An Augmented Risk Information Seeking Model: Perceived Food Safety Risk Related to Food Recalls
Abstract
:1. Introduction
2. Conceptual Framework
2.1. Models of Risk Information Seeking
2.2. Research Model and Hypothesis Development
2.2.1. Current Knowledge
2.2.2. Risk Perception
2.2.3. Information Need and Information Seeking Intention
2.2.4. Relevant Channel Beliefs
2.2.5. Perceived Information Gathering Capacity
3. Data and Methodology
3.1. Questionnaire Design and Measures
3.2. Procedure and Participants
4. Data Analysis and Results
4.1. Measurement Model Testing
4.2. Structural Model Testing
5. Discussion
6. Conclusions, Implications and Limitations
Author Contributions
Funding
Acknowledgments
Conflicts of Interest
Appendix A
Construct | No. | Items |
---|---|---|
INT | Int1 | I plan to seek more information about abuse of additive (e.g. sodium cyclamate) involved in white wine recall issues. |
Int2 | I intend to find out more about abuse of additive (e.g. sodium cyclamate) used in white wine recall issues. | |
Int3 | In the future, I will try to seek as much information as I can about abuse of additives used in other types of food. | |
PIGC | If I want to get more information about food recall issue and concerned food additive, | |
PIGC1 | I could obtain information from the CFDA and its sub-ordinates. | |
PIGC2 | I could obtain information from the social media. | |
PIGC3 | I will readily take time to gather any additional information I might need. | |
Need | Need1 | I need information about the abuse of food additive (e.g. sodium cyclamate) involved in the white wine recall. |
Need2 | I need lots of information about the possible effects of food additive (e.g. sodium cyclamate) involved in the white wine recall. | |
Need3 | I need to know the latest news related to white wine recall issue of abuse additive (e.g. sodium cyclamate). | |
Need4 | I want the government to provide more information about different types of food additives. | |
Know | Know1 | I know what food additives (e.g. sodium cyclamate) are. |
Know2 | I know the harm of food additives (e.g. sodium cyclamate). | |
Know3 | I Know the intended use of food additives (e.g. sodium cyclamate). | |
Know4 | I know the effect of over use of legal food additive (e.g. sodium cyclamate). | |
Know5 | I know the effect of use of illegal food additive (e.g. sodium cyclamate). | |
Risk | Risk1 | Consuming white wine with abuse of additive (e.g. sodium cyclamate) is dangerous to me. |
Risk2 | Consuming white wine with abuse of additive (e.g. sodium cyclamate) will seriously influence my health. | |
Risk3 | Consuming white wine with abuse of additive (e.g. sodium cyclamate) will bring loss to our property. | |
Risk4 | Effects of additive (e.g. sodium cyclamate) found in the recalled white wine will make me unhappy when I think of wine-drinking. | |
PCB | Do you believe you can obtain accurate information about the food recall issue and concerned food additive through the following channels? | |
PCB1 | Website and official account (in Wechat and Weibo) of governments, especially the China Food and Drug Administration, and the sub-Bureaus. | |
PCB2 | Social network software (i.e., WeChat, QQ, microblog, and others.). | |
PCB3 | Peers (i.e., relative, friend, and colleague). | |
PCB4 | Manufactures and retailers of the recalled food. |
References
- World Health Organization. WHO Estimates of the Global Burden of Foodborne Diseases: Foodborne Disease Burden Epidemiology Reference Group 2007–2015; WHO: Geneva, Switzerland, 2015. [Google Scholar]
- Zhao, X.; Li, Y.; Flynn, B.B. The financial impact of product recall announcements in China. Int. J. Prod. Econ. 2013, 142, 115–123. [Google Scholar] [CrossRef]
- Hallman, W.K.; Cuite, C.L.; Hooker, N.H. Consumer Responses to Food Recalls: 2008 National Survey Report. Rutgers, 2009. Available online: https://rucore.libraries.rutgers.edu/rutgers-lib/48426/PDF/1/play/ (accessed on 16 May 2018).
- Pozo, V.F.; Schroeder, T.C. Evaluating the costs of meat and poultry recalls to food firms using stock returns. Food Policy 2016, 59, 66–77. [Google Scholar] [CrossRef]
- Johnson-Hall, T.D. Essays on Product Recall Strategies and Effectiveness in the FDA-Regulated Food Sector; Clemson University: Clemson, SC, USA, 2012. [Google Scholar]
- AQSIQ (General Administration of Quality Supervision, Inspection and Guarantine of the the People’s Republic of China). Regulations on the Management of Food Recalls. 2007. Available online: http://www.aqsiq.gov.cn/xxgk_13386/jlgg_12538/zjl/20072008/200708/t20070831_239315.htm (accessed on 20 December 2017).
- CFDA Measures for the Management of Food Recalls. 2015. Available online: http://samr.cfda.gov.cn/WS01/CL1975/215945.html (accessed on 21 December 2017).
- Xu, F.; Chen, H.-H. Study of traceability system impact on food recall cost based on food recall cost model. J. China Agric. Univ. 2014, 2, 035. [Google Scholar]
- Wu, L.; Zhong, Y.; Shan, L.; Qin, W. Public risk perception of food additives and food scares. The case in Suzhou, China. Appetite 2013, 70, 90–98. [Google Scholar] [CrossRef] [PubMed]
- Rozin, P.; Pelchat, M.L.; Fallon, A.E. Psychological factors influencing food choice. Food Consum. 1986, 5, 85–106. [Google Scholar]
- Griffin, R.J.; Dunwoody, S.; Neuwirth, K. Proposed model of the relationship of risk information seeking and processing to the development of preventive behaviors. Environ. Res. 1999, 80, S230–S245. [Google Scholar] [CrossRef] [PubMed]
- Lindell, M.K.; Perry, R.W. The protective action decision model: Theoretical modifications and additional evidence. Risk Anal. 2012, 32, 616–632. [Google Scholar] [CrossRef] [PubMed]
- Kahlor, L. PRISM: A planned risk information seeking model. Health Commun. 2010, 25, 345–356. [Google Scholar] [CrossRef] [PubMed]
- Griffin, R.J.; Yang, Z.; ter Huurne, E.; Boerner, F.; Ortiz, S.; Dunwoody, S. After the flood: Anger, attribution, and the seeking of information. Sci. Commun. 2008, 29, 285–315. [Google Scholar] [CrossRef]
- Kellens, W.; Zaalberg, R.; De Maeyer, P. The Informed Society: An analysis of the public’s information-seeking behavior regarding coastal flood risks. Risk Anal. 2012, 32, 1369–1381. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Kahlor, L.A. An augmented risk information seeking model: The case of global warming. Media Psychol. 2007, 10, 414–435. [Google Scholar] [CrossRef]
- Griffin, R.J.; Neuwirth, K.; Dunwoody, S.; Giese, J. Information sufficiency and risk communication. Media Psychol. 2004, 6, 23–61. [Google Scholar] [CrossRef]
- Patrick, M.E.; Griffin, P.M.; Voetsch, A.C.; Mead, P.S. Effectiveness of recall notification: Community response to a nationwide recall of hot dogs and deli meats. J. Food Prot. 2007, 70, 2373–2376. [Google Scholar] [CrossRef] [PubMed]
- Steelfisher, G.; Weldon, K.; Benson, J.M.; Blendon, R.J. Public perceptions of food recalls and production safety: Two surveys of the American public. J. Food Saf. 2010, 30, 848–866. [Google Scholar] [CrossRef]
- Fleming, K.; Thorson, E.; Zhang, Y. Going beyond exposure to local news media: An information-processing examination of public perceptions of food safety. J. Health Commun. 2006, 11, 789–806. [Google Scholar] [CrossRef] [PubMed]
- Wilcock, A.; Pun, M.; Khanona, J.; Aung, M. Consumer attitudes, knowledge and behaviour: A review of food safety issues. Trends Food Sci. Technol. 2004, 15, 56–66. [Google Scholar] [CrossRef]
- Vij, V.; Ailes, E.; Wolyniak, C.; Angulo, F.J.; Klontz, K.C. Recalls of spices due to bacterial contamination monitored by the US Food and Drug Administration: The predominance of Salmonellae. J. Food Prot. 2006, 69, 233–237. [Google Scholar] [CrossRef] [PubMed]
- Qiang, L.; Wen, L.; Jing, W.; Yue, D. Application of content analysis in food safety reports on the internet in China. Food Control 2011, 22, 252–256. [Google Scholar] [CrossRef]
- Li, R.; Wu, H.L.; Yin, S.J.; Chen, X.J. Introductiong to 2017 China Developmet Report on Food Safety; Pecking University Press: Beijing, China, 2017; pp. 205–208. [Google Scholar]
- Wilson, T.D. Human information behavior. Inform. Sci. 2000, 3, 49–56. [Google Scholar] [CrossRef]
- Eagly, A. H.; Chaiken, S. The Psychology of Attitudes; Harcourt Brace Jovanovich College Publishers: Orlando, FL, USA, 1993; pp. 1–21. [Google Scholar]
- Johnson, E.J.; Tversky, A. Affect, generalization, and the perception of risk. J. Personal. Soc. Psychol. 1983, 45, 20. [Google Scholar] [CrossRef]
- Kosicki, G.M.; McLeod, J.M. Learning from political news: Effects of media images and information-processing strategies. Mass Commun. Political Inf. Process. 1990, 69–83. [Google Scholar]
- Ajzen, I. The theory of planned behavior. Organ. Behav. Hum. Decis. Process. 1991, 50, 179–211. [Google Scholar] [CrossRef]
- Williams, C.K. Applying a Model of Risk Information Seeking to a Newly Discovered Drug Risk; University of Georgia: Athens, GA, USA, 2012. [Google Scholar]
- Ter Huurne, E.F.; Griffin, R.J.; Gutteling, J.M. Risk information seeking among US and Dutch residents: An application of the model of risk information seeking and processing. Sci. Commun. 2009, 31, 215–237. [Google Scholar] [CrossRef]
- Yang, Z.J.; Aloe, A.M.; Feeley, T.H. Risk information seeking and processing model: A meta-analysis. J. Commun. 2014, 64, 20–41. [Google Scholar] [CrossRef]
- Johnson, J.D.; Meischke, H. A comprehensive model of cancer-related information seeking applied to magazines. Hum. Commun. Res. 1993, 19, 343–367. [Google Scholar] [CrossRef]
- Freimuth, V.S.; Stein, J.A.; Kean, T.J. Searching for Health Information: The Cancer Information Service Model; University of Pennsylvania Press: Philadelphia, PA, USA, 1989. [Google Scholar]
- Afifi, W.A.; Weiner, J.L. Toward a theory of motivated information management. Commun. Theory 2004, 14, 167–190. [Google Scholar] [CrossRef]
- Radecki, C.M.; Jaccard, J. Perceptions of knowledge, actual knowledge, and information search behavior. J. Exp. Soc. Psychol. 1995, 31, 107–138. [Google Scholar] [CrossRef]
- Drichoutis, A.C.; Lazaridis, P.; Nayga, R.M. Nutrition knowledge and consumer use of nutritional food labels. Eur. Rev. Agric. Econ. 2005, 32, 93–118. [Google Scholar] [CrossRef]
- Griffin, R.J. Energy in the eighties: Education, communication, and the knowledge gap. J. Q. 1990, 67, 554–566. [Google Scholar] [CrossRef]
- Yeung, R.M.; Morris, J. Food safety risk: Consumer perception and purchase behaviour. Br. Food J. 2001, 103, 170–187. [Google Scholar] [CrossRef]
- Chen, M.-F.; Li, H.-L. The consumer’s attitude toward genetically modified foods in Taiwan. Food Qual. Prefer. 2007, 18, 662–674. [Google Scholar] [CrossRef]
- Verdurme, A.; Viaene, J. Consumer beliefs and attitude towards genetically modified food: Basis for segmentation and implications for communication. Agribusiness 2003, 19, 91–113. [Google Scholar] [CrossRef]
- Baird, I.S.; Thomas, H. Toward a contingency model of strategic risk taking. Acad. Manag. Rev. 1985, 10, 230–243. [Google Scholar] [CrossRef]
- Bettman, J.R. Perceived risk and its components: A model and empirical test. J. Mark. Res. 1973, 184–190. [Google Scholar] [CrossRef]
- Ter Huurne, E. Information Seeking in a Risky World: The Theoretical and Empirical Development of FRIS: A Framework of Risk Information Seeking; University of Twente: Enschede, The Netherlands, 2008. [Google Scholar]
- Alaszewski, A. A person-centred approach to communicating risk. PLoS Med. 2005, 2, e41. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Huurne, E.T.; Gutteling, J. Information needs and risk perception as predictors of risk information seeking. J. Risk Res. 2008, 11, 847–862. [Google Scholar] [CrossRef]
- Feng, T.; Keller, L.R.; Wu, P.; Xu, Y. An empirical study of the toxic capsule crisis in China: Risk perceptions and behavioral responses. Risk Anal. 2014, 34, 698–710. [Google Scholar] [CrossRef] [PubMed]
- Verbeke, W. Agriculture and the food industry in the information age. Eur. Rev. Agric. Econ. 2005, 32, 347–368. [Google Scholar] [CrossRef]
- Rubin, A.M. Uses, gratifications, and media effects research. Perspect. Media Effic. 1986, 281–301. Available online: https://www.researchgate.net/profile/Thomas_Ruggiero/publication/233138016_Uses_and_Gratifications_Theory_in_the_21st_Century/links/5a119b03458515cc5aa98ba0/Uses-and-Gratifications-Theory-in-the-21st-Century.pdf (accessed on 27 December 2017).
- Griffin, R.J.; Yang, Z.; Boerner, F.; Bourassa, S.; Darrah, T.; Knurek, S.; Ortiz, S.; Dunwoody, S. Applying an Information Seeking and Processing Model to a Study of Communication About Energy; Annual Convention of the Association for Education in Journalism and Mass Communication: San Antonio, TX, USA, 2005; Volume 2005. [Google Scholar]
- Kuttschreuter, M.; Rutsaert, P.; Hilverda, F.; Regan, Á.; Barnett, J.; Verbeke, W. Seeking information about food-related risks: The contribution of social media. Food Qual. Prefer. 2014, 37, 10–18. [Google Scholar] [CrossRef]
- Bandura, A. Social Foundations of Thought and Action: A Social Cognitive Theory; Prentice-Hall, Inc.: Englewood Cliffs, NJ, USA, 1986. [Google Scholar]
- Erci, B. Reliability and validity of knowledge and behaviours related food additives scale. Adv. Nurs. Midwifery 2018, 27, 8–14. [Google Scholar]
- Gearhardt, A.N.; Corbin, W.R.; Brownell, K.D. Preliminary validation of the Yale food addiction scale. Appetite 2009, 52, 430–436. [Google Scholar] [CrossRef] [PubMed]
- Chen, M.-F. Modeling an extended theory of planned behavior model to predict intention to take precautions to avoid consuming food with additives. Food Qual. Prefer. 2017, 58, 24–33. [Google Scholar] [CrossRef]
- Griffin, R.J.; Neuwirth, K.; Giese, J.; Dunwoody, S. Linking the heuristic-systematic model and depth of processing. Commun. Res. 2002, 29, 705–732. [Google Scholar] [CrossRef]
- Hill, A.; Roberts, J.; Ewings, P.; Gunnell, D. Non-response bias in a lifestyle survey. J. Public Health 1997, 19, 203–207. [Google Scholar] [CrossRef] [Green Version]
- Kamakura, W.A. Common methods bias. Wiley Int. Encycl. Mark. 2010. [Google Scholar] [CrossRef]
- Schwarz, A.; Schwarz, C.; Rizzuto, T. Examining the “Urban Legend” of Common Method Bias: Nine Common Errors and Their Impact. In Proceedings of the 41st Annual Hawaii International Conference on System Sciences, Big Island, HI, USA, 7–10 January 2008; p. 441. [Google Scholar]
- Harman, H.H. Modern Factor Analysis; University of Chicago Press: Chicago, IL, USA, 1976. [Google Scholar]
- Petter, S.; Straub, D.; Rai, A. Specifying formative constructs in information systems research. MIS Q. 2007, 31, 623–656. [Google Scholar] [CrossRef]
- Barroso, C.; Carrión, G.C.; Roldán, J.L. Applying maximum likelihood and PLS on different sample sizes: Studies on SERVQUAL model and employee behavior model. In Handbook of Partial Least Squares; Springer: New York, NY, USA, 2010; pp. 427–447. [Google Scholar]
- Anderson, J.C.; Gerbing, D.W. Structural equation modeling in practice: A review and recommended two-step approach. Psychol. Bull. 1988, 103, 411. [Google Scholar] [CrossRef]
- Fornell, C.; Larcker, D.F. Evaluating structural equation models with unobservable variables and measurement error. J. Mark. Res. 1981, 18, 39–50. [Google Scholar] [CrossRef]
- Chiu, C.-M.; Wang, E.T. Understanding Web-based learning continuance intention: The role of subjective task value. Inf. Manag. 2008, 45, 194–201. [Google Scholar] [CrossRef]
- Baron, R.M.; Kenny, D.A. The moderator–mediator variable distinction in social psychological research: Conceptual, strategic, and statistical considerations. J. Personal. Soc. Psychol. 1986, 51, 1173. [Google Scholar] [CrossRef]
- Hair, J.F., Jr.; Hult, G.T.M.; Ringle, C.; Sarstedt, M. A Primer on Partial Least Squares Structural Equation Modeling (PLS-SEM); Sage Publications: Thousand Oaks, CA, USA, 2016. [Google Scholar]
- Tenenhaus, M.; Vinzi, V.E.; Chatelin, Y.-M.; Lauro, C. PLS path modeling. Comput. Stat. Data Anal. 2005, 48, 159–205. [Google Scholar] [CrossRef]
- Wetzels, M.; Odekerken-Schröder, G.; Van Oppen, C. Using PLS path modeling for assessing hierarchical construct models: Guidelines and empirical illustration. MIS Q. 2009, 33, 177–195. [Google Scholar] [CrossRef]
- Klerck, D.; Sweeney, J.C. The effect of knowledge types on consumer-perceived risk and adoption of genetically modified foods. Psychol. Mark. 2007, 24, 171–193. [Google Scholar] [CrossRef]
- He, G.; Mol, A.P.; Zhang, L.; Lu, Y. Nuclear power in China after Fukushima: Understanding public knowledge, attitudes, and trust. J. Risk Res. 2014, 17, 435–451. [Google Scholar] [CrossRef]
- Song, H.; Schwarz, N. If it’s difficult to pronounce, it must be risky: Fluency, familiarity, and risk perception. Psychol. Sci. 2009, 20, 135–138. [Google Scholar] [CrossRef] [PubMed]
- Jepsen, A.L. Factors affecting consumer use of the Internet for information search. J. Int. Mark. 2007, 21, 21–34. [Google Scholar] [CrossRef]
- Brucks, M. The effects of product class knowledge on information search behavior. J. Consum. Res. 1985, 12, 1–16. [Google Scholar] [CrossRef]
- Lobb, A.E.; Mazzocchi, M.; Traill, W.B. Risk perception and chicken consumption in the avian flu age—A consumer behaviour study on food safety information. Selected paper presented at the American Agricultural Economics Annual Meeting, Long Beach, CA, USA, 23–26 July 2006; Volume 2006, pp. 23–26. [Google Scholar]
- Yang, Z.J.; McComas, K.; Gay, G.; Leonard, J.P.; Dannenberg, A.J.; Dillon, H. Motivation for health information seeking and processing about clinical trial enrollment. Health Commun. 2010, 25, 423–436. [Google Scholar] [CrossRef] [PubMed]
- Yang, Z.J.; Kahlor, L. What, me worry? The role of affect in information seeking and avoidance. Sci. Commun. 2013, 35, 189–212. [Google Scholar] [CrossRef]
- Cuite, C.L.; Condry, S.C.; Nucci, M.L.; Hallman, W.K. Public Response to the Contaminated Spinach Recall of 2006; Pub. No. RR-0107-013; Food Policy Institute, Rutgers University: New Brunswick, NJ, USA, 2007. [Google Scholar]
- Carlson, C.C.; Peake, W.O. Rethinking food recall communications for consumers. Iridescent 2012, 2, 11–23. [Google Scholar] [CrossRef]
- Ajzen, I.; Fishbein, M. Understanding Attitudes and Predicting Social Behaviour; Prentice-Hall: Upper Saddle River, NJ, USA, 1980. [Google Scholar]
Demographics | Frequency | Percentage (%) |
---|---|---|
Gender | ||
0. Male | 290 | 46 |
1. Female | 341 | 54 |
Age | ||
1. under 15 | 26 | 4.1 |
2. 15–34 | 193 | 30.6 |
3. 35–54 | 355 | 56.3 |
4. 55 and above | 51 | 9.1 |
Education level | ||
1. Preliminary school or below | 15 | 2.4 |
2. Senior high school or below | 381 | 60.4 |
3. Associate degree or bachelor degree | 123 | 19.5 |
4. Master’s degree or PhD | 112 | 17.7 |
Income | ||
1. Less than ¥2000 | 124 | 19.7 |
2. ¥2001–¥4000 | 206 | 32.6 |
3. ¥4001–¥6000 | 125 | 19.8 |
4. ¥6001–¥8000 | 124 | 19.7 |
5. More than ¥8001 | 52 | 8.2 |
Total | 631 | 100 |
Construct | Item | Factor Loading | Cronbach’s Alpha | Composite Reliability | AVE | R-Squared |
---|---|---|---|---|---|---|
Perceived information-gathering capacity (PIGC) | PIGC1 | 0.92 | 0.91 | 0.94 | 0.84 | 0.23 |
PIGC2 | 0.94 | - | - | - | - | |
PIGC3 | 0.89 | - | - | - | - | |
Information-seeking intention (INT) | INT1 | 0.90 | 0.91 | 0.91 | 0.77 | 0.47 |
INT2 | 0.90 | - | - | - | - | |
INT3 | 0.82 | - | - | - | ||
Current Knowledge (Know) | Know1 | 0.8 | 0.92 | 0.94 | 0.75 | |
Know2 | 0.89 | - | - | - | - | |
Know3 | 0.89 | - | - | - | - | |
Know4 | 0.88 | - | - | - | - | |
Know5 | 0.86 | - | - | - | - | |
Information need (Need) | Need1 | 0.73 | 0.78 | 0.86 | 0.61 | 0.39 |
Need2 | 0.82 | - | - | - | - | |
Need3 | 0.77 | - | - | - | - | |
Need4 | 0.79 | - | - | - | - | |
Perceived channel beliefs (PCB) | PCB1 | 0.81 | 0.85 | 0.89 | 0.62 | - |
PCB2 | 0.82 | - | - | - | - | |
PCB3 | 0.78 | - | - | - | - | |
Risk perception (Risk) | Risk1 | 0.92 | 0.90 | 0.93 | 0.77 | 0.16 |
Risk2 | 0.9 | - | - | - | - | |
Risk3 | 0.82 | - | - | - | - | |
Risk4 | 0.86 | - | - | - | - |
Construct | Mean | SD | PIGC | INT | Know | Need | PCB | Risk |
---|---|---|---|---|---|---|---|---|
PIGC | 3.73 | 1.44 | 0.84 | - | - | - | - | - |
INT | 5.06 | 0.94 | 0.20 ** | 0.72 | - | - | - | - |
Know | 5.02 | 0.97 | 0.48 ** | 0.41 ** | 0.75 | - | - | - |
Need | 4.38 | 5.11 | 0.30 ** | 0.48 ** | 0.51 ** | 0.61 | - | - |
PCB | 5.52 | 0.77 | 0.15 ** | 0.48 ** | 0.37 ** | 0.42 | 0.62 | - |
Risk | 5.19 | 0.93 | 0.30 ** | 0.65 ** | 0.40 ** | 0.53 ** | 0.53 ** | 0.77 |
Path | Coefficient | t Statistics | t Statistics | Hypothesis | Results |
---|---|---|---|---|---|
Know -> Need | 0.36 *** | 8.56 | 8.56 | H1a | Supported |
Know -> INT | 0.13 * | 2.45 | 2.45 | H1b | Supported |
Know -> PIGC | 0.48 *** | 15.93 | 15.93 | H1c | Supported |
Know -> Risk | 0.40 *** | 9.08 | 9.08 | H1d | Not supported |
Risk -> Need | 0.39 *** | 8.47 | 8.47 | H2a | Supported |
Risk -> INT | 0.48 *** | 10.66 | 10.60 | H2b | Supported |
Need -> INT | 0.13 * | 2.51 | 2.51 | H3 | Supported |
PCB -> INT | 0.13 ** | 3.10 | 3.10 | H4 | Supported |
PIGC -> INT | −0.06 * | 2.14 | 2.14 | H5 | Not supported |
© 2018 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).
Share and Cite
Liao, C.; Zhou, X.; Zhao, D. An Augmented Risk Information Seeking Model: Perceived Food Safety Risk Related to Food Recalls. Int. J. Environ. Res. Public Health 2018, 15, 1800. https://doi.org/10.3390/ijerph15091800
Liao C, Zhou X, Zhao D. An Augmented Risk Information Seeking Model: Perceived Food Safety Risk Related to Food Recalls. International Journal of Environmental Research and Public Health. 2018; 15(9):1800. https://doi.org/10.3390/ijerph15091800
Chicago/Turabian StyleLiao, Chuanhui, Xiaomei Zhou, and Dingtao Zhao. 2018. "An Augmented Risk Information Seeking Model: Perceived Food Safety Risk Related to Food Recalls" International Journal of Environmental Research and Public Health 15, no. 9: 1800. https://doi.org/10.3390/ijerph15091800