Big Data Adoption

2022 ◽  
pp. 1817-1842
Author(s):  
Hemlata Gangwar
Keyword(s):  
Big Data ◽  
Data Privacy ◽  
Manufacturing Sector ◽  
Sem Analysis ◽  
Relative Advantage ◽  
Organizational Size ◽  
Management Support ◽  
Contributing Factors ◽  
Confirmatory Factor Analyses ◽  
Top Management Support

This study inspects how big data is comprehended by IT experts and the difficulties that they have in respect to the reception of big data examination. The study also looks into the contributing factors of big data adoption within the manufacturing and services sectors in India. The data were analyzed using exploratory and confirmatory factor analyses, and relevant hypotheses were derived and tested by SEM analysis. The findings revealed that relative advantage, compatibility, complexity, organizational size, top management support, competitive pressure, vendor support, data management, and data privacy are the factors that are important for both industries. Through a comparison of the industries, statistically significant differences between the service and the manufacturing sectors were found; in other words, it has been noted that the relative importance of all factors for big data adoption differs between the industries, with the only exception being its complexity – it was found to be insignificant for the manufacturing sector.

Big Data Adoption

2020 ◽  
pp. 138-171
Author(s):  
Hemlata Gangwar
Keyword(s):  
Big Data ◽  
Data Privacy ◽  
Manufacturing Sector ◽  
Sem Analysis ◽  
Relative Advantage ◽  
Organizational Size ◽  
Management Support ◽  
Contributing Factors ◽  
Confirmatory Factor Analyses ◽  
Top Management Support

This study inspects how big data is comprehended by IT experts and the difficulties that they have in respect to the reception of big data examination. The study also looks into the contributing factors of big data adoption within the manufacturing and services sectors in India. The data were analyzed using exploratory and confirmatory factor analyses, and relevant hypotheses were derived and tested by SEM analysis. The findings revealed that relative advantage, compatibility, complexity, organizational size, top management support, competitive pressure, vendor support, data management, and data privacy are the factors that are important for both industries. Through a comparison of the industries, statistically significant differences between the service and the manufacturing sectors were found; in other words, it has been noted that the relative importance of all factors for big data adoption differs between the industries, with the only exception being its complexity – it was found to be insignificant for the manufacturing sector.

Understanding the Determinants of Big Data Adoption in India

2018 ◽  
Vol 31 (4) ◽  
pp. 1-22 ◽  
Author(s):  
Hemlata Gangwar
Keyword(s):  
Big Data ◽  
Data Privacy ◽  
Service Sector ◽  
Manufacturing Sector ◽  
Sem Analysis ◽  
Relative Advantage ◽  
Organizational Size ◽  
Management Support ◽  
Confirmatory Factor Analyses ◽  
Top Management Support

This article sought to identify the drivers of Big Data adoption within the manufacturing and services sectors in India. A questionnaire-based survey was used to collect data from manufacturing and service sector organizations in India. The data was analyzed using exploratory and confirmatory factor analyses. Relevant hypotheses were then derived and tested by SEM analysis. The findings revealed that the following factors are important for both sectors: relative advantage, compatibility, complexity, organizational size, top management support, competitive pressure, vendor support, data management and data privacy. Statistically significant differences between the service and the manufacturing sectors were found. In other words, the relative importance of the factors for Big Data adoption differs between the sectors. The only exception was complexity, which was found to be insignificant in regard to the manufacturing sector. The factors identified can be used to facilitate Big Data adoption outcomes in organizations.

A Synergetic Model for Implementing Big Data in Organizations

2018 ◽  
pp. 281-298
Author(s):  
Mohanad Halaweh ◽  
Ahmed El Massry
Keyword(s):  
Big Data ◽  
Data Privacy ◽  
Primary Data ◽  
Extended Model ◽  
Management Support ◽  
Privacy And Security ◽  
Top Management Support ◽  
Top Executives ◽  
Research Findings ◽  
Synergetic Model

The term BIG DATA has been increasingly used recently. Big data refers to the massive amount of data that are processed and analyzed using sophisticated technology to gain relevant insights that will help top executives with the decision-making process. This study is an attempt to investigate the big data implementation in organizations. The literature review reveals an initial model of indicators that might affect big data implementation. This model was examined and extended by primary data collected from key people (CEO and managers) from ten organizations. The extended model of indicators, which is the result from this research, includes the factors that would affect the success or failure of big data implementation in organizations. The research findings showed the following factors: top management support, organizational change, IT infrastructure, skilled professional, contents (i.e. data), data strategy, data privacy and security.

A Synergetic Model for Implementing Big Data in Organizations

2017 ◽  
Vol 30 (1) ◽  
pp. 48-64 ◽  
Author(s):  
Mohanad Halaweh ◽  
Ahmed El Massry
Keyword(s):  
Big Data ◽  
Data Privacy ◽  
Primary Data ◽  
Extended Model ◽  
Management Support ◽  
Privacy And Security ◽  
Top Management Support ◽  
Top Executives ◽  
Research Findings ◽  
Synergetic Model

The term BIG DATA has been increasingly used recently. Big data refers to the massive amount of data that are processed and analyzed using sophisticated technology to gain relevant insights that will help top executives with the decision-making process. This study is an attempt to investigate the big data implementation in organizations. The literature review reveals an initial model of indicators that might affect big data implementation. This model was examined and extended by primary data collected from key people (CEO and managers) from ten organizations. The extended model of indicators, which is the result from this research, includes the factors that would affect the success or failure of big data implementation in organizations. The research findings showed the following factors: top management support, organizational change, IT infrastructure, skilled professional, contents (i.e. data), data strategy, data privacy and security.

Understanding the Determinants of Big Data Analytics Adoption

2022 ◽  
pp. 1549-1577
Author(s):  
Surabhi Verma ◽  
Sushil Chaurasia
Keyword(s):  
Big Data ◽  
Significant Influence ◽  
Data Analytics ◽  
Big Data Analytics ◽  
Relative Advantage ◽  
Technology Readiness ◽  
Management Support ◽  
Competitive Pressure ◽  
Top Management Support ◽  
Data Environment

This article aims to empirically investigate the factors that affects the adoption of big data analytics by firms (adopters and non-adopters). The current study is based on three feature that influence BDA adoption: technological context (relative advantage, complexity, compatibility), organizational context (top management support, technology readiness, organizational data environment), and environmental context (competitive pressure, and trading partner pressure). A structured questionnaire-based survey method was used to collect data from 231 firm managers. Relevant hypotheses were derived and tested by partial least squares. The results indicated that technology, organization and environment contexts impact firms' adoption of big data analytics. The findings also revealed that relative advantage, complexity, compatibility, top management support, technology readiness, organizational data environment and competitive pressure have a significant influence on the adopters of big data analytics, whereas relative advantage, complexity and competitive pressure have a significant influence on the non-adopters of big data analytics.

A Synergetic Model for Implementing Big Data in Organizations

Web Services ◽  
2019 ◽  
pp. 473-489
Author(s):  
Mohanad Halaweh ◽  
Ahmed El Massry
Keyword(s):  
Big Data ◽  
Data Privacy ◽  
Primary Data ◽  
Extended Model ◽  
Management Support ◽  
Privacy And Security ◽  
Top Management Support ◽  
Top Executives ◽  
Research Findings ◽  
Synergetic Model

The term BIG DATA has been increasingly used recently. Big data refers to the massive amount of data that are processed and analyzed using sophisticated technology to gain relevant insights that will help top executives with the decision-making process. This study is an attempt to investigate the big data implementation in organizations. The literature review reveals an initial model of indicators that might affect big data implementation. This model was examined and extended by primary data collected from key people (CEO and managers) from ten organizations. The extended model of indicators, which is the result from this research, includes the factors that would affect the success or failure of big data implementation in organizations. The research findings showed the following factors: top management support, organizational change, IT infrastructure, skilled professional, contents (i.e. data), data strategy, data privacy and security.

Understanding the Determinants of Big Data Analytics Adoption

2019 ◽  
Vol 32 (3) ◽  
pp. 1-26 ◽  
Author(s):  
Surabhi Verma ◽  
Sushil Chaurasia
Keyword(s):  
Big Data ◽  
Significant Influence ◽  
Data Analytics ◽  
Big Data Analytics ◽  
Relative Advantage ◽  
Technology Readiness ◽  
Management Support ◽  
Competitive Pressure ◽  
Top Management Support ◽  
Data Environment

This article aims to empirically investigate the factors that affects the adoption of big data analytics by firms (adopters and non-adopters). The current study is based on three feature that influence BDA adoption: technological context (relative advantage, complexity, compatibility), organizational context (top management support, technology readiness, organizational data environment), and environmental context (competitive pressure, and trading partner pressure). A structured questionnaire-based survey method was used to collect data from 231 firm managers. Relevant hypotheses were derived and tested by partial least squares. The results indicated that technology, organization and environment contexts impact firms' adoption of big data analytics. The findings also revealed that relative advantage, complexity, compatibility, top management support, technology readiness, organizational data environment and competitive pressure have a significant influence on the adopters of big data analytics, whereas relative advantage, complexity and competitive pressure have a significant influence on the non-adopters of big data analytics.

scholarly journals Predictors of physicians’ intentions to use clinical practice guidelines on antimicrobial in tertiary general hospitals of China: a structural equation modeling approach

2021 ◽  
Vol 10 (1) ◽  
Author(s):  
Qingwen Deng ◽  
Zhichao Zeng ◽  
Yuhang Zheng ◽  
Junhong Lu ◽  
Wenbin Liu
Keyword(s):  
Perceived Risk ◽  
Subjective Norms ◽  
Ease Of Use ◽  
Relative Advantage ◽  
Equation Modeling ◽  
Top Management ◽  
Intention To Use ◽  
Management Support ◽  
General Hospitals ◽  
Top Management Support

Abstract Background With inappropriate use of antimicrobials becoming a great public health concern globally, the issue of applying clinical practice guidelines (CPGs) to regulate the rational use of antimicrobials has attracted increasing attention. Taking tertiary general hospitals in China for example, this study aimed to identify factors to investigate the comprehensive influencing mechanism for physicians’ intention to use CPGs on antimicrobials. Methods Based on the integration of Theory of Planned Behavior (TPB), Technology Acceptance Model (TAM), and Technology-Organization-Environment framework (TOE), a questionnaire survey was conducted covering potential determinants of affecting physicians’ intentions to use CPGs on antimicrobials at the individual level (attitude, subjective norms and perceived risk), technical level (relative advantage and ease of use), and organizational level (top management support and organizational implementation). Data were collected from 644 physicians in tertiary general hospitals in eastern, central and western China, which were obtained by multi-stage random sampling. The structural equation modeling (SEM) was used to link three-level factors with physicians’ behavioral intentions. Results The majority of the participants (94.57%) showed a positive tendency toward intention to use CPGs on antimicrobials. The reliability and validity analysis showed the questionnaire developed from the theoretical model was acceptable. SEM results revealed physicians’ intentions to use CPGs on antimicrobials was associated with attitude (β = 0.166, p < 0.05), subjective norms (β = 0.244, p < 0.05), perceived risk (β = − 0.113, p < 0.05), relative advantage (β = 0.307, p < 0.01), top management support (β = 0.200, p < 0.05) and organizational implementation (β = 0.176, p < 0.05). Besides, subjective norms, perceived risk, relative advantage, ease of use, and top management support showed their mediating effects from large to small on the intentions, which were 0.215, 0.140, 0.103, 0.088, − 0.020, respectively. Conclusions This study revealed the significance of multifaceted factors to enhance the intention to use CPGs on antimicrobials. These findings will not only contribute to the development of targeted intervention strategies on promoting the use of CPGs on antimicrobials, but also provide insights for future studies about physicians’ adoption behaviors on certain health services or products.

Adoption of Big Data Analytics in Supply Chain Management

2021 ◽  
Vol 14 (2) ◽  
pp. 88-107
Author(s):  
Amin Khalil Alsadi ◽  
Thamir Hamad Alaskar ◽  
Karim Mezghani
Keyword(s):  
Big Data ◽  
Supply Chain ◽  
Supply Chain Management ◽  
Data Analytics ◽  
Big Data Analytics ◽  
Top Management ◽  
Management Support ◽  
Chain Management ◽  
Top Management Support ◽  
Intention To Adopt

Supported by the literature on big data, supply chain management (SCM), and resource-based theory (RBT), this study aims to evaluate the organizational variables that influence the intention of Saudi SCM professionals to adopt big data analytics (BDA) in SCM. A survey of 220 supply chain respondents revealed that both top management support and data-driven culture have a high significant influence on their intention to adopt BDA. However, the firm entrepreneurial orientation showed no significant effect. Also, the findings revealed that supply chain connectivity positively moderates the link between top management support and intention. This study contributes to the practical field, offering valuable insights for decision makers considering BDA adoption in SCM. It also contributes to the literature by helping minimize the research gap in BDA adoption in the Saudi context.

scholarly journals The influence of organizational factors to software-as-a-service (SaaS) adoption in Vietnamese enterprises

2020 ◽  
Vol 36 (2) ◽  
Author(s):  
Le Thi Thu Ha ◽  
Le Thi Minh Huyen ◽  
Le Thi Thu Huong ◽  
Le Nguyen Hoang Linh
Keyword(s):  
Organizational Factors ◽  
Organizational Readiness ◽  
Top Management ◽  
Organizational Size ◽  
Management Support ◽  
Information Technology Industry ◽  
Top Management Support ◽  
Technology Industry ◽  
Analyze Data

With the growth of the information technology industry, the literature exploring cloud computing, in particular, SaaS adoption has been developing considerably over the last few years. It is time to take stock of SaaS adoption’s determinant factors and its application to more specific contexts. This study endeavored to investigate the influence of three organizational factors (organizational size, organizational readiness, and top management support) to SaaS adoption in Vietnamese enterprises across sectors. Qualitative method was employed to analyze data gathered from 18 case-study companies. The findings reconfirmed that top management support is the strongest enabler for SaaS adoption while there are still some contradictions between organizational size as well as organizational readiness versus SaaS adoption in the context of a developing country as Vietnam.

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