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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.

The purpose of this study is to examine accountants’ application of principles-based accounting standards to a lawsuit contingency recognition scenario and the potential role that accounting work experience plays in mitigating accountants’ aggressive financial reporting.

This study presents a 2 × 2 between-subjects experiment with accounting experience (measured as high vs low) and contingency type (asset vs liability) as independent variables and accountants’ lawsuit contingency conservatism likelihood judgments and US$ recognition recommendations as the dependent variables.

Consistent with expectations, findings indicate that more experienced accountants are more likely to recognize liabilities and items that decrease income and less likely to recognize assets and items that increase income than their less experienced counterparts. Accountants also recommended recognizing lower (higher) mean US$ amounts for assets (liabilities), as expected. Supplemental analyses show a significant moderated-mediated effect whereby the interactive effect of contingency type and accounting experience on individuals’ US$ recognition recommendations is partially mediated through the nature of the conservatism judgment.

The finding that less experienced accountants report more aggressively than more experienced accountants when applying a principles-based standard supports the call for using judgment frameworks in imprecise standard settings and suggests that firms may want to ensure that accountants with adequate work experience are on hand as U.S. generally accepted accounting principles become more principles-based over time.

To the best of the authors’ knowledge, this study is the first to examine the impact of accounting work experience on the application of principles-based accounting standards and the mitigation of aggressive financial reporting. Our supplemental analyses also identify the nature of the conservatism judgment as a mediating mechanism which partially explains more experienced accountants’ US$ asset and liability recognition recommendations.

This paper aims to fulfil a need to identify assembly interfaces from existing products based on their Assembly Process Planning (APP). It proposes a tool to identify assembly interfaces responsible for reused components integration. It is integrated into a design for mixed model final assembly line approach by focusing on the identification of assembly interfaces as a generic tool. It aims to answer the problem of interfaces’ identification from the APP.

A tool is developed to identify assembly interfaces responsible for reused component integration. It is based on the use of a rule-based algorithm that analyses an APP and then submits the results to prohibition lists to check their relevance. The tool is then tested using a case study. Finally, the resulting list is subjected to a visual validation step to validate whether the identified interface is a real interface.

The results of this study are a tool named ICARRE which identify assembly interfaces using three steps. The tool has been validated by a case study from the helicopter industry.

As some interfaces are not contained in the same assembly operations and therefore, may not have been identified by the rule-based algorithm. More research should be done by testing and improving the algorithm with other case studies.

The paper includes implications for new product development teams to address the difficulties of integrating reused components into different products.

This paper presents a tool for identifying interfaces when sources of knowledge do not allow the use of current methods.

This paper aims to investigate whether the two dominant financial reporting regimes, US Generally Accepted Accounting Principles (US GAAP) and International Financial Reporting Standards (IFRS), are associated with audit pricing and audit report lags.

In 2007, the US SEC eliminated the requirement for foreign registrants to reconcile their financial statements to US GAAP from IFRS. In this post-reconciliation setting in the USA, the authors use panel ordinary least square regressions to examine a sample of foreign firms cross-listed in the USA reporting under IFRS and US domestic firms reporting under US GAAP during the fiscal year 2007–2019.

The authors find that the firms reporting under IFRS have longer audit report lags than firms reporting under US GAAP. In addition, the authors find that firms reporting under IFRS pay higher audit fees than their US GAAP counterparts. The results are robust after controlling for the firm- and country-specific characteristics as well as using propensity-score matching.

To the best of the authors’ knowledge, this study is the first to provide empirical evidence that the differences between the two reporting regimes are associated with auditor behavior, possibly through additional audit efforts and audit complexity associated with auditing the principle-based IFRS relative to the rule-based US GAAP.

The purpose of this study is to analyse historical events to argue the improbable prospect of radical accounting reform in corporate financial reporting (CFR) due to the absence of abstract accounting knowledge as part of accountancy professionalisation (AP).

A historical database of CFR and AP events in the UK is categorised and analysed to observe the evolution of accounting in CFR from the perspective of the sociology of professions relating to abstract knowledge in professionalisation.

CFR has always been a statutory function in the UK dependent on arbitrary accounting rules rather than expert measurements based on abstract accounting knowledge. Accounting rules have evolved as part of AP and currently form part of the statutory regulation of CFR. The accountancy profession has eschewed abstract accounting knowledge in a mutually beneficial and uncompetitive relationship with the law profession in CFR.

The study is limited to the history of CFR and AP in the UK and its findings are contrary to the sociology of professions regarding abstract knowledge, consistent with the accountancy profession’s 19th-century experience of court-related services, and indicative of normative accounting research’s redundancy.

Regarding CFR and AP in the UK, the accountancy profession is an expert subordinate branch of the law profession and has no incentive to alter the status quo of statutory accounting rule compliance prevailing over abstract accounting knowledge-based expertise in CFR.

The study questions the optimism of prior research of accounting in CFR that suggests the possibility of radical reform using abstract knowledge.

“Can artificial intelligence produce architectural plan schemes?” discussion is the starting point of this study. The aim of this paper is to question whether this will be a new method in architectural design by producing plans with artificial intelligence interfaces working with human–computer interaction and to create a discussion environment.

The main research topic is the evaluation of architectural design decisions with the text-to-image generation AI algorithms method based on shape grammar rules. First, a sample space consisting of Palladio plans or plan diagrams was created. Plan diagram production experiments were made with different interfaces (Midjourney, Dall-e2, Stable Diffusion, Craiyon, Nightcafe), and alternative plan diagrams were recorded as outputs. The discussion of the outputs has been made over architectural design and space.

In the conceptual design phase of the architectural discipline and in the production of architectural plan scheme, AI algorithms are trending. This interaction imposes a new responsibility on architects. AI can create paradigm shifts in architectural processes with its tools with high data processing potential. On the other hand, in this study, it is emphasized that architecture is not just an act of producing visuals, but a functional act of producing visuals.

The technology is effective in producing architectural plans and directing them to artificial intelligence algorithms. With this study, multi-alternative architectural plan productions were tried with text-to-image bots with fast results. In this direction, a new method proposal has been developed for the conceptual design phase in architecture.

This editorial for the 6th World Conference on Production and Operations Management (P&OM) 2022 Special Issue delves into the transformative role of advanced artificial intelligence (AI)-driven chatbots in reshaping operations, supply chain management and logistics (OSCM). It aligns with the conference’s theme of exploring the intersection between P&OM and strategy during the Technological Revolution.

Utilizing a conceptual approach, this paper introduces the “ERI Framework,” a tool designed to evaluate the impact of AI-driven chatbots in three critical operational dimensions: efficiency (E), responsiveness (R) and intelligence (I). This framework is grounded in disruptive debottlenecking theory and real-world applications, offering a novel structure for analysis.

The conceptual analysis suggests immediate benefits of chatbots in enhancing decision-making and resource allocation, thereby alleviating operational bottlenecks. However, it sees challenges such as workforce adaptation and potential impacts on creativity and sustainability.

The paper suggests that while chatbots present opportunities for optimizing operational processes, organizations must thoughtfully address the emerging challenges to maintain productivity and foster innovation. Strategic implementation and employee training are highlighted as key factors for successful integration.

Bridging the gap between the burgeoning proliferation of chatbots and their practical implications in OSCM, this paper offers a first perspective on the role of AI chatbots in modern business environments. By providing insights into both the benefits and challenges of chatbot integration, it offers a preliminary view essential for academics and practitioners in the digital age.

This research investigates how to optimize storage location assignment to decrease the order picking time and the waiting time of orders in the staging area of low-temperature logistics centers, with the goal of reducing food loss caused by temperature abuse.

The authors applied ABC clustering to the products in a simulated database of historical orders modeled after the actual order pattern of a large cold logistics company; then, the authors mined the association rules and calculated the sales volume correlation indices of the ordered products. Finally, the authors generated three different simulated order databases to compare order picking time and waiting time of orders in the staging area under eight different storage location assignment strategies.

All the eight proposed storage location assignment strategies significantly improve the order picking time (by up to 8%) and the waiting time of orders in the staging area (by up to 22%) compared with random placement.

The results of this research are based on a case study and simulated data, which implies that, if the best performing strategies are applied to different environments, the extent of the improvements may vary. Additionally, the authors only considered specific settings in terms of order picker routing, zoning and batching: other settings may lead to different results.

A storage location assignment strategy that adopts dispersion and takes into consideration ABC clustering and shipping frequency provides the best performance in minimizing order picker's travel distance, order picking time, and waiting time of orders in the staging area. Other strategies may be a better fit if the company's objectives differ.

Previous research on optimal storage location assignment rarely considered item association rules based on sales volume correlation. This study combines such rules with several storage planning strategies, ABC clustering, and two warehouse layouts; then, it evaluates their performance compared to the random placement, to find which one minimizes the order picking time and the order waiting time in the staging area, with a 30-min time limit to preserve the integrity of the cold chain. Order picking under these conditions was rarely studied before, because they may be irrelevant when dealing with temperature-insensitive items but become critical in cold warehouses to prevent temperature abuse.

Allocation of the marketing budget has become increasingly challenging due to the diverse channel exposure to customers. This study aims to enhance global marketing knowledge by introducing an ensemble attribution model to optimize marketing budget allocation for online marketing channels. As empirical research, this study demonstrates the supremacy of the ensemble model over standalone models.

The transactional data set for car insurance from an Indian insurance aggregator is used in this empirical study. The data set contains information from more than three million platform visitors. A robust ensemble model is created by combining results from two probabilistic models, namely, the Markov chain model and the Shapley value. These results are compared and validated with heuristic models. Also, the performances of online marketing channels and attribution models are evaluated based on the devices used (i.e. desktop vs mobile).

Channel importance charts for desktop and mobile devices are analyzed to understand the top contributing online marketing channels. Customer relationship management-emailers and Google cost per click a paid advertising is identified as the top two marketing channels for desktop and mobile channels. The research reveals that ensemble model accuracy is better than the standalone model, that is, the Markov chain model and the Shapley value.

To the best of the authors’ knowledge, the current research is the first of its kind to introduce ensemble modeling for solving attribution problems in online marketing. A comparison with heuristic models using different devices (desktop and mobile) offers insights into the results with heuristic models.

Analysis of architectural space is commonly conducted by examining architectural drawings that project spatial information by means of walls and partitions. To capture the lived experience of space, which is richer than what we can see from drawings, a new method is proposed to quantify the cognitive dimension of space and re-present it as an audible format.

Using an urban vernacular house in Seoul as a case study, this research takes a syntactic approach to quantify one's changing perception through their movement from the main gate to the most private reception room. Based on Luigi Moretti's theory of hollow space, a new method is proposed to measure the level of spatial pressure exerted on a navigating body. The numerical data of spatial pressure are then converted to a sound using musical techniques of the chromatic scale and chorale textures.

Building on Moretti's abstract concept, it has been shown that a rule-based quantification of users' spatial perception is possible. In addition, unlike conventional approaches of treating architecture as a static entity, this study showed an alternative approach to represent it as a sequence of sensorial experience that can be readily converted to a sound of music.

This research developed a quantification method to measure the perception of pressure inside buildings by revisiting Luigi Moretti's theory proposed in 1952. It has been also demonstrated that the visual stimuli in space can be translated into an audible experience. This new method is applicable to a wide range of buildings including important historic architecture.