scholarly journals The Strategic Role of Big Data Analytics in the Decision-Making Process

2021 ◽  
pp. 81-91
Author(s):  
Yahia Benyahia ◽  
Fatima Zohra Hennane
Keyword(s):  
Decision Making ◽  
Big Data ◽  
Data Analytics ◽  
Big Data Analytics ◽  
Decision Making Process ◽  
Strategic Role

scholarly journals A functional framework based on big data analytics for smart farming

2021 ◽  
Vol 24 (3) ◽  
pp. 1772
Author(s):  
Loubna Rabhi ◽  
Noureddine Falih ◽  
Lekbir Afraites ◽  
Belaid Bouikhalene
Keyword(s):  
Decision Making ◽  
Big Data ◽  
Internet Of Things ◽  
Data Analytics ◽  
New Products ◽  
Big Data Analytics ◽  
Decision Making Process ◽  
Time Production ◽  
Smart Farming ◽  
The Future

Big <span>data in agriculture is defined as massive volumes of data with a wide variety of sources and types which can be captured using internet of things sensors (soil and crops sensors, drones, and meteorological stations), analyzed and used for decision-making. In the era of internet of things (IoT) tools, connected agriculture has appeared. Big data outputs can be exploited by the future connected agriculture in order to reduce cost and time production, improve yield, develop new products, offer optimization and smart decision-making. In this article, we propose a functional framework to model the decision-making process in digital and connected agriculture</span>.

Big data analytics and competitive advantage: the strategic role of firm-specific knowledge

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Rajiv Dahiya ◽  
Son Le ◽  
John Kirk Ring ◽  
Kevin Watson
Keyword(s):  
Big Data ◽  
Competitive Advantage ◽  
Data Analytics ◽  
Management Strategies ◽  
Big Data Analytics ◽  
Specific Knowledge ◽  
Content Type ◽  
Absorptive Capacities ◽  
Strategic Role

PurposeWhile advances in big data analytics (BDA) provide valuable business insights and immense business value, many firms find it difficult to gain advantage from their BDA initiatives. Noting the strategic role of firm-specific knowledge, we develop a framework examining the relation between firm specificity of BDA knowledge and competitive advantage. We also examine the dynamic evolution of BDA capabilities and the associated knowledge management strategies.Design/methodology/approachWe review the resource-based view (RBV), capabilities life cycles and absorptive capacity perspectives along with the literature on BDA competitive advantage. Identifying two key BDA factors, application customization and data proprietorship, we develop a BDA competitive advantage framework. We also investigate the absorptive capacities employed by firms to advance their BDA capabilities. We use anecdotal cases to support our theoretical arguments.FindingsWe propose that BDA solutions with vendor-based applications (noncustomized) and public data will not generate firm-specific knowledge and therefore not provide competitive advantage. In contrast, BDA solutions with custom applications and proprietary data will provide high-level firm-specific knowledge and potentially result in sustained competitive advantage. We further suggest the relevant absorptive capacities and the knowledge management strategies for BDA capability development.Practical implicationsOur framework provides managers with insights into how to develop and enhance firm-specific knowledge from their BDA solutions to gain competitive advantage.Originality/valueOur study offers a new BDA firm-specific knowledge framework for competitive advantage.

scholarly journals A systematic review of the role of Big Data Analytics in reducing the influence of cognitive errors on the audit judgement

2019 ◽  
Vol 22 (2) ◽  
pp. 187-202 ◽  
Author(s):  
Fawad Ahmad
Keyword(s):  
Decision Making ◽  
Big Data ◽  
Information Processing ◽  
Data Analytics ◽  
Big Data Analytics ◽  
Future Research ◽  
Cognitive Errors ◽  
Revisión Sistemática ◽  
Toma De Decisiones

This systematic literature review provides the association between memory processes, auditors judgement and decision-making process under the influence of cognitive errors. Due to limited cognitive resources, auditors are unable to analyze the population of accounting transactions, therefore, they use sampling and heuristics for information processing. In the context of Big Data (BD), auditors may face a similar problem of information overload and exhibit cognitive errors, resulting in the selection and analysis of irrelevant information cues. But Big Data analytics (BDA) can facilitate information processing and analysis of complex diverse Big Data by reducing the influence of auditor’s cognitive errors. The current study adapts Ding et al., (2017) framework in the auditing context that identify causes of cognitive errors influencing auditor’s information processing. This review identified 75 auditing related studies to elaborate the role of BD and BDA in improving audit judgement. In addition, role of memory, cognitive errors, and judgement and decision-making are highlighted by using 61 studies. The analysis provides useful insight in different open areas by proposing research propositions and research questions that can be explored by future research to gain extensive understanding on the association between memory and audit judgement in the context of BD and BDA. La revisión sistemática de la literatura proporciona la asociación entre los procesos de la memoria, el juicio de los auditores y el proceso de toma de decisiones bajo la influencia de errores cognitivos. Debido a los limitados recursos cognitivos, los auditores no pueden analizar la población de transacciones contables; por lo tanto, utilizan el muestreo y la heurística para el procesamiento de la información. En el contexto de Big Data (BD), los auditores pueden enfrentarse a un problema similar de sobrecarga de información y exhibir errores cognitivos, lo que resulta en la selección y análisis de indicios de información irrelevantes. No obstante, la analítica de Big Data (BDA) puede facilitar el procesamiento de información y el análisis de datos complejos y diversos al reducir la influencia de los errores cognitivos del auditor. El presente estudio adapta el marco de trabajo de Ding et al (2017) en el contexto de la auditoría que identifica las causas de los errores cognitivos que influyen en el procesamiento de la información del auditor. Esta revisión identificó 75 estudios relacionados con la auditoría para elaborar el papel de BD y BDA en la mejora del juicio de auditoría. Además, el papel de la memoria, los errores cognitivos y el juicio y la toma de decisiones se destacan mediante el uso de 61 estudios. El análisis proporciona una visión útil de los diferentes aspectos abiertos de la cuestión proponiendo propuestas y preguntas de estudio que puedan ser exploradas por la investigación futura para obtener una comprensión amplia de la asociación entre la memoria y el juicio de auditoría en el contexto de BD y BDA.

Big data analytics capability and decision-making: The role of data-driven insight on circular economy performance

2021 ◽  
Vol 168 ◽  
pp. 120766
Author(s):  
Usama Awan ◽  
Saqib Shamim ◽  
Zaheer Khan ◽  
Najam Ul Zia ◽  
Syed Muhammad Shariq ◽  
...  
Keyword(s):  
Decision Making ◽  
Big Data ◽  
Circular Economy ◽  
Data Analytics ◽  
Big Data Analytics ◽  
Data Driven ◽  
Analytics Capability

scholarly journals Big Data Driven Decision Making Model: A case of the South African banking sector

2021 ◽  
Vol 33 (2) ◽  
Author(s):  
Komla Pillay ◽  
Alta Van der Merwe
Keyword(s):  
Decision Making ◽  
Big Data ◽  
South African ◽  
Data Analytics ◽  
Banking Sector ◽  
Big Data Analytics ◽  
Data Driven ◽  
Decision Making Process ◽  
Data Driven Decision Making ◽  
Decision Making Model

The quest to develop a Big Data Driven Decision Making framework to support the incorporation of big data analytics into the decision-making process resulted in the development of a decision making model. The study was conducted within the banking sector of South Africa, with participants from three leading South African banking institutions. The conducted research followed the design science research process of awareness, suggestion, development, evaluation and conclusion. This study developed a theoretical Big Data Driven Decision Making model which illustrates the decision-making process in banking using big data. The study further determined the organizational supports that need to be in place to support the big data analytics decision-making process.

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