Consequential factors of Big Data's Analytics Capability: how firms use data in the competitive scenario

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Luis Hernan Contreras Pinochet ◽  
Guilherme de Camargo Belli Amorim ◽  
Durval Lucas Júnior ◽  
Cesar Alexandre de Souza
Keyword(s):  
Big Data ◽  
Data Analytics ◽  
Structural Equation ◽  
Explanatory Power ◽  
Big Data Analytics ◽  
Strategic Decision ◽  
Analytical Models ◽  
Equation Modeling ◽  
Content Type ◽  
Analytics Capability

PurposeThe article's objective is to analyze the consequent factors of Big Data Analytics Capability for firms in the competitive scenario, using different analytical models.Design/methodology/approachThe research had a quantitative approach, using a survey of data from firms located in the state of São Paulo – Brazil. Structural Equation Modeling (SEM) was used to validate the model.FindingsThe results reveal that all hypotheses were accepted. Business value was the construct that had the most explanatory power in the model. It is necessary to invest more in analytical tools, as well as people trained in the analysis of these models, in addition to a change of mindset, which will dictate the bias of the firm's strategic decision-making. The Big Data analysis is evident from firms' growing investments, particularly those that operate in complex and fast-paced environments.Practical implicationsThe proposed theoretical model makes it possible to verify firms' analytical structure and whether they are better positioned to analyze customer data and information in real-time, generate insights and implement solutions to maintain and improve their market position.Originality/valueThe contribution of this article is to present a proposal to expand the research models in the literature that analyzed the direct and indirect relationship between “Big Data Analytics Capability” and “Product Innovation Performance”.

Big data analytics capabilities and knowledge management: impact on firm performance

2019 ◽  
Vol 57 (8) ◽  
pp. 1923-1936 ◽  
Author(s):  
Alberto Ferraris ◽  
Alberto Mazzoleni ◽  
Alain Devalle ◽  
Jerome Couturier
Keyword(s):  
Big Data ◽  
Knowledge Management ◽  
Data Analytics ◽  
Structural Equation ◽  
Positive Impact ◽  
Big Data Analytics ◽  
Integrated Approach ◽  
Content Type ◽  
Decision Making Processes ◽  
External Sources

Purpose Big data analytics (BDA) guarantees that data may be analysed and categorised into useful information for businesses and transformed into big data related-knowledge and efficient decision-making processes, thereby improving performance. However, the management of the knowledge generated from the BDA as well as its integration and combination with firm knowledge have scarcely been investigated, despite an emergent need of a structured and integrated approach. The paper aims to discuss these issues. Design/methodology/approach Through an empirical analysis based on structural equation modelling with data collected from 88 Italian SMEs, the authors tested if BDA capabilities have a positive impact on firm performances, as well as the mediator effect of knowledge management (KM) on this relationship. Findings The findings of this paper show that firms that developed more BDA capabilities than others, both technological and managerial, increased their performances and that KM orientation plays a significant role in amplifying the effect of BDA capabilities. Originality/value BDA has the potential to change the way firms compete through better understanding, processing, and exploiting of huge amounts of data coming from different internal and external sources and processes. Some managerial and theoretical implications are proposed and discussed in light of the emergence of this new phenomenon.

scholarly journals Decision support model to adopt big data analytics in higher education systems

2021 ◽  
Vol 8 (6) ◽  
pp. 67-78
Author(s):  
Adel Alkhalil ◽  
Keyword(s):  
Higher Education ◽  
Big Data ◽  
Data Analytics ◽  
Structural Equation ◽  
Data Science ◽  
Big Data Analytics ◽  
Equation Modeling ◽  
Systematic Research ◽  
Education Systems ◽  
Higher Education Systems

Data science or specifically data analytics systems have become an emerging trend in information technology and have attracted many organizations, including higher education. Higher Education Systems (HES) involve very active entities (students, faculty members, researchers, employers) who generate and require large volumes of data that go beyond the structured data stored in the house. The collection, analysis, and visualization of such big data present a huge challenge for HES. Big data analysis could be the solution to this challenge. However, the rationale and decision process for the adoption of big data analytics can be difficult. Such a knowledge-driven process requires a multitude of technical and organizational aspects that must be accounted for to ensure informed decisions are made. Existing research and development indicates that the decision to adopt, although systematic research with a theoretical background is rare and none of the existing studies have considered diffusion of innovation (DOI) theory. This paper aims to support HES, by providing a systematic analysis of the determinants for the decision to adopt big data analytics. An integrated framework referred to as the Technology Organization Environment (TOE) framework is proposed. The proposed framework is validated using structural equation modeling. Eleven determinants are confirmed that influence the TOE-driven framework for data analytics in HES. The result is expected to contribute to on-going research that attempts to address the complex and multidimensional challenge that relates to data science and analytics implementation in HES.

Big data analytics adoption model for small and medium enterprises

2020 ◽  
Vol 11 (4) ◽  
pp. 483-513 ◽  
Author(s):  
Parisa Maroufkhani ◽  
Wan Khairuzzaman Wan Ismail ◽  
Morteza Ghobakhloo
Keyword(s):  
Big Data ◽  
Data Analytics ◽  
Structural Equation ◽  
Small And Medium Enterprises ◽  
Big Data Analytics ◽  
Mediation Effect ◽  
Unified Framework ◽  
Content Type ◽  
Medium Enterprises ◽  
The Relationship

Purpose Big 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/approach A 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. Findings The 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 implications Providing 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/value Although 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.

Effect of big data analytics on improvement of corporate social/green performance

2020 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Bibhu Prasad Mishra ◽  
Bibhuti Bhusan Biswal ◽  
Ajay Kumar Behera ◽  
Harish Chandra Das
Keyword(s):  
Big Data ◽  
Corporate Culture ◽  
Data Analytics ◽  
Corporate Social Performance ◽  
Big Data Analytics ◽  
Equation Modeling ◽  
Content Type ◽  
Green Performance ◽  
Corporate Social ◽  
Control Direction

Purpose In spite of the fact that literature shows that big data analytics (BDA) pass on a distinct corporate ability, little is thought about their performance impacts, specifically logical conditions. Establishing this research in the dynamic capability view (DCV) and corporate culture and dependent on an sample of 310 Indian production industries, the purpose of this paper is to experimentally study the impacts of BDA on corporate social performance (CSP) and corporate green performance (CGP) using variance-based structural equation modeling (for example, PLS). Design/methodology/approach A questionnaire was used to accumulate data sets to examine research hypothesis. The authors pre-examined the survey with six scholastics and six directors from production firms in India. With the help of their sources of data, the authors have adjusted their wordings to improve the transparency and guarantee that length of the survey is accurate. Finally, the questionnaire was prepared for definite data collection. Findings The authors conclude that BDA has noteworthy effect on CSP/CGP. Notwithstanding, the authors did not find proof for directing role of flexible direction and control direction in the connections among BDA and CSP/CGP. This research offers a more nuanced comprehension of the performance ramifications of BDA, and in this way, it is tending to the critical inquiries of how and when BDA can improve in supply chains. Originality/value This investigation makes helpful commitments to the BDA research and its effect on CSP/CGP. To the authors’ best of information, this is the first hypothesis-focused approach to clarify the effect of BDA on ecological and social supportability. Second, this investigation likewise gives empirical proof that BDA impact on CSP/CGP and is free of flexible or control direction of the industry.

Much- needed business digital transformation through big data, internet of things and blockchain capabilities: implications for strategic performance in telecommunication sector

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Anam Bhatti ◽  
Haider Malik ◽  
Ahtisham Zahid Kamal ◽  
Alamzeb Aamir ◽  
Lamya Abdulrahman Alaali ◽  
...  
Keyword(s):  
Big Data ◽  
Internet Of Things ◽  
Data Quality ◽  
Data Analytics ◽  
Big Data Analytics ◽  
Digital Transformation ◽  
Equation Modeling ◽  
Content Type ◽  
Strategic Performance ◽  
Dependent Variables

PurposeIn the field of business, digital transformation is the integration of digital technology into all areas of business, from generating to deliver value to customers. This concept is essential for sustainable growth of a company and its overall economy. Based on this fact, this authentic and informative research is conducted whose major aim is to examine the importance of digital transformation within a business through big data, the Internet of things and blockchain-based capabilities for overall strategic performance within the telecom sector in China.Design/methodology/approachFor that aim, data quality and technology competence are considered as independent variables, strategic performance as dependent variable and big data analytics capabilities, Internet of things capabilities and blockchain capabilities routinization acted as mediators within this paper. In its data collection mechanism, an online survey was conducted in which questionnaires are randomly distributed to the telecom sector's professionals in which only 343 of them gave their valid outcomes. After collecting primary data, confirmatory factor analysis (CFA) and structural equation modeling (SEM)–based statistical outcomes have been generated.FindingsResults indicate that there is a significant relationship between data quality and strategic performance and between technological competence and strategic performance. Also, the big data analytics and Internet of Things capabilities acted as significant mediating role between both independent and dependent variables. But blockchain capabilities routinization is that variable that acts as an insignificant mediator between independent and dependent variables' relationship.Originality/valueOverall, this study is an informative and attractive source for the Chinese government, its telecom industry, administrative body and related ones to understand the importance of such IT capabilities' implications within their operating activities for their strategic performance management. Also, related field scholars can utilize its reliable data in their research analysis. Its major limitations are (1) lack of qualitative/ mixed method of research and (2) lack of comparative analysis that may impact the acceptability factor of this paper, and this weakness can be overcome by upcoming scholars in their research.

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

2020 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Sachin K. Mangla ◽  
Rakesh Raut ◽  
Vaibhav S. Narwane ◽  
Zuopeng (Justin) Zhang ◽  
Pragati priyadarshinee
Keyword(s):  
Big Data ◽  
Knowledge Management ◽  
Data Analytics ◽  
Big Data Analytics ◽  
Project Performance ◽  
Mediating Effect ◽  
Equation Modeling ◽  
Content Type ◽  
Operational Capabilities ◽  
Green Purchasing

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.

Leveraging big data analytics capabilities in making reverse logistics decisions and improving remanufacturing performance

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Surajit Bag ◽  
Sunil Luthra ◽  
Sachin Kumar Mangla ◽  
Yigit Kazancoglu
Keyword(s):  
Big Data ◽  
South African ◽  
Reverse Logistics ◽  
Data Analytics ◽  
Structural Equation ◽  
Online Survey ◽  
Big Data Analytics ◽  
Primary Data ◽  
Manufacturing Companies ◽  
Content Type

PurposeThe study investigated the effect of big data analytics capabilities (BDACs) on reverse logistics (strategic and tactical) decisions and finally on remanufacturing performance.Design/methodology/approachThe primary data were collected using a structured questionnaire and an online survey sent to South African manufacturing companies. The data were analysed using partial least squares based structural equation modelling (PLS–SEM) based WarpPLS 6.0 software.FindingsThe results indicate that data generation capabilities (DGCs) have a strong association with strategic reverse logistics decisions (SRLDs). Data integration and management capabilities (DIMCs) show a positive relationship with tactical reverse logistics decisions (TRLDs). Advanced analytics capabilities (AACs), data visualisation capabilities (DVCs) and data-driven culture (DDC) show a positive association with both SRLDs and TRLDs. SRLDs and TRLDs were found to have a positive link with remanufacturing performance.Practical implicationsThe theoretical guided results can help managers to understand the value of big data analytics (BDA) in making better quality judgement of reverse logistics and enhance remanufacturing processes for achieving sustainability.Originality/valueThis research explored the relationship between BDA, reverse logistics decisions and remanufacturing performance. The study was practice oriented, and according to the authors’ knowledge, it is the first study to be conducted in the South African context.

scholarly journals Sustainable Competitive Advantage Driven by Big Data Analytics and Innovation

2020 ◽  
Vol 10 (19) ◽  
pp. 6784
Author(s):  
Muawia Ramadan ◽  
Hana Shuqqo ◽  
Layalee Qtaishat ◽  
Hebaa Asmar ◽  
Bashir Salah
Keyword(s):  
Big Data ◽  
Competitive Advantage ◽  
Direct Effect ◽  
Data Analytics ◽  
Structural Equation ◽  
Big Data Analytics ◽  
Manufacturing Firms ◽  
Data Availability ◽  
Equation Modeling ◽  
Sustainable Competitive Advantage

Big data analytics (BDA) is one of the main pillars of Industry 4.0. It has become a promising tool for supporting the competitive advantages of firms by enhancing data-driven performance. Meanwhile, the scarcity of resources on a worldwide level has forced firms to consider sustainable-based performance as a critical issue. Additionally, the literature confirms that BDA and innovation can enhance firms’ performance, leading to competitive advantage. However, there is a lack of studies that examine whether or not BDA and innovation alone can sustain a firm’s competitive advantage. Drawing on previous studies and dynamic capability theory, this study proposes that big data analytics capabilities (BDAC), supported by a high level of data availability (DA), can improve innovation capabilities (IC) and, hence, lead to the development of a sustainable competitive advantage (SCA). This study examines the proposed hypotheses by surveying 117 manufacturing firms and analyzing responses via partial least squares–structural equation modeling (PLS-SEM) statistical software. Findings reveal that BDAC relies significantly on the degree of DA and has a significant role in increasing IC. Furthermore, the analysis confirms that IC has a significant and direct effect on a firm’s SCA, while BDAC has no direct effect on SCA. This study provides valuable insights for manufacturing firms to improve their sustainable business performance and theoretical and practical insights into BDA implementation issues in attaining sustainability in processes.

Impacts of big data analytics management capabilities and supply chain integration on global sourcing: a survey on firm performance

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Shokouh Razaghi ◽  
Sajjad Shokouhyar
Keyword(s):  
Big Data ◽  
Firm Performance ◽  
Data Analytics ◽  
Big Data Analytics ◽  
Global Sourcing ◽  
Content Type ◽  
Mediating Role ◽  
Management Capability ◽  
Analytics Capability

Purpose This study aims to show that management with big data analytics capability can achieve more advantages of the global sourcing process. Furthermore, this study using its conceptual attitude model aims to show that big data analytics management capability leads to an increase in firm performance by the mediating role of integration. Design/methodology/approach Using an online questionnaire, 158 managers from 13 Iranian companies taking advantage of the global sourcing process were surveyed. The validity of the hypotheses was evaluated using partial least squares based on structural equation modeling (PLS method). Findings The results of the study showed that big data analytics management capability has a positive impact on global sourcing and firm performance directly, and by the mediating role of integration. Originality/value Previous studies have carefully addressed the role of big data and big data analytics in firms. However, this is among a few studies addressing the role of big data analytics capability, especially management capability, in improving firms’ performance. The results of this study shed light on the fact that how global sourcing takes the best advantage of big data analytics management capability for better accomplishment of organizations’ duties. The results of this study also disclose how big data analytics management capability helps organizations with their performance and bring benefits to their units.

scholarly journals Big data analytics capability in supply chain agility

2019 ◽  
Vol 57 (8) ◽  
pp. 2092-2112 ◽  
Author(s):  
Rameshwar Dubey ◽  
Angappa Gunasekaran ◽  
Stephen J. Childe
Keyword(s):  
Big Data ◽  
Supply Chain ◽  
Competitive Advantage ◽  
Data Analytics ◽  
Dynamic Capabilities ◽  
Big Data Analytics ◽  
Content Type ◽  
Moderation Effect ◽  
Supply Chain Agility ◽  
Analytics Capability

Purpose The purpose of this paper is to examine when and how organizations build big data analytics capability (BDAC) to improve supply chain agility (SCA) and gain competitive advantage. Design/methodology/approach The authors grounded the theoretical framework in two perspectives: the dynamic capabilities view and contingency theory. To test the research hypotheses, the authors gathered 173 usable responses using a pre-tested questionnaire. Findings The results suggest that BDAC has a positive and significant effect on SCA and competitive advantage. Further, the results support the hypothesis that organizational flexibility (OF) has a positive and significant moderation effect on the path joining BDAC and SCA. However, contrary to the belief, the authors found no support for the moderation effect of OF on the path joining BDAC and competitive advantage. Originality/value The study makes some useful contributions to the literature on BDAC, SCA, OF, and competitive advantage. Moreover, the results may further motivate future scholars to replicate the findings using longitudinal data.

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