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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.

Culture is considered a critical aspect of social media usage. The purpose of this paper is to explore how cultures and languages influence multilingual users' cross-cultural information sharing patterns.

This study used a crowdsourcing survey with Amazon Mechanical Turk to collect qualitative and quantitative data from 355 multilingual users who utilize two or more languages daily. A mixed-method approach combined statistical, and cluster analysis with thematic analysis was employed to analyze information sharing patterns among multilingual users in the Chinese cultural context.

It was found that most multilingual users surveyed preferred to share in their first and second language mainly because that is what others around them speak or use. Multilingual users have more diverse sharing characteristics and are more actively engaged in social media. The results also provide insights into what incentives make multilingual users engage in social media to share information related to Chinese culture with the MOA model. Finally, the ten motivation factors include learning, entertainment, empathy, personal gain, social engagement, altruism, self-expression, information, trust and sharing culture. One opportunity factor is identified, which is convenience. Three ability factors are recognized consist of self-efficacy, habit and personality.

The findings are conducive to promoting the active participation of multilingual users in online communities, increasing global resource sharing and information flow and promoting the consumption of digital cultural content.

The purpose of this paper was to understand the difficulties related to asset management observed by experts in Brazilian organizations in light of the requirements outlined in the ISO 55001:2014 standard.

A survey was performed with asset management experts. The collected data were analyzed using frequency analysis, hierarchical cluster analysis and fuzzy technique for order preference by similarity to deal solution (TOPSIS).

Based on data analysis, the most critical difficulties observed were related to managing and controlling the impact of changes in the company that affect asset management objectives; to the committing to and supporting the asset management system by the top management of the organization; to manage the processes for dealing with risks and opportunities for the asset management system and plans, and correcting failures in asset performance; and to plan and conduct actions in an integrated manner to identify and minimize adverse impacts associated with the asset management system, and afterwards verifying their effectiveness.

The findings of this study have important theoretical and practical contributions, since they indicate the most critical points observed in asset management in Brazil, which can be used as a source for future research and by professionals to prioritize difficulties in future planning and develop action plans to overcome them. The step-by-step methodological approach presented in this study provides professionals and researchers with a replicable method of identifying potential asset management difficulties in a given specific reality.

We consider a joint optimization problem of product platform design and scheduling on unrelated additive/subtractive hybrid machines, and seek to find efficient solution approaches to solve such problem.

We propose a mathematical formulation for the problem of simultaneous product platform design and scheduling on unrelated additive/subtractive hybrid machines, and develop a simulated annealing-based hyper-heuristic algorithm with adjustable operator sequence length to solve the problem.

The simulated annealing-based hyper-heuristic algorithm with adjustable operator sequence length (SAHH-osla) that we proposed can be quite efficient in solving the problem of simultaneous product platform design and scheduling on unrelated additive/subtractive hybrid machines.

To the best of our knowledge, we are one of the first to consider both cost-related and time-related criteria for the problem of simultaneous product platform design and scheduling on unrelated additive/subtractive hybrid machines.

This study aims to examine the enablers of productivity of enterprise-level Agile development process using modified total interpretative structural modeling (TISM). The two main objectives of the current study are to determine the variables influencing enterprise-level agile development productivity and to develop modified TISM for the corresponding components.

To identify enablers of the productivity of enterprise-level agile software development process a literature review and opinions of domain experts were collected. A hierarchical relationship among variables that show direct and indirect influence is created using the modified TISM (M-TISM) technique with Cross Impact Matrix-Multiplication Applied to Classification analysis. This study examined and analyzed the relationships between the determinants within the enterprise using a M-TISM technique.

With the literature review, the study could identify ten enabling factors of the productivity of Agile development process at the enterprise level. Results depict that program increment (PI) planning and scalable backlog management, continuous integration and continuous delivery (CI/CD), agile release trains (ART), agile work culture, delivery excellence, lean and DevOps practices, value stream mapping (VMS), team skills and expertise, collaborative culture, agile coaching, customer engagement have an impact on the productivity of enterprise-level Agile development process. The results show that team collaboration, agile ways of working and customer engagement have a greater impact on productivity improvement for enterprise-level Agile development process.

The developed model is useful for organizations employing scaled Agile development processes in software development. This study provides a recommended listing of key enablers, that may enable productivity improvements in the Agile development process at the enterprise level. Strategists should focus on team collaboration and Agile project management. This study offers a modified TISM model to academicians to help them understand the effects of numerous variables on maintaining the productivity of an enterprise-level Agile. The identified characteristics and their hierarchical structure can help project managers during the execution of Agile projects at the enterprise level, more effectively, increasing their success and productivity.

The study addresses the gap in the literature by interpretative relationships between the identified enabling factors. The model validation is carried out by a panel of nine experts from several information technology organizations deploying Agile software development at the enterprise level. This unique method broadens the knowledge base in Agile software development at scale and provides project managers and practitioners with a practical foundation.

The purpose of this paper is to prepare a multifunctional nanocomposite that is slow-release and resistant to seawater corrosion and biofouling corrosion and to explore the synergistic effect between the two corrosion inhibitors.

The morphology, structure and release properties of CAP@HNTs, BTA@HNTs and CAP/BTA@HNTs were investigated by scanning electron microscopy, transmission electron microscopy, Fourier transform infrared spectroscopy, specific surface area analysis and UV spectrophotometry. The corrosion resistance and antimicrobial properties were investigated by electrochemical measurements and bioinhibition rate tests, and the synergistic effect between the two corrosion inhibitors was explored by X-ray photoelectron spectroscopy.

The CAP/BTA@HNTs are responsive to acidic environments and have significantly improved antibacterial and corrosion resistance compared with CAP@HNTs and BTA@HNTs. CAP and BTA have a positive synergistic effect on anticorrosion and antifouling.

Two types of inhibitors, anticorrosion and antifouling, were loaded into the same nanocontainer to prepare a slow-releasable and multifunctional nanocomposite with higher resistance to seawater corrosion and biocorrosion and to explore the synergistic effect of CAP and BTA on corrosion resistance.

With the rapid growth of the domestic lending industry, assessing whether the borrower of each loan is at risk of default is a pressing issue for financial institutions. Although there are some models that can handle such problems well, there are still some shortcomings in some aspects. The purpose of this paper is to improve the accuracy of credit assessment models.

In this paper, three different stages are used to improve the classification performance of LSTM, so that financial institutions can more accurately identify borrowers at risk of default. The first approach is to use the K-Means-SMOTE algorithm to eliminate the imbalance within the class. In the second step, ResNet is used for feature extraction, and then two-layer LSTM is used for learning to strengthen the ability of neural networks to mine and utilize deep information. Finally, the model performance is improved by using the IDWPSO algorithm for optimization when debugging the neural network.

On two unbalanced datasets (category ratios of 700:1 and 3:1 respectively), the multi-stage improved model was compared with ten other models using accuracy, precision, specificity, recall, G-measure, F-measure and the nonparametric Wilcoxon test. It was demonstrated that the multi-stage improved model showed a more significant advantage in evaluating the imbalanced credit dataset.

In this paper, the parameters of the ResNet-LSTM hybrid neural network, which can fully mine and utilize the deep information, are tuned by an innovative intelligent optimization algorithm to strengthen the classification performance of the model.

A maturity model for Industry 4.0 (I4.0 MM) with influencing factors is designed to address maturity issues in adopting Industry 4.0. Standardisation in I4.0 supports manufacturing industry transformation, forming reference architecture models (RAMs). This paper aligns key factors and maturity levels in I4.0 MMs with reputable I4.0 RAMs to enhance strategy for I4.0 transformation and implementation.

Three steps of alignment consist of the systematic literature review (SLR) method to study the current published high-quality I4.0 MMs, the taxonomy development of I4.0 influencing factors by adapting and implementing the categorisation of system theories and aligning I4.0 MMs with RAMs.

The study discovered that different I4.0 MMs lead to varied organisational interpretations. Challenges and insights arise when aligning I4.0 MMs with RAMs. Aligning MM levels with RAM stages is a crucial milestone in the journey toward I4.0 transformation. Evidence indicates that I4.0 MMs and RAMs often overlook the cultural domain.

Findings contribute to the literature on aligning capabilities with implementation strategies while employing I4.0 MMs and RAMs. We use five RAMs (RAMI4.0, NIST-SME, IMSA, IVRA and IIRA), and as a common limitation in SLR, there could be a subjective bias in reading and selecting literature.

To fully leverage the capabilities of RAMs as part of the I4.0 implementation strategy, companies should initiate the process by undertaking a thorough needs assessment using I4.0 MMs.

The novelty of this paper lies in being the first to examine the alignment of I4.0 MMs with established RAMs. It offers valuable insights for improving I4.0 implementation strategies, especially for companies using both MMs and RAMs in their transformation efforts.

For decades, consumer identification and motivation, either alone or jointly, have been essential constructs for behavioral researchers. The resultant output is significant in terms of both quality and quantity. However, at a deeper level, a lack of conceptual clarity in the relationship between these constructs has led to theoretical and practical irregularities, which this study aims to address.

An online questionnaire was distributed to sport consumers aged over 18 participating in an online panel, prompted 293 completed responses. Structural equations modeling was used to examine the data.

Findings show that identification mediates the effects of intrinsic and extrinsic motivation on sport supporters’ loyalty and explain 90% of the variance in that construct. In addition, identification mediates the adverse effects of extrinsic motivation on loyalty and strengthens loyalty when levels of satisfaction decline.

This study extends previous work by providing a theoretical perspective that clarifies the relationship between motivation and consumer identification; deepens theory by empirically observing the relationship at different levels of consumer satisfaction; and presents a parsimonious, valid and reliable method that managers can leverage to strengthen sport supporters’ loyalty.

The adoption of mobile payment remains low in certain regions, highlighting the need to identify the factors that enable and inhibit its adoption. This study aims to address this gap by investigating the role of information security, loss aversion and the moderating influence of the herd effect on Inertia and behavioral intentions in the adoption of mobile payment systems.

A structural equation model was developed and tested with 332 valid questionnaires to examine the proposed hypotheses.

The empirical results reveal that information security plays a significant role as an enabler, while loss aversion acts as an inhibitor of mobile payment adoption. Furthermore, the study uncovers the moderating influence of the herd effect on the relationship between Inertia and behavioral intentions.

This study was conducted in a specific region and may not be generalizable to other regions. Future studies could expand the sample size and scope to enhance the external validity of the findings.

This study offers practical implications for mobile payment service providers. Understanding the key enabling and inhibiting factors identified in this study can guide providers in designing and improving their services. Strengthening information security measures can help build trust among potential adopters, while offering incentives can mitigate the impact of loss aversion and encourage early adoption.

The findings of this study have social implications as they contribute to promoting the adoption of mobile payment systems. Increased adoption can enhance financial inclusion and stimulate economic development.

This study provides novel insights into the enabling and inhibiting factors of mobile payment adoption and highlights the moderating role of the herd effect. By shedding light on the influence of social norms on individual behavior in the context of mobile payment adoption, this study contributes to the existing literature and advances our understanding of this phenomenon.