The use of ambiguous language in contract adjustment: An empirical study of its impact on interfirm relationship performance

Zhao, Danyang; Gao, Angela Yi; Gu, Flora F.

doi:10.1016/j.indmarman.2022.10.024

Cited by 3 publications

(1 citation statement)

References 88 publications

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“…Table 2 shows comparative and improvement strategies in contract management of different types of industries [50][51][52][53][54][55][56]. This table indicates the role of clear, specific, and adaptable contracts in promoting technology transfer, collaboration, and dispute resolution across industries like oil, construction, manufacturing, and higher education.…”

Section: New Contracts Challenges On Oil and Gas Industrymentioning

confidence: 99%

Bibliometric Analysis of Intelligent Systems for Early Anomaly Detection in Oil and Gas Contracts: Exploring Recent Progress and Challenges

Cardona,

Guzmán-Luna,

Restrepo-Carmona

2024

Sustainability

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The oil and gas industries are crucial to global economies, influencing geopolitics, driving technological advancements, employing millions, and impacting financial markets. The complexity and the volume of data generated by these industries demonstrate the need for efficient information management, where effective contract audits play a key role in ensuring market stability, transparency, fair revenue distribution, corruption mitigation, and enhancing industry integrity to attract investors. This study employs bibliometric analysis to explore the application of machine learning (ML) in detecting anomalous contracts within the oil and gas industry. This analysis identifies key research and challenges, laying the groundwork for further computational ML advancements. The PRISMA guidelines identify ML’s role from 2018 to 2023, including post-COVID-19. Principal component analysis (PCA) evaluates the bibliometric contributions of different countries and institutions. China, Indonesia, Egypt, Saudi Arabia, the University of Antwerp Operations Research Group, and the University of Pittsburgh emerge as significant contributors. These findings underscore ML’s pivotal role in fraud detection, risk mitigation, and cost savings, concluding that anomalous contract detection remains open to newer ML techniques and ongoing research.

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