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.

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.

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 The Equilateral Agility Concept as Mediating Variable in Relationship between IT-Strategy Alignment and Sustainable Competitive Advantage

2020 ◽  
Vol 10 (2) ◽  
pp. 240-255
Author(s):  
Lutfi Nurcholis
Keyword(s):  
Competitive Advantage ◽  
Structural Equation ◽  
Mediating Effect ◽  
Equation Modeling ◽  
It Strategy ◽  
Sustainable Competitive Advantage ◽  
Business Relationship ◽  
Operational Flexibility ◽  
Business Decision ◽  
Strategy Alignment

This paper aims to investigate the effect of IT-strategy alignment, responsiveness, operational flexibility, and business relationship on sustainable competitive advantage and the mediating effect of responsiveness, operational flexibility, and business relationship in relationships between IT-strategy alignment and sustainable competitive advantage. Data were collected from 189 Batik SME in Pekalongan and analyzed by using Structural Equation Modeling (SEM). The result shows that IT-strategy alignment significantly affects responsiveness. Responsiveness, operational flexibility, and also business relationship significantly affect sustainable competitive advantage. Furthermore, responsiveness, operational flexibility, and business relationship mediate the correlation of IT-strategy alignment and sustainable competitive advantage. Responsiveness, operational flexibility, and business relationship have the confidence and value that puts customers on every business decision. It encourages Batik SME to improve the sustainable competitive advantage based on the customers’ expectations. IT-strategy alignment is essential to enhance responsiveness, operational flexibility, and business to gain a sustainable competitive advantage. That IT-strategy alignment can improve the sustainable competitive advantage of the Batik SME.

Performance-based evaluation of academic libraries in the big data era

2020 ◽  
pp. 016555152091851 ◽  
Author(s):  
A Y M Atiquil Islam ◽  
Khurshid Ahmad ◽  
Muhammad Rafi ◽  
Zheng JianMing
Keyword(s):  
Big Data ◽  
Technology Acceptance ◽  
Academic Libraries ◽  
Data Analytics ◽  
Structural Equation ◽  
Big Data Analytics ◽  
Data Practices ◽  
Ease Of Access ◽  
Acceptance Model ◽  
Performance Based Evaluation

The concept of big data has been extensively considered as a technological modernisation in organisations and educational institutes. Thus, the purpose of this study is to determine whether the modified technology acceptance model (MTAM) is viable for evaluating the performance of librarians in the use of big data analytics in academic libraries. This study used an empirical research method for collecting data from 211 librarians working in Pakistan’s universities. On the basis of the findings of the MTAM analysis by structural equation modelling, the performances of the academic libraries were comprehended through the process of big data. The main influential components of the performance analysis in this study were the big data analytics capabilities, perceived ease of access and the usefulness of big data practices in academic libraries. Subsequently, the utilisation of big data was significantly affected by skills, perceived ease of access and the usefulness of academic libraries. The results also suggested that the various components of the academic libraries lead to effective organisational performance when linked to big data analytics.

scholarly journals Decision Framework for Engaging Cloud-Based Big Data Analytics Vendors

2020 ◽  
Vol 22 (4) ◽  
pp. 60-74
Author(s):  
Emmanuel Wusuhon Yanibo Ayaburi ◽  
Michele Maasberg ◽  
Jaeung Lee
Keyword(s):  
Cloud Computing ◽  
Big Data ◽  
Competitive Advantage ◽  
Data Analytics ◽  
Big Data Analytics ◽  
Unique Contribution ◽  
Data Services ◽  
Business Environments ◽  
Digital Era ◽  
Service Oriented

Organizations face both opportunities and risks with big data analytics vendors, and the risks are now profound, as data has been likened to the oil of the digital era. The growing body of research at the nexus of big data analytics and cloud computing is examined from the economic perspective, based on agency theory (AT). A conceptual framework is developed for analyzing these opportunities and challenges regarding the use of big data analytics and cloud computing in e-business environments. This framework allows organizations to engage in contracts that target competitive parity with their service-oriented decision support system (SODSS) to achieve a competitive advantage related to their core business model. A unique contribution of this paper is its perspective on how to engage a vendor contractually to achieve this competitive advantage. The framework provides insights for a manager in selecting a vendor for cloud-based big data services.

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