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This paper aims to provide an overview of the current manufacturing methods for three-dimensional textile preforms while providing experimental data on the emerging techniques of combining yarn interlocking with yarn interlooping.

The paper describes the key textile technologies used for composite manufacture: braiding, weaving and knitting. The various textile preforming methods are suited to different applications; their capabilities and end performance characteristics are analysed.

Such preforms are used in composites in a wide range of industries, from aerospace to medical and automotive to civil engineering. The paper highlights how the use of knitting technology for preform manufacture has gained wider acceptance due to its flexibility in design and shaping capabilities. The tensile properties of glass fibre knit structures containing inlay yarns interlocked between knitted loops are given, highlighting the importance of reinforcement yarns.

The future trends of reinforcement yarns in knitted structures for improved tensile properties are discussed, with initial experimental data.

This paper aims to introduce a method to couple truss finite elements to the material point method (MPM). It presents modeling reinforced material using MPM and describes how to consider the bond behavior between the reinforcement and the continuum.

The embedded approach is used for coupling reinforcement bars with continuum elements. This description is achieved by coupling continuum elements in the background mesh to the reinforcement bars, which are described using truss- finite elements. The coupling is implemented between the truss elements and the continuum elements in the background mesh through bond elements that allow for freely distributed truss elements independent of the continuum element discretization. The bond elements allow for modeling the bond behavior between the reinforcement and the continuum.

The paper introduces a novel method to include the reinforcement bars in the MPM applications. The reinforcement bars can be modeled without any constraints with a bond-slip constitutive model being considered.

As modeling of reinforced materials is required in a wide range of applications, a method to include the reinforcement into the MPM framework is required. The proposed approach allows for modeling reinforced material within MPM applications.

With the help of basic physics, the application of computer algorithms in the form of recent advances such as machine learning and neural networking in textile Industry has been discussed in this review. Scientists have linked the underlying structural or chemical science of textile materials and discovered several strategies for completing some of the most time-consuming tasks with ease and precision. Since the 1980s, computer algorithms and machine learning have been used to aid the majority of the textile testing process. With the rise in demand for automation, deep learning, and neural networks, these two now handle the majority of testing and quality control operations in the form of image processing.

The state-of-the-art of artificial intelligence (AI) applications in the textile sector is reviewed in this paper. Based on several research problems and AI-based methods, the current literature is evaluated. The research issues are categorized into three categories based on the operation processes of the textile industry, including yarn manufacturing, fabric manufacture and coloration.

AI-assisted automation has improved not only machine efficiency but also overall industry operations. AI's fundamental concepts have been examined for real-world challenges. Several scientists conducted the majority of the case studies, and they confirmed that image analysis, backpropagation and neural networking may be specifically used as testing techniques in textile material testing. AI can be used to automate processes in various circumstances.

This research conducts a thorough analysis of artificial neural network applications in the textile sector.

The aim of this study was to identify interventions for and mechanisms of the digital transformation of purchasing and supply management (PSM) processes. The digital transformation of tactical and operational PSM processes has often progressed slowly despite the solid knowledge of advanced technologies.

This study used a qualitative exploratory approach based on 14 interviews with PSM executives from firms that are continuously working toward using advanced technologies in their PSM processes but have not yet gained full strategic benefits from digital transformation.

This study formulates five propositions regarding interventions and mechanisms that can positively influence the digital transformation of PSM processes. The main intervention in this regard is the renewal of data infrastructure, including platforms. PSM-related data should meet needs from both tactical and operational viewpoints. When applications serve as a source of data, they support digital transformation. Mechanisms such as supplier measurement and process improvement are outcomes of the digital transformation of PSM processes.

This study highlights the importance of common data sets for tactical and operational purchasing. These purchasing data should be owned and served by a cross-functional team. To create this interoperability, a firm needs global governance of open standards.

This study makes a theoretical contribution to the discussion of what kind of interventions positively influence on the digital transformation of PSM processes. Specifically, this study explains the integration needs of data and applications.

This study is the maiden attempt to understand the effect of specific human resource practices (HRPs) on employee retention (ER) with the mediation of job satisfaction (JS) and moderation of work experience (WE) and job hopping (JH) in the context of the textile industry.

This study adopted a quantitative methodology and applied quota sampling to gather data from employees (n = 365) of leading textile companies in India. The conceptual model and hypotheses were tested with the help of Partial Least Squares-Structural Equation Modelling (PLS-SEM).

The findings of a path analysis revealed that compensation and performance appraisal (CPA) have the highest impact on JS followed by employee work participation (EWP). On the other hand, EWP had the highest impact on ER followed by grievance handling (GRH). The study revealed that JS significantly mediates between HRPs like CPA and ER. During Multi-group analysis (MGA) it was found that the importance of EWP and health and safety (HAS) was more in employee groups with higher WE, but it was the opposite in the case of CPA. In the case of JH behavior, the study observed that EWP leads to JS in loyal employees. Similarly, JS led to ER, and the effect was more pronounced for loyal employees.

In the context of the Indian textile industry, this work is the first attempt to comprehend how HRPs affect ER. Secondly, it confirmed that JS is not a guaranteed mediator between HRPs and ER, it could act as an insignificant, partial or full mediator. Additionally, this study establishes the moderating effects of WE and JH in the model through multigroup analysis.

Organizations across industries are increasingly using Artificial Intelligence (AI) systems to support their innovation processes, supply chains, marketing and sales and other business functions. Implementing AI, firms report efficiency gains from automation and enhanced decision-making thanks to more relevant, accurate and timely predictions. By exposing the benefits of digitizing everything, COVID-19 has only accelerated these processes. Recognizing the growing importance of AI and its pervasive impact, this chapter defines the “social value of AI” as the combined value derived from AI adoption by multiple stakeholders of an organization. To this end, we discuss the benefits and costs of AI for a business-to-business (B2B) firm and its internal, external and societal stakeholders. Being mindful of legal and ethical concerns, we expect the social value of AI to increase over time as the barriers for adoption go down, technology costs decrease, and more stakeholders capture the value from AI. We identify the contributions to the social value of AI, by highlighting the benefits of AI for different actors in the organization, business consumers, supply chain partners and society at large. This chapter also offers future research opportunities, as well as practical implications of the AI adoption by a variety of stakeholders.

The purpose of this study was to investigate the role of human resource management and employee job satisfaction in predicting organizational commitment in the Saudi Arabian banking sector.

For the purpose of this study, quantitative survey research was employed. The independent variables are human resource management and employee job satisfaction, while the dependent variable is organizational commitment.

Human resource management correlated positively with employee job satisfaction and organizational commitment. On the other hand, employee job satisfaction was found to be positively correlated with organizational commitment. The two independent variables made significant individual contributions to the prediction of organizational commitment.

This study has some limitations. First, convenient sampling method was used to recruit the participants. Therefore, the findings of the study have limited generalizability in other regions and age groups. Second, as a cross-sectional study, there has to be caution in making any generalization of the results. Future researchers should get more respondents from wider geographical location, that is from different bans, private and public. Furthermore, self-report questionnaires were used to collect data from respondents. It is recommended that future researchers use different methods such as personal interview or telephone interview to collect data. This may help get reliable data after clarifying and removing what may be ambiguous.

The results of this study have corroborated with previous research and confirmed correlations between and among human resource management (HRM), employee job satisfaction and organizational commitment in the Saudi Arabian banking sector. These findings have practical implications. Having high levels of job satisfaction among bank employees will prevent low levels of turnover rate, absenteeism and levels of productivity and increase organizational commitment. HRM practices, on the other hand, help in career development opportunities and hence provide better job opportunities. This study can recommend that to ensure high levels of job satisfaction among employees and enhanced organizational commitment, organizations, especially banks, should emphasize more on HRM practices.

This study could contribute to the literature on HRM, job satisfaction and organizational commitment in the Saudi Arabian banking sector. There is a rapid growth in the banking sector in Saudi Arabia. Therefore, there is an urge for recruiting efficient and experienced human resources (Mizan et al., 2013). This study will contribute to the development of the Saudi Arabian banking sector, which may lead to maintain this sector work effectively that will have a positively impact on the economy of the Saudi society. It also will highlight the nature and importance of the HRM practices for the benefit of the banking sector.

This study examines how institutional quality influences variability in financial development among economies in Sub-Saharan Africa (SSA).

Empirical estimations verifying various relationships are performed using the limited information maximum likelihood (LIML) estimation technique.

The results suggest that institutional quality enhances the pace of financial development among economies in the sub-region all things being equal. In a further micro-level analysis where components of institutional quality index are examined separately, the study’s results suggest that effective governance, regulatory quality, rule of law and accountability tend to have a significant positive impact on financial sector development.

Findings of the study suggest that policies geared towards improving governance and regulatory institutions can augment development of the financial sector among economies in SSA; governments and policymakers are therefore encouraged to resource noted institutions to play effective roles for the development of the financial sector.

Compared to related studies, this study reorients existing paradigm, which emphasizes the role of governance and institutional variables in the economic growth discourse. The authors’ empirical inquiry rather focuses on how governance and institutional structures influence regional financial development dynamics. Specifically, this study differs from most macro-level studies found in literature because it examines the impact of hitherto unexamined governance and institutional variables on financial development among economies in SSA.

The purpose of this study is to investigate the moderating effect of board gender diversity on the relationship between sustainability reporting (SR) and earnings management (EM) in the East Africa Community (EAC).

The study analyzed a sample of 71 publicly traded companies from 2011 to 2021.

The study finds that both SR and board gender diversity have a negative and significant effect on EM and that board gender diversity moderates the relationship between SR and EM.

The findings suggest that boards should support the adoption of SR and increase female representation as a practical way to reduce EM. Policymakers should also implement appropriate measures, such as imposing mandatory SR and gender quotas on corporate boards, to address EM.

This research adds to the limited knowledge of SR and EM in the EAC and also fills a gap in the existing literature by investigating the influence of board gender diversity on the link between SR and EM.

This paper investigates the impact of institutional factors on financial deepening and its implications on bank credit in Africa.

The paper employs different panel econometric models to examine the heterogeneity of 50 African countries from 2000 to 2019. The estimators include panel corrected standard errors, system generalized method of moments, quantile and threshold regressions.

The results show that rule of law, regulatory quality, government effectiveness, voice and accountability, control of corruption and political stability significantly influence financial deepening in Africa. However, government effectiveness has a higher effect on middle- and high-income countries, while other indicators have a high impact on low-income countries. All institutional indicators have stronger effects, almost double, at higher financial depth levels than for countries with lower levels. Government effectiveness and regulatory quality impact financial deepening more for countries with strong institutions than weak ones. Thus, the relationship between institutional qualities and credit provided by banks is non-monotonic.

The findings suggest that strengthening appropriate institutional factors based on country heterogeneity may effectively stimulate debt financing in Africa, the primary source of financing for small and medium-sized enterprises and entrepreneurs.

The novelty of this paper is that previous studies did not sufficiently scrutinize the heterogeneity of the structure of African economies – i.e. differences in institution, credit and income levels.