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In the age of Industry 4.0 (I4.0), digital technologies (DTs) and the technologies' application in supply chain activities have become more important. On the other hand, global pressures for corporate social responsibility in the sustainable production of products are increasing. Accordingly, the purpose of this research is to develop and validate an instrument to measure the impact of digital supply chain (DSC) activities on the sustainable performance of manufacturing companies.

In the first step, through an in-depth study of the relevant literature, a conceptual model was developed and a questionnaire containing 51 indicators was designed. The questionnaire was distributed among 356 top managers and experts of the Iranian manufacturing companies, whereby finally 233 sound questionnaires were returned, yielding a response rate of about 64%. Exploratory factor analysis (EFA) was used to identify constructs and sub-constructs and the relationship between them was investigated using the partial least squares structural equation model (PLS-SEM) method.

Based on the obtained results, three constructs were identified, namely main activities (including sub-constructs: digital supplier, digital manufacturing, digital logistics and innovation and digital customer), support activities (with sub-constructs digital performance, DT and digital human resources) and sustainable performance (with sub-constructs of economic sustainability, environmental sustainability and social sustainability). The designed tool has excellent psychometric properties. The values of t-statistic = 11.07 and β = 0.602 indicate the positive impact of the DSC main activities on sustainable performance. Similarly, t = 2.42 and β = 0.149 prove that DSC support activities have a positive impact on sustainable performance. Also, based on the obtained values (t = 13.16 and β = 0.629), support activities have a significant impact on the main activities of the DSC. Finally, based on the calculated goodness-of-fit (GoF) index value (0.522), this paper concluded that the proposed model has high credibility.

Validation of the model is based on the answers received from the Iranian manufacturing companies. Therefore, caution should be taken regarding the generalizability of the results.

The proposed model presents a holistic view of the application of DTs in the supply chain and the DTs' impact on sustainable performance which might help manufacturing companies, particularly the Iranian companies to obtain a broader knowledge of the importance of DTs and DTs' usage toward responding to the challenges of today's complex business environment.

This study is among the first of the study's kind that attempts to develop and validate an instrument to measure the impact of DSC activities on the sustainable performance of manufacturing companies.

A number of recent artificial intelligence (AI)-enabled technologies, including summarisers, paraphrasers and the cutting-edge chatbots not only have outstanding potentials in modern educational systems but also could lead to a dramatic paradigm shift in the whole education process. This study aims to explore the factors that shape the academic community’s desire and intention to use AI conversational chatbot technology, with a particular focus on the leading ChatGPT.

This study uses a mixed method approach to explore the educators’ adoption of chatbots through an empirically validated model. The model, known as the “Educators’ Adoption of ChatGPT”, was developed by integrating the theoretical foundations of both the Unified Theory of Acceptance and Use of Technology and Status Quo Bias (SQB) frameworks, as well as insights gathered from interviews. The relationships within this model were then tested using a quantitative approach. The partial least squares-structural equation modelling method was used to analyse 243 valid survey responses.

The outcomes of the analysis indicated that perceived educators’ effort expectancy, educators’ autonomous motivation, perceived learners’ AI competency, perceived educators’ competency, innovative behaviour towards technological agility and perceived students’ engagement are significant determinants of educators’ intention to use chatbots. In contrast, perceived unfair evaluation of students, perceived students’ overreliance and perceived bias/inaccuracies were shown to have significant impacts on the resistance to use the technology, which typically implies a negatively significant influence on the educators’ use intention. Interestingly, perceived fraudulent use of ChatGPT was proven insignificant on the resistance to use chatbots.

This study makes a significant contribution to the field of educational technology by filling the gap in research on the use and acceptance of AI-enabled assistants in education. It proposes an original, empirically validated model of educator adoption, which identifies the factors that influence educators’ willingness to use chatbots in higher education and offers valuable insights for practical implementation.

This study aims to focus on the preparation and characterization of the silver nanowire (AgNWs), as well as their application as antimicrobial and antivirus activities either with incorporation on the waterborne coating formulation or on their own.

Prepared AgNWs are characterized by different analytical instruments, such as ultraviolet-visible spectroscope, scanning electron microscope and X-ray diffraction spectrometer. All the paint formulation's physical and mechanical qualities were tested using American Society for Testing and Materials, a worldwide standard test procedure. The biological activities of the prepared AgNWs and the waterborne coating based on AgNWs were investigated. And, their effects on pathogenic bacteria, antioxidants, antiviral activity and cytotoxicity were also investigated.

The obtained results of the physical and mechanical characteristics of the paint formulation demonstrated the formulations' greatest performance, as well as giving good scrub resistance and film durability. In the antimicrobial activity, the paint did not have any activity against bacterial pathogen, whereas the AgNWs and AgNWs with paint have similar activity against bacterial pathogen with inhibition zone range from 10 to 14 mm. The development of antioxidant and cytotoxicity activity of the paint incorporated with AgNWs were also observed. The cytopathic effects of herpes simplex virus type 1 (HSV-1) were reduced in all three investigated modes of action when compared to the positive control group (HSV-1-infected cells), suggesting that these compounds have promising antiviral activity against a wide range of viruses, including DNA and RNA viruses.

The new waterborne coating based on nanoparticles has the potential to be promising in the manufacturing and development of paints, allowing them to function to prevent the spread of microbial infection, which is exactly what the world requires at this time.

Using a hierarchical component model conceptualization, this study aims to investigate the moderating role of perceived corporate social responsibility (CSR) dimensions (i.e., economic, environmental and social) on the impact of brand awareness towards consumer relationship intention.

This study used a two-stage disjoint approach of partial least squares structural equation modeling using data from 325 respondents based in a Southeast Asian region. The case of ecolabel brands was chosen as the context of the investigation.

The results suggest that brand awareness and perceived CSR dimensions positively impact consumer relationship intention. It was also observed that perceived CSR dimensions moderate the relationship between brand awareness and relationship intention. Consumers with more favorable sentiments of economic, environmental and social dimensions as reflected by the firms' CSR programs exhibit a higher degree of relationship intention.

Using the stakeholder theory as well as the brand value chain framework, this study adds to the literature regarding the significance of perceived CSR dimensions to better build and maintain relationships with the targeted customers of an environmentally friendly product. CSR strategies should be emphasized for relevant companies in terms of the economic, environmental and social aspects. According to this research, customer views about the three CSR initiative characteristics may act as a moderator in the interactions between consumer awareness and relationship intention.

CSR may be utilized in addition to traditional marketing communication to represent the firm's unique value proposition in the market. It is vital to create a CSR program that combines economic, environmental and social factors. Firms may collaborate with various stakeholders to ensure that their CSR initiatives include three elements.

This study adds to the literature on the moderating role of perceived CSR dimensions on the relationship of consumer brand awareness and relationship intention using the theoretical lens of the stakeholder theory and the brand value chain.

Crack detection of pavement is a critical task in the periodic survey. Efficient, effective and consistent tracking of the road conditions by identifying and locating crack contributes to establishing an appropriate road maintenance and repair strategy from the promptly informed managers but still remaining a significant challenge. This research seeks to propose practical solutions for targeting the automatic crack detection from images with efficient productivity and cost-effectiveness, thereby improving the pavement performance.

This research applies a novel deep learning method named TransUnet for crack detection, which is structured based on Transformer, combined with convolutional neural networks as encoder by leveraging a global self-attention mechanism to better extract features for enhancing automatic identification. Afterward, the detected cracks are used to quantify morphological features from five indicators, such as length, mean width, maximum width, area and ratio. Those analyses can provide valuable information for engineers to assess the pavement condition with efficient productivity.

In the training process, the TransUnet is fed by a crack dataset generated by the data augmentation with a resolution of 224 × 224 pixels. Subsequently, a test set containing 80 new images is used for crack detection task based on the best selected TransUnet with a learning rate of 0.01 and a batch size of 1, achieving an accuracy of 0.8927, a precision of 0.8813, a recall of 0.8904, an F1-measure and dice of 0.8813, and a Mean Intersection over Union of 0.8082, respectively. Comparisons with several state-of-the-art methods indicate that the developed approach in this research outperforms with greater efficiency and higher reliability.

The developed approach combines TransUnet with an integrated quantification algorithm for crack detection and quantification, performing excellently in terms of comparisons and evaluation metrics, which can provide solutions with potentially serving as the basis for an automated, cost-effective pavement condition assessment scheme.