The study explored the attributes that affect consumers’ online purchase behavior in Bangladesh. Thirty-one attributes grouped into five categories (Product, Price, Accessibility & convenience, Company website, and Quality issues) are considered in the study. A questionnaire survey of 219 online shoppers has been conducted using non-probabilistic convenient sampling techniques. Foremost attributes noted are buying unavailable products, quality assurance, time saving, selective product purchase, and payment method. Other important factors include delivery time, discounts, geographical accessibility, size/ quantity assurance, response time, flexible shopping hours, and a wide range of product availability. Apart from these there are refund policy, warranty issues, delivery cost, salespeople behavior, relatively high price of certain products, satisfactory delivery system, and inventory availability. The least significant factors include activity of online store pages, smart filtering, and privacy. On the other hand, the non-agreeable significant attributes are personal relationships with owners, availability of used products, and no face-to-face interaction. It is noted that the respondents’ financial risk avoidance is emphasized by their considerations of payment method, discounts, refund policy, warranty issues, delivery cost, high price. As per group indices, the most influencing group in online purchases is quality issues, closely followed by product, accessibility & convenience and price. Comparatively, a less important group is company websites meaning that the consumers are not technophobic.
Dong Trung Chinh, Nguyen Thi Thu Hien, Pham Huong Quynh
et al.
Bank financial performance encapsulates an institution's capacity to effectively manage its assets, capital, and operational activities to generate profits and ensure stability. Evaluating this performance necessitates the integration of diverse metrics, including profitability indicators, loan growth rates, capital utilization efficiency, and more. Nevertheless, directly comparing the financial performance across different banks presents a complex challenge due to inherent disparities in their specific performance parameters. Multi-criteria decision-making (MCDM) techniques are frequently employed to navigate this intricate assessment. This study undertakes a comparative analysis of various MCDM approaches in evaluating bank financial performance. Our investigation encompasses both a comparison of methods for assigning weights to criteria and a comparison of methodologies for ranking the alternatives (banks). We examine five distinct weighting methods: Equal, Entropy, MEREC, LOPCOW, and SPC. Concurrently, three alternative ranking methods Probability, TOPSIS, and RAM are compared. These comparisons are conducted within the context of a case study involving the performance assessment of 19 banks. The findings indicate that the highest degree of stability in ranking bank financial performance is achieved when the Entropy method is utilized for criteria weighting in conjunction with the Probability method for ranking alternatives.
Central bank communication plays a critical role in shaping economic expectations and monetary policy effectiveness. This study applies supervised machine learning techniques to classify the sentiment of press releases from the Bank of Thailand, addressing gaps in research that primarily focus on lexicon-based approaches. My findings show that supervised learning can be an effective method, even with smaller datasets, and serves as a starting point for further automation. However, achieving higher accuracy and better generalization requires a substantial amount of labeled data, which is time-consuming and demands expertise. Using models such as Naïve Bayes, Random Forest and SVM, this study demonstrates the applicability of machine learning for central bank sentiment analysis, with English-language communications from the Thai Central Bank as a case study.
Agam Shah, Siddhant Sukhani, Huzaifa Pardawala
et al.
Central banks around the world play a crucial role in maintaining economic stability. Deciphering policy implications in their communications is essential, especially as misinterpretations can disproportionately impact vulnerable populations. To address this, we introduce the World Central Banks (WCB) dataset, the most comprehensive monetary policy corpus to date, comprising over 380k sentences from 25 central banks across diverse geographic regions, spanning 28 years of historical data. After uniformly sampling 1k sentences per bank (25k total) across all available years, we annotate and review each sentence using dual annotators, disagreement resolutions, and secondary expert reviews. We define three tasks: Stance Detection, Temporal Classification, and Uncertainty Estimation, with each sentence annotated for all three. We benchmark seven Pretrained Language Models (PLMs) and nine Large Language Models (LLMs) (Zero-Shot, Few-Shot, and with annotation guide) on these tasks, running 15,075 benchmarking experiments. We find that a model trained on aggregated data across banks significantly surpasses a model trained on an individual bank's data, confirming the principle "the whole is greater than the sum of its parts." Additionally, rigorous human evaluations, error analyses, and predictive tasks validate our framework's economic utility. Our artifacts are accessible through the HuggingFace and GitHub under the CC-BY-NC-SA 4.0 license.
Tarih boyunca bireyler, finansman ihtiyaçlarını karşılamak amacıyla farklı yöntemler geliştirmiştir. Bunlar arasında en yaygın kullanılanlardan biri olan borç ilişkisine dayalı yöntemler, konvansiyonel bankacılığın ortaya çıkışıyla birlikte büyük ölçüde faiz temelli bir hal almıştır. Ancak faiz hassasiyeti olan bireyler için bu durum, alternatif finansman modellerine olan ihtiyacı artırmış ve bu doğrultuda faizsiz bankacılık -Türkiye’deki adıyla katılım bankacılığı- ortaya çıkmış ve bu alanda kullanılabilecek faizsiz finansman enstrümanları tasarlanmıştır. Teknolojik gelişmeler ve dijitalleşmenin etkisiyle tüketim alışkanlıklarında hızlı değişimler yaşanmış, bireylerin ihtiyaç kalemleri çeşitlenmiş ve dolayısıyla nakit ihtiyacı artmıştır. Bu kapsamda faizsiz finansman yöntemlerini tercih eden bireyler yalnızca yatırım amaçlı finansman taleplerinde bulunmakla kalmamış; eğitim, sağlık, seyahat ve dayanıklı tüketim malları gibi çeşitli ihtiyaçlarını karşılamak üzere de nakit talep etmeye başlamışlardır. Katılım bankaları, bu taleplere yanıt vermek amacıyla çeşitli finansman modelleri geliştirmiştir. Bu modeller arasında teverruk, icâre temelli hizmet finansmanı, yatırım fonu temelli finansman ve Bireysel Emeklilik Sistemi (BES) teminatlı ihtiyaç finansmanı gibi yöntemler öne çıkmaktadır. Söz konusu bu çalışma, kapsamının sınırlı olması nedeniyle yalnızca BES birikimlerinin teminat gösterildiği, belli bankalarca sunulan BES teminatlı ihtiyaç finansmanı uygulamasına odaklanmaktadır. Çalışmanın temel amacı, Türkiye’de sunulan BES teminatlı ihtiyaç finansmanı uygulamasını hem işleyişi hem de fıkhî açıdan değerlendirmektir. Araştırmanın literatüre katkısı, konuya ilişkin müstakil akademik çalışmaların bulunmaması göz önüne alındığında, bu boşluğu kısmen de olsa doldurma yönündeki çabasına dayanmaktadır. Zira her ne kadar katılım bankacılığında nakit kullandırma yöntemleri ile sigorta ve BES üzerine çeşitli fıkhî çalışmalar mevcut olsa da özel olarak BES teminatlı ihtiyaç finansmanına dair müstakil bir çalışmaya rastlanmamıştır. Çalışmada katılım bankacılığı uygulamaları ve ilgili literatür esas alınarak nitel bir yöntem benimsenmiş, ürünün işlem seti de adım adım analiz edilmiştir. Makalenin birinci bölümünde BES teminatlı ihtiyaç finansmanının muhtevası, avantajları, dezavantajları ve işlem süreçleri incelenmiştir. İkinci bölümde ise katılım bankacılığında kullanılabilecek BES teminatlı bazı yöntemlere değinilmiştir. Ardından BES teminatlı ihtiyaç finansmanı, faizsiz finans ilke ve standartları çerçevesinde fıkhî bir analize tabi tutulmuştur. Araştırmalar sonucunda, BES teminatlı ihtiyaç finansmanının işlem setindeki adımlara riayet edilmesi şartıyla fıkhî açıdan meşru kabul edilebileceği tespit edilmiştir. Bununla birlikte özellikle herhangi bir varlığa dayanmayan tüketim amaçlı nakit finansman taleplerinde, bireylerin mali dengelerini koruyabilmeleri için dikkatli ve planlı kullanımın önem taşıdığı vurgulanmıştır. BES teminatlı modelin, bireylerin ödeme güçlüğü riskine karşı belirli ölçüde güvence sunduğu bulgusuna da ulaşılmıştır.
Digital wallet transactions have experienced a surge surpassing that of mobile banking transactions since 2021. Following a substantial merger with Tokopedia, GoPay has emerged as the digital wallet provider with the highest number of active users. The objective of this study is to examine the extent to which service innovation and service delivery, as antecedents of customer satisfaction, markedly influence customer loyalty. Employing the purposive sampling method, the research involved 170 respondents. The data collected were subjected to analysis using Structural Equation Modeling-Partial Least Squares (SEM-PLS). The findings of the research indicate a significant impact of service innovation, service delivery, and customer satisfaction on customer loyalty. Moreover, it is evident that service innovation and service delivery exert a noteworthy influence on customer satisfaction.
Banks are important for the development of economies in any financial ecosystem through consumer and business loans. Lending, however, presents risks; thus, banks have to determine the applicant's financial position to reduce the probabilities of default. A number of banks have currently, therefore, adopted data analytics and state-of-the-art technology to arrive at better decisions in the process. The probability of payback is prescribed by a predictive modeling technique in which machine learning algorithms are applied. In this research project, we will apply several machine learning methods to further improve the accuracy and efficiency of loan approval processes. Our work focuses on the prediction of bank loan approval; we have worked on a dataset of 148,670 instances and 37 attributes using machine learning methods. The target property segregates the loan applications into "Approved" and "Denied" groups. various machine learning techniques have been used, namely, Decision Tree Categorization, AdaBoosting, Random Forest Classifier, SVM, and GaussianNB. Following that, the models were trained and evaluated. Among these, the best-performing algorithm was AdaBoosting, which achieved an incredible accuracy of 99.99%. The results therefore show how ensemble learning works effectively to improve the prediction skills of loan approval decisions. The presented work points to the possibility of achieving extremely accurate and efficient loan prediction models that provide useful insights for applying machine learning to financial domains.
The rapid growth of Artificial Intelligence (AI) has underscored the urgent need for responsible AI practices. Despite increasing interest, a comprehensive AI risk assessment toolkit remains lacking. This study introduces our Responsible AI (RAI) Question Bank, a comprehensive framework and tool designed to support diverse AI initiatives. By integrating AI ethics principles such as fairness, transparency, and accountability into a structured question format, the RAI Question Bank aids in identifying potential risks, aligning with emerging regulations like the EU AI Act, and enhancing overall AI governance. A key benefit of the RAI Question Bank is its systematic approach to linking lower-level risk questions to higher-level ones and related themes, preventing siloed assessments and ensuring a cohesive evaluation process. Case studies illustrate the practical application of the RAI Question Bank in assessing AI projects, from evaluating risk factors to informing decision-making processes. The study also demonstrates how the RAI Question Bank can be used to ensure compliance with standards, mitigate risks, and promote the development of trustworthy AI systems. This work advances RAI by providing organizations with a valuable tool to navigate the complexities of ethical AI development and deployment while ensuring comprehensive risk management.
Rani Kartika Fitri, Nurabiah Nurabiah, Victoria Priyambodo
This study aims to examine whether financial technology moderates the relationship between intellectual capital and firm performance. Using secondary data obtained from the Indonesian stock exchange with a sample of banking companies listed on the Indonesian stock exchange. The dataset comprises a total of 230 observations. A panel datarandom effect regression model is applied to analyze the data. This study shows that intellectual capital moderated by financial technology has a significant and insignificant effect on company performance. However, overall, the average based on a prob>chi2 value of <0.05 indicates a significant positive result on the performance of banking companies in Indonesia during the 2018-2022 observation year. This research examines the importance of human resources and other intangible assets in achieving competitive advantages and enhancing company performance. Specifically, it explores the role of financial technology in addressing the challenges posed by complex competition within the financial sector, with a particular focus on the banking industry. Many studies confirm the importance of intellectual capital to improve company performance, as well as the importance of utilizing increasingly sophisticated financial technology that can affect company performance. However, to the researcher's knowledge, there has been no research linking intellectual capital with company performance moderated by financial technology. In addition, there is no research that measures company performance using various aspects such as accounting-based performance and market-based performance.
Business records management, Economics as a science
Nguyễn Anh Minh Thư, Mạch Khả Nhi, Đào Kim Huyền
et al.
Trong bối cảnh phát triển kinh tế - xã hội, tình trạng ô nhiễm môi trường đang ở mức báo động làm ảnh hưởng xấu tới sức khỏe và cuộc sống con người. Do đó, người tiêu dùng có xu hướng quan tâm nhiều hơn tới việc bảo vệ môi trường. Nghiên cứu này được thực hiện nhằm giải thích ý định mua sản phẩm xanh dưới sự tác động của các yếu tố bao gồm: tiếp thị xanh, kiến thức sức khỏe, kiến thức môi trường và định hướng dài hạn. Kết quả phân tích từ 321 mẫu khảo sát chứng minh các yếu tố đều có ảnh hưởng đến hành vi mua sản phẩm xanh. Trong đó, nổi bật lên vai trò quan trọng của tiếp thị xanh và định hướng dài hạn. Kết quả từ nghiên cứu ngoài việc đóng góp thêm về mặt lý thuyết, còn đưa ra những hàm ý thực tiễn cho các nhà quản trị, nhà tiếp thị trong lĩnh vực tiêu dùng xanh trong việc đưa ra những chiến lược phù hợp.
Paritosh Jha, Sona Chinngaihlian, Priyanka Upreti
et al.
The paper examines the direct and indirect implications of the risk factors relating to climate change on various parameters of agricultural production/productivity in India. India, being an emerging economy with considerable dependence on agriculture both for food security and employment generation, offers an important case study for understanding the macro-economic issues and designing the right set of policy approach for mitigating implications of climate change. Furthermore, unlike other central banks, the Reserve Bank of India (RBI) shares a close association with agriculture owing to the continued credit support to the sector as part of its priority sector lending policy, and because it is closely involved in addressing climate change. The paper focuses on the decade of 2010s, the recorded warmest decade till now and adopts a machine learning approach (sequential multivariate adaptive regression splines model) to assess the interaction between climate risk factors and agriculture. To the best of our knowledge, the modelling approach applied in this paper is unique, novel and of the first kind to assess the implications of climate change on agriculture. The results indicate that carbon dioxide (CO2) emission, precipitation, irrigation water used, and rainfall are the most prominent factors affecting different parameters of agricultural production. These factors are taken at the yearly aggregate level and their interactions among themselves, particularly CO2 emissions, affect productivity of foodgrains and oilseeds, providing a detailed insight about the recent decade of climate change in the context of Indian economy.
Cel artykułu. Celem opracowania jest ocena wpływu pandemii COVID-19 na aktywność polskich przedsiębiorstw w zakresie dokonywania transakcji fuzji i przejęć, zarówno występujących po stronie kupującej, jak i poszukujących inwestora.
Metodyka. Badanie zostało przeprowadzone w formie ankietowej na próbie 111 przedsiębiorstw działających w sektorze przemysłowym, a jego wyniki zestawiono z liczbą transakcji zawartych w okresie 2017–2022.
Wyniki/Rezultaty badania. Wpływ pandemii COVID-19 był odczuwalny dla większości ankietowanych podmiotów, jednak skala oddziaływania była istotnie różna pomiędzy potencjalnymi nabywcami, a podmiotami sprzedawanymi, które to podmioty deklarowały większy negatywny wpływ pandemii na działalność. Intencja do sprzedaży przedsiębiorstwa była motywowana skutkami wybuchu pandemii COVID-19 tylko w 22% przypadków. Spółki prowadzące aktywną politykę akwizycyjną w większości przypadków oceniały swoją sytuację finansową jako stabilną, podczas, gdy problemy finansowe przedsiębiorstwa były wymieniane jako jeden z motywów rozważanej sprzedaży w około 20% przypadków. Warto zwrócić uwagę, iż w przypadku wielu przedsiębiorstw wybuch pandemii nie spowodował zmiany we wcześniej przyjętej strategii, którą utrzymało 54% spółek planujących akwizycje i 35% spółek poszukujących inwestora. Wybuch pandemii spowodował, iż znaczna część podmiotów rozważających zakup przedsiębiorstwa oczekiwała spadku wycen oraz pojawienia się okazji inwestycyjnych. Zjawisko to nie było obserwowane wśród sprzedających, co mogło świadczyć o okresowym wystąpieniu różnicy w oczekiwaniach cenowych między stronami transakcji. Analiza liczby transakcji ex post pozwala wysnuć wniosek, iż ryzyka identyfikowane w początkowej fazie pandemii nie wpłynęły negatywnie na rynek fuzji i przejęć w Polsce, a po początkowym spowolnieniu spowodowanym lockdownem oraz niepewnością gospodarczą, polski rynek M&A powrócił do trendów wzrostowych obserwowanych przed wybuchem pandemii COVID-19.
Despite the extensive literature on prediction of banking crises by Early Warning Systems (EWSs), their practical use by policy makers is limited, even in the international financial institutions. This is a paradox since the changing nature of banking risks as more economies liberalise and develop their financial systems, as well as ongoing innovation, makes the use of EWS for informing policies aimed at preventing crises more necessary than ever. In this context, we assess the logit and signal extraction EWS for banking crises on a comprehensive common dataset. We suggest that logit is the most appropriate approach for global EWS and signal extraction for country-specific EWS. Furthermore, it is important to consider the policy maker's objectives when designing predictive models and setting related thresholds since there is a sharp trade-off between correctly calling crises and false alarms.