Pub Date : 2024-08-08DOI: 10.46281/bjmsr.v9i4.2239
Economic instability in emerging economies presents substantial challenges for firms, particularly in accessing debt funding, due to heightened perceived risk. This often results in a less favorable debt-to-equity ratio and complicates the overall composition of capital structure. Macroeconomic conditions play a pivotal role in influencing investor sentiment and risk perceptions, which in turn complicate capital structure decisions. This study aims to investigate the impact of various macroeconomic variables on the capital structure decisions of firms within the Indian automobile and automobile ancillary sectors over a comprehensive 17-year period from 2004 to 2020. They are utilizing secondary data collected from reputable sources like ProwessIQ, the Reserve Bank of India, and financial reports. The study employs various statistical tools, including descriptive statistics, correlation analysis, and dynamic panel data regression models, to analyze the data. The findings indicate that macroeconomic variables significantly shape the optimal capital structure decisions in the Indian automotive sector. Key variables such as the bank rate, GDP growth rate, inflation rate, and public debt substantially impact leverage ratios. For instance, an increase in the bank rate or public debt levels correlates with higher leverage ratios, suggesting that firms adjust their capital structures in response to changes in these macroeconomic indicators. This study provides valuable insights into the complex interplay between macroeconomic conditions and capital structure financing decisions. By highlighting the significant influence of these broader economic factors, the research underscores the necessity for firms, especially in emerging economies like India, to consider these determinants when making financial decisions. The findings thus contribute to a deeper understanding of capital structure dynamics in the face of macroeconomic challenges within the Indian automotive sector.
{"title":"A PANEL DATA EXPLORATION OF MACROECONOMIC FACTORS INFLUENCING THE OPTIMAL CAPITAL STRUCTURE OF THE INDIAN AUTOMOTIVE SECTOR","authors":"","doi":"10.46281/bjmsr.v9i4.2239","DOIUrl":"https://doi.org/10.46281/bjmsr.v9i4.2239","url":null,"abstract":"Economic instability in emerging economies presents substantial challenges for firms, particularly in accessing debt funding, due to heightened perceived risk. This often results in a less favorable debt-to-equity ratio and complicates the overall composition of capital structure. Macroeconomic conditions play a pivotal role in influencing investor sentiment and risk perceptions, which in turn complicate capital structure decisions. This study aims to investigate the impact of various macroeconomic variables on the capital structure decisions of firms within the Indian automobile and automobile ancillary sectors over a comprehensive 17-year period from 2004 to 2020. They are utilizing secondary data collected from reputable sources like ProwessIQ, the Reserve Bank of India, and financial reports. The study employs various statistical tools, including descriptive statistics, correlation analysis, and dynamic panel data regression models, to analyze the data. The findings indicate that macroeconomic variables significantly shape the optimal capital structure decisions in the Indian automotive sector. Key variables such as the bank rate, GDP growth rate, inflation rate, and public debt substantially impact leverage ratios. For instance, an increase in the bank rate or public debt levels correlates with higher leverage ratios, suggesting that firms adjust their capital structures in response to changes in these macroeconomic indicators. This study provides valuable insights into the complex interplay between macroeconomic conditions and capital structure financing decisions. By highlighting the significant influence of these broader economic factors, the research underscores the necessity for firms, especially in emerging economies like India, to consider these determinants when making financial decisions. The findings thus contribute to a deeper understanding of capital structure dynamics in the face of macroeconomic challenges within the Indian automotive sector.","PeriodicalId":479291,"journal":{"name":"Bangladesh journal of multidisciplinary scientific research","volume":"11 7","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-08-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141927495","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-07-28DOI: 10.46281/bjmsr.v9i2.2226
The study explores the effectiveness of sentiment analysis in predicting stock price movements, specifically focusing on the Indian Stock Market. The study investigates the reliability of social media sentiment analysis in financial markets and its implications for investors and traders. The research utilizes a sample of Twitter data comprising tweets containing hashtags related to the State Bank of India (SBI), used as a representative sample of the broader Indian Stock Market, collected from January 2021 to February 2024. The Valence Aware Dictionary for Sentiment Reasoning (VADER) algorithm was employed to analyse the sentiment of the Twitter data. Machine learning methods, including Random Forest, XGBoost, and AdaBoost, were used to integrate sentiment scores with technical indicators for predicting stock price trends. The results reveal that using only sentiment analysis achieved an accuracy of around 60% in predicting stock price direction. However, this accuracy increased to 70% with the AdaBoost method, 79% with the XGBoost method, and 82% with the Random Forest method combined with technical indicators while increasing the F1 scores from 0.4 to 0.8 in all three methods. Integrating sentiment analysis with technical indicators enhances financial market predictions by combining real-time investor sentiment with empirical historical data, leading to more accurate and adaptive trading strategies. Sentiment score was found to have a strong positive correlation with positive daily returns compared to negative daily returns, indicating that higher positive sentiment is associated with increased returns. Although negative sentiment exhibits a statistically significant correlation with daily returns, it shows a weaker positive association.
{"title":"VADER SENTIMENT ANALYSIS ON TWITTER: PREDICTING PRICE TRENDS AND DAILY RETURNS IN INDIA’S STOCK MARKET","authors":"","doi":"10.46281/bjmsr.v9i2.2226","DOIUrl":"https://doi.org/10.46281/bjmsr.v9i2.2226","url":null,"abstract":"The study explores the effectiveness of sentiment analysis in predicting stock price movements, specifically focusing on the Indian Stock Market. The study investigates the reliability of social media sentiment analysis in financial markets and its implications for investors and traders. The research utilizes a sample of Twitter data comprising tweets containing hashtags related to the State Bank of India (SBI), used as a representative sample of the broader Indian Stock Market, collected from January 2021 to February 2024. The Valence Aware Dictionary for Sentiment Reasoning (VADER) algorithm was employed to analyse the sentiment of the Twitter data. Machine learning methods, including Random Forest, XGBoost, and AdaBoost, were used to integrate sentiment scores with technical indicators for predicting stock price trends. The results reveal that using only sentiment analysis achieved an accuracy of around 60% in predicting stock price direction. However, this accuracy increased to 70% with the AdaBoost method, 79% with the XGBoost method, and 82% with the Random Forest method combined with technical indicators while increasing the F1 scores from 0.4 to 0.8 in all three methods. Integrating sentiment analysis with technical indicators enhances financial market predictions by combining real-time investor sentiment with empirical historical data, leading to more accurate and adaptive trading strategies. Sentiment score was found to have a strong positive correlation with positive daily returns compared to negative daily returns, indicating that higher positive sentiment is associated with increased returns. Although negative sentiment exhibits a statistically significant correlation with daily returns, it shows a weaker positive association.","PeriodicalId":479291,"journal":{"name":"Bangladesh journal of multidisciplinary scientific research","volume":"5 4","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141797021","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-07-27DOI: 10.46281/bjmsr.v9i2.2223
Understanding the relationship between tax avoidance and earnings management is crucial to evaluating tax policies and ensuring transparent financial reporting. Prior research has highlighted complexities and inconsistent findings, particularly concerning the impact of tax-related reporting incentives. This study addresses these issues by examining the influence of tax incentive recipient status on tax avoidance and earnings management among firms listed on the Kuala Lumpur Stock Exchange (KLSE). It examines whether firms receiving tax incentives from the Malaysian Investment Development Authority (MIDA) exhibit different earnings management behaviours than non-recipient firms. This study employs the effective tax rate (ETR) as a measure of tax avoidance and discretionary accruals (DEM) for earnings management. The dataset includes manually extracted financial information from firms listed on the KLSE for the financial year 2017 and a listing of tax incentive recipient firms from MIDA. Analytical techniques include ANOVA, independent samples t-test, and multiple regression analysis. The findings of this study suggest that higher tax avoidance relates to higher earnings management. Additionally, firms receiving tax incentives exhibit significantly higher ETRs than non-recipients. They are less likely to engage in earnings management, suggesting that tax incentives may deter aggressive financial reporting practices due to compliance pressures. The additional analysis indicates that tax incentives do not significantly moderate the relationship between tax avoidance and earnings management, implying that other pressures still play a crucial role. This study contributes to existing knowledge by emphasizing the need for robust regulatory frameworks that balance economic growth and financial reporting integrity.
了解避税与收益管理之间的关系对于评估税收政策和确保财务报告透明至关重要。先前的研究凸显了复杂性和不一致的研究结果，尤其是在与税收相关的报告激励措施的影响方面。本研究通过考察吉隆坡证券交易所（KLSE）上市公司中税收激励接受者身份对避税和收益管理的影响来解决这些问题。本研究探讨了从马来西亚投资发展局（MIDA）获得税收激励的公司是否表现出与未获得激励的公司不同的收益管理行为。本研究采用实际税率（ETR）来衡量避税行为，并采用酌情应计项目（DEM）来衡量收益管理行为。数据集包括人工提取的吉隆坡证交所上市公司2017财年的财务信息，以及MIDA的税收优惠政策受惠企业名单。分析技术包括方差分析、独立样本 t 检验和多元回归分析。研究结果表明，较高的避税率与较高的收益管理有关。此外，获得税收优惠的企业的 ETR 明显高于未获得优惠的企业。它们进行收益管理的可能性较低，这表明税收优惠可能会因合规压力而阻止激进的财务报告做法。补充分析表明，税收优惠政策并不能显著缓和避税与收益管理之间的关系，这意味着其他压力仍然起着至关重要的作用。本研究强调了在经济增长和财务报告完整性之间建立健全监管框架的必要性，从而为现有知识做出了贡献。
{"title":"TAX AVOIDANCE AND EARNINGS MANAGEMENT IN MALAYSIAN FIRMS: IMPACT OF TAX INCENTIVES","authors":"","doi":"10.46281/bjmsr.v9i2.2223","DOIUrl":"https://doi.org/10.46281/bjmsr.v9i2.2223","url":null,"abstract":"Understanding the relationship between tax avoidance and earnings management is crucial to evaluating tax policies and ensuring transparent financial reporting. Prior research has highlighted complexities and inconsistent findings, particularly concerning the impact of tax-related reporting incentives. This study addresses these issues by examining the influence of tax incentive recipient status on tax avoidance and earnings management among firms listed on the Kuala Lumpur Stock Exchange (KLSE). It examines whether firms receiving tax incentives from the Malaysian Investment Development Authority (MIDA) exhibit different earnings management behaviours than non-recipient firms. This study employs the effective tax rate (ETR) as a measure of tax avoidance and discretionary accruals (DEM) for earnings management. The dataset includes manually extracted financial information from firms listed on the KLSE for the financial year 2017 and a listing of tax incentive recipient firms from MIDA. Analytical techniques include ANOVA, independent samples t-test, and multiple regression analysis. The findings of this study suggest that higher tax avoidance relates to higher earnings management. Additionally, firms receiving tax incentives exhibit significantly higher ETRs than non-recipients. They are less likely to engage in earnings management, suggesting that tax incentives may deter aggressive financial reporting practices due to compliance pressures. The additional analysis indicates that tax incentives do not significantly moderate the relationship between tax avoidance and earnings management, implying that other pressures still play a crucial role. This study contributes to existing knowledge by emphasizing the need for robust regulatory frameworks that balance economic growth and financial reporting integrity.","PeriodicalId":479291,"journal":{"name":"Bangladesh journal of multidisciplinary scientific research","volume":"9 3","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141797778","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-07-07DOI: 10.46281/bjmsr.v9i2.2221
This study investigates the multifaceted determinants of malnutrition among Moroccan children under five, focusing on individual, household, and community influences. Utilizing data from the 2018 Population and Family Health Survey, the study analyzes 5,983 children aged 0–59 months. This study employs a multilevel modelling methodology to consider the data's hierarchical structure. The results reveal that 18% of Moroccan children suffer from undernutrition, while 10% experience overnutrition. Factors influencing malnutrition include child sex, age, birth weight, parental education, breastfeeding practices, household size, and poverty. Male children and those with a low birth weight are also at increased risk, with ORs of 1.49 and 1.93, respectively. Parental education, especially maternal education, protects against undernutrition (OR = 1.45). Breastfeeding practices impact child nutrition, with children not breastfed having higher odds of undernutrition (OR = 2.03). Children from poorer households are more likely to suffer from undernutrition (OR = 2.40). Conversely, children from wealthier households are at a higher risk of overnutrition (OR = 1.78). Community-level factors, such as poverty and regional disparities, influence undernutrition outcomes, with notable differences in regions like Beni Mellal-Khenifra (OR = 6.15). Children living in rural areas are more likely to experience undernutrition than their urban counterparts (OR = 1.87). The findings of this study conclude that addressing child malnutrition in Morocco requires multi-level interventions, focusing on parental education, breastfeeding promotion, support for low-birth-weight infants, and targeted strategies for socio-economic and geographic disparities.
{"title":"INDIVIDUAL AND CONTEXTUAL FACTORS OF MALNUTRITION IN MOROCCAN CHILDREN UNDER FIVE","authors":"","doi":"10.46281/bjmsr.v9i2.2221","DOIUrl":"https://doi.org/10.46281/bjmsr.v9i2.2221","url":null,"abstract":"This study investigates the multifaceted determinants of malnutrition among Moroccan children under five, focusing on individual, household, and community influences. Utilizing data from the 2018 Population and Family Health Survey, the study analyzes 5,983 children aged 0–59 months. This study employs a multilevel modelling methodology to consider the data's hierarchical structure. The results reveal that 18% of Moroccan children suffer from undernutrition, while 10% experience overnutrition. Factors influencing malnutrition include child sex, age, birth weight, parental education, breastfeeding practices, household size, and poverty. Male children and those with a low birth weight are also at increased risk, with ORs of 1.49 and 1.93, respectively. Parental education, especially maternal education, protects against undernutrition (OR = 1.45). Breastfeeding practices impact child nutrition, with children not breastfed having higher odds of undernutrition (OR = 2.03). Children from poorer households are more likely to suffer from undernutrition (OR = 2.40). Conversely, children from wealthier households are at a higher risk of overnutrition (OR = 1.78). Community-level factors, such as poverty and regional disparities, influence undernutrition outcomes, with notable differences in regions like Beni Mellal-Khenifra (OR = 6.15). Children living in rural areas are more likely to experience undernutrition than their urban counterparts (OR = 1.87). The findings of this study conclude that addressing child malnutrition in Morocco requires multi-level interventions, focusing on parental education, breastfeeding promotion, support for low-birth-weight infants, and targeted strategies for socio-economic and geographic disparities.","PeriodicalId":479291,"journal":{"name":"Bangladesh journal of multidisciplinary scientific research","volume":" 50","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141671163","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-05-01DOI: 10.46281/bjmsr.v9i1.2207
Fighting poverty is one of the most critical targets of development plans and initiatives. In the pursuit of lasting growth, emerging nations now face the most challenging issue of eliminating poverty, which remains one of the most significant challenges addressing humanity nowadays. The study explores the relationships between the institutional quality, financial development, and poverty-fighting initiatives of South Asian states. It goes beyond the potential bias in earlier studies caused by omitting variables by considering the impact of the interaction between the financial sector and institutional framework. The fixed effects models with STATA15 are employed in this study from 2000 to 2019. This study's analysis uses panel data and secondary sources to conduct the inquiry with a sample of 7 South Asian economies such as Bangladesh, Bhutan, India, the Maldives, Nepal, Pakistan, and Sri Lanka. This comprehensive compilation of annual data was done with consultation from the International Monetary Fund (IMF) and the World Bank Development Indicators (WDI). The study results show that a 1% increase in financial development is associated with a 39.88% decrease in poverty, which is statistically significant and favourable. It also reveals that institutional quality plays a vital role in poverty reduction in South Asia, with a 1% increase in institutional quality leading to a 2.61% increase in poverty. Besides, a 1% increase in GDP per capita growth correlates with a 0.12% decrease in poverty. The study's findings provide significant insights into poverty reduction by considering the relationship between institutional challenges and financial development through a flexible, functional structure in South Asian countries.
{"title":"THE FINANCIAL DEVELOPMENT, INSTITUTIONS, AND POVERTY REDUCTION: EMPIRICAL EVIDENCE FROM SOUTH ASIAN COUNTRIES","authors":"","doi":"10.46281/bjmsr.v9i1.2207","DOIUrl":"https://doi.org/10.46281/bjmsr.v9i1.2207","url":null,"abstract":"Fighting poverty is one of the most critical targets of development plans and initiatives. In the pursuit of lasting growth, emerging nations now face the most challenging issue of eliminating poverty, which remains one of the most significant challenges addressing humanity nowadays. The study explores the relationships between the institutional quality, financial development, and poverty-fighting initiatives of South Asian states. It goes beyond the potential bias in earlier studies caused by omitting variables by considering the impact of the interaction between the financial sector and institutional framework. The fixed effects models with STATA15 are employed in this study from 2000 to 2019. This study's analysis uses panel data and secondary sources to conduct the inquiry with a sample of 7 South Asian economies such as Bangladesh, Bhutan, India, the Maldives, Nepal, Pakistan, and Sri Lanka. This comprehensive compilation of annual data was done with consultation from the International Monetary Fund (IMF) and the World Bank Development Indicators (WDI). The study results show that a 1% increase in financial development is associated with a 39.88% decrease in poverty, which is statistically significant and favourable. It also reveals that institutional quality plays a vital role in poverty reduction in South Asia, with a 1% increase in institutional quality leading to a 2.61% increase in poverty. Besides, a 1% increase in GDP per capita growth correlates with a 0.12% decrease in poverty. The study's findings provide significant insights into poverty reduction by considering the relationship between institutional challenges and financial development through a flexible, functional structure in South Asian countries.","PeriodicalId":479291,"journal":{"name":"Bangladesh journal of multidisciplinary scientific research","volume":"18 6","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141044756","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-11-11DOI: 10.46281/bjmsr.v7i1.2115
Ethiopia has a large cattle and small ruminant population in Africa. Livestock production provided approximately 35 to 49% of the total agricultural GDP of Ethiopia. However, the commercialization of livestock genetic resources in the country is almost none. This review aims to present the application and efficiency of Artificial Insemination (AI) in cattle and small ruminants in Ethiopia. A bull is limited to less than 100 mating per year; however, a dairy sire, through AI, can provide semen for more than 60,000 services in one year. AI service in cattle was widely practised. The output of decades of crossbreeding programmes in Ethiopia through AI was relatively insignificant because the exotic breeds and their crossbreds account for about 1.44%. Applying AI service to small ruminants in Ethiopia is not a common practice except for a few ignitions. Conventional AI breeding indicated that the number of services per conception (NSC) ranged from 1.14 in local cows to 2.47 in different cows' genotypes under other management systems. The conception rate at first insemination (CR1) ranged from 7.14% in different genotypes of cows up to 75.5% in crossbred dairy cows kept under extensive and intensive management systems. Estrus synchronization followed AI breeding indicated that CR1 ranged from 24.69% in 95.8% of Zebu cows up to 70.6% in Boran cows kept under a semi-intensive management system. Strategic interventions for AI efficiency improvement should be identified and practised. Conventional AI breeding and estrus synchronization followed by AI breeding should be practised in small ruminant breeding in Ethiopia.
{"title":"A REVIEW OF THE STATUS OF APPLICATION AND EFFICIENCY OF ARTIFICIAL INSEMINATION IN CATTLE AND SMALL RUMINANTS BREEDING IN ETHIOPIA","authors":"","doi":"10.46281/bjmsr.v7i1.2115","DOIUrl":"https://doi.org/10.46281/bjmsr.v7i1.2115","url":null,"abstract":"Ethiopia has a large cattle and small ruminant population in Africa. Livestock production provided approximately 35 to 49% of the total agricultural GDP of Ethiopia. However, the commercialization of livestock genetic resources in the country is almost none. This review aims to present the application and efficiency of Artificial Insemination (AI) in cattle and small ruminants in Ethiopia. A bull is limited to less than 100 mating per year; however, a dairy sire, through AI, can provide semen for more than 60,000 services in one year. AI service in cattle was widely practised. The output of decades of crossbreeding programmes in Ethiopia through AI was relatively insignificant because the exotic breeds and their crossbreds account for about 1.44%. Applying AI service to small ruminants in Ethiopia is not a common practice except for a few ignitions. Conventional AI breeding indicated that the number of services per conception (NSC) ranged from 1.14 in local cows to 2.47 in different cows' genotypes under other management systems. The conception rate at first insemination (CR1) ranged from 7.14% in different genotypes of cows up to 75.5% in crossbred dairy cows kept under extensive and intensive management systems. Estrus synchronization followed AI breeding indicated that CR1 ranged from 24.69% in 95.8% of Zebu cows up to 70.6% in Boran cows kept under a semi-intensive management system. Strategic interventions for AI efficiency improvement should be identified and practised. Conventional AI breeding and estrus synchronization followed by AI breeding should be practised in small ruminant breeding in Ethiopia.","PeriodicalId":479291,"journal":{"name":"Bangladesh journal of multidisciplinary scientific research","volume":"2 6","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-11-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135041949","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-10-31DOI: 10.46281/bjmsr.v7i1.2110
The agriculture industry has been an enormous economic pillar in the production and consumption market value chain. The agriculture industry resets flower production factors with the agricultural technology revolution. The fastest technology provides innovative and intelligent decision-making strategies in seasonal cut flowers to increase production. This study briefs out existing farming practices, chain activity and farming technology’s significant impacts on the agriculture field and garden industry. Authors try to investigate cut flower production status and analyze production values to design innovative and intelligent strategies, especially for seasonal flower production. The study employs a flower dataset; hence, it applies floral parameter inputs and data mining association rules to create an output of the flower production category, which fits appropriately to evaluate flower market production value in a particular season. The article's result reveals that the proposed flower production strategy provides efficient and intelligent guidelines to increase flower production according to market demand. This study suggests an intelligent and friendly production strategy for gardeners that indicates the flower market gets continuous and quality production to meet consumers’ immediate market demand.
{"title":"SMART AND INTELLIGENT PRODUCTION STRATEGY FOR THE FLOWER MARKET USING DATA MINING KNOWLEDGE-BASED DECISION","authors":"","doi":"10.46281/bjmsr.v7i1.2110","DOIUrl":"https://doi.org/10.46281/bjmsr.v7i1.2110","url":null,"abstract":"The agriculture industry has been an enormous economic pillar in the production and consumption market value chain. The agriculture industry resets flower production factors with the agricultural technology revolution. The fastest technology provides innovative and intelligent decision-making strategies in seasonal cut flowers to increase production. This study briefs out existing farming practices, chain activity and farming technology’s significant impacts on the agriculture field and garden industry. Authors try to investigate cut flower production status and analyze production values to design innovative and intelligent strategies, especially for seasonal flower production. The study employs a flower dataset; hence, it applies floral parameter inputs and data mining association rules to create an output of the flower production category, which fits appropriately to evaluate flower market production value in a particular season. The article's result reveals that the proposed flower production strategy provides efficient and intelligent guidelines to increase flower production according to market demand. This study suggests an intelligent and friendly production strategy for gardeners that indicates the flower market gets continuous and quality production to meet consumers’ immediate market demand.","PeriodicalId":479291,"journal":{"name":"Bangladesh journal of multidisciplinary scientific research","volume":"12 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135928989","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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