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In an increasingly interconnected world, transportation infrastructure has emerged as a critical determinant of economic growth and global competitiveness. High-speed rail (HSR), characterized by its exceptional speed and efficiency, has garnered widespread attention as a transformative mode of transportation that transcends borders and fosters economic development. The Kuala Lumpur – Singapore (KL-SG) HSR project stands as a prominent exemplar of this paradigm, symbolizing the potential of HSR to serve as a catalyst for national economic advancement.

This paper is prepared to provide an insight into the benefits and advantages of HSR based on proven case studies and references from global HSRs, including China, Spain, France and Japan.

The findings that have been obtained focus on enhanced connectivity and accessibility, attracting foreign direct investment, revitalizing regional economies, urban development and city regeneration, boosting tourism and cultural exchange, human capital development, regional integration and environmental and sustainability benefits.

The KL-SG HSR, linking Kuala Lumpur and Singapore, epitomizes the potential for HSR to be a transformative agent in the realm of economic development. This project encapsulates the aspirations of two dynamic Southeast Asian economies, united in their pursuit of sustainable growth, enhanced connectivity and global competitiveness. By scrutinizing the KL-SG High-Speed Rail through the lens of economic benchmarking, a deeper understanding emerges of how such projects can drive progress in areas such as cross-border trade, tourism, urban development and technological innovation.

This study aims to analyze the relationship between the personal responsibility, the intrinsic knowledge of the norms and the knowledge of signs of money laundering of accountants.

The research was developed with responses from 381 Brazilian accounting professionals through a survey, statistically analyzed using structural equations.

The results indicate that personal responsibility directly affects the levels of intrinsic knowledge and knowledge about signs of money laundering; however, the different dimensions of knowledge were not related to each other.

From these results, organizations can clarify the individual about their responsibility, optimizing the use of training and mitigating costs, with greater sustainability and security for the organization, employees and business partners.

The results contribute to the construction and modeling of latent constructs on money laundering knowledge, with validity, reliability and statistical significance.

This research discusses and empirically explores the knowledge about money laundering of the accountants’, one of the main explanatory factors of whistleblowing in business.

Increasing overhang of serviced apartments poses a serious concern to the national property market. This study aims to examine the impacts of macroeconomic determinants, namely, gross domestic product (GDP), consumer confidence index (CF), existing stocks (ES), incoming supply (IS) and completed project (CP) on serviced apartment price changes.

To achieve more accurate, quality price changes, a serviced apartment price index (SAPI) was constructed through a self-developed hedonic price index model. This study has collected 1,567 transaction data in Kuala Lumpur, covering 2009Q1–2018Q4 for price index construction and data were analysed using the vector autoregressive model, the vector error correction model and the fully modified ordinary least squares (OLS) (FMOLS).

Results of the regression model show that only GDP, ES and IS were significantly associated with SAPI, with an R² of 0.7, where both ES and IS have inverse relationships with SAPI. More precisely, it is predicted that the price of serviced apartments will be reduced by 0.56% and 0.21% for every 1% increase in ES and IS, respectively.

Therefore, government monitoring of serviced apartments’ future supply is crucial by enforcing land use-planning regulations via stricter development approval of serviced apartments to safeguard and achieve more stable property prices.

By adopting an innovative approach to estimating the response of price change to supply and demand in a situation where there is no price indicator for serviced apartments, the study addresses the knowledge gap, especially in terms of understanding what are the key determinants of, and to what extent they influence, the SAPI.

The data for this case were obtained primarily through a series of in-person interviews in Penang between the authors and Pete Browning (a pseudonym) from 2017 to early 2019. The authors also consulted secondary data sources, including publicly available material on BMax and “Company B”.

This case examines a key decision, or set of decisions, in the life of a small- to medium-sized management consultancy group, namely, whether they might expand their operations in Southeast Asia, and if so, where. These key decisions came in the wake of their having already established a very modest scale presence there, with an operating base on the island of Penang just off the north western coast of Peninsular Malaysia. The initial establishment of a Southeast Asian branch had been somewhat spontaneous in nature – a former colleague of one of the two managing partners in the USA was on the ground in Malaysia and available: he became the local partner in the firm. But the firm had now been eyeing expansion within the region, with three markets under particular consideration (Singapore, Indonesia and Thailand) and a further two (Vietnam and China) also seen as possible targets, though at a more peripheral level. The questions facing the decision makers were “was it time they expand beyond Malaysia?” and “if so, where?”

This case could be used effectively in undergraduate courses in international business. The key concepts on which the case focuses are the factors affecting market entry, particularly the choice of market and the assessment of potential attractiveness such markets offer.

With the increasing number of Web services, correct and efficient classification of Web services is crucial to improve the efficiency of service discovery. However, existing Web service classification approaches ignore the class overlap in Web services, resulting in poor accuracy of classification in practice. This paper aims to provide an approach to address this issue.

This paper proposes a label confusion and priori correction-based Web service classification approach. First, functional semantic representations of Web services descriptions are obtained based on BERT. Then, the ability of the model is enhanced to recognize and classify overlapping instances by using label confusion learning techniques; Finally, the predictive results are corrected based on the label prior distribution to further improve service classification effectiveness.

Experiments based on the ProgrammableWeb data set show that the proposed model demonstrates 4.3%, 3.2% and 1% improvement in Macro-F1 value compared to the ServeNet-BERT, BERT-DPCNN and CARL-NET, respectively.

This paper proposes a Web service classification approach for the overlapping categories of Web services and improve the accuracy of Web services classification.

Pre-positioning and distributing relief items are important parts of disaster management as it simultaneously considers activities from both pre- and post-disaster stages. This study aims to address this problem with a novel mathematical model.

In this research, a bi-objective mixed-integer linear programming model is developed to tackle pre-positioning and distributing relief items, and it is formulated as an integrated location-allocation-routing problem with uncertain parameters. The humanitarian supply chain consists of relief facilities (RFs) and demand points (DPs). Perishable and imperishable relief commodities (RCs), different types of vehicles, different transportation modes, a time window for delivering perishable commodities and the occurrence of unmet demand are considered. A scenario-based game theory is applied for purchasing RCs from different suppliers and an integrated best-worst method-technique for order of preference by similarity to ideal solution technique is implemented to determine the importance of DPs. The proposed model is used to solve several random test problems for verification, and to validate the model, Iran’s flood in 2019 is investigated as a case study for which useful managerial insights are provided.

Managers can effectively adjust their preferences towards response time and total cost of the network and use sensitivity analysis results in their decisions.

The model locates RFs, allocates DPs to RFs in the pre-disaster stage, and determines the routing of RCs from RFs to DPs in the post-disaster stage with respect to minimizing total costs and response time of the humanitarian logistics network.

This study explores match related factors and their impact on the batting strike rate in Twenty20 cricket – an aspect which can generate excitement and fan engagement in cricket matches.

Data was collected from www.cricinfo.com using a web scraping tool based on R programming from February 17, 2005, to October 25, 2022, numbering 4,221 men’s Twenty20 international innings featuring 41 national teams that had taken place in 85 venues across 11 countries of play. Hypothesis testing was conducted using one-way ANOVA.

The findings indicate that batters score faster in the first inning of a match, and mean strike rates also vary significantly based on the country of play. Further, the study analyses the top performing national sides, venues and country of play in terms of mean batting strike rate, thus providing insights to cricket boards, international regulating bodies of cricket, sponsors, media companies and coaching staff for better decision-making based on batting strike rate.

The originality of the study lies in its focus on using non-marketing strategies to increase fan engagement. Further, this study is the first one to examine different venues from the perspective of batting strike rate in men’s Twenty20 international matches.

After completion of the case study, students are expected to demonstrate understanding of the process of strategy formulation (which include conducting situational analysis) and strategy implementation.

Perusahaan Azan, which trades under the brand name Roti Azan for its fresh bread and Azan for its dry bread or rusks, was established as a family business in 1968 by Haji Abu Bakar bin Ali in his hometown in Kuala Pilah, in the state of Negeri Sembilan in Malaysia. In the mid-1980s, the management of the business was passed on by Haji Abu Bakar to one of his sons, Haji Mohd Ghazali bin Haji Abu Bakar. Haji Ghazali was named managing director in 1985 and officially inherited his father’s company in 1987. By 2004, Perusahaan Azan breads had started to penetrate major grocery stores nationwide, and later the business began to expand internationally in 2010, with Oman and Iraq among the first countries it ventured into. The company sold both its fresh and dry bread in local stores; however, in the international market, only dry bread types were sold, specifically wholemeal rusks and long rusks, which had longer shelf lives. Post-pandemic, by 2022, the company had exited the retail fresh bread market and had focused only on its contractual fresh bread and retail dry bread markets. He thought about the main strategic choices he had of going forward, either to revive its retail fresh bread segment or venture into a coffee shop business. The former was the bread and butter of the company in the last 50 years. However, he knew that re-entering this market was getting more difficult, as it requires competing head-to-head with the giant breadmakers. There were also issues of rising costs and high wastage. For the latter coffee shop project, the company did not have experience in directly “serving” the customers, with its businesses so far had been mainly in production. He pondered on the best decision to undertake to sustain the company’s profitability into the next generation. Few family businesses can pass this crucial stage. He knew he had to act fast to ensure that the company’s plans for the future could be successfully implemented. The case study is suitable for use in teaching courses in strategic management, organisational management and integrated case study for advanced undergraduates and postgraduates in the programmes of business administration, Muamalat administration and accounting.

The case study is suitable for use in advanced undergraduate students in management, business administration, Muamalat administration and postgraduate students in MBA, Master in Muamalat Administration or other related master’s programmes with a course in strategic management, organisational management and integrated case study.

Teaching notes are available for educators only.

CSS 11: Strategy.

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.