| Literature DB >> 35153923 |
Zeying Huang1, Haijun Li2, Pei Wang3, Jiazhang Huang1.
Abstract
More and more packaged products in China have been labeled as low-calorie products since the official implementation of nutrition claims in 2007. But little was known about the impact of such claims on the Chinese consumption of low-calorie food on the background of increasing rates of obesity among the Chinese population. This study sought to fill the gap by applying a consumer behavior model to a nationally representative online survey by means of structural equation modeling. The findings revealed that nutrition claims significantly affect the consumption of low-calorie products. Specifically, marketing stimulus on low-calorie products first affected consumer psychology, then consumer decision-making, and finally consumer responses. Despite the significant role of consumer psychology and decision-making in consumption, consumers were susceptible to the influence of targeted marketing strategies for foods with a low-calorie claim. It is recommended that appropriate use of low-calorie nutrition claims by manufacturers and choices of low-calorie food by consumers according to their own needs should be encouraged.Entities:
Keywords: low-calorie products; model of consumer behavior; nutrition claim; nutrition labeling; packaged food
Year: 2022 PMID: 35153923 PMCID: PMC8833154 DOI: 10.3389/fpsyg.2021.799802
Source DB: PubMed Journal: Front Psychol ISSN: 1664-1078
FIGURE 1Model of consumer behavior. Source: Kotler and Armstrong (2011).
FIGURE 2The experimental design.
Socio-demographic characteristics of the sample (n = 930).
| Sample characteristics | Option | Sample size | Percentage (%) |
| Gender | Male | 528 | 56.77 |
| Female | 402 | 43.23 | |
| Age | <18 | 168 | 18.06 |
| From 18 to 44 | 294 | 31.61 | |
| From 45 to 59 | 275 | 29.57 | |
| ≥9.5 | 193 | 20.76 | |
| Education level | Primary school and below | 127 | 13.66 |
| Junior high school | 283 | 30.43 | |
| High school | 291 | 31.29 | |
| College/Bachelor | 198 | 21.29 | |
| Postgraduate or above | 31 | 3.33 | |
| Annual household income (after tax) | <10,000 Yuan | 117 | 12.58 |
| From 10,000 Yuan to 50,000 Yuan | 254 | 27.31 | |
| From 50,001 Yuan to 100,000 Yuan | 246 | 26.45 | |
| From 100,001 Yuan to 150,000 Yuan | 180 | 19.35 | |
| From 150,001 Yuan to 200,000 Yuan | 83 | 8.92 | |
| >200,000 Yuan | 50 | 5.38 |
One US dollar is equal to 6.524 Chinese Yuan and One Euro is equal to 7.960 Chinese Yuan from November 10 to December 28, 2020.
Description of latent variables and summary statistics.
| Latent variables | Scale items | Strongly disagree | Disagree | Neither agree nor disagree | Agree | Strongly agree | |||||
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| N | % | N | % | N | % | N | % | N | % | ||
| Marketing stimuli | I have seen food with low-calorie nutrition claim on sales. | 21 | 2.26 | 110 | 11.83 | 280 | 30.11 | 374 | 40.22 | 145 | 15.59 |
| I know that the price of food with low-calorie nutrition claim is affordable. | 108 | 11.61 | 288 | 30.97 | 376 | 40.43 | 138 | 14.84 | 20 | 2.15 | |
| I have seen food with low-calorie nutrition claim sold in many places. | 31 | 3.33 | 153 | 16.45 | 300 | 32.26 | 336 | 36.13 | 110 | 11.83 | |
| I have seen the food with low-calorie nutrition claim on promotion. | 64 | 6.88 | 301 | 32.37 | 338 | 36.34 | 178 | 19.14 | 49 | 5.27 | |
| Consumer psychology | I would follow my friends and relatives’ example if they all read low-calorie nutrition claim when shopping. | 24 | 2.58 | 85 | 9.14 | 258 | 27.74 | 455 | 48.92 | 108 | 11.61 |
| I would read low-calorie nutrition claim even if none of my friends and relatives did it when shopping. | 36 | 3.87 | 223 | 23.98 | 380 | 40.86 | 226 | 24.30 | 65 | 6.99 | |
| I would buy the food with low-calorie nutrition claim which is beyond my factual income. | 83 | 8.92 | 291 | 31.29 | 352 | 37.85 | 176 | 18.92 | 28 | 3.01 | |
| I would pay attention to the actual benefits of food with low-calorie nutrition claim. | 15 | 1.61 | 49 | 5.27 | 271 | 29.14 | 446 | 47.96 | 149 | 16.02 | |
| Consumer decision making | I believe low calorie nutrition claim helps make healthy food choice. | 14 | 1.51 | 70 | 7.53 | 231 | 24.84 | 453 | 48.71 | 162 | 17.42 |
| I believe low calorie nutrition claim helps understand nutritional properties of food. | 17 | 1.83 | 64 | 6.88 | 247 | 26.56 | 428 | 46.02 | 174 | 18.71 | |
| I have read low calorie nutrition claim when shopping. | 29 | 3.12 | 222 | 23.87 | 340 | 36.56 | 263 | 28.28 | 76 | 8.17 | |
| I have bought foods with low calorie nutrition claim when shopping | 14 | 1.51 | 135 | 14.52 | 397 | 42.69 | 289 | 31.08 | 95 | 10.22 | |
| Consumer responses | I have made choices among different kinds of foods through low-calorie nutrition claim. | 31 | 3.33 | 210 | 22.58 | 334 | 35.91 | 283 | 30.43 | 72 | 7.74 |
| I have made choices among different brands of similar foods through low-calorie nutrition claim. | 41 | 4.41 | 190 | 20.43 | 353 | 37.96 | 276 | 29.68 | 70 | 7.53 | |
| I have seized the moment to buy foods with low-calorie nutrition claim. | 42 | 4.52 | 232 | 24.95 | 376 | 40.43 | 228 | 24.52 | 52 | 5.59 | |
| I have made choices among different amounts of food through low-calorie nutrition claim. | 73 | 7.85 | 221 | 23.76 | 369 | 39.68 | 194 | 20.86 | 73 | 7.85 | |
Factor correlations and discriminant validity.
| Factors | Marketing stimuli | Consumer psychology | Consumer decision making | Consumer responses |
| Marketing stimuli | [0.792] | |||
| Consumer psychology | 0.494 | [0.787] | ||
| Consumer decision making | 0.451 | 0.913 | [0.705] | |
| Consumer responses | 0.333 | 0.674 | 0.739 | [0.733] |
Values in brackets [] indicate the square root of AVEs. A significance level is shown at ***p < 0.001, **p < 0.01, and *p < 0.05. Diagonals represent the square root of the average variance extracted, while the other entries represent the squared correlations.
Factor loadings and convergent validity results.
| Variables | Scale items Code | Scale items | Standard Loadings | AVE | Composite reliability | Cronbach’ s α |
| Marketing stimuli | X1 | I have seen food with low-calorie nutrition claim on sales. | 0.506 | 0.627 | 0.703 | 0.816 |
| X2 | I know that the price of food with low-calorie nutrition claim is affordable. | 0.641 | ||||
| X3 | I have seen food with low-calorie nutrition claim sold in many places. | 0.562 | ||||
| X4 | I have seen the food with low-calorie nutrition claim on promotion. | 0.554 | ||||
| Consumer psychology | X5 | I would follow my friends and relatives’ example if they all read low-calorie nutrition claim when shopping. | 0.588 | 0.619 | 0.734 | 0.853 |
| X6 | I would read low-calorie nutrition claim even if none of my friends and relatives did it when shopping. | 0.591 | ||||
| X7 | I would buy the food with low-calorie nutrition claim which is beyond my factual income. | 0.501 | ||||
| X8 | I would pay attention to the actual benefits of food with low-calorie nutrition claim. | 0.516 | ||||
| Consumer decision making | X9 | I believe low-calorie nutrition claim helps make healthy food choice. | 0.661 | 0.597 | 0.769 | 0.826 |
| X10 | I believe low-calorie nutrition claim helps understand nutritional properties of food. | 0.635 | ||||
| X11 | I have read low-calorie nutrition claim when shopping. | 0.718 | ||||
| X12 | I have bought foods with low-calorie nutrition claim when shopping. | 0.643 | ||||
| Consumer responses | X13 | I have made choices among different kinds of foods through low-calorie nutrition claim. | 0.729 | 0.537 | 0.792 | 0.899 |
| X14 | I have made choices among different brands of similar foods through low-calorie nutrition claim. | 0.755 | ||||
| X15 | I have seized the moment to buy foods with low-calorie nutrition claim. | 0.760 | ||||
| X16 | I have made choices among different amounts of food through low-calorie nutrition claim. | 0.752 |
Rotation technique: Promax; extraction technique: maximum likelihood; total variance elucidated: 59.05%; Bartlett’s test of sphericity: χ2 = 5,901.666, p < 0.001; Kaiser-Meyer-Olkin measure of sampling adequacy: 0.884 (p < 0.001).
FIGURE 3Structural equation modeling results. Comparative fit index = 0.850; goodness-of-fit index = 0.887; root mean square error of approximation = 0.075; degrees of freedom = 136; chi-square = 1,029.143; X1–X16 is the scale items code and e1–e20 is statistical error of four latent variables and 16 scale items.
Structural equation modeling fitting.
| Goodness-of-fit indices | Fitting index values | Fitting |
| Standard chi – square (SCS) | 2.199 | <3, good |
| Comparative fit index (CFI) | 0.950 | >0.9, good |
| Incremental fit index (IFI) | 0.951 | >0.9, good |
| Goodness-of-fit Index (GFI) | 0.987 | >0.9, good |
| Adjusted goodness-of-fit index (AGFI) | 0.957 | >0.9, good |
| Root mean square error of approximation (RMSEA) | 0.075 | <0.08, good |
| Non-normalizing fitting index (NNFI) | 0.928 | >0.9, good |
| Norm fitting index (NFI) | 0.927 | >0.9, good |
Test results of the hypothesis.
| Hypothesized paths | Normalized path coefficient | Accepted | ||
| H1: Marketing stimuli→Consumer psychology | 0.363 | 1.382 | 0.007 | Yes |
| H2: Consumer psychology→Consumer decision making | 0.913 | 3.411 | 0.005 | Yes |
| H3: Consumer decision making→Consumer responses | 0.739 | 3.835 | 0.008 | Yes |