Use Of Big Data Analytics Research Articles

Investigating the Determinants of Big Data Analytics Adoption in Decision Making: An Empirical Study in New Zealand, China, and Vietnam

Background: As a breakthrough technology, big data provides an opportunity for organizations to acquire business value and enhance competitiveness. Many companies have listed big data analytics (BDA) as one of their top priorities. However, research shows that managers are still reluctant to change their work patterns to utilize this new technology. In addition, the empirical evidence on what determines their adoption of BDA in management decision making is still rare. Method: To more broadly understand the determinants affecting managers’ actual use of BDA in decision making, a survey was conducted on a sample of 363 respondents from New Zealand, China, and Vietnam who work in different managerial roles. The dual process theory, the technology–organization–environment framework, and the key associated demographic characteristics are integrated to form the theoretical foundation to study the internal and external factors influencing the adoption. Results: The findings illustrate that the common essential factors across countries linking BDA in decision making are technology readiness, data quality, managers’ and organizational knowledge related to BDA, and organizational expectations. The factors that are more situation-dependent and evident in one or two countries’ results are managers’ predilection toward valuing intuition and experience over analytics and organizational size. Conclusion: The findings enrich the current literature and provide implications for practitioners on how they can improve the adoption process of this new technology.

Human-related capabilities in big data analytics: a taxonomy of human factors with impact on firm performance

This study intends to provide scholars and practitioners with an understanding of human resource challenges in the context of Big Data Analytics (BDA). This paper provides a holistic framework of human-related capabilities that organizations must consider when implementing BDA to facilitate decision-making. For this purpose, the authors conducted a systematic literature review adapted from Tranfield et al. (BJM 14:207–222, 2003) to identify relevant studies. The 75 publications reviewed provided the sample for an inductive, and systematic data evaluation following the well-known and accepted approach introduced by Gioia et al. (ORM 16:15–31, 2012). The comprehensive review uncovered 33 first-order concepts linked to human-related capabilities, which were distilled into 15 s-order themes and then merged into five aggregated dimensions: Personnel Capability, Management Capability, Organizational Capability, Culture and Governance Capability, and Strategy and Planning Capability. The study is, to the best of the authors’ knowledge, the first to categorize all relevant human-related capabilities for successful BDA application. As such, it not only provides the scientific basis for further research, but also serves as a useful overview of the critical factors for BDA use in decision-making processes.

Boosting innovation performance through big data analytics: An empirical investigation on the role of firm agility

While past studies proposed the role of big data analytics (BDA) as one of the primary pathways to business value creation, current knowledge on the link between BDA and innovation performance remains limited. In this regard, this study intends to fill this research gap by developing a theoretical framework for understanding how and under which mechanisms BDA influences innovation performance. Firm agility (conceptualised as sensing agility, decision-making agility and acting agility) is used in this research as the mediator between BDA and innovation performance. Besides, this research conceptualises two moderating variables: data-driven culture and BDA team sophistication. This study employs partial least squares (PLS) to test and validate the proposed hypotheses using survey data of 185 firms. The results show that firm agility significantly mediates the link between BDA use and innovation performance. Besides, the results suggest that data-driven culture moderates the relation between sensing agility and decision-making agility. This research also supports the moderating role of BDA team sophistication on the link between BDA use and sensing agility.

Harvesting reflective knowledge exchange for inbound open innovation in complex collaborative networks: an empirical verification in Europe

PurposeThis study aims to investigate the effects of open innovation (OI) and big data analytics (BDA) on reflective knowledge exchange (RKE) within the context of complex collaborative networks. Specifically, it considers the relationships between sourcing knowledge from an external environment, transferring knowledge to an external environment and adopting solutions that are useful to appropriate returns from innovation.Design/methodology/approachThis study analyzes the connection between the number of patent applications and the amount of OI, as well as the association between the number of patent applications and the use of BDA. Data from firms in the 27 European Union countries were retrieved from the Eurostat database for the period 2014–2019 and were investigated using an ordinary least squares regression analysis.FindingsBecause of its twofold lens based on both knowledge management and OI, this study sheds light on OI collaboration modes and highlights the crucial role they could play in innovation. In particular, the results suggest that OI collaboration modes have a strong effect on innovation performance, stimulating the search for RKE.Originality/valueThis study furthers a deeper understanding of RKE, which is shown to be an important mechanism that incentivizes firms to increase their efforts in the innovation process. Further, RKE supports firms in taking full advantage of the innovative knowledge they generate within their inter-organizational network.

Improving Agility Using Big Data Analytics: The Role of Democratization Culture

Abstract Background: Big data analytics (BDA) is considered an enabler of organizational agility because it helps firms to sense market-based changes and improve decision making in a more informed and timely manner. However, in reality, only a handful of firms have achieved improvement in their outcomes by using BDA. To address this inconsistency, our study explores the conditions under which BDA use translates into agility. We particularly focus on organizational culture because in the pursuit of agility, culture is emphasized as a source of stability that allows firms to successfully adapt to the changing environment. Therefore, by assuming organizational culture as a contextual factor, this study examines the moderating effect of organizational culture on the link between BDA use and agility. Method: We employ a concept from data democratization called “democratization culture,” which values the willingness to share information and the acceptance of diversity. We also adopt collectivistic culture for comparison with democratization culture. Further, BDA use is decomposed into advanced and basic use based on the functions and BDA types. A model is proposed and empirically validated through survey data collected from 304 senior-level managers. Results: Our findings suggest that the moderating effects of democratization culture on agility are different depending on whether it is combined with advanced or basic BDA use. Conclusions: This study provides initial empirical evidence that contributes to the scarce research on the role of organizational culture in the link between BDA use and agility. Available at: https://aisel.aisnet.org/pajais/vol12/iss2/2/ Recommended Citation Hyun, Youyung; Kamioka, Taro; and Hosoya, Ryuichi (2020) "Improving Agility Using Big Data Analytics: The Role of Democratization Culture," Pacific Asia Journal of the Association for Information Systems: Vol. 12: Iss. 2, Article 2. DOI: 10.17705/1pais.12202 Available at: https://aisel.aisnet.org/pajais/vol12/iss2/2

Improving Agility Using Big Data Analytics: The Role of Democratization Culture

Abstract Background: Big data analytics (BDA) is considered an enabler of organizational agility because it helps firms to sense market-based changes and improve decision making in a more informed and timely manner. However, in reality, only a handful of firms have achieved improvement in their outcomes by using BDA. To address this inconsistency, our study explores the conditions under which BDA use translates into agility. We particularly focus on organizational culture because in the pursuit of agility, culture is emphasized as a source of stability that allows firms to successfully adapt to the changing environment. Therefore, by assuming organizational culture as a contextual factor, this study examines the moderating effect of organizational culture on the link between BDA use and agility. Method: We employ a concept from data democratization called “democratization culture,” which values the willingness to share information and the acceptance of diversity. We also adopt collectivistic culture for comparison with democratization culture. Further, BDA use is decomposed into advanced and basic use based on the functions and BDA types. A model is proposed and empirically validated through survey data collected from 304 senior-level managers. Results: Our findings suggest that the moderating effects of democratization culture on agility are different depending on whether it is combined with advanced or basic BDA use. Conclusions: This study provides initial empirical evidence that contributes to the scarce research on the role of organizational culture in the link between BDA use and agility.

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.

Telling stories that sell: The role of storytelling and big data analytics in smart service sales

The emergence of digitally connected products and big data analytics (BDA) in industrial marketing has attracted academic and managerial interest in smart services. However, suppliers' provision of smart services and customers' adoption of these services have received scarce attention in the literature, demonstrating the need to address the changing nature of customer-supplier interactions in the digital era. Responding to prior research calls, this study utilizes ethnographic research and a storytelling lens to advance our knowledge of how stories and BDA can enhance customers' attitudes toward suppliers' smart services, their behavioral intentions and their actual adoption of smart services. The study's findings demonstrate that storytelling is a collective sensemaking and sensegiving process that occurs in interactions between customers and suppliers in which both parties contribute to the story development. The use of BDA in storytelling enhances customer sensemaking of smart services by highlighting the business value extracted from the digitized data of a reference customer. By synthesizing insights from servitization, storytelling, BDA and the customer reference literature, this study offers managers practical guidance regarding how to increase smart service sales. An example of a story used to facilitate customer adoption of a supplier's smart services in the manufacturing sector is provided.

Enhancing discrete-event simulation with big data analytics: A review

This article presents a literature review of the use of the OR technique of discrete-event simulation (DES) in conjunction with the big data analytics (BDA) approaches of data mining, machine learning, data farming, visual analytics, and process mining. The two areas are quite distinct. DES represents a mature OR tool using a graphical interface to produce an industry strength process modelling capability. The review reflects this and covers commercial off-the-shelf DES software used in an organisational setting. On the contrary the analytics techniques considered are in the domain of the data scientist and usually involve coding of algorithms to provide outputs derived from big data. Despite this divergence the review identifies a small but emerging literature of use-cases and from this a framework is derived for a DES development methodology that incorporates the use of these analytics techniques. The review finds scope for two new categories of simulation and analytics use: an enhanced capability for DES from the use of BDA at the main stages of the DES methodology as well as the use of DES in a data farming role to drive BDA techniques.

Insights from big Data Analytics in supply chain management: an all-inclusive literature review using the SCOR model

When supply chain management (SCM) intersects with Big Data Analytics (BDA), uncountable opportunities for research emerge. Unfortunately, how analytics can be applied to supply chain processes is still unclear for both academics and industries. To better connect SC processes needs and what BDA offer, we present a structured review of academic literature that addresses BDA methods in SCM using the supply chain operations reference (SCOR) model. The literature since 2001 is reviewed to provide a taxonomy framework resulting in a nomenclature grids and a SCOR-BDA matrix. The most important result of this paper indicates a clear disparity and points to an urgent need to bring the efforts closer in a collaborative way for more intelligent use of BDA in SCM. Furthermore, this paper highlights a misalignment between data scientists and SC managers in BDA applicability. It also highpoints upcoming research tracks and the main gaps that need to be stunned.

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.

Unlocking the drivers of big data analytics value in firms

Although big data analytics (BDA) is considered the next “frontier” in data science by creating potential business opportunities, the way to extract those opportunities is unclear. This paper aims to understand the antecedents of BDA value at a firm level. The authors performed a study using a mixed methodology approach. First, by carrying out a Delphi study to explore and rank the antecedents affecting the creation of BDA value. Based on the Delphi results, we propose an empirically validated model supported by a survey conducted on 175 European firms to explain the antecedents of BDA sustained value. The results show that the proposed model explains 62% of BDA sustained value at the firm level, where the most critical contributor is BDA use. We provide directions for managers to support their decisions on BDA strategy definition and refinement. For academics, we extend BDA value literature and outline some potential research opportunities.

How the Use of Big Data Analytics Affects Value Creation in Supply Chain Management

Despite numerous testimonials of first movers, the underlying mechanisms of organizations’ big data analytics (BDA) usage deserves close investigation. Our study addresses two essential research questions: (1) How does organizational BDA usage affect value creation? and (2) What are key antecedents of organizational-level BDA usage? We draw on dynamic capabilities theory to conceptualize BDA use as a unique information processing capability that brings competitive advantage to organizations. Furthermore, we employ the technology–organization–environment (TOE) framework to identify and theorize paths via which factors influence the actual usage of BDA. Survey data collected from 161 U.S.-based companies show that: organizational-level BDA usage affects organizational value creation; the degree to which BDA usage influences such creation is moderated by environmental dynamism; technological factors directly influence organizational BDA usage; and organizational and environmental factors indirectly influence organizational BDA usage through top management support. Collectively, these findings provide a theory-based understanding of the impacts and antecedents of organizational BDA usage, while also providing guidance regarding what managers should expect from usage of this rapidly emerging technology.

Human factors/ergonomics implications of big data analytics: Chartered Institute of Ergonomics and Human Factors annual lecture

In recent years, advances in sensor technology, connectedness and computational power have come together to produce huge data-sets. The treatment and analysis of these data-sets is known as big data analytics (BDA), and the somewhat related term data mining. Fields allied to human factors/ergonomics (HFE), e.g. statistics, have developed computational methods to derive meaningful, actionable conclusions from these data bases. This paper examines BDA, often characterised by volume, velocity and variety, giving examples of successful BDA use. This examination provides context by considering examples of using BDA on human data, using BDA in HFE studies, and studies of how people perform BDA. Significant issues for HFE are the reliance of BDA on correlation rather than hypotheses and theory, the ethics of BDA and the use of HFE in data visualisation.