Willingness Of Users Research Articles

Assessing the Quality and Behavior Change Potential of Vaping Cessation Apps: Systematic Search and Assessment.

An increasing number of people are using vapes (e-cigarettes), and with growing evidence of associated harms, there is a need for acceptable cessation support and interventions. Smartphone apps for health and well-being have increased in popularity and use. Limited published literature assesses the potential of apps to support vaping cessation. A systematic search of vaping cessation apps currently available in Australia for iOS and Android platforms was conducted. Apps were assessed against established health app assessment tools for quality and behavior change potential. A systematic search through the Australian Apple iTunes and Google Play stores was conducted using the search terms "vape"; "vaping"; "e-cigarette"; and "cessation," "quit," or "quitting" in May 2023. Only apps that encouraged the cessation of vaping were included. App descriptions were reviewed to determine if they were relevant for inclusion in this study, and relevant apps were downloaded onto the appropriate mobile device for review. The Mobile App Rating Scale (MARS) was used to rate the quality (engagement, functionality, aesthetics, and information) of the apps using an overall score out of 5. The App Behavior Change Scale (ABACUS) was used to assess the behavior change potential of each app using a score out of 21. An initial search of the app stores yielded 220 Android apps and 124 iOS apps. Screening against the inclusion criteria left 20 iOS apps and 10 Android apps for review. Six apps were available on both operating systems, and these were downloaded, reviewed, and reported separately for each operating system. The average MARS score for all apps assessed in this review was 3.1 (SD 0.41) out of 5. The reviewed apps overall performed well for the MARS elements relating to functionality, such as ease of use and navigation, but had the lowest scores for information-related elements, such as credibility. The number of ABACUS behavior change features per app ranged from 0 to 19 out of 21, with a mean of 8.9 (SD 4.51). The apps commonly included information-related features, such as requesting baseline information. The least common behavior change features were those relating to goal-setting, such as asking about the user's willingness for behavior change and providing feedback on current actions in comparison to future goals. The identified vaping cessation apps had moderate levels of quality and some behavior change components. Future vaping cessation apps could benefit from including more features that are known to support behavior change, such as goal-setting, to improve the potential benefit of these apps to support people to stop vaping. As guidelines for vaping cessation continue to be established, future apps need to reference these in their development.

A snapshot on a journey from frustration to readiness-A qualitative pre-implementation exploration of readiness for technology adoption in Public Health Protection in Ireland.

In an era of emergent infectious disease, the timely and efficient management of disease outbreaks is critical to public health protection. Integrated technologies for case and incident management (CIM) collect real-time health intelligence for decision making in Public Health. In Ireland, a Public Health reform program is preparing for implementation of a health information system for health protection. Project implementers seek to document and understand the readiness and willingness of future users to adopt the new system, prior to system procurement and implementation. Qualitative key informant interviews were conducted (n = 8) with Public Health personnel from a single regional department of Public Health representing medical, nursing, disease surveillance and administrative roles, at managerial and staff levels. A qualitative thematic analysis was performed. Participants were frustrated by weaknesses in the current practice of CIM and were ready and willing to adopt a digital CIM system if it met their needs. However, they were frustrated by lack of clear timelines. We identified 7 enablers and 3 barriers to readiness and willingness to adopt a CIM system. 'Newness of the workforce' was the main enabler of readiness and willingness, while 'lack of knowledge and familiarity with system' was the main barrier to readiness and willingness. Experiences during the COVID-19 pandemic gave a clear understanding of the problems and need for a digital CIM system and the reform program facilitated a culture of change, readying the workforce for the new health information system. New members of the Public Health departments are a likely ready and eager cohort for adoption of a modern, 'fit for purpose' CIM system and the execution of implementation will likely determine how ready and willing the wider network of departments will be to adopt a national CIMS.

Pseudo-eating behavior of service robot to improve the trustworthiness of product recommendations

Service robots are used in a range of service situations, including those where they are required to recommend products. However, when robots use subjective statements for product recommendations, users may not place sufficient trust in the robot's recommendations which may degrade its recommendation capability. This study proposes a method to improve the perceived trustworthiness of the robot's subjective statements and thus promote the user's food purchase behavior. The proposed recommendation method involves explicitly sharing the robot's experience with the user and improving the perceived trustworthiness of the robot's subjective statements by performing a pseudo-eating behavior. The results of two online surveys show that the robot's statements and recommendations accompanied by the pseudo-eating behavior improved the trust in the robot's subjective statement and consequently, the user's willingness to purchase products. Further, the results of a field experiment in a real bakery store showed that the proposed method is comparable to recommendation methods proposed in previous works. The results obtained through the online surveys and the field experiment indicate that robots are trusted even when they express subjective statements for recommendations by explicitly sharing the experience with the user.

42 Challenges for agricultural social media influencers engagement with young, non-ag adults

Abstract Segments of society question the welfare of animals in production, whether types of product consumption have a negative impact on personal health, and environmental sustainability of producing livestock. As most Americans are three or more generations removed from production agriculture (American Farm Bureau, 2020), they have minimal incentive to question unsubstantiated information purported by anti-animal agriculture groups. Ag communication strategies often seem to fall short compared with anti-agricultural messages shared throughout the media infosphere. Prominent forms of digital / social media (SM) platforms have infiltrated daily lives of society. With the significance of engagement factors such as likes, comments, shares, and saves on such platforms in mind, our research sought to assess the willingness of SM users to interact with content on Instagram created by agricultural social media influencers (SMIs). Participants (n = 855; ages 18-30 from the U.S. and a non-ag background) were recruited through Prolific and anonymously completed a Qualtrics survey. Quantitative data were responses to questions about willingness to engage with both general content and agriculture-related content. Participants were shown images and narrative content from well-established agricultural SMIs. Participants were surveyed both before and after being shown example image posts of agricultural SMIs. Data were analyzed utilizing SPSS with independent variables consisting of general content versus agricultural content while dependent variables were comprised of the summation of participant engagement type willingness which was operationalized by numerically converting the five-point Likert scale. Results showed differences (P < 0.05) in willingness to engage with agricultural SMI through likes, comments, shares, and saves than with general content. Results imply that agricultural SMIs will have more limited opportunities to facilitate engagement with young adult Instagram users than those that share generalized content. These outcomes reinforce a communication dilemma around SM engagement that focuses on reaching non-ag audiences with image and evidence-based agricultural content.

Edge computing‐based optimal dispatching of charging loads considering dynamic hosting capacity

AbstractOwing to the rapid increase in electric vehicle integration and the uncoordinated charging behaviour of electric vehicles, the overloading risk of distribution transformers has deteriorated. This impact caused by large‐scale electric vehicle integration can be effectively reduced through the orderly guidance of electric vehicle charging behaviours. Here, a dispatching strategy for charging loads is proposed to address the problems of the uncoordinated charging demand in electric vehicles and overloading risk of distribution transformers in residential areas. First, an edge‐side dynamic index of the electric vehicle hosting capacity is proposed to guide the optimal dispatching of charging loads. Subsequently, an optimal dispatching model of the charging loads is established based on edge computing. The edge‐side dispatching strategy for the charging loads is then further updated considering the participation willingness of electric vehicle users. Finally, the effectiveness of the proposed control strategy is validated using a modified residential distribution network in Tianjin. The results show that the proposed strategy can effectively decrease the overloading risk of distribution transformers while realizing the efficient operation of electric vehicles on the edge side.

Comparison of recognition methods for an asynchronous (un-cued) BCI system: an investigation with 40-class SSVEP dataset.

Steady-state visual evoked potential (SSVEP)-based brain-computer Interface (BCI) has demonstrated the potential to manage multi-command targets to achieve high-speed communication. Recent studies on multi-class SSVEP-based BCI have focused on synchronous systems, which rely on predefined time and task indicators; thus, these systems that use passive approaches may be less suitable for practical applications. Asynchronous systems recognize the user's intention (whether or not the user is willing to use systems) from brain activity; then, after recognizing the user's willingness, they begin to operate by switching swiftly for real-time control. Consequently, various methodologies have been proposed to capture the user's intention. However, in-depth investigation of recognition methods in asynchronous BCI system is lacking. Thus, in this work, three recognition methods (power spectral density analysis, canonical correlation analysis (CCA), and support vector machine (SVM)) used widely in asynchronous SSVEP BCI systems were explored to compare their performance. Further, we categorized asynchronous systems into two approaches (1-stage and 2-stage) based upon the recognition process's design, and compared their performance. To do so, a 40-class SSVEP dataset collected from 40 subjects was introduced. Finally, we found that the CCA-based method in the 2-stage approach demonstrated statistically significantly higher performance with a sensitivity of 97.62±02.06%, specificity of 76.50±23.50%, and accuracy of 75.59±10.09%. Thus, it is expected that the 2-stage approach together with CCA-based recognition and FB-CCA classification have good potential to be implemented in practical asynchronous SSVEP BCI systems.

Opinion attribution improves motivation to exchange subjective opinions with humanoid robots.

In recent years, the development of robots that can engage in non-task-oriented dialogue with people, such as chat, has received increasing attention. This study aims to clarify the factors that improve the user's willingness to talk with robots in non-task oriented dialogues (e.g., chat). A previous study reported that exchanging subjective opinions makes such dialogue enjoyable and enthusiastic. In some cases, however, the robot's subjective opinions are not realistic, i.e., the user believes the robot does not have opinions, thus we cannot attribute the opinion to the robot. For example, if a robot says that alcohol tastes good, it may be difficult to imagine the robot having such an opinion. In this case, the user's motivation to exchange opinions may decrease. In this study, we hypothesize that regardless of the type of robot, opinion attribution affects the user's motivation to exchange opinions with humanoid robots. We examined the effect by preparing various opinions of two kinds of humanoid robots. The experimental result suggests that not only the users' interest in the topic but also the attribution of the subjective opinions to them influence their motivation to exchange opinions. Another analysis revealed that the android significantly increased the motivation when they are interested in the topic and do not attribute opinions, while the small robot significantly increased it when not interested and attributed opinions. In situations where there are opinions that cannot be attributed to humanoid robots, the result that androids are more motivating when users have the interests even if opinions are not attributed can indicate the usefulness of androids.

Key factors capturing the willingness to use automated vehicles for travel in China.

With the continuous advancement of technology, automated vehicle technology is progressively maturing. It is crucial to comprehend the factors influencing individuals' intention to utilize automated vehicles. This study examined user willingness to adopt automated vehicles. By incorporating age and educational background as random parameters, an ordered Probit model with random parameters was constructed to analyze the influential factors affecting respondents' adoption of automated vehicles. We devised and conducted an online questionnaire survey, yielding 2105 valid questionnaires. The findings reveal significant positive correlations between positive social trust, perceived ease of use, perceived usefulness, low levels of perceived risk, and the acceptance of automated vehicles. Additionally, our study identifies extraversion and openness as strong mediators in shaping individuals' intentions to use automated vehicles. Furthermore, prior experience with assisted driving negatively impacts people's inclination toward embracing automated vehicles. Our research also provides insights for promoting the adoption of automated vehicles: favorable media coverage and a reasonable division of responsibilities can enhance individuals' intentions to adopt this technology.

Optimal Operation Strategy of Electric Vehicle Cluster in the Electricity Spot Market Considering Scheduling Capability

The widespread use of electric vehicles (EV) has put a strain on the stable operation of power grid. Therefore, the potential of EV cluster power load regulation has been paid attention. In the cluster, the electric vehicle aggregator (EVA) can gather a large number of EVs and participate in the electricity spot market by optimizing the charging/discharging power. In this study, a bi-objective optimization model for V2G enabled EV cluster operation is proposed to determine the optimal load of EV cluster considering the electricity spot market. First, the scheduling capability of EVs is modelled and aggregated considering the EV user willingness. Then, the demand response and electricity spot trade for EVA are analyzed. Based on the capability constraints and the market rules, an optimization model is established with two objectives of maximizing EVA profits and EV user satisfaction. Finally, a case study in Beijing, China is implemented to prove the feasibility of the proposed model. The results show that the EV user willingness for orderly charging/discharging is distributed in the range of 0.26 and 0.94 with an average value of 0.85. In addition, the proposed EV cluster operation strategy can improve the EVA daily profits by 81.27% and increase the EV user satisfaction by 70% compared with normal charging strategy.

UTAUT2 및 TRI 이론을 기반으로 중국 노인 이용자 디지털 미술관 사용 행동에 연구

The purpose of this study is to explore the factors that affect the willingness and behavior of elderly Chinese users to use digital art museums and related new technologies, by combining the UTAUT2 model and the TRI model, in order to provide theoretical basis and practical guidance for the future development and optimization of digital art museums. The research results demonstrate that higher performance expectations, effort expectations, hedonic motivation, price value, and social influence can positively enhance user willingness to use, ultimately affecting usage behavior. And the facilitating factors can directly and positively affect the user's willingness and behavior to use. On the contrary, higher levels of discomfort and lack of security can have a negative impact on the user's willingness to use, affecting their usage behavior.

Optimal lane management policy for connected automated vehicles in mixed traffic flow

The management of lanes for mixed traffic flow consisting of connected automated vehicles (CAVs) and human-driven vehicles (HDVs) can effectively improve the operational efficiency of highways. This paper proposes an optimal lane management policy for connected automated vehicles in mixed traffic flow on highways. Firstly, the functioning characteristics of the mixed traffic flow are analyzed in three aspects, including car-following mode, headway, and platoon size. Secondly, the vehicle probability distributions under different lane management strategies are derived based on the Markov chain and conditional probability. Then, a calculation model for the capacity of mixed traffic flow was constructed by considering the features of lane management strategies, and sensitivity analyses were performed on the model parameters. Finally, a joint optimization model of CAVs dedicated lanes is proposed with the objective of maximum throughput based on constraints such as lane operation strategies, vehicle type ratios, and road resources. The numerical experiments lead to the following conclusions. (1) For two-lane highways, when the traffic demand is less than 6000 veh/h, the mixed driving lane management strategy should be applied; and when the traffic demand reaches 8000 veh/h, and the CAVs penetration rate is more than 0.56, one CAVs dedicated lane is required. Also, the higher the traffic demand, the lower the critical penetration rate of CAVs. (2) In the three-lane highway scenario, no dedicated lane is required when traffic demand is less than 8000 veh/h; one CAVs dedicated lane is needed when demand exceeds 8000 veh/h with CAVs penetration rate greater than 0.38; and two CAVs dedicated lanes are necessary when demand increases to 12800 veh/h and CAVs penetration rate is more than 0.79. (3) As for four-lane highways, the minimum traffic demand for installing one dedicated lane is 15600 veh/h, and the critical penetration rates for one, two, and three dedicated lanes are 0.32, 0.71, and 0.96, respectively. (4) The increase of CAVs dedicated lane usage willingness and platoon size promote the throughput, and the former CAVs dedicated lane usage willingness works more obviously. These research conclusions can provide theoretical and applied practical support for CAVs dedicated lanes management in the future.

A Comprehensive Picture of Factors Affecting User Willingness to Use Mobile Health Applications

Mobile health (mHealth) applications have become increasingly valuable in preventive healthcare and in reducing the burden on healthcare organizations. The aim of this article is to investigate the factors that influence user acceptance of mHealth apps and identify the underlying structure that shapes users’ behavioral intention. An online study that employed factorial survey design with vignettes was conducted, and a total of 1,669 participants from eight countries across four continents were included in the study. Structural equation modeling was employed to quantitatively assess how various factors collectively contribute to users’ willingness to use mHealth apps. The results indicate that users’ digital literacy has the strongest impact on their willingness to use them, followed by their online habit of sharing personal information. Users’ concerns about personal privacy only had a weak impact. Furthermore, users’ demographic background, such as their country of residence, age, ethnicity, and education, has a significant moderating effect. Our findings have implications for app designers, healthcare practitioners, and policymakers. Efforts are needed to regulate data collection and sharing and promote digital literacy among the general population to facilitate the widespread adoption of mHealth apps.

Willingness to Keep or to Give Up the Social Media for a Certain Monetary Compensation

Millions of people around the world use social media every day for various reasons, whether it is sharing information, self-presentation, filling free time, or communicating with friends. The use of social networks brings with it advantages, but also risks associated with information overload, loss of privacy, as well as fraud or criminal acts. Mostly young people are connected to social networks almost constantly and find it more difficult to give up using social networks compared to older age groups. The aim of the present paper is to find out the willingness of social network users to give up social network such as Facebook and Instagram for certain monetary compensation. The necessary data were collected based on a questionnaire survey within 4 months. A total of 2 095 responses were collected. The static methods Chi-Square test of independence, logistic regression and the statistical program SAS Enterprise Guide were used to evaluate the results of the questionnaire survey. Analysis of Maximum Likelihood Estimates was tested for five predictors: Age, Gender, Income, Activity on the social network, Number of subscribers on the social network, especially for the social network Facebook and Instagram. Based on the results of the questionnaire survey, it can be concluded that Facebook users (in all age groups and regardless of gender) demand a higher average monthly financial compensation for giving up the social network compared to Instagram users. The same conclusion follows when categorizing respondents into age categories. Regarding gender, women demand lower monetary compensation than men in relation to both social networks. There was a significant difference in the Activity predictor, where active users asked for a higher amount of money for the Instagram social network and, conversely, inactive users asked for a higher average monthly payment for the Facebook social network.
 
 Received: 25 July 2023 / Accepted: 20 November 2023 / Published: 5 January 2024

Research on the double-layer clustering method of residential energy use characteristics under the background of energy system energy savings and carbon reduction

Accurate differentiation of energy consumption information of residential users is of great significance for load planning, scheduling, operation and management of power system, and is the basic premise for realizing intelligent perception of energy system and energy saving and carbon reduction. Considering that the conventional single-layer clustering method has limited clustering stability and clustering effect, this paper takes the key family feature factors as the modified feature quantity of quadratic clustering, and proposes a study of user energy characteristics based on double-layer clustering and modification. Firstly, the user’s energy consumption data is collected and pre-processed, and the user’s energy consumption curve is clustered and analyzed by using the integrated clustering algorithm based on voting and the advantages of each member algorithm. Then, the key family characteristic factors are obtained, and the results of one-layer clustering and key family characteristic factors are combined to carry out two-layer clustering of the same category of users in the form of questionnaire survey. Finally, the nonlinear mapping capability of Support Vector Machine (SVM) is used to reverse correct the results of the one-layer clustering. The actual algorithm data of the residents’ demand response experiment in a southeastern province are compared. The results show that compared with the single-layer clustering algorithm, the proposed method can accurately distinguish the energy consumption characteristics and adjustable potential of different users, and correct the wrong clustering results in the single-layer clustering. The clustering stability and clustering effect have been effectively improved.The example results show that the clustering results modified by SVM can better mine and distinguish user energy characteristics, and can be used to solve the problem of the current demand response clustering algorithm not being able to comprehensively and objectively describe the participation willingness and response-ability of residential users in the implementation process. It can also provide a basis for peak shaving and power grid frequency regulation.

A One-Way Car-Sharing Based Approach for Combined Shared Mobility of Freight and Passengers

Climate change stresses the need for research and development of innovative sustainable mobility solutions that provide reliable and convenient door-to-door services for both passengers and freight. The increase in urban population and the popularity of e-commerce further highlights the need for action. In this regard, crowd-shipping is often perceived as an efficient, cost-effective, and sustainable alternative (or complement) to the management of urban freight mobility through efficient utilization of current transportation capacities. In this framework, inspired by the concept of MaaS (Mobility as a Service) in integrating various forms of transport and transport-related services into a single on-demand mobility service, this paper proposes a car-sharing-based service for the combined mobility of passengers and freight. In doing so, one-way car-sharing and crowd-shipping concepts are integrated in order to serve part of the existing freight demand in a sustainable and cost-efficient way for users, societies, and the environment. An optimization model is proposed to optimally plan the activation of one-way car-sharing and crowd-shipping services and to determine the optimal number of vehicles to assign to them. Such decisions are aimed at minimizing the total imbalance by serving passenger and freight demand during different time periods. In doing so, the willingness of users to carry freight in their vehicles is also taken into consideration. The capability of the proposed approach is evaluated through representative numerical examples aimed at showing the impact of the model parameters on the solution.

Continuance intention and digital health resources from the perspective of elaboration likelihood model and DART model: a structural equation modeling analysis.

Internet hospitals, online health communities, and other digital health APPs have brought many changes to people's lives. However, digital health resources are experiencing low continuance intention due to many factors, including information security, service quality, and personal characteristics of users. We used cross-sectional surveys and structural equation modeling analysis to explore factors influencing user willingness to continue using digital health resources. Information quality (β = 0.31, p < 0.05), service quality (β = 0.19, p < 0.05), platform reputation (β = 0.34, p < 0.05), and emotional support (β = 0.23, p < 0.05) have significant positive effects on user value co-creation behavior. Additionally, user trust and perceived usefulness could mediate the association between user value co-creation behavior and continuance intention, with mediation effects of 0.143 and 0.125, respectively. User involvement can positively moderate the association between user value co-creation behavior and user trust (β = 0.151, t = 2.480, p < 0.001). Also, user involvement can positively moderate the association between value co-creation behavior and perceived usefulness (β = 0.103, t = 3.377, p < 0.001). The keys to solving the problem of low continuance intention are improving the quality and service level of digital health resources, and promoting users' value co-creation behavior. Meanwhile, enterprises should build a good reputation, create a positive communication atmosphere in the community, and enhance user participation and sense of belonging.

Research on the continuous use intention of intelligent express cabinets

In the background of China’s annual courier business volume surpassing 100 billion pieces, the issues within delivery service are becoming increasingly pronounced. One particular concern is that the growing occurrence of couriers depositing packages into courier cabinets without obtaining consent of users, leading to the dissatisfaction among users and a reluctance to utilize these cabinets. This paper is based on the technology acceptance model and the introduction of perceived cost and security variables, to illustrate the influence of intelligent express locker user willingness to continue to use factors. The study showed that the perceived cost had an impact on the user’s willingness to continue using express locker under the intermediary effect of satisfaction. Additionally, the study also found that security had a positive influence on user satisfaction. The intermediary effect of satisfaction acts as a masking factor between security and Continuance Intention.It also points to new directions for subsequent research.

Acceptance of IoT by Students in Universities of Sindh, Pakistan: A Proposed Framework

The Internet of Things (IoT) encompasses a convergence of physical and virtual technologies, connecting devices through networks to enhance the overall quality of life. These technologies offer diverse interactions, featuring unique user interfaces and functionalities. Beyond its technological prowess, IoT presents contemporary methodologies and tools, catering to both modern and traditional learning approaches. While IoT components have received considerable attention, there exists a notable gap in exploring factors influencing students' receptiveness to these services. This research centers on the Unified Theory of Acceptance and Use of Technology (UTAUT 2), a widely acknowledged framework for assessing user willingness to adopt new technologies. The primary objective of our pilot study is to establish a conceptual framework based on UTAUT 2 and assess its reliability and validity. This framework sets the stage for future investigations into IoT acceptance among higher education students in Pakistan. To achieve this, an online pilot research questionnaire was distributed via Google Forms to students across various institutions, including the University of Karachi, University of Sindh Jamshoro, University of Sufism and Modern Sciences Bhitshah, Government College University Hyderabad, and SALU Khairpur. Fifty participants completed the questionnaire, and the ensuing data attests to the questionnaire's high reliability (ranging from .795 to .961), affirming its suitability for broader surveys.

메타버스 박물관의 이용자 사용 의도에 영향을 미치는 요인 연구

The study explores factors affecting user acceptance of the Dunhuang Metaverse Museum using the UTAUT model. It aims to provide a theoretical basis for the museum's future development. The study introduces five variables: perceived immersion, perceived cost, perceived risk, perceived trust, and individual innovation. Results show that meeting user performance expectations and lower costs increase willingness to use the Metaverse museum. High expectations of effort and perceived trust enhance interest, while social influence, immersion, and innovation boost curiosity and willingness. Lower perceived risks also increase user willingness.

Utilizing User Bandwidth Resources in Information-Centric Networking through Blockchain-Based Incentive Mechanism

Idle bandwidth resources are inefficiently distributed among different users. Currently, the utilization of user bandwidth resources mostly relies on traditional IP networks, implementing relevant techniques at the application layer, which creates scalability issues and brings additional system overheads. Information-Centric Networking (ICN), based on the idea of separating identifiers and locators, offers the potential to aggregate idle bandwidth resources from a network layer perspective. This paper proposes a method for utilizing user bandwidth resources in ICN; specifically, we treat the use of user bandwidth resources as a service and assign service IDs (identifiers), and when network congestion (the network nodes are overloaded) occurs, the traffic can be routed to the user side for forwarding through the ID/NA (Network Address) cooperative routing mechanism of ICN, thereby improving the scalability of ICN transmission and the utilization of underlying network resources. To enhance the willingness of users to contribute idle bandwidth resources, we establish a secure and trustworthy bandwidth trading market using blockchain technology. We also design an incentive mechanism based on the Proof-of-Network-Contribution (PoNC) consensus algorithm; users can “mine” by forwarding packets. The experimental results show that utilizing idle bandwidth can significantly improve the scalability of ICN transmission under experimental conditions, bringing a maximum throughput improvement of 19.4% and reducing the packet loss rate. Compared with existing methods, using ICN technology to aggregate idle bandwidth for network transmission will have a more stable and lower latency, and it brings a maximum utilization improvement of 13.7%.