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This paper explores how conflicting institutional logics shape the behaviors of macro- and micro-level actors in their use of a calculative practice. Thereby, this paper explains how quantification can undermine the intended purpose of a governance system based on a single number.

The study draws upon the literature on calculative practices and institutional logics to present the case of how a single number—specifically the conversion factor for Atlantic Cod, established by macro-level actors for the purposes of governance within the Norwegian fishing industry—is interpreted and used by micro-level actors in the industry. The study is based on documents, field observations and interviews with fishers, landing facilities, and control authorities.

The use of the conversion factor, while intended to protect fish stock and govern industry actions, does not always align with the institutional logics of micro-level actors. Especially during the winter season, these actors may seek to serve their interests, leading to potential system gaming. The reliance on a single number that overlooks seasonal nuances can motivate unintended behaviors, undermining the governance system’s intentions.

Integrating the literature on calculative practices with an institutional logics perspective, this study offers novel insights into the challenges of using quantification for the governance of complex industries. In particular, the paper reveals that when the logics of macro- and micro-level actors conflict in a single-number governance system, unintended outcomes arise due to a domination of the macro-level logics.

This paper covers the development of a novel defect model for concrete highway bridges. The proposed defect model is intended to facilitate the identification of bridge’s condition information (i.e. defects), improve the efficiency and accuracy of bridge inspections by supporting practitioners and even machines with digitalised expert knowledge, and ultimately automate the process.

The research design consists of three major phases so as to (1) categorise common defect with regard to physical entities (i.e. bridge element), (2) establish internal relationships among those defects and (3) relate defects to their properties and potential causes. A mixed-method research approach, which includes a comprehensive literature review, focus groups and case studies, was employed to develop and validate the proposed defect model.

The data collected through the literature and focus groups were analysed and knowledge were extracted to form the novel defect model. The defect model was then validated and further calibrated through case study. Inspection reports of nearly 300 bridges in China were collected and analysed. The study uncovered the relationships between defects and a variety of inspection-related elements and represented in the form of an accessible, digitalised and user-friendly knowledge model.

The contribution of this paper is the development of a defect model that can assist inexperienced practitioners and even machines in the near future to conduct inspection tasks. For one, the proposed defect model can standardise the data collection process of bridge inspection, including the identification of defects and documentation of their vital properties, paving the path for the automation in subsequent stages (e.g. condition evaluation). For another, by retrieving rich experience and expert knowledge which have long been reserved and inherited in the industrial sector, the inspection efficiency and accuracy can be considerably improved.

Automation of detecting cracked surfaces on buildings or in any industrially manufactured products is emerging nowadays. Detection of the cracked surface is a challenging task for inspectors. Image-based automatic inspection of cracks can be very effective when compared to human eye inspection. With the advancement in deep learning techniques, by utilizing these methods the authors can create automation of work in a particular sector of various industries.

In this study, an upgraded convolutional neural network-based crack detection method has been proposed. The dataset consists of 3,886 images which include cracked and non-cracked images. Further, these data have been split into training and validation data. To inspect the cracks more accurately, data augmentation was performed on the dataset, and regularization techniques have been utilized to reduce the overfitting problems. In this work, VGG19, Xception and Inception V3, along with Resnet50 V2 CNN architectures to train the data.

A comparison between the trained models has been performed and from the obtained results, Xception performs better than other algorithms with 99.54% test accuracy. The results show detecting cracked regions and firm non-cracked regions is very efficient by the Xception algorithm.

The proposed method can be way better back to an automatic inspection of cracks in buildings with different design patterns such as decorated historical monuments.

The present work aimed to present the perception of Tunisian professionals towards companies engaged in social responsibility practices and describe the tax evasion strategies of socially responsible Tunisian companies following the coronavirus disease 2019 (COVID-19) pandemic (COVID-19) shock.

A survey was sent to 119 Tunisian tax administration auditors. Data analysis methods principal component analysis (PCA) and regression analysis were used. The data were collected through a questionnaire after the general containment of Tunisia from September 2020 to February 2021. These quantitative data were analysed using processing software (STATA).

Professionals of the tax authorities, particularly those in charge of the audit mission, aim for corporate profitability from the perspective of stakeholders that seek to integrate ethics and social responsibility into companies and consider employee morale a top priority. The results show that highly ethical and socially responsible professionals are far from practising aggressive strategies. Thus, an auditor from the tax administration is far from engaging in social responsibility to justify fraudulent acts. During the COVID-19 period, the role of these professionals was to prevent and detect fraud in the tax sector to fight corruption and investigate taxes based on sound regulations.

The results are consistent with optimal taxation theory, which postulates that a tax system should be chosen to maximise a social welfare function subject to a set of constraints. Professionals seek to make taxation much simpler for taxpayers by providing advice and consultation to manage tax obligations. The minimisation of tax or the play of tax values requires expertise in the field to respect legal constraints. Therefore, these professionals play a crucial role in tax collection, as the professionals' advice and suggestions can influence taxpayers' decision-making.

In recent years, academic researchers, policy makers and the public have become increasingly interested in corporate tax evasion behaviour. At the same time, companies are under increasing pressure to integrate CSR into the companies' decision-making processes, which has led to increased academic interest in CSR. Opportunistic tax minimisation reduces state resources and funds needed for government programmes to improve the social welfare of the entire community. This study represents an overriding concern not only for legal and tax authorities and companies, but also for shareholders and stakeholders.

The authors' study contributes to the existing literature by determining the state of play on corporate social responsibility (CSR) practices amongst Tunisian tax authorities' professionals. In Tunisia, an executive of the tax authorities in charge of the verification mission is required to verify the proper application of the accounting and tax legislation in force, follow up on tax control operations on declared taxes and validate the sincerity of the accounts. This study focussed on the tax evasion of companies engaged in social responsibility practices according to the judgements of Tunisian tax authorities' auditors during the global COVID-19 pandemic.

According to regulations, aircraft must be in an airworthy condition before they can be operated. To ensure airworthiness, they must be maintained by an approved component maintenance organisation. This study is aimed to identify potential errors that may arise during the final inspection and certification process of aircraft components, categorise them, determine their consequences and quantify the associated risks. Any removed aircraft components must be sent to an approved aircraft component maintenance organisation for further maintenance and issuance of European Union Aviation Safety Agency (EASA) Form 1. Thereafter, a final inspection and certification process must be conducted by certifying staff to receive an EASA Form 1. This process is crucial because any errors during this stage can result in the installation of unsafe components in an aircraft.

The Systematic Human Error Reduction and Prediction Approach (SHERPA) method was used to identify potential errors. This method involved a review of the procedures of three maintenance organisations, individual interviews with ten subject matter experts and a consensus group of 14 certifying staff from different maintenance organisations to achieve the desired results.

In this study, 39 potential errors were identified during the final inspection and certification process. Furthermore, analysis revealed that 48.7% of these issues were attributed to checking errors, making it the most common type of error observed.

This study pinpoints the potential errors in the final inspection and certification of aircraft components. It offers maintenance organisations a roadmap to assess procedures, implement preventive measures and reduce the likelihood of these errors.

This study aims to explore motives behind teachers' and students' use of translanguaging and how they use it in Tanzanian public secondary school classrooms.

Data were collected using interviews and non-participant observations.

The findings indicate that translanguaging was used to facilitate content comprehension, promote classroom interaction and increase students' motivation to learn. Translanguaging was implemented using three strategies: paraphrasing an English text into Kiswahili, translating an English text into its Kiswahili equivalent and word-level translanguaging.

By highlighting the motivations for translanguaging and corresponding strategies associated with translanguaging pedagogy in the Tanzanian context, this study has significant practical implications for teachers and students to showcase their linguistic and multimodal knowledge, while fostering a safe learning space that relates to students' daily experiences.

The study offers new insights into previous research on the role of language-supportive pedagogy appropriate for teachers and students working within bi-/multilingual education settings.