Purchase Decision Support with Internet of Things-Based Systems

2019 ◽  
pp. 173-185
Author(s):  
Monika Kulisz ◽  
Jerzy Lipski ◽  
Agnieszka Bojanowska
Keyword(s):  
Decision Support ◽  
Internet Of Things ◽  
Purchase Decision

Mobile Purchase Decision Support Systems for In-Store Shopping Environments

Data Mining ◽  
2013 ◽  
pp. 1339-1357
Author(s):  
Tobias Kowatsch ◽  
Wolfgang Maass
Keyword(s):  
Decision Support ◽  
Decision Support Systems ◽  
Information Needs ◽  
Product Information ◽  
Support Systems ◽  
Purchase Decision ◽  
Innovation Diffusion Theory ◽  
Shopping Environments ◽  
New Business ◽  
Communication Needs

Purchase decision-making is influenced by product information available in online or in-store shopping environments. In online shopping environments, the use of decision support systems increases the value of product information as information becomes adaptive and thus more relevant to consumers’ information needs. Correspondingly, mobile purchase decision support systems (MP-DSSs) may also increase the value of product information in in-store shopping environments. In this chapter, we investigate the use of a MP-DSS that is bound to a physical product. Based on Theory of Planned Behaviour, Innovation Diffusion Theory, and Technology Acceptance Model, we propose and evaluate a model to better understand MP-DSSs. Results indicate that perceived usefulness influences product purchases and predicts usage intentions and store preferences of consumers. We therefore discuss new business models for retail stores in which MP-DSSs satisfy both the information needs of consumers and the communication needs of retailers.

A self-adaptive distributed decision support model for Internet of Things applications

2017 ◽  
Vol 39 (4) ◽  
pp. 404-419
Author(s):  
Lizong Zhang ◽  
Nawaf R Alharbe ◽  
Anthony S Atkins
Keyword(s):  
Decision Support ◽  
Internet Of Things ◽  
Decision Support Model ◽  
Intelligent Support ◽  
Iot Applications ◽  
Environment Recognition ◽  
Artificial Neural Network Ann ◽  
Distributed Decision ◽  
Support Model ◽  
Self Adaptive

The term of Internet of Things (IoT) is an emerging concept that has already made an impact on many research domains by providing new solutions and ideas. However, we noticed that many IoT applications are more focused on the ‘communication’ part and are still relatively weak in ‘intelligent’ aspects. Consequently, in this paper we proposed an approach for a self-adaptive distributed decision support model to provide more intelligent support for IoT applications. The model is designed with three major approaches: an artificial neural network (ANN) for environment recognition, knowledge merging to create a local knowledge base and expert systems technology for decision making. In addition, a self-adaption feature is introduced to fix any possible improper usage of knowledge that may be caused by inaccuracies in the environment recognition. This strategy was confirmed in an experiment with a local Chinese medical clinical trials centre, in which the results indicate it may improve the accuracy of decisions from 42% to about 85%.

scholarly journals A Mobile Matchmaker for the Ubiquitous Semantic Web

2016 ◽  
pp. 994-1017 ◽  
Author(s):  
Floriano Scioscia ◽  
Michele Ruta ◽  
Giuseppe Loseto ◽  
Filippo Gramegna ◽  
Saverio Ieva ◽  
...  
Keyword(s):  
Performance Evaluation ◽  
Decision Support ◽  
Internet Of Things ◽  
Semantic Web ◽  
Knowledge Bases ◽  
Semantic Web Technologies ◽  
Experimental Performance ◽  
Web Technologies ◽  
Constrained Environments ◽  
And Performance

The Semantic Web and Internet of Things visions are converging toward the so-called Semantic Web of Things (SWoT). It aims to enable smart semantic-enabled applications and services in ubiquitous contexts. Due to architectural and performance issues, it is currently impractical to use existing Semantic Web reasoners. They are resource consuming and are basically optimized for standard inference tasks on large ontologies. On the contrary, SWoT use cases generally require quick decision support through semantic matchmaking in resource-constrained environments. This paper presents Mini-ME, a novel mobile inference engine designed from the ground up for the SWoT. It supports Semantic Web technologies and implements both standard (subsumption, satisfiability, classification) and non-standard (abduction, contraction, covering) inference services for moderately expressive knowledge bases. In addition to an architectural and functional description, usage scenarios are presented and an experimental performance evaluation is provided both on a PC testbed (against other popular Semantic Web reasoners) and on a smartphone.

scholarly journals SISTEM PENDUKUNG KEPUTUSAN PEMBELIAN MOBIL MENGGUNAKAN METODE TECHNIQUE FOR ORDER PREFERENCE BY SIMILARITY OF IDEAL SOLUTION (TOPSIS) DI KELIP MOTOR KARANGANYAR

2019 ◽  
Vol 7 (1) ◽  
Author(s):  
Yulian Tirta Saputra ◽  
Sri Hariyati Fitriasih ◽  
Setiyowati Setiyowati Setiyowati
Keyword(s):  
Decision Support ◽  
Research Method ◽  
Purchase Decision ◽  
Ideal Solution ◽  
Purchasing Decisions ◽  
Topsis Method ◽  
Application System ◽  
Order Preference ◽  
The Many ◽  
Collection Methods

Car is one of the means of transportation that is growing rapidly in the country of Indonesia, because of its comfort and safety. However, problems arise when buyers will buy a car vehicle often faced with a variety of choices of years, types, prices and various other criteria offered. Of the many criteria, buyers have difficulty comparing criteria with other criteria. So, a decision support system was built using the TOPSIS method to rank each alternatif cars based on criteria. The purpose of this study is to build a system of car purchase decision support applications using the TOPSIS method that can provide car recommendations in accordance with the wishes of the buyer. The research method used includes data collection methods and system design methods. The formulation of the problem in this study is how to make car purchasing decisions at Kelip Motor Karanganyar applying the TOPSIS method and how to build an application system for car purchasing decisions by applying the TOPSIS method at Kelip Motor Karanganyar. The result of this research is to build an application system for car purchasing decisions in Kelip Motor Karanganyar using the TOPSIS method.

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