Course marketing research topic research on factors affecting electric vehicles intention and use behavior in vietnam

  MINISTRY OF EDUCATION AND TRAINING UEH UNIVERSITY   COURSE: MARKETING RESEARCH TOPIC: RESEARCH ON FACTORS AFFECTING ELECTRIC VEHICLES INTENTION AND USE BEHAVIOR IN VIETNAM Lecturer: Lê Thị Hồng Minh Class: 22C1MAR50310102 Group: Ho Chi Minh City, 2022     TABLE OF CONTENTS Abstract 1 Introduction Theoretical Background 2.1 Foundation of the theoretically proposed research model 2.2 Conceptual research model Key topics 4 Hypotheses development Methodology 5.1 Qualitative research 5.2 Quantitative research Data analysis and results .8 6.1 Qualitative research 6.2 Quantitative research Discussion .8 Conclusion 8.1 Theoretical contribution 8.2 Practical contribution 8.3 Limitations and future research .8 References Abstract  Introduction Theoretical background 2.1 Foundation of the theoretically proposed research model 2.2 Conceptual research model Key topics Hypotheses development 4.1 Maintenance and Battery replacement cost 4.2 Vendor Credibility  4.3 Social Influence 4.4 Charging infrastructure  4.5 Emotion 4.6 Habit 4.7 Environmental concern  4.8 Government Policies  4.9 EV Behavioral Intention Methodology 5.1 Qualitative research 5.1.1 Data collection 5.1.2 Measurement 5.1.3 Methods 5.2 Quantitative research 5.2.1 Data collection 5.2.2 Measurement 5.2.3 Methods Data analysis and results 6.1 Qualitative research 6.2 Quantitative research 6.2.1 Descriptive statistics Table Demographic profile of the sample Demographics Age Gender Living Education Income N=347 % Under 18 2.3% 18 - 23 245 70.6% 24 - 34 67 19.3% 35 - 44 22 6.3% >45 1.4% Male 142 40.9% Female 204 58.8% Other 0.3% Hanoi 23 6.6% Ho Chi Minh City 263 75.8% Other 61 17.6% High school 17 4.9% University/College 276 79.5% Postgraduate 51 14.7% Other 0.9% Under million 150 43.2% 3- 10 million 122 35.2% 10 – 20 million 40 11.5% Above 20 million 35 10.1% 6.2.2 Reliability and validity 6.2.2.1 Exploratory factor (EFA) The results of reliability and validity of the research model are presented in Table Cronbach’s α and composite reliability were used to assess internal reliability of the constructs (Hair et al., 2012) In this study, the Cronbach’s α value is from 0.701 to 0.899, and the composite reliability value is from 0.829 to 0.921, which means that each structure has strong internal reliability The average variance extracted (AVE) and factor loadings were used to assess convergent validity (Fornell and Larcker, 1981) All standardized factor loadings are greater than 0.7 (except VC3, SI1 and EMO4 were deleted) and AVE values are greater than 0.5 (i.e., range from 0.549 to 0.682), which means that the horizontal convergence validity of the measurement instruments is acceptable Therefore, the research variables are kept for confirmatory factor analysis Bảng 2: Kết phân tích nhân tố (EFA) kiểm định Cronbach’s Alpha Biến Hệ số tải nhân số (>0,5) Dự đốn phấn khích (AE) 0,800 [AE1] Tơi phấn khích tham gia đợt giảm giá 0,794 [AE2] Tôi nghĩ thân bị đợt giảm giá thu hút mua hàng 0,818 [AE3] Tơi nghĩ hạnh phúc mua hàng giá tốt đợt giảm giá [AE4] Việc sở hữu hàng trực tuyến giảm giá khiến tơi cảm thấy phấn khích 0,708 0,834 Dự đốn đố kỵ (AEN) [AEN1] Tơi nghĩ người đố kỵ biết mua hàng đợt giảm giá mạng [AEN2] Tôi nghĩ bạn bè đố kỵ họ nghe việc mua hàng giá tốt mạng [AEN3] Tôi nghĩ người mua hàng giảm giá với đố kỵ có tơi mua hàng giảm giá 0,856 0,837 0,925 0,876 Sự hối tiếc chi phí dự tính trước (AER) 0,883 [AER1] Tơi cảm thấy thân lạc hậu bỏ lỡ đợt giảm giá mạng 0,816 [AER2] Tôi thấy tiếc bỏ lỡ đợt giảm giá mạng 0,879 [AER3] Tôi hối hận khơng mua hàng giảm giá để tiết kiệm tiền 0,860 Hệ số Cronbach’s Alpha (>0.7) [AER4] Tôi cảm thấy hội bỏ qua đợt giảm giá mạng 0,887 Điều bạn sợ bỏ lỡ (FOMO) đợt giảm giá mạng 0,779 [FM1] Tôi thường ngồi yên đợt giảm giá mạng 0,839 [FM2] Tơi thường mua hàng đợt giảm giá sợ bỏ lỡ giá tốt 0,655 [FM3] Tơi có xu hướng lướt sàn thương mại điện tử dù vốn thời gian không cho phép 0,822 [FM4] Tôi thường kiểm tra giao dịch sàn thương mại điện tử sợ bỏ lỡ điều 0,784 Sự tăng giá trị thân (SE) 0,845 [SE1] Tôi nghĩ người khác thích việc tơi mua hàng đợt giảm giá mạng 0,828 [SE2] Tôi nghĩ người có ấn tượng tốt tơi biết tơi mua hàng giá rẻ đợt giảm giá mạng 0,916 [SE3] Việc mua hàng giảm giá mạng làm người khác ấn tượng tốt 0,877 Ý định mua hàng (PD) 0,822 [PD1] Tôi thường thường chờ đợi đợt giảm giá mạng 0,816 [PD2] Tơi hay có danh sách cần mua trước đợt giảm giá 0,759 [PD3] Tôi cố gắng mua nhiều vào đợt giảm giá thay dịp mua sắm thơng thường 0,791 [PD4] Tôi bỏ qua đợt giảm giá mạng thấy mua hàng giá tốt thời gian giảm giá 0,858 6.2.2.2 Correlation coefficients between variables in the model Discriminant validity was assessed by the square root of the AVE and the cross-loading matrix (Hoque and Sorwar, 2017) The results show that all the structures meet the requirements and confirm that the discriminant validity of the data is satisfied (Hoque and Sorwar, 2017) The total correlation coefficients of the observed variables in each scale have to be higher than the limit of 0.3 to satisfy the discriminant validity The value of the Behavioral Intention, Charging Infrastructure, Environmental Concern, Emotion, Government Policies, Habit, Maintenance and Battery replacement cost, Social Influence, Use Behavior, Vendor Credibility are from 0.418 to 0,718, which means that there are strong positive correlations between variables On the other hand, the correlation coefficients between the moderator variable and other variables are from -0,117 to 0,381, which have negative correlation types.  Table Pair correlation matrix BI CI EC EMO GP HA MBC SI UB VC BI 1,000 CI 0,461 1,000 EC 0,652 0,664 1,000 EMO 0,613 0,642 0,676 1,000 GP 0,630 0,473 0,568 0,564 1,000 HA 0,657 0,402 0,503 0,585 0,601 1,000 MBC 0,508 0,576 0,540 0,552 0,506 0,505 1,000 SI 0,422 0,426 0,423 0,418 0,452 0,488 0,428 1,000 UB 0,718 0,561 0,686 0,676 0,597 0,624 0,539 0,456 1,000 VC 0,542 0,661 0,670 0,622 0,531 0,453 0,575 0,467 0,579 1,000 GPxBI -0,366 -0,317 -0,348 -0,305 -0,381 -0,247 -0,270 -0,117 -0,314 -0,335 GPxBI 1,000 Notes: BI: Behavioral Intention; CI: Charging Infrastructure; EC: Environmental Concern; EMO: Emotion; GP: Government Policies; HA: Habit; MBC: Maintenance and Battery replacement cost; SI: Social Influence; UB: Use Behavior; VC: Vendor Credibility 6.2.2.3 Analyze the discriminant validity The square roots of AVE in every latent variable are more than other correlation values among the latent variables (0,402 - 0,821), which means that Fornell-Larcker criterion of the variables meet the requirement.  Table Fornell-Larcker Criterion BI BI 0,817 CI EC EMO GP HA MBC SI UB VC CI 0,461 0,819 EC 0,652 0,664 0,821 EMO 0,613 0,642 0,676 0,801 GP 0,630 0,473 0,568 0,564 0,824 HA 0,657 0,402 0,503 0,585 0,601 0,810 MBC 0,508 0,576 0,540 0,552 0,506 0,505 0,746 SI 0,422 0,426 0,423 0,418 0,452 0,488 0,428 0,791 UB 0,718 0,561 0,686 0,676 0,597 0,624 0,539 0,456 0,851 VC 0,542 0,661 0,670 0,622 0,531 0,453 0,575 0,467 0,579 0,811 Notes: BI: Behavioral Intention; CI: Charging Infrastructure; EC: Environmental Concern; EMO: Emotion; GP: Government Policies; HA: Habit; MBC: Maintenance and Battery replacement cost; SI: Social Influence; UB: Use Behavior; VC: Vendor Credibility The HTMT results showed that all the ratio were different from value, and the HTMT ratio of correlation in Table illustrates that all the values are under the threshold of 0.90, which means that the discriminant validity of the reflective constructs Table Heterotrait-Monotrait Ratio (HTMT) BI CI EC EMO GP HA MBC SI UB BI CI 0,546 EC 0,726 0,780 EMO 0,785 0,817 0,824 GP 0,751 0,558 0,636 0,722 HA 0,815 0,485 0,571 0,785 0,752 MBC 0,620 0,724 0,647 0,744 0,620 0,662 SI 0,533 0,529 0,497 0,571 0,583 0,673 0,580 UB 0,871 0,675 0,789 0,881 0,720 0,786 0,666 0,592 VC 0,676 0,856 0,837 0,841 0,664 0,573 0,753 0,600 0,745 VC GPxBI GPxB I 0,400 0,345 0,365 0,353 0,412 0,276 0,294 0,131 0,348 0,391 Notes: BI: Behavioral Intention; CI: Charging Infrastructure; EC: Environmental Concern; EMO: Emotion; GP: Government Policies; HA: Habit; MBC: Maintenance and Battery replacement cost; SI: Social Influence; UB: Use Behavior; VC: Vendor Credibility Variance inflation factor (VIF) is frequently used for detecting collinearity, whose value should be or lower The SmartPls results in Table indicate that all VIF values are below (1,211-2,408), indicating the absence of collinearity among predictors Table Inner VIF Values BI CI EC EMO BI GP HA MBC SI UB VC GPxBI 2,148 CI 2,408 EC 2,506 EMO 2,513 GP 1,938 HA 1,824 MBC 1,867 SI 1,497 1,964 UB VC 2,386 GPxB I 1,211 Notes: BI: Behavioral Intention; CI: Charging Infrastructure; EC: Environmental Concern; EMO: Emotion; GP: Government Policies; HA: Habit; MBC: Maintenance and Battery replacement cost; SI: Social Influence; UB: Use Behavior; VC: Vendor Credibility 6.2.3 Research model analyzing Shown in Table 7, the results of the analyzing sem model indicate that there are hypotheses supported with the level of P value < 0,05 The constructs Environmental Concern (EC), Emotion (EMO), Habit (HA) were found to be significant variables in explaining Behavioral Intention (BI), thus accepting hypotheses H9, H5, and H6a Moreover, Government Policies (GP), Habit (HA) and Behavioral Intention (BI) were found to have significant influence on Use Behavior (UB), thus accepting hypotheses H8, H6b and H7 However, there are still variables not accepted in the SEM model namely Maintenance and Battery replacement cost (MBC), Vendor Credibility (CR), Social Influence (SI), Charging Infrastructure (CI) and the moderator variables Government Policies (GP) These variables are examined to construct relationships to the dependent variables in qualitative research results and quantitative research results The main reason for these rejection in the objective aspect is that there is not enough quantitative data collected Another reason recommended is that the items of these variables conducted in the survey didn’t match with the insight of the respondents Table Hypotheses results Ho Relationships Coefficient Std T-values P values Results H1 MBC → BI 0,067 0,054 1,239 0,215 Rejected H2 VC → BI 0,080 0,062 1,279 0,201 Rejected H3 SI → BI 0,018 0,058 0,307 0,759 Rejected H4 CI → BI -0,102 0,060 1,703 0,089 Rejected H5 EMO → BI 0,137 0,061 2,257 0,024 Supported H6a HA → BI 0,365 0,057 6,451 0,000 Supported H6b HA → UB 0,213 0,069 3,101 0,002 Supported H7 EC → BI 0,346 0,063 5,480 0,000 Supported H8 GP → UB 0,166 0,071 2,344 0,019 Supported H8 GP x BI → UB -0,019 0,047 0,403 0,687 Rejected H9 BI → UB 0,463 0,069 6,742 0,000 Supported Notes: BI: Behavioral Intention; CI: Charging Infrastructure; EC: Environmental Concern; EMO: Emotion; GP: Government Policies; HA: Habit; MBC: Maintenance and Battery replacement cost; SI: Social Influence; UB: Use Behavior; VC: Vendor Credibility Discussion EVs are innovative products so there are still many inadequacies that need examining To deeply research, our study adds up three new variables Firstly, incentives will include subsidies for purchases, operations, and construction of vehicle charging stations, and are significantly correlated with consumer electric vehicle use (Wang et al., 2017) Based on literature review, we took this factor as a new variable in this study, which became Government Policies (GP) later.  Secondly, consumers always consider carefully before deciding to buy, especially supplier credibility, for technical and operational expertise can reduce risk concerns; supplier reliability is central to purchasing behavior (Büttner and Goritz ă 2008; Pan and Chiou 2011; Reichelt et al., 2014) Therefore, Vendor Credibility (VC) was combined in our research model Thirdly, the final new variable Environmental Concern (EC) affects the intention to adopt EVs, which is the main influence in changing individuals from current behavior to more friendly with the environment (Bamberg, 2003; Schuitema et al., 2013) The study is built on the UTAUT2 model because it has the ability to explain well the contexts and new variables mentioned with the intention to use electric vehicles By using a semi-structured interview, we based on keywords in interviewees' answers to get their perspectives In each variable, we found the interviewees provided proper insights for our research Generally, all our interviewees had high awareness of environmental concern, and were positive about the role of electric vehicles They also showed their interest in the vendor's credibility, and accepted the cost of maintenance, and the waste of charging time, and tended to consider using EVs as their daily habit, which was not affected by any social influence but government support Based on the quantity research, we come to preliminary analyses: standard approach variance and confirmatory factor analysis to specify reliability and validity We also illustrate bivariate correlations between the independent and dependent variables before testing the proposed hypotheses Then, from the data received, we test our hypotheses The results reveal that EC, EMO, GP, and HA significantly and positively influence BI, and it is the same to BI towards the UB Besides, MBC, SI, VC, and GP are also positive in this research model, while CI is negative In other words, consumers are not willing to purchase electric vehicles if the charging infrastructure is not ensured The important theoretical and practical contributions raised by this study will be examined in the next section Conclusion 8.1 Theoretical contribution The study was conducted with the aim of finding out about consumer's intention to use electric vehicles in Vietnam, especially, the technology landscape has strongly developed in recent years In addition, it also helps diversify the legal literature in the field of accepting new technologies and makes some new theoretical contributions From a theoretical point of view, our study is built on the UTAUT2 model, but through the process of collecting qualitative data, we realize that it is necessary to adjust the model by adding variables to ensure more coverage and clarity on the factors affecting customers' intention to buy electric vehicles According to theories of innovation and planned behavior, in the context of EVs, it investigates factors that affect EVs behavioral intention and EV use behavior We focus on data that favors Vendor Credibility, Government Policies determine the group of people purchasing EV and Environmental Concern mention habits, lifestyle, activities related to environment, which influence behavior The research conclusions can provide a lot of factors that help theoretically related to consumer behavior more abundant 8.2 Practical contribution In terms of practical contribution, we found that the Electric Vehicles behavior intention in the future is increasing significantly Environmental problems make consumers want to use electric cars to protect the environment.  Empirical evidence obtained in this study may have important practical implications for encouraging policymakers, including confirming conclusions similar to former research and exploring new policy contributions Some of the incentives proposed in previous studies have been confirmed, including the effectiveness of continuing investment in EV infrastructure, such as charging stations and maintenance service facilities to enhance the perceived value of EVs, pricing subsidies, including purchase price subsidies, vehicle registration tax reductions, value-added tax relief, and operational subsidies In addition to policy incentives, policymakers need to consider the following new possibilities: customer satisfaction with electric vehicle infrastructure and functions By providing free test-drive opportunities of electric vehicles to everyone, this policy can increase people's interest It should be noted that policymakers should be satisfied with the level and experience of electric driving with these policies, and will then adjust their accounts based on the resulting survey This study predicts an increase in the demand for electric vehicles in the future, and electric vehicle manufacturers should also be fully prepared with technology and minimize possible problems Continuing to upgrade services will help users increasingly enhance their trust in the firm Therefore, electric vehicle manufacturers should upgrade and expand charging stations, ensuring the infrastructure can accommodate many users at the same time Manufacturers of batteries for electric vehicles should focus on minimizing the problems that users encounter such as fire and explosion problems, the overwhelming weight of the battery, In addition, the production of fast charging stations is also an important solution to save time for users In addition, EV manufacturers should apply proper marketing strategies to reach customers easily In addition, car manufacturers should focus on product quality development EV manufacturers should find reliable suppliers, build an experienced engineer team, and enhance customer service Our research also provides strategic strategies for electric vehicle manufacturers to encourage people to use electric vehicles We suggested and identified in current certifications, for example, providing a free long-term driving experience (eg months) to consumers can shape the habit of using electric vehicles, improve charging stations and shorten return times, to improve high-performance operations Emotion is the heart of new strategy to enhance the use of electric vehicles, e.g comfortable seats, low brightness, outstanding acceleration, efficient management technology and fast charging Government should have more policies to encourage customer demand for electric vehicles such as reducing taxes, increasing support, and communicating through government channels Moreover, the government and EV manufacturers should have the right support selections for charging stations along the way, such as increasing the area of charging stations, placing more charging stations or integrating with gas stations, and increasing the density of charging stations on highways and roads 8.3 Limitations and future research The study provides diverse perspectives on usage intentions, but it also has many limitations Firstly, the data collected from the key economic cities and the sample size are consistent with the recommendations set forth in previous studies, but the participants are relatively young Therefore, the generalization of the model's conclusions should be handled with caution The factors affecting the intention to use in the present will change rapidly as the development of technology and time lead to different conclusions Secondly, interviews show that policies have no significant influence, but they can affect behavior in other countries or at a particular time, so there are still limitations Thirdly, according to UTAUT2 applied in previous studies, social influence plays an important role in influencing behavioral intention (Venkatesh et al., 2012, Herrero et al., 2017) While researching and investigating the market, we discovered new variables including CI, MBC, GP, and VC, which clearly influence the consumers However, the p-value indicates the vague impact of those variables on BI This could be down to the fact that the radix is not optimal to collect objective data or the respondents not have enough intellectual background and intention to use EVs In addition to the limitations mentioned, the research process shows that the personal utilities brought by electric vehicles are always guaranteed and supported by many different factors As for charging infrastructure, construct definition can be used to provide insight into consumers' beliefs about daily motivating activities, that is, they will gradually change their habits through exposure to the charging station problem; Based on the premise model can continue to apply business activities to the corresponding model In our current study, we focus on intention to use, so in future research, charging will no longer be a negative factor but may increase anxiety before the trip, to determine stressors will likely be involved in needing more detailed recommendations to information or support systems In the future research, it will be repeated if the lifestyle of consumers changes as electric vehicles become more popular, at this time it will be necessary to further investigate the variables affecting the intention to use based on habits, emotions, or environmental factors In future research, the survey subjects will be a group of people with high income who will tend to consider and minimize risks on product features, or services to create competitive advantages (Ryabova, 2015).  On another hand, future researchers would deeply investigate to enhance their credibility and specialization of suppliers Therefore, the estimation would be more optimal in terms of the 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