Influencing Factors on Social Acceptance of Autonomous Vehicles and Policy Implications

2018 ◽  
Author(s):  
Jihye Lee ◽  
Hyungsik Chang ◽  
Young Il Park
Keyword(s):  
Influencing Factors ◽  
Autonomous Vehicles ◽  
Social Acceptance ◽  
Policy Implications

scholarly journals What drives you to use a driverless car?

2020 ◽  
Author(s):  
Sándor Huszár ◽  
Zoltán Majó-Petri
Keyword(s):  
Technology Acceptance ◽  
Influencing Factors ◽  
Behavioral Intention ◽  
Autonomous Vehicles ◽  
Structural Equation ◽  
Technological Development ◽  
Perceived Safety ◽  
Psychological Models ◽  
Acceptance Model ◽  
Self Driving Cars

The investigation of driverless car from the economic perspective is one of the most discussed topics nowadays. Although it can be approached from various perspectives there is still a lack of studies focusing on the behavioral intention to use self-driving cars and its influencing factors. Over the last few decades, various psychological models have been developed to investigate the influencing factors of usage of certain technologies, but most of them cannot provide clear answers on consumer attitudes and intentions with regard to autonomous vehicles. Thus, new models have appeared to better describe the psychological factors of this new technological development that will revolutionize the future of mobility. In our research CTAM (Car Technology Acceptance Model) was used to measure intention to using self-driving cars. In 2019, 314 participants responded to our questionnaire and provided answers to the given questions. We used structural equation modelling to investigate the linkages between the behavioral intention and influencing factors revealed during the literature review. According to the results, the most important influencing factors of intention are attitude, perceived safety and social norms, while anxiety (of using the technology), effort expectancy, performance expectancy, and self-efficacy have not been proven important factors. The model used in our investigation explains behavioral intention to a great extent (63%).

scholarly journals Assessment of Social Acceptance for Autonomous Vehicles in Southeastern Poland

Energies ◽  
2021 ◽  
Vol 14 (18) ◽  
pp. 5778
Author(s):  
Agnieszka Dudziak ◽  
Monika Stoma ◽  
Andrzej Kuranc ◽  
Jacek Caban
Keyword(s):  
Autonomous Vehicles ◽  
Social Acceptance ◽  
New Technologies ◽  
Consumer Preferences ◽  
Autonomous Vehicle ◽  
Developed Countries ◽  
Automotive Market ◽  
Advantages And Disadvantages ◽  
Autonomous Cars ◽  
The Impact

New technologies reaching out for meeting the needs of an aging population in developed countries have given rise to the development and gradual implementation of the concept of an autonomous vehicle (AV) and have even made it a necessity and an important business paradigm. However, in parallel, there is a discussion about consumer preferences and the willingness to pay for new car technologies and intelligent vehicle options. The main aim of the study was to analyze the impact of selected factors on the perception of the future of autonomous cars by respondents from the area of Southeastern Poland in terms of a comparison with traditional cars, with particular emphasis on the advantages and disadvantages of this concept. The research presented in this study was conducted in 2019 among a group of 579 respondents. Data analysis made it possible to identify potential advantages and disadvantages of the concept of introducing autonomous cars. A positive result of the survey is that 68% of respondents stated that AV will be gradually introduced to our market, which confirms the high acceptance of this technology by Poles. The obtained research results may be valuable information for governmental and local authorities, but also for car manufacturers and their future users. It is an important issue in the area of shaping the strategy of actions concerning further directions of development on the automotive market.

scholarly journals Analysis of Regional Differences and Influencing Factors on China’s Carbon Emission Efficiency in 2005–2015

Energies ◽  
2019 ◽  
Vol 12 (16) ◽  
pp. 3081 ◽  
Author(s):  
Zeng ◽  
Lu ◽  
Liu ◽  
Zhou ◽  
Hu
Keyword(s):  
Influencing Factors ◽  
Regional Differences ◽  
Carbon Emission ◽  
Industrial Structure ◽  
Tobit Model ◽  
Policy Implications ◽  
Dea Model ◽  
Emission Efficiency ◽  
Unit Root Testing ◽  
Technology Level

With the challenge to reach targets of carbon emission reduction at the regional level, it is necessary to analyze the regional differences and influencing factors on China’s carbon emission efficiency. Based on statistics from 2005 to 2015, carbon emission efficiency and the differences in 30 provinces of China were rated by the Modified Undesirable Epsilon-based measure (EBM) Data Envelopment Analysis (DEA) Model. Additionally, we further analyzed the influencing factors of carbon emission efficiency’s differences in the Tobit model. We found that the overall carbon emission efficiency was relatively low in China. The level of carbon emission efficiency is the highest in the East region, followed by the Central and West regions. As for the influencing factors, industrial structure, external development, and science and technology level had a significant positive relationship with carbon emission efficiency, whereas government intervention and energy intensity demonstrated a negative correlation with carbon emission efficiency. The contributions of this paper include two aspects. First, we used the Modified Undesirable EBM DEA Model, which is more accurate than traditional methods. Secondly, based on the data’s unit root testing and cointegration, the paper verified the influencing factors of carbon emission efficiency by the Tobit model, which avoids the spurious regression. Based on the results, we also provide several policy implications for policymakers to improve carbon emission efficiency in different regions.

scholarly journals Economic Efficiency and Its Influencing Factors on Urban Agglomeration—An Analysis Based on China’s Top 10 Urban Agglomerations

2019 ◽  
Vol 11 (19) ◽  
pp. 5380 ◽  
Author(s):  
Junwei Ma ◽  
Jianhua Wang ◽  
Philip Szmedra
Keyword(s):  
Total Factor Productivity ◽  
Influencing Factors ◽  
Economic Efficiency ◽  
Policy Implications ◽  
Factor Productivity ◽  
Urban Agglomerations ◽  
Capital Productivity ◽  
Industrial Specialization ◽  
New Findings ◽  
The Impact

Economic efficiency is the key issue of sustainable development in urban agglomerations. To date, more attention has been paid to the estimates of productivity gains from urban agglomerations. Differing from the previous studies, this paper focuses on the influencing factors and mechanisms of the economic efficiency of urban agglomerations, and check the effects of three different externalities (industrial specialization, industrial diversity and industrial competition) on the economic efficiency of urban agglomerations. The selected samples are multiple urban agglomerations, and the economic efficiency of urban agglomerations includes single factor productivity and total factor productivity. China’s top 10 urban agglomerations are selected as the case study and their differences in economic efficiency are portrayed comparatively. Firstly, a theoretical analysis framework for three different externalities effect mechanisms on the economic efficiency of urban agglomerations is incorporated. Secondly, economic efficiency measurement index system composes of labor productivity, capital productivity, land productivity and total factor productivity, and the impact of various factors on the economic efficiency of urban agglomerations is tested. The results confirm some phenomena (MAR externality, Jacobs externality and Porter externality) discussed or mentioned in the literature and some new findings regarding the urban agglomerations, derive policy implications for improving economic efficiency and enhancing the sustainability of urban agglomerations, and suggest some potentials for improving the limitations of the research.

scholarly journals Driving Factor Analysis and Forecasting of CO2 Emissions from Power Output in China Using Scenario Analysis and CSCWOA-ELM Method

Energies ◽  
2018 ◽  
Vol 11 (10) ◽  
pp. 2709 ◽  
Author(s):  
Weijun Wang ◽  
Weisong Peng ◽  
Jiaming Xu ◽  
Ran Zhang ◽  
Yaxuan Zhao
Keyword(s):  
Co2 Emissions ◽  
Influencing Factors ◽  
Power Output ◽  
Policy Implications ◽  
Emissions Reduction ◽  
Development Scenarios ◽  
Learning Machine ◽  
Set Up ◽  
Grey Relational ◽  
Average Contribution

With power consumption increasing in China, the CO2 emissions from electricity pose a serious threat to the environment. Therefore, it is of great significance to explore the influencing factors of power CO2 emissions, which is conducive to sustainable economic development. Taking the characteristics of power generation, transmission and consumption into consideration, the grey relational analysis method (GRA) is adopted to select 11 influencing factors, which are further converted into 5 main factors by hierarchical clustering analysis (HCA). According to the possible variation tendency of each factor, 48 development scenarios are set up from 2018–2025, and then an extreme learning machine optimized by whale algorithm based on chaotic sine cosine operator (CSCWOA-ELM) is established to predict the power CO2 emissions respectively. The results show that gross domestic product (GDP) has the greatest impact on the CO2 emissions from power output, of which the average contribution rate is 1.28%. Similarly, power structure and living consumption level also have an enormous influence, with average contribution rates over 0.6%. Eventually, the analysis made in this study can provide valuable policy implications for power CO2 emissions reduction, which can be regarded as a reference for China’s 14th Five-Year development plan in the future.

scholarly journals CORRUPTION PERCEPTIONS AND ENTREPRENEURIAL INTENTIONS IN A TRANSITIONAL CONTEXT–THE CASE OF RURAL BULGARIA

2017 ◽  
Vol 22 (03) ◽  
pp. 1750018
Author(s):  
DIANA TRAIKOVA ◽  
TATIANA S. MANOLOVA ◽  
JUDITH MÖLLERS ◽  
GERTRUD BUCHENRIEDER
Keyword(s):  
Social Norms ◽  
Perceived Control ◽  
Social Acceptance ◽  
Behavioral Control ◽  
Policy Implications ◽  
Entrepreneurial Intentions ◽  
Perceived Behavioral Control ◽  
Institutional Embeddedness ◽  
Entrepreneurial Attitudes ◽  
Farm Business

In this study, we augment Ajzen’s Theory of Planned Behavior (TPB) with an institutional embeddedness logic to develop and test a mediated model of the effects of perceived corruption on attitudes, social norms and perceived behavioral control, which in turn determine entrepreneurial intentions. We test our three hypotheses on a sample of 231 aspiring entrepreneurs seeking to start a non-farm business in three rural regions of Bulgaria. In our exploratory case study, we find that corruption perceptions are partially mediated by entrepreneurial attitudes and perceived control, but not by social norms. Corruption perceptions are positively associated with entrepreneurial intentions, indicative of the deeply rooted social acceptance of corruption in many transition economies. Theoretical, practitioner and public policy implications are discussed.

scholarly journals Cost Allocation in Integrated Community Energy Systems—Social Acceptance

2021 ◽  
Vol 13 (17) ◽  
pp. 9951
Author(s):  
Na Li ◽  
Rudi Hakvoort ◽  
Zofia Lukszo
Keyword(s):  
Decision Making ◽  
Local Community ◽  
Social Acceptance ◽  
Cost Allocation ◽  
Energy Systems ◽  
Energy System ◽  
Policy Implications ◽  
Fair Process ◽  
Allocation Method ◽  
The Cost

Integrated community energy systems (ICESs) are a good representative of local energy systems by integrating local distributed energy resources and local communities. It is proposed that costs should be allocated in a socially acceptable manner since there is no regulation in ICESs. In this paper, social acceptance is conceptualized from the dimension of community acceptance considering procedural and distributive justice. A fair process increases the understanding and the acceptance of the cost allocation outcomes, and a fair outcome leads to the acceptance of the cost allocation procedure. This approach adopted the multi-criteria decision-making technique to evaluate social acceptance to select a cost allocation method that was socially acceptable to local community members. The results show that our approach is unique and useful when multiple decision-making groups have to decide together upon the cost allocation method. It is able to provide quantitative results and optimal decisions from a multi-group decision-making perspective. The methodology developed in this research can be applied to any local community energy system to select a cost allocation method. Furthermore, the obtained results can be used by decision-makers to support them in the decision-making process. Based on our approach, policy implications are also analyzed to support the success of cost allocation in ICESs.

Sign in / Sign up

Export Citation Format

Share Document