Dissuasive effect, information provision, and consumer reactions to the term ‘Biotechnology’: The case of reproductive interventions in farmed fish

Autoři: Micaela M. Kulesz aff001;  Torbjörn Lundh aff002;  Dirk-Jan De Koning aff003;  Carl-Johan Lagerkvist aff001
Působiště autorů: Department of Economics, Swedish University of Agricultural Sciences, Uppsala, Sweden aff001;  Department of Animal Nutrition and Management, Swedish University of Agricultural Sciences, Uppsala, Sweden aff002;  Department of Animal Breeding and Genetics, Swedish University of Agricultural Sciences, Uppsala, Sweden aff003
Vyšlo v časopise: PLoS ONE 14(9)
Kategorie: Research Article
doi: 10.1371/journal.pone.0222494


Biotechnology can provide innovative and efficient tools to support sustainable development of aquaculture. It is generally accepted that use of the term ‘genetically modified’ causes controversy and conflict among consumers, but little is known about how using the term ‘biotechnology’ as a salient feature on product packaging affects consumer preferences. In an online discrete choice experiment consisting of two treatments, a set of 1005 randomly chosen Swedish consumers were surveyed about use of hormone and triploidization sterilization techniques for salmonids. The information given to the treatment group included an additional sentence stating that the triploidization technique is an application of biotechnology, while the control group received the same text but without reference to biotechnology. Analysis using a hierarchical Bayes approach revealed significant consumer reactions to the term biotechnology. When the term was included in information, variation in consumer willingness-to-pay (WTP) estimates increased significantly. Moreover, some participants were dissuaded towards an option guaranteeing no biotechnological intervention in production of fish. These results have multiple implications for research and for the food industry. For research, they indicate the importance of examining the distribution of variation in WTP estimates for more complete characterization of the effects of information on consumer behavior. For the food industry, they show that associating food with biotechnology creates more variability in demand. Initiatives should be introduced to reduce the confusion associated with the term biotechnology among consumers.

Klíčová slova:

Aquaculture – Biotechnology – Food consumption – Hormones – Marine fish – Triploidy – Agricultural biotechnology – Fish farming


1. Brummett R. (2013). Growing aquaculture in sustainable ecosystems (English). Agriculture and environmental services note; no. 5. Washington DC; World Bank.

2. EUMOFA (2015). The eu fish market. Technical report, European Market for Fisheries and Aquaculture Products.

3. Frewer L., Scholderer J., Downs C., and Bredahl L. (2003). Communicating about the risks and benefits of genetically modified foods. effects of different information strategies. Risk Analysis, 23(6):1117–1133. 14641888

4. McFadden B. and Lusk J. (2015). Cognitive biases in the assimilation of scientific information on global warming and genetically modified food. Food Policy, 54:35–43.

5. Wynne B. (2006). Public engagement as a means of restoring public trust in science—hitting the notes, but missing the music? Community Genetics, 9(3):211–220. doi: 10.1159/000092659 16741352

6. Frewer L. J., van der Lans I. A., Fischer A. R., Reinders M. J., Menozzi D., Zhang X., et al.(2013). Public perceptions of agrifood applications of genetic modification: A systematic review and meta-analysis. Trends in Food Science Technology, 30(2):142–152

7. FDA (2015). Guidance for industry: voluntary labeling indicating whether foods have or have not been developed using bioengineering; draft guidance. Technical report, United States Food and Drug Administration.

8. Torgersen, Helge, & Hampel, Jürgen. (2006). Promise, Problems and Proxies: 25 Years of European Biotechnology Debate and Regulation. http://hw.oeaw.ac.at/?arp=ita/ita-papers/HT_02_2.pdf.

9. Pardo R., Midden C., and Miller J. D. (2002). Attitudes toward biotechnology in the European Union. Journal of Biotechnology. 98(1):9–24. doi: 10.1016/s0168-1656(02)00082-2 12126802

10. Gaskell G., Bauer M. W., Durant J., and Allum N. C. (1999). Worlds apart? the reception of genetically modified foods in europe and the u.s. Science, 285(5426):384–387 doi: 10.1126/science.285.5426.384 10411496

11. Savadori L., Savio S., Nicotra E., Rumiati R., Finucane M., and Slovic P. (2004). Expert and public perception of risk from biotechnology. Risk Analysis, 24(5):1289–1299 doi: 10.1111/j.0272-4332.2004.00526.x 15563295

12. Aerni P., Scholderer J., and Ermen D. (2011). How would swiss consumers decide if they had freedom of choice? evidence from a field study with organic, conventional and gm corn bread. Food Policy, 36(6):830–838. Between the Global and the Local, the Material and the Normative: Power struggles in India’s Agrifood System.

13. Lusk J. L.; McFadden B., and Rickard B. J. (2015) Which biotech foods are most acceptable to the public? Biotechnology Journal, 10: 13–16. doi: 10.1002/biot.201400561 25388815

14. McFadden B. and Lusk J. (2016). What consumers don’t know about genetically modified food, and how that affects beliefs. The FASEB Journal, 30.

15. Lusk J. L., House L. O., Valli C., Jaeger S. R., Moore M., Morrow J., et al. (2004). Effect of information about benefits of biotechnology on consumer acceptance of genetically modified food: evidence from experimental auctions in the united states, england, and france. European Review of Agricultural Economics, 31(2):179–204.

16. Wuepper D., Wree P., & Ardali G.(2019). Does information change German consumers’ attitudes about genetically modified food? European Review of Agricultural Economics, 46(1), 53–78,

17. Zhou G., Hu W., and Huang W. (2016). Are consumers willing to pay more for sustainable products? A study of eco-labeled tuna steak. Sustainability, 8(5):494.

18. Lagerkvist C. J., Carlsson F., and Viske D. (2006). Swedish consumer preferences for animal welfare and biotech: A choice experiment. AgBioForum, 9(1).

19. Caputo V., Scarpa R., and Nayga R. M. Jr (2017). Cue versus indepen- dent food attributes: the effect of adding attributes in choice experiments. European Review of Agricultural Economics, 44(2):211–230.

20. Bradley M. and Daly A. (1994). Use of the logit scaling approach to test for rank-order and fatigue effects in stated preference data. Transportation, 21(2):167–184.

21. Adamowicz W., Boxall P., Williams M., and Louviere J. (1998). Stated preference approaches for measuring passive use values: Choice experiments and contingent valuation. American Journal of Agricultural Economics, 80(1):64–75.

22. Hess S., Hensher D. A., and Daly A. (2012). Not bored yet: revisiting respondent fatigue in stated choice experiments. Transportation Research Part A: Policy and Practice, 46(3):626–644.

23. Louviere J. J., Islam T., Wasi N., Street D., and Burgess L. (2008). Designing discrete choice experiments: Do optimal designs come at a price? Journal of Consumer Research, 35(2):360–375.

24. Johnson R. M. and Orme B. K. (1996). How many questions should you ask in choice-based conjoint studies? Technical report, Sawtooth Software, Inc.

25. Bateman, I., Carson, R., Day, B., au>Dupont, D., Louviere, J., Morimoto, S., et al. (2008). Choice set awareness and ordering effects in choice experiments. In 16th Annual Conference of the European Association of Environmental and Resource Economics (EAERE).

26. Carlsson F., Moerkbak M. R., and Olsen S. B. (2012). The first time is the hardest: A test of ordering effects in choice experiments. Journal of Choice Modelling, 5(2):19–37.

27. Carlsson F., Frykblom P., and Lagerkvist C. J. (2007). Consumer willingness to pay for farm animal welfare: mobile abattoirs versus transportation to slaughter. European Review of Agricultural Economics, 34(3):321–344.

28. McFadden D. (1973). Conditional logit analysis of qualitative choice behavior. In Zarembka P., editor, Frontiers in Econometrics, pages 105–142. Academic Press New York, New York, NY, USA.

29. McFadden D. (2001). Economic choices. American Economic Review, 91(3):351–378.

30. Hensher D. A. and Greene W. H. (2003). The mixed logit model: The state of practice. Transportation, 30(2):133–176.

31. Balcombe K., Chalak A., and Fraser I. (2009). Model selection for the mixed logit with bayesian estimation. Journal of Environmental Economics and Management, 57(2):226–237.

32. Huber J. and Train K. (2001). On the similarity of classical and bayesian estimates of individual mean partworths. Marketing Letters, 12(3):259–269.

33. Train K. E. (2001). A comparison of hierarchical bayes and maximum simulated likelihood for mixed logit

34. Train K. E. and McFadden D. (2002). Discrete choice methods with simulation.

35. Baker M. (2016). Bayesmixedlogitwtp: Stata module for bayesian estimation of mixed logit model in willingness-to-pay (wtp) space.

36. Sillano M. and de Dios Ortúzar J. (2005). Willingness to pay estimation with mixed logit models: some new evidence. Environment and Planning, 37(525):525–550

37. Scarpa R., Thiene M., and Train K. (2008). Utility in willingness to pay space: A tool to address confounding random scale effects in destination choice to the alps. American Journal of Agricultural Economics, 90(4):994–1010

38. Lagerkvist C. J., Berthelsen T., Sundström K., and Johansson H. (2014). Country of origin or EU/non-EU labelling of beef? Comparing structural reliability and validity of discrete choice experiments for measurement of consumer preferences for origin and extrinsic quality cues. Food Quality and Preference, 34:50–61.

39. Danthurebandara V. M., Yu J., and Vandebroek M. Designing choice experiments by optimizing the complexity level to individual abilities. (2015). Quantitative Marketing and Economics, 13:1–26

40. Lagerkvist C. J., Okello J., and Karanja N. (2012). Anchored vs. relative best-worst scaling and latent class vs. hierarchical bayesian analysis of best-worst choice data: Investigating the importance of food quality attributes in a developing country. Food Quality and Preference, 25(1):29–40.

41. Train K. and Weeks M. (2005). Discrete Choice Models in Preference Space and Willingness-to-Pay Space, pages 1–16. Springer Netherlands, Dordrecht

42. Qaim M. (2009). The economics of genetically modified crops. Annual Review of Resource Economics, 1(1):665–694.

43. Bech-Larsen, T. and Grunert, K. G. (2000). Can health benefits break down nordic consumers rejection of genetically modified foods?: A conjoint study of danish, norwegian, swedish and finnish consumers preferences for hard cheese. In ANZMAC 2000 Visionary Marketing for the 21st Century: Facing the Challenge Conference.

44. Chib, S. (2008). Bayesian econometrics. Bingley [etc.: Emerald/JAI].

45. van der Vaart A. W. (1998). Asymptotic Statistics. Cambridge Series in Statistical and Probabilistic Mathematics. CUP, Cambridge.

46. Bredahl L., Grunert K. G., and Frewer L. J. (1998). Consumer attitudes and decision-making with regard to genetically engineered food products–a review of the literature and a presentation of models for future research. Journal of Consumer Policy, 21(3):251–277.

47. Amin L., Azad M. A. K., Gausmian M. H., and Zulkifli F. (2014). Determinants of public attitudes to genetically modified salmon. PLOS ONE, 9(1):1–14.

48. Higgs S. and Thomas J. M. (2016). Social influences on eating. Current Opinion in Behavioral Sciences, 9:1–6.

49. Huffman W., Shogren J., Rousu M., and Tegene A. (2003). Consumer willingness to pay for genetically modified food labels in a market with diverse information: Evidence from experimental auctions. Journal of Agricultural and Resource Economics, 28(03).

50. Rousu M., Huffman W. E., Shogren J., and Tegene A. (2004). Are united states consumers tolerant of genetically modified foods? Review of Agricultural Economics, 26(1):19–31

51. Claret A., Guerrero L., Gartzia I., Garca-Quiroga M., and Gins R. (2016). Does information affect consumer liking of farmed and wild fish? Aquaculture, 454:157–162.

52. Claret A., Guerrero L., Gins R., Grau A., Hernandez M. D., Aguirre E., et al. (2014). Consumer beliefs regarding farmed versus wild fish. Appetite, 79:25–31. doi: 10.1016/j.appet.2014.03.031 24709486

53. Verbeke W., Vanhonacker F., Sioen I., Camp J. V., and Henauw S. D. (2007). Perceived importance of sustainability and ethics related to fish: A consumer behavior perspective. Ambio, 36(7):580–585 doi: 10.1579/0044-7447(2007)36[580:piosae]2.0.co;2 18074896

54. EFSA (2005). Opinion of the scientific panel on contaminants in the food chain on a request from the european parliament related to the safety assessment of wild and farmed fish. Technical Report 326, European Food Safety Authority.

55. Tegene A., Huffman W. E., Rousu M., and Shogren J. F. (2003). The effects of information on consumer demand for biotech foods: Evidence from experimental auctions. Technical bulletins, United States Department of Agriculture, Economic Research Service.

56. Simonson I., Carmon Z., and O’Curry S. (1994). Experimental evidence on the negative effect of product features and sales promotions on brand choice. Marketing Science, 13(1):23–40.

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