Determinants of credit risk at Vietnam bank for agriculture and rural developments in Can Tho City

The aim of this study is to investigate factors affecting credit risks of the borrowers (both corporate and individual customers) of Vietnam bank for agriculture and rural development's branch at Can Tho city (lender), thereby proposing several solutions to improve the bank’s operational efficiency in the upcoming years. Simultaneous qualitative and quantitative research methods are applied and secondary data from 102 corporate customers and 2100 individual clients are collected directly from the financial report of the Can Tho branch of Vietnam bank for agriculture and rural development (Agribank) until the end of 2018. A binary logistics model is employed to identify the determinant factors of the credit risk of bank customers. Estimation results reveal that the credit risk of corporate customers is affected by the factors of sales growth, return on sales ratio, Debt to equity ratio, collateral-to-outstanding loan balance ratio, and customer's loan history which are consistent with those of previous studies, whereas the credit risk of individual customers is influenced by the factors of age, educational level, loan purpose, loan maturity, type of collateral, customer income, and customer loan history, which are confirmed by previous studies. The empirical findings of the article imply that the Can Tho branch of Agribank should take precautions in order to limit the credit risk of bank customers. In addition, several governance recommendations are given for bank’s manager to improve the operational efficiency of bank.

Ship Operational Efficiency from AIS Data Using Big Data Technology

The Energy Efficiency Design Index (EEDI) is a necessary benchmark for all new ships to prevent pollution from ships. MARPOL has also applied the Ship Energy Efficiency Management Plan (SEEMP) to all existing ships. The Energy Efficiency Operational Indicator (EEOI) provided by SEEMP is used to measure a ship's operational efficiency. The shipowner or operator can make strategic plans, such as routing, hull cleaning, decommissioning, new construction, and so on, by monitoring the EEOI. Fuel Oil Consumption is the most important factor in calculating EEOI (FOC). It is possible to measure it when a ship is in operation. This means that the EEOI of a ship can only be calculated by the shipowner or operator. Other stakeholders, such as the shipbuilding firm and Class, or those who do not have the measured FOC, can assess how efficiently their ships are working relative to other ships if the EEOI can be determined without the real FOC. We present a method to estimate the EEOI without requiring the actual FOC in this paper. The EEOI is calculated using data from the Automatic Identification System (AIS), ship static data, and publicly available environmental data. Big data technologies, notably Hadoop and Spark, are used because the public data is huge. We test the suggested method with real data, and the results show that it can predict EEOI from public data without having to use actual FOC Keywords: Ship operational efficiency, Energy Efficiency Operational Indicator (EEOI), Fuel Oil Consumption (FOC), Automatic Identification System (AIS), Big data

Are smart manufacturing systems beneficial for all SMEs? Evidence from Korea

PurposeThrough the resource-based view (RBV) and contingency theory, this study empirically investigates the impacts of smart manufacturing systems' maturity levels on the performance of small and medium-sized enterprises (SMEs). Moreover, it aims to examine how industry types (i.e. high- and low-tech industries) and human-resource factors (i.e. the proportion of production workers to total workers) as contingency factors influence the effects of smart manufacturing systems.Design/methodology/approachThe study conducted an empirical investigation of a sample of 163 Korean manufacturing SMEs. This study used an ordinary least squares regression to examine the impacts of the maturity levels of smart manufacturing systems on financial performance. Moreover, the impacts on operational efficiency were analysed using data envelopment analysis based on bootstrap methods and Tobit regression.FindingsThe RBV results indicate that the higher the maturity levels of smart manufacturing systems, the higher the financial performance and operational efficiency. Moreover, based on contingency theory, this study reveals that the effect of the maturity levels of smart manufacturing systems on financial performance and operational efficiency depends on firms' industry types and the proportion of production workers.Research limitations/implicationsThis study shows that the introduction of smart manufacturing systems can help SMEs achieve better financial performance and operational efficiency. However, their effectiveness is contingent on firms' industry types and the characteristics of their human resources.Practical implicationsSince the effects of the maturity levels of smart manufacturing systems on SME performance differ depending on their industries and the characteristics of human resources, managers need to consider them when introducing or investing in smart manufacturing systems.Originality/valueBased on the RBV and contingency theory, this is the first empirical study to examine the moderating effects of industry types and the proportion of production workers on the impacts of the maturity levels of smart manufacturing systems on the financial performance and operational efficiency of SMEs.

Unifying Gene Expression Complexity and Cellular Operational Efficiency: Application of the Locality and Caching Principles via a Cell-to-Computer Analogy

Gene expression is time-consuming, and the delay from transcription activation to produced proteins is sequentially longer from bacteria to yeast and to humans. How human cells bypass the delay and attain operational efficiency, i.e., quick proteomic response to signals, is not well understood. The computer has endured the same system latency issue due to much slower information retrieval (hard drive (HD) to memory and to CPU) than CPU execution, and mitigated it via efficient memory management, namely, the spatiotemporal locality principles that control specialized user functions and the permanent caching of core system functions, the operating system (OS) kernel. Thus, in this study, we unified gene expression and HD-memory-CPU information flow as instances of the Shannon information theory, both supporting the respective system operations and consisting of three components: information storage, the execution/decoding step, and the channel for the dynamic storage-to-execution information flow; the gene expression machinery and their regulators, and the OS kernel, were deemed as the respective channels. This abstraction prompted a multi-omic comparative analysis, generating experimental evidence that transcriptome regulation shares the computer memory management principles. First, the temporal locality principle explains the mRNA stabilization-by-translation regulatory mechanism and controls specialized cellular functions. Second, the caching principle explains cytoplasmic mRNA sequestration and the defiance of the locality principle by highly sequestered mRNAs. Third, strikingly, in both systems, the caching principle controls the information channels; similar to permanent caching of OS kernel, basic translation/transcription machinery and their regulators are the top most sequestered mRNAs. Summarily, the locality and the caching principles differentially regulate specialized functions and core system functions, respectively, integrating the complexity of transcriptome regulation with cellular operational latency mitigation.

INFLUENCE OF LOG LENGTH ON THE PRODUCTIVITY OF WOOD HARVESTING AND TRANSPORTATION

The selection of machines and the development of operating systems are the major challenge for reducing costs in harvesting and forest transportation. This work aimed to carry out a technical analysis of harvesting and forest transport activities in two different log lengths (6 and 7m). The operational cycles of the Harvester, Forwarder and combined road train vehicle in mechanized harvest areas were evaluated. The technical analysis was performed through studies of times and movements, determining the operational efficiency and productivity of the machines. According to the results, processing consumed most of the harvester's operational cycle, while in the forwarder, the most time was consumed 35,2 and 45,2 m³·he-1 and 42,84 and 75,42 m³.he-¹. The larger log size led to an increase in the productivity of the harvester by 28% and the forwarder by 48%. Among the studied models of road train vehicles, the one that showed the best results both in the analysis made with a length of 6 m and 7 m, was the dimensions with 2.35 m in width and 2.85 in height. These vehicles had a total gross weight of 63.52 tonnes for logs with a length of 6m and 69.17 tons for logs of 7m, with an 8.17% higher performance compared to 6m logs. With the obtained results it can be concluded that the increase in the length of the logs increased the productivity and the performance of the harvest and the forest transport.

Pathways towards Green Banking adoption: moderating role of top management commitment

Purpose The purpose of this study is to empirically examine the determinants and outcomes of Green Banking adoption and the moderating influence of top management commitment under the corporate environmental ethics ideology. External stakeholder pressures are analyzed as determinants of Green Banking adoption. Brand image and operational efficiency are examined as outcomes of this business ideology. Green Banking adoption is adapted as a second-order construct with four first-order reflective constructs to ensure in-depth conceptualization of the construct. Design/methodology/approach Green Banking adoption is studied at the bank branch level in a developing country, i.e. Pakistan. The data is collected from the branch managers of 212 bank branches from five major cities of Pakistan through mails. Self-administered survey was used for the data collection. The data was analyzed through the application of partial least square-structured equation modeling in SMART PLS 3.2.9. The measurement model and the structural model of the research framework were analyzed through the two-stage approach of the second-order analysis. Findings The results indicate a positive relationship between customer and competitor pressure and Green Banking adoption among bank branches in Pakistan depicting an influence of various environmental ethical pressures on bank’s adoption practices. Community pressure was shown to have no significant influence on Green Banking adoption at the branch level. The moderator of top management commitment caused a positive influence on the relationship between all the studied stakeholder pressures and Green Banking adoption. Branch managers reported branch image and operational efficiency to be enhanced due to Green Banking adoption. Originality/value This study attempts to fill in the significant gap in Green Banking adoption literature through an empirical analysis of Green Banking’s second-order construct. Currently, limited literature exists on the various aspects of Green Banking adoption, and an empirical study has not been conducted at the bank branch level. The study contributes significant practical, theoretical and methodological contributions to the area of Green Banking.