A systematic review of food product conjoint analysis research

Published Online: Sep 13, 2023

Kristian PENTUS

Email: kristian.pentus@ut.ee
University of Tartu, Tartu, Estonia

Views: 663

Downloads: 37

Download PDF

Abstract:

Objectives: Conjoint research techniques have been employed in many articles. These are mainly in the field of food sciences. There has yet to be a thorough analysis of these papers. Reviewing food product conjoint analysis articles was the goal of the current literature review. Methods/Approach: A systematic literature review approach was used based PRISMA approach. Results: Published between years 2000 to 2020, 72 articles were reviewed. The article focussed on average sample size, most common subsampling methods, differences in conjoint evaluation questions, and most tested product categories. As a result of these findings, the author brought out steps to take when planning to conduct conjoint analysis and highlighted gaps in the current literature. Conclusions: 62 articles focused on hedonic goods and 38 on extrinsic qualities. Insights from this review champion conjoint analysis as an indispensable tool, highlighting its potential to refine future research endeavours in the domain. Results and supporting data from conjoint research conducted on utilitarian products still need to be included. The median sample size was 298, while the average was 459.

Keywords:

Product package design, conjoint analysis, experiment design, systematic literature review

Pages:

480-502

JEL Classification:

M31, M00, M39

DOI:

https://doi.org/10.46656/access.2023.4.3(11)

How to cite:

Pentus, K. (2023). A systematic review of food product conjoint analysis research. Access to science, business, innovation in the digital economy, ACCESS Press, 4(3), 480-502, https://doi.org/10.46656/access.2023.4.3(11)

References:

Agarwal, J., DeSarbo, W. S., Malhotra, N. K., Rao, V. R. (2015). An Interdisciplinary Review of Research in Conjoint Analysis: Recent Developments and Directions for Future Research. Customer Needs and Solutions, 2(1), 19–40. https://doi.org/10.1007/s40547-014-0029-5
Annunziata, A., Vecchio, R. (2013). Consumer perception of functional foods: A conjoint analysis with probiotics. Food Quality and Preference, 28(1), 348–355. https://doi.org/10.1016/j.foodqual.2012.10.009
Ares, G., Arrúa, A., Antúnez, L., Vidal, L., Machín, L., Martínez, J., Curutchet, M.R., Giménez, A. (2016). Influence of label design on children’s perception of two snack foods: Comparison of rating and choice-based conjoint analysis. Food Quality and Preference, 53, 1–8. https://doi.org/10.1016/j.foodqual.2016.05.006
Ares, G., Deliza, R. (2010). Identifying important package features of milk desserts using free listing and word association. Food Quality and Preference, 21(6), 621–628. https://doi.org/10.1016/j.foodqual.2010.03.010
Ares, G., Giménez, A., Gámbaro, A. (2009). Consumer perceived healthiness and willingness to try functional milk desserts. Influence of ingredient, ingredient name and health claim. Food Quality and Preference, 20(1), 50–56. https://doi.org/10.1016/j.foodqual.2008.07.002
Asioli, D., Næs, T., Øvrum, A., Almli, V. L. (2016). Comparison of rating-based and choice-based conjoint analysis models. A case study based on preferences for iced coffee in Norway. Food Quality and Preference, 48, 174–184. https://doi.org/10.1016/j.foodqual.2015.09.007
Asioli, Daniele, Varela, P., Hersleth, M., Almli, V. L., Olsen, N. V., Næs, T. (2017). A discussion of recent methodologies for combining sensory and extrinsic product properties in consumer studies. Food Quality and Preference, 56, 266–273. https://doi.org/10.1016/j.foodqual.2016.03.015
Balijepally, V., Mangalaraj, G., Iyengar, K. (2011). Are we wielding this hammer correctly? A reflective review of the application of cluster analysis in information systems research. Journal of the Association for Information Systems, 12(5), 1.
Barney, J. B., Hoskisson, R. E. (1990). Strategic groups: Untested assertions and research proposals. Managerial and decision Economics, 11(3), 187-198.
Baumgartner, B., Steiner, W. J. (2007). Are consumers heterogeneous in their preferences for odd and even prices? Findings from a choice-based conjoint study. International Journal of Research in Marketing, 24(4), 312–323. https://doi.org/10.1016/j.ijresmar.2007.05.003
Bernabéu, R., Prieto, A., Díaz, M. (2013). Preference patterns for wine consumption in Spain depending on the degree of consumer ethnocentrism. Food Quality and Preference, 28(1), 77–84. https://doi.org/10.1016/j.foodqual.2012.08.003
Chakravarti, A., Grenville, A., Morwitz, V., Tang, J., Ulkumen, G., Malleable C.P., Ülkümen, G. (2013). Malleable Conjoint Partworths: How the Breadth of Response Scales Alters Price Sensitivity. Journal of Consumer Psychology 23(4): 515–535.
Charette, P., Hooker, N. H., & Stanton, J. L. (2015). Framing and naming: A process to define a novel food category. Food Quality and Preference, 40(PA), 147–151. https://doi.org/10.1016/j.foodqual.2014.09.015
Claret, A., Guerrero, L., Aguirre, E., Rincón, L., Hernández, M. D., Martínez, I., Peleteiro, J.B., Grau, A., Rodríguez-Rodríguez, C. (2012). Consumer preferences for sea fish using conjoint analysis: Exploratory study of the importance of country of origin, obtaining method, storage conditions and purchasing price. Food Quality and Preference, 26(2), 259–266. https://doi.org/10.1016/j.foodqual.2012.05.006
de Andrade, J. C., Nalério, É. S., Giongo, C., de Barcellos, M. D., Ares, G., Deliza, R. (2016). Influence of evoked contexts on rating-based conjoint analysis: Case study with lamb meat. Food Quality and Preference, 53, 168–175. https://doi.org/10.1016/j.foodqual.2016.06.013
de Jonge, J., van der Lans, I. A., van Trijp, H. C. M. (2015). Different shades of grey: Compromise products to encourage animal friendly consumption. Food Quality and Preference, 45, 87–99. https://doi.org/10.1016/j.foodqual.2015.06.001
Dolnicar, S. (2002). A review of unquestioned standards in using cluster analysis for data-driven market segmentation.
Durach, C. F., Kembro, J., Wieland, A. (2017). A New Paradigm for Systematic Literature Reviews in Supply Chain Management. Journal of Supply Chain Management, 53(4), 67–85. https://doi.org/10.1111/jscm.12145
Endrizzi, I., Menichelli, E., Johansen, S. B., Olsen, N. V., Næs, T. (2011). Handling of individual differences in rating-based conjoint analysis. Food Quality and Preference, 22(3), 241–254. https://doi.org/10.1016/j.foodqual.2010.10.005
Endrizzi, I., Torri, L., Corollaro, M. L., Demattè, M. L., Aprea, E., Charles, M., Biasioli, F., Gasperi, F. (2015). A conjoint study on apple acceptability: Sensory characteristics and nutritional information. Food Quality and Preference, 40(PA), 39–48. https://doi.org/10.1016/j.foodqual.2014.08.007
Enneking, U., Neumann, C., Henneberg, S. (2007). How important intrinsic and extrinsic product attributes affect purchase decision. Food Quality and Preference, 18(1), 133–138. https://doi.org/10.1016/j.foodqual.2005.09.008
Feldmann, C., Hamm, U. (2015). Consumers’ perceptions and preferences for local food: A review. Food Quality and Preference, 40(PA), 152–164. https://doi.org/10.1016/j.foodqual.2014.09.014
Font, M., Realini, C., Montossi, F., Sañudo, C., Campo, M. M., Oliver, M. A., Nute, G. R. (2011). Consumer’ s purchasing intention for lamb meat affected by country of origin, feeding system and meat price: A conjoint study in Spain, France and United Kingdom. Food Quality and Preference, 22(5), 443–451. https://doi.org/10.1016/j.foodqual.2011.02.007
Grunert, K. G., Mueller, S., Zhou, Y., Tinggaard, S. (2015). Extrinsic and intrinsic quality cues in Chinese consumers’ purchase of pork ribs. Food Quality and Preference, 42, 37–47. https://doi.org/10.1016/j.foodqual.2015.01.001
Hersleth, M., Lengard, V., Verbeke, W., Guerrero, L., Næs, T. (2011). Consumers’ acceptance of innovations in dry-cured ham: Impact of reduced salt content, prolonged aging time and new origin. Food Quality and Preference, 22(1), 31–41. https://doi.org/10.1016/j.foodqual.2010.07.002
Jansen, J. J., Menichelli, E., Næs, T. (2015). Modeling target group heterogeneity in experimental consumer studies. Food Quality and Preference, 45, 50–57. https://doi.org/10.1016/j.foodqual.2015.05.005
Joško Brakus, J., Schmitt, B. H., & Zhang, S. (2014). Experiential product attributes and preferences for new products: The role of processing fluency. Journal of Business Research, 67(11), 2291–2298. https://doi.org/10.1016/j.jbusres.2014.06.017
Kildegaard, H., Olsen, A., Gabrielsen, G., Møller, P., Thybo, A. K. (2011). A method to measure the effect of food appearance factors on children’s visual preferences. Food Quality and Preference, 22(8), 763–771. https://doi.org/10.1016/j.foodqual.2011.06.009
Kimura, A., Mukawa, N., Yamamoto, M., Masuda, T., Yuasa, M., Goto, S., Oka, T., Wada, Y. (2012). The influence of reputational concerns on purchase intention of fair-trade foods among young Japanese adults. Food Quality and Preference, 26(2), 204–210. https://doi.org/10.1016/j.foodqual.2012.05.002
Kälviäinen, N., Roininen, K., Tuorila, H. (2003). The relative importance of texure, taste and aroma on a yogurt-type snack food preference in the young and the elderly. Food Quality and Preference. https://doi.org/10.1016/S0950-3293(02)00049-6
Le Roux, A., Bobrie, F., Thébault, M. (2016). A typology of brand counterfeiting and imitation based on a semiotic approach. Journal of Business Research, 69(1), 349–356. https://doi.org/10.1016/j.jbusres.2015.08.007
Lee, P. Y., Lusk, K., Mirosa, M., Oey, I. (2015). An attribute prioritization-based segmentation of the Chinese consumer market for fruit juice. Food Quality and Preference, 46, 1–8. https://doi.org/10.1016/j.foodqual.2015.06.016
Lim, J., Currim, I. S., Andrews, R. L. (2005). Consumer heterogeneity in the longer-term effects of price promotions. International Journal of Research in Marketing, 22(4), 441–457. https://doi.org/10.1016/j.ijresmar.2005.09.006
Lockshin, L., Jarvis, W., D’Hauteville, F., Perrouty, J. (2006). Using simulations from discrete choice experiments to measure consumer sensitivity to brand, region, price, and awards in wine choice. Food Quality and Preference, 17(3–4), 166–178. https://doi.org/10.1016/j.foodqual.2005.03.009
Louviere, J. J., Flynn, T. N., Carson, R. T. (2010). Discrete Choice Experiments Are Not Conjoint Analysis. Journal of Choice Modelling, 3(3), 57–72. https://doi.org/10.1016/S1755-5345(13)70014-9
Marreiros, C., Ness, M. (2009). CEFAGE-UE Working Paper a Conceptual Framework of Consumer Food Choice Behaviour. Agriculture.
Marshall, D., Bridges, J. F. P., Hauber, B., Cameron, R., Donnalley, L., Fyie, K., Johnson, F. R. (2010). Conjoint Analysis Applications in Health – How are Studies Being Designed and Reported? The Patient: Patient-Centered Outcomes Research, 3(4), 249–256. https://doi.org/10.2165/11539650-000000000-00000
Martínez-Carrasco Martínez, L., Brugarolas MolláBauzá, M., Del Campo Gomis, F. J., Martínez Poveda, Á. (2006). Influence of purchase place and consumption frequency over quality wine preferences. Food Quality and Preference, 17(5), 315–327. https://doi.org/10.1016/j.foodqual.2005.02.002
Menichelli, E., Veflen, N., Meyer, C., Næs, T. (2012). Combining extrinsic and intrinsic information in consumer acceptance studies. Food Quality and Preference, 23(2), 148–159. https://doi.org/10.1016/j.foodqual.2011.03.007
Moher D., Liberati A., Tetzlaff J., Altman D.G., The PRISMA Group (2009) Preferred Reporting Items for Systematic Reviews and Meta-Analyses: The PRISMA statement. PLoS Medicine, 6(7). https://doi.org/10.1371/journal.pmed.1000097
Moskowitz, H., Silcher, M., Beckley, J., Minkus-McKenna, D., Mascuch, T. (2005). Sensory benefits, emotions and usage patterns for olives: using Internet-based conjoint analysis and segmentation to understand patterns of response. Food Quality and Preference, 16(4), 369–382. https://doi.org/10.1016/j.foodqual.2004.01.003
Næs, T., Kubberød, E., Sivertsen, H. (2001). Identifying and interpreting market segments using conjoint analysis. Food Quality and Preference, 12(2), 133–143. https://doi.org/10.1016/S0950-3293(00)00039-2
Nguyen, T. T., Haider, W., Solgaard, H. S., Ravn-Jonsen, L., & Roth, E. (2015). Consumer willingness to pay for quality attributes of fresh seafood: A labeled latent class model. Food Quality and Preference, 41, 225–236. https://doi.org/10.1016/j.foodqual.2014.12.007
North, E., Vos, R. De. (2002). The use of conjoint analysis to determine consumer buying preferences: A literature review. Journal of Family Ecology and Consumer Sciences, 30, 32–39
O’Connor, E., Cowan, C., Williams, G., O’Connell, J., Boland, M. P. (2006). Irish consumer acceptance of a hypothetical second-generation GM yogurt product. Food Quality and Preference, 17(5), 400–411. https://doi.org/10.1016/j.foodqual.2005.05.003
Olson, J., Jacoby, J. (1972). Cue utilization in the quality perception process. Proceedings of the Third Annual Conference of the of the Association for Consumer Research, (1972), 167–179. https://doi.org/10.1108/eb026082
Orzechowski, M. A., Timmermans, H. J. P., & Vries, B. de. (2000). Measuring user satisfaction for design variations through virtual reality. Design & Decision Support Systems in Architecture - Proceedings of the 5th International Conference, (August), 278–288
Otter, T., Tüchler, R., Frühwirth-Schnatter, S. (2004). Capturing consumer heterogeneity in metric conjoint analysis using Bayesian mixture models. International Journal of Research in Marketing, 21(3), 285–297. https://doi.org/10.1016/j.ijresmar.2003.11.002
Pentus, K., Mehine, T., Kuusik, A. (2014). Considering emotions in product package design through combining conjoint analysis with psycho physiological measurements. Procedia - Social and Behavioral Sciences, 148, 280–290. https://doi.org/10.1016/j.sbspro.2014.07.044
Piqueras-Fiszman, B., Jaeger, S. R. (2014). The effect of product-context appropriateness on emotion associations in evoked eating occasions. Food Quality and Preference, 40(PA), 49–60. https://doi.org/10.1016/j.foodqual.2014.08.008
Piqueras-Fiszman, B., Velasco, C., Salgado-Montejo, A., Spence, C. (2013). Using combined eye tracking and word association in order to assess novel packaging solutions: A case study involving jam jars. Food Quality and Preference, 28(1), 328–338. https://doi.org/10.1016/j.foodqual.2012.10.006
Puyares, V., Ares, G., Carrau, F. (2010). Searching a specific bottle for Tannat wine using a check-all-that apply question and conjoint analysis. Food Quality and Preference, 21(7), 684–691. https://doi.org/10.1016/j.foodqual.2010.05.008
Sattler, H., Hensel-Börner, S. (2001). A comparison of conjoint measurement with self-explicated approaches (pp. 121-133). Springer Berlin Heidelberg
Schnettler, B., Ruiz, D., Sepúlveda, O., Sepúlveda, N. (2007). Importance of the country of origin in food consumption in a developing country. Food Quality and Preference, 19(4), 372–382. https://doi.org/10.1016/j.foodqual.2007.11.005
Tempesta, T., Arboretti, R., Corain, L., Salmaso, L., Tomasi, D., & Boatto, V. (2010). The importance of landscape in wine quality perception: An integrated approach using choice-based conjoint analysis and combination-based permutation tests. Food Quality and Preference, 21(7), 827–836. https://doi.org/10.1016/j.foodqual.2010.04.007
Tranfield, D., Denyer, D., Smart, P. (2003). Towards a Methodology for Developing Evidence-Informed Management Knowledge by Means of Systematic Review. British Journal of Management, 14(3), 207–222. https://doi.org/10.1111/1467-8551.00375
van der Zanden, L. D. T., van Kleef, E., de Wijk, R. A., van Trijp, H. C. M. (2015). Examining heterogeneity in elderly consumers’ acceptance of carriers for protein-enriched food: A segmentation study. Food Quality and Preference, 42, 130–138. https://doi.org/10.1016/j.foodqual.2015.01.016
Vieira, K. C., Alcantara, V. D. C., Prado, J. W. do, Pinto, C. L., Rezende, D. C. de. (2015). How Does Packaging Influence Consumer Behavior? A Multidisciplinary Bibliometric Study. International Business Research, 8(5), 66–80. https://doi.org/10.5539/ibr.v8n5p66
Voleti, S., Srinivasan, V., Ghosh, P. (2017). An approach to improve the predictive power of choice-based conjoint analysis. International Journal of Research in Marketing, 34(2), 325–335. https://doi.org/10.1016/j.ijresmar.2016.08.007

Latest issue

Our latest issues

Need help?

Access Journal