Abstract
Purpose
This study aims to identify the major factors influencing the consumers to prefer milk products and also to analyze the awareness level of the Indian consumers.
Design/methodology/approach
In this study, the data is obtained through a structured questionnaire from Indian consumers considering convenience sampling under the nonprobability sampling technique. The consumer preference is explained using a multiple-regression model followed by analysis of variance (ANOVA), which shed insight on the significant differences between the variables that influence consumer preference for dairy products.
Findings
Investigation is done to analyze the factors influencing the consumers' buying behavior toward milk and its products. The results showed that quality, health consciousness, price and availability are the most influencing factors to buy milk products. Quantity of milk showed a significant relationship between age, monthly income and family size.
Research limitations/implications
This study helps marketing managers to frame the marketing strategies based on consumer preference, quality, health consciousness, price and availability. The research outcome will not only be advantageous for the entrepreneurial perspective but also takes care of consumer likeliness. Though the research reveals the opinion of Indian consumers, it limits the likeliness of the western world. Because of the scarcity of resources, several dairy products are unexplored, which could pave the future scope of research.
Originality/value
The novelty of this study is to identify the quality, health consciousness, price and availability are the most influencing factors to buy milk products considering ANOVA and the multiple regression model.
Keywords
Citation
Bahety, P.K., Sarkar, S., De, T., Kumar, V. and Mittal, A. (2024), "Exploring the factors influencing consumer preference toward dairy products: an empirical research", Vilakshan - XIMB Journal of Management, Vol. 21 No. 1, pp. 15-32. https://doi.org/10.1108/XJM-03-2022-0062
Publisher
:Emerald Publishing Limited
Copyright © 2022, Pankaj Kumar Bahety, Souren Sarkar, Tanmoy De, Vimal Kumar and Ankesh Mittal.
License
Published in Vilakshan – XIMB Journal of Management. Published by Emerald Publishing Limited. This article is published under the Creative Commons Attribution (CC BY 4.0) licence. Anyone may reproduce, distribute, translate and create derivative works of this article (for both commercial and non-commercial purposes), subject to full attribution to the original publication and authors. The full terms of this licence maybe seen at http://creativecommons.org/licences/by/4.0/legalcode
1. Introduction
The dairy industry is one of India's most important industries. India is the largest producer of milk in the world, accounting for more than 18.50% of global milk production. In India, the unorganized sector, which includes local milk vendors, wholesalers, retailers and even the manufacturers themselves, accounts for 80% of milk sales. Despite the fact that cooperatives pay producers a fair price, the unorganized sector plays a significant role in milk marketing. Consumer motivations and thinking styles shape their judgments and decisions, which are fundamentally influenced by culture (Shavitt and Barnes, 2020). Dairy products have numerous advantages (Birla, 2022). These items provide health benefits, especially when it comes to creating and keeping strong bones. They assist you in maintaining a healthy weight. Dairy foods give nutrients that are essential for your body's health and maintenance. If the marketer fulfills the demanded factors by the consumers, then the goal of consumer satisfaction can be achieved (Kumar et al., 2021). Customer preferences and desires determine the success of consumer behavior (Jha et al., 2022). Analysis of consumer buying behavior is an entrance to success in the market (Nalluri et al., 2020). The consumer demand for the product now and then establishes the success of any product in the market (Kumar et al., 2017). Information on a customer's perception, taste, likes and dislikes is necessary for a marketer to understand how customers react when they are exposed to specific advertising strategies and messages (Amudha et al., 2016; Huang et al., 2019; Singh et al., 2022). Because of cultural differences in the demographic factors, consumer behavior issue has become more assorted. As a result of this occurrence, understanding the factors that drive it is becoming increasingly important (Auf et al., 2018).
This study is an effort to determine the factors influencing consumer preference toward dairy products. This will help in developing marketing strategies to promote wider acceptance of dairy products among consumers. In line with the purpose of this study, the following are the research objectives:
to determine the urban consumer preference toward dairy products;
to examine the factors influencing purchase decisions toward dairy products; and
to develop a marketing framework for wider acceptance of dairy products across the urban consumer.
2. Literature review and theoretical framework
India is recognized as the principal milk producer, which includes 18.50% and more of the world’s total milk production. Parasuraman et al. (2021) revealed that the quality and nutrient content of dairy products are the most important factors influencing customer behavior. Consumer preferences for dairy-based goods were determined by sociodemographic differences such as education and income level (Laassal and Kallas, 2019). Kumar and Babu (2014) explored the factors that influence consumer choice for dairy products in the study area, as well as the impact of identified socioeconomic factors on customer brand preference for dairy products. Availability, product quality, packaging, cost, ingredients, product popularity, product flavor and so on are the most important and fundamental factors that impact a brand's selection (Kumar and Babu, 2014). Consumer preferences for functional dairy product features and willingness-to-pay estimations in Germany were investigated using a discrete choice experiment (Bechtold and Abdulai, 2014) and revealed that class-specific preference variability depends on consumers' attitudes toward functional meals.
In the food defense program, employee satisfaction and the identification of end goods and raw materials are also regarded as important (Lopes et al., 2020). When it comes to dairy products, the categories sustainable, natural, healthful, ethical and trustworthy have some cognitive overlap, but consumers use them differently (Schiano et al., 2020). Cazacu et al.’s (2014) results support the theory of planned behavior model's contention that product knowledge, nutritional advantages, attitudes and social contacts have a favorable impact on the people of Thessaloniki and surrounding areas' intentions to purchase water buffalo milk products (WBMPs). According to the study, the same elements that are proven to favorably influence the intention to buy WBMPs also have an impact on the intention to buy dairy milk.
Solano et al.’s (2006) results show a strong correlation between managerial capacity and management strategies, as well as a strong correlation between management strategies and farm performance. On performance and efficiency, managerial variables have a limited impact, according to the research. Results are reviewed in relation to their potential impact on technology transfer initiatives and the future development of decision-support systems.
Cornelsen et al. (2016) revealed that the typical home purchased 48 grams of fresh milk each week per household member. Price was the most frequently reported impediment to consumption, whereas taste was the primary motivator for consumption. It is been observed that perceived quality affects the perceived price (Solakis et al., 2022). The cocreation can result in reduced earnings, particularly if enterprises have a greater ability to convert an investment into lower customer participation costs, which is surprising given competition (Kumar and Sharma, 2017a; Yang et al., 2021; Jha et al., 2022).
Kapaj et al.’s (2013) conjoint choice experiment survey was constructed to finalize the customer preferences related to the milk industry in a specific market. The finding of this study could benefit the milk industry and its marketers by allowing them to strategically advertise their milk to diverse groups.
The roles and resources of customers in value cocreation are investigated by Agrawal and Rahman (2015). They concluded that customers have been accepted as both resource and resource integrators in the firm's value creation plans and processes as a result of customer empowerment and the rise of interactive technologies. Structural, cognitive and relational connection based on social media, particularly structural linkage, is an important cocreation method for improving organizational performance (Zhang et al., 2020). As reliance on technology grows, so does the significance of connecting digital solutions with customers' motivations (Nöjd et al., 2020). To strengthen the attractiveness of physical retail spaces, the author emphasized using digitalization to leverage customer experiences.
Sillence et al. (2016) identified a tiered approach to online trust in which users first conduct a quick, heuristic review of a website before conducting a more thorough analysis of the material offered. Results are presented in connection to the creation of reliable online resources for food safety.
Aswini et al. (2021) used Garrett’s rank analysis to analyze the factors influencing the consumers' buying behavior toward milk and its products and concluded that Ghee was the major product preferred by the consumers. Packaging has been demonstrated to play a substantial effect in influencing customer purchasing behavior (Sultan, 2016). Quality, health consciousness, pricing, packaging, availability and door delivery are all important factors in affecting decision behavior toward aroma milk.
Hambardzumyan and Gevorgyan (2022) concluded that milk production and sale prices in Armenia have not changed significantly during the limitation era. Maitiniyazi and Canavari (2020) concluded that consumers' confidence in food safety is lowered as a result of a high prevalence of food safety incidents. Kiambi et al. (2020) determined the impact of numerous problems and governance issues on food safety was underlined. Aroma milk is preferred by customers because of its high quality. Furthermore, it is undeniable that consumers are health conscious about the products they buy on a daily basis (Dhanya and Palanichamy, 2019). Quality, price and taste were the most influential criteria in purchasing milk products, whereas flavor, color and packaging were the least influential (Koduru and Sree Krishna, 2021). Higher income, older persons and a smaller population all increase the likelihood of drinking skim milk (Siqueira et al., 2020). The stronger the brand, the greater the price (Ngo et al., 2019). Nonpurchase of goat milk and dairy products is because of a lack of eating habits, as well as the odor and flavor of the product (Paskaš et al., 2020). Packaging has been shown to have a significant impact on customer purchasing behavior when it comes to high-priced and fast-moving consumer goods (Sultan, 2016). There is a huge age disparity among the respondents, and there is no discernible link between monthly income and satisfaction with various dairy products (Iris et al., 2018). The importance of quality and taste in dairy products, as well as the limited influence of packaging in product selection, were emphasized by young respondents (Gaworski et al., 2021).
2.1 Research gap
The review of available literature and published work reveals that major of the work related to consumer preference is conducted in western and developed countries, while the developing countries are yet untapped. Particularly, research on consumer preference toward dairy products has gained very less attention in India, irrespective of the fact that India is the largest producer of milk in the world, accounting for more than 18.50% of global milk production. Along with this, the available literature is more concerned about the entrepreneurial perspective, whereas the consumer perception is ignored. This research is an effort to bridge the gap between existing literature and the concurrent demand of the industry.
Based on the above discussions, the following hypotheses are listed below and the theoretical framework is shown in Figure 1:
Quality has a significant impact on consumer preference toward dairy products in the urban areas of Chhattisgarh.
Health consciousness has a significant impact on consumer preference toward dairy products in the urban areas of Chhattisgarh.
Price has a significant impact on consumer preference toward rural dairy products in the urban areas of Chhattisgarh.
Availability has a significant impact on consumer preference toward dairy products in the urban areas of Chhattisgarh.
3. Research methodology
A quantitative survey-based research methodology was used in this study. This section presents the sampling, data collection, questionnaire design and mathematical framework. Figure 2 proposed the research framework used in this study.
3.1 Sampling and data collection
After extensive desk research, an exploratory survey was done using the qualitative research method of focus group discussion (FGD) to understand general perceptions about the urban consumer preference toward dairy products and the issues involved and to seek implementable suggestions. It is an excellent tool for creating hypotheses, examining undiscovered areas of consumer experience and clarifying ambiguous ones when dealing with a complex issue. The participants in the FGD included the general public consisting of a heterogeneous mix of consumers, government representatives for nonagricultural products, local people of destinations dealers, distributors, retailers, agents, customers and service providers.
3.2 Survey instrument
A close-ended structured questionnaire was designed and used to collect data on views and perceptions from the stakeholders of various destinations that were tested for understanding and comprehensiveness. Table 1 explains the summary of the research design.
3.3 Mathematical framework
Based on the literature review, the following variables have been identified, namely, independent variables include quality, health consciousness, price and availability, whereas demographics include age, monthly income and family size, occupation; dependent variables include only consumer preference. Consumer preference as an endogenous variable against quality, health consciousness, price and availability is taken to be the exogenous variables. Besides, the demographics like age, monthly income and family size are considered controlling variables.
Mathematical framework:
3.4 Data analysis
An effort has been made to collect information without any biases, as the male–female ratio was approximately 50%. Because the result could vary based on the age differences of the respondents. Table 4 presents the demographic details (income-wise distribution of all males and females) (Tables 2–4).
Income-wise distribution of data analysis reveals that 29.4% of the population belongs to ₹20,001–40,000. This age group is the true reflection of Indian behavior, as they have experienced both richness and poverty. Simultaneously, an effort has been made to collect facts from every aspect of life, including the respondent belonging to below ₹20,000 to the income greater than ₹60,000 group. To derive the true picture of consumer behavior toward dairy products, Pearson χ2 reflects the value of 81.2. The high positivity degree among the different income-wise populations brings the result worth full (Table 5).
Multiple regression model:
To estimate the associations between the dependent variable “consumer preference” and independent variables as explored quality, health consciousness, price and availability. The multiple regression analysis approach is used to measure these variables (Kumar and Sharma, 2018). The analysis of multiple regression is shown in Table 4 (Table 6).
The model was constituted for dependent variables into predictor variables such as quality, health consciousness, price and availability, measured by analysis of variance (ANOVA) (Kumar and Sharma, 2017b; Kumar et al., 2018) and shown in Table. These four constructs explain 54.2% variance of the model. There is no sign of autocorrelation as Durbin–Watson statistics is 2.063 as the value is between a range of 0 and 4 (Durbin and Watson, 1950) (Table 7).
Because the p-value is less than or equal to the significance level, the connection is statistically significant at the 95% confidence level. It is inferred that the independent variable and the dependent variable have a statistically significant relationship (i.e. quality, health consciousness, price and availability) with and dependent variable. The regression coefficients are shown in Table 8.
From the coefficients of the above table, out of four explanatory variables, namely, quality, health consciousness, price and availability, all the independent variables have a positive coefficient. Therefore, the hypothesis is accepted as a significant value well below 0.05 at a 95% confidence interval. The colinearity index shows that there are no chances of colinearity as the maximum variance inflation factor value of independent variables in the model fit is 1.154.
3.4.1 ANOVA between sociodemographic variables and consumer preference.
The ANOVA test is conducted to determine whether there exists any significant variance between sociodemographic variables and consumer preference toward dairy products. Consumer occupation has been taken as a sociodemographic variable to establish an ANOVA test with consumer preference. From the said ANOVA table, we find that differences concerning sociodemographic variables are insignificant at the 5% level as the calculated F-ratio of 1.14216 is less than the table value of 3.07, but the variety of differences concerning factors related to consumer preference are significant as the calculated F-ratio of 3.07 is more than its table value of 2.48 (Table 9).
Table 10 analyzes the independent variables (quality, health consciousness, price and availability) with dependent variables (consumer preference). The statistical test result reveals that there is no significant difference between the variables identified and the final consumer preference.
4. Results and discussion
Urban consumer preferences toward dairy products are identified through various literature. The present study has identified certain factors, which can be considered as drivers of pushing dairy products toward consumer preference. It is evident from the responses collected from the consumers that they are well versed with dairy products. The factors influencing purchase decisions toward dairy products are collected through various literature reviews from previous work and expert opinions through a pilot survey, four independent variables have been identified.
The consumer preference is explained using a multiple regression model with a positive intercept of 0.853 and a standard error of 0.986. Quality has a positive impact on consumer preference with a regression coefficient of 0.802, which includes the attributes like taste, uniqueness and services. Health consciousness has a positive impact on consumer preference with a regression coefficient of 0.168. Similarly, price has a positive impact on consumer preference with a regression coefficient of 0.138, which includes the attributes of affordability of consumers and mode of payment. Availability has a positive impact on consumer preference with a regression coefficient of 0.026, which includes the attributes of convenience, outlet design & layout, exhibition & fairs and online facilities. The two-way ANOVA test result reveals that there is a significant difference between the identified variables of consumer perception toward dairy products, while contrary to this, the difference is nothing to do with their occupation, i.e. sociodemographic variable. Thus, the marketer need not to concern more about consumer preferences concerning their respective occupation (Figure 3).
To develop a marketing framework for wider acceptance of dairy products across the urban consumer, the following strategies can be taken keeping in view the wider acceptance of dairy products across the urban consumer at its core and economic growth, employment generation, raising the standard of living and also financial stability as its intentions-training of manpower to create a pool of skilled manpower for marketing of dairy products. It is evident from the present scenario that because of a lack of skilled employees the organizations fail to attain the desired objective. To boost sales, the marketing department should focus on the areas where sales are low. They should be able to obtain current market trends and demand fluctuations through appropriate inquiry in a certain sector to supply stock to fulfill demand. The focus should be on expanding the number of dairy parlors in areas with higher market potential and providing convenience to consumers. The milk producers' plant should create and maintain high-quality, fairly priced products. To boost or improve the maximum sales performance, the marketing department should introduce the door delivery system to widen the product’s reach. Advertisement too plays a predominant role in augmenting the reach of the product. In this context, an effective advertising campaign in different types of media, i.e. print media and electronic media, would be the best method for increasing sales. Installation of signboards and banners in community areas helps in generating awareness among the people. The introduction of small size pouches of 250 ml of milk will result in a rise in the sales of the milk because low-income group consumers stimulate to procure the product. Marketing efforts should be directed toward fascinating novice consumers. Last but not least the marketing department should sponsor some social-welfare activities under corporate social responsibility, which will ultimately lead to a surge in the sales of the product.
The following strategies can be adopted, keeping in view the wider acceptance of dairy products across the urban consumer at its core and economic growth, employment generation, raising the standard of living and also financial stability as its intentions:
focus on expansion plans with explored as well as unfamiliar, implicit products;
to raise the amenities of consumer places to international levels in terms of quality, promotion, advertisement and a conducive platform; and
training of manpower to create a pool of skilled manpower of marketing professionals.
As far as a business perspective is concerned, the research will help to develop a marketing framework for the wider acceptance of dairy products across urban consumers. The present research will provide a pathway to attract new customers along with retaining the existing customers because it exerts over the factors influencing consumer preference over dairy products.
4.1 Theoretical implications and managerial contributions
This study helps marketing managers to frame the marketing strategies based on consumer preference, quality, health consciousness, price and availability. Once the feature is determined with which the consumers magnetize the most, it will be supportive for the managers to take decisions accordingly. Also, the consumer will obtain the preferred product as per their expectations. The production manager too will focus on those variables expected by the consumers while manufacturing the dairy products. Thus, the research outcome will not only be advantageous for the entrepreneurial perspective but also takes care of consumer likeliness.
5. Conclusions, research limitations and future scope
Consumer’s age, income, family size, education level and life cost all had a major impact on their choice and preferences, according to the examination of factors affecting their preferences. This effect could be related to living costs and health problems. The choice of low-fat yogurt and milk was influenced by the level of education. In fact, having a greater degree of knowledge has been linked to a preference for low-fat foods. One of the most important effective influences on willingness to consume some dairy products was the brand name of the product. Marketing managers can use these findings to improve their products based on consumer preference.
The population for the response comprises the preference of Indian consumers that too particularly from the state of Chhattisgarh, which itself is a heterogeneous characteristic. Though the research reveals the opinion of Indian consumers, it limits the likeliness of the western world. Because of scarcity of resources several dairy products are unexplored. From the available numerous statistical techniques, the research data is analyzed through ANOVA, while there is a scope of analysis through various other statistical tools.
From the above list of limitations, we can conclude that the research has several future scopes which are listed as follows:
The research respondents can be from different demographic profiles where their tastes and preferences differ. Along with the change in population profile similar research could be attempted with other untapped dairy products. The available data could be analyzed through other available statistical software with different tools. Thus, it is evident from the above statement that, there lies the enormous scope for future research with related changes in product, place and consumer behavior and analysis tools.
Figures
Figure 1.
Theoretical framework
Figure 2.
Proposed research framework used in this study
Figure 3.
Proposed marketing framework used in this study
Summary of research design and descriptive study
| Research design | Descriptive study |
|---|---|
| Data source | Primary data |
| Instrument used | Questionnaire |
| Sample unit | Chhattisgarh |
| Population | Consumers of Chhattisgarh |
| Sampling design | Nonprobability sampling–convenience sampling |
| Data analysis | Regression and ANOVA |
Reliability testing
| 1st method | |||
|---|---|---|---|
| k/k – 1 | 1.011235955 | Sum_Variance | 53.52173913 |
| Variance_Total | 885.1748792 | Cronbach's alpha = (k/k − 1) (1 − sum variance/ variance total) |
0.950091984 |
| 2nd method (ANOVA TABLE) | |||
| Cronbach’s alpha = 1 − (mean square error/mean square rows) | 0.952168534 | ||
| Interpretation: excellent data consistency | |||
Demographic profile (age-wise distribution)
| Age | Male (A) | Female (B) | A + B | |||||
|---|---|---|---|---|---|---|---|---|
| Range | N | (%) | N | (%) | N | (%) | χ2 | Probability value |
| 21–35 | 276 | 59.50 | 297 | 66.30 | 573 | 62.80 | Pearson χ2: 122.01 | 0 |
| 36–50 | 107 | 23.10 | 79 | 17.60 | 186 | 20.40 | ||
| 51–65 | 70 | 15.10 | 60 | 13.40 | 130 | 14.30 | ||
| 65+ | 11 | 2.40 | 12 | 2.70 | 23 | 2.50 | ||
| Total | 464 | 100 | 448 | 100 | 912 | 100 | ||
Demographic profile (income-wise distribution)
| Male (A) | Female (B) | A + B | ||||||
|---|---|---|---|---|---|---|---|---|
| Income per month | N | (%) | N | (%) | N | (%) | χ2 | Probability value |
| <₹20,000 | 112 | 24 | 150 | 33 | 262 | 28.7 | Pearson χ2: 81.2 | 0 |
| ₹20,001–40,000 | 154 | 33 | 114 | 25 | 268 | 29.4 | ||
| ₹40,001–60,000 | 76 | 16 | 120 | 26 | 196 | 21.5 | ||
| >₹60,000 | 122 | 26 | 64 | 14 | 186 | 20.4 | ||
| Total | 464 | 100 | 448 | 100 | 912 | 100 | ||
| Mean | Quality | Health consciousness | Price | |||||||||
| 6 | 4.7 | 4.74 | 4.98 | 5.87 | 4.72 | 5.06 | 4.66 | 4.82 | 5.01 | 4.81 | 4.63 | |
| Standard error | 0.03 | 0.08 | 0.09 | 0.09 | 0.09 | 0.08 | 0.09 | 0.09 | 0.09 | 0.09 | 0.08 | 0.09 |
| Median | 6 | 4 | 4 | 5 | 7 | 4 | 5 | 4 | 5 | 5 | 4 | 4 |
| Mode | 6 | 3 | 3 | 7 | 9 | 3 | 7 | 3 | 5 | 7 | 3 | 3 |
| Standard deviation | 0.8 | 2.51 | 2.59 | 2.62 | 2.74 | 2.5 | 2.76 | 2.64 | 2.57 | 2.69 | 2.44 | 2.58 |
| Sample variance | 0.65 | 6.31 | 6.72 | 6.89 | 7.52 | 6.24 | 7.64 | 6.96 | 6.62 | 7.24 | 5.94 | 6.64 |
| Kurtosis | –1.5 | −1 | –1.1 | –1.2 | −1.3 | –1 | –1.4 | −1.2 | –1.1 | –1.4 | –1 | −1.1 |
| Skewness | 0.01 | 0.36 | 0.3 | 0.11 | −0.4 | 0.38 | 0.08 | 0.35 | 0.27 | 0.12 | 0.41 | 0.34 |
| Range | 2 | 8 | 8 | 8 | 8 | 8 | 8 | 8 | 8 | 8 | 8 | 8 |
| Minimum | 5 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 |
| Maximum | 7 | 9 | 9 | 9 | 9 | 9 | 9 | 9 | 9 | 9 | 9 | 9 |
| Sum | 5468 | 4287 | 4322 | 4538 | 5356 | 4303 | 4613 | 4247 | 4398 | 4571 | 4383 | 4226 |
| Count | 912 | 912 | 912 | 912 | 912 | 912 | 912 | 912 | 912 | 912 | 912 | 912 |
| Confidence level (95.0%) | 0.05 | 0.16 | 0.17 | 0.17 | 0.18 | 0.16 | 0.18 | 0.17 | 0.17 | 0.17 | 0.16 | 0.17 |
Descriptive statistics
| Mean | Availability | Consumer preference | ||||||
|---|---|---|---|---|---|---|---|---|
| 4.7 | 4.77 | 2.9 | 2.86 | 2.67 | 2.94 | 2.83 | 2.9 | |
| Standard error | 0.09 | 0.09 | 0.07 | 0.07 | 0.07 | 0.06 | 0.07 | 0.07 |
| Median | 4 | 4 | 3 | 3 | 2 | 3 | 2 | 2 |
| Mode | 7 | 3 | 1 | 1 | 1 | 1 | 1 | 1 |
| SD | 2.8 | 2.72 | 2.09 | 1.98 | 2.1 | 1.89 | 2.11 | 2.11 |
| Sample variance | 7.86 | 7.38 | 4.38 | 3.91 | 4.4 | 3.58 | 4.44 | 4.43 |
| Kurtosis | –1.4 | –1.3 | 1.11 | 1.18 | 1.63 | 1.12 | 1.08 | 0.7 |
| Skewness | 0.25 | 0.28 | 1.19 | 1.15 | 1.43 | 1.02 | 1.22 | 1.06 |
| Range | 8 | 8 | 8 | 8 | 8 | 8 | 8 | 8 |
| Minimum | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 |
| Maximum | 9 | 9 | 9 | 9 | 9 | 9 | 9 | 9 |
| Sum | 4,284 | 4,352 | 2,645 | 2,612 | 2,435 | 2,678 | 2,585 | 2,648 |
| Count | 912 | 912 | 912 | 912 | 912 | 912 | 912 | 912 |
| Confidence level (95.0%) | 0.18 | 0.18 | 0.14 | 0.13 | 0.14 | 0.12 | 0.14 | 0.14 |
Regression model
| Model summaryb | ||||||||||
|---|---|---|---|---|---|---|---|---|---|---|
| Model | R (multiple regression coeff.) | R2 (coeff. of determination) | Adjusted R2 | SE of estimate | R2 change | F-change | df1 | df2 | Sig. | Durbin–Watson |
| 1 | 0.736a | 0.542 | 0.395 | 0.98643 | 0.542 | 268.088 | 4 | 907 | 0.000 | 2.063 |
aPredictors: (constant), quality, health consciousness, price, availability; bdependent variable: consumer preference
ANOVA
| ANOVAa | |||||
|---|---|---|---|---|---|
| Model | Sum of squares | df | Mean square | F | Sig. |
| Regression | 1,043.448 | 4 | 260.862 | 268.088 | 0.000b |
| Residual | 882.552 | 907 | 0.973 | ||
| Total | 1,926.000 | 911 | |||
aDependent variable: consumer preference; bPredictors: (constant), quality, health consciousness, price, availability
Regression coefficients
| Coefficientsa | |||||||
|---|---|---|---|---|---|---|---|
| S. no. | Dependent variable | Independent variable | β | T | Sig. | Colinearity statistics | |
| 1 | Consumer preference | (Constant) | 3.696 | 0.000 | Tolerance | VIF | |
| Quality | 0.730 | 31.491 | 0.000 | 0.937 | 1.067 | ||
| Health consciousness | 0.137 | 6.044 | 0.000 | 0.986 | 1.014 | ||
| Price | 0.096 | 3.982 | 0.000 | 0.867 | 1.154 | ||
| Availability | 0.020 | 0.873 | 0.043 | 0.925 | 1.081 | ||
aDependent variable: consumer preference
ANOVA between sociodemographic variables (occupation) and consumer preference (factors influencing consumer preference)
| Summary | Count | Sum | Average | Variance | ||
|---|---|---|---|---|---|---|
| Managerial | 4 | 187 | 46.75 | 278.9167 | ||
| Professional | 4 | 245 | 61.25 | 1,972.25 | ||
| Clerical | 4 | 84 | 21 | 164.6667 | ||
| Student | 4 | 123 | 30.75 | 90.91667 | ||
| Retired | 4 | 28 | 7 | 8.666667 | ||
| Unemployed | 4 | 36 | 9 | 68.66667 | ||
| Home maker | 4 | 96 | 24 | 304.6667 | ||
| Business | 4 | 113 | 28.25 | 632.25 | ||
| ANOVA | ||||||
| Source of variation | SS | df | MS | F | p-value | F crit |
| Rows | 9,319 | 7 | 1,331.286 | 3.078541 | 0.021427 | 2.487578 |
| Columns | 1,481.75 | 3 | 493.9167 | 1.142161 | 0.355057 | 3.072467 |
| Error | 9,081.25 | 21 | 432.4405 | |||
| Total | 19,882 | 31 | ||||
ANOVA between research variables (quality, health consciousness, price and availability) and consumer preference (dependent variable)
| ANOVA | |||||
|---|---|---|---|---|---|
| Sum of squares | df | Mean square | F | Sig. | |
| Quality | |||||
| Between groups | 1,816.116 | 24 | 75.671 | 15.923 | 0.000 |
| Within groups | 4,215.253 | 887 | 4.752 | ||
| Total | 6,031.368 | 911 | |||
| Health consciousness | |||||
| Between groups | 109.494 | 24 | 4.562 | 3.758 | 0.000 |
| Within groups | 1,076.817 | 887 | 1.214 | ||
| Total | 1,186.311 | 911 | |||
| Price | |||||
| Between groups | 1,839.901 | 24 | 76.663 | 17.032 | 0.000 |
| Within groups | 3,992.573 | 887 | 4.501 | ||
| Total | 5,832.474 | 911 | |||
| Availability | |||||
| Between groups | 1,902.289 | 24 | 79.262 | 15.953 | 0.000 |
| Within groups | 4,407.027 | 887 | 4.968 | ||
| Total | 6,309.316 | 911 | |||
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Further reading
Kumar, A.A. and Babu, D.S. (2019), “Factors influencing consumer buying behavior with special reference to dairy products in Pondicherry state”, Abhinav-International Monthly Refereed Journal of Research in Management & Technology, Vol. 3 No. 1, pp. 65-73.
Corresponding author
About the authors
Pankaj Kumar Bahety is Research Scholar at Faculty of Management Studies, Shri Shankaracharya Technical Campus, Bhilai, Chhattisgarh.
Souren Sarkar is currently working as Professor and Head Faculty of Management Studies, Shri Shankaracharya Technical Campus, Bhilai, Chhattisgarh, since 2003. He has more than 21 years of experience. He has successfully guided two PhD scholars. He has published 35+ research papers in various national and international journals.
Tanmoy De is currently working as Assistant Professor at SIBM-Hyderabad Symbiosis International (Deemed University), Hyderabad. He has more than 22 years of experience and has published more than 30 research papers in various national and international journals.
Vimal Kumar is Assistant Professor at the Department of Information Management Chaoyang University of Technology, Taichung, Taiwan (ROC). He completed his Postdoctoral Research at Chaoyang University of Technology, Taichung, Taiwan (ROC) in the Department of Business Administration in the domain of Technological Innovation and Patent Analysis. He has served as an Assistant Professor under TEQIP III, an initiative of MHRD, Government of India at AEC Guwahati in the Department of Industrial and Production Engineering. Prior to joining AEC, he served as Assistant Professor at MANIT, Bhopal in the Department of Management Studies and also served as Visiting Faculty at IMT Nagpur. He obtained his PhD in the domain of TQM and Manufacturing Strategy in the year 2017 and Masters in Supply Chain Management from the Department of Industrial and Management Engineering, IIT Kanpur in the year 2012. He graduated (BTech) in Manufacturing Technology from Jagadguru Sri Shivarathreeshwara Academy of Technical Education Noida, in the year 2010. He has published 50 articles in reputable international journals and presented 24 papers at international conferences. His research paper entitled “Time table scheduling for educational sector on an e-governance platform: a solution from an analytics company” has been selected for best paper award at the International Conference on Industrial Engineering and Operations Management (IEOM) held in Bandung, Indonesia, March 6–8, 2018. He was also invited to serve as session chair of session on “energy-related awareness” held on September 19 2018, at iCAST 2018, IEEE International Conference on Awareness Science and Technology and “Lean Six Sigma” at the International Conference on Industrial Engineering and Operations Management (IEOM-2018) at Bandung, Indonesia and “quality control and management” at the International Conference on Industrial Engineering and Operations Management (IEOM-2016) at Kuala Lumpur, Malaysia. He has been appointed as an editorial board member in the IEEE–TEMS Journal from January 1, 2022, to December 31, 2024. He is a contributing author in international journals including Journal of Informetrics, Technology in Society, CLSCN, Supply Chain Management: An International Journal, IJOA, JEEE, BSE, TFSC, JKM, CSREM, IJPPM, IJQRM, IJPMB, IJPQM, IJBIS, AJOR, The TQM Journal and Benchmarking: An International Journal, and also a guest reviewer of reputable journals like IEEE–TEMS, JOI, IJPPM, IJQRM, TQM & Business Excellence, The TQM Journal, Benchmarking: An International Journal, Journal of Asia Business Studies and JSIT.
Ankesh Mittal is Assistant Professor in the Mechanical Engineering Department of Asra College of Engineering and Technology, Punjab, India. He received his BTech and MTech degrees from Chitkara University, Punjab, India, and Thapar University, Patiala, India in 2013 and 2015 respectively, and a PhD degree from Sant Longowal Institute of Engineering and Technology Longowal, Punjab, in 2021. His areas of research are total quality management and production engineering. He has published various national and international journals of repute and conference proceedings.