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This study aims to identify the impact of intellectual capital (IC) on the bank’s performance using a cross-country approach with India and Gulf Cooperation Council (GCC) countries using the Skandia navigator model (SNM).

This study uses a mixed-methods research approach by taking financial and non-financial measures to assess the impact of the IC on the bank’s performance using the SNM. The study implies an analysis of the data from the top ten banks in India and twenty banks in GCC countries. The selection was done based on the volume of the bank’s business for three years (2019–2020, 2020–2021 and 2021–2022).

The research has three main findings: there is a positive impact of IC on the bank’s performance; amongst the factors of SNM, there is a direct impact of human capital and customer focus on the performance of the selected banks in both India and GCC countries; and the other factors of SNM such as structural capital and process focus, renewal and development focus also affect the selected banks.

The outcomes of the research may be useful for policymakers in India and GCC countries, as it identifies IC components that have a significant impact on the bank’s performance. This might enable them to develop policies that foster such factors, which, consequently, will improve the performance of the banks in the selected countries.

This study is an attempt to fill the gap in the existing literature on IC and bank’s performance for two different types of countries using the SNM.

This study aimed to develop a mathematical model for delamination and surface roughness during end milling by using grey relational analysis (GRA) and to determine how the input parameters (cutting speed, depth of cut, helix angle and feed rate) influence the output response (delamination and surface roughness) in machining of hybrid glass fibre-reinforced plastic (GFRP) (abaca and glass) composite using solid carbide end mill cutter.

The Four factors, three levels Taguchi orthogonal array design in GRA is used to conduct the experimental investigation. The Shop Vision inspection system is used to measure the width of maximum damage of the machined hybrid GFRP composite. The Shop Handysurf E-35A surface roughness tester is used to measure the surface roughness of the machined hybrid GFRP composite. “Minitab 14” is used to analyse the data collected graphically. Analysis of variance is conducted to validate the model in determining the most significant parameter.

The GRA is used to predict the input factors influencing the delamination and surface roughness on the machined surfaces of the hybrid GFRP composite at different cutting conditions with the chosen range of 95 per cent confidence intervals. Analysis on the influences of the entire individual input machining parameters on the delamination and surface roughness has been conducted using GRA.

Effect of milling of the hybrid GFRP composite on delamination and surface roughness with various helix angle solid carbide end mill has not been analysed yet using the GRA technique.

Although businesses continue to take up artificial intelligence (AI), concerns remain that companies are not realising the full value of their investments. The study aims to provide insights into how AI creates business value by investigating the mediating role of Business Process Management (BPM) capabilities.

The integrative model of IT Business Value was contextualised, and structural equation modelling was applied to validate the proposed serial multiple mediation model using a sample of 448 organisations based in the EU.

The results validate the proposed serial multiple mediation model according to which AI adoption increases organisational performance through decision-making and business process performance. Process automation, organisational learning and process innovation are significant complementary partial mediators, thereby shedding light on how AI creates business value.

In pursuing a complex nomological framework, multiple perspectives on realising business value from AI investments were incorporated. Several moderators presenting complementary organisational resources (e.g. culture, digital maturity, BPM maturity) could be included to identify behaviour in more complex relationships. The ethical and moral issues surrounding AI and its use could also be examined.

The provided insights can help guide organisations towards the most promising AI activities of process automation with AI-enabled decision-making, organisational learning and process innovation to yield business value.

While previous research assumed a moderated relationship, this study extends the growing literature on AI business value by empirically investigating a comprehensive nomological network that links AI adoption to organisational performance in a BPM setting.

Implementing food safety measures (FSMs) have become a prerequisite for food firms looking to export internationally. Many exporters find it difficult to comply with multiple regulations, and their consignments are often rejected at borders due to food safety concerns. Hence, harmonization in food safety standards is arguably the most contentious topic regarding the export market since it affects international trade. Accordingly, the paper uses the case of Indian seafood exporters to identify key FSMs, investigate stringency associated with them and rank international markets based on degree of stringency for selected FSMs.

First, the authors identify the key FSMs by using the Delphi method. Then, the authors apply the Fuzzy analytical hierarchical process (FAHP) method to calculate weights of the FSMs as criteria. Lastly, the authors apply the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) approach to rank markets. To compute fuzzy TOPSIS, weights are derived from fuzzy AHP.

This study’s findings suggest that product and process standards, traceability requirements and tolerance limits for residues are the most stringent FSMs, among others. Besides, the overall ranking of markets reveal that the European Union (EU), the USA and Japan ranked lowest and perceived to have the most stringent food safety requirements.

The paper offers guidance to firms and policymakers to manage their efforts and resources during food safety implementation by focussing on critical FSMs. Researchers will get insights about FSMs for further empirical investigation. To the authors’ knowledge, no study examined the stringency associated with various FSMs in the seafood industry.

The purpose of this paper is to identify the ideal process parameters to be set for the drilling of hybrid fibre-reinforced polymer (FRP) (kenaf and banana) composite using High-Speed Steel drill bits (5, 10, 15 mm) coated with tungsten carbide by means of statistical reproduction of the delamination factor and machining force using Taguchi–Grey Relational Analysis.

The contemplated process parameters are Feed, Speed and Drill Diameter. The trials were carried out by taking advantage of the L-27 factorial design by Taguchi. Three factors, the three level Taguchi Orthogonal Array design in Grey Relational Analysis was used to carry out the trial study. Video Measuring System was used to identify the damage around the drill region. “Minitab 18” was used to examine the data collected by taking advantage of the various statistical and graphical tools available. Examination of variance is used to legitimize the model in identifying the most notable parameter.

The optimised set of input parameters were found out successfully which are as follows: Feed Rate: 450 mm/min, Cutting Speed: 3,000 rpm and Drill Diameter of 5 mm. When these values are fed in as input the optimised output is being obtained. From ANOVA analysis, it is apparent that the Speed (contribution of 92.6%) is the most influencing parameter on the delamination factor and machining force of the FRP material.

Optimization of process parameters on drilling of natural fibres reinforced in epoxy resin matrices using Taguchi–Grey Relational Analysis has not been previously explored.

In the last years, the use of free-online instructional videos has gained popularity among educators and students. Its success is mainly based on the provision of fast and inexpensive access to educational contents which can be consulted at the own convenience of students, all over the world. Free-online platforms as YouTube offer access to more than ten million instructional videos. The purpose of this paper is to assess and rank the educational quality of free-online instructional videos from a multidimensional perspective.

In this paper, the authors propose a MCDM approach based on a compromise ranking method, VIKOR. The approach integrates a normalization process which is especially suitable for situations where the nature of the different decision-making criteria is such that it does not allow homogeneous aggregation.

With the proposed normalization approach, the initial valuations of the alternatives with respect to the criteria are transformed in order to reflect their similarity with a given reference point (ideal solution). The normalized data are then integrated in a VIKOR-based framework in order to obtain those mathematical videos closer to the ideal video from the instructors’ perspective.

The ranking of instructional videos based on their quality from an educational multidimensional perspective is a good example of a real decision-making problem where the nature of the criteria, qualitative and quantitative, implies heterogeneous data. The proposed IS-VIKOR approach overcomes some of the problems inherent to this real decision-making problem.

This paper aims to investigate the effect and parametric optimization of process parameters during milling of glass fibre-reinforced plastics (GFRP) composites using grey relational analysis (GRA).

Experiments are conducted using helix angle, spindle speed, feed rate, depth of cut and fibre orientation angle as typical process parameters. GRA is adopted to obtain grey relational grade for the milling process with multiple characteristics, namely, machining force and material removal rate (MRR). Analysis of variance is performed to get the contribution of each parameter on the performance characteristics.

It is observed that helix angle and fibre orientation angle are the most significant process parameters that affect the milling of GFRP composites. The experimental results reveal that the helix angle of 45°, spindle speed of 3000 rpm, feed rate of 1000 mm/min, depth of cut of 2 mm and fibre orientation angle of 15° is the optimum combination of lower machining force and higher MRR. The experimental results for the optimal setting show that there is considerable improvement in the process.

Optimization of process parameters on machining force and MRR during endmilling of GFRP composites using GRA has not been attempted previously.

This paper presents the effects of replacing fine aggregate (FA) with waste foundry sand (WFS) in natural aggregate and construction waste aggregate concrete specimens without and with superplasticizer (SP), silica fume (SF) and fiber (F) to solve the disposal problems of various wastes along with saving the environment. This study aims to investigate the effect of construction waste, WFS along with additives on the stress-strain behavior and development of compressive strength with age.

The various concrete specimen were prepared in mix proportion of 1: 2: 4 (cement (C): sand: coarse aggregate). The water-cement ratio of 0.5 (decreased by 10% for samples containing SP) to grading 1: 2: 4 under air-dry condition was adopted in the preparation of concrete specimens. The compressive strength of various concrete specimen were noticed for 3, 7 and 28 days by applying load through universal testing machine.

Upon adding construction and demolition waste aggregates, the compressive strength of concrete after 28 days was comparable to that of the control concrete specimen. An enhancement in the value of compressive strength is perceived when FA is replaced with WFS to the extent of 10%, 20% and 30%. If both construction and demolition waste aggregate and WFS replacing FA are used, the compressive strength increases. When FA is interchanged with WFS in natural aggregate or construction demolition waste aggregate concrete including usage of SF or F, the compressive strength improves significantly. Further, when construction and demolition waste aggregate and WFS replacing FA including SP are used, the compressive strength improves marginally compared to that of control specimen. The rate of strength development with age is observed to follow similar trend as in control concrete specimen. Therefore, construction and demolition waste and or WFS can be used effectively in concrete confirming an improvement in strength.

The utilization of these wastes in concrete will resolve the problem of their disposal and save the environment.

The purpose of this article is to analyze the state-of-the art in a systematic way, identifying the main research groups and their related topics. The types of studies found are fundamental for understanding the application of artificial neural networks (ANNs) in cemented soils and the potential for using the technique, as well as the feasibility of extrapolation to new geotechnical or civil and environmental engineering segments.

This work is characterized as being bibliometric and systematic research of an exploratory perspective of state-of-the-art. It also persuades the qualitative and quantitative data analysis of cemented soil improvement, biocemented or microbially induced calcite precipitation (MICP) soil improvement by prediction/modeling by ANN. This study sought to compile and study the state of the art of the topic which possibilities to have a critical view about the theme. To do so, two main databases were analyzed: Scopus and Web of Science. Systematic review techniques, as well as bibliometric indicators, were implemented.

This paper connected the network between the achievements of the researches and illustrated the main application of ANNs in soil improvement prediction, specifically on cemented-based soils and biocemented soils (e.g. MICP technique). Also, as a bibliometric and systematic review, this work could achieve the key points in the absence of researches involving soil-ANN, and it provided the understanding of the lack of exploratory studies to be approached in the near future.

Because of the research topic the article suggested other applications of ANNs in geotechnical engineering, such as other tests not related to geomechanical resistance such as unconfined compression test test and triaxial test.

This article systematically and critically presents some interesting points in the direction of future research, such as the non-approach to the use of ANNs in biocementation processes, such as MICP.

Regarding the social environment, the paper brings approaches on methods that somehow mitigate the computational use, or elements necessary for geotechnical improvement of the soil, thereby optimizing the same consequently.

Neural networks have been studied for a long time in engineering, but the current computational power has increased the implementation for several engineering applications. Besides that, soil cementation is a widespread technique and its prediction modes often require high computational strength, such parameters can be mitigated with the use of ANNs, because artificial intelligence seeks learning from the implementation of the data set, reducing computational cost and increasing accuracy.

This study aims to investigate the optimal process parameters of the wire-cut electrical discharge machining (WCEDM) for the machining of the GZR-AA7475 hybrid metal matrix composite (HMMC). HMMCs are prepared with 2 Wt.% graphite and 4 Wt.% zirconium dioxide reinforced with aluminium alloy 7475 (GZR-AA7475) composite by using the stir casting method. The objective is to enhance the mechanical properties of the material while preserving its unique features. WCEDM with a 0.18 mm molybdenum wire electrode is used for machining the composite.

To conduct experimental studies, a Taguchi L27 orthogonal array was adopted. Input variables such as peak current (I_p), pulse-on-time (T_ON) and flushing pressure (P_F) were used. The effect of process parameters on the output responses, such as material removal rate (MRR), surface roughness rate (SRR) and wire wear ratio (WWR), were investigated. The grey relational analysis (GRA) is used to obtain the optimal combination of the process parameters. Analysis of variance (ANOVA) was also used to identify the significant process parameters affecting the output responses.

Results from the current study concluded that the optimal condition for grey relational grade is obtained at T_ON = 105 µs, I_p = 100 A and P_F = 90 kg/cm². Peak current is the most prominent parameter influencing the MRR, whereas SRR and WRR are highly influenced by flushing pressure.

Identifying the optimal process parameters in WCEDM for machining of GZR-AA7475 HMMC. ANOVA and GRA are used to obtain the optimal combination of the process parameters.