Two-Part Modelling of Consumer Preferences for Native Chicken Meat: Sustainability Implications for Semi-Intensive and Intensive Production Systems

doi:10.21203/rs.3.rs-3395498/v1

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Two-Part Modelling of Consumer Preferences for Native Chicken Meat: Sustainability Implications for Semi-Intensive and Intensive Production Systems

https://doi.org/10.21203/rs.3.rs-3395498/v1

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Indigenous and native chickens play a pivotal role in rural economies, providing both a source of supplementary income and a means of nutritional security through egg and meat production. Native chicken farming practices often involve backyard and free-range systems with minimal input. India is one of the world's top producers of poultry, with a total chicken population of 851.8 million in 2019 and 37.2% representing native or indigenous breeds. Traditionally, backyards were used to raise native chickens, but semi-intensive and intensive farms have gained popularity in recent years because of the increasing demand for native chicken meat and related price increases. Government-backed projects in India also support raising native chickens to empower women and other marginalised communities. In this context, a structured online survey was conducted from April to May 2023 to study consumer preferences for native chicken meat in India and its impact on the sustainability of developing intensive production systems. The survey collected responses from 503 individuals who had confirmed their consumption of chicken meat. A two-part model, incorporating a logit regression for binary preference and an ordinary least square (log-normal OLS) regression for quantity purchased, was used to analyse the data. In addition, a factor analysis was conducted to identify the key components that shaped consumer attitudes, yielding nine unique rotated components.

Gender, age, income, education, marital status, cohabitation, household size, presence of senior citizens or children within households, and consumption frequency were found to have significantly influenced consumer preferences for native chicken meat. Factors such as psychological well-being, affordability, knowledge, nutrition, food quality, fitness, purity (natural), and sustainability consistently exerted a negative influence on both the preference and quantity of native chicken meat purchased. Despite a significant preference for local chicken owing to its perceived sustainability, concerns regarding sustainability arose from pricing and accessibility for specific quantities. Addressing pricing inequalities and increasing availability in the required quantity are essential for improving sustainability in semi-intensive and intensive native chicken production. The key to maintaining the long-term sustainability of native chicken farming is to match consumer demands with ethically and environmentally responsible production methods.

Animal Science

Agricultural Economics & Policy

Food Science & Technology

Native Chicken

Poultry Market

Consumer Preferences

Meat Consumption

Two-Part Model

Factor Analysis

Sustainable farming

In most developing and poor nations, native or indigenous chicken breeds play a pivotal role in rural economies. They are crucial for the rural underprivileged and marginalised groups of the population, not only for providing secondary income but also for supplying eggs and meat for their own sustenance (Ahlers et al. 2009; Turk 2013; Padhi 2016). Native chickens are produced worldwide, mostly using backyard and free-range systems with little to no suitable housing (Haunshi and Rajkumar 2020). As native birds do not require additional nutritional or management support to produce eggs or meat when individuals supply their kitchen and household garbage as supplementary feed, rural people prefer raising native chickens over high-yielding exotic birds (Padhi 2016; Wong et al. 2017). Native birds thrive when left to scavenge the owners' backyards without any assistance (Sonaiya 2004). Thus, raising local chickens gives rural residents a means of sustaining their way of life and ensuring nutritional security (Aklilu et al. 2008; Azzarri et al. 2015).

India is one of the world's top producers of poultry meat and eggs, and the consumption of poultry products across the country predominantly includes the consumption of chicken meat and eggs. In India, 37.2% of 851.8 million total chickens recorded in 2019 were of the native or indigenous type, and the remaining were improved. In addition, the native/desi poultry population in the country increased by 45.79% in 2019 over 2012, while the commercial poultry population expanded by only 4.5% during this period. Half of the meat produced in India is poultry (4.06 million tonnes in 2018-19). Because it is affordable and widely available, chicken meat is preferred by the majority of the population as the main meat option. The Indian poultry market is worth $22.97 billion by 2022 (DAHD 2023). Contributions from the chicken meat and egg industries were 75.32% and 24.67%, respectively, and it is projected that the poultry industry market in India will increase at a compound annual growth rate (CAGR) of 10.18% from 2023 to 2028, bringing the total market value to $41.94 billion (IMARC-2023).

The rural economy of India is mostly agrarian, with the majority (65% in 2021) of the country's population living in rural areas. Landless and individuals with tiny plots of land rely on raising livestock, especially chickens, to make a living. The Indian National Bureau of Animal Genetic Resources characterised and registered 19 chicken breeds in India (Kanakachari et al. 2022), and numerous nondescript desi chicken breeds have been reported by many authors (Mohapatra and Panda 1981; Aini 1990; Fumihito et al. 1994; Iqbal and Pampori 2008; Fadhil et al. 2016). Owing to their compatibility with regional taste preferences and cooking methods, native or indigenous chicken breeds are highly desired. Small-scale and backyard poultry farming practices have persisted and grown steadily in regions where large-scale commercially manufactured poultry products are available, which illustrates how local communities have chosen a sustainable production system that results in wholesome, culturally appropriate food (Aini 1990; Suganthi 2014; Wong et al. 2017). The demand for native chicken meat has been increasing, and it is priced at premiums over broiler chicken meat. Consumers are prepared to pay more for native chicken meat and eggs, particularly in metropolitan areas. Notably, the retail price of native chicken meat is currently almost three times that of broilers in metropolitan areas, irrespective of the production system by which they are raised (Haunshi and Rajkumar 2020).

Traditionally, native chickens have been reared mostly in backyard environments. However, semi-intensive and intense native chicken farms have become increasingly common in recent years, which can be attributed to the growing demand for native chicken meat and the lucrative prices it fetches. Additionally, a number of government-sponsored development initiatives in India aggressively promote the raising of native chickens with the aim of improving the livelihoods of women and other marginalised groups. On the demand side, consumers prefer native chickens to broilers because they are produced using a lot more organically. Given these circumstances, this study aimed to investigate consumer preferences for native chicken meat that might affect the sustainability of the evolving intensive systems of native chicken production.

Consumer preferences for native chicken in India were studied using a structured and pretested online questionnaire. The questionnaire was exclusively administered to individuals residing in India who confirmed their consumption of chicken meat. This Google form survey was conducted through social media from April to May 2023. The final dataset included responses from 503 individuals, with 305 expressing a preference for native chicken meat and 198 opting for broiler chicken meat as their choice. Extensive data encompassing demographic and socioeconomic attributes, along with consumers' preferences for consuming native chicken meat and the monthly quantities procured, were collected online. In addition, a set of 39 statements regarding chicken meat purchasing behaviour were presented to survey participants to elucidate their perceptions on a 5-point Likert scale (1, denoting "Strongly Disagree”, to 5, representing "Strongly Agree").

The statistical and econometric analyses of the data were performed using Stata®16.0. Socioeconomic associations with preferences for native chicken were assessed through chi-square analysis. A Factor Analysis was conducted on the 39 Likert scale statements, employing the Principal Component Analysis Extraction Method and the Varimax Rotation Method with Kaiser Normalisation, which reduced the dimensionality of the statements into nine distinct factors. The factor scores derived from these nine factors were then used as explanatory variables (from X₁₉ to X₂₇ in Table 1) in the two-part model to study the factors influencing consumers’ preferences for native chicken meat.

Two-part modelling represents a valuable but often overlooked statistical approach tailored to semicontinuous outcomes (Belotti et al. 2015). This method recognises outcomes shaped by a dual process, addressing both the presence or absence of an event and the quantitative dimension of that event. The application of a two-part model in consumer research was accompanied by a closely related counterpart for count data known as the "hurdle" model (Duan et al. 1984; Jones 1989; Hilbe 2005; Kathiravan et al. 2009; Colin and Pravin 2013). Additionally, it is essential to recognise the existence of the Heckman selection model (Heckman 1979), also referred to as the adjusted or generalised Tobit model (Maddala 1983; Harvey and Amemiya 1987). This model serves as a viable alternative to the two-part model when dealing with mixed discrete-continuous outcomes. However, it is imperative to acknowledge that conceptual and significant differences exist between these two modelling approaches (Poirier and Ruud 1981; Duan et al. 1984; Dow and Norton 2003). A two-stage analytical approach is preferred in consumption studies (Abdullah et al. 2019). The main reason for adopting such methods is that meat consumption involves a two-way decision: the decision to consume and the amount to be consumed. A problem frequently encountered in consumption research is that the amount of consumption cannot be observed for those who do not consume a product (Ayyıldız and Çiçek 2022). The two-part model assumes that the first and second parts of the model have errors that are independent of each other, whereas the Heckman selection model assumes that the errors between both equations are correlated, which causes endogenous selection. The zeros (no preference) in the two-part model are true zeros, whereas the zeros in the Heckman selection model denote censored values of the positive outcome. Monte Carlo studies found that the two-part model performs better than the sample selection model because the latter is susceptible to collinearity problems (Hay et al. 1987; Manning et al. 1987; Leung and Yu 1996). Therefore, a two-part model that does not make any assumptions about the correlation between the errors of the binary and continuous equations was adapted in this study to analyse the decision to consume (native chicken) and the amount consumed (quantity).

The structural expression of the two-part model is given by

E(Y|X) = Pr(Y > 0|X) ∗ E(Y|Y > 0, X).

The first part Pr(Y > 0|X) denotes the probability that a respondent has exhibited preference for native chicken meat given a set of variables X. The second part E(Y|Y > 0, X) denotes the expected monthly quantity of native chicken consumed, Y, given that the respondent has nonzero preference Y > 0 and a set of variables X. In this study, the first part employed a logit regression to predict the likelihood of a binary event, specifically whether consumers prefer to purchase native chicken (coded as 1) or not (coded as 0), while the second part utilised ordinary least squares (OLS) regression with a log-transformed dependent variable (y) to analyse the quantity associated with the amount of native chicken purchased if a consumer exhibited a preference in the first part. The same set of regressors shown in Table 1 was used in both parts of the model to maintain consistency in the analysis.

a). Preferences for Native Chicken Meat

This study received completed responses from 503 individuals who were chicken meat eaters and tried native chicken meat at least once. Only nine (1.79%) of 503 respondents had never tried broiler chicken meat more than once and restricted the eating of native chicken alone. It should be noted that although 305 of the 503 respondents expressed a preference for native chicken meat, they (except nine strict native chicken meat eaters) ate and purchased broiler chicken meat, which is often larger than native chicken, due to economic and cuisine considerations. They (305) preferred native chicken meat over broiler meat for its taste, nutritional quality, health, organic nature, and elegance when served to guests.

In Table 2, the preferences for and consumption of native chicken meat in the study population are described in terms of demographics. Among the 376 male respondents, 211 (56.12%) indicated that they preferred native chicken meat to broilers, whereas 165 (43.88%) did not. In contrast, among the 127 female respondents, 94 (74.02%) reported an inclination toward native chicken meat, with 33 (25.98%) indicating otherwise. There was also a highly significant (χ² = 12.74; p = 0.000) relationship between gender and preference for native chicken meat, where women showed a considerably higher predisposition for native chicken meat than men (Alam et al. 2020). Age segment-based analysis of the tendency to consume native chicken meat revealed a significant association (χ² = 13.52; p = 0.009) between the two groups. In contrast to the younger age groups, older age groups showed a reduced preference for native chicken meat. These results imply that the preferences of the population for native chicken meat are influenced by age (Setyawati et al. 2021). Similarly, a significant association (χ² = 13.91; p = 0.008) was observed between income levels and preferences for native chicken meat (Monisha et al. 2021). Accordingly, there was also a significant relationship (χ² = 6.26; p = 0.044) between the propensity to consume native chicken meat and the "educational levels" of respondents, with native chicken meat consumption rates of 61.54% for primary educated respondents, 88.89% for secondary-level qualified respondents, and 59.53% for graduates. This shows that consumer consumption patterns and preferences for chicken meat are influenced by educational background (Aikins-Wilson et al. 2013).

In contrast, there was no statistically significant association (χ² = 0.04; p = 0.846) between respondents' propensity to consume native chicken meat and their marital status. This suggests that native chicken meat consumption patterns were not greatly influenced by marital status (Higenyi 2014). Similarly, the analysis indicated no statistically significant association (χ² = 1.36; p = 0.243) between consumer consumption of native chicken meat and habitat (rural or urban). This indicates that habitat (urban or rural) does not play a significant role in determining the predilection for consuming native chicken meat (Bett et al. 2012). Likewise, the preference for eating native chicken meat did not have any statistically significant association (χ² = 1.81; p = 0.178) with cohabitation (with family) status. This suggests that living arrangements do not significantly affect chicken meat consumption habits in the population.

The preference for eating native chicken meat was significantly associated (χ² = 8.32; p = 0.004) with the "living with senior citizens’ status of respondents, where 66.93% of those living with the elderly preferred native chicken, while only 54.37% of those who did not live with elderly people preferred. The results showed that the consumption of native chicken meat was higher in households with senior citizens (Setyawati et al. 2021). Similarly, having "children at home" had a significant (χ² = 3.89; p = 0.049) association with the preference to eat native chicken meat, with 64.94% of those who had children preferring it and only 56.35% of those who did not. These findings imply that families with children are more likely to prefer buying chicken meat (Pym et al. 2006). Likewise, the preference for native chicken meat also had a statistically significant relationship (χ² = 9.91; p = 0.042) with respondents' "household size categories". Household size impacts the consumption patterns of chicken meat, with smaller households being less likely to prefer native chicken meat than other categories. In contrast, although Muslims were less likely (43.9%) to prefer native chicken meat, there was no statistically significant relationship (χ² = 5.56; p = 0.135) between religion and preference for native chicken meat, with 60.00% of Christians, 62.14% of Hindus, and 71.43% of those who practiced other religions preferring native chicken meat.

Notably, a highly significant association (χ² = 37.01; p = 0.000) was found between the frequency of chicken meat consumption and the desire for native chicken meat. Consumers who ate chicken "daily" had a lower preference (26.47%) for native chicken meat, compared to 53.97% of "alternate day" consumers, 55.56% of "twice weekly" consumers, 70.15% of "weekly" consumers, 50.00% of "fort nightly" consumers, and 79.22% of "occasional" consumers. The findings indicated that the preference for native chicken meat is significantly influenced by consumption frequency (Lee et al. 2017).

ii) Interrelationships in Customer Attitudes Toward Chicken Meat - Factor Analysis

The results of the factor analysis that illustrate the interrelationships among the attributes of customers' attitudes towards the purchase and consumption of chicken meat are shown in Table 3. The null hypothesis that the population correlation matrix is an identity matrix is rejected by Barlett’s test of sphericity, as the chi-square statistic was 14569.540 (p = 0.000). The value of the KMO statistic (0.872) was also large (> 0.50), indicating the suitability of the collected data for factor analysis (Kaiser 1970). Furthermore, all the extracted cummunalities were acceptable, and all variables were fit for the factor solution, as their extraction values were large enough. All the first nine components in the initial solution had over one, which accounted for 71.09 per cent of the observed variations. Factor loadings were used to measure the correlation between the variables and the factors. A strong correlation between a variable and the factor is indicated by a loading close to one, and the factors were rotated with the use of varimax with the Keyser normalisation rotation method (De Roover et al. 2017). Principal component analysis was used for factor extraction, and factors with values greater than 0.300 were considered for interpretation purposes (Jolliffe and Cadima 2016).

Six assertions, viz., 'Promotes relaxation', 'Cheers me up', 'Eases stress handling', 'Facilitates coping with life's challenges', 'Maintains alertness', and 'Enhances well-being (feel good)', are grouped as one factor that is related to the "Psychological Well-Being" of consumers. The advantages of eating chicken meat on an emotional and psychological level are represented by this component. If producers and market participants were aware of this psychological well-being element, they may better plan their strategies for the production and supply of chicken meat as a source of comfort and relaxation (Uzmay et al. 2013; Asante-Addo 2020).

By grouping the responses to the five statements, namely, (i) cost-effective, (ii) aware of meat prices, (iii) always hunt for deals, (iv) budget-friendly, and (v) excellent value for money, the factor analysis extracted the second component. Considering the associations among these statements, the component can be referred to as "Affordability". The outcomes showed that customers thought about how affordable and cost-effective chicken meat was. Additionally, a study showed that people who actively look for discounts and consider chicken meat to be a cost-effective option score higher on this factor (Mäkiniemi and Vainio 2014; Ares et al. 2017; Ali 2021).

The factor analysis derived the third component by clustering the consumers' responses to "familiar with chicken meat", "mindful of my chicken choices", "conscious of my dietary preferences", "distinguish chicken products" and "have confidence in my meat choice". This third element could be referred to as "knowledge" because it symbolises consumers’ knowledge, awareness, and consciousness regarding various aspects of chicken meat (Sanchez-Sabate and Sabaté 2019; Mtolo et al. 2022; Meeprom et al. 2023; Scudiero et al. 2023). This suggests that individuals who make informed dietary choices and differentiate between chicken products score higher on this factor.

"Nutrition," which included the statements "nutrient-rich," "promotes good health," "rich in protein," "abundant in vitamins and minerals," and "beneficial for skin, teeth, hair, nails, etc." was derived as the fourth factor. This factor underscores the perception of consumers that chicken meat is a source of nutrition and health benefits (Asante-Addo 2020; Neima et al. 2021; Valli et al. 2023). The results demonstrate the nutritional benefits of chicken meat that appeal to consumers who are health-conscious and highlight individuals' perceptions of chicken meat's nutritional value and health promotion.

In the factor analysis, the fifth component, "palatability," was created by combining the statements "appealing appearance," "pleasant aroma (smell)," "delicious taste," and "enjoyable texture." This variable reflects how consumers rate the sensory qualities of chicken meat (Sow and Grongnet 2010; Font-i-Furnols and Guerrero 2014; Mir et al. 2017; Damaziak et al. 2019; Neima et al. 2021). This implies that those who view chicken meat to be aesthetically pleasing and flavourful score higher on this component, reinforcing the importance of chicken meat's sensory appeal for any marketing initiative.

“Food Quality” was derived as the sixth component by the factor analysis. This factor was extracted by grouping the responses for "food quality is my key factor", "food quality guides my purchases", "there are multiple aspects to food quality", and "food quality serves as my motivator" (Vukasovič 2010; Collier et al. 2021; Grzybowska-Brzezińska et al. 2023). This component highlights the importance of food quality in individuals' decision making while buying chicken meat. In other words, people place a high priority on various aspects of food quality and use it as a deciding factor when making purchase decisions.

By integrating consumer responses to the assertions that "helps me control my weight," "is low in fat," and "is low in calories," the seventh factor, "Fitness," was derived by factor analysis (Kubberød et al. 2002; Melanson et al. 2003; Marangoni et al. 2015). This factor demonstrates the connection between eating chicken meat and staying physically fit. Therefore, people who identify chicken meat with fitness and weight management perform better on this factor.

The eighth factor, “Purity (Natural)”, was extracted through factor analysis by combining the consumers’ responses for the statements "free from artificial ingredients", "additive-free", and "comprises natural ingredients" (Asante-Addo 2020). This component exhibits the link between chicken meat choices and the purity or naturalness of chicken meat. Consumers who preferred chicken products with minimal additives scored higher on this factor.

With the statements "prefer purchasing organic chicken," "prefer to buy free-range chicken," "sustainability is important to me," and "support the local farming community," the factor analysis extracted the last component, "Sustainability," which reflects consumers' commitment to ethical and sustainable choices in chicken meat purchases (Busse et al. 2019; Kaygisiz et al. 2019; Garcez de Oliveira Padilha et al. 2021). Those who liked organic and free-range chickens and supported local farming communities scored higher on this factor.

iii) Factors Influencing Native Chicken Meat Preferences and Purchases: Two-Part Modelling

A two-part econometric model involving logit regression in part-1 and log-normal OLS in part-2 was used to analyse the factors affecting customers' preferences for native chicken meat and the quantity of their monthly purchases. The robustness of the logit model in describing preference dynamics was proven by the LR chi² statistic (332.52). The model's good fit was highlighted by the log likelihood (-170.924), and its efficiency in capturing a sizable portion of variation was shown by the pseudo R-squared (0.493). The significant F-statistic of 5.40 in Part-2 exhibited model fitness. These results indicated that the models used in part-1 and part-2 are valid and adequate.

The results from Table 4 for the two-part model showed the significance of gender in the first part (Logit), which indicated that males were 59.4% less likely to prefer native chicken meat than females. However, it did not significantly influence the quantity purchased in the second part. Therefore, while there may be gender-based preferences due to tastes and dietary preferences, they do not significantly affect consumption quantity (Grogan and Richards 2002). As people aged a stage, both their preferences for and purchases of local chicken significantly reduced, by 51.0% in the first part and by 6.39% in the second part. This trend may be due to growing health concerns' impact on dietary preferences (Sobal and Bisogni 2009). While income was not found to have a significant effect on the amount of native chicken meat purchased in Part-2, the results in Part-1 indicated that when income is raised by a category, consumers are 1.632 times (or 63.2%) more likely to choose native chicken. The findings support economic theories of consumer behaviour (Deaton and Muellbauer 1980) by showing that higher-income individuals had a larger range of food choices, including for specialty foods such as native chicken. As the levels of education rise, preferences for native chicken decline significantly by 81.70% in the first part and quantity purchases by 12.80% in the second. These results are consistent with studies showing that higher education is associated with more exposure to a broad range of foods and dietary alternatives (Beydoun et al. 2008).

Marital status had a significant impact on both consumers' preferences for native chicken (first-part) and the quantity purchased (second-part). Married consumers preferred native chicken meat 12.247 times more than unmarried consumers and purchased 38.40% more, possibly influenced by larger family sizes or shared dietary preferences. Consumer preferences for native chicken meat (first-part) were not significantly influenced by their habitat (urban or urban) of living. However, in the second part, it was found that urban consumers purchased in a quantity that was 19.27% lower than that of their counterparts, which might be because rural consumers had more convenient access to native chicken meat at all times.

The consumers having senior citizens at home had a significant odd of preferring native chicken by 2.119 times (part-1), whereas their presence did not significantly impact the quantity of native chicken purchased (pat-2). The results implied that older people could prefer traditional native chicken meat over quantity because they would consume it in relatively smaller quantities (Beardsworth and Keil 1992). However, neither the customers' preference for (part-1) nor the quantity of (part-2) native chicken meat they purchased were significantly influenced by having kids at home. This insignificance may be related to the children's choice for meals made primarily using broiler meat either at home or away. In part-1, a significant coefficient for household size indicates that there is a 42.10% greater likelihood of preferring native chicken for every additional unit in household size. In part 2, the household size coefficient is 0.045 (p = 0.054), which is barely significant. These findings suggest that larger households may have more varied eating habits and a propensity for traditional foods such as native chicken.

Hindu and other religious consumers did not show a significant preference for native chicken in the first part, which used Christianity as the reference group. Only Muslims, nevertheless, demonstrated a significantly lower preference, with a likelihood that was reduced by 90.80%, perhaps as a result of dietary limitations on certain meats. However, in part 2, only the consumers of other religions purchased significantly more (68.80%) than Christians, while Hindus and Muslims exhibited no significant difference in their quantities purchased. These results support the hypothesis that various religious and cultural groups may have unique dietary preferences and consumption patterns (Beydoun et al. 2008).

Notably, part-1 of the analysis revealed that respondents who consumed chicken daily, every other day, twice weekly, once weekly, or every two weeks had significantly lower preferences for native chicken meat than occasional or random chicken meat consumers (used as a reference); however, part-2 of the analysis revealed that these consumption frequencies had no significant impact on the quantity of native chicken purchased. As a result of increased native chicken meat prices, regular chicken consumers may have to spend more to satisfy their desire for native chicken; however, they might eat more broiler meat with the same budget.

With the exception of "Palatability," eight of the nine factor scores from factor analysis that were included as predictors in the two-part model had a significant negative impact on both part-1 (preference) and part-2 (quantity purchased). These eight significant factors, namely, (i) psychological well-being, (ii) affordability, (iii) knowledge, (iv) nutrition, (v) food quality, (vi) fitness, (vii) purity (natural), and (viii) sustainability, demonstrated a consistent and statistically significant negative impact on both preference for and quantity purchase of native chicken meat. Palatability may not have had a significant impact on native chicken meat preference and quantity purchase in the context of the analysis because it is universally desired, but the other eight factors may have had a different impact due to their unique characteristics and personal preferences.

The excessively high pricing (68%), the challenge in finding the precise quantities desired (23%), and just 9% dislike of the taste were the main justifications given by respondents who did not purchase native chicken meat. Even among those who preferred and purchased native chicken meat, their purchasing power was still significantly constrained by its greater price and difficulty in being found in the precise quantities they needed. On average, Native chicken meat typically costs approximately 75% more than broiler meat. People had to purchase complete chicken because native chicken meat, unlike broiler meat, was not available in portions. This means that for a family of two to three, the quantity from one bird will be greater, whereas for larger homes, it will either be insufficient and require the purchase of multiple birds.

With their economies of scale, the expanding semi-intensive and intensive systems of native chicken production have the potential to significantly increase market supply and thus address the issues of rising market pricing and limited availability of desired quantities. Furthermore, as the distinguished French economist once postulated, the supply of native chicken could stimulate its own demand, thereby contributing to the sustainability of the evolving native chicken production systems.

Preference for and consumption of native chicken meat in India were investigated using a two-part model and factor analysis. Consumer preferences have been found to be significantly influenced by demographic parameters, such as gender, age, income, education, marital status, cohabitation, household size, presence of senior citizens or children within households, and consumption frequency. Additionally, factors such as psychological well-being, affordability, knowledge, nutrition, food quality, fitness, purity (natural), and sustainability have a negative influence on both preferences and quantity of native chicken meat purchased.

An important problem in the market for native chicken meat is the price difference, which is approximately 75% higher for native chicken meat than for broiler meat. Additionally, consumers had to purchase the entire chicken because native chicken meat was not readily available in the required quantities. This brought on challenges for smaller households and may have resulted in under- or overconsumption for larger families. The results of this study indicated a significant preference for native chicken meat, which is driven by the sustainability advantages of this food, such as ethical and sustainable production. However, improving the sustainability of semi-intensive and intensive native chicken farming depends on eliminating pricing disparities and ensuring the availability of native chicken meat in desired quantities. The long-term sustainability of native chicken farming relies on integrating consumer demand with ethical and environmentally sustainable farming methods.

Informed Consent and Voluntary Survey Participation Statement:

In this research study, all survey participants provided informed consent and participated voluntarily. They were provided with clear information about the study's objectives and procedures. Participants were assured that their participation was entirely voluntary, with the right to refrain from submitting the survey form at any time without consequences. Strict ethical guidelines were followed to protect their rights and privacy.

Competing Interests Statement

The authors declare no competing interests associated with the research, including financial, personal, or professional affiliations that could influence or be perceived to influence the work presented in this manuscript.

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Table 1: List and levels of variables used in the two-part model

(First Part – Logit; Second Part – Log Linear)

Explanatory variable	Category	Specification	Variable in model
Gender	Male; Female	1 – If Male; 0 – If otherwise	X₁
Age Group	⋜ 26 years; 26–35 years; 36–45 years; 46–60 years; ˃ 60 years	1 – If ⋜ 26 years; 2 – If 26–35 years; 3 – If 36–45 years; 4 – If 46–60 years; 5 – If ˃ 60 years	X₂
Monthly Income Brackets (INR)	≤ 10000; 10,001 – 30,000; 30,001 – 60,000; 60,001 – 90,000; > 90,000	1 – If ≤ 10000; 2 – If 10,001 – 30,000; 3 – If 30,001 – 60,000; 4 – If 60,001 – 90,000; 5 – If > 90,000	X₃
Educational levels	Primary School; Secondary School; Collegiate	1 – If Primary; 2 – If Secondary; 3 – If Collegiate	X₄
Marital status	Married; Unmarried (single)	1 – If Married; 0 – If otherwise	X₅
Habitat (living area)	Urban; Rural	1 – If Urban; 0 – If otherwise	X₆
Cohabiting with family	Yes; No	1 – If Yes; 0 – if otherwise	X₇
Senior citizens at home	Yes; No	1 – If Yes; 0 – If otherwise	X₈
Children at home	Yes; No	1 – If Yes; 0 – If otherwise	X₉
Household size	Count	Count	X₁₀
Religion^a	Christian; Hindu; Muslim; Others	1 – If Hindu; 0 – If otherwise	X₁₁
		1 – If Muslim; 0 – If otherwise	X₁₂
		1 – If Others; 0 – If otherwise	X₁₃
Chicken consumption^b (frequency)	Daily; Alternate Days; Twice Weekly; Weekly; Fortnightly; Occasionally (randomly)	1 – If Daily; 0 – If otherwise	X₁₄
		1 – If Alternate Days; 0 – If otherwise	X₁₅
		1 – If Twice Weekly; 0 – If otherwise	X₁₆
		1 – If Weekly; 0 – If otherwise	X₁₇
		1 – If Fortnightly; 0 – If otherwise	X₁₈
Comfort	Factor Analysis	Factor Score	X₁₉
Affordability	Factor Analysis	Factor Score	X₂₀
Knowledge	Factor Analysis	Factor Score	X₂₁
Nutrition	Factor Analysis	Factor Score	X₂₂
Palatability	Factor Analysis	Factor Score	X₂₃
Food Quality	Factor Analysis	Factor Score	X₂₄
Fitness	Factor Analysis	Factor Score	X₂₅
Purity (natural)	Factor Analysis	Factor Score	X₂₆
Sustainability	Factor Analysis	Factor Score	X₂₇
Reference categories: a – Christian; b – Occasionally (randomly)

Table 2: Demographywise Desi-Chicken Meat Preferences and Consumption

Characteristics	Category	Total (N)	Response Frequency		Quantity (Mean kg.)	χ² [p value]
Characteristics	Category	Total (N)	Yes	No	Quantity (Mean kg.)	χ² [p value]
Gender	Male	376	211 (56.12)	165 (43.88)	1.495	12.74 [0.000]
Gender	Female	127	94 (74.02)	33 (25.98)	1.574	12.74 [0.000]
Age Groups (years)	⋜ 26	61	42 (68.85)	19 (31.15)	1.714	13.52 [0.009]
	26–35	103	66 (64.08)	37 (35.92)	1.492
	36–45	116	78 (67.24)	38 (32.76)	1.538
	46–60	178	101 (56.74)	77 (43.26)	1.446
	˃ 60	45	18 (40.00)	27 (60.00)	1.500
Income Brackets (INR)	≤ 10000	44	28 (63.64)	16 (36.36)	1.786	13.91 [0.008]
	10,001 – 30,000	75	43 (57.33)	32 (42.67)	1.698
	30,001 – 60,000	96	56 (58.33)	40 (41.67)	1.536
	60,001 – 90,000	103	78 (75.73)	25 (24.27)	1.378
	> 90,000	185	100 (54.05)	85 (45.95)	1.470
Education	Primary	13	8 (61.54)	5 (38.46)	1.750	6.26 [0.044]
	Secondary	18	16 (88.89)	2 (11.11)	1.563
	Collegiate	472	281 (59.53)	191 (40.47)	1.511
Marital status	Married	412	249 (60.44)	163 (39.56)	1.532	0.04 [0.846]
Marital status	Unmarried	91	56 (61.54)	35 (38.46)	1.464	0.04 [0.846]
Habitat (living area)	Urban	421	260 (61.76)	161 (38.24)	1.481	1.36 [0.243]
Habitat (living area)	Rural	82	45 (54.88)	37 (45.12)	1.744	1.36 [0.243]
Cohabiting with family?	Yes	437	260 (59.50)	177 (40.50)	1.521	1.81 [0.178]
Cohabiting with family?	No	66	45 (68.18)	21 (31.82)	1.511	1.81 [0.178]
Senior citizens at home?	Yes	251	168 (66.93)	83 (33.07)	1.595	8.32 [0.004]
Senior citizens at home?	No	252	137 (54.37)	115 (45.63)	1.427	8.32 [0.004]
Children at home?	Yes	251	163 (64.94)	88 (35.06)	1.543	3.89 [0.049]
Children at home?	No	252	142 (56.35)	110 (43.65)	1.493	3.89 [0.049]
Household size (count)	≤ 2	29	11 (37.93)	18 (62.07)	1.500	9.91 [0.042]
	3	117	65 (55.56)	52 (44.44)	1.515
	4	220	139 (63.18)	81 (36.82)	1.435
	5	77	49 (63.64)	28 (36.36)	1.663
	≥5	60	41 (68.33)	19 (31.67)	1.646
Religion	Christian	35	21 (60.00)	14 (40.00)	1.357	5.56 [0.135]
	Hindu	420	261 (62.14)	159 (37.86)	1.506
	Muslim	41	18 (43.90)	23 (56.10)	1.694
	Others	7	5 (71.43)	2 (28.57)	2.300
Chicken meat consumption frequency	Daily	34	9 (26.47)	25 (73.53)	1.667	37.01 [0.000]
	Alternate Days	63	34 (53.97)	29 (46.03)	1.471
	Twice Weekly	171	95 (55.56)	76 (44.44)	1.521
	Weekly	134	94 (70.15)	40 (29.85)	1.441
	Fortnightly	24	12 (50.00)	12 (50.00)	1.708
	Occasionally (randomly)	77	61 (79.22)	16 (20.78)	1.607
Figures in parenthesis indicate per cent to total number of respondents (N)

Table 3: Results of Factor Analysis (Rotated Factor Loadings)

Statements (about purchase and consumption of chicken meat…)	Component (Factors)
	1	2	3	4	5	6	7	8	9
Promotes relaxation	.804
Cheers me up	.788
Eases stress handling	.761
Facilitates coping with life's challenges	.756
Maintains alertness	.738
Enhances well-being (feel good)	.665
Cost-effective		.890
Aware of meat prices		.885
Always hunt for special deals		.869
Budget-friendly		.867
Excellent value for money		.584
Familiar with chicken meat			.813
Mindful of my chicken choices			.810
Conscious of my dietary preferences			.788
Distinguish chicken products			.785
Have confidence in my meat choice			.716
Nutrient-rich				.802
Promotes good health				.763
Rich in protein				.723
Abundant in vitamins and minerals				.673
Beneficial for skin, teeth, hair, nails, etc.				.632
Has appealing appearance					.789
Has pleasant aroma (smell)					.771
Has delicious taste					.743
Has enjoyable texture					.733
Food quality is my key factor						.828
Food quality guides my purchases						.784
There are multiple aspects to food quality						.776
Food quality serves as my motivator						.657
Helps me control my weight							.836
Is low in fat							.821
Is low in calorie							.813
Free from artificial ingredients								.798
Additive-free								.796
Comprises natural ingredients								.763
I prefer purchasing organic chicken									.791
I prefer to buy free-range chicken									.706
Sustainability is important to me									.657
I support the local farming community									.533
Extraction Method: Principal Component Analysis; Rotation Method: Varimax with Kaiser Normalisation.
Factors: 1 - Psychological Well-Being; 2 - Affordability; 3 - Knowledge; 4 - Nutrition; 5- Palatability; 6 - Food Quality; 7 - Fitness; 8 - Purity (natural); 9 - Sustainability

Table 4: Results of the Two-Part Model (First Part – Logit; Second Part – Log Linear)

Explanatory Variables	Part 1: LOGIT				Part 2: LOG-LINEAR REG.
Explanatory Variables	Coefficient	Exp(B)	z	P > z	Coefficient	z	P > z
X₁	-0.901 (0.380)	0.406	2.37	0.018	0.026 (0.057)	0.45	0.650
X₂	-0.714 (0.193)	0.490	3.70	0.000	-0.066 (0.032)	2.04	0.041
X₃	0.490 (0.138)	1.632	3.54	0.000	0.010 (0.024)	0.44	0.657
X₄	-1.701 (0.450)	0.183	3.78	0.000	-0.137 (0.069)	2.00	0.046
X₅	2.505 (0.665)	12.247	3.77	0.000	0.325 (0.094)	3.47	0.001
X₆	0.377 (0.385)	1.458	0.98	0.327	-0.214 (0.071)	3.02	0.002
X₇	-2.145 (0.581)	0.117	3.69	0.000	-0.033 (0.074)	0.44	0.660
X₈	0.751 (0.317)	2.119	2.37	0.018	0.094 (0.053)	1.78	0.075
X₉	0.329 (0.331)	1.389	0.99	0.321	0.014 (0.056)	0.25	0.803
X₁₀	0.351 (0.144)	1.421	2.45	0.014	0.045 (0.024)	1.93	0.054
X₁₁	-0.350 (0.555)	0.704	0.63	0.528	0.107 (0.101)	1.06	0.288
X₁₂	-2.383 (0.774)	0.092	3.08	0.002	0.008 (0.146)	0.06	0.954
X₁₃	0.524 (1.177)	1.688	0.44	0.656	0.750 (0.213)	3.53	0.000
X₁₄	-3.427 (0.770)	0.032	4.45	0.000	0.165 (0.155)	1.07	0.286
X₁₅	-1.759 (0.638)	0.172	2.76	0.006	0.035 (0.098)	0.36	0.717
X₁₆	-2.266 (0.555)	0.104	4.08	0.000	-0.036 (0.073)	0.50	0.617
X₁₇	-1.369 (0.567)	0.254	2.41	0.016	-0.089 (0.071)	1.25	0.211
X₁₈	-2.414 (0.759)	0.089	3.18	0.001	0.052 (0.131)	0.40	0.693
X₁₉	-0.672 (0.153)	0.511	4.38	0.000	-0.095 (0.023)	4.19	0.000
X₂₀	0.785 (0.161)	0.456	4.88	0.000	-0.092 (0.022)	4.14	0.000
X₂₁	-0.417 (0.151)	0.659	2.75	0.006	-0.044 (0.023)	1.94	0.052
X₂₂	-0.841 (0.170)	0.431	4.96	0.000	-0.072 (0.023)	3.20	0.001
X₂₃	-0.241 (0.164)	0.786	1.47	0.143	-0.013 (0.024)	0.54	0.591
X₂₄	-0.552 (0.138)	0.576	4.00	0.000	-0.064 (0.022)	2.89	0.004
X₂₅	-1.563 (0.188)	0.209	8.31	0.000	-0.169 (0.025)	6.69	0.000
X₂₆	-0.673 (0.149)	0.510	4.51	0.000	-0.098 (0.023)	4.18	0.000
X₂₇	1.166 (0.166)	3.209	7.01	0.000	0.136 (0.027)	5.13	0.000
Constant	6.693 (1.819)	806.385	3.68	0.000	0.322 (0.240)	1.34	0.180
	N = 503 LR chi2(27) = 332.52 Prob > chi2 = 0.000 Log likelihood = -170.924 Pseudo R² = 0.493				N = 305 F (27, 277) = 5.40 Prob > F = 0.000 R² = 0.345 Adj. R² = 0.281 Log likelihood = -140.313 Root MSE = 0.4022
	Log pseudolikelihood = -311.23659
Figures in parenthesis indicate Standard Errors

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Two-Part Modelling of Consumer Preferences for Native Chicken Meat: Sustainability Implications for Semi-Intensive and Intensive Production Systems

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Abstract

INTRODUCTION

METHODOLOGY

RESULTS AND DISCUSSION

a). Preferences for Native Chicken Meat

ii) Interrelationships in Customer Attitudes Toward Chicken Meat - Factor Analysis

iii) Factors Influencing Native Chicken Meat Preferences and Purchases: Two-Part Modelling

CONCLUSIONS

Declarations

References

Tables

Status:

Version 1