Usage Of Data Analytics Research Articles

Big Data Analytics and Management Forecasting Behavior

SYNOPSIS This paper investigates whether the use of Big Data analytics by firms has a spillover effect on management forecasting behavior. Insights provided by Big Data could potentially improve firms’ ability to forecast earnings (supply channel) and investor demand for earnings information is likely higher for firms engaging in data analytics (demand channel). Using a text-based measure of firms’ commitments to and usage of Big Data analytics, we find that Big Data analytics usage is positively associated with the propensity to issue management earnings forecasts. Consistent with the “supply channel” explanation, we find that Big Data analytics usage is positively associated with management forecast accuracy as well. Also, supporting the “demand channel” explanation, we find that Big Data analytics usage is associated with greater analyst following. Our findings of improved disclosure following commitments to Big Data analytics highlight a potentially unintended benefit of the Big Data revolution.

Bridging realities into organizations through innovation and productivity: Exploring the intersection of artificial intelligence, internet of things, and big data analytics in the metaverse environment using a multi-method approach

This study investigates how organizations may increase innovation and productivity through the Metaverse environment efficacy (MVEE), Artificial intelligence usage (AIU), Internet of Things usage (IoTU), and Big Data Analytics usage (BDAU). The study gathers responses from the gaming, information technology, and entertainment industries, using a multi-method involving Partial Least Squares Structural Equation Modeling, Fuzzy-set Qualitative Comparative Analysis, and Artificial Neural Networks to investigate how these technologies might be used to improve the linking of disparate realities in a business context. The use of AI in personalized and decision-support applications, IoT for real-time data collecting, and BDAU for an insights-driven strategy all combine to create a dynamic MVEE ecosystem. The research also delves into theoretical implications concerning the viability of using the MVEE to boost innovation and productivity. This research identifies the applications of using AI, IoT, and BDA to drive organizational performance in terms of innovation and productivity. Also, the research lays out the role of AI, IoT, and BDA in creating a dynamic metaverse ecosystem.

An SME approach to data analytics by management accountants in the transition economy of Vietnam

Purpose Data analytics (DA) is an emerging topic in management science at large organizations; however, accountants in small and medium-sized enterprises (SMEs) are believed to be lagging far behind in the usage of DA. This study aims to provide a deep understanding of the actual DA activities undertaken by management accountants (MA) and to draw on SMEs’ characteristics to take advantage of DA applications. Design/methodology/approach This study used a qualitative approach by conducting in-depth interviews with 31 accounting and finance practitioners at senior levels of SMEs, following the use of the MAXQDA 2022 application for data analysis. Findings Findings in this study suggest variance and trend analysis as the two most popular tasks of DA, and advanced tasks such as contingency analysis, financial modeling, sentiment analysis and regression analysis are unfully performed in SMEs. The outcomes revealed that DA is not anticipated to affect the responsibilities but to expand the role and scope of management accounting. Practical implications This study simultaneously builds a theoretical framework about the antecedents, in terms of the external and internal drives and the characteristics of SMEs’ owner-managers, that are most common in all types of SMEs that encourage MA to use a specific technology, data analysis in a more advanced way instead of searching for determinants that affect the adoption of technology in general, as previous studies have conducted. Originality/value Although there are studies on DA usage, little has approached the mutual interconnections between how DA is applied by MA in SMEs and what changes in management accounting responsibilities by DA affect. Therefore, the point of this study was to look into how SMEs use DA and what activities MA actually do with DA to discover what traits SMEs need to use DA applications effectively as DA applications become more advanced.

Big data analytics usage in the banking industry in Tanzania: does perceived risk play a moderating role on the technological factors

PurposeThe main purpose of this paper is to examine the adoption of big data analytics (BDA) in the Tanzania banking industry by investigating the influence of technological, environmental and organizational (TOE) factors while exploring the moderating role of perceived risk (PR).Design/methodology/approachThe study employed a qualitative research design, and the research instrument was developed using per-defined measurement items adopted from prior studies; the items were slightly adjusted to fit the current context. The questionnaires were distributed to top and middle managers in selected banks in Tanzania using the snowball sampling technique. Out of 360 received responses, 302 were considered complete and valid for data analysis. The study employed partial least squares structural equation modeling (PLS-SEM) to examine the developed conceptual framework.FindingsTop management support and financial resources emerged as influential organizational factors, as did competition intensity for the environmental factors. Notably, bank size and perceived trends showed no significant impacts on BDA adoption. The study's novelty lies in revealing PR as a moderating factor, weakening the link between technological readiness, perceived usefulness and the intent to adopt BDA.Originality/valueThis study extends literature by extending the TOE model, through examining the moderating roles of PR on technological factors. Furthermore, the study provides useful managerial support for the adoption of BDA in banking in emerging economies.

Identification and prediction of standard times in machining for precision steel tubes through the usage of data analytics

An approach to predict standard times for upcoming individual customer orders based on historical production orders is developed. The approach follows the CRISP-DM model with regard to the steps data preparation, modeling and evaluation. A precision steel tube straightening machine is provided as a validation example. Furthermore, the paper focuses on how the standard time prediction approach can be successfully integrated into the IT infrastructure of a production focused company. Especially data availability and accessibility in enterprise resource planning systems are discussed. The presented approach offers users the value of more precise scheduling in production than established approaches.

Impact of data analytics on reporting quality of forensic audit: a study focus in Malaysian auditors

The primary objective of this research is to study the impact of data analytics on reporting quality of forensic audit, a study focussed on auditors in Malaysia. In this research, the researcher intends to discover the impact caused by emerging technology, in particular the data analytics, in audit profession, as audit field is the most complex field where tremendous volume of data, and research is conducted to investigate a certain situation, to report findings. The researcher uses the primary method to conduct the data. In this research, four different variables that causes impact to data analytics usage in forensic audit reporting quality, are tested. The data was collected from forensic audit and accounting service providers, operating around Malaysia. To analyse the findings, statistical tool, Statistical Package of the Social Sciences (SPSS) is used, to further analyse the responses, and to derive at a conclusion relating to impact of data analytics to reporting quality of forensic audits. The findings discovered that all the variables have significant relationship with the data analytics and its impact to the forensic audit reporting quality, among forensic auditors and practitioners in Malaysia. The conclusion has been discussed in this research which caters for and provides confidence and data privacy, for users of data analytic tools for their forensic audit work. This research creates the awareness of the use of data analytics in order to assist forensic auditors in their routine data gathering and analysis, in preparing reports, statistics and trend analysis etc, by saving their efforts and time, to focus more on performing further analysis and findings based on case or audit assignment.

How big data analytics enables the alliance relationship stability of contract farming in the age of digital transformation

Notwithstanding the potential of big data analytics technology for alliance management, there is a lack of understanding of how such digital technology influences alliance relationship stability (ARS). Drawing on the information technology-enabled organizational capabilities (IT-enabled OCs) perspective, this study empirically verifies that big data analytics promotes ARS and risk management capability. Moreover, market risk management capability (MRM) enhances ARS, and data quality moderates the relationship between big data analytics usage (BDU) and MRM. This research reveals the impact mechanism of BDU on the ARS. Implications for management and future research are presented as well.

Big data analytics in auditing and the consequences for audit quality: A study using the technology acceptance model (TAM)

The study examines the impacts of using two dimensions of the technology acceptance model (TAM), perceived usefulness and perceived ease of use, on the adoption of big data analytics in auditing, and the subsequent impact on audit quality. Five hypotheses were developed. A questionnaire survey was undertaken with external affiliated audit companies and offices in Jordan. Eventually, 130 usable questionnaires were collected, representing a 72.22% response rate. Structural equation modelling (SEM) was employed for diagnosing the measurement model, and to test the hypotheses of the study. The study finds that perceived usefulness and perceived ease of use have a direct effect on audit quality, without mediating the actual use of data analytics. However, the use of big data analytics is shown to moderate the relationship between perceived usefulness and audit quality, but not between the perceived ease of use and audit quality. The study is one of the first to examine auditors’ acceptance of big data analytics in their work and the impact of this acceptance and actual use on audit quality. It contributes to the existing literature in auditing through its application of SEM to examine the impact of big data analytics usage on audit quality by using the TAM.

Evaluating the impact of big data analytics usage on the decision-making quality of organizations

Big data initiatives are critical for transforming traditional organizational decision making into data-driven decision making. However, prior information systems research has not paid enough attention to the impact of big data analytics usage on decision-making quality. Drawing on the dynamic capability theory, this study investigated the impact of big data analytics usage on decision-making quality and tested the mediating effect of data analytics capabilities. We collected data from 240 agricultural firms in China. The empirical results showed that big data analytics usage had a positive impact on decision-making quality and that data analytics capabilities played a mediating role in the relationship between big data analytics usage and decision-making quality. Hence, firms should not only popularize big data analytics usage in their business activities but also take measures to improve their data analytics capabilities, which will improve their decision-making quality toward competitive advantages.

Big Data Analytics and Natural Data Design for Enterprise Management

The advancement of Internet of Things (IoT), economic globalization, consumer satisfaction and competitive advancements have stimulated companies to transform significantly. In that regard, competitive rivalry among various firms is being replaced by the existing rivalry among businesses and their various enterprises. In the present competitive environments, enterprise experts are focusing on dealing with Big Data (BD) to reach agile, effective, efficient and integrated enterprises. Therefore, explosive development in volume and several data types in the business have presented the need to establish technological advancements that can quickly and intelligently assess large sets of data. The concept of Data Analytics (DA) is one of the most effective remedies that can assist firms to overcome challenges. DA provides an instrument for retrieving insightful data and patterns in massive volumes of information. In that case, this study explores the usage of DA.

Correlates of the internal audit function’s use of data analytics in the big data era: Global evidence

In the big data era, internal audit functions (IAFs) should innovate their techniques so as to add value to their organizations. The use of data analytics (DA) increases IAFs’ ability to extract value from big data, helping IAFs to enhance their activities’ efficiency and effectiveness. We use responses from 1,681 Chief Audit Executives (CAEs) in 82 countries to investigate the correlates of IAFs’ DA usage. From the literature, we identify five main variables expected to be associated with IAFs’ DA use. We find a positive and significant association between DA use and (i) the IAF reporting to the audit committee (AC) and (ii) CAEs’ ability to build positive relationships with managers. These findings suggest that IAF independence and CAEs’ soft skills are important to innovate IAF techniques favoring DA use. We also find a positive association between DA use and IAFs’ involvement in the assurance of enterprise risk management, fraud detection, and IT risk audit activities. Our findings contribute to the internal auditing and DA literatures, and should be of interest to CAEs, ACs, corporate boards, and professional associations.

Screening of Murabaha business process through Quran and hadith: a text mining analysis

Purpose This paper revolves around the usage of data analytics in the Qur’an and Hadith through a new text mining technique to answer the main research question of whether the activities and the data flows of the Murabaha financing contract is compatible with Sharia law. The purpose of this paper is to provide a thorough and comprehensive database that will be used to examine existing practices in Islamic banks’ and improve compliancy with Islamic financial law (Sharia). Design/methodology/approach To design a Sharia-compliant Murabaha business process originated on text mining, the authors start by identifying the factors deemed necessary in their text mining techniques of both texts; using a four-step strategy to analyze those text mining analytics; then, they list the three basic approaches in text mining used for new knowledge discovery in databases: the co-occurrence approach based on the recursive co-occurrence algorithm; the machine learning or statistical-based; and the knowledge-based. They identify any variation and association between the Murabaha business processes produced using text mining against the one developed through data collection. Findings The main finding attained in this paper is to confirm the compatibility of all activities and the data flows in the Murabaha financing contract produced using data analytics of the Quran and Hadith texts against the Murabaha business process that was developed based on data collection. Another key finding is revealing some shortcomings regarding Islamic banks business process compliance with Sharia law. Practical implications Given Murabaha as the most popular mode of Islamic financing with more than 75% in total transactions, this research has managed to touch-base on an area that is interesting to the vast majority of those dealing with Islamic finance instruments. By reaching findings that could improve the existing Islamic Murabaha business process and concluding on Sharia compliance of the existing Murabaha business process, this research is quite relevant and could be used in practice as well as in influencing public policy. In fact, Islamic Sharia law experts, Islamic finance professionals and Islamic banks may find the results of this study very useful in improving at least one aspect of the Islamic finance transactions. Originality/value By using a novel, fresh text mining methods built on recursive occurrence of synonym words from the Qur’an and Hadith to enrich Islamic finance, this research study can claim to have been the first of its kind in using machine learning to mine the Quran, Hadith and in extracting valuable knowledge to support and consolidate the Islamic financial business processes and make them more compliant with the i.

Big Data Analytics Usage and Business Performance: Integrating the Technology Acceptance Model (TAM) and Task Technology Fit (TTF) Model

The purpose of this study is to propose a unified model integrating both the technology acceptance model (TAM) and task technology fit (TTF) model, and explore the organizational and environmental fit of the integrated model in order to investigate usage of Big Data Analytics and its effect on business performance. A questionnaire was used to collect data from 280 companies in CPG & Retail, Healthcare, Banking and Telecom in India. The data were analysed using exploratory and confirmatory factor analyses. Further, structural equation modelling was used to test the proposed model. The findings show that the research model for integrating the TAM for adoption and TTF model for utility provides a more comprehensive understanding of Big Data Analytics usage. The study identified task technology fit , individual technology fit, organizational data fit, organizational process fit, and business strategy fit as Tidd important variables for affecting Big Data Analytics usage using perceived ease of use (PEOU) and perceived usefulness (PU) as mediating variables. Competitive fit and partner support/customer fit were also found to be directly affecting Big Data Analytics usage, which in turn has significant influence on business performance. The model explained 71.4 percent of Business performance. The integrated model may be used as a guideline to ensure a positive outcome of Big Data Analytics usage in organizations. This study combined both the key ideas of TAM and TTF to show that they were necessary in predicting Big Data Analytics usage and business performance.

A survey on cancer prediction and detection with data analysis

World Health Organization reports cancer as a leading cause worldwide in mortality and morbidity. Accurate and early cancer risk assessment in average- to high-risk population is vital in controlling the cancer-related suffering and mortality. Advanced bioinformatics and data mining techniques along with computer-aided cancer prediction and risk assessment are used extensively to assist in identifying the high-risk population as well as individual cancer diagnosis and treatment. An early detection minimizes the risk of cancer spreading to secondary sites and ensures appropriate treatment at the onset of the malignancy. The scope of our survey was to review over 90 publications centered around works done in the area of data analysis studies in the field of cancer prediction and detection. The motivation was to accumulate and categorize knowledge on the usage of data analytics for cancer prediction and detection. The aim was to do a comparative study of few of the major analytical approaches in cancer data analysis and highlight their effectiveness.

Assessing Organizational Maturity in Predictive Analytics of Telecommunications Companies in the Republic of Macedonia

Data analytics and predictive analytics are among major trends companies are facing worldwide. In a highly digitalized environment it is not only to question the usage of data analytics, but how analytically mature organizations are. The goal of this paper is to assess organizational maturity in predictive analytics of telecommunications companies in the country.
 In order to assess the level of organizational maturity in predictive analytics we use Predictive Analytics Maturity Framework Assessment (PAMFA) (Capgemini, 2012), since it best describes maturity levels in telecommunications sector. The method of analysis is based on interviewing managers with questionnaire that guides respondents through all dimensions and levels proposed by the framework. According to the PAMFA five dimensions are analyzed (Vision and strategy, Enablers, Competence, Deployment and Governance). For each dimension, four maturity levels are defined: Level 1: Impromptu, Level 2: Solo, Level 3: Ensemble and Level 4: Symphony.
 Survey results confirmed that analysed companies fully understand the benefits of predictive analytics as valuable source of gaining competitive advantage from data. The overall level of predictive analytics maturity is set between levels 2 or 3 for almost all dimensions.
 This research is the first attempt to analyze organizational maturity in predictive analytics in the country. Its originality derives from the specific characteristics and development of the telecommunications sector. This sector is one of the most advanced service sectors in the country and hence represents a benchmark concerning digital transformation. Results of this survey provide useful information needed to design a roadmap for migrating towards higher maturity levels.

Understanding the adoption and usage of data analytics and simulation among building energy management professionals: A nationwide survey

Improving the energy-efficiency of our building stock is critical to meeting our worldwide sustainability goals. In response to this need, two key tools have emerged to help engineers, building scientists and energy managers understand building energy usage and derive energy-efficiency solutions: data analytics and simulation. While both data analytics and simulation hold significant promise, we lack a clear understanding on the use, barriers and expectations of both in the building energy management decision-making process. This study conducts a nationwide survey of 448 building energy management professionals in the United States to help elucidate: 1) what impacts the adoption of data analytics and simulation among building energy management professionals; 2) in what phases of the building energy management decision-making process are data analytics and simulation currently used; and 3) what are the barriers of use for data analytics and simulation and how can they be improved to better support building energy management decision-making. Overall, results indicate that professional domain plays a large role in associating the uses, barriers and expectations for data analytics and simulation. Results also indicate that data analytics and simulation could be coupled to leverage functionality as they are used in similar phases of the decision-making process. Lastly, results point to opportunities for improving the applicability of data analytics and simulation tools as well as training for both. In the end, this study aims to provide a quantitative basis for improving the efficacy and integration of data analytics and simulation in the building energy management domain.

Does data analytics use improve firm decision making quality? The role of knowledge sharing and data analytics competency

Despite the huge investment in data analytics tools, the necessary conditions required to obtain benefit from such investment deserves close investigation. In this study we utilize the knowledge-based view and data analytics competency literature to address two important research questions: (1) Does knowledge sharing have a mediating role on the impact of data analytics usage on the quality of firm decisions?; and (2) What is the role of data analytics competency in enhancing the quality of firm decisions through increasing knowledge sharing? Survey data collected from top and middle-level managers from 133 U.S.-based firms indicates that: the impact of data analytics use on the quality of firm decisions is fully mediated by knowledge sharing; the impact of knowledge sharing on firm decision quality is not significant and it is moderated by data analytics competency; and data analytics competency does not moderate the impact of data analytics use on knowledge sharing. Collectively, the findings provide a theory-based understanding of how data analytics use improves firm decision quality. The results also provide actionable guidelines for firms regarding the critical resources they need to invest in order to obtain benefits from using data analytics tools.

Are firms ready to use big data analytics to create value? The role of structural and psychological readiness

ABSTRACTIn this study, we leverage Information Technology (IT) readiness literature and resource-based view (RBV) to investigate the impact of firm structural and psychological readiness on firm value creation, as mediated by big data analytics usage. The proposed research model is empirically validated using survey data from 179 senior IT managers. The findings demonstrate the importance of both structural (i.e. IT infrastructure capability, tools functionality, employee analytical capability, and bigness of data) and psychological readiness (i.e. IT proactive climate) in enhancing firm value creation through big data analytics usage. These results provide interesting theoretical and practical insights.

The role of positive and negative valence factors on the impact of bigness of data on big data analytics usage

The number of firms that intend to invest in big data analytics has declined and many firms that invested in the use of these tools could not successfully deploy their project to production. In this study, we leverage the valence theory perspective to investigate the role of positive and negative valence factors on the impact of bigness of data on big data analytics usage within firms. The research model is validated empirically from 140 IT managers and data analysts using survey data. The results confirm the impact of bigness of data on both negative valence (i.e., data security concern and task complexity), and positive valence (i.e., data accessibility and data diagnosticity) factors. In addition, findings show that data security concern is not a critical factor in using big data analytics. The results also show that, interestingly, at different levels of data security concern, task complexity, data accessibility, and data diagnosticity, the impact of bigness of data on big data analytics use will be varied. For practitioners, the findings provide important guidelines to increase the extent of using big data analytics by considering both positive and negative valence factors.

Data analytics and firm performance: An empirical study in an online B2C platform

Data analytics has become an increasingly eye-catching practice in both the academic and the business communities. The importance of data analytics has also prompted growing literature to focus on the design of data analytics. However, the boundary conditions for data analytics in creating value have been largely overlooked in the literature. The objective of this article therefore is to examine the business value of data analytics usage and explore how such value differs in different market conditions. We rely on an online B2C platform as our empirical setting and obtain several important insights. First, both demand-side and supply-side data analytics usage has a positive effect on the performance of merchants. Second, when merchants’ product variety is high, the influence of usage toward demand-side data on performance is strengthened, whereas such impact is weakened for supply-side data analytics. Third, when competitive intensity is high, the performance implication of demand-side data analytics usage is strengthened, whereas such impact is not strengthened for supply-side data analytics. This study contributes by advancing the overall understanding of business value of data analytics.