A study on preferences of female customers in the purchase decision of cars in Chennai

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This study employs Conjoint Analysis to ask to know the preference of Women in Cars. Results of the study are discussed with future directions for research.

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International Journal of Mechanical Engineering and Technology (IJMET) Volume 10, Issue 03, March 2019, pp. 1227-1237. Article ID: IJMET_10_03_125 Available online at http://www.iaeme.com/ijmet/issues.asp?JType=IJMET&VType=10&IType=3 ISSN Print: 0976-6340 and ISSN Online: 0976-6359 © IAEME Publication Scopus Indexed A STUDY ON PREFERENCES OF FEMALE CUSTOMERS IN THE PURCHASE DECISION OF CARS IN CHENNAI Balaji Jayakrishnan and Dr. R. Aruna VIT University,India ABSTRACT In the recent years, women’s driving four wheeler has become a very common sight all over the India. According to a survey, 85% of women are in the driving seat for all purchases. Yet, many independent marketing and communication agencies reveal that 80% of women believe that the automotive industry as a whole is not doing a good job at representing women, and 88% of women do not see themselves represented in the sector’s advertising or websites. For years, women have been considered as primary decision makers for most household products and forward thinking companies have found ways to capitalize on this by developing marketing plans that address women’s multifaceted lifestyles, by evaluating and retraining existing sales and customer service forces to better serve women’s needs and interests. Many studies show that women feel advertising has made them aware of different cars in the market. Also many women agree about the perceived exclusivity of car ads in the ads being aimed only at men. This study employs Conjoint Analysis to ask to know the preference of Women in Cars. Results of the study are discussed with future directions for research. Keywords: Contextual Targeting, Automobiles, Women, Conjoint Analysis and Consumer Behaviour. Cite this Article: Balaji Jayakrishnan and Dr. R. Aruna, A Study on Preferences of Female Customers in the Purchase Decision of Cars in Chennai, International Journal of Mechanical Engineering and Technology, 10(3), 2019, pp. 1227-1237. http://www.iaeme.com/IJMET/issues.asp?JType=IJMET&VType=10&IType=3 1. INTRODUCTION India has proved itself as one of the world’s largest and fastest growing automobile markets over the years, with production in 2018 of 29 million vehicles and it is estimated to grow about 60 million vehicles in 2022 (Automobile sector report, IBEF, April 2017). Currently contributing 7.1 percent to the GDP and around 3.2 crore people are employed directly or indirectly by the Indian Automobile Industry. Since the purchase of car requires high involvement, it is also equally important to promote the advertisement of cars to get the attention of customers. Presently Indian ad industry contributes 0.4% of GDP. Advertising http://www.iaeme.com/IJMET/index.asp 1227 editor@iaeme.com
Balaji Jayakrishnan and Dr. R. Aruna plays a pivotal role in increasing brand awareness as well as creating strong brand associations (Yoo, Donthu, & Lee, 2000). Understanding a consumer is complex as it is a blend of personal, economic, political, social, cultural, technological, demographic, psychological, and natural factors. Some of these factors are uncontrollable and hence an effective marketing strategy needs to be developed to understand these complexities. Hence it is time for advertising industry to implement new strategies and plans to implement innovative developments. In this context both automobile and ad industry can jointly present new avenues towards enhancing the business. Unlike earlier times, a woman driving is hardly a rare sight nowadays. An interesting fact is that, women in smaller cities start driving earlier than those in metros. Most women drive all days of the week, with various purposes such as work commuting, shopping and ferrying kids. Also, most women cover a weekly range of 25-50km, while a significant number manage to clock around 50-100km in a week. Women are smart browsers of the social web and digital world compared to men. When buying cars, women focus mainly on the advertisements, in particular digital ads than print media, while men concentrate on vehicle’s technical specifications. Relatively it is now found that Contextual targeting can provide a clear advantage to promote ads to in order to engage specific customer. Targeted automobile ads have neglected women in their promotional campaigns. There is a need to establish a finer communication between advertising and purchase decision involvement when targeting female customers. This paper focuses primarily on analysing the advertising cues and purchase behaviour cues related to female customers. According to Barletta (2003), “Women are the world’s most powerful consumers. They are the big spenders, whether that is goods purchasing for households, corporate purchasing, or small businesses” (p. xix). Packaged goods companies and retailers have long recognized that women form the core of their market, however, until recently the big businesses – automotive, financial services, computers, consumer electronics, home improvements, and travel for example – appear to have overlooked female customers almost entirely. Lots of advertisements and marketing communications that resonate with men don’t simply hit the spot with women. A company that is aware of consumers’ reaction to different characteristics of goods, prices and advertisement tricks has advantage over its competitors. (Kotler & Keller 2009) Gender- based differences in perceptions, attitudes and communication styles generate gender-differentiated responses in priorities, decision processes, and purchase outcomes. (Barletta, 2003) Menon (2012); and Jacob and Khan (2010) reported in their studies that there was considerable proportion of modern women car buyers, which had increased three fold in the recent years. Companies have started to dig deep into the Indian women's psyche and attention for details. Marketers may need to look at the needs of women customers, who are increasingly growing in the segment. Westbrook and Fornell (1979) classified the respondents based on the extent of physical shopping, and the use of neutral sources versus personal sources while searching for pre purchase information. Their findings show four segments objective shoppers, moderate shoppers, store intense shoppers, and personal advice seekers. The buyer’s age and education, her/his satisfaction with a previously purchased product and the working condition of the same, the evoked set size, and joint husband-wife decision making are shown to influence the information seeking process (Westbrook & Fornell, 1979). http://www.iaeme.com/IJMET/index.asp 1228 editor@iaeme.com
A Study on Preferences of Female Customers in the Purchase Decision of Cars in Chennai Targeted selling will have a direct. Positive impact and five ways to get women buyer’s attention is to get the innovative selling strategies, diversification, a tailored approach, Automotive Ad campaigns filling a wide range of professionals, dealership that fills her framework for their purchases and a positive pre and post sales experience. (Anne Fleming, 2017) Goswami (2007) segmented college-goers psychographically into five clusters life-loving go-getters, politically-conscious positivists, independent-minded, destiny-believing pessimists, and happy-go-lucky dependants. Kumar and Sarkar(2008) segmented metropolitan consumers into six behavioural groups well-settled, strugglers, enjoyers, conservatives, self-concerned, and realists in order to understand their consumption patterns. The segments were profiled in terms of their product ownership, activities and interests, financial investment avenues, and media habits. Sesa Sen (2017) expresses that in the past five years, the percentage of women buying cars has nearly doubled, from 10-12 per cent to 25 per cent. And, to tap this growing market, automakers are increasingly making their products women friendly. While Korean carmaker Hyundai Motors India attributes 20-25 per cent of sales to women drivers, nearly 20 per cent of the demand for Renault’s Kwid is from women. For Maruti, the country’s largest car maker, as much as 15 per cent of sales across brands are driven by women car buyer, and the company expects that number to grow. Nielsen India (2014) an end to end consumer insights company, reported interestingly on how women are now having a greater say in car purchase decisions in India. According to the report Safety and convenience are two such top areas that women consider important. Dorsch, Grove and Darden (2000) studied consumer buying behaviour using the 5 step process (need – information search – evaluation of alternatives – purchase – post purchase evaluation) problem solving paradigm or through the progression of consumer choice from a product class to brand choice and found that marketers can do a better segmentation and targeting of their campaigns based on the terminal values customers desire when choosing a service category. Another factor considered was the knowledge about prior experience on intentions to use a service category. White (2004) discussed the factors that affect car buyers ‘brand preference and he pointed out that with an increase in multi-car households, car dealers and advertisers needed to target the right audience, taking into account the pester power of children and the importance of life stage. Despite the fact that women are the primary buyers of most new cars, he admits that the motor trade has traditionally been contemptuous of women’s role in the car buying process. Jacob and Khan (2010) reported in their studies that there is also a substantial influence of women in the car purchase decision of the family. The purpose of the study is to find the involvement, preference and influence of Women in Chennai in the purchase decision of Automobiles. 1. To assess the feature preferences for the prospective women purchasers. 2. To assess the factors influencing the car purchasing behaviour of women. 2. METHODOLOGY Simple random sampling and Primary data collection methodology will be used to collect data and conjoint analysis will produce the relatively important features of women car consumers.We contacted a total of 600 women customers from Chennai through email and requested to respond to an online questionnaire. The purpose of the study was communicated in the email and an informed consent was taken. Out of 600 customers contacted, 504 customers responded to the questionnaire. Due to incompleteness of questionnaire, 4 of them were deleted http://www.iaeme.com/IJMET/index.asp 1229 editor@iaeme.com
Balaji Jayakrishnan and Dr. R. Aruna and 500 respondents were chosen finally for the research. A self-constructed questionnaire with the aim of exploring the influencing factors of car purchasing decisions was designed. Necessary deletions, additions, and editions were made into the questionnaire after having conducted a pre-test. Acceptable level of understanding was aimed while selecting the list of attributes from both industry professionals and women consumers. The selection of attributes was based on deductive approach and was irrespective of the aims or purpose of the reviewed studies from all levels of detail and various terms were used to refer them. Consumer preference modelling among multiattribute analysis has been one the major activities in consumer research. Conjoint analysis places the participant in a hypothetical use scenario and allows for the evaluation of preference for multi-attribute alternatives (Myung, 2003). Variables being examined within conjoint analysis are regarded as attributes. Each attribute (e.g. point size, column width, etc.) is then broken down into variations regarded as levels (e.g. 12 points, 4 points, etc.). The combinations of each attribute and the respective levels are classified as profiles. Participants are given profiles to evaluate via rating or choosing. Utility values are calculated for each level, based on the respondent’s choices. Upon calculating utility values, importance scores can also be determined. These scores help illustrate the impact the attributes have in the individual respondent’s selections. There are three main methods of conjoint analysis: traditional, adaptive, and choice-based. Kelly's (1955) repertory grid, focus group interviews, or judgments of product managers, retailers and others knowledgeable about the product/service and its uses can be used for this purpose. The more difficult and often subjective task is to reduce the number of attributes to a manageable size so that the estimation procedures are reliable while at the same time accounting for consumer preferences sufficiently well. 3. DEMOGRAPHIC CHARACTERISTICS From the above figure 1,2 and 3 we could interpret that women who owned cars were more in the age between 26- 35 and from the data we could also interpret that married women used and owned cars more than single ladies. Many women used cars for commutation to work and homemakers used cars for ferrying the kids and shopping. From the survey, even though many http://www.iaeme.com/IJMET/index.asp 1230 editor@iaeme.com
A Study on Preferences of Female Customers in the Purchase Decision of Cars in Chennai Indian women get their driving license by 20-25, still due to the heavy traffic in cities they prefer using a two-wheeler in place of a car. 4. PREFERENCE FEATURES The above figure gives the feature preferences of women in the purchase decision of cars. Out of which automated manual transmission and Anti-lock Braking System (ABS) are two top areas that women consider important. Even in the price conscious segments, automated manual transmission is preferred for the comfort and ease of driving. The ABS which is a critical safety feature is also considered important among the women buyers. GPS navigation is also considered as must have car features. Rear parking camera is another additional safety and comfort feature offered by many car dealers as an extra option with cost. Followed by the feature called Cruise control, which is popular only in C-segment cars, opted by less number of women. Then Rain sensing wipers, Road side assistance, Electric wing mirrors and flat tyres are some of the features preferred by women. From this we conclude that they are keener on the safety and comfort features. 4.1. Preference Models First, let p = 1, 2,..., t (1) denote the set of t attributes or factors that have been chosen. Next, let Yjp denote the level of the pth attribute for the jth stimulus. We first consider the case where Yjp is inherently a continuous variable (e.g., travel time or price). The case of categorical (or polytomous) attributes will be considered later. The vector model of preference, referred to as the Composite Criterion Model by Srinivasan and Shocker (1973b) and Parker and Srinivasan (1976), posits that the preference Sj for the jth stimulus is given by Sj = ∑𝒕𝒑=𝟏 𝑾𝒑 𝒀𝒋𝒑 , (2) Where the {Wp} are the individual's weights for the t attributes. Thus, the vector model is identical in mathematical form to the Fishbein-Rosenberg class of multiattribute models. As remarked earlier, the weights {Wp} will, in general, be different for different individuals in the sample. Geometrically, the preference Sj can be represented as the projection of the stimulus point {Yjp} on the vector {wp} in the t-dimensional attribute space. The ideal-point model posits that the preference Sj is negatively related to the squared (weighted) distance dl of the location {Yjp} of the jth stimulus from the individual's ideal point {xp}, where dj is defined as http://www.iaeme.com/IJMET/index.asp 1231 editor@iaeme.com
Balaji Jayakrishnan and Dr. R. Aruna 𝟐 dj2= ∑𝒕𝒑=𝟏 𝒘𝒑 (𝒚𝒋𝒑 − 𝒙𝒑 ) (3) 2 Thus, stimuli which are closer to the ideal point (smaller dj ) will be the more preferred ones (larger Sj). It turns out that the simultaneous estimation of {Wp} and {xp} is feasible for the weighted Euclidean measure of distance as specified in equation (3). If, however, the exponent 2 in equation (3) is replaced by a general Minkowski metric r, the estimation of {xp} becomes very difficult. Fortunately, however, the Euclidean metric is often a close enough approximation to the general Minkowski metric (Green 1975). The part-worth function model posits that Sj =∑ fp(yjp) , (4) Where fp is the function denoting the part worth of different levels of Yjp for the pth attribute. In practice, fp(Yjp) is estimated only for a selected set of levels for Yjp (usually three or four), with the part worth for intermediate Yjp obtained by linear interpolation. The part-worth function model provides the greatest flexibility in allowing different shapes for the preference function along each of the attributes. In particular, by defining fp(Yjp) = - Wp(Yjp - Xp)2 we get the idealpoint model and by setting fp(Yjp) = WpYjp we obtain the vector model. Similarly, the ideal-point model is more flexible than the vector model since it can be shown (Carroll 1972) that the vector model is a special case of the ideal-point model as Xp ~ ± 00. Intuitively, as Xp ~ + 00, preference a long the pth dimension increases as Yjp increases (since the ideal is at plus infinity) and this is essentially the same as the vector model with Wp > O. Although the part-worth function model seems to be the most attractive in terms of being compatible with any arbitrary shape for the preference function, this benefit comes at the cost of having to estimate additional parameters (thereby lowering their reliability) and the need to approximate intermediate values by linear interpolation. In particular, estimation of the vector model involves only the t parameters {wp }. For the ideal-point model, 2t parameters have to be estimated, namely {wp} and {xp}. If there are q levels, say, for each of the t attributes then (q - l)t parameters have to be estimated for the part-worth function model. Replacing fp(Yjp) by fp(Yjp) + ap does not alter the model in equation (4) in any essential way so that the part worth for level I , say, can be taken to be zero without any loss of generality. Consequently, only (q - 1) parameters need to be estimated for the pth attribute. 4.2. Predictive Modelling with Conjoint Analysis Conjoint Analysis attempts to determine the relative importance consumers attach to important attributes and the utilities associated with them, on different levels of the attributes. Conjoint utilities or part-worths are scaled to an arbitrary additive constant within each attribute and are interval data. The arbitrary origin of the scaling within each attribute results from dummy coding in the design matrix. We could add a constant to the part-worths for all levels of an attribute or to all attribute levels in the study, and it would not change our interpretation of the findings. Many manufacturers with their choice of technological and innovative product features, with quality and reliability, various advertising and marketing strategies started to target the Indian consumers, with the changing scenario in Automobile Industry in India. Purchasers or consumers in Automotive Industry started developing personal preferences and purchasing patterns with the multiplicity of choices available. The major objective was to build up a choice (forced) based Consumer Purchase Behaviour Model, with major parameters influencing the behavioural patterns of Women Car Customers. The data collected from the respondents was examined, verified, edited wherever necessary, for completeness, accuracy and reliability. http://www.iaeme.com/IJMET/index.asp 1232 editor@iaeme.com
A Study on Preferences of Female Customers in the Purchase Decision of Cars in Chennai More specifically, the major factors that influence purchase decision by women consumers identified 4 key attributes which on careful analysis and previous experience considered important and relevant. These are: 1. Reason to Purchase 2. Awareness 3. Preferences of Car features 4. Influence Factors based on model of the car Thereafter, different levels are assigned to each attribute and the data was further analyzed using statistical package IBM SPSS version 23.0. Respondents ranked each of the set of cards according to the most preferred choice of combination. Attribute Levels Upgrade Reason to Purchase Status Comfortability Magazines Awareness Social media Peers Anti-lock Braking System(ABS) GPS navigation Preferences of Car Features Rear parking camera Automated manual Transmission Price Influence Factors based on car Resale value model Security and safety features Ergonomics, comfort and entertainment features Given the attributes and their levels, there are 144 product profiles (3*3*4*4=144). To apply the full profile method in a feasible and practical manner, fractional factorial design is used to generate orthogonal arrays, resulting in a 16 profiles called design cards or plan cards. Some cards are also generated as hold out cards for validation purpose. After running Conjoint Analysis, we obtained the following results. Pearson’s R = 0.81 Significance 0.0000 Kendall’s Tau = 0.912 Significance 0.0005 The following utilities, Part-worth Utilities also known as attribute importance scores and level values are numerical values that measure the influence of consumer’s decision on each attribute and their levels. Attribute and Levels are interrelated. Attributes Relative Importance in % Reason to Purchase 46 Awareness 10 Preferences of Car Features 26 Influence Factors based on car model 20 http://www.iaeme.com/IJMET/index.asp 1233 editor@iaeme.com
Balaji Jayakrishnan and Dr. R. Aruna Relative Importance 20% Preferences… 26% Reason to 10% Purchase Reason to… 46% 0 50 The graph shown in the figure suggests that amongst the levels of reason to purchase, Women’s most preferred reason to purchase a car is for an upgradation from a two wheeler to car, as a safety view car with auto transmission made women drivers more comfortable to upgrade themselves from scooter to car. They give more importance to comfortability in terms of the interiors and exterior rather than using a car as a status symbol. Levels Part-worth Utility Upgrade 1.18 Status 0.29 Comfortability 0.81 1.5 1 0.5 Upgrade 0 Status comfortability The graph shown in the figure suggests that the awareness about various cars is obtained through peers and then through social media and then from magazines. Nowadays digital and social media are used during the shopping process but are not the principal source of information gathering. Part-worth Levels Utility magazines 0.27 social media 0.78 peers 1.57 2 1 magazines 0 social media peers The graph shown in the figure suggests that Women have gravitated towards Automatic Manual Transmission and being an emerging trend in the car segment it is quite likely to http://www.iaeme.com/IJMET/index.asp 1234 editor@iaeme.com
A Study on Preferences of Female Customers in the Purchase Decision of Cars in Chennai become a common segment in the near future. Women see Anti-lock Braking system as a critical safety feature during hard braking by preventing the brakes from locking up and hence avoidance rather than collision. GPS navigation is becoming a necessary feature among the female customers as it helps to find their way in crowded cities without having to stop to ask for directions. Rear parking Camera an another important feature appreciated by many women drivers as parking in tight spots have been made easier with this feature but since it is still given as an additional option with extra cost, hence it is been given least preference. Levels Part-worth Utility Anti-lock Braking System(ABS) 0.78 GPS navigation 0.86 Rear parking camera -0.59 Automated manual Transmission 1.56 2 Anti lock Braking 1.5 System(ABS) 1 GPS navigation 0.5 0 -0.5 rear parking -1 camera Automated manual Transmission The graph shown in the figure suggests that Women consider the price and resale value to be the factors that influence them to purchase based on the model. After that comes the safety and security features, even though many considered this also to be an important factor but still they were more comfortable in choosing price and resale value as their main preference. The least preference was given to ergonomics and comfort features. Part-worth Levels Utility Price 1.12 resale value 0.78 security and safety features 0.57 ergonomics, comfort and entertainment 0.39 features 1.5 1 price 0.5 0 resale value 5. LIMITATIONS AND FUTURE RESEARCH: We have seen limited empirical research has been conducted on the Indian automobile Industry and very few researches have been done on Female consumers in this industry. The Global http://www.iaeme.com/IJMET/index.asp 1235 editor@iaeme.com
Balaji Jayakrishnan and Dr. R. Aruna automobile industry has done substantial research in this sector and we are yet to target the female consumers as it is reflected in the form of a change in the purchase behavior pattern of variety of products. The rise of educated women having more independent job, self-identity have reflected in the market place. Our study is limited geographically and needs to be extended to a universal data. This study has revealed that women prefer a car over two wheeler and they feel it is much safer to use in the city traffic. Further, word of mouth appeals to women rather than advertisements or social media. They are not very keen in the technological aspect of the vehicle but the price. They showed positive attitude towards resale value of the vehicle. Their four wheeler purchases are driven by factors such as preference, decision making, attitude and satisfaction of women consumers in Chennai. Many women showed varied interest in new and enhanced features for their car, which sounded necessary and user friendly. There are lot of scope for research in this field especially their preferences in car advertisements will be dealt in our next study as a further development to the study and extending the study to global Women car buyers. REFERENCES [1] Azizi, I., Saaghi, F., & Sardo, S. Y. (2014). The impact of corporate social responsibility on consumer satisfaction (a case study: car industry in Shiraz). Indian J. Sci. Res, 7(1), 163- 168.Bhatia, Jeannie. (2013). Marketing Strategies of Car Manufacturers in India. (Ph.D. Dissertation). [2] Menon, B., & Raj, J. (2012). Logistic regression modelling for consumer purchase behaviour of passenger cars. AIMA Journal of Management & Research, 6(3/4), 0974-497. [3] Chattopadhyay, T., Shivani, S., & Krishnan, M. (2009). Determinants of brand equity-A blue print for building strong brand: A study of automobile segment in India. African Journal of Marketing Management, 1(4), 109-121. [4] Green, P. E., & Srinivasan, V. (1978). Conjoint analysis in consumer research: issues and outlook. Journal of consumer research, 5(2), 103-123. [5] Gould, G. M. (2006). A spatial anaylsis of passenger vehicle attributes. Environmental Impact and Policy. [6] Goswami, P. (2007). Psychographic segmentation of college-goers of Kolkata. IIMB Management Review, 19(1), 41-51. [7] Gupta, S. (2013). A study of buying decision influencers for passenger car segment in New Delhi. International journal of business and Management Invention, 2(12), 64-71. [8] Jacob, S., & Khan, S. (2010). Designed for the women today. The Economic Times, 25. [9] Kotler, P., Keller, K. L., Koshy, A., & Jha, M. (2008). Marketing management: A south Asian perspective. Pearson Education India [10] Diels, C., Erol, T., Kukova, M., Wasser, J., Cieslak, M., Payre, W., Miglani, A., Mansfield, N. J., Hodder , S. G. & Bos, J. (2017). Designing for comfort in shared and automated vehicles (SAV): A conceptual framework. [11] Kumar, R. V., & Sarkar, A. (2008). Psychographic segmentation of Indian urban consumers. Journal of the Asia Pacific Economy, 13(2), 204-226. [12] Lakshmi, P. (2011). Purchasing involvement: A potential mediator of buyer behavior. Economic Sciences Series, 8(2), 23-37. [13] MacNeill, S., & Liu, X. (2008). Innovation Networks and Regional Clusters in the Automotive Sector: The UK West Midlands. [14] Barletta, M. (2003). Marketing to women: How to understand, reach, and increase your share of the world's largest market segment. Dearborn Trade Publishing. [15] Menon, B. (2012). Parameters and framework development to study consumer behaviour patterns of passenger cars. Drishtikon: A Management Journal, 3(1), 26. http://www.iaeme.com/IJMET/index.asp 1236 editor@iaeme.com
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