Usage Of Big Data Analytics Research Articles

The organizational learning capability as an antecedent to the implementation of Big data Analytics: Some evidence from an Asian country

This study aims to investigate the relationship between an organization's capacity for learning and its use of big data analytics in an Asian nation. Using a quantitative research design, the investigation collects data from a number of local companies. According to the findings, the deployment of big data analytics is positively correlated with an organization's capacity to learn. This indicates that in order to implement these technologies effectively, a culture of learning must be established. Some qualities of an organization's learning capability, such as knowledge transfer and experimentation, have a stronger correlation with the usage of big data analytics than others, according to the study. The findings have implications for regional managers and policymakers and underline the need of investing in the development of organizational learning capability to improve the use of big data analytics. This study greatly contributes to our understanding of the function of organizational learning capability as a prerequisite for the deployment of big data analytics in the context of an Asian country through its findings. In order to maximize the use and impact of big data analytics, the findings imply that businesses may benefit from a strategic approach to organizational learning
 

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.

The good, the bad, and the ugly: impact of analytics and artificial intelligence-enabled personal information collection on privacy and participation in ridesharing

ABSTRACT Big data analytics (BDA) and artificial intelligence (AI) may provide both bright and dark sides that may affect user participation in ridesharing. We do not know whether the juxtaposed sides of these IT artefacts influence users’ cognitive appraisals, and if so, to what extent will their participative behaviour be affected. This paper contributes to the IS research by uncovering the interplay between the dark and bright sides of BDA and AI and the underlying mechanisms of cognitive appraisals for user behaviour in ridesharing. We performed two phases of the study using mixed-methods. In the first study, we conduct 21 semi-structured interviews to develop the research model. The second study empirically validated the research model using survey data of 332 passengers. We find that the usage of BDA and AI on ridesharing platforms have a bright side (usefulness, “the good”) but also a dark side (uncertainty and invasion of privacy, “the bad and the ugly”). The bright side generates perceived benefits, and the dark side shape perceived risks in users, which discount the risks from the benefits of using the ridesharing platform. Privacy control exerts a positive effect on the perceived benefits to encourage individuals to use the ridesharing platform.

Characteristics of consulting firms associated with the diffusion of big data analytics

PurposeThis study investigates the characteristics of business and management consulting firms (firm size, international affiliation and scope of operation) affecting the adoption rate (i.e. recency of adopting big data analytics (BDA) as a new idea) and usage level of BDA. Ten critical areas of BDA application to business and management consulting were investigated, (1) Human Resource Management; (2) Risk Management; (3) Financial Advisory Services; (4) Innovation and Strategy; (5) Brand Building and Product Positioning; (6) Market Research/Diagnostic Studies; (7) Scenario-Based Planning/Business Simulation; (8) Information Technology; (9) Internal Control/Internal Audit; and (10) Taxation and Tax Management.Design/methodology/approachSurvey data was obtained through a structured questionnaire from one hundred and eighteen (118) consultants in Nigeria from diverse consulting firm settings in terms of size, international affiliation and scope of operation (Big 4/non-Big 4 firms). Data was analyzed using descriptive statistics, cluster analysis, multivariate analysis of variance (MANOVA), multivariate discriminant analysis and multivariable logistic regression.FindingsWhereas organizational characteristics such as firm size, international affiliation and scope of operation significantly determine the adoption rate of BDA, two attributes (international affiliation and scope of operation) significantly explain BDA usage level. Internationally affiliated consulting firms are more likely to record higher usage level of BDA than local firms. Also, the usage level of BDA by the Big 4 accounting/consulting firms is expected to be higher in comparison to non-Big 4 firms.Practical implicationsContrary to common knowledge that firm size is positively associated with the adoption of an innovation, the study found no evidence to support this claim in respect of the diffusion of BDA. Overall, it appears that the scope of operation is the strongest organizational factor affecting the diffusion of BDA among consulting firms.Originality/valueThe study contributes to knowledge by exposing the factors promoting the uptake of BDA in a developing country. The originality of the current study stems from the consideration that it is the first, to the researchers' knowledge, to investigate the application of BDA by consulting firms in the Nigerian context. The study adds to literature on management accounting in the digital economy.

Effective and efficient usage of big data analytics in public sector

PurposeThis study aims to achieve three goals: present a holistic, flexible and dynamic model; define the model’s factors and explain how these factors lead to effective and efficient usage of big data; and generate indexes based on experts’ input to rank them based on their importance.Design/methodology/approachThis paper uses the analytic hierarchy process, a quantitative method of decision-making, to evaluate the importance of the factors presented in the model. The fundamental principle of the overall model is that of a dynamo which is borrowed from electromagnetic physics. The model is also based on three IS theories.FindingsTechnological advancements and data security are among the most important factors that may impact the effectiveness and efficiency of big data usage. Authentication, governments’ focus on it and transparency and accountability are the most important factors in techno-centric, governmental-centric and user-centric factors, respectively.Research limitations/implicationsThe findings of this paper confirmed earlier findings in the literature and quantitatively assessed some of the factors that were conceptually presented. This paper also presented a framework that can be used in future studies.Practical implicationsPolicy and decision-makers may need to upgrade pertinent technologies such as internet security, frame policies toward information technology (IT) and train the users.Originality/valueThis paper fills a gap in the literature by presenting a comprehensive study of how different factors dynamically contribute to the effective usage of big data in the public sector. It also quantitatively presents the importance of the factors based on the data collected from 12 IT experts.

Managing big data in the retail industry of Singapore: Examining the impact on customer satisfaction and organizational performance

Much of the research on big data analytics has been centered on technical or system development. Research has been carried out on the usage of big data analytics to understand customer relationships and experience, amongst others. Still, there is a lack of research in the retail industry considering big data management, examining the impact on customer satisfaction and organizational performance in the retail sector. Retailers explore analytics to gain a unified picture of their customers and operations across the store or online channels and make strategic decisions contributing to the growth of the retail industry. Thereof, this study has been conducted by majorly focusing on the Singapore retail industry to clarify the feasibility of big data management analytics. Quantitative research method was employed involving 500 participants from the retail industry of Singapore. The results of the study stated that amongst the different big data analytics utilized within the retail industry of Singapore, social media analytics had been majorly answered by the participants. Future researchers can study about the upcoming retail trends in Singapore and how the effects of big data analysis changed in the past few years and deal with the unexpected future recessions in the retail industry within Singapore.

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.

The performance effects of big data analytics and supply chain ambidexterity: The moderating effect of environmental dynamism

The importance of big data analytics–enabled dynamic capability has been at the forefront of research for information systems management, operations management, and strategic management community. Prior studies have reported on the influence of big data analytics–enabled dynamic capability (BDA) for improved organizational agility and organizational performance, but there has been a paucity of literature regarding the role of big data analytics–enabled dynamic capability in untangling the supply chain ambidexterity dilemma and organizational performance. To address these research gaps, this paper draws on the dynamic capability view of the organization under the contingent effect of environmental dynamism. We tested our research hypotheses using 281 surveys, gathered using a pre-tested questionnaire. Our results suggest that BDA has positive effects on improving supply chain agility (SCAG), supply chain adaptability (SCAD) and performance measures (cost performance and operational performance). However, we noted that hypotheses regarding the moderating effect of environmental dynamism (ED) on the paths joining BDA and SCAG/SCAD were not supported. To address these unexpected results, we conducted post hoc analysis to explain the rationale behind the insignificant moderating effects of ED on the paths joining BDA and SCAG/SCAD. We found that the effects of BDA on SCAG/SCAD were higher under intermediate levels of environmental dynamism but comparatively weak when the environmental dynamism is low or high. Hence, we can argue that big data analytics can help enhance supply chain agility, supply chain adaptability, and organizational performance, but these effects are contingent upon the level of environmental dynamism. Moreover, a non-linear, inverse U-shaped moderating effect of environmental dynamism exists. Collectively, these findings provide a theory-based understanding of the organizational level of usage of big data analytics and its effects on supply chain agility, supply chain adaptability, and organizational performance. Moreover, they further shape our understanding of how big data analytics–enabled dynamic capabilities yield differential results under the moderating effect of environmental dynamism. Hence, we believe that our results will be useful for managers who are highly optimistic about the usage of these emerging technologies and their effects on supply chain characteristics. Finally, we have outlined our study limitations and offered numerous research directions.

Motivation to use big data and big data analytics in external auditing

Purpose This paper aims to explore organisational intentions to use Big Data and Big Data Analytics (BDA) in external auditing. This study conceptualises different contingent motivating factors based on prior literature and the views of auditors, business clients and regulators regarding the external auditing practices and BDA. Design/methodology/approach Using the contingency theory approach, a literature review and 21 in-depth interviews with three different types of respondents, the authors explore factors motivating the use of BDA in external auditing. Findings The study presents a few key findings regarding the use of BD and BDA in external auditing. By disclosing a comprehensive view of current practices, the authors identify two groups of motivating factors (company-related and institutional) and the circumstances in which to use BDA, which will lead to the desired outcomes of audit companies. In addition, the authors emphasise the relationship of audit companies, business clients and regulators. The research indicates a trend whereby external auditors are likely to focus on the procedures not only to satisfy regulatory requirements but also to provide more value for business clients; hence, BDA may be one of the solutions. Research limitations/implications The conclusions of this study are based on interview data collected from 21 participants. There is a limited number of large companies in Lithuania that are open to co-operation. Future studies may investigate the issues addressed in this study further by using different research sites and a broader range of data. Practical implications Current practices and outcomes of using BD and BDA by different types of respondents differ significantly. The authors wish to emphasise the need for audit companies to implement a BD-driven approach and to customise their audit strategy to gain long-term efficiency. Furthermore, the most challenging factors for using BDA emerged, namely, long-term audit agreements and the business clients’ sizes, structures and information systems. Originality/value The original contribution of this study lies in the empirical investigation of the comprehensive state-of-the-art of BDA usage and motivating factors in external auditing. Moreover, the study examines the phenomenon of BD as one of the most recent and praised developments in the external auditing context. Finally, a contingency-based theoretical framework has been proposed. In addition, the research also makes a methodological contribution by using the approach of constructivist grounded theory for the analysis of qualitative data.

Big Data Analytics and the Management Accountant

A business exists to earn more revenue with less costs and thus making it a profitable venture. Therefore it will strive cost containment with investment to develop its competitive position. To arrive at this balance is not easy and it requires deep understanding of the drivers of cost, risk and value in the business. All three factors are highly dynamic and management requires constant and current information for better decision making and performance management. Usage of Big Data Analytics have become imminent to improve operational performance and sustaining or improving competitive position. Research findings indicate that organizations are struggling to get valuable insights from their data. Businesses need to adopt strategic thinking and be innovative to ensure that they have the needed capabilities to exploit advances in big data analytics for not only success but also for survival in the technology and digital age. Management Accountants have an important role to play in ensuring business success in today’s data-driven era. Therefore they need to develop their skills continually so that they remain relevant to their employer’s needs. They also required to stay abreast of developments and upskill themselves in the discipline of analytics and data science so that they continue to contribute their value to the business and to the profession they serve. This paper explains the meaning of business analytics, its value to business and the evolution of roles of management accountants in the era of globalization and digital age and the need for management accountants to acquire the skills of business analytics and data science to retain their relevance to the corporate world.

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.

A decade of big data literature: analysis of trends in light of bibliometrics

Bibliometrics is a quantitative tool for the analysis of literature published in a scientific field. Using Scopus as the data source, we perform a thorough analysis of scholarly works published in the field of big data from 2008 to 2017. The objective of the work is to find the most cited articles in the given time frame, the citation trends, the authorship trends as well as the trends of research work in the related area. The analysis shows that over 50% of publications do not receive any citations, and the average number of citations per publication is 3.17. It is also observed that single authorship of research publications has declined over the time. The analysis reveals the pioneering role played by the USA in advancing the research in big data, which has lately been taken over by China, and the large-scale usage of big data analytics in various domains of science.

A Comprehensive Study on The Usage of Big Data Analytics for Wireless and Wired Networks

Big networks, such as those of Google and also Facebook, or even little as well as medium sized business networks suffer from a myriad of web traffic. This happens due to the gigantic amount of data being refined either in set or live applications. This paper provides a comprehensive study on the usage of big data analytics for wireless and wired networks

A survey towards an integration of big data analytics to big insights for value-creation

Big Data Analytics (BDA) is increasingly becoming a trending practice that generates an enormous amount of data and provides a new opportunity that is helpful in relevant decision-making. The developments in Big Data Analytics provide a new paradigm and solutions for big data sources, storage, and advanced analytics. The BDA provide a nuanced view of big data development, and insights on how it can truly create value for firm and customer. This article presents a comprehensive, well-informed examination, and realistic analysis of deploying big data analytics successfully in companies. It provides an overview of the architecture of BDA including six components, namely: (i) data generation, (ii) data acquisition, (iii) data storage, (iv) advanced data analytics, (v) data visualization, and (vi) decision-making for value-creation. In this paper, seven V's characteristics of BDA namely Volume, Velocity, Variety, Valence, Veracity, Variability, and Value are explored. The various big data analytics tools, techniques and technologies have been described. Furthermore, it presents a methodical analysis for the usage of Big Data Analytics in various applications such as agriculture, healthcare, cyber security, and smart city. This paper also highlights the previous research, challenges, current status, and future directions of big data analytics for various application platforms. This overview highlights three issues, namely (i) concepts, characteristics and processing paradigms of Big Data Analytics; (ii) the state-of-the-art framework for decision-making in BDA for companies to insight value-creation; and (iii) the current challenges of Big Data Analytics as well as possible future directions.

Big data analytics sentiment: US-China reaction to data collection by business and government

As society continues its rapid change to a digitized individual, corporate, and government environment it is prudent for researchers to investigate the zeitgeist of the global citizenry. The technological changes brought about by big data analytics are changing the way we gather and view data. This big data analytics sentiment research examines how Chinese and American respondents may view big data collection and analytics differently. The paper follows with an analysis of reported attitudes toward possible viewpoints from each country on various big data analytics topics ranging from individual to business and governmental foci. Hofstede's cultural dimensions are used to inform and frame our research hypotheses. Findings suggest that Chinese and American perspectives differ on individual data values, with the Chinese being more open to data collection and analytic techniques targeted toward individuals. Furthermore, support is found that US respondents have a more favorable view of businesses' use of data analytics. Finally, there is a strong difference in the attitudes toward governmental use of data, where US respondents do not favor governmental big data analytics usage and the Chinese respondents indicated a greater acceptance of governmental data usage. These findings are helpful in better understanding appropriate technological change and adoption from a societal perspective. Specifically, this research provides insights for corporate business and government entities suggesting how they might adjust their approach to big data collection and management in order to better support and sustain their organization's services and products.

Big data analytics: does organizational factor matters impact technology acceptance?

Ever since the emergence of big data concept, researchers have started applying the concept to various fields and tried to assess the level of acceptance of it with renown models like technology acceptance model (TAM) and it variations. In this regard, this paper tries to look at the factors that associated with the usage of big data analytics, by synchronizing TAM with organizational learning capabilities (OLC) framework. These models are applied on the construct, intended usage of big data and also the mediation effect of the OLC constructs is assessed. The data for the study is collected from the students pertaining to information technology disciplines at University of Liverpool, online programme. Though, invitation to participate e-mails are sent to 1035 students, only 359 members responded back with filled questionnaires. This study uses structural equation modelling and multivariate regression using ordinary least squares estimation to test the proposed hypotheses using the latest statistical software R. It is proved from the analysis that compared to other models, model 4 (which is constructed by using the constructs of OLC and TAM frameworks) is able to explain 44% variation in the usage pattern of big data. In addition to this, the mediation test performed revealed that the interaction between OLC dimensions and TAM dimensions on intended usage of big data has no mediation effect. Thus, this work provided inputs to the research community to look into the relation between the constructs of OLC framework and the selection of big data technology.

Perceived strategic value-based adoption of Big Data Analytics in emerging economy

PurposeThe purpose of this paper is to provide an insight about factors affecting Big Data Analytics (BDA) utilization and adoption in Indian firms. Research studies have so far focused on BDA adoption in developed economies. This study examines the factors that influence BDA usage and adoption in the context of emerging economies.Design/methodology/approachThis study proposed a theoretical model of factors influencing BDA utilization and adoption. Two independent research streams – first, the top managers’ perceived strategic value (PSV) in BDA and second, the factors that influence the adoption of BDA theoretically – have been integrated with the technology-organization-environment (TOE) framework. In the BDA context, there was a theoretical necessity to identify the driver and barriers of BDA from the TOE framework on PSV and adoption of BDA. A qualitative exploratory study using face-to-face semi-structured interviews was carried out to collect data from 22 different enterprises and service providers in India. India was selected as the context as it is one of the fastest growing large economies of the world with huge potential of BDA to improve the business landscape.FindingsThe results showed that the major reason behind BDA non-adoption is that the organizations did not realize the strategic value (SV) of BDA, and they were not ready to make the changes because of technological, organizational and environmental difficulties. The findings corroborate previous results about significant factors affecting IT adoption and implementation and provide new and interesting insights. The main factors identified as playing a significant role in organizations’ adoption of BDA were SV of BDA, complexity, compatibility, IT assets, top management support, organization data environment, perceived costs, external pressure and industry type.Research limitations/implicationsThe main limitation related to this study is the difficulty in generalizing the findings to a larger population of enterprises. To overcome this, a statistical survey has been planned to be conducted in the future.Practical implicationsThe BDA adoption model in this study will have both managerial implications for practitioners in India, as well as those in other developing countries, and academic implications for researchers who are interested in BDA adoption in developing counties, in terms of formulating better strategies for BDA adoption. For managers, using the research model of this study could assist in increasing their understanding of why some organizations choose to adopt BDA, while similar ones facing similar conditions do not. Also, the understanding of the strategic utilization of BDA in different business processes may improve the adoption of BDA in organizations.Originality/valueThis paper contributes in exploring and enhancing the understanding of the factors affecting the utilization and adoption of BDA in organizations from an Indian perspective. This study is an attempt to develop and explore a BDA adoption model by the fusion of PSV and TOE framework. The effect of the three contexts of this framework (technological, organizational and environmental) on the strategic utilization of BDA has been studied for the first time.

Big Data Analytics in Heart Attack Prediction

Introduction: Acute myocardial infarction (heart attack) is one of the deadliest diseases patients face. The key to cardiovascular disease management is to evaluate large scores of datasets, compare and mine for information that can be used to predict, prevent, manage and treat chronic diseases such as heart attacks. Big Data analytics, known in the corporate world for its valuable use in controlling, contrasting and managing large datasets can be applied with much success to the prediction, prevention, management and treatment of cardiovascular disease. Data mining, visualization and Hadoop are technologies or tools of big data in mining the voluminous datasets for information. Aim: The aim of this literature review was to identify usage of Big Data analytics in heart attack prediction and prevention, the use of technologies applicable to big data, privacy concerns for the patient, and challenges and future trends as well as suggestions for further use of these technologies. Methods: The national and international databases were examined to identify studies conducted about big data analytics in healthcare, heart attack prediction and prevention, technologies used in big data, and privacy concerns. A total of 31 studies that fit these criteria were assessed. Results: Per the studies analyzed, Big Data analytics is useful in predicting heart attack, and the technologies used in Big Data are extremely vital to the management and tailoring of treatment for cardiovascular disease. And as the use of Big Data in healthcare increases, more useful personalized medicine will be available to individual patients. Conclusion: This review offers the latest information on Big Data analytics in healthcare, predicting heart attack, and tailoring medical treatment to the individual. The results will guide providers, healthcare organizations, nurses, and other treatment providers in using Big Data technologies to predict and manage heart attack as well as what privacy concerns face the use of Big Data analytics in healthcare. Effective and tailored medical treatment can be developed using these technologies.

Are enterprises ready for big data analytics? A survey-based approach

Big data analytics is a fascinating topic in the field of computer science and information systems as it challenges fundamental aspects of computation. The aim of this study is to investigate the acceptance and usage of big data analytics in enterprises, and to understand how end-users perceptions and organisational factors influence the implementation of big data projects. This study unfolds in two objectives. The first objective is to examine the concepts and elements of big data and big data analytics. The second objective is to propose and empirically validate an extensive model of technology acceptance incorporating individual perceptions and organisational elements. The proposed research model expands the traditional technology acceptance model by incorporating the dimensions of organisational learning capabilities. This study surveyed 359 information technology professionals from 83 countries in various industries who are studying at the University of Liverpool Online. This study provides relevant academic and practical insight as it integrates two technology adoption frameworks.