Adoption Of Data Analytics Research Articles

Integrating sports education with data analysis and computer technology: A new paradigm for enhanced athletic performance

This comprehensive study delves into the transformative integration of sports science, data analytics, and computer technology in athletic training and education. It highlights the multidisciplinary approach to enhancing athletic performance through evidence-based methodologies, including biomechanical analysis, physiological assessment, and psychological strategies. With a focus on data-driven training methodologies, real-time performance monitoring, educational implications, and the incorporation of ethical considerations, the article showcases how modern technology and data analysis can personalize training programs, optimize performance, and minimize injury risks. Through quantitative analysis and case studies, it demonstrates the significant impact of these integrations on athletic performance metrics, while also addressing the challenges and limitations faced in adopting these approaches. The findings suggest a promising future for sports education and training, advocating for interdisciplinary collaboration and the adoption of data analytics in sports.

Strategic Digital Integration: Unravelling the Impact of Social Media, Data Analytics, and Branding on Sustainable Marketing Strategies and Consumer Engagement in the Tourism Industry in Bali Province

This study examines the complex dynamics of digital integration in Bali's travel and tourist sector, with a particular emphasis on the effects of branding, social media, and data analytics on customer engagement and sustainable marketing tactics. 355 different enterprises in the province responded to a quantitative analysis that was carried out. The structural model analysis validated all of the proposed hypotheses, while the measurement model assessment confirmed the constructs' validity and reliability. The findings highlight the beneficial connections between consumer involvement, branding tactics, data analytics adoption, social media integration, and sustainable marketing techniques. The study's conclusions can be trusted because the model fit indices show a respectable fit. This research highlights the strategic significance of digital integration for sustainability and customer connections, providing useful insights for companies trying to navigate Bali's tourism sector in the digital era.

Innovations in accounting and auditing: A comprehensive review of current trends and their impact on U.S. businesses

The landscape of accounting and auditing is undergoing a transformative evolution, driven by rapid technological advancements, regulatory changes, and shifting business paradigms. This comprehensive review delves into the contemporary innovations shaping the realm of accounting and auditing and assesses their profound implications for U.S. businesses. Technological innovations, particularly the integration of artificial intelligence and machine learning, have revolutionized traditional accounting processes. Automation tools and advanced data analytics are streamlining routine tasks, enhancing accuracy, and enabling accountants to focus on more strategic, value-added activities. The paper explores how these technological advancements are reshaping the skill sets required in the accounting profession and the consequent impact on professional development and education. Furthermore, the study investigates the evolving role of auditors in an era dominated by Big Data. The adoption of data analytics and blockchain technology is enhancing audit efficiency, transparency, and the overall quality of financial reporting. The analysis emphasizes the importance of auditors embracing these innovations to provide more timely, accurate, and insightful assessments of financial statements. Regulatory changes, such as the implementation of new accounting standards and reporting requirements, also feature prominently in this review. The study examines the challenges and opportunities arising from these regulatory shifts and assesses their impact on financial reporting practices in U.S. businesses. As businesses adapt to these innovations, the paper explores the broader implications for decision-making, risk management, and corporate governance. It sheds light on how the changing landscape of accounting and auditing influences the strategic decision-making processes of organizations, with a focus on fostering transparency and accountability. In conclusion, this comprehensive review synthesizes the multifaceted innovations in accounting and auditing, offering insights into the current trends shaping the industry and their far-reaching consequences for U.S. businesses. The research contributes to a deeper understanding of the challenges and opportunities associated with these transformations, guiding stakeholders in navigating this dynamic landscape effectively.

Firm characteristics and the adoption of data analytics in performance management: a critical analysis of EU enterprises

PurposeThis study aims to investigate the factors influencing the adoption of data analytics in performance management. By examining the role of organizational and environmental contexts, this study contributes to the existing literature by proposing a novel and detailed technology-organization-environment (TOE) model for the complex interplay between firm characteristics and the adoption of data analytics. The results offer valuable insights and practical implications for organizations seeking to leverage data analytics for effective performance management.Design/methodology/approachThe research draws upon a data set encompassing over 21,869 companies operating across all European Union member states. A multilevel logistic regression model was developed to evaluate the influence of organizational and environmental factors on the likelihood of adopting performance analytics in organizations.FindingsThe findings indicate that the lack of awareness of the benefits of data analytics and its practical application to address specific business challenges is a significant barrier to its adoption. Organizational contexts, such as variable-pay systems, employee training, hierarchical structures and frequency of monetary rewards, also influence the adoption of data analytics.Research limitations/implicationsThe study informs managers about the strategic role of data analytics capabilities in performance management for improved business intelligence and driving data culture.Practical implicationsThe study helps managers understand the strategic role of data analytics capabilities in performance management, leading to improved business intelligence and fostering a data-driven culture in five key areas: structural alignment, strategic decision-making, resource allocation, performance improvement and change management.Originality/valueThe study advances the TOE theory, making it a more detailed and complete framework, particularly applicable to the adoption of performance analytics. It identifies the main factors of adoption that play a crucial role in this process.

Overcoming Challenges and Unlocking the Potential: Empowering Small and Medium Enterprises (SMEs) with Data Analytics Solutions

In today's data-driven business landscape, Data Analytics (DA) has emerged as a vital tool for organizations to extract insights from their existing data, enabling informed decision-making. While large enterprises have wholeheartedly embraced DA as a strategic asset for operational enhancement, SMEs have been comparatively slower in adopting these transformative solutions. To remain competitive and surpass their rivals, SMEs must recognize the significance of harnessing their data assets effectively to drive decision-making processes. This research aims to delve into the challenges hindering the adoption of DA among SMEs, particularly focusing on issues such as inadequate information infrastructure and limited awareness of the benefits that DA can offer. Furthermore, this study investigates the implementation of data analytics as a practical solution to address these challenges, providing a comprehensive analysis of both the advantages and disadvantages associated with DA adoption in the SME context. By shedding light on the untapped potential of data analytics, this research aims to empower SMEs and equip them with the necessary tools to thrive in today's digitally-driven era of business.

Data analytics as a catalyst for operational optimization: A comprehensive review of techniques in the oil and gas sector

Data analytics has emerged as a critical enabler for operational optimization in the oil and gas sector, driving efficiency and profitability through data-driven insights. This comprehensive review examines the various techniques employed in the industry, focusing on the role of predictive analytics, machine learning, and artificial intelligence (AI) in enhancing exploration, production, and distribution processes. The integration of real-time data analytics with traditional engineering methodologies allows for more accurate reservoir simulations, improved drilling precision, and predictive maintenance of critical infrastructure. Furthermore, the adoption of big data and cloud computing enables faster data processing and more scalable solutions for large and complex datasets, enhancing decision-making capabilities. The review also highlights the significance of prescriptive analytics, which aids in scenario planning and optimizing supply chain logistics, minimizing operational downtime, and improving overall asset management. Techniques such as seismic data interpretation, remote sensing analytics, and IoT-enabled sensors are discussed in the context of their application to real-time monitoring and risk mitigation in oil and gas operations. These technologies contribute to reducing environmental impact by optimizing resource allocation and minimizing operational inefficiencies, supporting the industry's transition to more sustainable practices. Moreover, the review identifies key challenges in the adoption of data analytics, such as data silos, cybersecurity risks, and the need for advanced technical literacy among the workforce. Solutions to these challenges, including the implementation of integrated data platforms and enhanced cybersecurity protocols, are explored. Finally, the review underscores the future potential of analytics-driven technologies in driving digital transformation and operational excellence in the oil and gas sector.

Pull and Push Factors of Data Analytics Adoption and Its Mediating Role on Operational Performance

Despite the influx of data analytics (DA) practices among firms, their impact on operational performance remains ambiguous. This study examined the pull and push factors affecting the data analytics adoption (DAA) from the theoretical perspectives of the technology–organization–environment (TOE) model, theory of perceived risk (TPR), and resource-based view (RBV). The study analyzed data from 169 firms on the basis of the positivist paradigm and employed the partial least square to run the reflective–formative two-stage analysis. Accordingly, the results indicated that the three TOE model aspects exhibited a positive direct impact on DAA and indirectly impacted operational performance through DAA. However, the perceived risk did not display a similar effect in both situations. This study further revealed that the environment push factor had more explanatory power than the perceived risk pull factor, suggesting that a conducive TOE environment would motivate DAA, subsequently enhancing operational performance. The study provided valuable empirical evidence on the determinants of DAA and its subsequent effect on firms’ operational performance. Uniquely, the study also contributed to the literature from the perspective of higher-order-construct analysis in examining the determinants of DAA and its effect on operational performance. Furthermore, the mediation analysis covered the interaction of indirect-path coefficients, minimizing errors in interpreting the mediation effect.

Accelerating data analytics adoption in small and mid-size enterprises: A Singapore context

As with other technologies, decisions to adopt data analytics (DA) can create challenges, particularly for small and mid-size enterprises (SMEs), due to resource constraints. Our research assessed the factors that could accelerate DA adoption in SMEs. In Singapore, one-third of SMEs have yet to adopt DA, and it remains unknown how they plan their business strategies to stay ahead of competitors. To investigate how SMEs in Singapore embrace DA, this study used a framework of technological acceptance. Our analysis suggests that management and government support are critical to accelerating DA adoption in SMEs. Management and owners of SMEs must take the lead to explain the importance and relevance of data analytics to their companies, while the government could assist SMEs through grants, tax relief and subsidies to minimize the cost burden of adopting DA.

The use of data analytics in external auditing: a content analysis approach

PurposeThis study examines the perceptions of interested stakeholders on the factors affecting the use of data analytics (DA) in financial statement audits. Response letters submitted by stakeholders of the auditing services to the International Auditing and Assurance Standards Board's (IAASB) Data Analytics Working Group (DAWG) served as sources for analysis.Design/methodology/approachThe modified information technology audit model was used as a framework to perform a direct content analysis of all the 50 response letters submitted to the DAWG.FindingsThe analysis showed that a range of attributes, such as the usefulness of DA in auditing, authoritative guidance (auditing standards), data reliability and quality, auditors' skills, clients' factors and costs, were the factors perceived by stakeholders to be affecting the use of DA in external auditing.Research limitations/implicationsThis study is subjected to the limitations inherent to all content analysis studies. Nonetheless, the findings offer additional insights about potential factors affecting the adoption of DA in audit practices.Originality/valueThe data noted in the published statements highlighted the perceptions of a range of stakeholders with regards to the factors affecting the use of DA in auditing.

Factors associated with the adoption of data analytics by internal audit function

PurposeThe benefits of data analytics in the internal audit function (IAF) are clear; less is known about IAF adoption of analytics. The purpose of this study is to examine the factors driving IAF adoption of analytics.Design/methodology/approachThe Common Body of Knowledge of Internal Auditing Database (IIA, 2015) provides auditor responses on key variables of analysis.FindingsThe results of this study indicate the most critical adoption factor is data-specific IT knowledge in the IAF. Critical thinking skills and business knowledge of chief audit executive (CAEs) also contribute to adoption. IAFs with fraud risk detection responsibly are more likely to adopt. IAFs in technologically advanced cultures are more likely to adopt analytics.Originality/valueThe results of this study document the critical factors driving adoption of audit analytics, benefitting both industry and research.

Digitalizing Swedish industry: What is next?: Data analytics readiness assessment of Swedish industry, according to survey results

Digitalization refers to enabling, improving, and transforming operations, functions, models, processes, or activities by leveraging digital technologies. Furthermore, digitalization is considered one of the most powerful drivers of innovation with the potential to trigger the next wave of innovation.Today, the importance of digitalization is well-understood in Swedish government agencies and industry. Although there are several initiatives working to actively drive change, one question is key: What is the next step? Data analytics is a promising way to turn information into outcomes, enhance decision-making, make data-driven discoveries, minimize risk, and unearth valuable insights that would otherwise remain hidden. This paper presents survey results on data analytics adoption and usage within Swedish industry, to highlight post-digitalization industry needs. To this end, a questionnaire was designed and distributed. Answers from more than 100 respondents from the manufacturing, technology, engineering, telecommunications, and automotive industries in Sweden were collected and analyzed.The assessment results show that Swedish industry has a high resources readiness score. This suggests that the necessary tools, and human resources are in place. Moreover, its cultural readiness level, which focuses on the acceptance of data-driven decision-making, scores between high and very high. At the same time, the information systems readiness level is in between medium and high, except in the telecommunication domain. However, the organizational readiness level is between medium and low, which shows that the organizations are not structured to enable the adaptation of data analytics and the business impacts of data analytics are not in place yet. These findings suggest that the industry should use the advantages of the current cultural, information systems, and resources readiness capabilities and concentrate efforts on exploring the business impacts of data analytics, ensuring the support from executive managers, and implementing data analytics protocols to improve organizational readiness. Moreover, the industry should consider structural changes in organizations, in addition to systematically initiating proper planning, timing, budgeting, and setting of clear key performance indicators/metrics in order to ameliorate the organizational readiness of data analytics.

Overcome Entrenched Institutional Hurdles to Smart Water Systems

It's vital for the water and wastewater industry to welcome the next generation of water professionals by updating its regulatory framework, certification process, and workforce expectations. Such changes will help bolster the industry's adoption of data analytics and smart technology processes to advance infrastructure renewal.

An empirical study on attribute selection of student performance prediction model

Despite improvement in the standard of education globally, students' failure rates have risen. Data mining has been implemented in several domains, including education, for extracting valuable information from raw data. The aim of this study was to develop a model for predicting student performance and thereby identifying the students who might under perform in examinations. Student data used for the study consisted of demographic and academic information of students. Systematic analysis of different attributes of the student data was done using feature subset selection algorithms. The model was tested using classification algorithms. Based on these results a small attribute set, namely student data feature set (SDFS) was proposed. The experimental results demonstrate that the learning model using SDFS gives the best results and also minimises the errors. This model can be utilised to identify the academically weaker students so that appropriate preventive action can be taken to avoid failures. Adoption of data analytics in education can help create a smart education system beneficial for society.

Data analytics in auditing: Opportunities and challenges

In this article, I provide background regarding a hot topic in the public accounting profession: the rise of big data and the related field of data analytics (DA). The tax and advisory practices of public accounting firms have embraced the use of DA, and firms have made significant investments in growing these practice areas. Although DA holds great promise for the auditing practice as well, the use of widespread DA on audit engagements has lagged behind other practice areas. This is due to the fact that auditing presents unique challenges in the adoption of DA that are not relevant for other practice areas. Despite the impression that DA is not being embraced as readily in auditing, public accounting firms are continuing to make significant investments in developing audit-related DA, and it is only a matter of time before we start to see the transformational impact of these efforts. The purpose of this article is (1) to explain how DA applies to financial statement audits and why it could represent a game changer in how audits are conducted, and (2) to provide a context for researchers in terms of problems to be addressed related to DA.