On big data-guided upstream business research and its knowledge management

Nimmagadda, Shastri L.; Reiners, Torsten; Wood, Lincoln C.

doi:10.1016/j.jbusres.2018.04.029

Cited by 16 publications

(21 citation statements)

References 5 publications

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“…Classification algorithms from this group were used, for example, as a management tool for the creation of a DSS that enabled credit risk assessment in the financial market (Hayashi, 2016), for building modernization assessment allowing the decision-maker to select the best alternatives in terms of energy consumption and installation cost (Rasiulis, Ustinovichius, Vilutiene & Popov, 2016), to deal with business data heterogeneity and multidimensionality (Nimmagadda, Reiners & Wood, 2018). We also found application in route optimization models through DSS to view traffic volume and weather interactions applicable to transport planners, traffic control rooms and urban infrastructure DSS (Sathiaraj, Punkasem, Wang & Seedah, 2018) and through an online route generation system with Dijkstra algorithm, which resulted in changes of route paradigms that were determined together (Rönnqvist, Svenson, Flisberg & Jö).…”

Section: Resultsmentioning

confidence: 99%

Big data as a value generator in decision support systems: a literature review

Grander

Silva

González

2021

REGE

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PurposeThis paper aims to analyze how decision support systems manage Big data to obtain value.Design/methodology/approachA systematic literature review was performed with screening and analysis of 72 articles published between 2012 and 2019.FindingsThe 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/valueAs 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.

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Section: Resultsmentioning

confidence: 99%

Big data as a value generator in decision support systems: a literature review

Grander

Silva

González

2021

REGE

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“…Com base nisso, conota-se a importância de uma boa gestão informacional, na qual deve realizar-se um levantamento de mercado interno e externo para que se possa traçar a melhor estratégia para a empresa. Nimmagadda, et al, (2018), dizem que gerenciar e entregar informações precisas em várias unidades operacionais são tarefas desafiadoras para tomar decisões nos negócios, e nesse ponto encontra-se o marketing da informação. Amaral (2011), explica o marketing da informação como o processo de gestão da informação, no tocante as etapas do ciclo da informação, como a coleta, tratamento, disseminação e o uso.…”

Section: Introductionunclassified

Informação – um Produto Insubstituível na Estratégia de Marketing: Estudo de Caso Empresa E-Commerce

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Filho

Franco

2022

J. Perspect. Manag.

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Este artigo objetiva analisar de que forma uma empresa de e-commerce, localizada em Curitiba-PR, coletam dados e como os transforma em informações úteis para construção da estratégia de marketing. O estudo tem cunho qualitativo, por meio de uma revisão bibliográfica de artigos sem corte temporal, estudo de caso único por meio de entrevista semiestruturada, com a finalidade de descrever um evento contemporâneo específico. Como resultado do estudo, foi constatado que a principal fonte de dados internos são os históricos de vendas. Por outro lado, como fontes externas, a empresa monitora informações de sítios de internet de empresas concorrentes. Após transformarem esses dados em informações, a empresa realiza seu planejamento e define sua estratégia de marketing, direcionando os anúncios e suas promoções, reduzindo os custos da operação e maximizando os resultados.

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“…The advantages of Big Data Analytics are manifold including better decision making, better 'targeted' marketing, much improved client based support, direct and stringent business insights, better planning and forecasting among several others (Chen and Zhang., 2014;Nimmagadda, Reiners and Wood, 2018) However, despite having several strategic and operational advantages, it has been observed that the adoption rate of Big Data Analytics within industries and sectors at large hasn't been impressive (Shin 2016). Thus further research is needed in order to understand the inertia coupled with the risk and challenges affecting large scale adoption; having said that, it's imperative to note that there's a lot of research that is on-going in the technology itself, wherein scholars and exponents of this technology are working together in order to address some plaguing businessrelated issues of Big Data Analytics (Chen, Mao and Liu, 2014;Verma, Bhattacharyya and Kumar, 2018).…”

Section: Introductionmentioning

confidence: 99%

Big Data Analytics Usage and Business Performance: Integrating the Technology Acceptance Model (TAM) and Task Technology Fit (TTF) Model

Gangwar¹

2020

EJISE

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The purpose of this study is to propose a unified model integrating both the technology acceptance model (TAM) and task technology fit (TTF) model, and explore the organizational and environmental fit of the integrated model in order to investigate usage of Big Data Analytics and its effect on business performance. A questionnaire was used to collect data from 280 companies in CPG & Retail, Healthcare, Banking and Telecom in India. The data were analysed using exploratory and confirmatory factor analyses. Further, structural equation modelling was used to test the proposed model. The findings show that the research model for integrating the TAM for adoption and TTF model for utility provides a more comprehensive understanding of Big Data Analytics usage. The study identified task technology fit , individual technology fit, organizational data fit, organizational process fit, and business strategy fit as Tidd important variables for affecting Big Data Analytics usage using perceived ease of use (PEOU) and perceived usefulness (PU) as mediating variables. Competitive fit and partner support/customer fit were also found to be directly affecting Big Data Analytics usage, which in turn has significant influence on business performance. The model explained 71.4 percent of Business performance. The integrated model may be used as a guideline to ensure a positive outcome of Big Data Analytics usage in organizations. This study combined both the key ideas of TAM and TTF to show that they were necessary in predicting Big Data Analytics usage and business performance.

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On big data-guided upstream business research and its knowledge management

Cited by 16 publications

References 5 publications

Big data as a value generator in decision support systems: a literature review

Big data as a value generator in decision support systems: a literature review

Informação – um Produto Insubstituível na Estratégia de Marketing: Estudo de Caso Empresa E-Commerce

Big Data Analytics Usage and Business Performance: Integrating the Technology Acceptance Model (TAM) and Task Technology Fit (TTF) Model

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