| Literature DB >> 36076874 |
Jing Zhang1, Scott Waldron1, Xiaoxia Dong2.
Abstract
China is the largest global consumer of infant milk formula (IMF). Chinese consumer preferences towards IMF have evolved over time but have also been rocked in recent years by COVID-19 with major implications for the IMF industry, globally and within China. This study is the first to document parents' preferences toward IMF since the outbreak. We used novel methods to do so, through an online choice experiment of 804 participants that included risk perceptions and socio-demographic variables. Our study finds that Chinese parents continue to prioritize quality and safety attributes of IMF represented by functional ingredients, organic labelling and traceability information. Notably, it also finds greatly increased confidence in Chinese domestically produced IMF and an underlying preference away from expensive products. This implies that the era of 'go for foreign' and 'go for the most expensive' in IMF purchasing may be coming to an end. The shift in sentiment is driven by the longer-term revitalization of the Chinese dairy industry, accelerated by COVID-19. Understanding these trends will be of major benefit to both Chinese producers and non-Chinese exporters of IMF.Entities:
Keywords: China; choice experiment; consumer preference; infant milk formula
Year: 2022 PMID: 36076874 PMCID: PMC9455783 DOI: 10.3390/foods11172689
Source DB: PubMed Journal: Foods ISSN: 2304-8158
Product attributes and attribute levels.
| Product Attribute | Description | Attribute Levels |
|---|---|---|
| Product origin | Origin of main ingredients (‘nai yuan’), origin of manufacturing (processing location) and country-of-purchase | 1 = domestic main ingredients, produced domestically |
| Organic | Logo or other trademark to show organic certification, regardless of Chinese or non-Chinese origin | 1 = With organic label |
| Functional ingredients | Functional ingredients, such as DHA/ARA for brain development, prebiotics for digestive health, lutein for vision and cognitive function etc. | 1 = functional ingredients contained |
| Traceability | QR (Quick Response) code to trace any supply chain information covering milk producing, IMF processing and marketing. | 1 = With traceable QR code |
| Price | Average price of IMF available in markets at the time of study, with intervals of CNY 50 | 1 = 180 CNY/900 g (around 26 USD/900 g) |
Figure 1Example of one choice set ((A–C) stand for different options).
Demographic frequency distribution of samples (N = 804).
| Demographic/Perception | Category | % |
|---|---|---|
| Gender | Male | 41.5 |
| Female | 58.5 | |
| Age (years) | 20–30 | 37.7 |
| 30–40 | 56.7 | |
| 40–50 | 4.9 | |
| Over 50 | 0.7 | |
| Education | Junior high | 1.6 |
| Senior high | 10.8 | |
| College (2–3 years) | 24.8 | |
| Undergraduate | 53.8 | |
| Postgraduate & above | 9.0 | |
| Family size (peoples) | 3 | 39.6 |
| 4 | 31.7 | |
| 5 | 17.9 | |
| 6 and more | 10.8 | |
| Income (CNY) | ≤100,000 | 11.8 |
| 100,000–200,000 | 46.4 | |
| 200,000–300,000 | 27.4 | |
| 300,000–400,000 | 8.8 | |
| 400,000–500,000 | 2.2 | |
| >500,000 | 3.4 | |
| The quality and safety of domestic produced IMF is reliable to me | Strongly disagree | 3.7 |
| Disagree | 12.6 | |
| Somewhat disagree | 15.4 | |
| Neutral | 27.7 | |
| Somewhat agree | 18.8 | |
| Agree | 11.0 | |
| Strongly agree | 10.8 | |
| China’s quality and safety supervision of IMF is the strictest in history | Strongly disagree | 2.6 |
| Disagree | 4.6 | |
| Somewhat disagree | 8.5 | |
| Neutral | 23.8 | |
| Somewhat agree | 17.3 | |
| Agree | 24.0 | |
| Strongly agree | 19.3 | |
| I would be concerned about imported IMF due to the possible delays, shortages or virus risks | Strongly disagree | 1.6 |
| Disagree | 5.1 | |
| Somewhat disagree | 6.2 | |
| Neutral | 12.9 | |
| Somewhat agree | 24.1 | |
| Agree | 26.0 | |
| Strongly agree | 24.0 |
Figure 2Respondents’ trust in IMF chain.
Results of conditional logit and random parameter logit main effects.
| Conditional Logit Model (A) | Random Parameter Logit Model (B) | |||||
|---|---|---|---|---|---|---|
| Choice | Coef. | Std. Err. | Mean Coef. | Std. Err. | SD Coef. | Std. Err. |
| Origin | −0.3641 *** | 0.0185 | −0.5412 *** | 0.0334 | 0.6257 *** | 0.0358 |
| Functional ingredient | 0.1130 ** | 0.0450 | 0.1324 ** | 0.0553 | 0.1427 * | 0.1641 |
| Organic | 0.4175 *** | 0.0491 | 0.5849 *** | 0.0645 | 0.5581 *** | 0.1161 |
| Tracible | 1.2909 *** | 0.0382 | 1.8237 *** | 0.0753 | 1.2046 *** | 0.0818 |
| Price | −0.0048 | 0.0190 | −0.0052 | 0.0248 | 0.2908 *** | 0.0366 |
| ASC | 0.6732 *** | 0.0777 | 1.5624 *** | 0.1298 | −1.8538 *** | 0.1324 |
| Log likelihood | −5851.6058 | −5298.18 | ||||
| Number of obs. | 19,296 | 19,296 | ||||
| LR chi2(6) | 2429.34 | 1106.86 | ||||
| Prob>chi2 | 0.0000 | 0.0000 | ||||
| Pseudo R2 | 0.1719 | - | ||||
| AIC | 11,715.21 | 10,620.35 | ||||
| BIC | 11,762.42 | 10,714.76 | ||||
*, **, *** denotes significance level at the 10%, 5% and 1% respectively; ASC stands to Alternative-specific Constant.
Results of a random parameter logit model interacting the case-specific with alternative-specific variables.
| Interacting Case-Specific Variables with ASC (B1) | Interacting Case-Specific Variables with Origin (B2) | Interacting Case-Specific Variables with Price (B3) | ||||
|---|---|---|---|---|---|---|
| Choice 1 | Coef. | Std. Err. | Coef. | Std. Err. | Coef. | Std. Err. |
| Origin | −0.5817 *** | 0.0372 | −0.8749 *** | 0.1690 | −0.5837 *** | 0.0373 |
| Functional ingredient | 0.1247 ** | 0.0558 | 0.1379 ** | 0.0561 | 0.1266 ** | 0.0558 |
| Organic | 0.5980 *** | 0.0659 | 0.6239 *** | 0.0642 | 0.6004 *** | 0.0660 |
| Tracible | 1.8464 *** | 0.0759 | 1.8586 *** | 0.0779 | 1.8474 *** | 0.0759 |
| Price | −0.0063 | 0.0252 | −0.0062 | 0.0249 | −0.1294 | 0.0944 |
| ASC | −0.2639 | 0.6921 | 1.6459 *** | 0.1391 | −0.6796 | 0.4999 |
| Gender * | −0.3000 | 0.2052 | −0.1840 *** | 0.0517 | 0.0214 | 0.0439 |
| Education * | −0.1861 | 0.1497 | 0.1134 *** | 0.0390 | −0.0232 | 0.0323 |
| Income * | 0.0857 | 0.1208 | 0.0460 | 0.0304 | 0.0686 *** | 0.0258 |
| Trust_Dqual * | 0.2364 *** | 0.0658 | −0.1598 *** | 0.0175 | 0.2343 *** | 0.0660 |
| Trust_Dregu * | 0.2096 *** | 0.0715 | −0.1127 *** | 0.0184 | 0.2094 *** | 0.0725 |
| Impact_CV * | 0.0297 | 0.0728 | −0.0929 *** | 0.0172 | 0.0288 | 0.0702 |
| Log likelihood | −5278.5997 | −5165.5757 | −5277.2314 | |||
| Number of obs | 19,296 | 19,296 | 19,296 | |||
| LR chi2(6) | 1104.94 | 1060.80 | 1118.49 | |||
| Prob>chi2 | 0.0000 | 0.0000 | 0.0000 | |||
| AIC | 10,593.20 | 10,367.15 | 10,590.46 | |||
| BIC | 10,734.82 | 10,508.77 | 10,732.08 | |||
1 Other variables omitted for lack of statistical significance were age and family size; *, **, *** denotes significance level at the 10, 5 and 1% level respectively; ASC stands to Alternative-specific Constant; Trust_Dqual stands for Trust of Domestic product quality; Trust_Dregu stands for Trust of Domestic industry regulation; Impact_CV stands for Impacts of COVID-19.
Figure 3Policy and measures toward the Chinese Infant Milk Formula sector, 2008–2022. Source: Authors collected and translated from multiple Chinese official websites including State Council, Ministry of Agriculture, China Food and Drug Administration, State Administration for Market Regulation, Development and Reform Committee and Ministry of Industry and Information Technology.