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The number of construction dispute cases has maintained a high growth trend in recent years. The effective exploration and management of construction contract risk can directly promote the overall performance of the project life cycle. The miss of clauses may result in a failure to match with standard contracts. If the contract, modified by the owner, omits key clauses, potential disputes may lead to contractors paying substantial compensation. Therefore, the identification of construction project contract missing clauses has heavily relied on the manual review technique, which is inefficient and highly restricted by personnel experience. The existing intelligent means only work for the contract query and storage. It is urgent to raise the level of intelligence for contract clause management. Therefore, this paper aims to propose an intelligent method to detect construction project contract missing clauses based on Natural Language Processing (NLP) and deep learning technology.

A complete classification scheme of contract clauses is designed based on NLP. First, construction contract texts are pre-processed and converted from unstructured natural language into structured digital vector form. Following the initial categorization, a multi-label classification of long text construction contract clauses is designed to preliminary identify whether the clause labels are missing. After the multi-label clause missing detection, the authors implement a clause similarity algorithm by creatively integrating the image detection thought, MatchPyramid model, with BERT to identify missing substantial content in the contract clauses.

1,322 construction project contracts were tested. Results showed that the accuracy of multi-label classification could reach 93%, the accuracy of similarity matching can reach 83%, and the recall rate and F1 mean of both can reach more than 0.7. The experimental results verify the feasibility of intelligently detecting contract risk through the NLP-based method to some extent.

NLP is adept at recognizing textual content and has shown promising results in some contract processing applications. However, the mostly used approaches of its utilization for risk detection in construction contract clauses predominantly are rule-based, which encounter challenges when handling intricate and lengthy engineering contracts. This paper introduces an NLP technique based on deep learning which reduces manual intervention and can autonomously identify and tag types of contractual deficiencies, aligning with the evolving complexities anticipated in future construction contracts. Moreover, this method achieves the recognition of extended contract clause texts. Ultimately, this approach boasts versatility; users simply need to adjust parameters such as segmentation based on language categories to detect omissions in contract clauses of diverse languages.

This article aims to establish a dynamic Energy Performance Contract (EPC) risk allocation model for commercial buildings based on the theory of Incomplete Contract. The purpose is to fill the policy vacuum and allow stakeholders to manage risks in energy conservation management by EPCs to better adapt to climate change in the building sector.

The article chooses a qualitative research approach to depict the whole risk allocation picture of EPC projects and establish a dynamic EPC risk allocation model for commercial buildings in China. It starts with a comprehensive literature review on risks of EPCs. By modifying the theory of Incomplete Contract and adopting the so-called bow-tie model, a theoretical EPC risk allocation model is developed and verified by interview results. By discussing its application in the commercial building sector in China, an operational EPC three-stage risk allocation model is developed.

This study points out the contract incompleteness of the risk allocation for EPC projects and offered an operational method to guide practice. The reasonable risk allocation between building owners and Energy Service Companies can realize their bilateral targets on commercial building energy-saving benefits, which makes EPC more attractive for energy conservation.

Existing research focused mainly on static risk allocation. Less research was directed to the phased and dynamic risk allocation. This study developed a theoretical three-stage EPC risk allocation model, which provided the theoretical support for dynamic EPC risk allocation of EPC projects. By addressing the contract incompleteness of the risk allocation, an operational method is developed. This is a new approach to allocate risks for EPC projects in a dynamic and staged way.

Contract risk management has become a critical mission, as contract issues may lead to a loss of vast amounts of money to parties involved or cause project failure. This study sought to identify effective management strategies to mitigate construction contract issues that might emerge during construction.

A quantitative research approach was adopted for the study. Structured questionnaires made up of close-ended questions were distributed to construction professionals in South Africa via the SurveyMonkey platform. The data were then analysed using descriptive statistics.

The findings indicate that the critical sources of contract-related disputes are ambiguous definitions of the contract parties' scope of their rights and obligations, lack of precise arrangements regarding the calculation of contractual penalties for failure to meet the deadline, lack of detailed specification of the works and specific milestones, lack of provisions regulating changes to the project documentation during the construction stage, an excessive amount of contractual penalties on contractor's side and lack of provisions regarding the rules of performing additional and replacement works and their settlement. However, for these disputes to be effectively managed, strategies such as reduction uncertainties in project's phases, setting up contingency plans, construction guarantee, extension of time claims, payment guarantee, retention and escalation clause should be implemented by the parties involved.

Even though the empirical study focused on construction professionals in South Africa, the findings could be applied to other countries outside of South Africa.

To effectively manage and prevent contract disputes from averting project failures and losses to parties involved in the contract, construction professionals need to be aware of strategies that must be implemented before and during the project execution. If well implemented, these strategies will help a construction project be successful and experience fewer contractual disputes.

The study has identified the knowledge gap concerning suitable contract risk management strategies available for implementation to effectively prevent any contract parties from losing money, time and project failure.

Integrated project delivery (IPD) project that does not use multiparty agreement is identified as IPD-ish. The use of IPD-ish arrangement by incorporating integration practices in conventional contract can be viewed as the part of the adoption process of IPD. Moreover, inappropriate integration practices invite new forms of risks and the absence of multiparty agreement adds to the challenges of risk management in IPD-ish projects. This study discusses such challenges and proposes the use of joint risk management to address the potential pitfalls in IPD-ish arrangement.

A mixed research method was applied. First, the criticality of IPD-ish general and integration-specific risks was examined through a survey. Second, a real IPD-ish project was used to exemplify the use of joint risk management (JRM) to manage IPD-ish risks.

Two types of risks, namely integration risks (IRs) and general risks (GRs), are identified in IPD-ish projects. Two major findings for the IRs: (1) the most critical IRs are related to unbalanced incentivization and inefficient multidisciplinary teams; and (2) only team formation related pre-contract JRM strategies affect IRs. As for the GRs, the most critical ones are associated with design issues and can be effectively mitigated by post-contract JRM.

Using IPD-ish arrangement is an inevitable part of implementation of full IPD. This happens as many change-averse owners would like to test the integration principles using a conventional contract that they are familiar with. In fact, success in IPD-ish would pave the path for further adoption of IPD. This study offers insight into categorization of risks in IPD-ish projects. Appropriate use of post-contract and organization related pre-contract JRM would improve the chance of teasing out the values of IPD through IPD-ish arrangements. Care should be taken to introduce some contracting integration initiatives, such as risk/reward sharing incentive.

Contract incompleteness with deficiency, inconsistency, defectiveness, and ambiguity in contract clauses, which can cause misunderstandings and misinterpretations, may result with disputes in projects. This study aims to investigate contract incompleteness factors with a hybrid fuzzy multi-criteria decision approach.

Contract incompleteness factors were ranked by fuzzy VIKOR (Visekriterijumska Optimizacija I Kompromisno Resenje) method, and the most significant factors were subjected to fuzzy decision-making trial and evaluation laboratory (DEMATEL) to examine their causal relationships. The study is not limited to ranking the identified factors solely, since their cause-effect interactions are also essential for proper risk management in construction projects.

Hybrid use of multi-criteria analysis reveals that ambiguity in enforceability including excessive demands and significant amendments in the scope of works are the top two causal contract incompleteness factors, while lack of implementation details and focus of focal point, and insufficient supporting and technical documents are the most affected ones.

Contractual causes of disputes due to contract incompleteness factors other than requirements of the contracts have been rarely investigated in the literature. The research is one of the first studies in the literature investigating the causal relationship among factors in construction contracts, which might lead to project disputes. Findings are expected to improve contract drafting, eventually contributing to effective risk management in construction projects.

This paper investigates the influence of the ongoing crisis of Russia's incursion on Ukraine on the risk dynamics of energy futures contracts with high-frequency data on four different futures contracts using risk metrics of value at risk (VaR) and conditional value at risk (CVaR) for the USA market.

The author used different generalised autoregressive conditional heteroscedasticity - Extreme Value Theory (GARCH)-EVT models and compared the performance of each of the competing models. Backtesting evidence shows that VaR and CVaR combined with GARCH-EVT better estimate risk.

The study results show that combined risk metrics are efficient and adaptive to estimating the risk dynamics and backtesting of the models, revealing that the autoregressive moving average (ARMA) (1,1)-asymmetric power autoregressive conditional heteroscedasticity (APARCH) model performs relatively better than other models.

The paper has practical implications for different market participants. From the risk manager's and day traders' angles, the market participants can estimate the risk exposure in the energy futures contract and take positions accordingly. The results are important for oil-importing countries due to the developing supply crisis and price escalation, which can brew inflation in the economy.

To the best of the author's knowledge, the paper is the first to throw light on the risk angle of energy futures contracts during the ongoing crisis of the Russia–Ukraine war.

The purpose of this paper is to propose a disruption claim management (DCM) life cycle and a risk management framework to provide comprehensive guidance to construction practitioners for facilitating effective and efficient DCM.

DCM life cycle was initially developed through a focus group discussion (FGD) with the participation of the construction practitioners who have diverse experiences about DCM. The life cycle is comprised of 6 phases and also includes proper reactions of the owners and contractors. Then, 42 risk factors that can impact the deliverables of DCM were identified through a literature review and an additional FGD session. This was then followed by a Fuzzy Analytical Hierarchy Process (FAHP) which was performed to evaluate the importance of each risk factor in terms of the factor's impact on the success of DCM. Additionally, consistency analysis was performed to further maximize the reliability of the results.

Findings revealed that a proactive and systematic approach should be adopted and DCM practices should be initiated before any disruption event is triggered. Accordingly, the proposed framework recommends DCM practices to be initiated early in the contract development phase since compensation for the disruption might be recovered only to the extent that the contract permits. The contract-related risks were given top priority by the experts so that the results of the fuzzy AHP analysis also verified the significance of the contract development phase. Besides contract-related risks, risks related to insufficient site observation, ignorance of the project team, cognitive bias and conflict of interest were determined as the most significant DCM risks, needing an urgent and sophisticated risk response plan. Lastly, results suggested that “Site observation and record-keeping” is the most formidable phase since the phase's implementation on a continuous basis could create unforeseen organizational challenges such as mismanagement of project records, especially in the dynamic and turbulent environment of the construction projects.

Disruption – which is caused mostly by change – is inevitable in construction projects due to their sophisticated nature. DCM, therefore, becomes crucial to compensate losses of contractors and eliminate or diminish the prolonged dispute resolution process. Existing studies, however, do not provide a comprehensive theoretical basis for the DCM life cycle and DCM life cycle's potential risks so that DCM life cycle's promising benefits can hardly be materialized. Thus, developing a DCM life cycle and associating DCM life cycle with risk management, this study is highly believed to make a promising theoretical contribution to the DCM domain since this is one of the earliest attempts in the literature. Additionally, this research provides construction practitioners with an insight into the effective implementation of DCM practices in construction projects.

This study aims to examine the value of the call option contract in hedging the risks in the supply chain. The decentralized supply chain without call option contract is first studied as the criterion model for evaluations. This paper addresses several questions: What will be the optimal manufacturer’s production quantity, retailer’s ordering and pricing policies in the presence of random demand and random yield by applying the downconversion approach? How will the call option contract influence the optimal decisions for the members of the supply chain? Can the risk from randomness be divided among the members in the supply chain through the call option contract?

This paper considers a two-level decentralized supply chain under random yield and random demand in which the manufacturer takes advantage of the downconversion approach with two scenarios, with and without option contract. To the best of the authors’ knowledge, no article or study uses the downconversion approach in a supply chain regarding random yield and random demand. Furthermore, the paper considers pricing with option contract in the supply chain, which makes this article stands out significantly from other articles in the literature.

This study shows that the downconversion approach would reduce the risk caused by the random yield, which appears to be the appropriate method for the environmental goal of the supply chains. Moreover, adopting a call option contract can increase flexibility and mitigate risks, resulting in more expected members’ profits.

To simplify the model, the authors assume one manufacturer and one retailer, so extending the model to consider multiple retailers instead of one retailer and inventory sharing between them would be interesting. Considering the option and exercise prices as decision variables would be important future research topics. Put option and bidirectional option contracts could be investigated in the future. Another extension is modeling asymmetry of information in supply chain.

This paper provides managerial insights on dealing with both demand and yield risks in a manufacturer–retailer supply chain. The manufacturer has a random yield production and produces two types of vertical products: low-end and high-end. To reduce waste caused by the random yield, the manufacturer uses a downconversion approach in which low-end products are made by converting the defective high-end products. The manufacturer purchased a shortage of high-end products from the secondary market (i.e. emergency sourcing). High-end products are sold through the retailer, and low-end products are sold directly by the manufacturer. The customer demand for high-end products in the end market is random and depends on the selling price, and the customer demand for the low-end products in the secondary market is independent and random. The retailer contracts the manufacturer with the call option to obtain high-end products to meet a random demand; in fact, by using the call option contract, the authors try to balance the risks between two members. Two scenarios of with and without call option contract are proposed. After the high-end product demand is observed, the retailer would exercise the option order quantity in the call option contract scenario and then place an instant order with the manufacturer if necessary. In each scenario, the manufacturer and the retailer make their decisions simultaneously (static game) to determine the retailer’s optimal ordering and pricing policies and the optimal production quantity of the manufacturer (Nash equilibrium) by maximizing their expected profits. Finally, the impact of the model parameters on the supply chain is expressed through numerical examples. The numerical analysis shows that the call option contract provides greater profit than the wholesale price contract.

This study considers a contracting problem between a fairness concerned entrepreneur (EN) and a fair-neutral venture capitalist (VC) to explore the effects of asymmetry, agency conflicts and fairness concerns.

The authors construct the model by assuming the EN's risk aversion degree is private information, which is more realistic but ignored in most studies. Under the principal–agent framework, the authors solve the VC's optimal contracting models by identifying the ranges of feasible solution, where the optimal solutions of these models are explicit and nicely reconcile the “private equity” puzzle. Moreover, validity of the optimal solutions is verified by numerical simulations.

In accordance with empirical evidence, information asymmetry lowers the optimal equity share that the VC provides to EN but raises EN's profit due to lower effort disutility and information rent. Moreover, the authors find that the fairness concerns is beneficial for the EN, where it not only increases the EN's optimal equity share, but also enhances the certainty equivalence of the EN's utility regarding its profit. Relative to the benchmark model where the EN's risk aversion degree is common knowledge, the EN's efforts recommended by the optimal contract is less sensitive to the EN's fairness concerns degree when the EN does not actually announce its risk aversion degree.

First, the authors incorporate asymmetry to study a two-period contracting problem and explore how it affects the equity shares allocated to the contractual parties. Second, the authors incorporate fairness concerns and analyze its effect regarding the decision-makings and profits.

This study investigates the impact of contract farming in onion, okra and pomegranate production on profits of smallholder farmers in India. It also investigates the determinants of farmers’ participation in contract farming. The study is based on a survey of 1,131 farmers from Maharashtra, India engaged in the cultivation of these three crops.

This study uses instrumental variable regressions and quasi-experimental methods to decipher the impact of contract farming.

The study reveals that contract farming ensures higher returns for smallholders, enables their access to high-end markets and brings in risk-sharing with protection during price fluctuations. Farm size and farmers’ risk perceptions are significantly associated with their participation in contract farming.

The study is based on cross-sectional data, which presents limitations on considering unobserved farmer-level individual heterogeneity.

The study shows that contracts highlight the functioning of the contractor/integrator on both the input and output sides of the market. By providing better-quality inputs on credit and at discounted prices and by providing training, the integrator helps small farmers meet international food safety and quality standards, a historically difficult challenge for smallholders in India.