Technological and Economic Development of Economy

Supporting the circular economy transition using the emergent role of the Internet of Things

Abbas Mardani, Charbel Jose Chiappetta Jabbour, Mario Köppen — Tue, 30 Apr 2024 00:00:00 +0300

Supporting the circular economy transition using the emergent role of the Internet of Things. Technological and Economic Development of Economy, 30(2), pp. 338-343.

Enabling technologies challenges of green Internet of Things (IoT) towards sustainable development in the era of Industry 4.0

Lei Liu, Arunodaya Raj Mishra — Tue, 30 Apr 2024 00:00:00 +0300

The extensive adoption of the Internet of Things (IoT) has increased the carbon footprint on a large scale across the globe. To handle this challenge, scholars and policymakers are making efforts to propose novel energy-efficient solutions to provide a desirable environment for green-IoT (G-IoT). Additionally, further research is required to analyze the G-IoT-related challenges to elucidate the difficulties of its implementation for researchers. Moreover, the GIoT requirements have been considered in different network levels, namely software, hardware, architecture, communication. To present a comprehensive framework to identify the challenges of G-IoT, a survey using literature review and expert’s opinion is carried. Total 23 challenges are taken to evaluate and implement G-IoT technologies towards sustainable development achievements (SDA). Consequently, this article aims to rank and evaluate the challenges to implement the G-IoT towards the SDA. An integrated approach is proposed with stepwise weight assessment ratio analysis (SWARA) and additive ratio assessment (ARAS) under Pythagorean fuzzy sets. As a result, an machine-to-machine (M2M) standardization protocol with a weight value of 0.0508 has the first rank, followed by adaptation to natural energy sources with a weight value of 0.0479, information security and privacy protection with a weight value of 0.0469, and internet protocol version-6 (IPv6) for low-end devices with weight 0.0467. To validate the proposed method, sensitivity analysis and comparison using existing methods have been conducted.

First published online 30 March 2022

Investigating the Internet-of-Things (IoT) risks for supply chain management using q-rung orthopair fuzzy-SWARA-ARAS framework

Yalan Hu, Abdullah Al-Barakati, Pratibha Rani — Tue, 30 Apr 2024 00:00:00 +0300

Modern “Supply Chains (SCs)” have recently been introduced as value networks of high complexity, and firms have focused on its efficiency as an important support for staying competitive in the market. Firms are currently capable of observing, tracking, and monitoring their products, activities, and processes throughout their value chain networks using new technologies, namely the “Internet of Things (IoT)”. Though, the influencing factors of IoT are highly complex and diverse, which result in the information-intensiveness of the SCs processes. This, in turn, leads to lots of barriers to SCs. In this paper, we evaluate and rank the IoT risks for “Supply Chain Management (SCM)” by utilizing “Stepwise Weight Assessment Ratio Analysis (SWARA)” and “Additive Ratio Assessment (ARAS)” under “q-Rung Orthopair Fuzzy Sets (q-ROFSs)”. A case study is presented for investigating the IoT risks for SCM in the q-ROFSs setting. Moreover, the obtained results were compared to those of some methods currently used in the literature. The outcomes of the study show that the security and privacy risks with a weight value of 0.0572 is the main IoT risk factor for the SCM and the organization-I with the utility degree 0.8208 is the best option with diverse IoT risks for SCM.

First published online 25 April 2022

The shadow banking behaviour in internet of things: evidence from economy operation mode in China

Ling Yang, Chung-Hua Shen — Tue, 30 Apr 2024 00:00:00 +0300

With the acceleration of world economic integration and enterprise management globalization, the advent of Internet of things based on Internet and information technology has become inevitable. The Internet of things also brings about a cascading effect between firms’ shadow banking behaviour and bank connections. This study investigates the relationship between firms’ shadow banking behaviour and bank connections by analysing a sample of Chinese listed firms in Internet of things industry. The results show that bank connections eliminate information asymmetry between banks and firms, bank connections are positively related to firms’ long-term debt, and as long-term debt increases, firms’ shadow banking behaviour also increases. Furthermore, this finding shows very strong robustness, the empirical analysis provides sufficient evidence that firms’ shadow banking behaviour increased with bank connections in Internet of things industry. In addition, the evidence also shows that the tendency of shadow banking behaviour is more pronounced in non-state-owned enterprises (NSOEs) than state-owned enterprises (SOEs) by sub-sample sensitivity analysis.

First published online 17 March 2022

Internet of things-enabled tourism economic data analysis and supply chain modeling

Shuyong Wang, Zhuo Fang, Dongshuo Wu — Tue, 30 Apr 2024 00:00:00 +0300

The purpose is to cut the costs of Supply Chain enterprises in Ice-Snow Tourism (IST) and improve the intelligence and automation of Supply Chain Management (SCM). First, the spatial-temporal characteristics of economic data of the IST Supply Chain are analyzed based on the Internet of Things (IoT). Second, the annual Online Public Attention (OPA) data to IST in domestic cities and regions are collected. The quarterly concentration index and Gini coefficient are used to analyze their spatial and temporal characteristics. Then, the weighted fusion algorithm used for the Supply Chain scenario modeling is improved to solve data redundancy and improve information accuracy. Finally, the framework of the IST-oriented Supply Chain scenario ontology model is proposed. The experimental results show that Internet users give much attention to IST from 2011 to 2021. OPA to IST increased first and decreased and peaked in 2016. The final fusion value of the proposed data fusion algorithm is 20.0221, and that of the adaptive Weighted Average Method (WAM) is 20.0724. Thus, the proposed algorithm outperforms the adaptive WAM. The traditional scenario-based ontology model takes people as the center. In contrast, the Supply Chain scenario-based ontology model centers around product state and scenario. Therefore, the proposed Supply Chain scenario-based ontology model is entirely new. The proposed scenariobased ontology model using polymorphic IoT lays the foundation for developing an intelligent and automatic SCM. It has great practical significance in realizing efficient tourism industry management and SCM.

First published online 10 August 2022

Exploring factors influencing the digital economy: uncovering the relationship structure to improve sustainability in China

Tue, 30 Apr 2024 13:23:07 +0300

Digital economy is a great route to promote the efficient utilization of natural resources and promote sustainability due to its high-tech, rapid growth, extensive penetration, deep integration and other characteristics. Existing study on the influencing factors of the digital economy is not deep enough and lacks the analysis on the relationship structure of factors influencing the digital economy, which is not conducive for an overall grasp of the digital economy. To correctly understand how to better develop the digital economy, this paper studies its influencing factors and the relationships between them. Based on the time-series data of China from 2002 to 2018, grey correlation analysis was applied to calculate the correlation between these influencing factors and the digital economy, and determine the major influencing factors of digital economy development in China. The Granger causality test and a review of existing research were used to judge the interrelationship of various factors. The interpretative structure model was utilized to determine the relationship structure of the main factors affecting the development of China’s digital economy. The results show that the number of digital talents, state of the technology market, and degree of digitalization are direct influencing factors of the digital economy. The results help to better understand the development of the digital economy and will enable the implementation of policies to improve towards more sustainable cities.