The Variances of Consumers’ Materialistic Personality Traits and Reduced Consumption Behavior Among Demographics in Egypt

doi:10.7176/jmcr/88-02

Cited by 3 publications

(3 citation statements)

References 36 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…noise) [3], and analyzed to reveal its hidden relations and patterns [4]. Informatics processing needs has today transformed our society [5] with tools that advance effective resource sharing with its inherent benefits [6]. These also yield a range of threats and complications to the normal operations of systems deployed to ease living at every frontier [7].…”

Section: Introductionmentioning

confidence: 99%

Handling Transactional Data Features via Associative Rule Mining for Mobile Online Shopping Platforms

Akazue,

Okofu,

Ojugo

et al. 2024

IJACSA

View full text Add to dashboard Cite

Transactional data processing is often a reflection of a consumer's buying behavior. The relational records if properly mined, helps business managers and owners to improve their sales volume. Transaction datasets are often rippled with the inherent challenges in their manipulation, storage and handling due to their infinite length, evolution of product features, evolution in product concept, and oftentimes, a complete drift away from product feat. The previous studies' inability to resolve many of these challenges as abovementioned, alongside the assumptions that transactional datasets are presumed to be stationary when using the association ruleshave been found to also often hinder their performance. As it deprives the decision support system of the needed flexibility and robust adaptiveness to manage the dynamics of concept drift that characterizes transaction data. Our study proposes an associative rule mining model using four consumer theories with RapidMiner and Hadoop Tableau analytic tools to handle and manage such large data. The dataset was retrieved from Roban Store Asaba and consists of 556,000 transactional records. The model is a 6-layered framework and yields its best result with a 0.1 value for both the confidence and support level(s) at 94% accuracy, 87% sensitivity, 32% specificity, and a 20-second convergence and processing time.

show abstract

Section: Introductionmentioning

confidence: 99%

Handling Transactional Data Features via Associative Rule Mining for Mobile Online Shopping Platforms

Akazue,

Okofu,

Ojugo

et al. 2024

IJACSA

View full text Add to dashboard Cite

show abstract

“…Despite the many benefits, basket analysis still faces lots of challenges in mining large transactional stream dataset. These include [23]- [25]: 1. Nature of the Dataset: Data mining systems are successfully grouped into the kind of data they do mine as knowledge.…”

Section: Introductionmentioning

confidence: 99%

FePARM: The Frequency-Patterned Associative Rule Mining Framework on Consumer Purchasing-Pattern for Online Shops

Malasowe,

Okpako,

Ashioba

et al. 2024

AIMS Research Journal

View full text Add to dashboard Cite

Transaction data often is a true presentation of consumers’ buying behavior, stored as a set of relational records, which properly harnessed via mining – can help businesses improve their sales volume as a decision support system. Managing such a system can pose many issues to biz such as feature evolution, concept evolution, concept drift, and infinite data length – and often makes it impractical to effectively store such big-data. To curb this, previous studies have assumed data to be stationary in using associative rule mining. This has deprived such systems of the flexibility and adaptiveness required to handle the dynamics of concept drift that characterizes transaction datasets. Our study thus proposes a basket frequent pattern growth trained associative rule mining model to handle large data. The dataset was retrieved from the Delta-Mall Asaba and consists of 556,000 transaction consumer records. The model consists of six-layers, and yields the best result with a 0.1 value for both the confidence and support level(s) at a 94% accuracy, sensitivity of 87%, and a specificity of 32% with a 20-second convergence and processing time. Keywords: Big-Data Analysis, Feature Evolution, Inventory Model, Market Basket Analysis, Businesses, Transactional Data Streams, Concept Drift CISDI Journal Reference Format Malasowe, B.O., Okpako, E.A., Ashioba, N.O., Ejeh, P.O., Ojugo, A.A. & Ako, R.E. (2024): FePARM: The Frequency-Patterned Associative Rule Mining Framework on Consumer Purchasing-Pattern for Online Shops. Computing, Information Systems, Development Informatics & Allied Research Journal. Vol 15 No 2, Pp 1-14. dx.doi.org/10.22624/AIMS/CISDI/V15N2P2. Available online at www.isteams.net/cisdijournal

show abstract

“…With over-stocking of inventories, it will increase the cost of maintenance and storage, and thus, reduce the profit margins [68]- [70]. With nation left devastated by the pandemic, the International Monetary Fund reported by 2024 ending, it will cost globally about US$13.8trillion [71], [72] for recovery. This socioeconomic impact while enormous, also had indirect impacts to both businesses and society so that all nations through her citizens are now exposed and educated with disaster management due to the rippled consequences [73].…”

Section: Introductionmentioning

confidence: 99%

NiCuSBlockIoT: Sensor-based Cargo Assets Management and Traceability Blockchain Support for Nigerian Custom Services

Obasuyi, D.A.,

Yoro, R.E.,

Okpor, M.D.

et al. 2024

AIMS Research Journal

View full text Add to dashboard Cite

As competitive market and globalization continue to ripple a range of issues across the asset chain (i.e. safety, quality, tracing, and overall management efficiency). Pandemics are bound to occur without warning and has revealed the unpreparedness of many nations. Thus, the Nigerian Government aiming to shore up revenue/monetization via customs exercise duties to augment the nosedive in revenue of the oil sector – must formulate policies and adapt technology to harness its inherent benefits therein. Study advances a sensor-based blockchain NiCuSBlockIoT, which will provision a decision-support scheme for cargo goods traceability and asset movement on a value-chain by first ensuring that accurate records of cargo goods are registered, tagged and reported using the sensor-based units. These are then broadcasted on to the NiCuSBlockIoT as record and/or blocks via a P2P chain on the network as a decentralized framework executed on a distributed hyper-ledger fabric via smart-contract transaction logic. Result show model eliminate fraud that often accompanies a centralized scheme via its sensor-layered model that reports all such errors as data on NiCuSBlockIoT supply value chain. Keywords: BlockChain, Food supply chain, Nigerian Customs Service, NISBlockIoT framework CISDI Journal Reference Format Obasuyi, D.A., Yoro, R.E., Okpor, M.D., Ifioki, A.., Brizimor, S.., Ojugo, A.A., Odiakaose, C.C., Emordi, F.U., Ako, R.E., Geteloma, V.C., Abere, R.A., Atuduhor, R.R. & Akiakeme, E. (2024): NiCuSBlockIoT: Sensor-based Cargo Assets Management and Traceability Blockchain Support for Nigerian Custom Services. Computing, Information Systems, Development Informatics & Allied Research Journal. Vol 15 No 2, Pp 45-64. dx.doi.org/10.22624/AIMS/CISDI/V15N2P4. Available online at www.isteams.net/cisdijournal

show abstract

The Variances of Consumers’ Materialistic Personality Traits and Reduced Consumption Behavior Among Demographics in Egypt

Cited by 3 publications

References 36 publications

Handling Transactional Data Features via Associative Rule Mining for Mobile Online Shopping Platforms

Handling Transactional Data Features via Associative Rule Mining for Mobile Online Shopping Platforms

FePARM: The Frequency-Patterned Associative Rule Mining Framework on Consumer Purchasing-Pattern for Online Shops

NiCuSBlockIoT: Sensor-based Cargo Assets Management and Traceability Blockchain Support for Nigerian Custom Services

Contact Info

Product

Resources

About