Game theoretic approach of a novel decision policy for customers based on big data

Li, Shasha; Shao, Bingjia; Gao, Yuan; Hu, Su; Li, Yang; Zhou, Weigui

doi:10.1007/s10660-017-9259-6

Cited by 9 publications

(8 citation statements)

References 16 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…This paper complements this journal's articles depicting how e-commerce companies can deploy their own data [27] as well as data found in their environment [28] for improved decision making, of which some also use time series methods [29].…”

Section: Literaturementioning

confidence: 84%

Cash flow prediction: MLP and LSTM compared to ARIMA and Prophet

Weytjens¹,

Lohmann²,

Kleinsteuber

2019

Electron Commer Res

View full text Add to dashboard Cite

Cash flow prediction is important. It can help increase returns and improve the allocation of capital in healthy, mature firms as well as prevent fast-growing firms, or firms in distress, from running out of cash. In this paper, we predict accounts receivable cash flows employing methods applicable to companies with many customers and many transactions such as e-commerce companies, retailers, airlines and public transportation firms with sales in multiple regions and countries. We first discuss "classic" forecasting techniques such as ARIMA and Facebook's TM Prophet before moving on to neural networks with multi-layered perceptrons (MLP) and, finally, Long Short-Term Memory (LSTM) networks, that are particularly useful for time series forecasting but were until now not used for cash flows. Our evaluation demonstrates this range of methods to be of increasing sophistication, flexibility and accuracy. We also introduce a new performance measure, interest opportunity cost (IOC), that incorporates interest rates and the cost of capital to optimize the models in a financially meaningful, money-saving, way. Keywords cash flow prediction • accounts receivable • neural networks • LSTM • MLP • ARIMA • Prophet

show abstract

Section: Literaturementioning

confidence: 84%

Cash flow prediction: MLP and LSTM compared to ARIMA and Prophet

Weytjens¹,

Lohmann²,

Kleinsteuber

2019

Electron Commer Res

View full text Add to dashboard Cite

show abstract

“…This is followed by Suki’s [ 62 ] article on online consumer shopping insights, and Chen et al’s [ 63 ] article on information markets. Other articles in this cluster have investigated topics such as Internet queries and marketplace prediction [ 64 ], cross-border electronic commerce using the information systems success model [ 65 ], and electronic [ 66 ] and social [ 67 ] commerce using big data. Thus, ECR articles in this cluster appear to be centered on online market sentiments and analyses , with the use of advanced modeling techniques to unpack fresh insights on electronic commerce being relatively prominent.…”

Section: Bibliographic Coupling: Thematic Clustersmentioning

confidence: 99%

20 years of Electronic Commerce Research

Kumar

Lim

Pandey

et al. 2021

Electron Commer Res

140

View full text Add to dashboard Cite

2021 marks the 20th anniversary of the founding of Electronic Commerce Research ( ECR ). The journal has changed substantially over its life, reflecting the wider changes in the tools and commercial focus of electronic commerce. ECR ’s early focus was telecommunications and electronic commerce. After reorganization and new editorship in 2014, that focus expanded to embrace emerging tools, business models, and applications in electronic commerce, with an emphasis on the innovations and the vibrant growth of electronic commerce in Asia. Over this time, ECR ’s impact and volume of publications have grown rapidly, and ECR is considered one of the premier journals in its discipline. This invited research summarizes the evolution of ECR ’s research focus over its history.

show abstract

“…Four OTs investigated decision-making. These theories explain how BI and BD can be used to enhance the decision-making process (Liu, Shao, Gao, Hu, Li, & Zhou, 2017;Hazen, Skipper, Ezell, & Boone, 2016;Wilburn, & Wilburn, 2016).…”

Section: Findings and Discussionmentioning

confidence: 99%

“…BI and BD related studies include Chang, Hsu, &Wu, 2015, andFiorini, Seles, Jabbour, Mariano, Jabbour, 2018. 11. Game theory applies analytical tools to predict, explain and prescribe what players with various degrees of rationality will do in specific situations (Liu, Shao, Gao, Hu, Li, & Zhou, 2017). BI and BD related studies include Fiorini, Seles, Jabbour, Mariano, Jabbour, 2018, Fu & Zhu, 2017, Liu, Shao, Gao, Hu, Li, & Zhou, 2017, and Liu & Yi, 2017 12.…”

Section: Organizational Theories Supporting Business Intelligence Andmentioning

confidence: 99%

“…Game theory applies analytical tools to predict, explain and prescribe what players with various degrees of rationality will do in specific situations (Liu, Shao, Gao, Hu, Li, & Zhou, 2017). BI and BD related studies include Fiorini, Seles, Jabbour, Mariano, Jabbour, 2018, Fu & Zhu, 2017, Liu, Shao, Gao, Hu, Li, & Zhou, 2017, and Liu & Yi, 2017 12. Goal contagion theory explains how individuals automatically adopt and pursue a goal of another person's behavior (Aarts, Gollwitzer, & Hassin, 2004).…”

Section: Organizational Theories Supporting Business Intelligence Andmentioning

confidence: 99%

See 1 more Smart Citation

An examination of the organizational impact of business intelligence and big data based on management theory

Alnoukari¹

2020

JISIB

View full text Add to dashboard Cite

Big data and big data analytics have been considered to be a disruptive technologythat will rebuild business intelligence. The purpose of this study is to enrich the literature onthe organizational impact of business intelligence and big data based on management theory.While the majority of the organizational theories have had research dedicated to enhance theunderstanding of the impact of business intelligence and big data on organizational performanceand decision-making, the research lacks scholarly work capable of identifying the other mainorganizational outcomes. To achieve this goal, a semi-systematic literature review was carriedout to find all studies related to the research topic. Then, an analysis was conducted tounderstand the use of the organizational theory in accordance with business intelligence andbig data. Finally, a grouping was developed to assign each organizational theory the relatedimpact. The main findings of this work, after examining thirty-three related organizationaltheories, was that there are other important organizational impacts including innovation,agility, adoption, and supply-chain support.

show abstract

Game theoretic approach of a novel decision policy for customers based on big data

Cited by 9 publications

References 16 publications

Cash flow prediction: MLP and LSTM compared to ARIMA and Prophet

Cash flow prediction: MLP and LSTM compared to ARIMA and Prophet

20 years of Electronic Commerce Research

An examination of the organizational impact of business intelligence and big data based on management theory

Contact Info

Product

Resources

About