Emerging Technologies and Quality of Financial Reporting of Selected Quoted Firms

Emerging Technologies and Quality of Financial Reporting of Selected Quoted Firms

Adeniran, Olusegun Samson, Ajagun, Olusegun Peter, Kehinde James, Bolarinwa, Abike Sehilat

Lagos State University, Ojo, Nigeria

DOI: https://dx.doi.org/10.47772/IJRISS.2025.90700028

Received: 14 June 2025; Accepted: 18 June 2025; Published: 28 July 2025   

ABSTRACT

This study examines the influence of emerging technologies—Artificial Intelligence (AI), Blockchain, and Data Analytics—on the quality of financial reporting within Fidelity Bank Plc, Lasaco Assurance Plc, and AXA Mansard Insurance Plc. the study seeks to explore how they enhance financial reporting practices and address the shortcomings of traditional methods. The methodology involves a quantitative approach, employing regression analysis to empirically evaluate the impact of AI, Blockchain, and Data Analytics on the quality of financial reporting. Data were collected using primary source via structured questionnaire and analyzed to determine the statistical significance and magnitude of the impact of these technologies. The study concludes that AI, Blockchain, and Data Analytics significantly improve the quality of financial reporting by increasing accuracy, transparency, and efficiency. AI enhances anomaly detection and predictive analytics, The recommendations highlight the necessity for these firms to invest in these technologies and for policymakers to update regulatory frameworks to support their integration. Continuous professional development and training programs are essential to equip financial professionals with the skills required to effectively leverage these technologies. By adopting these recommendations, Fidelity Bank Plc, Lasaco Assurance Plc, and AXA Mansard Insurance Plc can enhance their financial reporting quality, meet stakeholder expectations, and maintain a competitive edge in the evolving financial landscape.

Keywords: Artificial intelligence, blockchain technologies, data analytics, Comparability Reliability, Accuracy

INTRODUCTION

Financial statements not only reveal an organization’s financial situation but also include information on value-added, changes in equity, and cash flows over a given period (Lev, 2018). This information benefits various users in making educated financial decisions. Users of financial reporting rely on its fundamental needs being met, requiring accuracy, relevance, dependability, comparability, and ease of understanding (Palepu, Healy, Wright, Bradbury, & Coulton, 2020). Qualitative financial reports improve securities pricing and reduce information asymmetry, aiding resource allocation (Alade, 2018). Regulatory organizations set accounting standards to ensure the reliability and uniformity of financial statements (Oyedokun & Emmanuel, 2015). IFRS implementation improves stakeholder communication and reduces information inconsistencies (Ball, 2016), lowering costs associated with producing multiple versions of financial statements for global companies (Leuz & Wysocki, 2016).

Developments in AI, machine learning, blockchain technology, cloud computing and data analytics have transformed financial data analysis, enhancing audit efficiency and fraud detection (Goodell, Kumar, Lim, & Pattnaik, 2021). Saggi and Jain (2018); George and Patatoukas (2021) noted that technology like big data analytics and blockchain offers significant potential for improving financial reporting quality and transparency. The ongoing adoption of technological tools like AI and blockchain continues to reshape the accounting profession, creating both opportunities and challenges (Moll & Yigitbasioglu, 2019; Goh, Pan, Seow, Lee, & Yong, 2019). The internet has facilitated global financial reporting, integrating database technology to enhance data accessibility (Stallkamp & Schotter, 2021). As business environments become more dynamic, there is a push for improvements in financial reporting, driven by the accounting community’s interest in understanding its role in an increasingly online and technology driven world (Palepu et al., 2020; Bilsland, Nagy, & Smith, 2020).

Despite its growing significance, few studies provide a theoretical basis for the impact of emerging technology on financial reporting. Most research adopts a technological imperative perspective, neglecting non-technical issues and relying on limited personal experiences without historical context. This gap makes it difficult to evaluate theories accurately and base policy decisions on them. Therefore, this research explores emerging technologies and qualities of financial reporting of selected quoted firms.

Statement of the Problem:

The evolution of financial reporting, driven by technological advancements and globalization, has significantly transformed the way organizations present and stakeholders interpret financial data. Historically, financial reporting has evolved from simple transaction recording to complex systems that provide comprehensive insights into an organization’s financial health. However, this evolution has not been without challenges. Despite the numerous advancements in technology and the adoption of standardized frameworks like the International Financial Reporting Standards (IFRS), several gaps and issues persist in the realm of financial reporting.

One of the primary problems in financial reporting is the technological disparity among organizations. While larger corporations may have the means to integrate advanced technologies such as artificial intelligence (AI), machine learning, and blockchain into their financial reporting processes, smaller entities often lag. This technological gap not only creates discrepancies in the quality, reliability, and comparability of financial reports but also increases the risk of cybersecurity threats and data breaches, as not all organizations have the same level of access to robust security measures.

Furthermore, the rapid pace of technological change poses a continuous challenge for financial professionals who must keep up with evolving tools and methodologies. The need for ongoing education and skill development in new technologies can strain resources and time, particularly for professionals in smaller firms. This gap in technological proficiency can lead to inefficiencies and inaccuracies in financial reporting, undermining the reliability of the data presented. However, these technologies are still in the early stages of adoption, and their long-term impact on the accounting profession remains unclear. Hence, there is a need to study the relationship between emerging technologies and the quality of financial reporting.

Objectives of the Study

This study aims to investigate the relationship between emerging technologies and the quality of financial reporting of selected quoted firms. The typical objectives include to:

• Assess the influence of artificial intelligence on the accuracy of financial report of quoted firms.
• Assess the influence of blockchain technology on the reliability of financial report of quoted firms.
• Analyze the barriers hindering the adoption of emerging technologies in financial reporting of quoted firms.

Research Question

• To what extent does artificial intelligence influence the accuracy of financial report of quoted firms?
• To what extent does blockchain technology influence the reliability of financial report of quoted firms?
• Are there barriers hindering the adoption of emerging technologies in financial reporting of quoted firms?

Research Hypothesis

• Ho₁: There is no significant relationship between artificial intelligence and the quality of financial report of selected quoted firms.
• Ho₂: There is no significant relationship between blockchain technology and the quality of financial report of selected quoted firms.

Scope of the Study

This study focuses on a select group of quoted firms listed on the exchange as of December 2023 operating in diverse industries in Nigeria, aiming to provide a comprehensive analysis of the influence of emerging technologies on their financial reporting practices. The examination encompasses various aspects, including data integrity, transparency, timeliness, and compliance with accounting standards. Furthermore, the study explores both the opportunities and challenges associated with the adoption of emerging technologies in financial reporting.

LITERATURE REVIEW

Conceptual Review

Emerging Technologies:

Emerging technologies are those whose potential for development, real-world applications, or both remains substantially untapped. While most of these technologies are new, several older technologies are also finding new uses. It is generally perceived that emerging technologies have the power to alter the status quo (Ding, Korolov, Wallace, & Wang, 2021). Radical originality (in application, if not in sources), coherence, significant influence, uncertainty and ambiguity, and relatively quick expansion are characteristics of emerging technologies (Sarker, 2022).

A wide range of technologies, including robotics, artificial intelligence, internet of things, blockchain technology, and machine learning, data analytics, are considered emerging technologies. The technological convergence of disparate systems evolving toward common aims may give rise to new technology domains (Agarwal, Swami & Malhotra, 2024). Converging technologies are those once independent sectors that are in some way heading towards stronger interconnection and similar goals. Emerging technologies are those technological advancements that reflect progressive developments within a field for competitive advantage (Enholm, Papagiannidis, Mikalef, & Krogstie, 2022).

Artificial Intelligence:

According to Ahmed, Mardini, Burton, and Dunne (2018), the simulation of human intelligence processes by machines, particularly computer systems, is known as artificial intelligence. Expert systems, natural language processing (NLP), speech recognition, and machine vision are a few applications of artificial intelligence. Machine learning algorithms in AI require certain hardware and software in order to be written and trained. Although there isn’t just one programming language used for AI, Python, R, Java, C++, and Julia are all widely used by AI engineers.

Artificial Intelligence (AI) represents a transformative force in financial reporting, offering numerous advantages that enhance the overall quality and reliability of financial data (Enholm, Papagiannidis, Mikalef, & Krogstie, 2022). Integrating AI into financial processes has revolutionized how firms manage and report their financial information, significantly improving accuracy, efficiency, and strategic decision-making. One of the primary benefits of AI in financial reporting is the automation of routine tasks. Traditionally, financial reporting involves labor-intensive activities such as data entry, reconciliation, and preliminary analysis, which are prone to human error (Hasan, 2021). AI systems can automate these processes, ensuring that data is consistently accurate and reducing the likelihood of mistakes. This automation not only enhances the reliability of financial reports but also frees up valuable time for finance professionals, allowing them to focus on more strategic and analytical tasks that add greater value to the organization.

Furthermore, AI excels in predictive analytics, a capability that has become increasingly vital in today’s fast-paced business environment (Bharadiya, 2023). By analyzing vast amounts of historical data, AI algorithms can identify patterns and trends that are not immediately apparent to human analysts (M-Alshater, 2022). This enables firms to forecast future financial performance more precisely and identify potential risks and opportunities well in advance. For instance, predictive analytics can help companies anticipate market fluctuations, optimize cash flow management, and make informed investment decisions. As a result, businesses can adopt a proactive approach to financial management, thereby enhancing their overall strategic planning and competitive edge. AI also contributes significantly to the accuracy and integrity of financial reporting through its advanced data processing capabilities. Financial data often originates from multiple sources and formats, making it challenging to ensure consistency and completeness. AI-powered systems can seamlessly integrate and standardize data from diverse sources, providing a comprehensive and coherent view of the financial landscape (Allioui & Mourdi, 2023). This holistic perspective is crucial for stakeholders who rely on financial reports for decision-making, as it ensures that the information presented is both accurate and reliable. Moreover, the use of AI in financial reporting enhances transparency and accountability (Li, Zhou, Luo, Benitez, & Liao, 2022).

Blockchain Technology

Gurzawska (2020) asserted that blockchain is a crucial decentralized ledger that functions as a definitive source of information in contrast to the conventional accounting ledger. The conventional accounting process, which generates the ledger, involves the entry of financial information into personal ledgers and the subsequent reconciliation of those entries by accountants (Nurhayati & Muda, 2022). This stands in contrast to the decentralized and observational blockchain ledger, which involves all participants in a sequence of transactions. According to Nnamezie (2021), the prevailing conventional approach to financial reporting is arduous and labour-intensive, resulting in increased expenditures on human resources and constraining the productivity and efficacy of accountants as the workload and transaction volume escalate within a specific business community. Contrary to the conventional approach to financial reporting, Thimm and Rasmussen (2023) contend that the incorporation of blockchain technology into financial reporting will result in significant modifications to established procedures pertaining to billing, documentation, contracts, and cash flow management. According to Javaid, Haleem, Singh, Suman, and Khan (2022), a novel approach to managing accounting data will be facilitated by blockchain technology. This approach is described as a disbursed ledger architecture with a distinct structure that stores transaction records in multiple blocks that are cryptographically linked. Moving forward, the field of accounting will be required to implement digital ledgers comprising account statements and recordings of blockchain-enabled transactions. In the future, a portion of the organization’s accounting systems will be implemented in a decentralized fashion, featuring open access and assessments as stated by Chong, Lim, Hua, Zheng, and Tan (2019). This implies that the accounting data of a company can be uploaded to a blockchain-enabled platform, allowing the company’s stakeholders to observe data and business transactions as they transpire. Furthermore, the stakeholders will have the ability to scrutinize any alterations made to the transactions or records, as such modifications will be reflected in the company’s blockchain ledger.

Data Analytics

The logical decision-making process used to improve the company’s performance, particularly its operational and financial performance, is built on information. According to systems theory, data is the raw material for information creation since it is subjected to categorization, filtering, and analysis procedures to generate comprehensible and perceptible outcomes (Heidt, Gerlach, & Buxmann, 2019).  Data Analytics is the distinguishing feature of the business environment of today. Data Analytics is described as an extremely vast and complicated collection of structured and unstructured data that are difficult to manage, handle, or analyze using typical data processing methods (Bharadiya, 2023). According to Emovon, Onogholo, and Agbo (2023), data analytics is knowledge that is too massive, complicated, and dynamic for traditional technologies, skills, and infrastructure to process in a reasonable amount of time. Ghasemaghaei and Calic (2019) argued that data analytics refers to the volume, velocity, and variety of information assets that necessitate novel and cost-effective information processing methods to increase insight and decision-making effectiveness. (Ajagun, Arowolo, & Adeyemi (2025).

Relevance is also enhanced through data analytics, as these tools can sift through vast amounts of data to identify the most pertinent information. By focusing on key performance indicators and critical financial metrics, data analytics ensures that financial reports highlight the most relevant data for decision-making. This targeted approach helps stakeholders understand the financial health and performance of the organization more clearly. Additionally, data analytics can incorporate external data, such as market trends and economic indicators, providing a broader context for financial performance and helping firms anticipate future trends. Furthermore, data analytics supports compliance with regulatory requirements by automating the monitoring and reporting of financial activities. Compliance analytics can ensure that financial reports adhere to the relevant accounting standards and regulations, reducing the risk of non-compliance and associated penalties (Oguejiofor, Omotosho, Abioye, Alabi, Oguntoyinbo, Daraojimba, & Daraojimba, 2023). Automated compliance checks can also identify potential issues early, allowing firms to address them proactively.

Purpose of Financial Reporting

Financial reporting serves as a means to disclose pertinent details regarding the financial consequences of activities, transactions, and events that impact the operational outcomes and overall financial health of an entity. The information provided can furnish investors, creditors, and other external users with a comprehensive understanding of an economic unit’s profitability. The overarching aim is further divided into distinct objectives, and the provision of particular information is necessary to accomplish each of these objectives:

• Disclosure of data to facilitate the evaluation of financial standing and economic capability.
  Provision of data for evaluating the capability and functionality of profitability.
• Provision of data to facilitate the evaluation of cash supply and consumption.
• Provision of information for evaluating the execution of legal obligations and management responsibilities.
• The provision of supplementary information to enhance comprehension of the information provided and enable anticipation of future developments.
• Dissemination of information about supplementary value in services and production, profitability, operational efficiency, and staff health promotion; as well as initiatives concerning environmental preservation and other social and cultural undertakings.

Characteristics of Quality Financial Reporting

Relevance: Relevance is a cornerstone of quality financial reporting. Financial information must be pertinent to the decision-making needs of users. Relevant information helps stakeholders, such as investors, creditors, and regulators, to make well-informed decisions by providing insights into the organization’s past performance, current financial status, and future prospects. For financial information to be relevant, it must possess predictive value, enabling users to predict future outcomes, and confirmatory value, allowing them to confirm or adjust their previous evaluations. Relevance ensures that financial reports provide meaningful insights that directly impact stakeholders’ economic decisions.

Reliability: Reliability, also known as faithful representation, is critical for ensuring that financial information accurately reflects an organization’s financial position and performance. Reliable financial reports are complete, neutral, and free from material error. Completeness ensures that all necessary information is included, neutrality guarantees that the information is unbiased, and freedom from error ensures that the data is accurate. Reliable financial reporting builds stakeholder trust and confidence, as users can depend on the information to be an accurate reflection of the organization’s financial reality. This trust is essential for maintaining the integrity of financial markets and for fostering long-term business relationships.

Comparability: Comparability allows stakeholders to analyze financial statements across different periods and between different organizations. This characteristic is crucial for identifying trends, assessing relative performance, and making investment and credit decisions. Consistent application of accounting principles and standards over time and across entities ensures that financial statements are comparable. When financial information is comparable, users can better evaluate the performance of an organization relative to its peers, and investors can make more informed decisions about where to allocate their resources.

Understandability: Understandability ensures that financial information is presented clearly and concisely, making it accessible to users with a reasonable knowledge of business and economic activities. Financial statements should be free from unnecessary complexity and should use plain language wherever possible. Notes and explanations accompanying financial statements should provide additional context to help users comprehend the information. When financial information is easy to understand, it broadens the range of stakeholders who can effectively use the data, including non-experts who may rely on financial reports for decision-making purposes.

Timeliness: Timeliness refers to the provision of financial information within a time frame that makes it useful for decision-making. Timely financial reporting ensures that the information is available when stakeholders need it to make decisions, rather than being outdated. This characteristic is particularly important in today’s fast-paced business environment, where delays in reporting can result in missed opportunities or failure to mitigate risks. Timely financial reporting also enhances transparency, as it shows that the organization is responsive and proactive in sharing its financial information with stakeholders.

While the five primary characteristics form the foundation of quality financial reporting, there are other enhancing qualities that contribute to its overall effectiveness:

Verifiability: Ensures that different knowledgeable and independent observers can reach a consensus that an event is represented faithfully.

Consistency: Requires that accounting methods remain unchanged over time or that changes in methods are adequately explained and justified.

Transparency: Ensures that all material information is disclosed in a clear and unbiased manner, providing a complete picture of the organization’s financial health.

Emerging Technology’s Influence on Financial Reporting

The implementation of emerging technology has significantly altered the landscape of financial reporting, presenting a multitude of advantages:

• Efficiency enhanced in financial reporting processes through the automation of repetitive tasks and the streamlining of data processing workflows; this results in time and resource savings and eradication of manual errors.
• The implementation of sophisticated error detection mechanisms and data validation checks serves as a tool to augment the precision and dependability of financial information, thereby mitigating the potential for inaccurate reporting.
• The utilisation of online reporting platforms and real-time data processing empowers organisations to promptly disseminate financial information, thereby furnishing stakeholders with current reliable and relevant insights regarding the financial performance of the entity.
• Transparency and accountability is attained in financial reporting through the use of electronic data exchange mechanisms and standardised reporting formats, which enable stakeholders to compare and analyse financial information more efficiently.

Theoretical Review

Task-Technology Fit Theory (TTF)

Task-Technology Fit (TTF), as described by Goodhue and Thompson (1995), is the appropriateness of implementing a new technology to carry out a task. An example of this would be the use of artificial intelligence (AI) as well as other emerging technologies in the input, processing, and output stages of the accounting and financial reporting process. According to the concept, a technology is considered task-fit if its features match the requirements of the task and its users anticipate that using it will improve performance. While the AI technology has characteristics like speed, accuracy, diligence, versatility, reliability, memory, and consistency, the task characteristics are those of financial reporting, such as relevance, faithful representation, comparability, verifiability, timeliness, and understandability. Users will be more inclined to employ AI technology in financial reporting if both traits match and they will be certain of the performance impact.

Empirical Review

Oyeniyi, Ugochukwu and Mhlongo (2024) conducted a critical review and analysis of the influence of AI on financial reporting quality. The study, which was based on a qualitative research technique, carefully examines how AI technologies are incorporated into the financial reporting environment with the goal of illuminating the ways in which AI can support conventional reporting procedures. The paper examines the development of financial reporting, the fundamental ideas of artificial intelligence, and the mutually beneficial relationship between financial analytics and AI applications through the prism of this investigation. It concludes with a sophisticated knowledge of AI’s potential to transform financial reporting. The major conclusions of the study show that while AI presents issues relating to ethical considerations, regulatory compliance, and the possibility of biases, it also considerably improves reporting accuracy, analytical depth, and efficiency. The findings open the door for a carefully considered list of recommendations that support the creation of thorough regulatory frameworks, the standardization of AI systems in financial reporting, and the encouragement of AI literacy among financial professionals. It was however concluded that the strategic integration of artificial intelligence (AI) into financial reporting is not only a choice but also a requirement for the field’s advancement. It encourages stakeholders to embrace this technological evolution in a way that balances innovation with moral and legal obligations.

Oladejo and Yinus (2020) evaluated the impact of e-accounting practices on financial reporting quality of selected banks in Nigeria. Primary data were gathered through the use of a questionnaire, while secondary data spanning the years 2010–2017 was obtained from the chosen banks’ annual reports. Using homogeneous purposive sampling, ten deposit money banks in Nigeria were chosen. A total of two hundred and sixty of the three hundred copies of the questionnaire that were randomly distributed to the bank employees who were chosen for the study were returned. At a 95% confidence level, descriptive statistics like tables and percentages were used to analyze the data, and inferential statistics like pooled regression analysis were also used. The findings indicated that Bank Size (BS) at 92%; Cost of ICT Deployment (CID) at 69%; Perceived Ease of Use (PEOU) at 74%; and Perceived Benefit (PB) at 86% were specific factors influencing electronic accounting adoption in the selected banks. There were notable variations in the factors influencing the adoption of e-accounting (R² = 0.9661; Adj R² = 0.9633; F= 1318.61; p= 0.000). The fixed effect estimation result revealed that electronic accounting promotes users confidence on the financial statement of the selected banks (R² =0.6203; Adj R² =0.6150; F=67.20; p=0.000). The study concluded that the adoption of e-accounting was influenced by all of the variables that were taken into consideration, including BS, CID, PEOU, and PB. It also found that e-accounting practice improved accounting procedure, report generation timeliness, and bank financial reporting quality. It was suggested that deposit money banks work more to create systems that encourage the adoption of e-accounting and give consumers more faith in the financial data that banks release.

Danach, Hejase, Faroukh, Fayyad-Kazan, and Moukadem (2024) assessed the impact of blockchain technology on financial reporting and audit practices. The study examines the potential consequences of blockchain adoption in accounting, with a focus on regulatory compliance, smart contracts, cost-effectiveness, audit trail integrity, and accuracy of financial reporting. Using examples from real-world applications and expanding regulatory frameworks, it provides a comprehensive understanding of the ways in which blockchain technology is transforming financial reporting and auditing procedures. The report also highlights how important it is to comprehend how blockchain technology alters conventional accounting procedures as more businesses integrate it into their financial operations. It highlights the advantages of blockchain technology. To fully achieve its potential in audit assurance and financial reporting, it also highlights the necessity of flexibility and adherence to regulatory standards. Through this inquiry, the study was able to furnish invaluable information about the potential of blockchain technology to transform the accounting sector to accounting managers, professionals, policymakers, and other relevant stakeholders.

Research Methods

Research Design

A descriptive survey research design was used for this study due to its suitability in collecting answers regarding

specific and essential questions. It is also suitable in asking questions in multiple formats as per the target audience and the intent of the study. The survey research design was used because the study involved the use of primary data through distribution of questionnaire to some selected respondents in the companies considered by the study.

Area of Study

This study covers the area where emerging technologies are applied in financial reporting process of the selected

firms ‘Fidelity Bank Plc, Lasaco Assurance Plc, and AXA Mansard Insurance Plc.’. For the purpose of this study, the influence of emerging technologies on the quality of financial reporting was evaluated.

Population of the Study

The population for this study consists of staffs of the selected quoted firms. Specifically, the population size was estimated to be 10000 staffs. These individuals play a crucial role in financial reporting processes within their respective organizations, either directly involved in preparing financial reports or contributing to the automation of financial reporting tasks. As such, they represent a key demographic for studying the impacts of Information Technology on financial reporting practices within the selected quoted firms.

Sample Size and Sampling Technique

n=N/(1+Ne^2 )

N=Population size

n=sample size

e=Error limit (0.05 based on a 95% confidence level)

=10000/(1+(10000*〖0.05〗^2 ) )

n=385

The sample size was determined using Yaro Yamani formula:

Instrumentation

The instrumentation for this study primarily consists of a structured questionnaire designed to gather data from the target population of staffs working in the selected quoted firms.

Validity of the Instrument

Validity testing was conducted through a comprehensive process involving crosschecking by the supervisor and expert validation. The supervisor, an experienced academic and subject matter expert reviewed the questionnaire for alignment with research objectives, clarity, and content validity. Additionally, a panel of 10 subject matter experts in Information Technology and financial reporting provide input on the questionnaire’s content, structure, and relevance. Their feedback was instrumental in identifying shortcomings and guiding revisions to enhance the questionnaire’s validity.

Reliability of the Instrument

The reliability test employed Cronbach’s alpha coefficient, which measures the extent to which items within a scale or instrument are correlated with each other. A high Cronbach’s alpha value indicates strong internal consistency among the questionnaire items, indicating that they measure the same underlying construct reliably. A Cronbach’s alpha value of 0.8081 was gotten and this suggests that the items within the scale reliably measure the same underlying construct which indicates internal consistency.

METHOD OF DATA ANALYSIS

The data collected were presented using tables, facilitating a clear visualization of the findings. Regression analysis was employed to compare the relationships between the variables outlined in the hypotheses. The significance of these relationships was assessed using p-values obtained from the regression model at a 5% level of significance. The collected data for both dependent and independent variables were sourced from the administered questionnaires. Subsequently, the data were meticulously examined and qualitatively analyzed using the Statistical Package for Social Sciences (SPSS), version 27. This software facilitated comprehensive statistical analysis, enabling a thorough exploration of the research findings and their implications.

The responses were obtained through a five-point Likert-type scale which was subsequently converted into quantitative data and used to do the regression analysis model to test the strength of the relationship between the dependent and independent variables which are as follows: Where:

Dependent Variable:

Financial Reporting quality (Y)

Independent Variables:

• Artificial Intelligence
• Big data analytics
• Blockchain

The regression model is as follows:

Meanwhile the error term takes care of unaccounted factors not included in the model.

In addition, spearman correlation was used to assess the strength of the relationship between the variables. Spearman rank is calculated as follows:

r=1-(6∑_(i=1)^n▒〖d_i〗^2 )/n(n^2-1)

d = Difference between the ranks of two corresponding variables for each data pair.

Data Analysis and Presentation of Findings

RESULT

Table 4.1 delineates the demographic characteristics of the surveyed population. Lasaco Assurance Plc commands the largest representation at 39%, followed closely by Fidelity Bank Plc at 31%, and AXA Mansard Insurance Plc at 30%. This distribution underscores the diversity of organizational backgrounds within the surveyed population, potentially reflecting the perspectives and practices prevalent across different firms.

Gender distribution within the sample population appears relatively balanced, with male respondents comprising 55% and female respondents representing 45% of the total. The age demographics of the surveyed population exhibit a diverse spread across different age brackets. Notably, respondents aged 25 to 30 years constitute the largest cohort at 26%, followed by those aged 31 to 35 years at 23%. Additionally, individuals under the age of 25 comprise 21% of the sample, while those aged 36 to 40 years and 41 and above represent 17% and 13%, respectively.

Furthermore, the position of respondents is delineated based on their professional roles. Financial Analysts emerge as the largest group, accounting for 26% of the sample, followed by IT Professionals and Finance managers at 20% and 18%, respectively. Auditors constitute 16% of the respondents, while Regulatory Authorities and Other categories each represent 10% of the sample.

Table 4.1:       Demographic Characteristic

Table 4.2 presents insights into the perceptions regarding financial reporting quality in the context of emerging technologies. The majority of respondents (70%) strongly agree with this statement, indicating a widespread belief in the positive impact of emerging technologies such as artificial intelligence, blockchain, and big data analytics on the accuracy of financial reporting. Additionally, 15% agree, while the remaining 15% exhibit varying degrees of neutrality or disagreement. A significant proportion of respondents (55%) agree that emerging technologies contribute to enhancing the reliability of financial reporting by reducing errors and inconsistencies. Moreover, 20% strongly agree with this assertion. However, there is a notable segment (20%) expressing neutral or opposing views on the matter. 40% of respondents strongly agree that the adoption of emerging technologies in financial reporting practices leads to increased transparency and accountability. An additional 25% agree with this statement, reflecting a widespread perception regarding the positive influence of technology adoption on transparency and accountability. Nearly half of the respondents (48%) strongly agree that emerging technologies facilitate timely and efficient financial reporting processes. Moreover, 27% express agreement with this statement. However, a portion of respondents (20%) exhibit neutral or opposing views regarding the role of emerging technologies in enhancing reporting efficiency.

Table 4.2: Financial Reporting Quality

Table 4.3 presents the drivers influencing the adoption of technology within a specific context, categorized based on frequency and percentage distribution. Representing 20% of the responses, cost reduction and efficiency gains emerge as significant drivers of technology adoption. This suggests that organizations are motivated to leverage technology to streamline processes, optimize resource utilization, and enhance operational efficiency, thereby reducing costs and improving overall productivity. Nearly a quarter of the respondents (25%) cite competitive pressures and market demands as key drivers influencing technology adoption decisions. This indicates that organizations are compelled to embrace technology to stay competitive in dynamic market environments, meet evolving customer expectations, and capitalize on emerging opportunities to gain a strategic edge over rivals. 16% of the respondents highlighted regulatory compliance requirements as a pivotal factor driving technology adoption. This underscores the significance of adhering to regulatory standards and mandates, necessitating the adoption of technology solutions to ensure compliance, mitigate risks, and maintain regulatory alignment. 21% of respondents recognize the potential for improved decision-making as a compelling driver of technology adoption. This suggests that organizations perceive technology as a catalyst for enhancing data analytics capabilities, facilitating real-time insights, and enabling informed decision-making processes to drive strategic outcomes and business performance. 14% of respondents identify enhanced data security and integrity as crucial drivers influencing technology adoption. This underscores the growing concerns surrounding data privacy, cybersecurity threats, and the imperative to safeguard sensitive information, prompting organizations to invest in technology solutions that bolster data security measures and ensure data integrity. A smaller segment, comprising 5% of the responses, cites other factors explicitly enumerated in the predefined categories as drivers of technology adoption such as industry-specific challenges, organizational priorities, or technological innovations.

Table 4.3:  Drivers of Emerging Technology Adoption

Table 4.4 outlines the challenges and opportunities associated with integrating emerging technologies.  A notable proportion of respondents (35%) agree, with an additional 20% strongly agreeing, that emerging technologies introduce complexity and necessitate substantial investments in infrastructure and training. This highlights the perceived challenges associated with adopting and integrating emerging technologies, underscoring the need for organizations to allocate resources effectively to address these complexities and ensure successful implementation. Thirty percent (30%) of respondents express strong agreement, while 25% agree that concerns about data security and privacy pose significant challenges to the widespread adoption of emerging technologies in financial reporting. This indicates a widespread recognition of the importance of safeguarding data integrity and privacy, signalling potential barriers that organizations must address to foster trust and confidence in technology-enabled financial reporting processes. Nearly half of the respondents (49%) strongly agree that the lack of skilled personnel and compatibility issues with existing systems present significant challenges to the widespread adoption of emerging technologies in financial reporting. An additional 31% agree with this assertion. These findings underscore the critical importance of addressing skill gaps and ensuring seamless integration with legacy systems to overcome barriers to technology adoption effectively. Thirty-five percent (35%) of respondents agree, with an additional 25% expressing strong agreement, that emerging technologies offer opportunities to enhance data analytics capabilities and improve decision-making processes in financial reporting. This highlights the potential benefits of leveraging advanced analytics tools and technologies to derive actionable insights from financial data, driving informed decision-making and strategic outcomes. Thirty-five percent (35%) of respondents agree, while 30% express strong agreement that the integration of emerging technologies can lead to increased efficiency and cost savings in financial reporting practices. This underscores the perceived benefits of technology adoption in streamlining processes, optimizing resource utilization, and driving operational efficiency to achieve tangible cost savings and performance improvements.

Table 4.4: Challenges and Opportunities of Integrating Emerging Technologies

Table 4.6 illustrates the Spearman rank correlation coefficients among four key constructs: Financial Report quality, AI, Blockchain, and Data analytics. The coefficients reveal significant strong positive correlations between Financial Report quality and each of the other constructs, with coefficients ranging from 0.7653 to 0.9322. Similarly, strong positive correlations are observed between Financial Report quality, AI, Blockchain, and Data Analytics, reflecting the interrelated nature of these constructs within the research context.

Table 4.6: Spearman Rank Correlation

Table 4.7 presents the model summary statistics derived from a regression analysis. Adjusted R-square is reported as 0.875, indicating that approximately 87.5% of the variance in the dependent variable is explained by the independent variables included in the model. However, the high values of R-square and adjusted R-square suggest that the regression model effectively captures the relationship between the independent and dependent variables which shows that the model is fit.

Table 4.7: Model Summary

The ANOVA table (Table 4.8) reveals a highly significant relationship between the independent and dependent variables in the regression model, as indicated by the extremely low p-value (Sig) of 0.0000, which suggests that there is a linear relationship between the dependent and independent variables.

Table 4.8  ANOVA

Table 4.9 presents the coefficients of a regression model where the quality of financial reporting is the dependent variable. The coefficient for AI is 7.284 with a standard error of 5.092, yielding a t-statistic of 1.43048 and a p-value of 0.0000. The p-value indicates that the impact of AI on financial reporting quality is statistically significant. The positive estimate of 7.284 suggests that the implementation of AI significantly enhances the quality of financial reporting. This improvement can be attributed to AI’s capabilities in automating complex accounting processes, detecting anomalies, and providing predictive analytics, which together increase the accuracy, reliability, and efficiency of financial reporting.

The coefficient for Blockchain is 3.671 with a standard error of 2.061, resulting in a t-statistic of 1.78117 and a p-value of 0.0000. The p-value signifies that Blockchain’s impact on financial reporting quality is also statistically significant. The positive estimate of 3.671 indicates that the adoption of Blockchain technology contributes positively to financial reporting quality. Blockchain enhances transparency, traceability, and security of financial transactions, reducing the risk of fraud and ensuring the integrity of financial data. These attributes are crucial for producing high-quality financial reports that stakeholders can trust.

The coefficient for Data Analytics is 4.819 with a standard error of 2.068, leading to a t-statistic of 2.33027 and a p-value of 0.0000. The p-value confirms that Data Analytics significantly impacts the quality of financial reporting. The positive estimate of 4.819 demonstrates that the use of Data Analytics significantly improves financial reporting quality. Data Analytics provides deep insights into financial data, aids in accurate forecasting, and enhances decision-making processes. It ensures that financial reports are relevant and timely, meeting the critical needs of stakeholders.

Table 4.9: Coefficients

DISCUSSION OF FINDINGS

Table 4.1 presents the demographic features of the population that was surveyed. This information helps to understand the attitudes and views that are represented in the following tables. We can contextualise the results and derive more nuanced interpretations based on demographic considerations by examining the demographic distribution among firms, gender, age, and educational attainment. These insights provide information about the variety and representativeness of the sample.  However, how people perceived the quality of financial reporting in relation to developing technologies was examined in Table 4.2. The findings show that respondents’ perceptions of the possible advantages of incorporating cutting-edge technologies into financial reporting procedures were largely favorable. A significant percentage of participants strongly concur that new technologies improve financial reporting’s accuracy and dependability, transparency and accountability, and ability to process reports quickly and effectively. These findings, which are consistent with the larger industry trend towards digital transformation and innovation, highlight the revolutionary potential of emerging technologies in enhancing many areas of the quality of financial reporting.

Turning now to Table 4.3, which lists the elements influencing technology adoption, we see a wide range of incentives encouraging businesses to use technology in their daily operations. The main drivers are cost reduction and efficiency improvements, underscoring the necessity for organisations to optimise resources and improve operational efficiency in the current competitive environment. Other important factors influencing technology adoption decisions include the need to comply with regulations, competitive pressures, and the possibility of making better judgements. The multiple nature of technological adoption dynamics is shown by these findings, which show the intricate interaction between internal and external elements influencing organisational decision-making processes.

The ANOVA test results are also included in Table 4.8, emphasising the regression model’s overall relevance. The regression model’s incredibly low p-value (0.0000) suggests that there is a strong correlation between the independent and dependent variables. This implies that all of the model’s predictors significantly affect the quality of financial reporting, supporting the validity and dependability of the regression analysis’s conclusions. Lastly, Table 4.9 sheds light on the regression model’s coefficients and offers estimates of the relationship between the dependent variable (financial reporting quality) and the explanatory variables (AI, Blockchain, and Data analytics). The extent to which each explanatory variable influences the quality of financial reporting is indicated by the coefficient estimations. Positive coefficients, for example, show that improved financial reporting quality is associated with an increase in the corresponding variable which is consistent with the work of Salehi et al., (2023); Oladejo and Yinus (2020) and Oladejo, Yinus, and Aina-David (2020). The statistical significance of this relationship is indicated by the corresponding p-values, which validate the validity of the regression model’s conclusions. These coefficients aid in providing a clearer picture of the precise elements influencing increases in the calibre of financial reporting within the context of the organisation.

SUMMARY, CONCLUSION AND RECOMMENDATION

Summary of Study

This study investigates the impact of emerging technologies (Artificial Intelligence (AI), Blockchain, and Data Analytics) on the quality of financial reporting among selected quoted firms (Fidelity Bank Plc, Lasaco Assurance Plc and AXA Mansard Insurance Plc). The primary objective is to understand how these technologies enhance various aspects of financial reporting, including accuracy, reliability, transparency, and efficiency. The study’s findings are based on a robust analysis, including regression models that measure the effect of each technology on financial reporting quality.

In the modern business environment, financial reporting quality is critical for stakeholders, including investors, regulators, and management. High-quality financial reports provide a clear, accurate, and reliable depiction of a firm’s financial position and performance, essential for informed decision-making. The study is grounded in the understanding that traditional financial reporting methods face limitations, particularly in handling large volumes of data and ensuring real-time accuracy. Hence, the adoption of advanced technologies is seen as a necessary evolution to meet the increasing demands for precise and timely financial information. The research employs a quantitative approach, utilizing regression analysis to examine the relationship between the dependent variable (financial reporting quality) and the independent variables (AI, Blockchain, and Data Analytics). The coefficients and p-values obtained from the regression model provide insights into the significance and impact of each technology on financial reporting quality. The analysis is conducted on data collected from selected quoted firms, ensuring a relevant and representative sample.

Summary of Findings

The findings highlight the substantial positive impact of AI, Blockchain, and Data Analytics on financial reporting quality. Each technology was found to significantly enhance various attributes of financial reporting. First, the implementation of AI significantly improves financial reporting accuracy and efficiency. AI automates complex accounting processes, detects anomalies, and provides predictive analytics, which collectively enhance the reliability and precision of financial reports. The positive coefficient of 7.284 and a p-value of 0.0000 underscore AI’s critical role in elevating financial reporting quality. In addition, Blockchain technology significantly contributes to transparency and security in financial reporting. The immutable and decentralized nature of Blockchain ensures that financial transactions are recorded accurately and cannot be altered, reducing the risk of fraud. The positive coefficient of 3.671 and a p-value of 0.0000 confirm that Blockchain enhances the trustworthiness and integrity of financial reports. Lastly, the use of Data Analytics significantly enhances the relevance and timeliness of financial reporting. Data Analytics tools provide deep insights into financial data, facilitate accurate forecasting, and improve decision-making processes. The positive coefficient of 4.819 and a p-value of 0.0000 indicate that Data Analytics plays a crucial role in ensuring financial reports meet stakeholders’ needs for timely and relevant information.

Conclusion

In conclusion, this study demonstrates that AI, Blockchain, and Data Analytics are transformative tools that significantly improve the quality of financial reporting. As the financial landscape continues to evolve, embracing these technologies will be essential for firms aiming to maintain transparency, accuracy, and efficiency in their financial reporting. The adoption of these technologies is not just a strategic advantage but a necessary step towards ensuring that financial reporting meets the high standards demanded by stakeholders in today’s dynamic business environment.

Recommendation

Based on the findings presented in the study, several recommendations can be proposed to help organizations improve financial reporting quality and effectively navigate the challenges and opportunities associated with technology adoption:

• Firms should prioritize investing in AI technologies to automate complex accounting tasks, improve anomaly detection, and enhance predictive analytics capabilities. This investment will lead to more accurate, efficient, and timely financial reporting.
• Companies should adopt Blockchain technology to enhance the transparency and security of financial transactions. Blockchain can significantly reduce the risk of fraud and ensure the integrity of financial data, thereby increasing stakeholder trust in financial reports.
• Organizations should utilize advanced Data Analytics tools to gain deeper insights into their financial data. This will improve the relevance and timeliness of financial reports, aiding in better decision-making and strategic planning.
• Continuous training and development programs should be established to equip financial professionals with the necessary skills to effectively use AI, Blockchain, and Data Analytics. Keeping the workforce updated with technological advancements will ensure the efficient implementation of these tools.

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