Introduction

Dairying in India has come forth as an important sub-sector with an encouraging growth rate of five per cent over the years. It also plays a significant role in changing the social and economic status of people in India by providing a subsidiary source of income. At present, the leading light of dairy world is India, which occupies the first position in milk production with a production level of 146 million tones of milk in 2011-12 (Basic Animal Husbandry Statistics, 2015-16). The per capita availability of milk increased from 130 grams per day in 1950-51 to 322 grams per day in 2014-15 (Basic Animal Husbandry Statistics, 2015-16). The value of output from dairy sector (Rs.3496720 million rupees during 2012-13) was found to be the highest among the various subsectors of livestock. Urbanization is positively correlated with the production and consumption of dairy products. The population of Chennai city was 14.16 lakhs in 1951 which has increased to 46.81 lakhs in 2011 as per the census of India (Vinayakam and Sekar, 2013). The most observable fact in Chennai city was its massive urbanization. Due to this, consumers of Chennai city were highly skewed towards value added livestock products in which dairy products occupy a major space. From the consumer point of view, the price for the dairy products will be fixed, based on quality attributes like colour, texture, fat content, freshness, taste, nutrition and safety. The demand for quality parameters of dairy products at Chennai had undergone a perceptible change in the recent years. Keeping all these factors into mind, the present study was conducted to assess the determinants of consumer preferences for quality attributes of ghee in Chennai city.

Data and Methodology

For the present study, Chennai metro city, the capital of Tamil Nadu was purposively selected because of high human density and also massive urbanization. The Chennai city has three regions viz., North, Central and South and each region has five zones and thus the city is composed of fifteen zones. From each zone, 30 household consumers were selected by a simple random sampling procedure thus yielding a total sample size of 450 household consumers for the study.

Semi-Log Functional Model

In order to assess the interrelationships between consumption of ghee and the socio-demographic factors, as described by Rani et al. (1999), Semi-log function was fitted. The functional form was as below:

lnY = α + β₁X₁ + β₂X₂ + β₃X₃ +β₄X_{4 +} β₅X_{5 +} β₆X_{6 +} β₇X_{7 +} β₈X_{8 +} β₉X_{9 +} β₁₀X_{10 +}+μ

Where,

Y_{1 =} Quantity of ghee consumed per household per month (in kgs)

X_{1 =} Family size in consumption units

X₂ = Age of the head of the household – continuous

X₃ = Educational status of the head of the household (0 – if illiterate; 1 – if primary; 2 – if secondary; and 3 – if college)

X₄ = Family income in Rupees

X₅ = Dummy – Hindu religion (1 – if the household is a Hindu; 0 – otherwise)

X₆ = Dummy – Christian religion (1 – if the household is a Christian; 0 – otherwise)

X₇ = Dummy – Presence of child in the family (1 – if a family had a child (ren) below 14 years; 0 – otherwise)

X₈ = Dummy – Presence of aged person (1 – if household with aged person(s) above 60 years; 0 – otherwise)

X₉ = Dummy – Food habit of the family (1 – If non – vegetarian; 0 – otherwise)

X₁₀ = Dummy – Type of family (1 – if nuclear; 0 – joint)

α = Intercept

β_i = Regression coefficients to be estimated

μ = stochastic disturbance term

Conjoint Analysis

Conjoint analysis is a multivariate technique used specifically to understand how consumers develop preferences for products or services and to formulate predictions about market attitude towards product concepts and it is also called as trade-off analysis. This method is based on the multi-attribute product concepts, i.e. on the premise that consumers evaluate the value or utility of a product by combining the separate amounts of utility provided by each attribute. The power of the method is to provide an explanatory model of consumers’ preferences, which can then be used to define the product concepts constituting the optimum combination of the attribute levels (Connor et al., 2006 and Shetti et al., 2006).

The most important decision in conjoint analysis is selecting the attributes to characterize the dairy products. Attributes selected for ghee are given in the Table 1. Based on the attributes and levels given in the table, if the full profile method is used, the number of combinations is 32 (2*4*2*2).

Table 1: List of attributes and its levels of ghee

When a large number of combinations are presented to consumers, the non response rate (due to fatigue, boredom) becomes very high. Fortunately, the number of cards can be reduced by generating an orthogonal array method using SPSS statistical software. Hence the number of cards created for ghee is eight (Table 2). Therefore, each card is the combination of attribute levels of a dairy product. Now, the created card list was given to household consumer for ranking each card from 1 to 8 (1 – highly preferred to 8 – least preferred). With this preference data, conjoint analysis was run.

Table 2: Card list for ghee

The results of conjoint analysis show the part-worth utility values and standard error for attribute levels. Utility refers to a number that represents the value that consumers place on an attribute (the relative worth of the attribute) i.e. higher utility values indicate greater preference. Utilities can be added together to arrive at the total utility of any combination. The range of the utility values for each attribute provides a measure of how important the attribute was to the overall preference. Attributes with greater utility ranges play a more significant role than those with smaller ranges. Finally, the measure of the relative importance of each attribute known as an importance score or value was calculated.

Relative importance =

The values represented in percentages and had the property that they sum to 100. Thus, the conjoint analysis identified the attribute combinations that confer the highest utility to the consumers.

Results and Discussion

Factors Influencing the Consumption of Ghee

The semi-log functional model constructed to explain the factors influencing the household consumption of ghee. The model showed a good fit with the adjusted R² of 0.959, indicating that 95.90 per cent of variation in the dependant variable was explained by the independent variables incorporated (Table 3). The ANOVA also exhibited that the model had a good fit with a significant ‘F’ value of 873.08. Of the variables used to explain the variation in the consumption of ghee in Chennai city, family size, educational status of the head of the household, monthly family income and food habit of the household consumer were found to be significantly influencing the consumption of ghee. Of which, family size, educational status of the head of the household and monthly family income were found to be positively influencing the ghee consumption. Similar result was observed by Keshari and Malik (1998) whereas contrary to the findings of Ingavale and Thakar (2012). The variable-food habit of the family was found to be negatively influencing the consumption of ghee.The results indicated that a unit change in the family size could alter the monthly household consumption of ghee by 31.10 per cent, from its mean level.

Table3: Factors influencing the consumption of ghee

Figures in parentheses indicate standard errors; Dependent variable- Quantity of ghee consumed per household per month (in Kgs); *Significant (P ≤ 0.05); ** Significant (P ≤ 0.01)

Similarly, as the educational level of the household consumer increased consumption of ghee increased by 7.10 per cent from its mean level. This might be attributed to education level of the household consumer when increases their awareness on dairy products gets increased. Similar result was reported by Das et al., 2011. The monthly family income positively influenced the consumption of ghee by 13.30 per cent from the mean level. The consumption of ghee was increased by 23.10 per cent when the sample households were vegetarians over their non-vegetarian counterparts. This might be due to the reason that dairy products were the only source of animal protein for vegetarians.

Consumers’ Preferences for Quality Attributes of Ghee

The result of conjoint analysis for quality attributes of ghee was presented in Table 4. Pearson’s R and Kendall’s tau values were 0.90 and 0.88 respectively, indicating a better fit to the data. The brand was found to be the most important influencing factor in purchasing behaviour of ghee for household consumers in Chennai city (33.81%; n = 450) followed by price of the product (24.78%), texture (18.47%) and availability (17.21%). The brand was therefore more than twice as important for household consumers in Chennai city as compared to other attributes of ghee. Similar result was observed by Hu et al. (2012) when they conducted a similar study on processed food product. Within attributes, the utilities at each level were investigated. For example, in brand, the most utility was obtained for Aavin (U = 0.583) followed by Amul (U = 0.274), GRB Udhayam (U=0.161), whereas the utility of other brand was lower (U = -0.596).

Table 4: Conjoint analysis for quality attributes of ghee

Pearson’s R value =0.90**; Kendall’s tau value = 0.88**, n = 450

It is interesting to note that a price of Rs.150 to Rs.250 per kg of ghee had the highest utility (U=0.432) when compared to a ghee that was priced at Rs.251 to Rs.350 (U = -0.432). This indicated that consumers in this survey were highly price conscious, deriving a higher utility from ghee that was offered at a lower price. If a producer were to change the price towards higher levels, there would be a consequent loss in utility value of 0.864.

In case of texture, respondents obtained a higher utility from ghee that is grainy in nature (U=0.322) than from ghee that is smooth in nature (U= -0.322). The overall 450 respondents obtained a higher utility from ghee at the availability level of 50g – 250g packets (U=0.300) than from a ghee at the availability level of 250g – 500g packets (U= -0.300). If a producer were to sell the ghee with the availability levels of 50g – 250g packets, there would be a consequent rise in utility value of 0.600 for the consumers. The conjoint analysis for the overall 450 household consumers found that the ideal ghee had the following attributes- brand specific (Aavin), the price level of Rs.150 to Rs.250 per kg of ghee, grainy texture and availability level of 50g -250g.

Conclusion

The study of analysing the factors that influence the household consumption of ghee by using semi-log functional model revealed that family size, educational status of the head of the household, monthly family income and food habit of the household consumer were found to be significantly influencing the consumption of ghee. Of which, family size, educational status of the head of the household and monthly family income were found to be positively influencing the ghee consumption whereas, food habit of the family was found to be negatively influencing the consumption of ghee. The conjoint analysis for ghee revealed that the ideal ghee had the following attributes: Brand specific (Aavin), the price level of Rs.150 to Rs.250 per kg of ghee, grainy texture and availability level of 50g -250g packets. The results indicated that the consumers are given more importance on quality factor while purchasing the product. So, this imparts the need for enhancing the production of quality dairy products. The study suggested that a producer should analyze the part-worth utilities of each of the attribute to ascertain how he can increase the consumer’s utility from his product. These results have the potential to assist in the construction of a market strategy.

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