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

Interest in smart cities (SCs) and big data analytics (BDA) has increased in recent years, revealing the bond between the two fields. An SC is characterized as a complex system of systems involving various stakeholders, from planners to citizens. Within the context of SCs, BDA offers potential as a data-driven decision-making enabler. Although there are abundant articles in the literature addressing BDA as a decision-making enabler in SCs, mainstream research addressing BDA and SCs focuses on either the technical aspects or smartening specific SC domains. A small fraction of these articles addresses the proposition of developing domain-independent BDA frameworks. This paper aims to answer the following research question: how can BDA be used as a data-driven decision-making enabler in SCs? Answering this requires us to also address the traits of domain-independent BDA frameworks in the SC context and the practical considerations in implementing a BDA framework for SCs' decision-making. This paper's main contribution is providing influential design considerations for BDA frameworks based on empirical foundations. These foundations are concluded through a use case of applying a BDA framework in an SC's healthcare setting. The results reveal the ability of the BDA framework to support data-driven decision making in an SC.

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Boosting innovation performance through big data analytics:An empirical investigationon the role of firm agility

Journal of Information Science ◽

10.1177/01655515211047425 ◽

2021 ◽

pp. 016555152110474

Author(s):

Ahad ZareRavasan

Keyword(s):

Decision Making ◽

Big Data ◽

Current Knowledge ◽

Big Data Analytics ◽

Innovation Performance ◽

Data Driven ◽

Moderating Variables ◽

Research Gap ◽

Moderating Role

While past studies proposed the role of big data analytics (BDA) as one of the primary pathways to business value creation, current knowledge on the link between BDA and innovation performance remains limited. In this regard, this study intends to fill this research gap by developing a theoretical framework for understanding how and under which mechanisms BDA influences innovation performance. Firm agility (conceptualised as sensing agility, decision-making agility and acting agility) is used in this research as the mediator between BDA and innovation performance. Besides, this research conceptualises two moderating variables: data-driven culture and BDA team sophistication. This study employs partial least squares (PLS) to test and validate the proposed hypotheses using survey data of 185 firms. The results show that firm agility significantly mediates the link between BDA use and innovation performance. Besides, the results suggest that data-driven culture moderates the relation between sensing agility and decision-making agility. This research also supports the moderating role of BDA team sophistication on the link between BDA use and sensing agility.

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Attributes of Big Data Analytics for Data-Driven Decision Making in Cyber-Physical Power Systems

2020 14th International Conference on Protection and Automation of Power Systems (IPAPS) ◽

10.1109/ipaps49326.2019.9069391 ◽

2019 ◽

Author(s):

Jalal Moradi ◽

Hossein Shahinzadeh ◽

Hamed Nafisi ◽

Mousa Marzband ◽

Gevork B. Gharehpetian

Keyword(s):

Decision Making ◽

Big Data ◽

Power Systems ◽

Data Analytics ◽

Big Data Analytics ◽

Data Driven ◽

Data Driven Decision Making

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Big Data Analytics in Government: Improving Decision Making for R&D Investment in Korean SMEs

Sustainability ◽

10.3390/su12010202 ◽

2019 ◽

Vol 12 (1) ◽

pp. 202

Author(s):

Eun Sun Kim ◽

Yunjeong Choi ◽

Jeongeun Byun

Keyword(s):

Decision Making ◽

Big Data ◽

Data Analytics ◽

Big Data Analytics ◽

Data Driven ◽

Medium Size ◽

The Government ◽

Using Data ◽

High Level ◽

D Investment

To expand the field of governmental applications of Big Data analytics, this study presents a case of data-driven decision-making using information on research and development (R&D) projects in Korea. The Korean government has continuously expanded the proportion of its R&D investment in small and medium-size enterprises to improve the commercialization performance of national R&D projects. However, the government has struggled with the so-called “Korea R&D Paradox”, which refers to how performance has lagged despite the high level of investment in R&D. Using data from 48,309 national R&D projects carried out by enterprises from 2013 to 2017, we perform a cluster analysis and decision tree analysis to derive the determinants of their commercialization performance. This study provides government entities with insights into how they might adjust their approach to Big Data analytics to improve the efficiency of R&D investment in small- and medium-sized enterprises.

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

Revista de Contabilidad ◽

10.6018/rcsar.382251 ◽

2019 ◽

Vol 22 (2) ◽

pp. 187-202 ◽

Cited By ~ 1

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.

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