Adoption Of Big Data Analytics Research Articles

Erratum: Big data analytics adoption: A case study in a large South African telecommunications organisation

Erratum: Big data analytics adoption: A case study in a large South African telecommunications organisation

Information technology capability and firm performance in the era of Big Data analytics: Evidence from the United States

Research Question: This study investigates the relationship between information technology (IT) capability and firm performance in the 2010s, the era of big data analytics (BDA), in the context of US companies. Motivation: With the evolution of business intelligence and the proliferation of analytic tools that further improve IT capability, it is more important than ever to understand whether firms with stronger IT capabilities perform better. Idea: After categorizing firms into pairs of IT leaders and control groups, the performance of each pair of firms in each group was compared. Data: All data are publicly available from Compustat and InformationWeek (IW) 500. Tools: The Wilcoxon signed-rank test and regression analysis were used to examine how the performance of IT leaders and control groups changed during the 2010–2017 period. Findings: This results show no significant relationship between a firm’s IT capability and its performance in the sample of US companies during this period. Contribution: This study will help academicians and practitioners to better understand how the adoption and application of BDA derived from IT capability affects firm performance.

Big data analytics adoption: Determinants and performances among small to medium-sized enterprises

Big data analytics (BDA) adoption is a game-changer in the current industrial environment for precision decision-making and optimal performance. Nonetheless, the determinants or consequences of its adoption in small and medium enterprises remain unclear, hence the objective of this study. Data analysis of 171 Iranian small and medium manufacturing firms revealed that complexity, uncertainty and insecurity, trialability, observability, top management support, organizational readiness, and external support affect significantly on BDA adoption. The findings confirm the strong impact of BDA adoption in small to medium-sized enterprises, marketing and financial, performance enhancement. Understanding the drivers of BDA adoption helps managers to employ appropriate initiatives that are vital for effective implementation. The results enable BDA service providers to attract and diffuse BDA in small to medium-sized enterprises.

Mediating effect of big data analytics on project performance of small and medium enterprises

PurposeThis study aims to investigate the mediating role of “Big Data Analytics” played between “Project Performance” and nine factors including top management, project knowledge management focus on sustainability, green purchasing, environmental technologies, social responsibility, project operational capabilities, project complexity, collaboration and explorative learning, and project success.Design/methodology/approachA sample of 321 responses from 106 Indian manufacturing small and medium-scaled enterprises (SMEs) was collected. Data were analyzed using empirical analysis through structural equation modeling.FindingsThe result shows that project knowledge management, green purchasing and project operational capabilities require the mediating support of big data analytics. The adoption of big data analytics has a positive influence on project performance in the manufacturing sector.Practical implicationsThis study is useful to SMEs managers, practitioners and government policymakers to develop an understanding of big data analytics, eliminate challenges in the adoption of big data, and formulate strategies to handle projects efficiently in SMEs in the context of Indian manufacturing.Originality/valueFor the first time, big data for manufacturing firms handing innovative projects was discussed in the Indian SME context.

Editorial: The personalisation of insurance: Data, behaviour and innovation

The adoption of Big Data analytics (BDA) in insurance has proved controversial but there has been little analysis specifying how insurance practices are changing. Is insurance passively subject to the forces of disruptive innovation, moving away from the pooling of risk towards its personalisation or individualisation, and what might that mean in practice? This special theme situates disruptive innovations, particularly the experimental practices of behaviour-based personalisation, in the context of the practice and regulation of contemporary insurance. Our contributors argue that behaviour-based personalisation in insurance has different and broader implications than have yet been appreciated. BDAs are changing how insurance governs risk; how it knows, classifies, manages, prices and sells it, in ways that are more opaque and more extensive than the black boxes of in-car telematics.

Big data analytics adoption model for small and medium enterprises

PurposeBig data analytics (BDA) is recognized as a turning point for firms to improve their performance. Although small- and medium-sized enterprises (SMEs) are crucial for every economy, they are lagging far behind in the usage of BDA. This study aims to provide a single and unified model for the adoption of BDA among SMEs with the integration of the technology–organization–environment (TOE) model and resource-based view.Design/methodology/approachA survey of 112 manufacturing SMEs in Iran was conducted, and the data were analysed using structural equation modelling to test the model of this study.FindingsThe results offer evidence of a BDA mediation effect in the relationship between technological, organizational and environmental contexts, and SMEs performance. The findings also demonstrated that technological and organizational elements are the more significant determinants of BDA adoption in the context of SMEs. In addition, the result of this study confirmed that BDA adoption could enhance the financial and market performance of SMEs.Practical implicationsProviding a single unified framework of BDA adoption for SMEs enables them to appreciate the importance of most influential elements (technology, organization and environment) in the adoption of BDA. Also, this study may encourage SMEs to be more willing to use BDA in their businesses.Originality/valueAlthough there are studies on BDA adoption and firm performance among large companies, there is a lack of empirical research on SMEs, in particular, based on the TOE model. SMEs differ from large companies in terms of the availability of resources and size. Therefore, this study aimed to initiate a conceptual framework of BDA adoption for SMEs to assist them to be able to take advantage of the adoption of such technology.

A survey on context awareness in big data analytics for business applications

The concept of context awareness has been in existence since the 1990s. Though initially applied exclusively in computer science, over time it has increasingly been adopted by many different application domains such as business, health and military. Contexts change continuously because of objective reasons, such as economic situation, political matter and social issues. The adoption of big data analytics by businesses is facilitating such change at an even faster rate in much complicated ways. The potential benefits of embedding contextual information into an application are already evidenced by the improved outcomes of the existing context-aware methods in those applications. Since big data is growing very rapidly, context awareness in big data analytics has become more important and timely because of its proven efficiency in big data understanding and preparation, contributing to extracting the more and accurate value of big data. Many surveys have been published on context-based methods such as context modelling and reasoning, workflow adaptations, computational intelligence techniques and mobile ubiquitous systems. However, to our knowledge, no survey of context-aware methods on big data analytics for business applications supported by enterprise level software has been published to date. To bridge this research gap, in this paper first, we present a definition of context, its modelling and evaluation techniques, and highlight the importance of contextual information for big data analytics. Second, the works in three key business application areas that are context-aware and/or exploit big data analytics have been thoroughly reviewed. Finally, the paper concludes by highlighting a number of contemporary research challenges, including issues concerning modelling, managing and applying business contexts to big data analytics.

Influencing models and determinants in big data analytics research: A bibliometric analysis

Incorporating big data analytics into a particular context brings various challenges that rest on the model or framework through which individuals or organisations adopt big data to achieve their objectives. Although these models have recently triggered scholars’ attention in various domains, in-depth knowledge of using each of these models in big data research is still blurred. This study enriches our knowledge on emerging models and theories that shape big data analytics adoption (BDAD) research through a bibliometric analysis of 229 studies (143 journal articles and 86 conference papers) published in indexed sources between 2013 and 2019. As a result, twenty models on BDAD have emerged (e.g., “Dynamic Capabilities”, “Resource-Based View”, “Technology Acceptance Model”, “Diffusion of Innovation”, etc.). The analysis reveals that BDAD research to demonstrate attributes suggestive of a topic at an initial stage of development as it is broadly dispersed across different domains employs a wide range of models, some of which overlap. Most of the applied models are generic in nature focusing on variance-based relationships and snapshot prediction with little consensus. There is a conspicuous dearth of process models, firm-level analysis and cultural orientation in contemporary BDAD research. Insights of this bibliometric study could guide rigorous big data research and practice in various contexts. The study concludes with research implications and limitations that offer promising prospects for forthcoming research.

A systematic literature review with bibliometric analysis of big data analytics adoption from period 2014 to 2018

PurposeThe volume of data being generated by various sectors in recent years has increased exponentially. Consequently, professionals struggle to process essential data in the current competitive world. The purpose of the study is to explore and provide insights into the Big Data Analytics (BDA) studies in different sectors.Design/methodology/approachThis study performs a systematic literature review (SLR) with bibliometric analysis of BDA adoption (BDAA) in the supply chain and its applications in various sectors from 2014 to 2018. This paper focuses on BDAA studies have been carried out across different countries and sectors. Also, the paper explores different tools and techniques used in BDAA studies.FindingsThe benefits of adopting BDA, coupled with a lack of adequate research in the field, have motivated this study. This literature review categorizes paper into seven main areas and found that most of the studies were carried out in manufacturing and service.Practical implicationsThis research insight and observations can provide practitioners and academia with guidance on implementing BDA in different sustainable supply chain sectors. The article indicates a few remarkable gaps in the future direction and trends regarding the integration of BDA and sustainable supply chain development.Originality/valueThe study derives a new categorization of BDA, which investigates how data is generated, organized, captured, interpreted and evaluated to give valuable insights to manage the sustainable supply chain.

BIG DATA ANALYTICS IN BANKING AND FINANCIAL SERVICES SECTOR

‘He who holds the wealth of information, holds the solution to the toughest of the situations’ this quote is very much apt for the recent ongoing scenario, where there is an invisible struggle going on among the organizations as well as nations in the search for more and more information. Now, when the Banking and Financial services sectors are put under the scanner, it becomes evident that they are sitting on top of a humungous heap of valuable data. This data can be used for the betterment and advancement of the industry as well as the people. While it is good to have large amount of data available, it will be termed a big pile of trash if it is not analyzed properly and the results obtained from it are not put to use. With the adoption of Big Data analytics into the banking and financial services, many obvious as well as concealed problems can be addressed to and even solved quickly. The main objective of this paper is to highlight the meaning of Big Data analysis, study the types of data analytics with respect to Banking and Financial services sector and to showcase the potential benefits of embracing Big Data analytics into the Banking & Financial services sector. KEYWORDS: Information, Banking and Financial services, Advancement, Big Data, Data Analytics

An Adoption model for a big data analytics system for improving healthcare services in Burundi’s public hospitals

One of the key responsibilities of a government is to provide efficient health care services that are better and affordable. In Burundi, patients’ health records are collected using handwritten forms and stored in filing cabinets. Evidence’ based research and practice shows that adoption of a Big Data Analytics (BDA) system can significantly improve health care services. Unfortunately, BDA adoption models and automated assessment tools are lacking not to mention the dearth caused by researchers’ predominant focus on the technical aspects. Therefore, the aim of this study was to propose a BDA system adoption model for improving health care services in Burundi’s public hospitals. This was achieved through a mixed research method a large part being qualitative. The factors that influence the adoption of BDA in public healthcare services using the Technology Organization Environment (TOE) adoption theory through a desk research. Semi-structured interviews, observations and document reviews were used to investigate the methods used to collect, store and analyze data in Public hospitals of Burundi. Afterwards, a web based automated Adoption Readiness Assessment Tool (ARAT) was developed then used to assess the readiness of Burundi in adopting a BDA system in its public hospitals. The assessment results showed that the country has adequate telecommunication infrastructures and has started using information systems like OpenClinic and District Health Information Software 2 (DHIS2) in some public hospitals, the government has set up policies for e-Health and the level of awareness is high as well among health workers. But there are improvements to be made in order to assure that the adoption is successful. Lastly, a tailored adoption model was proposed describing what should be done and how in order to assure a successful adoption of a BDA in public hospitals.

Adopt Big-Data Analytics to Explore and Exploit the New Value for Service Innovation

Big-data analytics is gaining substantial attention due to its contribution to the process of determining business strategy and providing valuable information for the design and development of service innovation. The principal objective of this research is to study the adoption of big-data analytics for service innovation. The focus will be on leveraging features of data analytics to capture genuine customer’s requirements from the communication data through the digital service channel. This study used mixed methods research of documentary research, with supplementary semi-structured interviews. The interviews were conducted with 11 executive managements who have more than ten years of experience in data analytics or service development. The result of the research found that organizations in the services industry are using big data analytics to build capabilities to gain competitive advantages as well as the ability to rapidly and accurately respond to the market’s demands. The process of adopting big-data analytics for service innovation described in this article consists of seven essential procedural steps that impact the success of the development of service innovation, and also considered with the objective of increasing effectiveness in opportunity identification and reduce complexity in the fuzzy frond-end service innovation development theory.

Big data adoption in academic libraries: a literature review

PurposeThe purpose of this paper is to analyse the publication of big data in the library from Scopus database by looking at the writing time period of the papers, author's country, the most frequently occurring keywords, the article theme, the journal publisher and the group of keywords in the big data article. The methodology used in this study is a quantitative approach by extracting data from Scopus database publications with the keywords “big data” and “library” in May 2019. The collected data was analysed using Voxviewer software to show the keywords or terms. The results of the study stated that articles on big data have appeared since 2012 and are increasing in number every year. The big data authors are mostly from China and America. Keywords that often appear are based on the results of terminology visualization are including, “big data”, “libraries”, “library”, “data handling”, “data mining”, “university libraries”, “digital libraries”, “academic libraries”, “big data applications” and “data management”. It can be concluded that the number of publications related to big data in the library is still small; there are still many gaps that need to be researched on the topic. The results of the research can be used by libraries in using big data for the development of library innovation.Design/methodology/approachThe Scopus database was accessed on 24 May 2019 by using the keyword “big data” and “library” in the search box. The authors only include papers, which title contain of big data in library. There were 74 papers, however, 1 article was dropped because of it not meeting the criteria (affiliation and abstract were not available). The papers consist of journal articles, conference papers, book chapters, editorial and review. Then the data were extracted into excel and analysed as follows (by the year, by the author/s’s country, by the theme and by the publisher). Following that the collected data were analysed using VOX viewer software to see the relationship between big data terminology and library, terminology clustering, keywords that often appear, countries that publish big data, number of big data authors, year of publication and name of journals that publish big data and library articles (Alagu and Thanuskodi, 2019).FindingsIt can be concluded that the implementation of big data in libraries is still in an early stage, it is shown from the limited number of practical implementation of big data analytics in library. Not many libraries that use big data to support innovation and services since there were lack of librarian skills of big data analytics. The library manager’s view of big data is still not necessary to do. It is suggested for academic libraries to start their adoption of big data analytics to support library services especially research data. To do so, librarians can enhance their skills and knowledge by following some training in big data analytics or research data management. The information technology infrastructure also needs to be upgraded since big data need big IT capacity. Finally, the big data management policy should be made to ensure the implementation goes well.Originality/valueThis paper discovers the adoption and implementation of big data in library, many papers talk big data in business and technology context. This is offering new idea for many libraries especially academic library about the adoption of big data to support their services. They can adopt the big data analytics technology and technique that suitable for their library.

Moderating Effects of Gender and Resistance to Change on the Adoption of Big Data Analytics in Healthcare

The big data analytics (BDA) has dragged tremendous attention in healthcare organizations. Healthcare organizations are investing substantial money and time in big data analytics and want to adopt it to get potential benefits. Thus, this study proposes a BDA adoption model in healthcare organizations to explore the critical factors that can influence its adoption process. The study extends the technology acceptance model (TAM) with the self-efficacy as an external factor and also includes gender and resistance to change (RTC) as moderators to strengthen the research model. The proposed research model has been tested on 283 valid responses which were collected through a structured survey, by applying structural equation modeling. Our results portray that self-efficacy is a strong predictor of intention to use BDA along with other TAM factors. Moreover, it is confirmed by the results that RTC dampens the positive relationship between intention to use and actual use of BDA in healthcare organizations. The outcomes revealed that male employees as compared to female employees are dominant towards the positive intention to use BDA. Furthermore, females create more RTC than males while adopting BDA in healthcare organizations. Theoretical and practical implications, limitations, and future research directions also underlined in this study.

Customer-driven supply chains: Trends and practices in leading Italian companies

This paper aims at understanding whether and to what extent companies are facing the challenge of improving their supply chains according to a customer-driven approach. Although the most recent supply chain management literature developed theoretical reflections and conceptualizations on the need for customer centricity in supply chain management, companies' practice does not seem to follow these prescriptions and the empirical research highlighted a frequent misalignment between market strategy and supply chain management processes. The aim of this paper is to bridge these two perspectives by answering two research questions. First, how are companies revising their supply chains, that is, what is the nature of the most recent projects concerning supply chain improvements? Second, to what extent are companies that invest in such projects prioritizing those specific projects that make a concrete alignment between market orientation and supply chain operating conditions possible? The paper reports and discusses the findings of an empirical investigation conducted among leading Italian companies or Italian subsidiaries of multinational companies. In particular, a two-step research was conducted, consisting of ten indepth interviews and a survey. According to our study, Italian companies are revising their supply chains to provide prompt availability of the product in different (but coordinated) distribution channels. This led to the launch of projects related to Demand Forecasting and to Omnichannel strategy adoption. However, in most cases, the managerial and technological readiness of companies is not in line with the relevance of the challenges. Another area of improvement concerns projects aimed at adopting up-to-date technologies, mostly connected to the Industry 4.0 paradigm, to improve operational performance. In this case the major opportunities perceived by the companies relate to the adoption of Big Data Analytics in order to better understand market trends

Factors influencing Brazilian internal auditors' behavioural intention to adopt big data analytics

The evolution of internal audit is driven by changes in IT and business environment. Academic literature presented that big data analytics (BDA) represents a paradigm shift but it is not widespread in auditing compared to other fields. A cross-sectional study was conducted to explore the IT tools and data currently in use by 305 internal auditors working in Brazilian private companies and investigates the factors influencing the intention of these professionals to adopt BDA, based on the unified theory of acceptance and use of technology (UTAUT). Data from an online survey were analysed using partial least squares structural equation modelling (PLS-SEM) with an R2 of 63.1%. The results revealed performance expectancy and social influence as the two key drivers for increasing the likelihood of BDA adoption while effort expectancy and facilitating conditions showed no significant effect. Our findings provide insights for practitioners and researchers to advance this research stream.

Factors influencing Brazilian internal auditors' behavioural intention to adopt big data analytics

The evolution of internal audit is driven by changes in IT and business environment. Academic literature presented that big data analytics (BDA) represents a paradigm shift but it is not widespread in auditing compared to other fields. A cross-sectional study was conducted to explore the IT tools and data currently in use by 305 internal auditors working in Brazilian private companies and investigates the factors influencing the intention of these professionals to adopt BDA, based on the unified theory of acceptance and use of technology (UTAUT). Data from an online survey were analysed using partial least squares structural equation modelling (PLS-SEM) with an R2 of 63.1%. The results revealed performance expectancy and social influence as the two key drivers for increasing the likelihood of BDA adoption while effort expectancy and facilitating conditions showed no significant effect. Our findings provide insights for practitioners and researchers to advance this research stream.

Big data analytics and artificial intelligence pathway to operational performance under the effects of entrepreneurial orientation and environmental dynamism: A study of manufacturing organisations

The importance of big data analytics, artificial intelligence, and machine learning has been at the forefront of research for operations and supply chain management. Literature has reported the influence of big data analytics for improved operational performance, but there has been a paucity of research regarding the role of entrepreneurial orientation (EO) on the adoption of big data analytics. To address this gap, we draw on the dynamic capabilities view of firms and on contingency theory to develop and test a model that describes the role of EO on the adoption of big data analytics powered by artificial intelligence (BDA-AI) and operational performance (OP). We tested our research hypotheses using a survey of 256 responses gathered using a pre-tested questionnaire from manufacturing firms in India with the help of the National Association of Software and Services Companies (NASSCOM) and the Federation of Indian Chambers of Commerce and Industry (FICCI). The results from our analysis indicate that EO enables an organisation to exploit and further explore the BDA-AI capabilities to achieve superior OP. Further, our results provide empirical evidence based on data analysis that EO is strongly associated with higher order capabilities (such as BDA-AI) and OP under differential effects of environmental dynamism (ED). These findings extend the dynamic capability view and contingency theory to create better understanding of dynamic capabilities of the organisation while also providing theoretically grounded guidance to the managers to align their EO with their technological capabilities within their firms.

Big Data Analytics and Organizational Performance: A Meta-Analysis Study

Big data analytics gained a great deal of attention from both, the business world and the academia. Investments of time, capital and personnel are significant, to the level that pioneers of this field reached a point of no return. Although there are clear insights of successful companies built on a big data analytics strategy, the results from the researchers’ work show mixed outcomes. It is not explicit under which circumstances big data analytics adoption leads to positive results. The purpose of this study is to run a meta-analysis review on the empirical articles that tackle big data analytics and organizational performance topics. As a methodology, we searched all the articles indexed on ISI Web of Knowledge during period January 2010 – May 2019. After the first filter, we selected 375, out of which we included only those studies which respected our full criteria: empirical study, provided results, at least one effect size and measurement error displayed. Based on this standard, we included 37 articles in our meta-analysis. The findings confirm that big data analytics play an important role in organizational performance. Finally, implications for practice are discussed, and future research directions are proposed considering the limitations of our study.

Big data analytics adoption: A case study in a large South African telecommunications organisation

Background: Big data analytics (BDA) offers a frontier of opportunities across all industries enabling improvements in marketing, customer service and product development. The adoption process for BDA is often challenging for organisations, given the complexities associated with it. Objective: The objective of this study was hence to understand factors that influence the BDA adoption process in organisations. The technology–organisation–environment framework was combined with factors from a Big Data Adoption model and used as a foundation for the study. Method: A case study research strategy was performed on a large telecommunication organisation. Themes were identified which provided rich explanations into the factors influencing the BDA adoption process in organisations. Results: Five technological factors were confirmed to influence the BDA adoption process. These were: (1) relative advantage, (2) complexity, (3) compatibility, (4) trialability and (5) data quality. Four organisational factors were confirmed to influence the BDA adoption process. These were: (1) top management support, (2) human resource expertise, (3) business and information technology (IT) alignment and (4) organisation size. Five environmental factors were confirmed to influence the BDA adoption process. These were: (1) competitive pressure, (2) data privacy, (3) vendor support, (4) IT fashion and (5) regulatory requirements. Two factors were confirmed as influencing an organisations’ ability to move from intention to adopt BDA to actual deployment. These were: (1) complexity tolerance and (2) paradigm shifts. Conclusion: This study provided evidence that organisations that have a high tolerance for complexity are more likely to move rapidly from intention to adopt BDA to actual deployment and effectively reduce the deployment gap.