{"title":"财务数据建模：影响大数据分析驱动的财务决策质量的因素分析","authors":"Manaf Al-Okaily, Aws Al-Okaily","doi":"10.1108/jm2-08-2023-0183","DOIUrl":null,"url":null,"abstract":"<h3>Purpose</h3>\n<p>Financial firms are looking for better ways to harness the power of data analytics to improve their decision quality in the financial modeling era. This study aims to explore key factors influencing big data analytics-driven financial decision quality which has been given scant attention in the relevant literature.</p><!--/ Abstract__block -->\n<h3>Design/methodology/approach</h3>\n<p>The authors empirically examined the interrelations between five factors including technology capability, data capability, information quality, data-driven insights and financial decision quality drawing on quantitative data collected from Jordanian financial firms using a cross-sectional questionnaire survey.</p><!--/ Abstract__block -->\n<h3>Findings</h3>\n<p>The SmartPLS analysis outcomes revealed that both technology capability and data capability have a positive and direct influence on information quality and data-driven insights without any direct influence on financial decision quality. The findings also point to the importance and influence of information quality and data-driven insights on high-quality financial decisions.</p><!--/ Abstract__block -->\n<h3>Originality/value</h3>\n<p>The study for the first time enriches the knowledge and relevant literature by exploring the critical factors affecting big data-driven financial decision quality in the financial modeling context.</p><!--/ Abstract__block -->","PeriodicalId":16349,"journal":{"name":"Journal of Modelling in Management","volume":"41 1","pages":""},"PeriodicalIF":1.8000,"publicationDate":"2024-07-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Financial data modeling: an analysis of factors influencing big data analytics-driven financial decision quality\",\"authors\":\"Manaf Al-Okaily, Aws Al-Okaily\",\"doi\":\"10.1108/jm2-08-2023-0183\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<h3>Purpose</h3>\\n<p>Financial firms are looking for better ways to harness the power of data analytics to improve their decision quality in the financial modeling era. This study aims to explore key factors influencing big data analytics-driven financial decision quality which has been given scant attention in the relevant literature.</p><!--/ Abstract__block -->\\n<h3>Design/methodology/approach</h3>\\n<p>The authors empirically examined the interrelations between five factors including technology capability, data capability, information quality, data-driven insights and financial decision quality drawing on quantitative data collected from Jordanian financial firms using a cross-sectional questionnaire survey.</p><!--/ Abstract__block -->\\n<h3>Findings</h3>\\n<p>The SmartPLS analysis outcomes revealed that both technology capability and data capability have a positive and direct influence on information quality and data-driven insights without any direct influence on financial decision quality. The findings also point to the importance and influence of information quality and data-driven insights on high-quality financial decisions.</p><!--/ Abstract__block -->\\n<h3>Originality/value</h3>\\n<p>The study for the first time enriches the knowledge and relevant literature by exploring the critical factors affecting big data-driven financial decision quality in the financial modeling context.</p><!--/ Abstract__block -->\",\"PeriodicalId\":16349,\"journal\":{\"name\":\"Journal of Modelling in Management\",\"volume\":\"41 1\",\"pages\":\"\"},\"PeriodicalIF\":1.8000,\"publicationDate\":\"2024-07-22\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Journal of Modelling in Management\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1108/jm2-08-2023-0183\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q3\",\"JCRName\":\"MANAGEMENT\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Modelling in Management","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1108/jm2-08-2023-0183","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"MANAGEMENT","Score":null,"Total":0}
Financial data modeling: an analysis of factors influencing big data analytics-driven financial decision quality
Purpose
Financial firms are looking for better ways to harness the power of data analytics to improve their decision quality in the financial modeling era. This study aims to explore key factors influencing big data analytics-driven financial decision quality which has been given scant attention in the relevant literature.
Design/methodology/approach
The authors empirically examined the interrelations between five factors including technology capability, data capability, information quality, data-driven insights and financial decision quality drawing on quantitative data collected from Jordanian financial firms using a cross-sectional questionnaire survey.
Findings
The SmartPLS analysis outcomes revealed that both technology capability and data capability have a positive and direct influence on information quality and data-driven insights without any direct influence on financial decision quality. The findings also point to the importance and influence of information quality and data-driven insights on high-quality financial decisions.
Originality/value
The study for the first time enriches the knowledge and relevant literature by exploring the critical factors affecting big data-driven financial decision quality in the financial modeling context.
期刊介绍：
Journal of Modelling in Management (JM2) provides a forum for academics and researchers with a strong interest in business and management modelling. The journal analyses the conceptual antecedents and theoretical underpinnings leading to research modelling processes which derive useful consequences in terms of management science, business and management implementation and applications. JM2 is focused on the utilization of management data, which is amenable to research modelling processes, and welcomes academic papers that not only encompass the whole research process (from conceptualization to managerial implications) but also make explicit the individual links between ''antecedents and modelling'' (how to tackle certain problems) and ''modelling and consequences'' (how to apply the models and draw appropriate conclusions). The journal is particularly interested in innovative methodological and statistical modelling processes and those models that result in clear and justified managerial decisions. JM2 specifically promotes and supports research writing, that engages in an academically rigorous manner, in areas related to research modelling such as: A priori theorizing conceptual models, Artificial intelligence, machine learning, Association rule mining, clustering, feature selection, Business analytics: Descriptive, Predictive, and Prescriptive Analytics, Causal analytics: structural equation modeling, partial least squares modeling, Computable general equilibrium models, Computer-based models, Data mining, data analytics with big data, Decision support systems and business intelligence, Econometric models, Fuzzy logic modeling, Generalized linear models, Multi-attribute decision-making models, Non-linear models, Optimization, Simulation models, Statistical decision models, Statistical inference making and probabilistic modeling, Text mining, web mining, and visual analytics, Uncertainty-based reasoning models.
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