The role of intelligent agents and data mining in electronic partnership management

Warkentin, Merrill; Sugumaran, Vijayan; Sainsbury, Robert S.

doi:10.1016/j.eswa.2012.05.074

Cited by 19 publications

(4 citation statements)

References 39 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Systemic character – as the most important feature of intellectual production – is pointed out in the work of Dincelli et al (2017). Increase of the role and importance of intellectual production in the modern economy under the influence of establishment of the information society is considered in Warkentin et al (2012) and Inshakova et al (2019); under the influence of transformation of consumer behavior based on the Internet of things is considered in Caputo et al (2018). The change of approaches to corporate social responsibility due to formation of intellectual production is described in Coluccia et al (2016).…”

Section: Literature Reviewmentioning

confidence: 99%

The model of distribution of human and machine labor at intellectual production in industry 4.0

Inshakova

Frolova

Rusakova

et al. 2020

JIC

View full text Add to dashboard Cite

PurposeThe purpose of the paper is to develop a model of distribution of human and machine labor at intellectual production in Industry 4.0.Design/methodology/approachThe basis of the methodology of the research is regression analysis. The analyzed variables are independent variables that characterize the level of development of human and machine labor in the economy of a country; dependent variables that reflect the effectiveness of the production, marketing and innovative business processes in the economy of country according to “The Global Competitiveness Report” (World Economic Forum); and dependent variables, which show the share of the sphere (agriculture, mining industry, processing industry and service sphere) in the structure of GDP of a country according to the statistics of the World Bank. For determining the change of regression dependencies in dynamics in the interests of reduction of the probability of statistical error, the research is conducted for 2010 and 2018 with application of trend analysis.FindingsBased on the full selection of modern countries that conduct digital modernization, the authors determine statistical dependencies of effectiveness of business processes and development of the spheres of economy on the intensity of application of machine and human labor. This allowed determining significant differences in automatization of business processes: perspectives of application of machine labor are the widest in production and the narrowest in marketing, differentiated logic of organization of intellectual production in different spheres of economy and the specifics of automatization of business processes and spheres of economy in countries of different categories, one of which has to be taken into account during organization of intellectual production in Industry 4.0.Originality/valueThe developed model of optimal distribution of human and machine labor at intellectual production in Industry 4.0 will allow reducing disproportions in effectiveness of different business processes, development of different spheres of economy and growth rate of developed and developing countries. This explains its contribution into provision of well-balanced development of the modern global economic system.

show abstract

Section: Literature Reviewmentioning

confidence: 99%

The model of distribution of human and machine labor at intellectual production in industry 4.0

Inshakova

Frolova

Rusakova

et al. 2020

JIC

View full text Add to dashboard Cite

show abstract

“…This is especially attractive when dealing with big data, attempting to automate data gathering and analysis. Warkentin et al (2012) suggested opportunities to utilize knowledge discovery through data mining in Web-based electronic commerce. Balasubramaniam and Thigarasu (2015) used agents in generating decision tree analysis to avoid over-fitting when measuring supply chain efficiency with respect to both cost and customer satisfaction.…”

Section: Data Mining Methodologiesmentioning

confidence: 99%

A review of supply chain data mining publications

Olson

2020

Journal of Supply Chain Management Science

View full text Add to dashboard Cite

The use of data mining in supply chains is growing, and covers almost all aspects of supply chain management. A framework of supply chain analytics is used to classify data mining publications reported in supply chain management academic literature. Scholarly articles were identified using SCOPUS and EBSCO Business search engines. Articles were classified by supply chain function. Additional papers reflecting technology, to include RFID use and text analysis were separately reviewed. The paper concludes with discussion of potential research issues and outlook for future development.

show abstract

“…Warkentin, Sugumaran, and Sainsbury produce a study in which they discuss the role of intelligent agents and data mining in electronic partnership management. The procedures of data mining used in this process can be enhanced by using intelligent agents [14]. Nahar, Imam, Tickle, and Chen discussed a paper in which they used association rule mining and a computational intelligence to identify the factors which contributes to heart diseases for males and females.…”

Section: Related Workmentioning

confidence: 99%

Association Rules Algorithms for Data Mining Process Based on Multi Agent System

Imane¹,

Alaoui²,

Miloud³

et al. 2020

Lecture Notes in Computer Science

View full text Add to dashboard Cite

In this paper, we present a collaborative multi-agent based system for data mining. We have used two data mining model functions, clustering of variables in order to build homogeneous groups of attributes, association rules inside each of these groups and a multi-agent approach to integrate the both data mining techniques. For the association rules extraction, we use both apriori algorithm and genetic algorithm. The main goal of this paper is the evaluation of the association rules obtained by running apriori and genetic algorithm using quantitative datasets in multi agent environment.

show abstract

The role of intelligent agents and data mining in electronic partnership management

Cited by 19 publications

References 39 publications

The model of distribution of human and machine labor at intellectual production in industry 4.0

The model of distribution of human and machine labor at intellectual production in industry 4.0

A review of supply chain data mining publications

Association Rules Algorithms for Data Mining Process Based on Multi Agent System

Contact Info

Product

Resources

About