Search

The primary objective of the paper is to predict the price of the "Toyota Cars" based on the requirement of the customer. The primary need is to identify the minimum price of the "Toyota Cars" based on the 37 key attributes/factors. The levels of each factor in the data set varies from 2 to 312, moreover the data sets used for the analysis, contain both feature as well as numerical based data. The data sets are taken from the "KAGGLE" an open-source real time data provider. The total number of data available for the analysis is 1442, since the data are large and the factors are more with different levels, the prediction done using the Machine Learning (ML) approach. Ridge regression, a supervised machine learning algorithm is used to predict the sales price of the "Toyota Cars", thereby identifying the minimum price based on different combinations of attributes. The prime novelty lies on the usage of the Ridge regression, a linear regression algorithm for the prediction process. The ridge regression model was developed by means of the Python programming and Google Colab, an open-source online compiler for the ML was used for the code compilation process. Based on the result, the minimum price was found to be 6520.8 dollars, with the basic customer requirements such as Model: Corolla 1.6-16v, Type: Sedan, 4 Doors, CNG, 69hp, 1854 cc, with air conditioner, centre lock and power steering. [ABSTRACT FROM AUTHOR]

Purpose: Drawing on social information processing theory, this study constructs a multilevel moderated mediation model. This model seeks to delve into the intricate and previously overlooked interplay between supervisor bottom-line mentality (BLM) and knowledge hiding. Within this context, we introduce self-interest as a mediating factor and incorporate performance climate as a team-level moderating variable. Design/methodology/approach: The time-lagged data involve 336 employees nested in 42 teams from 23 automobile sales companies in five regions of China. The analysis was meticulously executed using Hierarchical Linear Modeling, complemented by bias-corrected bootstrapping techniques. Findings: The findings reveal that self-interest acts as a full mediator in the positive link between supervisor BLM and knowledge hiding. Furthermore, the performance climate plays a moderating role in both the relationship between supervisor BLM and self-interest, and the entire mediation process. Notably, these relationships are intensified in environments with a high performance climate compared to those with a low one. Originality/value: This research stands as one of the pioneering efforts to integrate supervisor BLM into the discourse on knowledge hiding, elucidating the underlying psychological mechanisms and delineating the boundary conditions that shape the "supervisor BLM–knowledge hiding" relationship. Further, our insights provide organizations with critical guidance on strategies to curtail knowledge hiding among their employees. [ABSTRACT FROM AUTHOR]

AbstractThe policy-driven development of China’s new energy vehicle (NEV) has been accompanied by significant fluctuations in the industrial chain due to the substantial fiscal subsidy removal. The study aims to examine the impact that three typical fiscal subsidy removal events enacted by China’s government have on various segments of the NEV industrial chain. The three events were enacted in 2016 (a 20% reduction and a limit for local subsidies), 2018 (raising the threshold, no subsidies for range below 150 km, and a 4-month transitional period), and 2019 (a 50% reduction, full cancelation of local subsidies, and a 3-month transitional period). This is done by applying the event study method and a difference-in-difference framework to finance data from capital market and sales data traffic management bureau. Empirical evidence suggests that, on the supply side, the three removal policies have caused various negative influences on companies involved in the industrial chain, with the midstream (e.g. battery, motor, and electric control) being the component of the chain affected the most by the policy removal. On the demand side, the events in 2016 and 2019 have decreased the vehicle sales in NEV pilot cities nationwide. Rather than discouraging consumer purchases, however, the event in 2018 has counter-intuitively boosted vehicle sales due to proper establishment of the transitional period (in comparison to 2019) of the policy. Further, NEV sales in cities with small population are found to be more dependent on government fiscal subsidies. We offer management suggestions and make policy recommendations for subsidy removal. [ABSTRACT FROM AUTHOR]

The Detroit Big Three General Motors (GMs), Ford, and Stellantis predict that electric vehicle (EV) sales will comprise 40–50% of the annual vehicle sales by 2030. Among the key components of LIBs, the LiNixMnyCo1−x−yO2 cathode, which comprises nickel, manganese, and cobalt (NMC) in various stoichiometric ratios, is widely used in EV batteries. This review reveals NMC cathodes from laboratory research. Furthermore, this study examines the environmental effect of NMC cathode production for EV batteries (including coating technologies), encompassing aspects such as energy consumption, water usage, and air emissions. Although gaps persist in NMC cathode environmental assessments (NMC111, NMC532, NMC622, and NMC811), limited life cycle assessments "(LCA)" have been conducted. Most available data originate from Asia (primarily China), accounting for 85% of the production of EV LIB cathode materials. The concept of battery passports for data collection on LIB components has been proposed to facilitate material traceability as a system for ensuring a sustainable supply chain for critical minerals. The automotive industry's shift to electrification necessitates a sustainable supply chain from mine to vehicle end-of-life. As the critical mineral supply moves from Asia to North America, environmentally friendly industrial methods must be studied to provide this supply chain direction. [ABSTRACT FROM AUTHOR]

Purpose: The purpose of the paper is to identify the factors shaping the sales volume of passenger electric cars in selected European countries that differ in the level of development and advancement of the electric car market. Design/methodology/approach: The empirical analysis covers annual EV sales data from five countries annually, spanning 2012-2022. To achieve the aim of the study, a single-equation linear regression model was used, estimated using the classic least squares method. Appropriate fit measures and statistical tests were used to verify the estimated models. Findings: The study showed that there are differences in the factors determining electric car sales in the countries surveyed. Driver preferences vary by region. However, due to the demands of climate change, interest in electric cars has increased dramatically in recent years. Research limitations/implications: The study concerned the demand for electric cars in selected countries. It constitutes the basis for further research, covering a larger number of countries in Europe and beyond. Practical implications: The obtained research results allowed us to indicate what elements constitute the main reasons for purchasing electric cars by European residents. They signal to decision-makers what elements should be paid attention to increase the ecological awareness of European residents, influencing decisions to purchase an electric vehicle. Social implications: Research indicates the need to promote electromobility to increase the number of publicly available fast charging points and policies related to facilitating the purchase of electric vehicles due to the need to reduce greenhouse gas emissions. Originality/value: The article is an attempt to identify the factors determining the demand for electric cars, indicate the causes of differences in demand in European countries, and recommendations for increasing interest in this type of car in the light of sustainable development goals. [ABSTRACT FROM AUTHOR]

The car market in Mexico has undergone substantial change over the last twenty years, as sales have increased dramatically and as policy measures have been introduced to improve fuel economy so that decarbonization targets can be achieved. The argument presented in this paper is that overall fuel economy is driven by vehicle sales and the fuel economy standards imposed. In addition, this picture is complicated by the purchasing preferences of buyers, and this might reduce the effectiveness of the policy measures introduced. A case study approach allows analysis of the shifts in transport policy (2003–2020) to be undertaken by linking the fuel consumption of cars (L/100 km) to the purchasing patterns of consumers, and from this estimate the levels of CO2 emissions. From the empirical analysis, it is found that, although there have been fuel economy gains every year, this is countered by (a) increasing sales of SUVs, and (b) a car market that is increasingly being dominated by larger cars. The current fuel standards are not sufficient to control the continued growth in fuel consumption, and levels of carbon emissions are continuing to increase. In conclusion, tighter emissions standards are needed, together with stronger governance structures and a range of further policy measures to improve car efficiencies and limit growth of the use of larger vehicles. [ABSTRACT FROM AUTHOR]

This paper provides insights into differences in carsharing users' attitudes, motives for joining carsharing, and transport behaviour between users with and without another car at their disposal. It builds on revealed and stated data about members of the oldest carsharing company in the Czech Republic. Carsharing adopters without a car utilise shared cars more intensively than carsharing users with a car available in their household. On the other hand, unlike the second group, they drive fewer kilometres by car in total. The car availability in households also influences the shift in car use after joining carsharing. The sale of a car thanks to adopting carsharing is a factor leading to a decrease in overall car use. Those who have a car at their disposal within their household have a lower probability of decreasing kilometres driven after joining carsharing. Households without an additional car available seem to be less car-dependent on average than those utilising carsharing as a second or third car. They tend to be more environmentally conscious and more inclined towards policies supporting alternative modes and restricting private car use, although both groups share these beliefs. The findings open a debate over whether carsharing increases the legitimacy of restrictive transport measures against private car ownership and use. [ABSTRACT FROM AUTHOR]

Intelligent design has been the direction pursued by international electric vehicle (EV) research and development (R&D) teams in recent years. This paper analyzes the problems of unsustainable development in the current product design of EVs in China, such as high R&D investment, high innovation risks, and low R&D input–output ratios. It explores the issues related to intelligent design, R&D investment, car prices, and safety in the field of EVs in China, and it proposes the concept of optimizing intelligence to optimize the design investment of EVs in China. On the basis of the development situation and the existing problems of social organization standards that gather innovative technologies for EVs, this paper used data from the national social organization standard information platform as the research object and analyzed important data, such as the quantity of the information of relevant social organizations and professional fields of social organization standards, through mathematical methods. The article proposes an optimization design scheme for EV products in China, combining intelligence and practicality from the perspective of the optimizing intelligent design, and it models the construction of EV optimization design. The quantitative relationship between the two schemes before and after optimization design is compared in terms of cost savings in intelligent design, the improvement of social benefits, and the enhancement of EV cost performance. The comparative study found that intelligent optimization design reduced the R&D cost of EVs by 45.24%, and the social benefits of R&D investment increased by 29.51%. [ABSTRACT FROM AUTHOR]

In Indonesia, the purchase and sale of used vehicles is a common practice. The valuation of a used vehicle is influenced by several factors, making it challenging to determine an appropriate selling price. To address this issue, this study employs a stacked generalization (stacking) algorithm to integrate Machine Learning (ML) techniques that have demonstrated efficacy in prior research on used car valuations. The Sequential Model-Based Optimization (SMBO) algorithm is employed to achieve high accuracy while ensuring an efficient hyperparameter optimization process. The initial price of a vehicle is undoubtedly a significant determinant of its resale value. However, this fact is frequently overlooked in previous studies on developing car price estimation models. This study makes a contribution to the field by addressing this issue. The use of the initial price as an input for the model enables two distinct types of analysis: one for the assessment of used car prices and the other for the measurement of the degree of residual valuation of used cars in relation to their initial costs. The results demonstrated that the optimized stacking model exhibited superior predictive ability compared to the other algorithms in both analyses. Feature analysis substantiated the considerable influence of the initial price on the used car's price. This study also corroborates the assertion that accurately predicting the valuation of a used car cannot be achieved by solely considering the usage of the previous owner, such as the car's age and mileage. It is crucial to take into account the car's original attributes, particularly its initial price. [ABSTRACT FROM AUTHOR]

Past research in operations management and marketing on inventory levels and product variety has predominantly focused on their effects on brand performance indicators, such as sales and market share while overlooking the influence on consumers' perceptions of brands. Brand perceptions, encompassing reputation, quality, credibility, and emotional associations, go beyond typical revenue metrics and offer foresight into a brand's future performance. Hence, understanding the effects of inventory and product variety on brand perceptions is crucial, and that constitutes the main contribution of this paper. Through a consumer-facing automobile search platform, we collect data on new cars' inventories of more than 20,000 dealerships in the United States from August 2020 to March 2021. We measure brand perceptions using 273,991 responses by in-market consumers collected by YouGov. To address endogeneity concerns, we model the effects of inventory and variety on perceived brand strength using three different empirical approaches: (1) High-dimensional fixed effects, (2) instrumental variables, and (3) causal forest. Across all analyses, we find that inventory has a positive effect on perceived brand strength but the main effect of product variety is not significant. Our second contribution is related to the fact that past research on inventory and variety does not, for the most part, investigate systematic heterogeneity due to brand- or consumer-specific factors that impact the effectiveness of inventory or variety; to help fill this gap in the literature, we investigate the role of two important and theoretically motivated moderators: Consumer income and luxury status of the brand. We find that consumers' income levels and brands' luxury status negatively (positively) moderate the effects of product inventory (variety) on perceived brand strength. Our results have managerial implications for effective assortment planning under scarcity, determining the right range of product offerings for luxury versus nonluxury brands, optimizing the customer's online browsing experience, and targeting advertisements based on brand and consumer characteristics. [ABSTRACT FROM AUTHOR]

Governments around the world promote the purchase of electric vehicles (EVs) as part of their climate change strategy, with many committing to EV-only sales of new passenger vehicles by 2035 and complete use of EVs by 2045 (California) or 2050 (e.g., Canada, EU). Rebates (purchase subsidies) are offered to consumers to promote uptake of EVs, and growth in their uptake has been quite strong, although EVs remain a small proportion of registered vehicles. In this study, we first analyze the economics of EV subsidies and then use Canadian electrical generation capacity, EV efficiency data, and distances driven, along with Monte Carlo simulation, to project the increased demands that greater numbers of EVs will place on an electrical grid. We find that the current grid's capacity will not be adequate to power the anticipated growth in EVs, and major new power plants or hydroelectric dams will need to be constructed. The analysis suggests that Canada might need to build 17 new hydroelectric facilities or 14 additional gas plants, as there is likely to be much resistance to new hydroelectric projects. [ABSTRACT FROM AUTHOR]

Accurate automobile-demand forecasting can provide effective guidance for automobile-manufacturing enterprises in terms of production planning and supply planning. However, automobile sales volume is affected by historical sales volume and other external factors, and it shows strong non-stationarity, nonlinearity, autocorrelation and other complex characteristics. It is difficult to accurately forecast sales volume using traditional models. To solve this problem, a forecasting model combining trend extrapolation and causality analysis is proposed and derived from the historical predictors of sales volume and the influence of external factors. In the trend-extrapolation model, the historical predictors of sales series was captured based on the Seasonal Autoregressive Integrated Moving Average (SARIMA) and Polynomial Regression (PR); then, Empirical Mode Decomposition (EMD), a stationarity-test algorithm, and an autocorrelation-test algorithm were introduced to reconstruct the sales sequence into stationary components with strong seasonality and trend components, which reduced the influences of non-stationarity and nonlinearity on the modeling. In the causality-analysis submodel, 31-dimensional feature data were extracted from influencing factors, such as date, macroeconomy, and promotion activities, and a Gradient-Boosting Decision Tree (GBDT) was used to establish the mapping between influencing factors and future sales because of its excellent ability to fit nonlinear relationships. Finally, the forecasting performance of three combination strategies, namely the boosting series, stacking parallel and weighted-average parallel strategies, were tested. Comparative experiments on three groups of sales data showed that the weighted-average parallel combination strategy had the best performance, with loss reductions of 16.81% and 4.68% for data from the number-one brand, 25.60% and 2.79% for data from the number-two brand, and 46.26% and 14.37% for data from the number-three brand compared with the other combination strategies. Other ablation studies and comparative experiments with six basic models proved the effectiveness and superiority of the proposed model. [ABSTRACT FROM AUTHOR]

Disruptive policy packages that fundamentally change the current unsustainable passenger transport structures and enable low-carbon mobility transformation are inevitable. This implies the use of more stringent and multiple restrictive (i.e. push) measures. To enable successful implementation, public acceptance is critical, but what drives this acceptance? In this study, two main hypotheses were tested using survey methods that combined a two-group framing with a conjoint experiment: (1) The acceptance of push measures decreases as disruption increases; (2) disruptive push measures are less likely to be rejected when communicated as part of a policy package that included complementary pull measures (i.e. incentives). We conducted this survey with a quota-representative sample of 1,032 respondents from Austria. Two main findings emerge: First, we find low public acceptance of push measures, but observe differences based on the level of disruption (i.e. the measures' intensity or rapidity of implementation). The more disruptive the measure (e.g. a registration ban for fossil fuel cars or a fuel price increase), the more negatively these measures were evaluated by survey respondents. Second, our results indicate the need to communicate and implement high-impact, more restrictive push measures (e.g. car bans) as part of policy packages that include acceptance-boosting pull measures. Successful and acceptable mitigation policies for low-carbon mobility require multiple push measures that are communicated in policy packages together with pull measures. Acceptance levels of push measures are generally low, but acceptance differs depending on how disruptive the respective policy is, with the more disruptive policies being less well accepted. Acceptable combinations of push measures may include higher parking fees, car-free city centres, and street redesign to, e.g. prioritize non-motorized modes of transport. Responses to the presented push measures vary across society, with frequent car users showing higher levels of rejection dependent on the included policy measures, underlining the need to tailor policies for specific contexts. [ABSTRACT FROM AUTHOR]

This research explores the effective sales strategy of Wuling Almaz vehicles among the Millennial Generation, with a focus on the Wuling Arista Bogor showroom. Through a qualitative approach, this research evaluates the preferences and needs of the Millennial Generation in purchasing vehicles and analyzes the effectiveness of the Wuling Almaz Vehicle sales strategy on the Millennial Generation. The results illustrate that the sales strategy of Wuling Almaz Vehicles is positively successful with the Millennial Generation. [ABSTRACT FROM AUTHOR]

The article examines consumers' perceptions about electric vehicle (EV) adoption and user satisfaction in Southern Kerala, India. The study evaluates the vehicle preferences of youthful, educated men and factors that influence buying decisions including demographic characteristics, economic issues, environmental concerns, financial gains and operational efficiency. It summarizes EV sales trends and cost-benefit of EVs and outlines opportunities and challenges in EV adoption.

This article provides a detailed theoretical and empirical justification for the effectiveness of the Ethanol Blended Petroleum (EBP) program in India in the context of reducing the burden of Current Account Deficit (CAD). The most important research issue is whether the increase in ethanol blending in petroleum reduces the burden of the current account deficit via decreasing oil import bills or not. In order to capture the effect of the EBP program in India, the study uses data on the percentage of ethanol blending in petroleum since the launch of the program, the Oil import bill and the current account deficit. For analysis, the study has applied the Augmented Dickey-Fuller Test, Granger Causality, Regression model and descriptive statistical methods. The descriptive statistical trend line analysis proved the success of the EBP program in India which is observed using the increasing percentage of ethanol blend in vehicular petroleum. As a result of the implementation of the EBP program, the study has observed a constant trend for oil imports and current account deficits even though there was a huge increase in per capita income and automobile sales during the study period. Granger causality test result observed bidirectional causality indicating that the EBP program has a significant impact on oil imports and the current account deficit in India. The regression results also validated Granger causality results by detecting that the EBP program had a significant impact on CAD in India. Finally, the study concludes that the effect of the Ethanol Blending Petroleum Program on the oil Import Bill is effective in curtailing the current account deficit in India. The E20 (Ethanol blending 20 per cent) initiative will have a significant impact on the current account deficit in India. Hence, the government of India must relax the legal issues relating to the production of ethanol from sugarcane and speed up the process of ethanol blending in petroleum products. [ABSTRACT FROM AUTHOR]

Objective: To investigate the current situation of child safety seat use and children's cognition of safe riding in Leshan City, and to provide a basis for the promotion and application of child safety seats in Leshan City. Methods: From November 2021 to February 2022, a total of 500 car-owning families who visited or were hospitalized in the department of pediatrics of our hospital were surveyed by self-filling questionnaire method. Through the investigation of personal situation, family situation, travel habits, use of child safety seat, cognition of safe riding, etc., the influencing factors of possession and use of child safety seat and cognition were analyzed. Results: The ownership rate of car seats was 57.8%, the use rate was 47.6%, and the always use rate was 18.8% among all families with children surveyed. There were no statistically significant differences in the use rate and always use rate of child safety seats among different genders of children, parents' education background, and car ownership price. The use rate and constant use rate of child safety seats in urban households were significantly higher than those in rural areas. When the main driver was the mother, the use rate of child safety seats was higher, and the difference was statistically significant. From the perspective of cognition, the use rate and constant use rate of the child safety seat of parents with correct cognition were 64.5% and 25.7% respectively, while the use rate and constant use rate of the child safety seat of parents with incorrect cognition were only 11.1% and 4.3%, which were significantly lower than those with correct cognition, and the difference was statistically significant. In terms of cognition, 10.4% of parents think it is safe for children to sit in the back seat with adults in their arms, and 9.0% of family members think it is safe for children to sit in the back seat with seat belts alone. Conclusions: At present, the ownership rate, utilization rate, and parents' correct cognition of child safety seats in Leshan City are all at a low level, and there is a lack of regional legal guidance, so the safety situation of children in cars is worrying. To effectively improve the use of child safety seats, the promotion of child safety seats should be promoted from three aspects: improving parents' cognition, forming good traffic habits, and legal guidance. [ABSTRACT FROM AUTHOR]

At the end of April 2024, national car sales for the domestic market were recorded to have decreased by 34.91% compared to the previous month. This negative trend, of course, disrupts company profits, including PT XYZ as a supplier of automotive components in Indonesia. This decline triggered the company to carry out cost reduction activities in the production area. However, in reality there is still a lot of waste during production, one of which is in the 650T injection molding process. The high cycle time of the injection process resulted in the Overall Equipment Effectiveness (OEE) target not being achieved. The lean manufacturing approach and DMAIC cycle as a research method aims to identify non-value added activities as well as make improvements by reducing the injection process cycle time from 86 to 57 seconds. These improvements made the OEE target for the 650T injection machine achieved because it increased from 67.9% to 76.3%. [ABSTRACT FROM AUTHOR]

As a result of incredible technological advancements and research of new technical expertise and huge economic growth of our country, people started to buy cars more than other vehicles. Therefore, there arises an enormous demand for cars, as the demand increases for new cars the used car market also booms alike. But the used car market is highly manipulated by few numbers of people who govern the rates of the used cars and also online selling websites designate the values for the used cars. This paper tries to study and investigate the trends in used car prices and predicts the price of used cars with the help of supervised machine learning algorithms. And to suggest which machine learning algorithm performs well among the selected methods for predicting the cars price. There has been related work done with machine learning algorithms like linear regression, multiple regression, random forest and so on. We wanted to study which algorithm predicts the car price more reliably and accurately. [ABSTRACT FROM AUTHOR]

In modern times, people predominantly use personal vehicles as a means of transportation, and, as this trend has developed, services that enable consumers to rent vehicles instead of buying their own have emerged. These services have grown into an industry, and the demand for predicting rental prices has arisen with the number of consumers. This study addresses the challenge in accurately predicting rental prices using big data with numerous features, and presents the experiments conducted and results obtained by applying various machine learning (ML) algorithms to enhance the prediction accuracy. Our experiment was conducted in two parts: single- and multi-step forecasting. In the single-step forecasting experiment, we employed random forest regression (RFR), multilayer perceptron (MLP), 1D convolutional neural network (1D-CNN), long short-term memory (LSTM), and the autoregressive integrated moving average (ARIMA) model to predict car rental prices and compared the results of each model. In the multi-step forecasting experiment, rental prices after 7, 14, 21 and 30 days were predicted using the algorithms applied in single-step forecasting. The prediction performance was improved by applying Bayesian optimization hyperband. The experimental results demonstrate that the LSTM and ARIMA models were effective in predicting car rental prices. Based on these results, useful information could be provided to both rental car companies and consumers. [ABSTRACT FROM AUTHOR]

The aim of this paper is to analyse the effects of the Green Innovation event and corporate announcements regarding green innovation on the stock price of the Automobile Industry and the performance of firms. The authors also aim to assess the impact of these events on business performance and identify the effective innovation strategy influenced by the type of corporate announcement. The study focuses on the corporate announcements made by the automobile industry and their impact on company performance, specifically in relation to the application of green innovation methods. Furthermore, there is no universally agreed upon standard for defining and categorising corporate announcements. The writers also exclude the impact of media and other events that occur during the event window when categorising these announcements. The findings of this study have important practical consequences. They suggest that the release of green innovations, which aim to protect the environment, can have an impact on an organization's stock market success. Specifically, the type of innovation and the trade segment in which the organisation operates can influence its stock market performance. [ABSTRACT FROM AUTHOR]