On the Adoption of Big Data Analytics: Interdependencies of Contextual Factors

Schüll, Anke; Maslan, Natalia

doi:10.5220/0006759904250431

Cited by 15 publications

(21 citation statements)

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“…These changes will consequently force other firms to adopt the new technological innovation to gain a competitive advantage, thus protecting their competitive position. (Petersen & Nguyen, 2017) Previous empirical studies confirmed the positive effect of competitive pressure on the intent to adopt BDA (Agrawal, 2015;Chen et al, 2015;Gangwar, 2018;Jang et al, 2019;Lai et al, 2018;Petersen & Nguyen, 2017;Schüll & Maslan, 2018;Shiwei Sun et al, 2019). Moreover, Lai et al (2018) argued that 'contextual factors can work as facilitators or inhibitors in firms' adoption behaviours when deciding whether to embrace a new information technology or not.'…”

Section: The Effects Of Competitive Pressure On Intentionmentioning

confidence: 93%

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Examining the adoption of Big data analytics in supply chain management under competitive pressure: evidence from Saudi Arabia

Alaskar

Mezghani

Alsadi

2020

Journal of Decision Systems

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Section: The Effects Of Competitive Pressure On Intentionmentioning

confidence: 93%

“…However, these sets of data require new forms of processing capability to enable better decision-making (Chen et al, 2015;Schüll & Maslan, 2018).…”

Section: Bda and The Digital Transformation Of Scmmentioning

confidence: 99%

Examining the adoption of Big data analytics in supply chain management under competitive pressure: evidence from Saudi Arabia

Alaskar

Mezghani

Alsadi

2020

Journal of Decision Systems

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Section: Findings and Discussionmentioning

confidence: 99%

“…Another study by [45] highlighted some of the most important factors related to BDA. [45] followed the Technology, Organization, and Environment (TOE) framework to categorize the influencing factors. [45] extended the TOE framework from the perspective of dynamic capability which requires a mixture of technology, management, and skills as a requirement to the successful adoption of Big Data Analytics.…”

Section: Findings and Discussionmentioning

confidence: 99%

“…[45] followed the Technology, Organization, and Environment (TOE) framework to categorize the influencing factors. [45] extended the TOE framework from the perspective of dynamic capability which requires a mixture of technology, management, and skills as a requirement to the successful adoption of Big Data Analytics. [45] focused on the importance of dynamic capabilities and Big Data capabilities to enable the organization to evolve according to the requirements of a changing environment [45].…”

Section: Findings and Discussionmentioning

confidence: 99%

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Critical Success Factors for Big Data: A Systematic Literature Review

2020

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During the last few decades, many organizations have started recognizing the benefits of Big Data (BD) to drive their digital transformation and to gain faster insights from faster data. Making smart data-driven decisions will help the organizations to ride the waves toward invaluable investments. The successful implementation of Big Data projects depends on their alignment with the current organizational, technological, and analytical aspects. Identifying the Critical Success Factors (CSFs) for Big Data is fundamental to overcome the challenges surrounding Big Data Analytics (BDA) and implementation. In recent years, the investigations related to identifying the CSFs of Big Data and Big Data Analytics expanded on a large scale trying to address the limitations in existing publications and contribute to the body of knowledge. This paper aims to provide more understanding about the existing CSFs for Big Data Analytics and implementation and contributes to the body of knowledge by answering three research questions: 1) How many studies have investigated on Big Data CSFs for analytics and implementation?, 2) What are the existing CSFs for Big Data Analytics, and 3) What are the categories of Big Data Analytics CSFs?. By conducting a Systematic Literature Review (SLR) for the available studies related to Big Data CSFs in the last twelve years (2007-2019), a final list of sixteen (16) related articles was extracted and analyzed to identify the Big Data Analytics CSFs and their categories. Based on the descriptive qualitative content analysis method for the selected literature, this SLR paper identifies 74 CSFs for Big Data and proposes a classification schema and framework in terms of 5 categories, namely Organization, Technology, People, Data Management, and Governance. The findings of this paper could be used as a referential framework for a successful strategy and implementation of Big Data by formulating more effective data-driven decisions. Future work will investigate the priority of the Big Data CSFs and their categories toward developing a conceptual framework for assessing the success of Big Data projects.

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The barriers to big data adoption in developing economies

Alalawneh

Alkhatib

2020

E J Info Sys Dev Countries

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Big data adoption (BDA) and its barriers, opportunities, and expected benefits are increasingly attracting researchers' and practitioners' attention. For developing economies, BDA has special importance; however, they have struggled to identify the barriers hindering it. This study aims to identify the barriers to BDA in developing economies and their relative importance across sectors to identify sectors' readiness for BDA. Based on a review of the BDA literature, semi‐structured interviews with big data (BD) experts, and a questionnaire‐based survey, five barrier sets, and 19 sub‐barriers were identified. Thereafter, barrier sets for each sector were identified, analyzed, discussed, and compared. Analytic hierarchy process (AHP) was used to identify the relative importance of these barriers across sectors, while the technique for order of preference by similarity to ideal solution (TOPSIS) was used to rank sectors according to their readiness for BDA. This study positions itself as a reference for scholars and practitioners interested in BDA in developing economies.

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On the Adoption of Big Data Analytics: Interdependencies of Contextual Factors

Cited by 15 publications

References 0 publications

Examining the adoption of Big data analytics in supply chain management under competitive pressure: evidence from Saudi Arabia

Examining the adoption of Big data analytics in supply chain management under competitive pressure: evidence from Saudi Arabia

Critical Success Factors for Big Data: A Systematic Literature Review

The barriers to big data adoption in developing economies

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