The Impact of Health Risk Perception on Blockchain Traceable Fresh Fruits Purchase Intention in China
Abstract
:1. Introduction
2. Literature Review
3. Data and Methods
3.1. Data Collection
3.2. Data Description and Summary Statistics
3.3. Model Specification
4. Results and Discussion
4.1. Basic Regression Analysis
4.2. Heterogeneity Analysis
4.3. Robustness Test
5. Conclusions
6. Implications
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
References
- Han, G.; Zhai, Y. Risk society and the politics of food safety problems in China. Jpn. J. Polit. Sci. 2022, 23, 73–87. [Google Scholar] [CrossRef]
- Zheng, J.; Hu, H. Study on food quality and safety management model based on industrial agglomeration theory. Acta Agric. Scand. Sect. B Soil Plant Sci. 2022, 72, 429–439. [Google Scholar] [CrossRef]
- Wang, Y.; Wang, J.; Shen, Q. A consumer segmentation study of nutrition information seeking and its relation to food consumption in Beijing, China. Foods 2022, 11, 453. [Google Scholar] [CrossRef]
- Dionysis, S.; Chesney, T.; McAuley, D. Examining the influential factors of consumer purchase intentions for blockchain traceable coffee using the theory of planned behaviour. Brit. Food J. 2022. [Google Scholar] [CrossRef]
- Yin, S.; Hu, W.; Chen, Y.; Han, F.; Wang, Y.; Chen, M. Chinese consumer preferences for fresh produce: Interaction between food safety labels and brands. Agribusiness 2019, 35, 53–68. [Google Scholar] [CrossRef]
- Hamilton, J.G.; Lobel, M. Psychosocial factors associated with risk perceptions for chronic diseases in younger and middle-aged women. Womens Health 2015, 55, 921–942. [Google Scholar] [CrossRef] [Green Version]
- Brewer, N.T.; Chapman, G.B.; Gibbons, F.X.; Gerrard, M.; McCaul, K.D.; Weinstein, N.D. Meta-analysis of the relationship between risk perception and health behavior: The example pf Vaccination. Health Psychol. 2007, 26, 136–145. [Google Scholar] [CrossRef] [Green Version]
- Larsman, P.; Eklof, M.; Torner, M. Adolescents’ risk perceptions in relation to risk behavior with long-term health consequences, antecedents and outcomes: A literature review. Saf. Sci. 2012, 50, 1740–1748. [Google Scholar] [CrossRef]
- Lin, W.; Ortega, D.L.; Ufer, D.; Caputo, V.; Awokuse, T. Blockchain-based traceability and demand for US beef in China. Appl. Econ. Perspect. Policy 2022, 44, 253–272. [Google Scholar] [CrossRef]
- Islam, S.; Cullen, J.M. Food traceability: A generic theoretical framework. Food Control 2021, 123, 107848. [Google Scholar] [CrossRef]
- Robson, K.; Dean, M.; Brooks, S.; Haughey, S.; Elliott, C. A 20-year analysis of reported food fraud in the global beef supply chain. Food Control 2020, 116, 107310. [Google Scholar] [CrossRef]
- Garaus, M.; Treiblmaier, H. The influence of blockchain-based food traceability on retailer choice: The mediating role of trust. Food Control 2021, 161, 108082. [Google Scholar] [CrossRef]
- Sander, F.; Semeijn, J.; Mahr, D. The acceptance of blockchain technology in meat traceability and transparency. Brit. Food J. 2018, 120, 2066–2079. [Google Scholar] [CrossRef] [Green Version]
- Lin, Q.; Wang, H.; Pei, X.; Wang, J. Food safety traceability system based on blockchain and EPCIS. IEEE Access 2019, 7, 20698–20707. [Google Scholar] [CrossRef]
- Han, Y.; Qiao, J. Influence factors on consumers’ attitude and willingness to buy traceable foods in China: A test and analysis on the survey from Beijing. Technol. Econ. 2009, 28, 37–43. [Google Scholar]
- Rodriguez-Salvador, B.; Dopico, D.C. Understanding the value of traceability of fishery products from a consumer perspective. Food Control 2020, 112, 107142. [Google Scholar] [CrossRef]
- Caro, M.P.; Ali, M.S.; Vecchio, M.; Giaffreda, R. Blockchain-based traceability in Agri-Food supply chain management: A practical implementation. In Proceedings of the 2018 IoT Vertical and Topical Summit on Agriculture-Tuscany (IOT Tuscany), Tuscany, Italy, 8–9 May 2018. [Google Scholar] [CrossRef]
- Mangla, S.K.; Kazançoğlu, Y.; Yıldızbaşı, A.; Öztürk, C.; Çalık, A. A conceptual framework for blockchain-based sustainable supply chain and evaluating implementation barriers: A case of the tea supply chain. Bus. Strateg. Environ. 2022. [Google Scholar] [CrossRef]
- Creydt, M.; Fischer, M. Blockchain and more-Algorithm driven food traceability. Food Control 2019, 105, 45–51. [Google Scholar] [CrossRef]
- Lakhani, K.R.; Iansiti, M. The truth about blockchain. Harvard Bus. Rev. 2017, 95, 119–127. [Google Scholar]
- Khan, S.A.; Mubarik, M.S.; Kusi-Sarpong, S.; Gupta, H.; Zaman, S.I.; Mubarik, M. Blockchain technologies as enablers of supply chain mapping for sustainable supply chains. Bus. Strateg. Environ. 2022. [Google Scholar] [CrossRef]
- Galvez, J.F.; Mejuto, J.C.; Simal-Gandara, J. Future challenges on the use of blockchain for food traceability analysis. TRAC-Trends Anal. Chem. 2018, 107, 222–232. [Google Scholar] [CrossRef]
- Collart, A.J.; Canales, E. How might broad adoption of blockchain-based traceability impact the U.S. fresh produce supply chain? Appl. Econ. Perspect. Policy 2022, 44, 219–236. [Google Scholar] [CrossRef]
- Montecchi, M.; Plangger, K.; Etter, M. It’s real, trust me! Establishing supply chain provenance using blockchain. Bus. Horizons 2019, 62, 283–293. [Google Scholar] [CrossRef] [Green Version]
- Feng, H.; Wang, X.; Duan, Y.; Zhang, J.; Zhang, X. Applying blockchain technology to improve agri-food traceability: A review of development methods, benefits and challenges. J. Clean. Prod. 2020, 260, 121031. [Google Scholar] [CrossRef]
- Kamath, R. Food traceability on blockchain: Walmart’s pork and mango pilots with IBM. J. Brit. Blockchain Assoc. 2018, 1, 3712. [Google Scholar] [CrossRef]
- Visser, C.; Hanich, Q.A. How blockchain is strengthening tuna traceability to combat illegal fishing. Jan. Conversat. 2018, 22, 1–4. [Google Scholar]
- National Bureau of Statistics. China Statistical Yearbook 2021; China Statistics Press: Beijing, China, 2021. [Google Scholar]
- Wang, L.; Wang, J.; Huo, X. Consumer’s willingness to pay a premium for organic fruits in China: A double-hurdle analysis. Int. J. Environ. Res. Public Health 2019, 16, 126. [Google Scholar] [CrossRef] [Green Version]
- Ding, L.; Liu, M.; Yang, Y.; Ma, W. Understanding Chinese consumers’ purchase intention towards traceable seafood using an extended Theory of Planned Behavior model. Mar. Policy 2022, 137, 104973. [Google Scholar] [CrossRef]
- Lin, X.; Chang, S.C.; Chou, T.H.; Chen, S.C.; Ruangkanjanases, A. Consumers’ intention to adopt blockchain food traceability technology towards organic food products. Int. J. Environ. Res. Public Health 2021, 18, 912. [Google Scholar] [CrossRef]
- Cavite, H.J.; Mankeb, P.; Kerdsriserm, C.; Joedsak, A.; Direksri, N.; Suwanmaneepong, S. Do behavioral and socio-demographic factors determine consumers’ purchase intention towards traceable organic rice? Evidence from Thailand. Org. Agric. 2022, 12, 243–258. [Google Scholar] [CrossRef]
- Quevedo-Silva, F.; Lucchese-Cheung, T.; Spers, E.E.; Alves, F.V.; de Almeida, R.G. The effect of Covid-19 on the purchase intention of certified beef in Brazil. Food Control 2022, 133, 108652. [Google Scholar] [CrossRef]
- Li, Z.; Zhang, Y.; Zhao, Q.; Wang, C.; Cui, Y.; Li, J.; Chen, A.; Liang, G.; Jiao, B. Occurrence, temporal variation, quality and safety assessment of pesticide residues on citrus fruits in China. Chemosphere 2020, 258, 127381. [Google Scholar] [CrossRef]
- O’Brien, E.K.; Baig, S.A.; Persoskie, A. Absolute and Relative Smokeless Tobacco Product Risk Perceptions: Developing and Validating New Measures that are Up-to-Snuff. Nicotine Tob. Res. 2022, 24, 265–269. [Google Scholar] [CrossRef]
- Cao, H.; Zhang, H.; Yang, L.; Li, L.; Zhang, J.; Zada, B.; Li, X.; Liu, W.; Su, T.; Zhao, Y. Analysis of the mediating effect of risk perception on the relationship between time perception and mental health of college students during the COVID-19 epidemic. Front. Psychiatry 2021, 12, 749379. [Google Scholar] [CrossRef] [PubMed]
- Ban, J.; Lan, L.; Yang, C.; Wang, J.; Chen, C.; Huang, G.; Li, T. Public perception of extreme cold weather-related health risk in a cold area of Northeast China. Disaster Med. Public 2017, 11, 417–421. [Google Scholar] [CrossRef] [PubMed]
- Liu, H.; Li, J.; Li, H.; Li, H.; Mao, P.; Yuan, J. Risk perception and coping behavior of construction workers on occupational health risks: A case study of Nanjing, China. Int. J. Environ. Res. Public Health 2021, 18, 7040. [Google Scholar] [CrossRef]
- Kim, Y.; Lee, H. Association of health risk perception and physical activity among adolescents. Rev. Psicol. Deporte 2017, 26, 45–50. [Google Scholar]
- Kim, Y.; Park, I.; Kang, S. Age and gender differences in the health risk perception. Cent. Eur. J. Publ. Health 2018, 26, 54–59. [Google Scholar]
- Yoo, E.; Baek, K. Gender Differences in Environmental and Health-Related Risk Perception in Korea. Asian Women 2019, 35, 47–67. [Google Scholar] [CrossRef]
- Godovykh, M.; Pizam, A.; Bahja, F. Antecedents and outcomes of health risk perceptions in tourism following the COVID-19 pandemic. Tour. Rev. 2021, 76, 737–748. [Google Scholar] [CrossRef]
- Ding, Y.; Xu, J.; Huang, S.; Li, P.; Lu, C.; Xie, S. Risk perception and depression in public health crises: Evidence from the COVID-19 crisis in China. Int. J. Environ. Res. Public Health 2020, 17, 5728. [Google Scholar] [CrossRef] [PubMed]
- Spinks, J.; Nghiem, S.; Byrnes, J. Risky business, healthy lives: How risk perception, risk preferences and information influence consumer’s risky health choices. Eur. J. Health Econ. 2021, 22, 811–831. [Google Scholar] [CrossRef] [PubMed]
- Chien, P.M.; Sharifpour, M.; Ritchie, B.W.; Watson, B. Travelers’ health risk perceptions and protective behavior: A psychological approach. J. Travel Res. 2017, 56, 744–759. [Google Scholar] [CrossRef]
- Ferrer, R.A.; Klein, W.M.P.; Avishai, A.; Jones, K.; Villegas, M.; Sheeran, P. When does risk perception predict protection motivation for health threats? A person-by-situation analysis. PLoS ONE 2018, 13, E0191994. [Google Scholar] [CrossRef] [PubMed]
- Corrales-Gutierrez, I.; Mendoza, R.; Gomez-Baya, D.; Leon-Larios, F. Pregnant women’s risk perception of the teratogenic effects of alcohol consumption in pregnancy. J. Clin. Med. 2019, 8, 907. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Pieniak, Z.; Verbeke, W.; Scholderer, J.; Brunso, K.; Olsen, S.O. Impact of consumers’ health beliefs, health involvement and risk perception on fish consumption: A study in five European countries. Brit. Food J. 2008, 110, 898–915. [Google Scholar] [CrossRef]
- Dono, J.; Ettridge, K.A.; Wakefield, M.; Pettigrew, S.; Coveney, J.; Roder, D.; Durkin, S.; Wittert, G.; Martin, J.; Miller, C.L. Intentions to reduce sugar-sweetened beverage consumption: The importance of perceived susceptibility to health risks. Public Health Nutr. 2021, 24, 5663–5672. [Google Scholar] [CrossRef]
- Demestichas, K.; Peppes, N.; Alexakis, T.; Adamopoulou, E. Blockchain in agriculture traceability systems: A review. Appl. Sci. 2020, 10, 4113. [Google Scholar] [CrossRef]
- Prashar, D.; Jha, N.; Jha, S.; Lee, Y.; Joshi, G.P. Blockchain-based traceability and visibility for agricultural products: A decentralized way of ensuring food safety in india. Sustainability 2020, 12, 3497. [Google Scholar] [CrossRef] [Green Version]
- Surasak, T.; Wattanavichean, N.; Preuksakarn, C.; Huang, S.C.H. Thai agriculture products traceability system using blockchain and internet of things. Int. J. Adv. Comput. Math. 2019, 10, 578–583. [Google Scholar] [CrossRef]
- Mirabelli, G.; Solina, V. Blockchain and agricultural supply chains traceability: Research trends and future challenges. Procedia Manuf. 2020, 42, 414–421. [Google Scholar] [CrossRef]
- Violino, S.; Pallottino, F.; Sperandio, G.; Figorilli, S.; Antonucci, F.; Ioannoni, V.; Fappiano, D.; Costa, C. Are the innovative electronic labels for extra virgin olive oil sustainable, traceable, and accepted by consumers? Foods 2019, 8, 529. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Rocha, G.S.R.; de Oliveira, L.; Talamini, E. Blockchain applications in agribusiness: A systematic review. Future Internet 2021, 13, 95. [Google Scholar] [CrossRef]
- Williamson, J. Consumer Willingness to Pay for Blockchain Verified Lamb. 2019. Available online: https://www.mla.com.au/research-and-development/reports/2019/consumer-willingness-to-pay-for-blockchain-verified-lamb/# (accessed on 2 March 2022).
- National Bureau of Statistics. China Statistical Yearbook 2020; Yearbook Editorial Board: Beijing, China, 2020. [Google Scholar]
- Xu, L.; Liu, J.; Kim, J.; Chon, M. Are Chinese netizens willing to speak out? The spiral of silence in public reactions to controversial food safety issues on social media. Int. J. Environ. Res. Public Health 2021, 18, 13114. [Google Scholar] [CrossRef]
- Su, Y.; Yu, H.; Wang, M.; Li, X.; Li, Y. Why did China’s cost-reduction-oriented policies in food safety governance fail? The collective action dilemma perspective. Cand. J. Agr. Econ. 2022. [Google Scholar] [CrossRef]
- Baptista, R.C.; Rodrigues, H.; Sant’Ana, A.S. Consumption, knowledge, and food safety practices of Brazilian seafood consumers. Food Res. Int. 2020, 132, 109084. [Google Scholar] [CrossRef]
- Wooldridge, J.M. Econometric Analysis of cross Section and Panel Data, 2nd ed.; MIT Press: London, UK, 2010; pp. 51–88. [Google Scholar]
- Li, Q.; Wang, J.; Wang, X.; Wang, Y. The impact of alternative policies on livestock farmers’ willingness to recycle manure: Evidence from central China. China Agr. Econ. Rev. 2020, 12, 583–594. [Google Scholar] [CrossRef]
- Lim, J.; Moon, K. Does political participation strengthen the relationship between civic morality and environmentally friendly attitudes? Evidence from South Korean. Int. J. Environ. Res. Public Health 2022, 19, 2095. [Google Scholar] [CrossRef]
- Ferrer-i-Carbonell, A.; Frijters, P. How important is methodology for the estimates of the determinants of happiness? Econ. J. 2004, 114, 641–659. [Google Scholar] [CrossRef] [Green Version]
- Shew, A.M.; Snell, H.A.; Nayga, R.M.; Lacity, M.C. Consumer valuation of blockchain traceability for beef in the U nited S tates. Appl. Econ. Perspect. Policy 2022, 44, 299–323. [Google Scholar] [CrossRef]
- Menozzi, D.; Halawany-Darson, R.; Mora, C.; Giraud, G. Motives towards traceable food choice: A comparison between French and Italian consumers. Food Control 2015, 49, 40–48. [Google Scholar] [CrossRef]
- Borrelli, B.; Hayes, R.B.; Dunsiger, S.; Fava, J.L. Risk perception and smoking behavior in medically ill smokers: A prospective study. Addiction 2010, 105, 1100–1108. [Google Scholar] [CrossRef] [PubMed]
- Lee, J.E.C.; Lemyre, L.; Turner, M.C.; Orpana, H.M.; Krewski, D. Health risk perceptions as mediators of socioeconomic differentials in health behaviour. J. Health Psychol. 2008, 13, 1082–1091. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Marcon, A.; Nguyen, G.; Rava, M.; Braggion, M.; Grassi, M.; Zanolin, M.E. A score for measuring health risk perception in environmental surveys. Sci. Total Environ. 2015, 527, 270–278. [Google Scholar] [CrossRef] [PubMed]
- Dellino, G.; Laudadio, T.; Mari, R.; Mastronardi, N.; Meloni, C. A reliable decision support system for fresh food supply chain management. Int. J. Prod. Res. 2018, 56, 1458–1485. [Google Scholar] [CrossRef]
Variables | Definition | Mean | SD | Min | Max |
---|---|---|---|---|---|
Dependent variable | |||||
Purchase intention | Possibility of purchasing blockchain traceable fresh fruit, assign from 1 to 5 in turn | 4.174 | 0.745 | 1 | 5 |
Independent variable of interest | |||||
Health risk perception | Value of attitude towards fresh fruit quality and safety, assign from 1 to 5 in turn | 4.093 | 0.793 | 1 | 5 |
Control variables | |||||
Individual characteristics | |||||
Gender | Female = 1, male = 0 | 0.604 | 0.489 | 0 | 1 |
Age | the high school and below educated = 1, the college educated = 2, the graduate educated or above = 3 | 1.560 | 0.559 | 1 | 3 |
Education | young (29 years and under) = 1, the middle-aged (30~49 years old) = 2, the elderly (50 years and above) = 3 | 2.028 | 0.414 | 1 | 3 |
Occupation | Whether occupation related to food industry, yes = 1, no = 0 | 0.100 | 0.300 | 0 | 1 |
Marriage | Married = 1, unmarried = 0 | 0.422 | 0.494 | 0 | 1 |
Family characteristics | |||||
Family scale | Total household population/person | 3.955 | 1.400 | 1 | 20 |
Number of children | family with fewer children (with 1 children and under) = 1, family with two children = 2, family with more children (with 3 children and above) = 3 | 1.757 | 0.662 | 1 | 3 |
Family income | low-income (less than $7450/year) = 1, middle income ($7.450~22,350/year) = 2, high-income (more than $22,350/year) = 3 | 2.411 | 0.667 | 1 | 3 |
Individual cognitive experience | |||||
Traceability cognition | Whether heard of food traceability system of traceable food, yes = 1, no = 0 | 0.847 | 0.360 | 0 | 1 |
Purchase experience | Whether purchased traceable fresh agricultural products, yes = 1, no = 0 | 0.832 | 0.374 | 0 | 1 |
Food poisoning experience | Whether experienced food poisoning or not. Yes = 1, no = 0 | 0.063 | 0.244 | 0 | 1 |
Variables | OLS Model | Ologit Model (Marginal Effects) | ||||
---|---|---|---|---|---|---|
(1) | (2) | (3) | (4) | (5) | (6) | |
Independent variable of interest | ||||||
Health risk perception | 0.153 *** (0.030) | 0.108 *** (0.029) | 0.108 *** (0.029) | 0.092 *** (0.018) | 0.064 *** (0.017) | 0.064 *** (0.017) |
Control variables | ||||||
Gender | −0.040 (0.044) | −0.038 (0.044) | −0.027 (0.026) | −0.026 (0.026) | ||
Age (Elderly is the reference group) | ||||||
Young | 0.040 (0.133) | 0.021 (0.134) | 0.016 (0.080) | 0.005 (0.081) | ||
Middle-aged | −0.022 (0.134) | −0.035 (0.134) | −0.023 (0.080) | −0.032 (0.081) | ||
Education (The high school and below educated is the reference group) | ||||||
College-educated | 0.209 ** (0.106) | 0.215 ** (0.105) | 0.122 ** (0.051) | 0.125 ** (0.050) | ||
Graduate educated or above | 0.294 ** (0.125) | 0.314 ** (0.127) | 0.192 *** (0.065) | 0.206 *** (0.064) | ||
Occupation | −0.129 (0.083) | −0.135 (0.082) | −0.054 (0.045) | −0.057 (0.045) | ||
Marriage | −0.068 (0.047) | −0.067 (0.047) | −0.037 (0.027) | −0.037 (0.027) | ||
Family scale | −0.022 (0.018) | −0.025 (0.018) | −0.012 (0.011) | −0.014 (0.011) | ||
Number of children (family with fewer children is the reference group) | ||||||
With two children | 0.157 *** (0.058) | 0.154 *** (0.058) | 0.088 *** (0.033) | 0.086 *** (0.033) | ||
With more children | 0.042 (0.091) | 0.029 (0.091) | 0.034 (0.051) | 0.028 (0.051) | ||
Family income (low-income is the reference group) | ||||||
Middle-income | 0.201 ** (0.091) | 0.199 ** (0.091) | 0.105 ** (0.047) | 0.104 ** (0.047) | ||
High-income | 0.191 ** (0.091) | 0.192 ** (0.091) | 0.099 ** (0.047) | 0.101 ** (0.047) | ||
Traceability cognition | 0.272 *** (0.067) | 0.273 *** (0.068) | 0.160 *** (0.037) | 0.161 *** (0.037) | ||
Purchase experience | 0.321 *** (0.063) | 0.319 *** (0.063) | 0.194 *** (0.034) | 0.191 *** (0.035) | ||
Food poisoning experience | −0.006 (0.092) | −0.014 (0.092) | 0.005 (0.052) | 0.001 (0.052) | ||
_cons | 3.546 *** (0.127) | 2.921 *** (0.214) | 3.012 *** (0.221) | — | — | — |
Regions effect | Uncontrolled | Uncontrolled | Controlled | Uncontrolled | Uncontrolled | Controlled |
R2 | 0.027 | 0.137 | 0.139 | — | — | — |
Pseudo R2 | — | — | — | 0.013 | 0.067 | 0.068 |
Wald chi | — | — | — | 25.17 | 124.52 | 130.49 |
Number of obs | 1058 | 1058 | 1058 | 1058 | 1058 | 1058 |
Variables | Grouped by Gender (Marginal Effect) | Grouped by Age (Marginal Effect) | Grouped by Income (Marginal Effect) | Grouped by Education (Marginal Effect) | ||||
---|---|---|---|---|---|---|---|---|
Male | Female | Low-Aged | High-Aged | Low-Income | High-Income | Low-Educated | High-Educated | |
Independent variable of interest | ||||||||
Health risk perception | 0.101 *** (0.026) | 0.045 ** (0.022) | 0.048 ** (0.022) | 0.080 *** (0.026) | 0.053 ** (0.026) | 0.069 *** (0.024) | 0.051 * (0.030) | 0.063 *** (0.020) |
Control variables | Controlled | Controlled | Controlled | Controlled | Controlled | Controlled | Controlled | Controlled |
Regions effect | Controlled | Controlled | Controlled | Controlled | Controlled | Controlled | Controlled | Controlled |
Pseudo R2 | 0.076 | 0.079 | 0.092 | 0.061 | 0.057 | 0.096 | 0.078 | 0.070 |
Wald chi | 63.76 | 94.14 | 106.05 | 54.62 | 58.85 | 116.19 | 48.29 | 109.99 |
Number of obs | 419 | 639 | 556 | 502 | 516 | 542 | 267 | 791 |
Variables | Replace Model | Replace Independent Variable of Interest | Replace Dependent Variable | ||
---|---|---|---|---|---|
Oprobit Model | Ologit Model | Oprobit Model | Ologit Model | Oprobit Model | |
Independent variable of interest | |||||
Health risk perception | 0.060 *** (0.016) | 0.025 * (0.013) | 0.025 * (0.013) | 0.047 *** (0.013) | 0.046 *** (0.012) |
Control variables | Controlled | Controlled | Controlled | Controlled | Controlled |
Regions effect | Controlled | Controlled | Controlled | Controlled | Controlled |
Pseudo R2 | 0.067 | 0.063 | 0.062 | 0.118 | 0.117 |
Wald chi | 132.99 | 121.59 | 123.04 | 111.00 | 120.14 |
Number of obs | 1058 | 1058 | 1058 | 1058 | 1058 |
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Zhai, Q.; Sher, A.; Li, Q. The Impact of Health Risk Perception on Blockchain Traceable Fresh Fruits Purchase Intention in China. Int. J. Environ. Res. Public Health 2022, 19, 7917. https://doi.org/10.3390/ijerph19137917
Zhai Q, Sher A, Li Q. The Impact of Health Risk Perception on Blockchain Traceable Fresh Fruits Purchase Intention in China. International Journal of Environmental Research and Public Health. 2022; 19(13):7917. https://doi.org/10.3390/ijerph19137917
Chicago/Turabian StyleZhai, Qianqian, Ali Sher, and Qian Li. 2022. "The Impact of Health Risk Perception on Blockchain Traceable Fresh Fruits Purchase Intention in China" International Journal of Environmental Research and Public Health 19, no. 13: 7917. https://doi.org/10.3390/ijerph19137917