Search results

This study aims to validate a proposed conceptual model for the implementation of sustainable institutional repositories (IRs) in Nigeria.

A quantitative approach shaped the survey research design. This study used structural equation modeling (SEM) analysis to evaluate the proposed model. The population of the study comprises 117 librarians, information technology staff and researchers knowledgeable about IR implementation status across 14 public universities in Nigeria. The data was collected using an online survey. The Smart-PLS v3.3 software was used to facilitate the analysis.

The findings indicate that the nine identified factors of the IR sustainability model have a significant influence on the implementation of sustainable IRs. This signified that the model has adequately depicted the relationship between the implementation of sustainable IRs and the identified factors.

This study provides an integrated synthesis of factors that influence the implementation of sustainable IRs. This study also presents the first-ever empirically validated model for sustainable IRs. The findings of this study addressed the challenge of implementing sustainable IRs and institutionalized the idea of IRs’ sustainability assessment.

The purpose of this study is to examine whether the choice of a firm to spend resources on corporate social responsibility (CSR) activities is associated with its actual social impacts as measured by its energy consumption and the quality of its financial reporting. Based on legitimacy theory, the authors argue firms in India use CSR expenditures as mere smoke screens to build a positive public image.

By using energy consumption per unit of sale as a measure of real environmental impact, the authors model firms' CSR investment behavior. Additionally, the authors use earnings management measures to examine whether CSR spenders engage in manipulating reported earnings, a practice socially responsible firms would not engage in. These hypotheses are tested using a panel data set of Indian firms for the period 2012–2014.

Consistent with legitimacy theory, the authors show firms that participate in socially undesirable activities such as heavy energy consumption and accounting manipulation are more likely to pursue CSR voluntarily. Additionally, the authors find evidence suggesting firms that voluntarily engage in CSR tend to have lower firm values.

This study examines the social and environmental concerns of firms that invest in CSR, especially in an emerging market context. The findings help understand the motivation for CSR behavior of corporate firms and may well explain the observed negative relationship between firm value and voluntary CSR spending observed in many emerging market contexts, especially in India.

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.

In this paper, the authors investigate whether an independent and gender-diverse compensation committee strengthens the relationship between top managers' pay and firm performance in Chinese companies. The authors also investigate whether the independent compensation committee composed of all male directors is effective in designing the optimal contract for executives.

The authors use data from A-share listed companies on the Shenzhen and Shanghai stock exchanges from 2005 to 2015. As a baseline methodology, the authors use pooled ordinary least square (OLS) regression to draw inferences. In addition, cluster OLS regression, two-stage least square regression, the two-stage Heckman test and the propensity score matching method are also used to control for endogeneity issues.

The authors find evidence that an independent or gender-diverse compensation committee strengthens the link between top managers' pay and firm performance; that the presence of a woman on the compensation committee enhances the positive influence of committee independence on this relationship; that a compensation committee's independence or gender diversity is more effective in designing top managers' compensation in legal-person-controlled firms than they are in state-controlled firms; that gender diversity on the compensation committee is negatively associated with top managers' total pay; and that an independent compensation committee pays top managers more.

The study results highlight the role of an independent compensation committee in designing optimal contracts for top managers. The authors provide empirical evidence that a woman on the compensation committee strengthens its objectivity in determining top managers' compensation. The study finding supports regulatory bodies' recommendations regarding independent and women directors.

The study findings contribute to the recent debate about gender equality around the globe. Given the discrimination against women, many regulatory bodies mandate a quota for women on corporate boards. The study findings support the regulatory bodies' recommendations by highlighting the economic benefit of having women in top management positions.

This study contributes to literature by investigating the largely overlooked questions of whether having a gender-diverse or independent compensation committee strengthens the relationship between top managers' pay and firm performance; whether an independent compensation committee is more efficient in setting executives' pay when it is gender-diverse; and whether the effect of independent directors and female directors on top managers' compensation varies based on the firm's ownership structure. Overall, the main contribution of the study is that the authors provide robust empirical evidence in support of the managerial power axiom.

This study examines the improvement of rice production in Nigeria through the government’s policy of the Anchor Borrowers’ Programme (ABP). Rationalized by the production theory, the study utilizes quantitative methods such as surveys. The study investigates the influence of ABP on rice production, with particular emphasis on the experiences of smallholder rice farmers in Ebonyi State, Nigeria. The study employed a cross-sectional survey research approach, and the target population comprised smallholder rice farmers in the state who have benefitted from ABP. The study obtained a representative sample of 400 respondents through stratified random sampling. The study adopts the administration of a structured questionnaire with rice farmers in Ebonyi State, Nigeria, as the primary data collection method. Regression was used to test the hypotheses in SPSS version 23. From the findings, the study reveals the need for the ABP, banks and other lending agencies/programmes to train farmers. It shows that modern farming contributes to rice production in Ebonyi State.

The organizations focus on environmental factors as stakeholders exert pressure to reduce the environmental influence arising from production tasks. This study aims to see the influence of the environmental management control system (MCS) package and perceived environmental uncertainty on green performance with the mediating role of green dynamic capabilities in Pakistani manufacturing organizations. Moreover, this study aims to see the moderating role of investment in environmental management between green dynamic capabilities and green performance.

The partial least square structural equation modeling technique is used to test hypotheses. The data was collected through questionnaires using simple random sampling, and a total of 404 questionnaires were used in the final analysis. The data was collected from September 2021 to November 2021. Multiple regression analysis followed to see the influence of environmental MCS package, perceived environmental uncertainty, green dynamic capabilities and investment in environmental management on green performance.

Environmental MCS package, green dynamic capabilities and investment in environmental management are positively related to green performance. Despite this, perceived environmental uncertainty is negatively related to green performance. Moreover, green dynamic capabilities significantly mediate between environmental MCS package, perceived environmental uncertainty and green performance. Finally, investment in environmental management significantly moderates between green dynamic capabilities and green performance.

This research covers vital issues that how top management uses the environmental MCS package, perceived environmental uncertainty, green dynamic capabilities and investment in environmental management in determining green performance.

This research adds value to the existing literature by focusing on predictors that determine green performance. This pioneering study observes the influence of environmental MCS package and perceived environmental uncertainty on green performance and green dynamic capabilities as mediators by applying natural resource-based view theory. Moreover, investment in environmental management is used as a moderator between green dynamic capabilities and green performance. Finally, this study can benefit management, industrial policymakers and academicians.

This chapter offers a preliminary investigation into the impact of the COVID-19 pandemic on consumer purchasing behaviour within the grocery retail industry and supports evidence that since the pandemic began at the end of 2019, there have been changes in the demands and behaviours of consumers (Donthu & Gustafsson, 2020). Previous research has reported that the pandemic resulted in retail consumers spending less and saving more (Jorda, Singh, & Taylor, 2020), as well as panic buying (Nazir, 2021), both of which initially contributed to the limited availability of goods. This preliminary study reports upon survey data collected from retail consumers and answers the question ‘What were the changes in consumer behaviour in the grocery sector as a result of the COVID-19 pandemic?’ Findings reveal that an increase in online shopping occurred more distinctly during the first of the UK’s lockdowns, which waned over time. Findings also reveal a lower shopping frequency, but higher shopping spends during lockdown, and that social distancing and discipline were key drivers of this behaviour change. Findings also reveal an intention to maintain a combination of new and old shopping behaviours and habits after lockdown, giving rise to the continuing importance of meeting consumers’ grocery needs online as well as in-store. This chapter further discusses the implications arising from the reported findings.

Unlike previous research that primarily utilized structural equation modelling (SEM) to evaluate safety hazards in subway projects, this research aims to utilize a hybrid approach to investigate and scrutinize the key indicators of safety hazards leading to accidents, thereby hindering the progress of subway projects in China, taking into cognizance the multiple stakeholder’s perspective.

By administering a survey questionnaire to 373 highly involved stakeholders in subway projects spanning Changsha, Beijing and Qingdao, China, our approach incorporated a four-staged composite amalgamation of exploratory factor analysis (EFA), confirmatory factor analysis (CFA), covariance-based structural equation modelling (CB-SEM) and artificial neural network (ANN) to develop an optimized model that determines the causal relationships and interactions among safety hazards in subway construction projects.

The optimized model delineated the influence of individual safety hazards on subway projects. The feasibility and applicability of the model developed was demonstrated on an actual subway project under construction in Changsha city. The outcomes revealed that the progress of subway projects is significantly influenced by risks associated with project management, environmental factors, subterranean conditions and technical hazards. In contrast, risks related to construction and human factors did not exhibit a significant impact on subway construction progress.

While our study provides valuable insights, it is important to acknowledge the limitation of relying on theoretical approaches without empirical validation from experiments or the field. In future research, we plan to address this limitation by assessing the SEM using empirical data. This will involve a comprehensive comparison of outcomes derived from CB-SEM with those obtained through SEM-ANN methods. Such an empirical validation process is crucial for enhancing the overall efficiency and robustness of the proposed methodologies.

The established hybrid model revealed complex non-linear connections among indicators in the intricate project, enabling the recognition of primary hazards and offering direction to improve management of safety in the construction of subways.