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This paper examines the relationship between the extent and quality of the four dimensions of corporate social responsibility disclosure (CSRD) namely community, environment, workplace and marketplace with the long-run share price performance of Malaysian initial public offering (IPO) companies.

This study utilised secondary data by the content analysis of the annual reports and Datastream of 115 IPOs listed from 2007 to 2015 in Malaysia. The IPO’s performance was determined by calculating the return measures under the equally weighted and value-weighted schemes of the mean abnormal returns and buy-and-hold abnormal returns covering the three years post-listing using the event-time approach.

The findings demonstrate that Malaysian IPOs experience substantial overperformance and underperformance when both the IPO performance measures are benchmarked against the matched companies and market. The results indicated that the extent and quality of the community and environment CSRD dimensions are positively and significantly correlated to the IPO’s performance. On the other hand, the extent and quality of the workplace and marketplace CSRD dimensions are negatively and significantly correlated to the IPO performance.

Malaysian regulators could benefit from these findings in their endeavour to carry out a reform process on CSRD to improve its quality. The results of this study are important to investors, regulators, non-government organisations, communities and policymakers. They also enhance the understanding of companies about the importance of disclosing greater CSR information to improve their performance and profitability.

To the researchers' best knowledge, this study provides new insights into the association between CSRD and the performance of Malaysian IPO companies, which is considered important.

This paper aims to explore the changes that an organizational performance and control system should perform to adapt to the current dynamic environment.

This paper first presents the features of a typical organizational control system. It identifies current technological developments that affect the control process and evaluates the changes that should be introduced in order for the organization to function effectively.

Although the components of organizational performance and control systems remain the same, most of them have changed their features, such as strategy should be visited more frequently, processes are digitized and therefore customers are more involved in the process, and actual performance can and should be monitored automatically.

This paper presents a comprehensive model of a control system that can be implemented by every organization, as well as points out control features that should be continuously reviewed and updated.

This paper evaluates the risk-adjusted returns, selectivity, market timing skills and persistence of the performance of Nigerian pension funds.

Annual return data of 23 pension funds that operated in Nigeria between 2018 and 2022 were obtained from the National Pension Commission (PenCom). Risk-adjusted return was appraised using the Treynor ratio, Sharpe ratio and Jensen alpha, while the Treynor–Mazuy and Henriksson–Merton multiple regression models were applied to decompose selective and timing skills. Performance persistence was assessed using the contingency table and rank correlation models.

Evidence shows that pension funds deliver excess risk-adjusted returns and exhibit selective skills. However, the evidence does not support the presence of timing skills, and there is overwhelming evidence that good (bad) performance does not repeat.

An evaluation of the investment performance of pension funds is crucial for ensuring the financial stability of retirees, maintaining economic stability and making informed investment decisions. It serves the interests of pensioners, pension fund managers, regulators and the broader economy. Our evidence that pension funds generate positive excess returns is a departure from most of the literature on managed funds. We recommend that more Nigerians should leverage the pension fund industry to grow their wealth and prepare for retirement.

This study, to our knowledge, is the first to appraise all the key facets of the investment performance of pension funds in the Nigerian context.

Logistics sector is recognized as one of the core enablers of the economic development of a nation. However, inefficiency in logistics operations impedes the achievement of intended targets by increasing the cost of doing business. Also, it is difficult to improve the efficiency of a country’s logistics operations without a metric for evaluating and understanding logistics capabilities and efficiency. Therefore, the present study has developed In-country Logistics Performance Index (ILP Index) to propose a benchmarking tool to measure the in-country logistics competitiveness, particularly in the setting of emerging economies, i.e. India.

This study has developed a unified index using principal component analysis and quintile approach. In addition, the proposed index relies on several dimensions that are developed and illustrated using quantitative secondary panel data.

The findings of this study reveal that the quality of infrastructure, economy, and telecommunications are the three most important dimensions that may significantly support the growth of the transportation and logistics sector. The results reveal that Gujarat, Tamil Nadu, and Maharashtra are the top performers whereas, Bihar, Jharkhand, and Jammu and Kashmir scores the least due to the insufficient logistics infrastructure as compared to other Indian states.

Given the extensive focus on international-level logistics index (like World Bank’s LPI) in the existing literature, this study intends to develop in-country logistics index to evaluate the logistics capabilities at the regional and state level. In addition, unlike prior studies, this study utilizes quantitative secondary data to eliminate cognitive and opinion bias. Moreover, this benchmarking tool would assist decision-makers in idealizing standard practices toward sustainable logistics operations. Additionally, the ILP index could serve the international investors in crucial decision-making, as it provides valuable insights into a country’s logistics readiness, influencing their investment choices and trade preferences. Finally, the proposed approach is adaptable to measuring the overall performance of any other industry/economy.

Supply chain management (SCM)-embedded valuable resources, such as capital, raw-materials, products, partners, customers and finished inventories, where the evaluation of environmental texture and flexibilities are needed to perceive sustainability. The present study aims to identify and evaluate the directory of green and agile (G-A) attributes based on decision support framework (DSF) for identifying dominating measures in SCM.

DSF is developed by exploiting generalized interval valued trapezoidal fuzzy numbers (GIVTFNs). Two technical approaches, i.e. degree of similarity approach (DSA) and distance approach (DA) under the extent boundaries of GIVTFNs, are implicated for data analytics and for recognizing constructive G-A measures based on comparative study for robust decision. A fuzzy-based performance indicator, i.e. fuzzy performance important index (FPII), is presented to enumerate the weak and strong G-A characteristics to manage knowledge risks in allied business environment.

The modeling is illustrated from the insights of decision-makers for augmenting business value based on cognitive identification of measures, where the best performance score is identified by the “sustainable packaging” under the traits of green supply chain management (GSCM). “The use of Web-based applications” under the traits of agile supply chain management (ASCM) and “Outsourcing flexibility” under traits of ASCM is found as the second and third most significant performance characteristics for business sustainability. Additionally, the “Reutilization (recycling) and reprocessing” under GSCM in manufacturing and “Responsiveness and speed toward customers needs” under ASCM are found difficult in attainment.

The G-A evaluation will assist in attaining performance excellence in day-to-day operations and overall functioning. The outcomes will help executives to plan strategic objectives and attaining success.

To reinforce the capabilities of SCM, wide extent of G-A dimensions are presented, concept of FPII is reported to manage knowledge risks based on identification of strong attributes and two technical approaches, i.e. DSA and DA under GIVTFNs are presented for attaining robust decision and directing managerial decision-making process.

This study explores the relationships among sustainability implementation barriers (resource, managerial and regulatory barriers), sustainability practices (sustainable construction materials, sustainable construction design, modern construction methods and environmental provisions and reporting) and sustainability performance (environmental, economic and social) in hill road construction (HRC).

Primary data were collected from the 313 HRC practitioners with the help of a questionnaire, and research hypotheses were tested employing structural equation modeling.

The findings reveal a mixed effect of sustainability implementation barriers. Resource (managerial) barriers are negatively related to all practices except environmental provisions and reporting (sustainable construction materials), while regulatory barriers only negatively impact modern construction methods. On the other hand, all sustainability practices positively impact environmental performance, whereas economic (social) performance is positively influenced by all practices, except environmental provisions and reporting (modern construction methods), and positively affects economic performance.

In order to transform HRC toward sustainability, the barriers to sustainability implementation, sustainability practices and performance need to be understood by practitioners; however, the relationships have not previously been empirically assessed in extant literature. Besides, past research appears to be predominantly focused on the environmental aspect, thereby neglecting economic and social aspects. This study is a modest attempt to bridge these research gaps.

This paper aims to design a hybrid model of knowledge-based performance management system (KBPMS) for facilitating Lean Six-Sigma (L6s) application to increase contractor productivity without compromising human safety in Indonesian upstream oil field operations that manage ageing and life extension (ALE) facilities.

The research design applies a pragmatic paradigm by employing action research strategy with qualitative-quantitative methodology involving 385 of 1,533 workers. The KBPMS-L6s conceptual framework is developed and enriched with the Analytical Hierarchy Process (AHP) to prioritize fit-for-purpose Key Performance Indicators. The application of L6s with Human Performance Modes analysis is used to provide a statistical baseline approach for pre-assessment of the contractor’s organizational capabilities. A comprehensive literature review is given for the main pillars of the contextual framework.

The KBPMS-L6s concept has given an improved hierarchy for strategic and operational levels to achieve a performance benchmark to manage ALE facilities in Indonesian upstream oil field operations. To increase quality management practices in managing ALE facilities, the L6s application requires an assessment of the organizational capability of contractors and an analysis of Human Performance Modes (HPM) to identify levels of construction workers’ productivity based on human competency and safety awareness that have never been done in this field.

The action research will only focus on the contractors’ productivity and safety performances that are managed by infrastructure maintenance programs for managing integrity of ALE facilities in Indonesian upstream of oil field operations. Future research could go toward validating this approach in other sectors.

This paper discusses the implications of developing the hybrid KBPMS- L6s enriched with AHP methodology and the application of HPM analysis to achieve a 14% reduction in inefficient working time, a 28% reduction in supervision costs, a 15% reduction in schedule completion delays, and a 78% reduction in safety incident rates of Total Recordable Incident Rate (TRIR), Days Away Restricted or Job Transfer (DART) and Motor Vehicle Crash (MVC), as evidence of achieving fit-for-purpose KPIs with safer, better, faster, and at lower costs.

This paper does not discuss social implications

This paper successfully demonstrates a novel use of Knowledge-Based system with the integration AHP and HPM analysis to develop a hybrid KBPMS-L6s concept that successfully increases contractor productivity without compromising human safety performance while implementing ALE facility infrastructure maintenance program in upstream oil field operations.

Due to its high leverage nature, a bank suffers vitally from the credit risk it inherently bears. As a result, managing credit is the ultimate responsibility of a bank. In this chapter, we examine how efficiently banks manage their credit risk via a powerful tool used widely in the decision/management science area called data envelopment analysis (DEA). Among various existing versions, our DEA is a two-stage, dynamic model that captures how each bank performs relative to its peer banks in terms of value creation and credit risk control. Using data from the largest 22 banks in the United States over the period of 1996 till 2013, we have identified leading banks such as First Bank systems and Bank of New York Mellon before and after mergers and acquisitions, respectively. With the goal of preventing financial crises such as the one that occurred in 2008, a conceptual model of credit risk reduction and management (CRR&M) is proposed in the final section of this study. Discussions on strategy formulations at both the individual bank level and the national level are provided. With the help of our two-stage DEA-based decision support systems and CRR&M-driven strategies, policy/decision-makers in a banking sector can identify improvement opportunities regarding value creation and risk mitigation. The effective tool and procedures presented in this work will help banks worldwide manage the unknown and become more resilient to potential credit crises in the 21st century.

This study aims to investigate contextual factors affecting the deployment of strategy-driven manufacturing accounting techniques (SMAT), as well as the impact of SMAT usage on organisational competitiveness. Seven major SMAT were investigated, namely, benchmarking, integrated performance measurement, environmental management accounting, strategic costing, strategic pricing, strategic investment and life cycle costing.

By using multi-informant strategy, structured questionnaire was used to gather survey data from 129 senior accounting, finance and production personnel of publicly quoted manufacturing companies in Nigeria. Data was analysed using structural equation modelling and propensity score matching.

Result shows that the usage rate of the SMAT is generally moderate. Market orientation and deliberate strategy formulation are notable determinants of SMAT usage. The inability of competition intensity and perceived environmental uncertainty to notably affect SMAT usage suggests that external environmental pressure to use SMAT is weak.

Although the impact of SMAT usage on organisational competitiveness is positive and statistically significant, it is conceivable that the impact of SMAT could have been more assuming SMAT recorded extensive usage. Thus, the lack of competitiveness of manufacturing companies in Nigeria may not be unconnected to the superficial usage of SMAT.

The study contributes to knowledge in three ways. First, it extends studies on the contingency theory that contextual factors influence the adoption of management accounting innovations. Second, it exposes the contextual factors affecting the adoption of SMAT in a developing country. Third, it provides evidence on the value relevance of management accounting innovation in enhancing organisational competitiveness.

The paper aims to tackle the classification of Remote Sensing Images (RSIs), which presents a significant challenge for computer algorithms due to the inherent characteristics of clustered ground objects and noisy backgrounds. Recent research typically leverages larger volume models to achieve advanced performance. However, the operating environments of remote sensing commonly cannot provide unconstrained computational and storage resources. It requires lightweight algorithms with exceptional generalization capabilities.

This study introduces an efficient knowledge distillation (KD) method to build a lightweight yet precise convolutional neural network (CNN) classifier. This method also aims to substantially decrease the training time expenses commonly linked with traditional KD techniques. This approach entails extensive alterations to both the model training framework and the distillation process, each tailored to the unique characteristics of RSIs. In particular, this study establishes a robust ensemble teacher by independently training two CNN models using a customized, efficient training algorithm. Following this, this study modifies a KD loss function to mitigate the suppression of non-target category predictions, which are essential for capturing the inter- and intra-similarity of RSIs.

This study validated the student model, termed KD-enhanced network (KDE-Net), obtained through the KD process on three benchmark RSI data sets. The KDE-Net surpasses 42 other state-of-the-art methods in the literature published from 2020 to 2023. Compared to the top-ranked method’s performance on the challenging NWPU45 data set, KDE-Net demonstrated a noticeable 0.4% increase in overall accuracy with a significant 88% reduction in parameters. Meanwhile, this study’s reformed KD framework significantly enhances the knowledge transfer speed by at least three times.

This study illustrates that the logit-based KD technique can effectively develop lightweight CNN classifiers for RSI classification without substantial sacrifices in computation and storage costs. Compared to neural architecture search or other methods aiming to provide lightweight solutions, this study’s KDE-Net, based on the inherent characteristics of RSIs, is currently more efficient in constructing accurate yet lightweight classifiers for RSI classification.