scholarly journals A SEM–Neural Network Approach to Predict Customers’ Intention to Purchase Battery Electric Vehicles in China’s Zhejiang Province

2019 ◽  
Vol 11 (11) ◽  
pp. 3164 ◽  
Author(s):  
Yueling Xu ◽  
Wenyu Zhang ◽  
Haijun Bao ◽  
Shuai Zhang ◽  
Ying Xiang
Keyword(s):  
Neural Network ◽  
Electric Vehicles ◽  
Purchase Intention ◽  
Structural Equation ◽  
Motor Vehicle ◽  
Zhejiang Province ◽  
Chinese Government ◽  
Second Phase ◽  
Neural Network Approach ◽  
Battery Electric Vehicles

As part of the increasing efforts toward the prevention and control of motor vehicle pollution, the Chinese government has practiced a range of policies to stimulate the purchase and use of battery electric vehicles (BEVs). Zhejiang Province, a key province in China, has proactively implemented and monitored an environmental protection plan. This study aims to contribute toward streamlining marketing and planning activities to introduce strategic policies that stimulate the purchase and use of BEVs. This study considers the nature of human behavior by extending the theory of planned behavior model to identify its predictors, as well as its non-linear relationship with customers’ purchase intention. To better understand the predictors, a substantial literature review was given to validate the hypotheses. A quantitative study using 382 surveys completed by customers in Zhejiang Province was conducted by integrating a structural equation model (SEM) and a neural network (NN). The initial analysis results from the SEM revealed five factors that have impacted the customers’ purchase intention of BEVs. In the second phase, the normalized importance among those five significant predictors was ranked using the NN. The findings have provided theoretical implications to scholars and academics, and managerial implications to enterprises, and are also helpful for decision makers to implement appropriate policies to promote the purchase intention of BEVs, thereby improving the air quality.

Artificial intelligent chatbots as brand promoters: a two-stage structural equation modeling-artificial neural network approach

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Crystal T. Lee ◽  
Ling-Yen Pan ◽  
Sara H. Hsieh
Keyword(s):  
Neural Network ◽  
Social Support ◽  
Artificial Neural Network ◽  
Purchase Intention ◽  
Structural Equation ◽  
Equation Modeling ◽  
Network Approach ◽  
Neural Network Approach ◽  
Content Type ◽  
Artificial Neural Network Approach

PurposeThis study investigates the determinants of effective human and artificial intelligence (AI) relationship-building strategies for brands. It explores the antecedents and consequences of consumers' interactant satisfaction with communication and identifies ways to enhance consumer purchase intention via AI chatbot promotion.Design/methodology/approachMicrosoft Xiaoice served as the focal AI chatbot, and 331 valid samples were obtained. A two-stage structural equation modeling-artificial neural network approach was adopted to verify the proposed theoretical model.FindingsRegarding the IQ (intelligence quotient) and EQ (emotional quotient) of AI chatbots, the multi-dimensional social support model helps explain consumers' interactant satisfaction with communication, which facilitates affective attachment and purchase intention. The results also show that chatbots should emphasize emotional and esteem social support more than informational support.Practical implicationsBrands should focus more on AI chatbots' emotional and empathetic responses than functional aspects when designing dialogue content for human–AI interactions. Well-designed AI chatbots can help marketers develop effective brand promotion strategies.Originality/valueThis research enriches the human–AI interaction literature by adopting a multi-dimensional social support theoretical lens that can enhance the interactant satisfaction with communication, affective attachment and purchase intention of AI chatbot users.

A SEM-Neural Network Approach for Predicting Antecedents of Factors Influencing Consumers' Intent to Install Mobile Applications

2018 ◽  
pp. 262-308 ◽  
Author(s):  
Yakup Akgül
Keyword(s):  
Neural Network ◽  
Mobile Applications ◽  
Behavioral Intention ◽  
Structural Equation ◽  
Equation Modeling ◽  
Second Phase ◽  
Intention To Use ◽  
Neural Network Approach ◽  
The Neural Network ◽  
Behavioral Intention To Use

This chapter explores the present gap in the literature regarding the acceptance of mobile applications by investigating the factors that affect users' behavioral intention to use apps in Turkey. First, structural equation modeling (SEM) was used to determine which variables had significant influence on intention to install. In a second phase, the neural network model was used to rank the relative influence of significant predictors obtained from SEM. The results reveal that habit, performance expectancy, trust, social influence, and hedonic motivation affect the users' behavioral intention to use apps.

scholarly journals Predicting Purchase Intention towards Battery Electric Vehicles: A Case of Indonesian Market

2021 ◽  
Vol 12 (4) ◽  
pp. 240
Author(s):  
Ade Febransyah
Keyword(s):  
Electric Vehicles ◽  
Purchase Intention ◽  
Analytic Network Process ◽  
Purchase Intentions ◽  
Early Adopters ◽  
Highly Educated ◽  
Battery Electric Vehicles ◽  
Intention To Purchase ◽  
Cost Of Ownership ◽  
Network Process

The emergence of electric vehicles (EV) is inevitable. In Indonesia, EVs in various forms have been introduced to the market. However, the adoption of EV in the Indonesian market is still negligible. The purpose of this paper is to make an early prediction of consumers’ purchase intentions towards EV, particularly battery electric vehicles (BEV), in Indonesia. A multi-criteria decision model based on the analytic network process (ANP) approach has been proposed. There are several main criteria used to explain the purchase/don’t purchase decision towards BEV, namely functionality, emotion, cost of ownership, and car identity. Through a series of pairwise comparisons involving a number of target customers of senior level professionals, their purchase intentions towards BEV have been predicted. The results of this study show that these early wealthy, highly educated consumers have a moderate preference towards purchasing BEV. Their intention to purchase is influenced by criteria as follows: emotion (42.64%), functionality (25.94%), car identity (21.87%), and cost of ownership (9.55%). Even though the invited target customers do not represent the mass market, the findings of this study could help BEV makers in Indonesia choose who the early adopters are and find the BEV product-market fit in order to accelerate the adoption of electric vehicles.

State-of-Charge Estimation of Li-Ion Battery in Electric Vehicles: A Deep Neural Network Approach

2020 ◽  
Vol 56 (5) ◽  
pp. 5565-5574
Author(s):  
Dickshon N. T. How ◽  
Mahammad A. Hannan ◽  
Molla S. Hossain Lipu ◽  
Khairul S. M. Sahari ◽  
Pin Jern Ker ◽  
...  
Keyword(s):  
Neural Network ◽  
Electric Vehicles ◽  
Deep Neural Network ◽  
State Of Charge ◽  
Li Ion Battery ◽  
Network Approach ◽  
Neural Network Approach ◽  
Li Ion ◽  
State Of Charge Estimation

scholarly journals An Evaluation Study on Investment Efficiency: A Predictive Machine Learning Approach

Complexity ◽  
2021 ◽  
Vol 2021 ◽  
pp. 1-9
Author(s):  
Weiwei Hao ◽  
Hongyan Gao ◽  
Zongqing Liu
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Structural Equation ◽  
Evaluation Index System ◽  
Corporate Debt ◽  
Investment Efficiency ◽  
Neural Network Approach ◽  
Ownership Reform ◽  
Nonlinear Autoregressive ◽  
Autoregressive Neural Network

This paper proposes a nonlinear autoregressive neural network (NARNET) method for the investment performance evaluation of state-owned enterprises (SOE). It is different from the traditional method based on machine learning, such as linear regression, structural equation, clustering, and principal component analysis; this paper uses a regression prediction method to analyze investment efficiency. In this paper, we firstly analyze the relationship between diversified ownership reform, corporate debt leverage, and the investment efficiency of state-owned enterprises (SOE). Secondly, a set of investment efficiency evaluation index system for SOE was constructed, and a nonlinear autoregressive neural network approach was used for verification. The data of A-share state-owned listed companies in Shanghai and Shenzhen stock exchanges from 2009 to 2018 are taken as a sample. The experimental results show that the output value from the NARNET is highly fitted to the actual data. Based on the neural network model regression analysis, this paper conducts a descriptive statistical analysis of the main variables and control variables of the evaluation indicators. It verifies the direct impact of diversified ownership reform on the investment efficiency of SOE and the indirect impact on the investment efficiency of SOE through corporate debt leverage.

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