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The literature on the influence of audit committees (AC) and cosmetic accounting (CA) is scarce. AC plays a unique and vital role in boosting earnings reliability in countries with weaker application of accounting standards or weaker legal protection for investors. AC, therefore, are considered to be one of the essential tools available to directors in supervising management decisions regarding financial reporting. This paper aims to examine the influence of AC characteristics (ACC) on CA and how this relationship is moderated by the audit fee.

This study used probit regression to analyze 1,218 firm-year observations of listed companies in Tehran Stock Exchange from 2014 to 2020.

The results show that AC financial accounting expertise, AC independence, female AC membership and AC tenure were negatively related to CA. The negative relationship is highly pronounced when a firm incurs higher audit fees, and audit fees moderate the relationship between ACC and CA. Results for the robustness checks show that only AC independence was significant, and the results of other characteristics were not significant.

This research was conducted in an Iranian setting where the formation of ACs is on the verge of regulation; therefore, the data used for the study only contains the seven-year period of ACs’ statutory activity. In addition, a lack of consensus on the precise measures of an AC’s effectiveness could be considered as a restrictive factor.

The findings provide an initial insight into the effect AC on CA and moderating effect of audit fee on the relationship between ACC and CA.

This largely conceptual study aims to draw from the author’s experience of conversations with Svalbard’s educators, lessons for international higher education institutions’ engagement with climate change education and thinking for non-specialists.

In situ discussions with Svalbard’s educators informed the theoretical work of the author towards the development of conceptual conclusions. The theoretical frame used – “Red Biocentrism” – draws on both radical left and green thought to posit an emplaced, materialist understanding of author’s, participants’ and place’s intra-related contributions.

That, insofar as universities represent nodes in an ethical ecology, they have a capacity to realise that which is obvious in Svalbard – their role as embassies for their learning places, generative of spokespeople or ambassadors.

There is sparse published research into the work of Svalbard’s climate educators, as a pedagogical project undertaken under such extreme and rapidly changing environmental conditions. This study represents the first to reflect on what can be learnt from the educators of Svalbard by Universities elsewhere.

The purpose of this article is to provide information about interactions between pink-pigmented facultative methylotroph (PPFM) organisms and plants, their molecular mechanisms of methylotrophic metabolism, application of PPFMs in agriculture, biotechnology and bioremediation and also to explore lacuna in PPFMs research and direction for future research.

Research findings on PPFM organisms as potent plant growth promoting organisms are discussed in the light of reports published by various workers. Unexplored field of PPFM research are detected and their application as a new group of biofertilizer that also help host plants to overcome draught stress in poorly irrigated crop field is suggested.

PPFMs are used as plant growth promoters for improved crop yield, seed germination capacity, resistance against pathogens and tolerance against drought stress. Anti-oxidant and UV resistant properties of PPFM pigments protect the host plants from strong sunshine. PPFMs have excellent draught ameliorating capacity.

To meet the ever increasing world population, more and more barren, less irrigated land has to be utilized for agriculture and horticulture purpose and use of PPFM group of organisms due to their draught ameliorating properties in addition to their plant growth promoting characters will be extremely useful. PPFMs are also promising candidates for the production of various industrially and medicinally important enzymes and other value-added products. Wider application of this ecofriendly group of bacteria will reduce crop production cost thus improving economy of the farmers and will be a greener alternative of hazardous chemical fertilizers and fungicides.

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Intent detection (ID) and slot filling (SF) are two important tasks in natural language understanding. ID is to identify the main intent of a paragraph of text. The goal of SF is to extract the information that is important to the intent from the input sentence. However, most of the existing methods use sentence-level intention recognition, which has the risk of error propagation, and the relationship between intention recognition and SF is not explicitly modeled. Aiming at this problem, this paper proposes a collaborative model of ID and SF for intelligent spoken language understanding called ID-SF-Fusion.

ID-SF-Fusion uses Bidirectional Encoder Representation from Transformers (BERT) and Bidirectional Long Short-Term Memory (BiLSTM) to extract effective word embedding and context vectors containing the whole sentence information respectively. Fusion layer is used to provide intent–slot fusion information for SF task. In this way, the relationship between ID and SF task is fully explicitly modeled. This layer takes the result of ID and slot context vectors as input to obtain the fusion information which contains both ID result and slot information. Meanwhile, to further reduce error propagation, we use word-level ID for the ID-SF-Fusion model. Finally, two tasks of ID and SF are realized by joint optimization training.

We conducted experiments on two public datasets, Airline Travel Information Systems (ATIS) and Snips. The results show that the Intent ACC score and Slot F1 score of ID-SF-Fusion on ATIS and Snips are 98.0 per cent and 95.8 per cent, respectively, and the two indicators on Snips dataset are 98.6 per cent and 96.7 per cent, respectively. These models are superior to slot-gated, SF-ID NetWork, stack-Prop and other models. In addition, ablation experiments were performed to further analyze and discuss the proposed model.

This paper uses word-level intent recognition and introduces intent information into the SF process, which is a significant improvement on both data sets.

The purpose of this study is to investigate the association between three corporate governance (CG) idiosyncrasies, namely audit committee characteristics, external audit quality (AQ), board diversity and firm performance (FP) in the South Asian Association for Regional Cooperation (SAARC) nations.

The study used a sample of 200 listed nonfinancial firms in the SAARC nations from 2012 to 2021. The System Generalized Method of Moment model was applied to the data consisting of 2000 firm-year observations. The Generalized Estimating Equation population-averaged model was also employed for added robustness. The study employed Tobin's Q as the measure of FP.

The findings revealed that amongst the CG variables tested, external AQ exhibited a significantly positive relationship with Tobin's Q. Significant negative influences on FP have been demonstrated by the variables of audit committee meeting and board's independence. Furthermore, gender diversity, CEO duality, audit committee strength and independence failed to record any significant association.

This study is one of the first to investigate the association between CG idiosyncrasies and FP in the SAARC nations. The study findings have important implications for policymakers and regulators in the region.

This paper shows how Bruno Latour’s novel work and methodological approach can enrich management and organization studies, accounting and science and technology studies on what it takes to redesign sustainable societal infrastructures. Latour’s notions of trials of strength, macro-actor and design as redesign are used in a case study to describe and analyse how the laboratory becomes decisive in negotiating the bridge design and project budget to the benefit of a more sustainable transport infrastructure.

Latour’s notion of the detective-author is used to research and write a longitudinal qualitative case study that reconstructs the project processes and chain of related events by following the actors/actants.

The case analysis shows how a project design becomes an emerging powerful macro-actor through the mobilization of laboratory simulations and calculations. The role of the project budget changes; from a strong supporting role as input to a decision option in favour of a cheaper stayed bridge to a weak role as an output from a process of redesign supporting a much larger, costlier and more sustainable suspension bridge.

We use Latour’s methodological approach to engage primarily in detailed process descriptions to go beyond the often-pointless call for further theory development and to rather account for what is at work in specific situations. Latour’s notions of redesign as an outcome from trials of strength, we consider a useful approach to further our understanding since it also takes account of the distributed knowledge production that is integral to the actors’ cognitions and recognitions. Relatedly, the specific Latourian notion of redesign opens up new avenues for researching the more or less powerful role accounting devices such as a project budget can play in valuing, supporting and/or undermining the design of sustainable societal infrastructures.

The compliance checking of Building Information Modeling (BIM) models is crucial throughout the lifecycle of construction. The increasing amount and complexity of information carried by BIM models have made compliance checking more challenging, and manual methods are prone to errors. Therefore, this study aims to propose an integrative conceptual framework for automated compliance checking of BIM models, allowing for the identification of errors within BIM models.

This study first analyzed the typical building standards in the field of architecture and fire protection, and then the ontology of these elements is developed. Based on this, a building standard corpus is built, and deep learning models are trained to automatically label the building standard texts. The Neo4j is utilized for knowledge graph construction and storage, and a data extraction method based on the Dynamo is designed to obtain checking data files. After that, a matching algorithm is devised to express the logical rules of knowledge graph triples, resulting in automated compliance checking for BIM models.

Case validation results showed that this theoretical framework can achieve the automatic construction of domain knowledge graphs and automatic checking of BIM model compliance. Compared with traditional methods, this method has a higher degree of automation and portability.

This study introduces knowledge graphs and natural language processing technology into the field of BIM model checking and completes the automated process of constructing domain knowledge graphs and checking BIM model data. The validation of its functionality and usability through two case studies on a self-developed BIM checking platform.

Existing performance assessment frameworks, such as the Sustainable Development Goals (SDGs), struggle to incorporate diverse voices and representations of heterogeneous contexts. Cities, in particular, present a challenging context for sustainability performance assessment as they pursue new forms of governance based on the multiplicity of actors and inter-organisational collaboration. This study explores how sustainability performance accounts are created at the urban level within collaborative forms of governance and amidst the plethora of available devices for performance assessment.

This study adopts a case study approach. Qualitative methods are mobilised to study a large European project focused on the urban transition to a circular economy in six participating cities. The construction of sustainability performance accounts is studied via the Actor-Network Theory lens.

The study highlights that when it comes to sustainability assessment in city initiatives, existing performance assessment devices are adapted and modified to fit local needs and other sources of performance information are spontaneously mobilised to address the different dimensions of sustainability.

This study contributes to the public sector accounting literature by explaining the process of modifying existing devices for performance assessment to allow for the co-creation of accounts and by illuminating the spontaneous way in which performance information is generated and combined.

This paper aims to shed light on the relationship between audit committee attributes and integrated reporting quality (IRQ).

Data on a sample of 360 European firms selected from the STOXX Europe 600 index between 2010 and 2021 were used to test the model based on multiple regression for panel data to analyze the effect of audit committee attributes on IRQ. This paper considers generalized least squares (GLS) estimation for panel data models.

The findings of this study confirm expectations concerning the impact of audit committee attributes on the IRQ. Indeed, audit committee independence and meetings have a significant positive impact on IRQ. However, no significant association is found between financial expertise and IRQ.

The findings of this paper have significant implications for policymakers, who, through proper legislation, should encourage the formation of larger audit committees and ones with a higher percentage of independent members. They should also establish a minimum number of audit committee meetings per year. These regulations, which aim to increase the efficacy of audit committees’ supervisory and monitoring tasks, would promote corporate transparency and improve IRQ.

This study supports the existing literature. First, it expands the scientific debate on IRQ. Second, unlike previous studies, which used more subjective methods to measure the degree of integrated reporting (IR), this study relied on the CGVS variable from the DataStream ASSET 4 Database. Third, the research is novel because it indicates the crucial role of internal assurance mechanisms in wide managerial reporting practices in European companies. The sample consisted of European firms only, whereas previous studies used a global sample. Finally, this study is based on recent data (2010–2021), while other studies covered the period between 2008 and 2013.

For a better understanding of the preferences and differences of young consumers in emerging wine markets, this study aims to propose a clustering method to segment the super-new generation wine consumers based on their sensitivity to wine brand, origin and price and then conduct user profiles for segmented consumer groups from the perspectives of demographic attributes, eating habits and wine sensory attribute preferences.

We first proposed a consumer clustering perspective based on their sensitivity to wine brand, origin and price and then conducted an adaptive density peak and label propagation layer-by-layer (ADPLP) clustering algorithm to segment consumers, which improved the issues of wrong centers' selection and inaccurate classification of remaining sample points for traditional DPC (DPeak clustering algorithm). Then, we built a consumer profile system from the perspectives of demographic attributes, eating habits and wine sensory attribute preferences for segmented consumer groups.

In this study, 10 typical public datasets and 6 basic test algorithms are used to evaluate the proposed method, and the results showed that the ADPLP algorithm was optimal or suboptimal on 10 datasets with accuracy above 0.78. The average improvement in accuracy over the base DPC algorithm is 0.184. As an outcome of the wine consumer profiles, sensitive consumers prefer wines with medium prices of 100–400 CNY and more personalized brands and origins, while casual consumers are fond of popular brands, popular origins and low prices within 50 CNY. The wine sensory attributes preferred by super-new generation consumers are red, semi-dry, semi-sweet, still, fresh tasting, fruity, floral and low acid.

Young Chinese consumers are the main driver of wine consumption in the future. This paper provides a tool for decision-makers and marketers to identify the preferences of young consumers quickly which is meaningful and helpful for wine marketing.

In this study, the ADPLP algorithm was introduced for the first time. Subsequently, the user profile label system was constructed for segmented consumers to highlight their characteristics and demand partiality from three aspects: demographic characteristics, consumers' eating habits and consumers' preferences for wine attributes. Moreover, the ADPLP algorithm can be considered for user profiles on other alcoholic products.