Financial data modeling: an analysis of factors influencing big data analytics-driven financial decision quality

IF 1.8 Q3 MANAGEMENT

Journal of Modelling in Management Pub Date : 2024-07-22 DOI:10.1108/jm2-08-2023-0183

Manaf Al-Okaily, Aws Al-Okaily

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引用次数: 0

Abstract

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.

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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

目的金融企业正在寻找更好的方法来利用数据分析的力量，以提高其在财务建模时代的决策质量。本研究旨在探讨影响大数据分析驱动的财务决策质量的关键因素，相关文献对这些因素的关注度较低。研究结果SmartPLS分析结果显示，技术能力和数据能力对信息质量和数据驱动的洞察力有积极的直接影响，但对财务决策质量没有任何直接影响。研究结果还指出了信息质量和数据驱动型洞察力对高质量财务决策的重要性和影响。原创性/价值该研究首次在财务建模背景下探讨了影响大数据驱动型财务决策质量的关键因素，丰富了相关知识和文献。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Journal of Modelling in Management MANAGEMENT-

CiteScore

5.50

自引率

12.50%

发文量

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.