Gustavo Grander, Luciano Ferreira da Silva and Ernesto Del Rosário Santibañez Gonzalez
This paper aims to analyze how decision support systems manage Big data to obtain value.
Abstract
Purpose
This paper aims to analyze how decision support systems manage Big data to obtain value.
Design/methodology/approach
A systematic literature review was performed with screening and analysis of 72 articles published between 2012 and 2019.
Findings
The findings reveal that techniques of big data analytics, machine learning algorithms and technologies predominantly related to computer science and cloud computing are used on decision support systems. Another finding was that the main areas that these techniques and technologies are been applied are logistic, traffic, health, business and market. This article also allows authors to understand the relationship in which descriptive, predictive and prescriptive analyses are used according to an inverse relationship of complexity in data analysis and the need for human decision-making.
Originality/value
As it is an emerging theme, this study seeks to present an overview of the techniques and technologies that are being discussed in the literature to solve problems in their respective areas, as a form of theoretical contribution. The authors also understand that there is a practical contribution to the maturity of the discussion and with reflections even presented as suggestions for future research, such as the ethical discussion. This study’s descriptive classification can also serve as a guide for new researchers who seek to understand the research involving decision support systems and big data to gain value in our society.
Details
Keywords
Based on the theory of planned behavior (TPB) and stakeholder theory, the model proposes that responsible leadership (RL) is mediated by affective commitment (AC) on both outcome…
Abstract
Purpose
Based on the theory of planned behavior (TPB) and stakeholder theory, the model proposes that responsible leadership (RL) is mediated by affective commitment (AC) on both outcome variables (organizational citizenship behavior [OCB] and patient satisfaction [PS]) while distributive justice (DJ) moderates the relationship among RL, OCB and PS through the mediator of AC.
Design/methodology/approach
Overall, data collected from 275 employees and patients in India’s healthcare sector support this model both in online and offline mode. SPSS 25, AMOS 22 and PROCESS macro were used to analyze the data.
Findings
The influence of RL, OCB and PS was seen insignificant in the Indian healthcare sector. This study examines the role of AC as a mediator which does not affect extra-role behavior and PS. The findings also show that the moderation-mediation effect of DJ through AC strengthened the link between RL and OCB, but not PS. Commitment does not affect extra-role behavior and PS.
Originality/value
Until now, there has been no research in the Indian context that has tested the effect of RL on extra-role behaviors and PS, as mediated by AC, according to researchers’ knowledge. Since RL and outcome variables are related through AC, the current study aims to understand how DJ acts as a moderator to that relationship.