Understanding the Determinants of Big Data Analytics Adoption

2022 ◽  
pp. 1549-1577
Author(s):  
Surabhi Verma ◽  
Sushil Chaurasia

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.

2019 ◽  
Vol 32 (3) ◽  
pp. 1-26 ◽  
Author(s):  
Surabhi Verma ◽  
Sushil Chaurasia

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.


Author(s):  
Amin Khalil Alsadi ◽  
Thamir Hamad Alaskar ◽  
Karim Mezghani

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.


2017 ◽  
Vol 15 (1) ◽  
pp. 260-270 ◽  
Author(s):  
Billy Mathias Kalema ◽  
Motau Mokgadi

Regardless of the nature, size, or business sector, organizations are now collecting burgeoning various volumes of data in different formats. As much as voluminous data are necessary for organizations to draw good insights needed for making informed decisions, traditional architectures and existing infrastructures are limited in delivering fast analytical processing needed for these Big Data. For success organizations need to apply technologies and methods that could empower them to cost effectively analyze these Big Data. However, many organizations in developing countries are constrained with limited access to technology, finances, infrastructure and skilled manpower. Yet, for productive use of these technologies and methods needed for Big Data analytics, both the organizations and their workforce need to be prepared. The major objective for this study was to investigate developing countries organizations’ readiness for Big Data analytics. Data for the study were collected from a public sector in South Africa and analyzed quantitatively. Results indicated that scalability, ICT infrastructure, top management support, organization size, financial resources, culture, employees’ e-skills, organization’s customers’ and vendors are significant factors for organizations’ readiness for Big Data analytics. Likewise strategies, security and competitive pressure were found not to be significant. This study contributes to the scanty literature of Big Data analytics by providing empirical evidence of the factors that need attention when organizations are preparing for Big Data analytics.


2022 ◽  
pp. 1817-1842
Author(s):  
Hemlata Gangwar

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.


2018 ◽  
Vol 29 (2) ◽  
pp. 676-703 ◽  
Author(s):  
Yuanyuan Lai ◽  
Huifen Sun ◽  
Jifan Ren

PurposeBased on previous literature on big data analytics (BDA) and supply chain (SC) management, the purpose of this paper is to address the factors determining firms’ intention to adopt BDA in their daily operations. Specifically, this study classifies potential factors into four categories: technological, organizational, environmental factors, and SC characteristics.Design/methodology/approachDrawing on the innovation diffusion theory, a model consisted of direct technological and organizational factors as well as moderators was proposed. Subsequently, survey data was collected from 210 organizations. Then we used SPSS and SmartPLS to analyze the collected data.FindingsThe empirical results revealed that perceived benefits and top management support can significantly influence the adoption intention. And environmental factors, such as competitors’ adoption, government policy, and SC connectivity, can significantly moderate the direct relationships between driving factors and the adoption intention.Research limitations/implicationsGiven the fact that big data (BD) usage in logistics and SC management is still in the start-up stage, the interpretations toward BDA might vary from different perspectives, thus causing some ambiguity in understanding the meaning and potential BD has. In addition, we collected data through questionnaires completed by IT managers, whose viewpoint may not fully represent that of an organization.Practical implicationsThis paper tests the organizational adoption intention of BDA and extends the literature streams of BD and SC management simultaneously.Social implicationsThis research helps top managers assess the benefits of BDA as well as how to adjust their business strategy along the changes of environment and SC maturity.Originality/valueThis paper contributes to the literature of organizational adoption intention of BDA and extends the literature streams of BD and SC management simultaneously.


Author(s):  
Alireza Mohammadi ◽  
Armin Saeedikondori ◽  
Hossein Nezakati ◽  
Naghmeh Sabermajidi ◽  
Amer Hamzah Jantan

Cloud computing is one of the most popular technology services, and its usage has increased significantly in recent years. This study aims to understand the factors that influence cloud computing adoption by Malaysian information technology (IT) companies. An in-depth review in the previous literature demonstrated a relationship between relative advantage, complexity, compatibility, top management support, firm size, technology readiness, competitive pressure, and trading partner pressure with cloud computing adoption in Malaysia. The study's findings displayed that relative advantage, compatibility, top management support, and competitive pressure significantly affect cloud computing adoption. The study contributes to applying new technological features of cloud computing adoption in the industry through a wide range of variables. The results also help companies foresee their IT investment when implementing cloud computing. The relative advantage is identified to have the highest impact on cloud computing adoption.


Author(s):  
Hemlata Gangwar

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.


2018 ◽  
Vol 31 (4) ◽  
pp. 1-22 ◽  
Author(s):  
Hemlata Gangwar

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.


10.28945/4447 ◽  
2019 ◽  
Vol 14 ◽  
pp. 367-403
Author(s):  
Omar Hasan Salah ◽  
Prof.Dr. Zawiyah Mohammad Yusof ◽  
Dr.Hazura Mohamed

Aim/Purpose: This study aimed to examine the relationships among compatibility, relative advantage, complexity, IT Infrastructure, security, top Management Support, financial Support, information Policies, employee engagement, customer pressure, competitive pressure, information integrity, information sharing, attitude toward adopting technology factors, and CRM adoption Background: Customer relationship management (CRM) refers to the use of the process, information, technology, and people for the management of the interactions between the organization and its customers. Therefore, there is a need for SMEs to implement CRM practices in their businesses for competitive advantage. However, in developing nations, the adoption rate of such practices remains low. This low rate may be attributed to the lack of important factors that guide CRM adoption, and as such, the present study attempts to investigate the factors affecting CRM adoption in Palestinian SMEs. This paper used the Diffusion of Innovation Theory (DOI), Resource-Based View (RBV), and Technology, Organization, and Environment Framework (TOE) framework to identify the determinant factors from the technological, organizational, environmental, and information culture perspectives. Methodology: This study uses a quantitative approach to investigate the relationships between the variables. A questionnaire was designed to collect data from 420 SMEs in Palestine. 331respondents completed and returned the survey. The Partial Least Square-Structural Equation Model (PLS-SEM) approach was used to assess both the measurement and structural models. Contribution: This study contributes to both theory and practitioners by providing insights into factors that affect CRM adoption in Palestinian SMEs, which did not explore before. Future research suggestions are also provided. Findings: The results of the study prove that the adoption of CRM depends on compatibility (CMP), security (SEC), top management support (TMS), information policies (INP), financial resources (FR), employee engagement (EEN), competitive pressure (COP), customers pressure (CUP), attitude toward adopting technology (ATA), information integrity (INI), and information sharing (INS). Surprisingly, complexity (CMX), IT infrastructure (ITI), and relative advantage (RLA) do not play any role in CRM adoption in Palestine. Recommendations for Practitioners: This study provides practitioners with the important factors for CRM adoption upon its successful implementation in the context of Palestinian SMEs. Recommendation for Researchers: Our findings may be used to conduct further studies about compatibility, security, top management support, information policies, financial resources, employee engagement, competitive pressure, customers pressure, attitude toward adopting technology, information integrity, information sharing factors, and CRM adoption by using different countries, procedure, and context. Impact on Society: The proposed framework provides insights for SMEs which have significant effects for research and practice to help facilitate the adoption of CRM Future Research: The findings may also be compared to other studies conducted in different contexts and provide deeper insights into the influence of the examined contexts on the employees’ intention toward CRM adoption in banking and universities. It would be fruitful to test whether the results hold true in developed and developing countries.


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