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Funding green activities: The national cultural profiles of high green bond issuance countries
No full textScholars have informed important findings related to the relationships between Hofstede's cultural dimensions and various green financing activities. Yet, we observe a lack of research that investigates the complex interdependency between the six key cultural dimensions of a society (i.e., individualism, uncertainty avoidance, long-term orientation, power distance, masculinity, indulgence) and how they jointly drive green bond issuance. In this paper, we employ Fuzzy-set Qualitative Comparative Analysis (FsQCA) to examine the link between cultural dimensions and green bond issuance across 64 countries. We contribute to offering novel insights into the three cultural configurations or profiles that are associated with high green bond issuance. Our work challenges existing research on the national culture and green practice relationship, such that we observe no best condition for any single Hofstede's cultural value. Instead, depending on the condition of other values, each cultural value/dimension can be high, low, or not play a major role in predicting high green bond issuance. Finally, our paper represents a methodological contribution to culture and green practice literature. We discuss our study's practical and theoretical implications in this line of research.</p
The Use of Zero-Shot Classification in Complex Emotion Detection
No full textNatural language processing techniques have been developing rapidly over the years. Their aim is to better understand what the people are communicating, starting by classifying their messages into sentiments including positive and negative. From there, researchers developed machine learning techniques that could help us not only extract people’s words but also get an abstract meaning out of whole sentences. With those abstractive algorithms, like zero-shot classification, messages as a whole can be better classified into themes and emotions. Furthermore, recent studies have shown that humans do not only have basic but complex emotions, which are summarized up to twenty-eight. Both rapid advancements in psychology and technology fields have opened up a research gap and a technical challenge relating to the use of abstract zero-shot classification in complex emotion detection. This study has found that the new zero-shot classification is significantly more effective than the conventional text classification in detecting complex emotions, contributing to the theoretical understanding of the effectiveness of zero-shot classification, and its practical use for highly-accuracy emotion detection that the current text classification techniques cannot achieve.</p
Employee ambidexterity and adaptive resources in the face of digital HRM changes: evidence from frontline banking staff
No full textPurposeThe study introduces and validates an adaptive self-regulatory approach for service organizations in the digital era, addressing the evolving landscape of human resource management. Focusing on frontline service workers, the research explores the mechanism that fosters ambidextrous behavior, aiming to enhance proactive service performance (PSP) and address layoff concerns in today’s volatile economy.Design/methodology/approachDrawing on the Career Construction Model of Adaptation (CCMA) and Job Demands-Resources (JD-R) theories, the study examines the impact of proactive personality (PP) and hardiness on activating ambidextrous behavior to achieve higher PSP. The survey findings of 368 frontline banking employees in Vietnam support the hypothesized model.FindingsThe study reveals that higher PP levels in frontline staff lead to enhanced ambidextrous behavior and PSP while adapting to digital human resource management (HRM). Hardiness, identified as a personal adapting resource, acts as a mediator between PP and ambidexterity, influencing both direct and indirect effects. Consequently, employees with superior PSP perceive lower job insecurity.Research limitations/implicationsThe findings contribute to the debate on the value of personality and adaptability traits in employee selection within the Industry 4.0 context. Emphasizing the importance of ambidexterity and staff adaptation in uncertain times, the study positions employees as either drivers or barriers in the change management process.Originality/valueThe study integrated various adaptability theories, shedding light on self-regulated mechanisms for ambidextrous workers to excel in e-HRM. It underscores the significance of individual-level ambidexterity in navigating changing environments resulting from HRM digitalization efforts.</p
Discursive Assemblages in Vietnamese Higher Education: A Critical Discourse Analysis of Global South Digital Texts
No full textThis chapter examines the “statements of purpose” featured on Vietnamese university websites within the context of higher education. It approaches these digital texts as more than mere functional documents, instead proposing an intellectual framework for analyzing the contexts and settings that shape them. By treating online texts and websites as purposeful digital artifacts—designed to inform, engage, and support social interaction—the study views Vietnamese university websites as embodiments of the country’s higher education policy, social standing, and institutional autonomy. In doing so, it situates these sites as discursive assemblages that highlight the role and status of higher education within Vietnamese society.</p
Meat the Reality: Unpacking the Exploitation of PALM Scheme Workers in Australia’s Meat Industry
No full textDespite important recent reforms to the PALM scheme in 2024, the study found that many Pacific Island workers in Australia’s meat processing sector remain trapped in exploitative conditions. The research highlights:
Restricted freedoms – Workers are tied to a single employer and face immense barriers if they wish to change jobs, reinforcing a system akin to indentured labour.
Severe underpayment – PALM workers earn less than colleagues on other visa schemes, despite often doing the most physically demanding work, a disparity fuelled by racial stereotypes about their strength.
Unpaid work and excessive hours – Many are forced to work unpaid overtime, undertake unpaid leadership roles, or serve as drivers for their colleagues with no extra compensation.
High-cost, low-quality accommodation – Workers are placed in isolated rural areas and forced into overcrowded, high-rent housing with automatic deductions from their wages.
Lack of access to workplace protections – Despite government promises, workers continue to struggle with illegal wage deductions, lack of tenancy rights, and limited representation from their home countries’ attachés.</p
Policy Brief: What kinds of environments support the wellbeing of young people with disability?
No full textThis policy brief presents a summary of findings from a scoping review. The review set out to map out what has currently been studied about the types of geographic environments that support good health and wellbeing for young people (ages 15-30 years) with disability.</p
AI-enabled Integration in the Supply Chain: A Solution in the Digitalization Era
No full textArtificial Intelligence (AI) is getting increased attention from various manufacturing industries, including fashion and textiles, due to its ability to work effectively, similar to human intelligence. This Systematic Literature Review (SLR) paper proposes potential future research directions that emphasize the impacts of AI on supply chain integration (SCI) efforts through information sharing (IS). A structured literature review of articles in the 2010-2021 period, addressing geographic location, journals, publishers, authors, research designs, and applied theories, has been used to prepare this paper. The additional discussion of AI incorporates information from the structured review to conclude the findings and suggest future research directions. The authors have used the Scopus database and prestigious peer-reviewed journals to search for relevant papers using suitable keywords. This paper concluded that the Asian region has the highest concentration of publications and that AI adoption positively affects the IS-SCI relationship. Empirical quantitative research design and resource-based view theory are prominent among the reviewed publications. This SLR paper is limited by not having the impacts of AI discussed at the subset level. </p
Interference Modelling, Detection, and Mitigation for Next Generation Spaceborne SAR
No full textSynthetic Aperture Radar (SAR) systems are critical for Earth observation, providing high-resolution imaging capabilities under all weather conditions and independent of daylight. These systems are widely utilized in environmental monitoring, disaster response, and security operations. However, SAR imaging is susceptible to anomalies caused by unintentional electromagnetic emissions, intentional jamming, multipath propagation, terrestrial radars, and system-induced artifacts, which can degrade the quality and reliability of SAR data.
The growing use of the Radio Frequency (RF) spectrum has exacerbated the issue of Radio Frequency Interference (RFI), posing a significant threat to SAR systems. RFI can obscure features, introduce artifacts in raw data and focused images, and complicate data interpretation, potentially undermining SAR’s effectiveness in critical applications. Terrestrial RFI, particularly Narrow-Band Interference (NBI), is a dominant source of anomalies, and its mitigation is challenging due to the complexity of SAR data and the need for real-time processing in operational scenarios.
This research explores the challenges of RFI in spaceborne SAR systems by investigating both traditional and Machine Learning (ML)-based detection and mitigation methods. Traditional approaches, such as notch filtering, adaptive filtering, and signal decomposition, offer established methodologies for addressing interference under certain conditions. At the same time, ML and
Deep Learning (DL) approaches present promising capabilities in handling complex, non-Gaussian interference environments by leveraging their ability to model and isolate interference patterns. This study assesses and quantifies the potential and limitations of each approach to identify their suitability for various RFI scenarios.
Akey contribution of thiswork is SEMUS (SAREMUlator for Spaceborne Applications), an open-source emulator designed to simulate RFI effects on SAR data at the RF level. SEMUS generates Phase History Data (PHD) and employs the Range-Doppler Algorithm (RDA) to produce focused images. It incorporates an empirical azimuth matched filter and injects various types of interference, including NBI, to evaluate their impact on SAR data. This enables comprehensive testing of RFI detection and mitigation methods in a controlled environment, advancing the understanding of radar signatures and interference
suppression in spaceborne SAR systems.
Two innovative approaches are employed to detect and mitigate RFI. The first approach leverages advanced signal processing techniques, including a two-dimensionalVariableAttenuation Space-Frequency Filter (VASFF), which exploits the time-frequency characteristics of RFI signals. This method has been validated on real TerraSAR-X raw data in collaboration with the German
Aerospace Center (DLR). The second approach apply DL, featuring a U-Net-based segmentation model, along with a mitigation model, developed in collaboration with the European Space Agency (ESA). These models are trained and tested on synthetic datasets emulating Sentinel-1 SAR data with radar interference and quantitatively verified on the synthetic data and qualitatively
validated on real Sentinel-1 data, demonstrating its effectiveness in detecting and mitigating NBI.
As an extension of radar signature recognition beyond SAR, this work explores air-writing recognition. The proposed approach utilizes a single ultrawideband (UWB) radar, leveraging five ML models and multiple data representations to achieve robust and practical performance. With a simplified design requiring minimal preprocessing, the system adapts effectively to various
handwriting styles, hand orientations, and writing speeds. Experimental results validate its effectiveness in realistic scenarios, showcasing its potential to advance human-computer interaction and gesture-based control applications.
The findings of this research offer significant contributions to both signal processing and deep learning approaches, setting a newbenchmark for enhancing SAR performance in the presence of RFI. These innovative methodologies not only demonstrate feasibility for practical implementation but also pave the way for real-time processing across diverse Earth observation applications. By addressing critical global monitoring and management challenges, this work establishes a robust foundation for the operational deployment of high-quality SAR systems, reinforcing their pivotal role in tackling pressing environmental
and societal needs. Additionally, the exploration of air-writing recognition using UWB radar extends the scope of radar applications, showcasing its potential to revolutionize human-computer interaction and gesture-based technologies through robust and adaptable machine learning frameworks.</p
Evaluating air transport barriers to tourism development in island states – A case study of an air service subsidy scheme in the Maldives
No full textOne-third of the Maldives’ economic output is generated by tourism. However, Maldivian tourism efforts are unevenly distributed impacting the socioeconomic developments in the north and south regions. This case study evaluates air transport barriers as one factor contributing to the sluggish tourism development in these regions and investigates how an air service subsidy scheme could be a potential solution to those barriers. Using 38 semi-structured questionnaires, perceptions on these two areas were collected from a selected group of senior-level management personnel in the Maldivian tourism and air transport industry in 2021. Barriers identified included factors such as geography, operations, market, air transfer cost, air transport policy, and infrastructure. The majority of participants supported an air service subsidy scheme as a possible solution to stimulate tourism activities in the north and south of the Maldives. Opportunities, challenges and implementation of an air subsidy scheme are discussed. Findings shed light not only on possible solutions for the Maldives but also on other island locations and contribute to the pool of knowledge of air connectivity economics.</p
Tailoring Robust 2D Nanochannels by Radical Polymerization for Efficient Molecular Sieving
No full textTwo-dimensional (2D) nanochannels have demonstrated outstanding performance for sieving specific molecules or ions, owing to their uniform molecular channel sizes and interlayer physical/chemical properties. However, controllably tuning nanochannel spaces with specific sizes and simultaneously achieving high mechanical strength remain the main challenges. In this work, the inter-sheet gallery d-spacing of graphene oxide (GO) membrane is successfully tailored with high mechanical strength via a general radical-induced polymerization strategy. The introduced amide groups from N-Vinylformamide significantly reinforce the 2D nanochannels within the freestanding membranes, resulting in an ultrahigh tensile strength of up to 105 MPa. The d-spacing of the membrane is controllably tuned within a range of 0.799–1.410 nm, resulting in a variable water permeance of up to 218 L m−2 h−1 bar−1 (1304% higher than that of the pristine GO membranes). In particular, the tailored membranes demonstrate excellent water permeance stability (140 L m−2 h−1 bar−1) in a 200-h long-term operation and high selectivity of solutes under harsh conditions, including a wide range of pH from 4.0 to 10.0, up to a loading pressure of 12 bar and an external temperature of 40 °C. This approach comprehensively achieves a balance between sieving performance and mechanical strength, satisfying the requirements for the next-generation molecular sieving membranes.</p