Usage Of Big 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.

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.

Izazovi primene digitalnih tehnologija u reviziji

Every company must consider the changes in a digital environment. In the era of digital transformation, the company understands the importance of information technologies, and makes adjustments in the form of structural changes. Modern technology usage provides the possibility of the analysis of previously unimaginable types and amounts of data, and introduces significant changes in the field of auditing. By simplifying the audit work, digitalization has also created new opportunities for IT auditing. The aim of this paper is to present the opportunities and challenges of key digital trends in auditing, or the usage of big data analytics, artificial intelligence, blockchain technology, and robotic process automation. There is an emerging need for auditors to take advantage of digital technology usage and respond to the challenges of digitalization in a systematic and high-quality way. The progress of using digital technologies in auditing is contributing to more reliability and better quality reporting, which is leading to increased trust among stakeholders in the results of audit work.

IDLTM-DMT: Intelligent Deep Learning based Trust Management with Decision Making Tool for Healthcare Internet of Things and Big Data Environment with Neutrosophic Set Analysis

Over the last few years development of Internet of Things (IoT) devices and communication technologies have resulted in the massive generation of health-related data. In the context of healthcare, IoT offers several advantages, including being able to observe patients very closely and using data for analytics. A major challenging issue that exists in the usage of IoT and big data in the medical field is security. As healthcare data is highly vulnerable and becomes a target for attacks, there are significant privacy issues related to the usage of big data analytics. Besides, implementing new data analysis tools and strategies for handling big data decision-making is a major issue. The capability to examine this amount of data is a significant aspect of big data in health care. For resolving these issues, this paper presents a new intelligent deep learning-based trust management with decision making tool (IDLTM-DMT) for IoT healthcare big data environments, incorporating Neutrosophic Set Analysis (NSA). The proposed IDLTM-DMT model enables IoT devices to gather healthcare data. The IDLTM-DMT model involves a DL based bidirectional long short-term memory (BiLSTM) model for vulnerability detection and thereby identifies the malicious traffic in the Network. Hadoop MapReduce is used for handling big data and a decision-making tool using Deep Stacked Auto Encoder (DSAE) is used for the classification of diseases that exist in big data. To optimize the DSAE model's hyperparameters and improve classification performance, the Sandpiper Optimization (SPO) Algorithm is employed. Neutrosophic Set Analysis is integrated to manage the indeterminacy and inconsistency of the data, enhancing the decision-making process. Extensive experimental analysis is conducted on the EEG Eye State Dataset, with results analyzed using various performance measures. The findings indicate that the proposed method achieves improved accuracy compared to existing methods, demonstrating the effectiveness of incorporating Neutrosophic Set Analysis in IoT healthcare big data environments.

Examining the Dimensions of Adopting Natural Language Processing and Big Data Analytics Applications in Firms

Big data analytics (BDA) is an advanced analytic technique used with very large and diverse sets of data from different sources. Natural language processing (NLP) is a technology that interfaces with different fields such as computer science, linguistics, and human-computer interactions. Over the past few years, there is a growing number of firms, which are using different BDA and NLP applications in their businesses. Only a few of the research have investigated different dimensions of NLP and BDA and their impacts on the overall organizational performance. There is a growing interest among researchers and practitioners in understanding the consequences for firms that adopt BDA and NLP applications. In this context, the aim of this article is to determine the factors for the usage of BDA and NLP applications in business. With the help of dynamic capability view theory and existing literature, a theoretical model was developed conceptually. Later, the model was validated using structural equation modeling approach considering 1287 samples from 23 firms, primarily based in Asia and Europe, which use NLP and BDA applications. The article finds that NLP and BDA applications help the firms to improve their operational efficiency, which in turn improves the overall firm performance.

Examining The Role of Big Data Analytics and Data Availability on Sustainable Competitive Advantage and Business Performance: Mediating Role of Innovative Capabilities

The purpose of this investigation is to examine the role of big data analytics and data availability on sustainable competitive advantage and business performance. Furthermore, the researchers also investigate the mediating role of innovative capabilities between observed variables. The current research used quantitative research design and positivism philosophy to conduct this research. The population of the study, comprised of managers working in different manufacturing companies in Pakistan, the researchers used an adopted questioner to collect data from respondents. Furthermore, a structural equational model was used to test the hypothesis of the study. The research findings indicate that there is a significant mediating influence of innovative capabilities (IC) in the relationship between big data analytics (BDA) and data availability. This mediating effect has a favourable impact on both sustainable competitive advantage (SCA) and firm performance. This highlights the crucial significance of IC in converting data-derived insights into innovative strategies, thereby contributing to improved competitiveness and the overall performance of the organization. This study is significant for manufacturing organisations and policymakers to make appropriate decisions regarding the usage of big data analytics. This study is limited to manufacturing organisations and selected variables. Future studies may use government policies as a moderator to test the model of current research.

The Impact of Data Science Solutions on the Company Turnover

This study explores the potential of data science software solutions like Customer Relationship Management Software (CRM) for increasing the revenue generation of businesses. We focused on those businesses in the accommodation and food service sector across the European Union (EU). The investigation is contextualized within the rising trend of data-driven decision-making, examining the potential correlation between data science applications and business revenues. By employing a comprehensive evaluation of Eurostat datasets from 2014 to 2021, we used both univariate and multivariate analyses, assessing the percentage of companies that have e-commerce sales across the EU countries, focusing on the usage of big data analytics from any source and the use of CRM tools for marketing purposes or other activities. Big data utilization showed a clear, positive relationship with enhanced e-commerce sales. However, CRM tools exhibited a dualistic impact: while their use in marketing showed no significant effect on sales, their application in non-marketing functions had negative effects on sales. These findings underscore the potential role of CRM and data science solutions in enhancing business performance in the EU’s accommodation and food service industry.

Big Data Analytics Capability and Firm Performance in the Hotel Industry: The Mediating Role of Organizational Agility

The emergence of the Covid-19 pandemic and restrictions on international mobility have negatively impacted the tourism market. Tourism players, particularly the hotel industry, have turned to big data analytics to mitigate uncertainties and offer better products and services. Nonetheless, the central question for researchers and practitioners is how the usage of big data analytics can help the hotel industry improve firm performance. Drawing on the resource-based view and dynamic capability theories, this study analyses the relationship between big data analytics capability and firm performance in the hotel industry. This study expands the current research by examining the role of organizational agility in mediating the relationship between big data analytics capability and firm performance. To empirically test the research model, the author used survey data from 115 star-rated hotels throughout Malaysia. Through partial least square equation modeling, the findings revealed that big data analytics capability positively affects organizational agility and firm performance. The result also demonstrated that organizational agility mediates the relationship between big data analytics capability and firm performance. This study can also guide hoteliers to identify resources required to build big data analytics capability and further highlight the significance of organizational agility in improving firm performance in the hotel industry.

Determinants of the adoption of big data analytics in business consulting service: a survey of multinational and indigenous consulting firms

This study investigates the factors affecting the adoption of big data analytics (BDA) by business consulting firms. With the aid of a structured questionnaire, survey data was obtained from one hundred and eighteen (118) business and management consultants working in multinational and indigenous consulting firms in Nigeria. Discriminant analysis and multinomial logistic regression were applied to assess the determinants of BDA adoption, while structural equation modelling (path analysis) was used to assess the complexity of the interrelationship among the determinants. Robustness check using least square regression, correlation, covariance (Cov) and sum of squares and cross-products (SSCPs) analysis confirms that results are valid. Whilst the desire to enhance competitive position will cause incremental improvement in BDA adoption, consulting firms are likely to intensify BDA usage because of the need to increase market share. The determinants of BDA adoption are interrelated, implying that the advantages of BDA are systemic and could yield synergistic benefits.

Information as a tool for personalized medicine development. How to rule for patient benefit. Big data science

The article summarizes modern approaches of big data analytics implementation in healthcare, including OMIC’s data. The existing challenges for big data in medicine, barriers for implementation, reasons for slow development of practical usage of big data analytics and artificial intelligence in healthcare are discusses as well as perspective for the nearest future.

Data-based drivers of big data analytics utilization: moderating role of IT proactive climate

PurposeThis study uses the resource-based view (RBV) and isomorphism to investigate the influence of data-based resources (i.e. bigness of data, data accessibility (DA) and data completeness (DC)) on big data analytics (BDA) use under the moderation effect of organizational culture (i.e. IT proactive climate). It also analyzes the possible relationship between BDA implementation and value creation.Design/methodology/approachThe empirical validation of the research model was performed through a cross-sectional procedure to gather survey-based responses. The data obtained from a sample of 190 IT executives having relevant educational backgrounds and experienced in the field of big data and business analytics were analyzed using structural equation modeling.FindingsBDA usage can generate significant value if supported by proper levels of DA and DC, which are benefits obtained from the bigness of data (high volume, variety and velocity of data). In addition, data-driven benefits have stronger impacts on BDA usage in firms with higher levels of IT proactive climate.Originality/valueThe present paper has extended the existing literature as it demonstrates facilitating characteristic of data-based resources (i.e. DA and DC) on BDA implementation which can be intensified with an established IT proactive climate in the firm. Additionally, it provides further theoretical and practical insights which are illustrated ahead.

The Use of Inside-Out and Outside-In Big Data Analytics on E-Platforms: Performance Impacts and Heterogeneity Analysis

ABSTRACT Big data has brought unprecedented opportunities and challenges, prompting global firms to grow big data analytics (BDA) investments, especially in a turbulent business environment. However, there is insufficient empirical evidence in scholarly research on whether and how using BDA functions of various types creates business value. The current study divides BDA into inside-out and outside-in types and explores whether and how firms can create value by using functions of these two types of BDA. Then, the knowledge-based view (KBV) is applied as a theoretical foundation to investigate the independent and combined impacts of inside-out and outside-in BDA usage on firms’ sales performance. Furthermore, we build a quantile regression model to analyze the heterogeneity of independent and combined impacts among firms with different performance levels. The empirical study is based on a unique dataset collected on one of the largest electronic platforms (e-platforms) in China from 785 firms in 35 weeks. The results of the benchmark model based on two-way fixed effects show that both inside-out and outside-in BDA usage, as well as their interactions, are positively related to the sales performance of firms on e-platforms. The heterogeneity analysis indicates that inside-out (outside-in) BDA functions have a greater degree of impact on firms with lower (higher) sales performance. Through the theoretical and empirical analysis of the complex performance impacts of BDA usage, this study enriches the understanding of value creation in using multiple BDA functions and extends the theoretical account of KBV in the field of BDA.

Determinants of the Intention to Use Big Data Analytics in Banks and Insurance Companies: The Moderating Role of Managerial Support

Aim/Purpose: The aim of this research paper is to suggest a comprehensive model that incorporates the technology acceptance model with the task-technology fit model, information quality, security, trust, and managerial support to investigate the intended usage of big data analytics (BDA) in banks and insurance companies. Background: The emergence of the concept of “big data,” prompted by the widespread use of connected devices and social media, has been pointed out by many professionals and financial institutions in particular, which makes it necessary to assess the determinants that have an impact on behavioral intention to use big data analytics in banks and insurance companies. Methodology: The integrated model was empirically assessed using self-administered questionnaires from 181 prospective big data analytics users in Moroccan banks and insurance firms and examined using partial least square (PLS) structural equation modeling. The results cover sample characteristics, an analysis of the validity and reliability of measurement models’ variables, an evaluation of the proposed hypotheses, and a discussion of the findings. Contribution: The paper makes a noteworthy contribution to the BDA adoption literature within the finance sector. It stands out by ingeniously amalgamating the Technology Acceptance Model (TAM) with Task-Technology Fit (TTF) while underscoring the critical significance of information quality, trust, and managerial support, due to their profound relevance and importance in the finance domain. Thus showing BDA has potential applications beyond the finance sector. Findings: The findings showed that TTF and trust’s impact on the intention to use is considerable. Information quality positively impacted perceived usefulness and ease of use, which in turn affected the intention to use. Moreover, managerial support moderates the correlation between perceived usefulness and the intention to use, whereas security did not affect the intention to use and managerial support did not moderate the influence of perceived ease of use. Recommendations for Practitioners: The results suggest that financial institutions can improve their adoption decisions for big data analytics (BDA) by understanding how users perceive it. Users are predisposed to use BDA if they presume it fits well with their tasks and is easy to use. The research also emphasizes the importance of relevant information quality, managerial support, and collaboration across departments to fully leverage the potential of BDA. Recommendation for Researchers: Further study may be done on other business sectors to confirm its generalizability and the same research design can be employed to assess BDA adoption in organizations that are in the advanced stage of big data utilization. Impact on Society: The study’s findings can enable stakeholders of financial institutions that are at the primary stage of big data exploitation to understand how users perceive BDA technologies and the way their perception can influence their intention toward their use. Future Research: Future research is expected to conduct a comparison of the moderating effect of managerial support on users with technical expertise versus those without; in addition, international studies across developed countries are required to build a solid understanding of users’ perceptions towards BDA.

The Impact of Data Analytics on High Efficiency Supply Chain Management

Change is inevitable, so when supply chain (SC) managers strategize for future years, they must deal with challenges of the global supply chain management (SCM) issues. Leading trends over the last couple of years tend to be the increasing value of big data and analyzing the information through analytics. The information has tremendous value; businesses must capitalize on the assortment of information by proper and in-depth evaluation with the usage of big data analytics (BDA). This article seeks to spotlight the changing dynamics of the SC managing atmosphere, to recognize the way the two leading trends will influence SCM in future, to demonstrate the advantages which may be derived, and to generate suggestions for providing SC managers if BDA is adopted. The process of deriving value from the large quantities of information within the SCM is defined. It is demonstrated, through examples, the way SCM location might be influenced by these brand-new developments and trends. Within the examples, BDA have been adopted, utilized and applied effectively. Big data and analytics to draw out value coming from the information can create a big influence. It is clearly suggested that chain administrators pay attention to these two trends, since better usage of BDA can ensure they keep abreast with innovations modifications, which could help improve company competitiveness.

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.

THE IMPACT OF BIG DATA ANALYTICS ON SUPPLY CHAIN MANAGEMENT PRACTICES IN FAST MOVING CONSUMER GOODS INDUSTRY: EVIDENCE FROM DEVELOPING COUNTRIES

Leading trends over the last couple of years tend to be the increasing value of big data and analyzing the information through analytics. The information has tremendous value; fast moving consumer goods (FMCG) businesses must capitalize on the assortment of information by proper and in-depth evaluation with the usage of big data analytics (BDA). Objective: This article seeks to spotlight the changing dynamics of the SC managing atmosphere, to recognize the way the two leading trends will influence supply chain management (SCM) in future, to demonstrate the advantages which may be derived, and to generate suggestions to provide SC managers if BDA is adopted. Method: A survey was done amongst workers of multinational FMCG businesses across the world: the Americas, Asia etc. Systemic situation modeling is used in the quantitative evaluation to analyze the information gathered from surveys. The process of deriving value from the large quantities of information within the SCM is defined. Results: The adoption of BDA technologies can develop extensive value-added as well as a financial gain for companies and can quickly be a regular during the entire market. It is demonstrated, through examples, the way SCM location might be influenced by these brand-new developments and trends. Within the examples, BDA have been adopted, utilized, and applied effectively. Big data and analytics to draw out value coming from the information can create a big influence. Conclusion: It is clearly suggested chain administrators pay attention to these 2 trends, since better usage of BDA can ensure they hold abreast with innovations modifications, which could help improve company competitiveness. Keywords: Supply chain management; Big data analytics; FMCG; Developing countries

Positioning big data analytics capabilities towards financial service agility

PurposeEnterprises are increasingly taking actionable steps to transform existing business models through digital technologies for service transformation such as big data analytics (BDA). BDA capabilities offer financial institutions to source financial data, analyse data, insight and store such data and information on collaborative platforms for a quick decision-making process. Accordingly, this study identifies how BDA capabilities can be deployed to provide significant improvement for financial services agility.Design/methodology/approachThe study relied on survey data from 485 banking professionals' perspectives with BDA usage, IT capability development and financial service agility. The PLS-SEM technique was used to evaluate the underlying relationship and the applicability of the research framework proposed.FindingsBased on the empirical test from this study, distinctive BDA usage grounded on the concept of IT capability viewpoint proof that financial service agility could be enhanced provided enterprises develop technical capabilities alongside other relevant resources.Practical implicationsThe study further highlights the need for financial service managers to identify BDA technologies such as data mining, query and reporting, data visualisation, predictive modelling, streaming analytics, video analytics and voice analytics to focus on financial knowledge gathering and market observation. Financial managers can also deploy BDA tools to develop a strategic road map for data management, data transferability and knowledge discovery for customised financial products.Originality/valueThis study is a useful contribution to the burgeoning discussion with emerging technologies such as BDA implication to improving enterprises operations.

Aligning Tasks, Technology, People, and Structures to Leverage the Value of Big Data Analytics

This study provides an understanding of how task, technology, people and structures (TTPS) can be used by developing countries as a baseline for future alignment evaluations to leverage big data analytics (BDA) in healthcare. Enabling the adoption of patient-empowering technologies is being used to cultivate a more patient-centric and collaborative healthcare ecosystem. Creating simple and reliable technology then integrating it into the existing public healthcare infrastructure has generated new challenges for big data analytics. Challenges arise because of complex interactions among the tasks that need to be supported by the technology, people using the technology and the distinct contextual characteristics of the organization. Therefore, TTPS are organisational elements that affect the value generated by BDA. However, little is still known about how these interacting organizational elements can be aligned to obtain maximum BDA value. A study involving participants from different industrial sectors in South Africa was conducted to determine what level of alignment of TTPS yields the best value from BDA. We adopted the notion of alignment as Gestalts. Results show strong alignment between TTPS where IT experts work closely with financial planners. Adequate training, coupled with structures encouraging usage of BDA results in higher organisational performance when technology is in sync with the tasks.

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.