The selection of an appropriate discount rate is a critical yet often neglected aspect of valuing flood protection and climate adaptation projects. We examine current discounting practices in flood risk management and climate adaptation literature and compare these practices with a proposed fair market value (FMV) discounting approach, in which we discount project cash flows based on their levels of systematic market risk, consistent with practice in the private sector. Using publicly available data, we demonstrate minimal correlation between flood risk–related cash flows and overall market returns, suggesting flooding-related cash flows are most appropriately discounted near the risk-free rate. We consider the example of a proposed storm surge barrier in Boston Harbor and compare prevailing public-sector discounting approaches with this FMV approach. Our results suggest prevailing discounting approaches systematically undervalue flood risk reduction benefits and climate adaptation investments relative to the private-sector valuation implied via the FMV approach.

Replacing equipment at the most economical time not only helps to save state transportation agencies (STAs) costs for operating the fleet but also keeps the fleet’s level of service at an optimal level. Prior research studies focused on developing alternative economic-oriented equipment replacement models rather than the equivalent annual cost (EAC) model to achieve better economic decisions. In addition, various optimization techniques were applied to equipment replacement problems with different objectives, constraints, and contexts. However, few studies examined the impact of depreciation estimation on the equipment replacement decision within STAs by minimizing total equipment cost over a finite study period using the dynamic programming optimization method. This study performed a case study of two class codes of equipment [1.5 m³ (2-yd) diesel engine front-end loaders and 0.453 t (half-ton) fleetside pickup trucks] to analyze the impact of different depreciation calculations on equipment replacement decisions. Using real-world data provided by the Oklahoma Department of Transportation, the study showed that the double-declining balance depreciation method substantially reduces the number of pieces of equipment recommended for replacement compared with the result of the straight-line depreciation method. This study contributes to the understanding of the impact of depreciation methods on equipment replacement decisions as well as the importance of properly estimating equipment depreciation to minimize the equipment costs over a designated study period among STA communities. The demonstrated manual calculation procedures of dynamic programming for cost optimization potentially may facilitate STA’s adoption of dynamic programming for equipment economic decisions.