Assessing Blockchain Adoption in Supply Chain Management, Antecedent of Technology Readiness, Knowledge Sharing and Trading Need

The present research aimed to establish a framework integrating the concept of technology readiness with variables that accomplished the blockchain adoption theory to identify the impact of blockchain adoption on supply chain transparency, blockchain transparency, and supply chain performance. The methodology used was quantitative with PLS-SEM as the analysis method. There were 295 validated datasets used. The procedure of data collection involved questionnaires. The key finding of the research confirmed the six proposed hypotheses. It was also confirmed that technology readiness, knowledge sharing, and trading needs were significant for the profitability of blockchain technology adoption in supply chain management. On the other hand, blockchain adoption played a significant role in supply chain transparency, blockchain transparency, and supply chain performance. The novelty of this research is in the integration of technology readiness into blockchain in the field of supply chain management. This research can be used to improve and analyze the success rate of blockchain adoption in supply chain management systems. The findings of this study contribute to several aspects, namely practical and academic implications, by providing more insights that correlate with blockchain integration into supply chain management systems. Doi: 10.28991/ESJ-2022-06-05-01 Full Text: PD

Examining the Antecedents of Blockchain Usage Intention: An Integrated Research Framework

Blockchain is considered one of the key technologies that can accelerate the Industrial Revolution 4.0. The intention to use Blockchain can still be improved in several ways, and how users perceive Blockchain is likely to be influenced by how well they understand the underlying theory. This study examines several important factors, namely government regulation, social influence, perceived security, and Blockchain functional benefits, to measure trust and satisfaction with relationship quality, which may influence the intention to use Blockchain. A sample of 460 people participated in the online questionnaire survey, which was then evaluated with SmartPLS 3. The findings reveal that the social influence and Blockchain functional benefits have a substantial impact on relationship quality, which further results in a positive impact on Blockchain usage intention as well. This study can serve as a reference for companies that need to consider the factors discussed in this study when implementing Blockchain technology to achieve marketing goals and generate sustainable Blockchain usage intentions

A Blockchain-Enabled IoT Logistics System for Efficient Tracking and Management of High-Price Shipments: A Resilient, Scalable and Sustainable Approach to Smart Cities

The concept of a smart city is aimed at enhancing the quality of life for urban residents, and logistic services are a crucial component of this effort. Despite this, the logistics industry has encountered issues due to the exponential growth of logistics volumes, as well as the complexity of processes and lack of transparency. Consequently, it is necessary to develop an efficient management system that offers traceability and condition monitoring capabilities to ensure the safe and high-quality delivery of goods. Moreover, it is crucial to guarantee the accuracy and dependability of distribution data. In this context, this paper proposes a blockchain-enabled IoT logistics system for the efficient tracking and management of high-price shipments. A smart contract based on blockchain technology has been designed for automatic approval and payment, with the aim of distributing shipping information exclusively among legitimate logistics parties. To ensure authentication, a zero-knowledge proof is used to conceal the blockchain address. Moreover, an intelligent parcel (iParcel) containing piezoresistive sensors is developed to pack delivered goods during the shipping process for violation detection such as severe falls or theft. The iParcels are automatically tracked and traced, and if a violation occurs, the contract is cancelled, and payment is refunded. The transaction fee per party is reasonable, particularly for high-price products that guarantee successful shipment

New Optimized Adaptive Time Series IDS Classifier Algorithm: Beyond Deep Learning

Adaptive time series Intrusion Detection System (IDS) Classifier is essential to detect real-time cyber-threats. Meanwhile, optimized hyperparameters on time series IDS classifier model will ensure swift detection. However, current studies on Time Series IDS classifier involve additional RNN-LSTM layers and multiple gates to optimize the training and feedback process. Notwithstanding, RNN-LSTM has powerful features to memorize data sequences. The nature of multiple complex hidden states in RNN-type model requires intensive training or epoch to achieve optimized loss function. This paper aims to go beyond conventional deep learning model by removing complex gated states and conventional hidden layers. The goal is to create an optimized adaptive time series classifier. The model leverages various fitting algorithms which include Sinusoidal, Linear, Power Function, Taylor Series and a new “Staircase” function that is introduced in this study. These functions adapt gradually to the real-time target distribution pattern. This will eliminate the need for feedback process to optimize hyperparameters. The model’s performance is evaluated against the realistic benchmarked IDS dataset; a dataset that simulates recent malware attacks and has imbalanced distribution property. This property reflects a realistic low cyber-attack footprint. After 10 epoch over randomized stratified testing samples, the Mean Absolute Error (MAE) rate achieved almost 0.0% after a fitting process reached 100% as compared with the conventional LSTM model that achieved 17%