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This paper aims to evaluate the relationship between financial inclusion and economic development in Pakistan based on available sources of detailed data and assess its outcome of financial inclusion on basic standards of life, then accord relevant recommendations to prompt economic growth and development.

The research design selected for data analysis was meta-analysis, besides, data analysis over the period 2010-2015 was performed by using a descriptive statistical approach, regression and correlation analysis, i.e. the Pearson correlation matrix.

The authors find a positive relationship between financial inclusion and economic development, resultantly; increase in financial inclusion may lead to an increase in economic development. In detail, the number of the number of bank accounts (per 1,000 adult population) and the number of bank branches (per 100,000 people) have a positive relationship with human development index (HDI). Where else the amount of automated teller machines per 1,000 km² (per cent) reveals a negative relationship.

The study has shown that expand financial access such as strengthen the establishment of bank accounts and bank branches can increase economic development in Pakistan. That is the government should focus on the financial inclusion policies as a means of ameliorating poverty, through a participation of all economic agents in the financial system. There is an utmost need for the Government of Pakistan to prioritize the importance of financial inclusion.

The novelty of the study is taken HDI and three representative indicators as a measurement of economic growth and financial inclusion, respectively, meanwhile, meta-analysis, multivariate regression model sum up that poverty alleviation is connected with the development of a more inclusive financial services sectors.

Prevention of chronic diseases by means of dietary modification can be achieved by consuming healthier foods with lower sugar content. However, reducing sugar content causes significant impact on the sensory quality and consumers’ acceptance towards local healthy products. This study aims to evaluate the effects of cross-modal interactions of Aroma-Taste-Texture (ATT) in yoghurt drink.

Fifteen sugar reduced yoghurt drinks [S, sugar percentage (0%, 2%, 4%); T, stevia dosage (0%, 0.01%, 0.02%); P, pectin dosage (0%, 0.3%, 0.6%)] based on ATT were evaluated by 300 consumer panellists on aroma, taste, texture and overall acceptability on a nine-point hedonic scale while sweetness intensity was measured using a seven-point just-about-right (JAR) scale. Interactions between ATT were determined.

Significant interaction was found between sugar and stevia, with F14 (4S0.02T0P) rated as sweeter than F4 (0S0.02T0P) (p = 0.003) and higher overall liking score than F11 (4S0T0P) (p = 0.001). Similarly, significant interaction was found between sugar and pectin, with F2 (0S0T0.6P) rated as significantly lower overall liking score and less sweet than F1 (0S0T0P) (p = 0.0001). Likewise, significant interaction was found between stevia and pectin, with F2 (0S0T0.6P) rated as significantly lower overall liking score and less sweet than F1 (0S0T0P) (p = 0.0001). Overall, F5 (0S0.02T0.6P) scored highest in overall liking and closest to the ideal sweetness (JAR = 4) indicating the possibility to apply 100% sugar replacement.

Application from the present study could be great potential solution in developing healthier range products while meeting consumer preference. The present study concluded that interactions of ATT of yoghurt drink will induce desirable changes in sensory and sweetness perception.

Negative media coverage has important impacts on firm financial performance, but existing studies have inconsistent views of this relationship and lack a unified theoretical framework to explain how such impacts arise. This study aims to bridge this gap in the literature.

This study uses two sets of data encompassing publicly listed companies in Shanghai and Shenzhen stock exchanges from 2013 to 2019, which are covered by the China Stock Market and Accounting Research Database.

This study finds that the number of negative news coverages has an inverted U-shaped relationship with firm financial performance; this relationship is weakened by the proportion of shares held by institutional investors and strengthened by advertising intensity.

This study suggests that corporate executives should be aware of the potential value of a limited amount of negative news coverage and react with tolerance and caution when their companies encounter it.

This study uses two different routes provided in the elaboration likelihood model theory to fully explain the processes underlying changes in investors’ attitudes toward firms experiencing negative media coverage.

The purpose of this paper is to conduct a comprehensive review of the noteworthy contributions made in the area of the Feedforward neural network (FNN) to improve its generalization performance and convergence rate (learning speed); to identify new research directions that will help researchers to design new, simple and efficient algorithms and users to implement optimal designed FNNs for solving complex problems; and to explore the wide applications of the reviewed FNN algorithms in solving real-world management, engineering and health sciences problems and demonstrate the advantages of these algorithms in enhancing decision making for practical operations.

The FNN has gained much popularity during the last three decades. Therefore, the authors have focused on algorithms proposed during the last three decades. The selected databases were searched with popular keywords: “generalization performance,” “learning rate,” “overfitting” and “fixed and cascade architecture.” Combinations of the keywords were also used to get more relevant results. Duplicated articles in the databases, non-English language, and matched keywords but out of scope, were discarded.

The authors studied a total of 80 articles and classified them into six categories according to the nature of the algorithms proposed in these articles which aimed at improving the generalization performance and convergence rate of FNNs. To review and discuss all the six categories would result in the paper being too long. Therefore, the authors further divided the six categories into two parts (i.e. Part I and Part II). The current paper, Part I, investigates two categories that focus on learning algorithms (i.e. gradient learning algorithms for network training and gradient-free learning algorithms). Furthermore, the remaining four categories which mainly explore optimization techniques are reviewed in Part II (i.e. optimization algorithms for learning rate, bias and variance (underfitting and overfitting) minimization algorithms, constructive topology neural networks and metaheuristic search algorithms). For the sake of simplicity, the paper entitled “Machine learning facilitated business intelligence (Part II): Neural networks optimization techniques and applications” is referred to as Part II. This results in a division of 80 articles into 38 and 42 for Part I and Part II, respectively. After discussing the FNN algorithms with their technical merits and limitations, along with real-world management, engineering and health sciences applications for each individual category, the authors suggest seven (three in Part I and other four in Part II) new future directions which can contribute to strengthening the literature.

The FNN contributions are numerous and cannot be covered in a single study. The authors remain focused on learning algorithms and optimization techniques, along with their application to real-world problems, proposing to improve the generalization performance and convergence rate of FNNs with the characteristics of computing optimal hyperparameters, connection weights, hidden units, selecting an appropriate network architecture rather than trial and error approaches and avoiding overfitting.

This study will help researchers and practitioners to deeply understand the existing algorithms merits of FNNs with limitations, research gaps, application areas and changes in research studies in the last three decades. Moreover, the user, after having in-depth knowledge by understanding the applications of algorithms in the real world, may apply appropriate FNN algorithms to get optimal results in the shortest possible time, with less effort, for their specific application area problems.

The existing literature surveys are limited in scope due to comparative study of the algorithms, studying algorithms application areas and focusing on specific techniques. This implies that the existing surveys are focused on studying some specific algorithms or their applications (e.g. pruning algorithms, constructive algorithms, etc.). In this work, the authors propose a comprehensive review of different categories, along with their real-world applications, that may affect FNN generalization performance and convergence rate. This makes the classification scheme novel and significant.

The purpose of this paper is three-fold: to review the categories explaining mainly optimization algorithms (techniques) in that needed to improve the generalization performance and learning speed of the Feedforward Neural Network (FNN); to discover the change in research trends by analyzing all six categories (i.e. gradient learning algorithms for network training, gradient free learning algorithms, optimization algorithms for learning rate, bias and variance (underfitting and overfitting) minimization algorithms, constructive topology neural networks, metaheuristic search algorithms) collectively; and recommend new research directions for researchers and facilitate users to understand algorithms real-world applications in solving complex management, engineering and health sciences problems.

The FNN has gained much attention from researchers to make a more informed decision in the last few decades. The literature survey is focused on the learning algorithms and the optimization techniques proposed in the last three decades. This paper (Part II) is an extension of Part I. For the sake of simplicity, the paper entitled “Machine learning facilitated business intelligence (Part I): Neural networks learning algorithms and applications” is referred to as Part I. To make the study consistent with Part I, the approach and survey methodology in this paper are kept similar to those in Part I.

Combining the work performed in Part I, the authors studied a total of 80 articles through popular keywords searching. The FNN learning algorithms and optimization techniques identified in the selected literature are classified into six categories based on their problem identification, mathematical model, technical reasoning and proposed solution. Previously, in Part I, the two categories focusing on the learning algorithms (i.e. gradient learning algorithms for network training, gradient free learning algorithms) are reviewed with their real-world applications in management, engineering, and health sciences. Therefore, in the current paper, Part II, the remaining four categories, exploring optimization techniques (i.e. optimization algorithms for learning rate, bias and variance (underfitting and overfitting) minimization algorithms, constructive topology neural networks, metaheuristic search algorithms) are studied in detail. The algorithm explanation is made enriched by discussing their technical merits, limitations, and applications in their respective categories. Finally, the authors recommend future new research directions which can contribute to strengthening the literature.

The FNN contributions are rapidly increasing because of its ability to make reliably informed decisions. Like learning algorithms, reviewed in Part I, the focus is to enrich the comprehensive study by reviewing remaining categories focusing on the optimization techniques. However, future efforts may be needed to incorporate other algorithms into identified six categories or suggest new category to continuously monitor the shift in the research trends.

The authors studied the shift in research trend for three decades by collectively analyzing the learning algorithms and optimization techniques with their applications. This may help researchers to identify future research gaps to improve the generalization performance and learning speed, and user to understand the applications areas of the FNN. For instance, research contribution in FNN in the last three decades has changed from complex gradient-based algorithms to gradient free algorithms, trial and error hidden units fixed topology approach to cascade topology, hyperparameters initial guess to analytically calculation and converging algorithms at a global minimum rather than the local minimum.

The existing literature surveys include comparative study of the algorithms, identifying algorithms application areas and focusing on specific techniques in that it may not be able to identify algorithms categories, a shift in research trends over time, application area frequently analyzed, common research gaps and collective future directions. Part I and II attempts to overcome the existing literature surveys limitations by classifying articles into six categories covering a wide range of algorithm proposed to improve the FNN generalization performance and convergence rate. The classification of algorithms into six categories helps to analyze the shift in research trend which makes the classification scheme significant and innovative.

The purpose of this paper is to predict the response and perforation of fibre metal laminates (FMLs) subjected to impact by projectiles at different velocities.

A finite element (FE) model is constructed in which recently proposed dynamic constitutive models for fibre reinforced plastic (FRP) laminates and metals are used. Moreover, a recently developed dynamic cohesive element constitutive model is also used to simulate the debonding between FRP laminates and metal sheets. The FE model is first validated against the test data for glass laminate aluminum reinforced epoxy (GLARE) both under dropped object loading and ballistic impact, then used to perform a parametric study on the influence of projectile nose shape on the perforation of FMLs.

It is found that the present model predicts well the response and perforation of GLARE subjected to impact loading in terms of load-time history, load-displacement curve, residual velocity and failure pattern. It is also found that projectile nose shape has a considerable effect on the perforation of GLARE FMLs and that the ballistic limit is the highest for a flat-ended projectile whilst for a conical-nosed missile the resistance to perforation is the least.

Recently developed constitutive models for FRPs and metals, together with cohesive element model which considers strain rate effect, are used in the FE model to predict the behaviour of FMLs struck by projectiles in a wider range of impact velocities; the present model is advantageous over such existing models as Johnson-Cook (JC) + Chang-Chang and JC (+BW) + MAT162 in terms of failure pattern though they produce similar results for residual velocity.

The purpose of this paper is to investigate different types of innovations and their effects on eco-innovation practices by firms practicing green technology in Malaysia.

This paper collected data via postal and online surveys. The population frame for this study was obtained from the External Trade Development Corporation directory 2010 and the MyHijau Directory published in 2013. Surveys were distributed to the top management of green technology companies.

Based on the reflections from the managers who participated in the survey, this study found that environmental compliance has become a critical issue and lack of enforcement can no longer be ignored. Despite that fact that eco-innovation inspection activities to comply with environmental regulations are carried out periodically by the studied firms, the challenges in deploying the latest green technologies and their potential benefits have remain evident. Often, a shortage of resources has restricted firms in setting up in-house research and development units.

The findings of this study lead to the suggestion that firms practicing green technology should engage in an information-sharing culture with key suppliers regarding the latest know-how and technological developments. Among the recommendations are that firms should engage employees, customers, suppliers and competitors through co-production. This engagement will help improve product marketability, lean production processes and the design of desired green products.

This study investigated the level of eco-innovation practices for each dimension of its construct and compared that level to the different stages of firm innovation. The results will fill in the gaps in the literature because previous studies only focused on theory testing and modelling of eco-innovation from a multidisciplinary approach.

Established on the detailed exploration of Chinese ancient management philosophies (CAMPs), the purpose of this paper is to extract enlightenments from CAMPs to see whether there exist some similarities between CAMPs and contemporary human resources management thoughts (CHRMTs) and pinpoint CAMPs' implications on human resource management practices nowadays.

Inspired by Lao Tzu's “When we can lay hold of the Dao of old to direct the things of the present day, and are able to know it as it was of old in the beginning, this is called (unwinding) the clue of Dao”, the paper explores, categorizes and integrates wisdom stemmed from CAMPs to evaluate whether there exist some commonly accepted arguments between CAMPs and CHRMTs.

CAMPs which have been passed on by generations for the past 2,500 years in China provide firm ground for human resources management thoughts and practices development; CAMPs' emphasis on people's well cultured morality and highly developed virtues has kindled a light to illuminate human resources management practices, not only in the past but also in the future. CHRMTs' principles concerning “people‐centered strategies”, employee recruitment and selection strategies, employee training and education strategies, staffing as well as employee retention strategies, can all trace their sources from CAMPs.

The research on CAMPs is not only significant to complement and extend CHRMTs but also useful to direct current human resource management practices.

Using the theoretical frameworks of public diplomacy and public relations, we mapped how the Chinese government has used panda imagery to build its national brand on Twitter and how this ‘panda diplomacy’ has facilitated its para-diplomatic actions. Our findings uncover new attempts by the Chinese government to engage in digital diplomacy. Mobilizing panda imagery on Twitter enhanced friendly relations with foreign political leaders and people and established a friendly and peaceful image of China on Twitter.

Academic contention occurs when research evidence is amenable to more than one interpretation. China has a long tradition of Shang Que (商榷), in which authors argue for their preferred interpretation. The modern form of this tradition is the Shang Que article, which often takes the form of research papers in Chinese-language journals and which tends to be question-oriented. Shang Que articles usually take the views of a particular author or article as the focus of independent and complete criticism by another, independent, academic. This paper explains the role of Shang Que articles in Chinese scholarship and their influence on international academia.

A bibliometric analysis was used to explore the characteristics and evolution of Chinese Shang Que articles using 30,577 articles published between 1979 and 2018. Microsoft Excel and Gephi were used for data analysis and visualization.

Findings suggest a decline in the number of Shang Que articles and an increase in the number of co-authors. Shang Que articles remained particularly prominent in Philosophy and Humanities and Social Sciences, where they focused on local issues such as classical Chinese, the Sinicization of Marxism and Chinese literature. This suggests that the number of Shang Que articles is related to the degree of internationalization of a research field.

Shang Que articles, which have been influenced by academic paradigms in English, are a fusion of China's Shang Que tradition and of the modern academic system. Through considering Shang Que articles, this paper explores the benefits of local academic traditions in non-English-speaking cultures.