1 Introduction
1.1 The Economic Problem
Electric vehicle adoption depends on whether a new technology offers an acceptable combination of price, convenience, reliability, and expected running costs. A household may recognise the environmental advantages of an electric car yet choose a conventional vehicle because it cannot afford the initial payment or cannot charge conveniently. Conversely, a household may purchase an electric vehicle for financial or practical reasons without having detailed environmental knowledge. Awareness, favourable attitudes, and purchasing behaviour therefore represent different stages of a decision.
This distinction matters for public policy. An information campaign addresses uncertainty about available products or incentives. Charging investment addresses access to a complementary service. A purchase subsidy reduces a financial barrier. Each intervention has a different mechanism and public cost. Treating low adoption as a single problem of insufficient awareness can lead to spending on an intervention that leaves the binding constraint untouched.
The survey examined in this thesis provides evidence on several of these stages. Respondents reported whether they knew electric vehicles were available, whether they knew about government incentives, which obstacles they associated with limited adoption, and, for professionals, whether they would consider buying an electric car under specified hypothetical conditions. The central empirical result is that recognition of electric vehicle availability was widespread in this sample, while awareness of incentives was considerably lower. Charging access was the most frequently selected obstacle among the five reported categories.
The thesis evaluates these patterns as an exploratory contribution to the economics of technology adoption. Its findings concern the surveyed respondents. They are not estimates of the proportion of all Kerala residents who are aware of electric vehicles or would buy one.
1.2 Historical Setting and Scope
The empirical material belongs to a bachelor project completed in 2021. Historical context is therefore drawn from policy announcements and literature available by that year. The survey cannot describe the subsequent expansion of vehicle models, charging networks, or incentive arrangements. The fieldwork dates are not specified in the original report, so particular policy changes within 2021 cannot be matched to respondents' answers.
Electric car adoption was expanding internationally around the study period. The International Energy Agency reported approximately three million new electric car registrations worldwide in 2020 and a 4.6% share of new car sales. Those figures describe an international market and are not evidence of local adoption in Kerala (IEA, 2021). They establish why consumer acceptance and infrastructure provision were relevant research questions at the time.
India's FAME II scheme, approved in February 2019, combined purchase incentives with support for charging infrastructure. The original announcement provided an outlay of INR 10,000 crore over three years from April 2019. It emphasised public and shared transport; three- and four-wheeler incentives primarily concerned public transport or commercially registered vehicles, while private two-wheelers were included (Press Information Bureau, 2019). Recognition that incentives existed did not necessarily mean that a respondent understood eligibility or personally qualified for a benefit.
The title refers to electric vehicles because the common awareness and barrier items use that term. Several questions, especially those administered to professionals, explicitly concern electric cars. Consequently, the results should not be extended to electric buses, commercial fleets, or two-wheelers without additional evidence. General awareness results and car purchase consideration are reported separately throughout the analysis.
1.3 Research Questions and Objectives
The main research question is: What do the available survey tables reveal about electric vehicle awareness and perceived adoption barriers among the surveyed students and professionals in Kerala, and what policy questions follow from those patterns?
The analysis addresses four subsidiary questions. First, how does reported awareness of electric vehicle availability compare with awareness of government incentives? Second, how do these indicators vary across the respondent groups for which cross-tabulations are available? Third, which reported barriers are selected most often, using a consistent respondent denominator? Fourth, how does professionals' aggregate purchase consideration differ across hypothetical price, range, and charging conditions?
The objectives are to reconstruct these descriptive results accurately, interpret them using an explicit economic framework, and distinguish policy proposals that warrant investigation from conclusions the data cannot establish. The thesis does not estimate market demand, willingness to pay, the causal effect of a subsidy, or the economic return to a particular charging programme.
1.4 Contribution and Organisation
The contribution is local descriptive evidence rather than a new structural model of vehicle choice. Separating product awareness from incentive awareness helps specify the content of a possible information intervention. Recalculating multiple-response results clarifies how many respondents selected each obstacle. Examining the hypothetical scenarios identifies conditions associated with greater stated consideration within the professional sample.
Chapter 2 develops the economic framework and reviews relevant literature. Chapter 3 describes the data, measurement decisions, and analytical limits. Chapter 4 presents corrected descriptive results. Chapter 5 discusses competing interpretations. Chapter 6 sets out policy implications and a programme for evaluation. Chapter 7 concludes. The appendices provide the calculation rules, a record of substantive corrections, and the original questionnaire pages.
2 Economic Framework and Literature
2.1 Consumer Choice and Total Ownership Costs
A vehicle provides transport services over time. Its economic attractiveness depends on the initial price and on costs incurred during ownership. These can include financing, energy, maintenance, insurance, charging equipment, and the expected loss in resale value. A lower running cost can compensate for a higher purchase price, but the compensation depends on annual travel, the ownership period, and how future savings are discounted.
A conceptual expression for private total cost of ownership is:
Here P is the purchase price, S an applicable purchase incentive, H the initial charging installation cost, Et energy expenditure, Mt maintenance expenditure, Ot other ownership expenditure, VT residual value at the end of the ownership period, and r the discount rate. Financing costs must be treated consistently with the discounting convention to avoid counting the same cost twice. The expression organises relevant costs; no numerical TCO estimate is calculated from this survey.
Private choice also depends on convenience, space, performance, perceived reliability, and environmental preferences. Time spent locating a charger, waiting, or adjusting a trip can impose a cost even when electricity is inexpensive. An electric car can therefore be competitive in monetary terms but unattractive to a household with difficult charging access. Conversely, a household with predictable daily travel and home charging may face fewer practical disadvantages.
An initial-price constraint is different from an unfavourable ownership-cost calculation. A buyer may expect future savings and still lack access to credit or funds for the purchase. The survey's price scenario cannot distinguish this liquidity constraint from a preference for lower lifetime costs because income, financing conditions, and planned travel are not linked to the reported responses.
2.2 Awareness and Information Frictions
Knowing that electric vehicles exist is a limited form of awareness. It does not demonstrate knowledge of their operating costs, suitability for particular journeys, available warranties, or eligibility for incentives. The two binary survey items are therefore treated as separate indicators of self-reported product availability awareness and incentive awareness.
Information can affect decisions through expected costs and perceived risks. A consumer who does not know that a relevant incentive exists may overestimate the net purchase price. Another consumer may know about a scheme but misunderstand the application process or qualifying vehicle category. Clear information can help both, although an information campaign cannot make an ineligible consumer eligible.
The distinction also sets a boundary on interpretation. A respondent who answers yes to an awareness question has not been tested on the details of a scheme. A respondent who answers no may be unfamiliar with a particular policy while understanding electric vehicles well. The survey measures reported recognition, not a comprehensive stock of technical or policy knowledge.
2.3 Infrastructure and Coordination
Vehicles and charging facilities are complementary investments. Consumers value a vehicle more when charging is accessible, while charging operators are more likely to invest when they expect sufficient vehicle use. This relationship can create a coordination problem: limited adoption discourages infrastructure investment, and limited infrastructure discourages adoption.
Li, Tong, Xing and Zhou (2017) examine this interdependence using electric vehicle sales and charging deployment in United States metropolitan areas. Their analysis finds indirect network effects on both sides of the market. It provides a reason to evaluate infrastructure alongside purchase incentives. Its estimated policy effects depend on the market studied and cannot be transferred numerically to Kerala.
The Kerala questionnaire asks professionals about rapid charging within a five-kilometre radius together with a home charging facility. That scenario is relevant to the coordination mechanism, but it changes two dimensions simultaneously. The answers cannot identify whether home charging or the public network is more important. Nor can they establish that a five-kilometre radius is the efficient spacing for chargers.
2.4 Environmental Externalities and Public Expenditure
A private vehicle choice can impose costs on people other than the owner, including air pollution and greenhouse gas emissions. Where these costs are not fully reflected in market prices, a privately attractive choice may differ from a socially desirable one. This provides a possible economic justification for intervention, but it does not imply that every subsidy or charging project increases welfare.
Social evaluation should compare the incremental environmental benefit of a policy with its resource cost and alternative uses of public funds. It should also consider whether the programme causes additional adoption or mainly pays buyers who would have adopted anyway. Subsidising private cars may distribute benefits differently from investing in public transport or shared mobility. Those comparisons require evidence that the survey does not contain.
Environmental claims must use a clear system boundary. A battery electric car has no exhaust emissions from a combustion engine during driving. Electricity generation, vehicle and battery manufacture, and end-of-life treatment can nevertheless generate emissions and other environmental impacts. Hawkins et al. (2013a) show that vehicle lifetime, electricity sources, and production assumptions affect lifecycle comparisons. The associated corrigendum revised part of their inventory (Hawkins et al., 2013b). The relevant lesson is the need for lifecycle assessment rather than a universal numerical emissions advantage.
For this thesis, the lifecycle distinction is also a measurement issue. Agreement with an unqualified statement about zero emissions cannot safely be classified as correct environmental knowledge. A well-informed respondent may disagree because the statement fails to distinguish tailpipe and lifecycle emissions.
2.5 Evidence on Preferences and Adoption
Hidrue et al. (2011) study stated vehicle choices using a survey of 3,029 respondents and a latent-class random utility model. Their experiment varies electric vehicle attributes and prices, allowing monetary valuations of features such as range, charging time, and fuel savings. These attributes influence choices in their setting. The study illustrates why vehicle demand should be understood as a combination of attributes rather than a response to environmental concern alone.
The present survey has a different design. Respondents select perceived obstacles and answer a small number of hypothetical consideration questions. It contains neither the controlled combinations of attributes nor the linked choice records needed to estimate willingness to pay. The literature informs the choice of concepts; it does not supply missing valuations for the Kerala sample.
Khurana, Ravi Kumar and Sidhpuria (2020), first published online in 2019, examine adoption among existing car owners in India using structural equation modelling. Their abstract identifies attitude as an important mediator in the adoption framework. This supports distinguishing attitudes from actual adoption, but a descriptive tabulation cannot reproduce their mediation analysis. In particular, this thesis cannot establish that awareness affects purchasing through attitudes because the necessary joint observations are unavailable.
The literature reviewed here addresses different empirical objects. Choice experiments estimate preferences under specified alternatives. Studies of sales and infrastructure examine market outcomes and investment relationships. Attitudinal models investigate relationships among reported constructs. None is interchangeable with a table of responses to an online questionnaire. Interpretation improves when the method used to produce a finding remains visible.
2.6 Analytical Expectations and Research Gap
The framework suggests that product availability awareness may exceed detailed policy awareness and that charging convenience may matter alongside purchase price. It also suggests that interest in electric cars may rise when practical or financial disadvantages are reduced. These expectations organise the descriptive analysis; they are not treated as hypotheses confirmed by causal estimation.
The study adds an exploratory account of these issues among respondents in Kerala at an early stage of the market. Its specific contribution is the comparison of two awareness indicators, the relative prevalence of reported barriers, and the pattern of conditional consideration. It does not claim to be the first study of Kerala or to fill a gap demonstrated by a systematic literature search. Establishing the broader extent of local evidence would require a separate literature review protocol.
3 Data and Methodology
3.1 Study Design and Analytical Material
The original project reports an online questionnaire with 400 participants, divided into 299 students and 101 professionals. It also mentions a snowball survey of existing electric vehicle users. However, a separate owner sample size and a corresponding set of owner results are not recoverable from the reported analysis. The empirical scope here is therefore limited to the documented student and professional tables (Original survey report, 2021, pp. 9-10 and 44-76).
The unit of observation underlying the survey is the respondent. The material available for this analysis consists of aggregate frequencies and cross-tabulations, rather than respondent-level records. Counts are retained where the tables are internally consistent, and percentages are recalculated from them. This permits transparent descriptive comparisons while preventing analyses that require unreported combinations of individual characteristics and responses.
The label professional is used as a respondent category, following the original report. It should not be interpreted as a verified occupational class or as a representative sample of Kerala's employed population. The original report also uses working class for this group, but it supplies no definition that would justify treating it as a socioeconomic class.
3.2 Recruitment and Population Inference
The report does not establish a sampling frame or known probabilities of inclusion for the main online survey. The recruitment channels, number of invitations, response rate, district composition, and exact fieldwork dates are not documented sufficiently to reproduce recruitment. The safest interpretation is a non-probability sample of people who were reached and chose to respond.
An online mode can restrict coverage to people with internet access and the ability or willingness to complete the questionnaire. Recruitment through personal or educational networks may also produce clusters of similar respondents. The predominance of students is a directly observed feature: they account for 74.75% of the sample. Increasing the number of such respondents does not by itself resolve coverage or selection bias.
The AAPOR task force report explains why inference from non-probability samples requires explicit assumptions and evaluation of the selection process (AAPOR, 2013). Those conditions cannot be established from the available documentation. Accordingly, no population margin of error, survey weight, or design-based confidence interval is reported. The thesis describes the sample and does not treat its percentages as estimates for all Kerala residents.
3.3 Measurement and Interpretation
Availability awareness is a yes or no response about the presence of electric vehicles or electric cars in the Indian market. Incentive awareness is a yes or no response about government incentives for purchase. Both are self-reported indicators. The wording differs slightly across questionnaire branches, which limits strict comparability between groups.
Perceived barriers are measured by a select-all-that-apply item asking about reasons for limited popularity in India. Respondents could select unavailable charging stations, range anxiety, high price, limited options or brands, and long charging time. The questionnaire also allows an other response, but no frequency for that category is reported. The findings therefore concern the five tabulated categories. They measure respondents' perceptions of a wider market, not verified reasons why each respondent personally rejected an electric vehicle.
Student attitudes are captured by questions on environmental benefits, whether electric cars are better than conventional cars, the need for promotion, infrastructure adaptation, and future vehicle preference. These questions do not identify purchase timing, a feasible household budget, or a particular vehicle specification. Students' future preferences are consequently interpreted as an orientation toward vehicle types, not a forecast of future sales.
Professionals were asked whether they would consider an electric car generally and under conditions involving a price of about INR 600,000, a range of about 600 kilometres per full charge, or rapid public charging nearby together with home charging. Responses were yes, no, or maybe. The outcome is consideration, not a purchase commitment. INR 600,000 is the questionnaire's scenario value; it is not an estimated market-clearing price.
3.4 Percentage Rules and Comparisons
For a single-response item, the percentage for response k in group g is calculated as 100 multiplied by the reported count for k divided by the relevant group total. The denominators are 400 for the combined sample, 299 for students, and 101 for professionals unless a table explicitly uses another subgroup. Percentages are displayed to two decimal places; small deviations from 100% can result from rounding.
For the multiple-response barrier item, the main percentage is the number selecting a barrier divided by the number of respondents in the group. A person can select several barriers, so these percentages can sum to more than 100%. A second measure, the share of all tabulated selections, uses the sum of barrier counts as its denominator. This measure is valid for describing the distribution of selections but must not be described as the percentage of respondents.
Differences between percentages are expressed in percentage points. For example, the difference between 92.50% availability awareness and 37.75% incentive awareness is 54.75 percentage points. This is a difference between two marginal proportions; it is not a documented transition for the same individuals. The joint table relating those two answers is not reported.
Gender comparisons are descriptive and use within-gender denominators. Additional comparisons within student and professional groups are reconstructed only where the published margins uniquely identify the relevant counts. No unreported individual record is created. Residence comparisons are restricted to students because corresponding professional awareness cross-tabulations by residence are not available.
3.5 Data Validation and Analytical Exclusions
The principal tables were checked for arithmetic consistency. Student and professional awareness counts sum to the combined totals; barrier counts also add across the two groups. Several prose statements in the original report do not match their tables. In these cases, the internally consistent counts take priority, and the correction is recorded in Appendix B.
The agreement-scale items are excluded from quantitative scoring of knowledge. The instrument asks respondents to evaluate several broad claims, while the original analysis treats agreement through neutrality as correct. This approach combines uncertainty with knowledge. In addition, the questionnaire states that electric cars cost about the same to buy as petrol or diesel vehicles, while several result charts label the item as electric vehicles costing more. Without response records and a coding history, the direction of any recoding cannot be verified.
The original charts on the importance of car attributes also lack a complete tabulation of category frequencies. Selected rounded labels cannot establish the full distribution or a validated ranking of importance. Those charts are therefore not converted into additional numerical results. Their omission protects the analysis from precision that the evidence cannot sustain.
3.6 Ethical Documentation and Reproducibility
The questionnaire includes a name field. This thesis uses aggregate results only and reproduces no respondent identities. The source report does not provide enough detail to verify consent procedures, privacy safeguards, ethical review, or duplicate-response checks. No claim that those procedures occurred is made here.
Reproducibility is strongest at the level of the published tables. Each result table identifies the original table or page and its denominator. Appendix A describes calculation rules and provides examples. The survey itself cannot be replicated exactly without recruitment and fieldwork documentation, and its responses cannot be independently audited without the original records.
3.7 Limits of the Design
The analysis faces three distinct constraints. Selection limits generalisation beyond respondents. Measurement limits what awareness and hypothetical consideration mean. Aggregation limits the relationships that can be investigated. These problems require different remedies: a documented sampling strategy, clearer and tested questions, and retention of anonymised individual records, respectively.
The available data can support accurate descriptions, comparisons between reported groups, and proposals for subsequent evaluation. They cannot support causal language, household willingness-to-pay estimates, district-level infrastructure allocation, or forecasts of statewide electric vehicle adoption.
4 Descriptive Results
4.1 Composition of the Sample
The sample consists of 299 students and 101 professionals. Of the 400 respondents, 171 are reported as female and 229 as male. Rural residents account for 190 respondents, suburban residents for 100, and urban residents for 110. Table 4.1 presents the corresponding percentages.
| Characteristic | Category | Count | Share of 400 |
|---|---|---|---|
| Respondent category | Student | 299 | 74.75% |
| Professional | 101 | 25.25% | |
| Reported gender | Female | 171 | 42.75% |
| Male | 229 | 57.25% | |
| Residence | Rural | 190 | 47.50% |
| Suburban | 100 | 25.00% | |
| Urban | 110 | 27.50% |
Source: Original survey report (2021), Tables 4.1.1-4.1.3, pp. 44-45. Counts retained; percentages recalculated.
The distribution of residence differs between the two groups. Students include 154 rural, 68 suburban, and 77 urban respondents. Professionals include 36 rural, 32 suburban, and 33 urban respondents. Near-equal professional counts across residence categories describe this sample's balance; they do not establish proportional representation of Kerala's population.
The professional group also has a different reported gender composition: 26 female and 75 male respondents, compared with 145 female and 154 male students. The questionnaire's student gender item offers additional categories, but the published tables report only female and male counts. It cannot be established from the tables alone whether the other categories had no responses or were handled differently during reporting.
| Characteristic | Category | Count | Share of 101 |
|---|---|---|---|
| Age | 18-24 | 11 | 10.89% |
| 25-34 | 42 | 41.58% | |
| 35-44 | 20 | 19.80% | |
| 45-54 | 23 | 22.77% | |
| 55-64 | 4 | 3.96% | |
| 65 and above | 1 | 0.99% | |
| Vehicles owned | None | 6 | 5.94% |
| One | 38 | 37.62% | |
| Two | 36 | 35.64% | |
| Three or more | 21 | 20.79% |
Source: Original survey report (2021), Tables 4.4.2-4.4.3, pp. 67-68. Counts retained; percentages recalculated.
Professionals aged 25-34 comprise the largest age category, with 42 of 101 respondents. Only five professionals are aged 55 or above. Vehicle ownership is common within this group: 95 report owning at least one vehicle, although the ownership counts do not identify the propulsion type. These characteristics make the group relevant to discussions of vehicle choice, but do not establish that respondents were about to buy a car.
4.2 Availability Awareness and Incentive Awareness
Reported awareness of electric vehicle availability is high within both groups. Among students, 279 of 299 report awareness; among professionals, 91 of 101 do so. In contrast, 109 students and 42 professionals report awareness of government incentives.
| Group | Group size | Availability yes | Incentives yes |
|---|---|---|---|
| Students | 299 | 279 (93.31%) | 109 (36.45%) |
| Professionals | 101 | 91 (90.10%) | 42 (41.58%) |
| Combined sample | 400 | 370 (92.50%) | 151 (37.75%) |
Source: Original survey report (2021), Tables 4.2.1, 4.3.2 and 4.4.4, pp. 48, 55 and 69. Counts retained; percentages recalculated.
Self-reported awareness, not a validated knowledge score. Each percentage uses its stated group denominator.
Source: Original survey report (2021), Tables 4.2.1, 4.3.2 and 4.4.4, pp. 48, 55 and 69. Counts retained; percentages recalculated.
The combined availability-awareness proportion is 92.50%, compared with 37.75% for incentive awareness. The difference of 54.75 percentage points indicates that product recognition and policy recognition are not interchangeable measures. It does not show how many of the same respondents knew about availability but not incentives because their joint responses are not tabulated.
Professional incentive awareness is 41.58%, compared with 36.45% among students, a difference of 5.13 percentage points. Student availability awareness exceeds that of professionals by 3.21 percentage points. These group contrasts could reflect differences in exposure, recruitment, or personal circumstances. The tables do not identify their explanation.
4.3 Awareness by Reported Gender
Within the female sample, 151 of 171 respondents report availability awareness and 47 report incentive awareness. Within the male sample, the corresponding counts are 219 and 104 out of 229. The resulting proportions are shown in Table 4.4.
| Gender | Group size | Availability yes | Incentives yes |
|---|---|---|---|
| Female | 171 | 151 (88.30%) | 47 (27.49%) |
| Male | 229 | 219 (95.63%) | 104 (45.41%) |
Source: Original survey report (2021), Table 4.2.2, pp. 49-50. Counts retained; percentages recalculated.
Male respondents' availability awareness exceeds female respondents' awareness by 7.33 percentage points. The incentive-awareness difference is 17.93 percentage points. The latter comparison uses the yes responses of 104 out of 229 males, or 45.41%; the original prose mistakenly reports the complementary no percentage.
The published student gender table allows a limited comparison within respondent category. Subtracting its counts from the combined gender table identifies professional counts without inventing respondent records. This reconstruction is shown in Table 4.5.
| Category | Gender | Size | Availability yes | Incentives yes |
|---|---|---|---|---|
| Students | Female | 145 | 129 (88.97%) | 39 (26.90%) |
| Male | 154 | 150 (97.40%) | 70 (45.45%) | |
| Professionals | Female | 26 | 22 (84.62%) | 8 (30.77%) |
| Male | 75 | 69 (92.00%) | 34 (45.33%) |
Source: Original survey report (2021), Tables 4.1.2, 4.2.2 and 4.3.3, pp. 45, 49 and 56. Counts retained; percentages recalculated. Professional counts are reconstructed by subtraction; see Appendix A2.
For incentive awareness, the male-female difference is 18.56 percentage points among students and 14.56 percentage points among professionals. Thus, in the reported data, the overall contrast does not disappear when the two broad respondent categories are considered separately. This does not adjust for age, income, occupation, recruitment networks, or other influences, and it does not establish a causal effect of gender.
No inference about an inherent difference in interest or ability follows from these observations. A defensible response is to investigate whether access to information, purchasing roles, or survey selection differed across the groups, using evidence designed to test those possibilities.
4.4 Student Awareness by Residence
Among students, reported availability awareness is highest in the rural group, at 96.75%, followed by 91.18% in suburban areas and 88.31% in urban areas. Incentive awareness is much closer across the three groups.
| Residence | Group size | Availability yes | Incentives yes |
|---|---|---|---|
| Rural | 154 | 149 (96.75%) | 58 (37.66%) |
| Suburban | 68 | 62 (91.18%) | 23 (33.82%) |
| Urban | 77 | 68 (88.31%) | 28 (36.36%) |
Source: Original survey report (2021), Table 4.3.4, pp. 57. Counts retained; percentages recalculated.
Incentive awareness is 37.66% among rural students, 33.82% among suburban students, and 36.36% among urban students. The largest difference is 3.84 percentage points. These descriptive differences do not justify claiming that rural residents are more interested in government support. The survey does not measure that explanation, and the comparison concerns students reached by the survey rather than all residents of each area.
Residence categories also lack a documented operational definition. A respondent's self-classification may not correspond to administrative classifications. No district identifier or spatial information is available to relate awareness to actual charging coverage.
4.5 Perceived Barriers to Adoption
Charging-station unavailability is the most frequently selected category in the combined sample, followed by limited model or brand choice and high price. Table 4.7 recalculates the proportion selecting each barrier using respondents, rather than selections, as the denominator.
| Barrier | Combined n 400 | Students n 299 | Professionals n 101 |
|---|---|---|---|
| Unavailable charging stations | 269 (67.25%) | 203 (67.89%) | 66 (65.35%) |
| Range anxiety | 107 (26.75%) | 80 (26.76%) | 27 (26.73%) |
| High price | 182 (45.50%) | 128 (42.81%) | 54 (53.47%) |
| Limited models or brands | 193 (48.25%) | 148 (49.50%) | 45 (44.55%) |
| Long charging time | 124 (31.00%) | 92 (30.77%) | 32 (31.68%) |
Source: Original survey report (2021), Tables 4.2.4, 4.3.11 and 4.4.7, pp. 52, 63-64 and 74. Counts retained; percentages recalculated. Multiple responses permitted. Percentages are shares of respondents and need not sum to 100%. The unreported other category is excluded.
Multiple answers permitted; percentages use 400 respondents, not 875 reported selections. Percentages need not sum to 100. The other category was offered but not tabulated.
Source: Original survey report (2021), Tables 4.2.4, 4.3.11 and 4.4.7, pp. 52, 63-64 and 74. Counts retained; percentages recalculated.
The sum of the five counts is 875 because respondents could select more than one answer. Charging-station unavailability accounts for 269 selections, corresponding to 67.25% of respondents and 30.74% of all tabulated selections. These are two different statistics. The former answers how prevalent this reported concern is; the latter answers how selections are distributed across the listed categories.
Charging unavailability is selected by 67.89% of students and 65.35% of professionals. High price is selected by 42.81% of students and 53.47% of professionals. Limited model choice is selected by 49.50% of students and 44.55% of professionals. The professional-student difference for price is 10.66 percentage points, larger than the corresponding difference for charging access.
That price contrast is consistent with the possibility that respondents closer to vehicle purchasing give more attention to purchase costs, but purchasing proximity is not observed. Differences in income, household responsibility, or recruitment could also account for it. The result is therefore presented as a sample comparison rather than an explanation of behaviour.
Range anxiety is the least selected of the five categories, but 107 respondents, or 26.75%, still select it. Its lower relative position does not demonstrate that range is unimportant. Charging access, charging time, and range may overlap in respondents' understanding. The reported marginal counts cannot determine which concerns were selected together.
4.6 Attitudes and Future Preferences among Students
Student responses indicate considerable support for electric vehicle promotion and infrastructure adaptation. A majority also believes electric vehicle introduction could reduce global warming and considers electric cars better than conventional cars. The meaning of better is not defined in the questionnaire.
| Statement or question | Yes | No | Maybe |
|---|---|---|---|
| EV introduction could reduce global warming | 224 (74.92%) | 12 (4.01%) | 63 (21.07%) |
| Electric cars are better than conventional cars | 168 (56.19%) | 26 (8.70%) | 105 (35.12%) |
| Infrastructure adaptation is timely | 217 (72.58%) | 13 (4.35%) | 69 (23.08%) |
| More effort should go into promotion | 227 (75.92%) | 22 (7.36%) | 50 (16.72%) |
Source: Original survey report (2021), Tables 4.3.6-4.3.9, pp. 60-62. Counts retained; percentages recalculated. Denominator 299 for every row. Statements are abbreviated for display; original wording appears in Appendix C.
These are reported opinions. The environmental question does not test lifecycle knowledge, and the comparison with conventional cars does not specify price, range, or another evaluation criterion. Support for promotion should likewise not be equated with agreement to fund a particular subsidy from public revenue.
The distribution of future vehicle preferences is less decisive. Electric vehicles are selected by 104 students, whereas 108 select do not know. Table 4.9 retains the not applicable responses in the denominator.
| Response | Count | Share of 299 |
|---|---|---|
| Electric | 104 | 34.78% |
| Conventional | 60 | 20.07% |
| Bicycle | 15 | 5.02% |
| Do not know | 108 | 36.12% |
| Not applicable | 12 | 4.01% |
Source: Original survey report (2021), Table 4.3.10, pp. 63. Counts retained; percentages recalculated.
The 34.78% electric preference is therefore a sample orientation, not an expected future market share. The questionnaire does not specify whether respondents will purchase a vehicle, when they will do so, or whether the eventual choice will involve a car or another vehicle. The 36.12% uncertain category is particularly relevant: a large group has not expressed a settled preference.
Students also rated exposure to electric vehicle advertising on a scale from zero, labelled none, to ten, labelled frequently. Counts for ratings zero through five total 259, or 86.62% of students. This is a proportion scoring at or below the midpoint. It cannot be described uniformly as seeing advertisements very rarely because the intermediate scale points have no reported verbal definition. The survey also lacks a comparable measure of exposure to conventional vehicle advertising.
4.7 Conditional Purchase Consideration among Professionals
Professionals' answers to the four consideration questions are reported in Table 4.10. The baseline question asks whether respondents would consider an electric car if buying a new car. The subsequent scenarios introduce specific conditions.
| Condition | Yes | No | Maybe |
|---|---|---|---|
| Baseline consideration | 39 (38.61%) | 16 (15.84%) | 46 (45.54%) |
| Price about INR 600,000 | 52 (51.49%) | 12 (11.88%) | 37 (36.63%) |
| Range about 600 km | 76 (75.25%) | 3 (2.97%) | 22 (21.78%) |
| Rapid charging nearby and home charging | 79 (78.22%) | 6 (5.94%) | 16 (15.84%) |
Source: Original survey report (2021), Table 4.4.5, pp. 71-72. Counts retained; percentages recalculated. Denominator 101 for each question. Nearby means within a five-kilometre radius in the questionnaire. The scenarios are separate questions, not randomised treatments.
Separate hypothetical questions to 101 professionals. Aggregate margins cannot show individual changes. These are not causal effects, actual sales or estimates of willingness to pay.
Source: Original survey report (2021), Table 4.4.5, pp. 71-72. Counts retained; percentages recalculated.
The yes proportion is 38.61% under the baseline question. It is 51.49% under the INR 600,000 price scenario, 75.25% under the 600-kilometre range scenario, and 78.22% under the combined charging scenario. Relative to the baseline, the aggregate yes shares are higher by 12.87, 36.63, and 39.60 percentage points, respectively, calculated from the underlying counts before rounding.
These differences are not estimated causal effects. All respondents were offered hypothetical questions rather than randomly assigned changes in actual conditions. The published table also does not show individual transitions. It cannot establish that 40 previously unwilling respondents became willing under the charging scenario, even though the total yes count rises from 39 to 79.
The maybe category is substantial at baseline, with 46 respondents. Under the charging condition it contains 16 respondents. This pattern suggests that the hypothetical conditions are associated with less expressed uncertainty in the aggregate. However, it does not identify which individuals changed categories, whether the responses would survive a real budget constraint, or whether respondents understood the scenarios in the same way.
The scenarios cannot be compared as changes of equal magnitude. No common baseline vehicle, baseline range, or baseline price is specified. It is therefore inappropriate to rank the scenarios as policy treatments of equal magnitude. The combined charging condition cannot isolate a public-charger effect from a home-charger effect.
4.8 Support for Infrastructure Adaptation
Across the common infrastructure item, 290 respondents answer yes, 20 no, and 90 maybe. This yields 72.50%, 5.00%, and 22.50%, respectively. Student and professional yes shares are nearly identical, at 72.58% and 72.28%.
The wording asks whether it is high time for India to adapt its infrastructure to accommodate electric vehicles. It invites a broad judgement and may encourage agreement. The result records support for the general direction of development. It does not measure willingness to pay for infrastructure, approval of particular locations, or the benefit-cost ratio of an investment programme.
5 Discussion
5.1 What the Awareness Contrast Means
The clearest informational result is the difference between recognising electric vehicle availability and recognising government incentives. Within this sample, a campaign limited to telling people that electric vehicles exist would address a less prevalent reported gap than one explaining relevant incentives. This is a statement about the content suggested by the observations, not a prediction of the campaign's sales effect.
Low incentive awareness can have several meanings. Some respondents may not have encountered policy information; some may regard it as irrelevant because they do not plan to buy a qualifying vehicle. Others may be unsure which government or vehicle category the question refers to. The survey does not ask respondents to identify a scheme or explain its conditions, so it cannot separate these interpretations.
An information intervention should consequently provide eligibility details and a way to verify understanding. Its effectiveness should be assessed through accurate knowledge and subsequent feasible choices, rather than through recall of promotional material alone. High product awareness does not make further information unnecessary; it changes which information would be most useful to test.
5.2 Charging Access and Financial Constraints
Charging access is the most common reported obstacle in both respondent groups. It is also the focus of the hypothetical condition associated with the highest professional yes share. The two results concern different questions, but together they make charging convenience a plausible subject for further investigation.
The data cannot establish that charger numbers are the binding constraint on actual adoption. Respondents were asked about market unpopularity, not whether a specific missing charger prevented their own purchase. A charging station that is distant, unreliable, incompatible, or inconvenient may also be counted as unavailable. Effective access therefore requires more information than the number of installations.
Financial constraints remain material: high price is selected by 45.50% of the combined sample and 53.47% of professionals. Even the price scenario produces only a 51.49% yes share among professionals, with 36.63% answering maybe. A lower quoted purchase price may leave uncertainty about running costs, charging access, warranties, or household affordability unresolved.
The survey therefore supports a multi-constraint interpretation of adoption. It does not show that a single intervention would solve the problem. Charging and affordability may reinforce each other, but their interaction cannot be measured without linked responses and a design that varies the relevant conditions systematically.
5.3 The Limits of Stated Consideration
Considering a vehicle is an early step in a purchasing process. It requires less commitment than choosing a model, arranging finance, or placing an order. A respondent can answer yes to consideration while remaining unlikely to buy an electric car at any currently available price.
The scenarios are especially vulnerable to hypothetical interpretation because they offer attractive conditions without requiring respondents to confront their costs or availability. The charging question combines home installation with a dense rapid-charging network. A high yes share may reveal that respondents find this package appealing, but it says little about how much they would pay for it or whether the package is efficient to supply.
The scenario comparisons also cannot estimate demand elasticities. An elasticity requires a defined proportional change in price and a corresponding change in demand, with an appropriate basis for identifying the relationship. Here there is one hypothetical price value, no observed purchasing quantity, and no documented common initial price.
The useful conclusion is narrower: expressed consideration is conditional on the attributes described. Future research should examine realistic choices among clearly specified alternatives and, where possible, follow actual purchasing outcomes.
5.4 Group Differences and Distributional Questions
The descriptive gender contrast deserves investigation without stereotyped explanation. Its persistence within the student and professional categories suggests that those two categories alone do not account for it in the reported tables. Yet the professional female subgroup contains only 26 respondents, and no linked controls for age or income are available. Recruitment and exposure remain plausible alternative explanations.
The student residence comparison provides still less basis for a regional policy ranking. Incentive-awareness differences are modest, residence is self-classified, and the professional awareness table by residence is absent. The data cannot identify rural households' purchasing power or prove that rural charging provision would have a higher return than urban provision.
Distribution nevertheless belongs in policy evaluation. Homeowners with private parking may benefit differently from tenants or people who park on the street. Car-purchase subsidies may benefit people with the means to buy a car more than people dependent on public transport. These are economic considerations to investigate; they are not findings demonstrated by the present sample.
5.5 Relation to the Literature
The observed prominence of charging, price, and product attributes is compatible with the consumer-choice framework reviewed in Chapter 2. Compatibility is weaker than confirmation. Similar concerns in different settings do not establish that consumers assign the same monetary values to them or that a policy effective elsewhere will have the same effect in Kerala.
The main analytical advance over a simple list of obstacles is to connect each reported concern to a possible mechanism. Price can act through affordability or lifetime costs. Charging can act through convenience and coordination. Limited models can act through the absence of a vehicle suited to a buyer's needs. Incentive awareness can act through perceived net cost and application difficulty. These mechanisms help formulate testable questions without treating the survey as evidence that the mechanisms have already been identified.
5.6 Evidential Boundaries
Three conclusions are firmly supported as descriptions of this sample: availability awareness exceeds incentive awareness; charging unavailability is the most commonly selected reported barrier; and professional yes shares differ across hypothetical consideration scenarios. These results are internally recoverable from counts and do not require reading rounded chart segments as precise data.
The evidence is weaker for explanations of group differences, comparisons of intervention effectiveness, and statements about future behaviour. It is insufficient for statewide prevalence estimates, an optimal charger spacing rule, or a recommendation to subsidise a particular model or industry. Stating these boundaries preserves the value of the observed patterns by preventing them from carrying claims they cannot support.
6 Policy Implications and Further Research
6.1 A Policy Sequence Based on Identifiable Questions
The survey warrants further evaluation of incentive communication, charging access, and affordability. It does not establish the order in which Kerala should allocate its entire transport budget. A defensible sequence begins with clarifying policy information and collecting evidence on practical access, followed by pilot interventions whose costs and outcomes can be compared.
The distinction between a reported concern and an evaluated intervention is essential. A frequently selected barrier identifies a problem respondents perceive. Public spending should additionally require evidence that a proposed programme reduces that problem, changes feasible choices, and produces benefits greater than its costs.
6.2 Clearer and Testable Incentive Information
Public information should distinguish national and state arrangements, eligible vehicle categories, application requirements, and the date on which information applies. Communication that merely announces subsidies can create incorrect expectations if private car purchasers do not qualify. Dealerships, educational institutions, and public service channels are possible delivery points, but the sample does not establish which would reach the relevant population most effectively.
An evaluation could compare an accurate information treatment with usual information provision. Outcomes should include correct answers to eligibility questions, understanding of net purchase costs, and subsequent enquiries or applications among eligible consumers. If purchasing is measured, follow-up should distinguish additional purchases from transactions that would have occurred anyway. Random assignment of information, subject to appropriate research procedures, would provide a stronger basis for causal inference than this survey.
6.3 Charging Pilots Designed around Actual Access
Charging investment should be informed by travel patterns, parking conditions, existing station use, electricity capacity, and the cost of serving candidate locations. Public fast charging and home charging should be investigated separately before selecting a package. A five-kilometre radius appears in the questionnaire as a hypothetical condition and should not become a planning standard on the strength of these responses.
Pilot sites should report utilisation, uptime, compatibility, waiting time, and the full cost of installation and operation. Evaluation should ask whether access improves for users with limited charging alternatives. Locations with high expected utilisation may reduce unit costs, while some socially useful locations may require explicit support because low early demand makes commercial investment unattractive. Assessing that trade-off requires spatial and financial evidence.
Measures of adoption should be complemented by measures of mobility and environmental outcomes. Replacing a conventional trip with an electric trip can have a different social effect from generating additional private-car travel. The survey does not resolve this distinction.
6.4 Affordability and the Design of Financial Support
The price responses justify examining affordability; they do not identify an appropriate subsidy amount. Analysis should distinguish initial-price constraints, access to finance, and expected lifetime costs. A transparent ownership-cost comparison using realistic travel and energy assumptions can improve information, while a financing intervention would address a different constraint.
Before proposing broad purchase subsidies, policymakers should estimate fiscal cost, additional adoption, distribution across income groups, and environmental benefits per unit of expenditure. Possible alternatives include support for shared transport, high-use commercial vehicles, or charging facilities. Their merits depend on local evidence and the objective being pursued.
The survey provides no basis for concluding that domestic battery production would necessarily reduce consumer prices. Such an industrial policy would require analysis of scale, input costs, technology, trade, and the public resources involved. Similarly, limited model choice does not by itself justify a tax concession to every manufacturer. Competition and market-entry policy should be assessed separately from respondents' perceptions of product availability.
6.5 Environmental Safeguards and Policy Objectives
Transport policy should define the outcome it seeks: reduced local air pollution, lower lifecycle greenhouse gas emissions, improved mobility, or some combination. The appropriate intervention may differ by objective. Environmental communication should specify that battery electric vehicles avoid combustion exhaust during driving while lifecycle impacts remain dependent on electricity and production conditions.
Charging policy should also consider electricity system demands and battery end-of-life arrangements. The present survey contains no measurements of grid impacts, battery recovery, or lifecycle emissions. These considerations therefore enter as requirements for subsequent evaluation rather than estimates supplied by this thesis.
6.6 A Stronger Follow-Up Survey
A follow-up should define its target population and document recruitment. If the aim is to describe adult residents of Kerala, a sampling strategy should address geographic coverage, age, and household characteristics rather than relying mainly on students. If the aim is to understand near-term car buyers, eligibility should instead identify people considering a purchase within a defined period. These are different research populations.
The questionnaire should distinguish electric cars from two-wheelers and other vehicle types. It should ask about income bands, purchase budgets, planned purchase timing, annual travel, parking access, and familiarity with specific policies. Technical knowledge should be tested with unambiguous factual questions, with incorrect and uncertain responses retained separately. Agreement with broad positive statements should remain an attitude measure.
Vehicle scenarios should use realistic alternatives, a common description of relevant attributes, and explicit prices. Public charging access and home charging should vary independently. A carefully designed choice experiment could estimate trade-offs; a follow-up linked to actual decisions could examine the difference between intentions and behaviour. Those analyses require anonymised respondent records and a documented coding scheme.
Research procedures should explain consent, data minimisation, storage, and access. Names should not be collected unless needed for a defined purpose, and contact details for follow-up should be kept separate from analytical responses. The final report should retain the questionnaire, fieldwork dates, recruitment description, cleaning rules, and count tables so that both the study design and its arithmetic can be reviewed.
7 Conclusion
The survey offers a useful but bounded account of electric vehicle awareness and perceived adoption barriers among 400 respondents in Kerala in the 2021 study context. Awareness of market availability is reported by 92.50% of respondents, while incentive awareness is reported by 37.75%. Charging-station unavailability is selected by 67.25%, making it the most frequently selected of the five tabulated barriers. Limited models and high prices are also common concerns.
Among the 101 professionals, 38.61% would consider an electric car under the baseline question. Aggregate consideration is higher under the specified price, range, and charging conditions. The highest yes share, 78.22%, concerns rapid public charging nearby together with a home charging facility. This is evidence of the appeal of a hypothetical package within the sample, not a causal estimate of adoption or an optimal infrastructure specification.
The economic interpretation is that product recognition can coexist with uncertainty about policy, affordability, and the practical use of a vehicle. Information, price, and charging interventions address related but distinct constraints. Their effectiveness and social return require evaluation beyond a descriptive survey.
The strongest policy implication is to test accurate incentive communication and charging access improvements while measuring costs, distribution, and actual outcomes. The study supports those questions without claiming to determine Kerala's investment priorities. Its descriptive value rests on explicit denominators, cautious interpretation, and a clear separation between observations and proposals.
References
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- Hawkins, T. R., Singh, B., Majeau-Bettez, G., and Stromman, A. H. (2013b). Corrigendum to Comparative environmental life cycle assessment of conventional and electric vehicles. Journal of Industrial Ecology. Read the source ↗
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Appendix A Calculation Rules and Source Traceability
A1 Recoverable Data and Percentage Denominators
All empirical counts used in Chapters 3-7 originate in the frequency tables of the 2021 survey report. Percentages are recalculated. No respondent-level dataset, additional survey response, simulated observation, or estimated missing category is introduced. The source map below identifies the original evidence behind the revised tables.
| Revised table or result | Original table | Original pages | Denominator |
|---|---|---|---|
| 4.1 | 4.1.1-4.1.3 | 44-45 | 400 |
| 4.2 | 4.4.2-4.4.3 | 67-68 | 101 |
| 4.3 | 4.2.1, 4.3.2, 4.4.4 | 48, 55, 69 | 400, 299, 101 |
| 4.4 | 4.2.2 | 49 | 171, 229 |
| 4.5 | 4.2.2, 4.3.3 and group margins | 45, 49, 56 | 145, 154, 26, 75 |
| 4.6 | 4.3.4 | 57 | 154, 68, 77 |
| 4.7 | 4.2.4, 4.3.11, 4.4.7 | 52, 63-64, 74 | 400, 299, 101 |
| 4.8 | 4.3.6-4.3.9 | 60-62 | 299 |
| 4.9 | 4.3.10 | 63 | 299 |
| 4.10 | 4.4.5 | 71-72 | 101 |
| Advertising paragraph | 4.3.5 | 59 | 299 |
| Infrastructure paragraph | 4.2.3, 4.3.8, 4.4.6 | 51, 61, 73 | 400, 299, 101 |
Page numbers refer to the printed page numbers of the original PDF. Figures 4.1-4.3 are generated from the counts in revised Tables 4.3, 4.7 and 4.10.
For single-response items, use the count divided by the group size and multiply by 100. Thus, combined incentive awareness is 151 / 400 multiplied by 100 = 37.75%. For within-gender awareness, use the corresponding gender total: male incentive awareness is 104 / 229 multiplied by 100 = 45.41%.
For the barrier item, respondent percentages use 400, 299, or 101. The alternative selection denominator is 875 for the combined sample, 651 for students, and 224 for professionals. Thus, charging unavailability is 269 / 400 multiplied by 100 = 67.25% of respondents, but 269 / 875 multiplied by 100 = 30.74% of tabulated selections.
For scenario comparisons, calculate the difference from the underlying counts before rounding. The charging-scenario contrast is (79 - 39) / 101 multiplied by 100 = 39.60 percentage points. It is an aggregate marginal difference, not the number of individual converts.
A2 Reconstruction of Professional Awareness by Gender
The combined gender table reports 47 female and 104 male respondents aware of incentives. The student gender table reports 39 female and 70 male respondents aware. Subtraction therefore yields 8 female and 34 male professional respondents aware of incentives. Professional denominators are similarly 171 - 145 = 26 females and 229 - 154 = 75 males. The reconstructed counts sum to 42, the independently reported professional incentive-awareness total.
Availability awareness is reconstructed in the same way: 151 - 129 = 22 female professionals and 219 - 150 = 69 male professionals. These sum to the reported professional total of 91. Reconstruction is limited to counts uniquely identified by the published margins. It supplies no information about links to age, residence, vehicle ownership, or purchase consideration.
A3 Tabulated Selections Compared with Respondent Prevalence
The respondent percentages are the main results because they answer how many people selected each listed concern. Selection shares are retained here to make the original denominator recoverable. Neither statistic identifies which combinations of concerns each person selected. The unreported other category prevents treating the tabulated selection distribution as exhaustive of every answer that may have been given.
Appendix B Corrections and Unresolved Reporting Issues
The following record separates arithmetic corrections from analytical exclusions. An arithmetic correction can be made where reported counts establish the appropriate value. An unresolved measurement problem requires a narrower interpretation or omission because the tables do not establish what occurred during collection or coding.
B1 Multiple Response Percentages
The original report describes 31% of respondents as selecting charging unavailability. Its combined count is 269 of 400, giving 67.25%. Approximately 31% instead describes the share of the 875 tabulated selections. The same denominator issue affects student and professional barrier descriptions. Chapter 4 consistently reports respondent percentages and Appendix A separately reports selection shares.
B2 Male Awareness of Incentives
On original page 50, male incentive awareness is reported as 54.59%. Table 4.2.2 shows 104 yes and 125 no responses among 229 males. The correct yes percentage is 45.41%; 54.59% describes no. The revised analysis uses the internally consistent table counts.
B3 Infrastructure Support and Charging Time
The original findings on page 77 state that one-third support infrastructure adaptation. Table 4.2.3 instead reports 290 yes responses out of 400, or 72.50%. On page 52, the prose gives long charging time as 21%, whereas the table gives about 14% of selections. The corrected respondent percentage is 124 / 400 multiplied by 100 = 31.00%.
B4 Price Consideration and Purchase Readiness
The original summary suggests that more than two-thirds of professionals are ready to buy under a lower price or increased range. The separate yes counts are 52 for price and 76 for range out of 101, giving 51.49% and 75.25%. No joint table identifies how many answer yes to at least one of these conditions. The question asks about considering a car; the revised thesis preserves that outcome rather than describing purchase readiness.
B5 Advertising Exposure
The original report describes ratings zero through five as very rare advertising exposure. Their counts sum to 259 of 299, or 86.62%. The instrument labels zero as none and ten as frequently; the intermediate values do not justify assigning one verbal category to the entire lower half. The revised description states only that these respondents scored at or below the midpoint. There is no observed comparison with conventional vehicle advertising.
B6 Agreement Scales and Category Reporting
The agreement-scale questionnaire includes the statement that electric cars cost about the same to buy as petrol or diesel vehicles. Result charts use a statement about electric vehicles costing more. The published report does not document reversal or another recoding, so the equivalence cannot be established. The environmental statement also uses zero emissions without a lifecycle boundary. Consequently, agreement charts are not scored as validated knowledge, and their rounded segments are not reconstructed as counts.
The student questionnaire offers non-binary and prefer-not-to-say gender options, but the published demographic tables report only male and female respondents. The professional questionnaire pages do not visibly include the same gender item, although gender counts appear in the tables. The revised analysis retains the reported categories and identifies the documentation limit; it does not infer the missing collection or coding procedure.
B7 Scope of the Owner Survey and Regional Conclusions
The original methodology mentions existing owners and snowball recruitment but does not supply a separately recoverable owner sample or result table. Owner-specific claims are therefore excluded. Broad assertions about rural purchasing power, technical interest by gender, or charging as the only rural adoption obstacle are also excluded because no reported table tests them.
B8 Material Excluded from the Main Analysis
The extended list of subsidised models and the technical component catalogue are not required to answer the research questions. They are replaced by a focused historical policy account and an economic framework. Partial agreement-chart labels are not converted into numerical observations. Certification and declaration pages from the original submission are not reproduced as certifications of this revised analysis.
Appendix C Original Questionnaire
The following facsimiles reproduce the questionnaire pages from the original survey report, pp. 82-89. Their wording and response scales are retained so that the measurement choices can be inspected. They include a name field, questionnaire branching, multiple-response barrier items, and the hypothetical professional questions. They do not document recruitment, consent, or the coding of responses. The original report's final page is blank and is not reproduced. Select a page to enlarge it.
