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Автор: Grafiati

Опубліковано: 14 березня 2023

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1

Kim, Jinmin, Kwangtae Park, and Hosun Rhim. "Analysis of Product-Service Integration Effect using Choice-based Conjoint Analysis." Journal of the Korean Operations Research and Management Science Society 39, no. 1 (March 31, 2014): 101–12. http://dx.doi.org/10.7737/jkorms.2014.39.1.101.

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2

Cunningham, Charles E., Ken Deal, and Yvonne Chen. "Adaptive Choice-Based Conjoint Analysis." Patient: Patient-Centered Outcomes Research 3, no. 4 (December 2010): 257–73. http://dx.doi.org/10.2165/11537870-000000000-00000.

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3

BARBOSA, Eduardo Campana, Carlos Henrique Osório SILVA, Moysés NASCIMENTO, Fabyano Fonseca e. SILVA, Valéria Paula Rodrigues MINIM, Rosires DELIZA, and Suzana Maria DELLA LUCIA. "Choice-Based Conjoint Analysis: A Bayesian Approach." REVISTA BRASILEIRA DE BIOMETRIA 36, no. 1 (April 3, 2018): 1. http://dx.doi.org/10.28951/rbb.v36i1.102.

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Анотація:

This paper presents a Bayesian approach to Frequentist Logit Multinomial model used in Choice-based Conjoint Analysis. The analysis was conducted with data of choice for the assessment by 144 consumers, eight samples of light strawberry flavored yogurts, ranging information from three ingredients (sugar, fat and protein) in a full factorial design. the results and inferences obtained by the Bayesian approach are presented in terms of estimating the main effect of the attributes, the choice probabilities and choice ratio. The frequentist results are also reported and discussed. The Bayesian analysis showed similar results to frequentist and allowed the construction of credibility intervals for choice probabilities and choice ratio, allowing statistically compare such quantities. About the practical results, the most likely choice was associated with yoghurt containing strawberry flavor light information "0% sugar", "0% fat" and "bioactive proteins enriched"

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4

Vilikus, Ondřej. "Hybrid Approach to Choice-Based Conjoint Analysis." Acta Oeconomica Pragensia 21, no. 4 (August 1, 2013): 3–19. http://dx.doi.org/10.18267/j.aop.407.

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5

Desarbo, Wayne S., Venkatram Ramaswamy, and Steven H. Cohen. "Market segmentation with choice-based conjoint analysis." Marketing Letters 6, no. 2 (March 1995): 137–47. http://dx.doi.org/10.1007/bf00994929.

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6

Kim, Bu-Yong. "New Design of Choice Sets for Choice-based Conjoint Analysis." Korean Journal of Applied Statistics 25, no. 5 (October 31, 2012): 847–57. http://dx.doi.org/10.5351/kjas.2012.25.5.847.

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7

Toubia, Olivier, John R. Hauser, and Duncan I. Simester. "Polyhedral Methods for Adaptive Choice-Based Conjoint Analysis." Journal of Marketing Research 41, no. 1 (February 2004): 116–31. http://dx.doi.org/10.1509/jmkr.41.1.116.25082.

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8

Meißner, Martin, and Reinhold Decker. "Eye-Tracking Information Processing in Choice-Based Conjoint Analysis." International Journal of Market Research 52, no. 5 (September 2010): 593–612. http://dx.doi.org/10.2501/s147078531020151x.

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9

Schlereth, Christian, and Bernd Skiera. "Schätzung von Zahlungsbereitschaftsintervallen mit der Choice-Based Conjoint-Analyse." Schmalenbachs Zeitschrift für betriebswirtschaftliche Forschung 61, no. 8 (December 2009): 838–56. http://dx.doi.org/10.1007/bf03373670.

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10

Verma, Vivek Kumar, and Bibhas Chandra. "Sustainability and customers’ hotel choice behaviour: a choice-based conjoint analysis approach." Environment, Development and Sustainability 20, no. 3 (April 5, 2017): 1347–63. http://dx.doi.org/10.1007/s10668-017-9944-6.

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11

Jayadevappa, R., S. Chhatre, S. B. Malkowicz, and J. S. Schwartz. "PR2 Concordance of Preferences and Treatment Choice Using Choice Based Adaptive Conjoint Analysis." Value in Health 24 (June 2021): S196. http://dx.doi.org/10.1016/j.jval.2021.04.982.

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12

Bakhurji, E., T. Scott, and W. Sohn. "Factors Associated with Pediatric Dentists’ Choice of Amalgam: Choice-Based Conjoint Analysis Approach." JDR Clinical & Translational Research 4, no. 3 (February 19, 2019): 246–54. http://dx.doi.org/10.1177/2380084418822977.

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13

Hoffmann, Sascha, Felix Eggers, and Claudia Fantapié Altobelli. "Titelblattoptimierung bei Zeitschriften mit Hilfe der Choice-based Conjoint-Analyse." MedienWirtschaft 7, no. 3 (2010): 18–25. http://dx.doi.org/10.15358/1613-0669-2010-3-18.

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Анотація:

Viele Zeitschriften werden spontan im Kiosk bzw. im Einzelhandel gekauft, ohne dass die Käufe einem ausführlichen Auswahlprozess unterliegen. Die Aufmachung des Titelbildes und die Auswahl der Coverthemen haben damit einen großen Einfluss auf die Verkaufszahlen am Point of Sale. Für die Zeitschriftenredaktion ergibt sich daraus von Ausgabe zu Ausgabe das Problem, aus einer Vielzahl denkbarer Titelblattalternativen eine besonders verkaufsstarke Zeitschriftenausgabe zu erstellen. Meist entscheiden die Verlagsexperten intuitiv bzw. auf der Basis von Erfahrungswerten, welche Titelmotive den höchsten Kaufanreiz erwarten lassen und welche Themen wahre Auflagentreiber darstellen. Im vorliegenden Beitrag wird an einem Fallbeispiel dargestellt, wie mit Hilfe der Choice-based Conjoint-Analyse Konsumenten frühzeitig in den Entscheidungsprozess optimaler Titelbildalternativen eingebunden werden können.

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14

Altınay Özdemir, Meltem. "Travellers’ Destination Attribute Preferences: A Choice-Based Conjoint (CBC) Analysis." Academica Turistica 15, no. 2 (August 2022): 217–31. http://dx.doi.org/10.26493/2335-4194.15.217-231.

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15

Eggers, Fabian, Felix Eggers, and Sascha Kraus. "Entrepreneurial branding: measuring consumer preferences through choice-based conjoint analysis." International Entrepreneurship and Management Journal 12, no. 2 (November 4, 2014): 427–44. http://dx.doi.org/10.1007/s11365-014-0344-1.

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16

Chrzan, Keith. "Three kinds of order effects in choice-based conjoint analysis." Marketing Letters 5, no. 2 (April 1994): 165–72. http://dx.doi.org/10.1007/bf00994106.

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17

Halme, Merja, and Markku Kallio. "Estimation methods for choice-based conjoint analysis of consumer preferences." European Journal of Operational Research 214, no. 1 (October 2011): 160–67. http://dx.doi.org/10.1016/j.ejor.2011.03.049.

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18

Halme, Merja, and Markku Kallio. "Likelihood estimation of consumer preferences in choice-based conjoint analysis." European Journal of Operational Research 239, no. 2 (December 2014): 556–64. http://dx.doi.org/10.1016/j.ejor.2014.05.044.

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19

Jedidi, Kamel, Rajeev Kohli, and Wayne S. Desarbo. "Consideration Sets in Conjoint Analysis." Journal of Marketing Research 33, no. 3 (August 1996): 364–72. http://dx.doi.org/10.1177/002224379603300310.

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Анотація:

The authors model product consideration as preceding choice in a segment-level conjoint model. They propose a latent-class tobit model to estimate cardinal, segment-level preference functions based on consumers’ preference ratings for product concepts considered worth adding to consumers’ self-explicated consideration sets. The probability with which the utility of a product profile exceeds an unobserved threshold corresponds to its consideration probability, which is assumed to be independent across product profiles and common to consumers in a segment. A market-share simulation compares the predictions of the proposed model with those obtained from an individual-level tobit model and from traditional ratings-based conjoint analysis. The authors also report simulations that assess the robustness of the proposed estimation procedure, which uses an E-M algorithm to obtain maximum likelihood parameter estimates.

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20

Frosch, Zachary AK, Esin C. Namoglu, Nandita Mitra, Daniel Jeffrey Landsburg, Sunita Nasta, Judy Shea, Justin E. Bekelman, Carmen E. Guerra, and Marilyn M. Schapira. "Willingness to travel for CAR-T: A choice-based conjoint analysis." Journal of Clinical Oncology 39, no. 15_suppl (May 20, 2021): e18503-e18503. http://dx.doi.org/10.1200/jco.2021.39.15_suppl.e18503.

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e18503 Background: Chimeric antigen receptor T-cell therapy (CAR-T) is a novel therapy only available at specialized centers. Because patients prefer treatment close to home and provider continuity, preferences may result in unequal access. Sharing follow-up (f/u) with a local provider could increase willingness to travel, but its appeal to patients is unknown. Methods: We conducted a choice-based conjoint analysis to determine the clinical and care delivery factors that patients with diffuse-large B cell lymphoma (DLBCL) value when deciding whether to travel for CAR-T. We recruited from 13 sites within an integrated health system and asked participants to make choices between 12 pairs of treatment options that varied on 5 attributes: travel time, oncologist continuity, whether shared f/u was offered, two-year overall survival (OS), and intensive care unit (ICU) admission rate. Preferences for specific attributes were determined using a generalized estimating equation model with treatment choice as the outcome and the attributes as the variables. Each attribute’s coefficient (importance weight [IW]) represented its value to participants: higher IWs indicated greater value; negative IWs indicated undesirable attributes. Results: We invited 489 patients and 303 (62%) responded. IWs ranged from -0.56 to 1.15 (total cohort) and -2.83 to 2.50 (when modeled by race). Treatments at cancer centers ≥60 minutes away were valued less than those 30 minutes away without collaborative f/u; but were desirable if shared f/u care was offered (Table). Black respondents valued treatments ≥60 minutes away less despite shared f/u care. Participants also valued oncologist continuity (IW 1.15 [0.99, 1.32]), greater OS (IW 0.71 [0.63, 0.79] per 5 percentage point increase), and lower ICU admission rates (IW -0.21 [-0.25, -0.17] per 5 percentage point increase). Oncologist continuity was more highly valued by Black than by White respondents (IW 2.50 [1.74, 3.27] vs 1.09 [0.92, 1.25]). Conclusions: Patients with DLBCL are more likely to select CAR-T therapy at distant cancer centers if f/u care is shared locally. This could represent a strategy to expand access to CAR-T. Travel remains a barrier for Black patients despite shared f/u, suggesting the need to investigate targeted strategies that promote equitable access to CAR-T. [Table: see text]

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21

Bridges, John F. P., Elizabeth T. Kinter, Annette Schmeding, Ina Rudolph, and Axel Mühlbacher. "Can Patients Diagnosed with Schizophrenia Complete Choice-Based Conjoint Analysis Tasks?" Patient: Patient-Centered Outcomes Research 4, no. 4 (December 2011): 267–75. http://dx.doi.org/10.2165/11589190-000000000-00000.

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22

Oetzel, Sebastian. "Anwendung der Choice Based Conjoint-Analyse mit Hilfe hierarchisch bayesianischer Methoden." WiSt - Wirtschaftswissenschaftliches Studium 42, no. 10 (2013): 592–98. http://dx.doi.org/10.15358/0340-1650_2013_10_592.

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23

Oetzel, Sebastian. "Anwendung der Choice Based Conjoint-Analyse mit Hilfe hierarchisch bayesianischer Methoden." WiSt - Wirtschaftswissenschaftliches Studium 42, no. 9 (2013): 523–30. http://dx.doi.org/10.15358/0340-1650_2013_9_523.

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24

Garver, Michael S., Richard L. Divine, and Samuel A. Spralls. "Choice-Based Conjoint Analysis of the Local Coupon Preferences of Millennials." Journal of Promotion Management 20, no. 2 (March 15, 2014): 240–49. http://dx.doi.org/10.1080/10496491.2014.894956.

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25

Mansuy, Jean, Sara Verlinde, and Cathy Macharis. "Understanding preferences for EEE collection services: A choice-based conjoint analysis." Resources, Conservation and Recycling 161 (October 2020): 104899. http://dx.doi.org/10.1016/j.resconrec.2020.104899.

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26

Kateeb, Elham T., Susan C. McKernan, Gary J. Gaeth, Raymond A. Kuthy, Nancy B. Adrianse, and Peter C. Damiano. "Predicting dentists’ decisions: a choice-based conjoint analysis of Medicaid participation." Journal of Public Health Dentistry 76, no. 3 (November 17, 2015): 171–78. http://dx.doi.org/10.1111/jphd.12126.

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27

Elrod, Terry, Jordan J. Louviere, and Krishnakumar S. Davey. "An Empirical Comparison of Ratings-Based and Choice-Based Conjoint Models." Journal of Marketing Research 29, no. 3 (August 1992): 368–77. http://dx.doi.org/10.1177/002224379202900307.

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Анотація:

The authors compare two approaches to conjoint analysis in terms of their ability to predict shares in a holdout choice task. The traditional approach is represented by three models fit to individual-level ratings of full profiles, whereas the other approach is represented by four multinomial logit models fit to choice shares for sets of full profiles. Both approaches predict holdout shares well, with neither the ratings-based nor the choice-based approach dominant, though some models predict better than others. Particularly promising is a new aggregate model that captures departures from independence of irrelevant alternatives (IIA).

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28

Kim, Bu-Yong. "An Empirical Comparison of Predictability of Ranking-based and Choice-based Conjoint Analysis." Korean Journal of Applied Statistics 27, no. 5 (October 31, 2014): 681–91. http://dx.doi.org/10.5351/kjas.2014.27.5.681.

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29

Moore, William L. "A cross-validity comparison of rating-based and choice-based conjoint analysis models." International Journal of Research in Marketing 21, no. 3 (September 2004): 299–312. http://dx.doi.org/10.1016/j.ijresmar.2004.01.002.

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30

Azizah, Anisah, Turniningtyas Ayu R, and I. Nyoman Suluh Wijaya. "PREFERENSI BERMUKIM MASYARAKAT PERBATASAN KOTA DAN KABUPATEN MALANG (DESA NGIJO KECAMATAN KARANGPLOSO)." Jurnal Planologi 15, no. 2 (October 14, 2018): 180. http://dx.doi.org/10.30659/jpsa.v15i2.2810.

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ABSTRACT Development of city center encourages development in frontier city. Ngijo Village, one of frontier areas of Malang City and Malang Regency, was identified as expanding urban activity from Malang City which keep increasing from time to time, resulted in the rapid growth of settlements in the area. Increase of settlements in Ngijo village is growing rapidly, it is influenced by housing preference of residents. The method used in research is quantitative research with analytical technique used is the technique of conjoin analysis. Based on result of conjoint analysis, importance value in each attribute / variable is choice of housing unit with the importance value of 38,3%, environmental factor with importance value of 35,9%, accessibility with importance value of 14,3% and the last, choice of infrastructure with importance value of 11.5%. Choice of housing units to be the most dominant choice, while choice of infrastructure to be the lowest choice of living factors due to facilities and infrastructure available in the neighborhood of Ngijo Village is relatively low when compared with the city center.Keywords: housing preference, conjoin analysis ABSTRAKPerkembangan pusat kota mendorong terjadinya perkembangan di daerah perbatasan kota. Desa Ngijo salah satu kawasan perbatasan Kota Malang dan Kabupaten Malang, teridentifikasi terkena perluasan kegiatan perkotaan dari Kota Malang yang terus meningkat dari waktu ke waktu, yang mengakibatkan pertumbuhan permukiman yang cukup pesat pada kawasan tersebut. peningkatan permukiman pada Desa Ngijo semakin pesat, hal tersebut tentunya sangat dipengaruhi oleh preferensi bermukim dari masyarakatnya. Metode yang digunakan dapam penelitian merupakan penelitian kuantitatif dengan teknik analisis yang digunakan adalah teknik analisis konjoin. Berdasarkan hasil analisis konjoin nilai kepentingan pada masing-masing atribut/variabel adalah pilihan unit rumah dengan nilai kepentingan 38,3%, faktor lingkungan dengan nilai kepentingan 35,9%, aksesibilitas dengan nilai kepentingan 14,3% dan yang terakhir pilihan infrastruktur dengan nilai kepentingan 11,5%. Pilihan unit rumah menjadi pilihan yang paling dominan, sedangkan pilihan infrastruktur menjadi faktor pilihan bermukim yang paling rendah dikarenakan sarana dan prasarana yang tersedia di lingkungan permukiman Desa Ngijo tergolong rendah apabila dibandingkan dengan pusat kota.Kata kunci:preferensi bermukim, analisis konjoin

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31

Lebeau, Philippe, Cathy Macharis, and Joeri Van Mierlo. "Exploring the choice of battery electric vehicles in city logistics: A conjoint-based choice analysis." Transportation Research Part E: Logistics and Transportation Review 91 (July 2016): 245–58. http://dx.doi.org/10.1016/j.tre.2016.04.004.

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32

Wang, Keqiang, Hongmei Liu, Wuyang Hu, and Linda Cox. "Using online self-assessment tool to improve conjoint analysis." Internet Research 26, no. 3 (June 6, 2016): 644–60. http://dx.doi.org/10.1108/intr-04-2014-0105.

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Purpose – Dolphin excursions have become increasingly popular worldwide. Many past studies assessing the value of dolphin excursions use choice-based methods such as the conjoint analysis. However, this method is often criticized as being hypothetical. The purpose of this paper is to describe a relatively low cost but effective approach to enhance understanding of consumer preference obtained by conjoint analysis. The method relies heavily on using internet-based survey tools. Design/methodology/approach – Enabled by an online tool, individuals are asked to self-explicate their preferred alternatives using the same attributes as are found in the conjoint design. The difference between the self-constructed, preferred alternatives and those offered in conjoint experiment are incorporated into choice models. Unlike previous research where only rough estimates can be provided, the proposed method allows precise capture of respondents’ preferred alternative through the automated online survey design. Findings – Results show that although the extra effort involved in data collection is small, the gain in model fit, choice interpretation, and the value (welfare) estimation is sizeable. Evidence indicates that consumers would be willing to pay up to $50 more for adventurous excursions and guarantees that they will interact with dolphins could worth up to $70 per trip. The approach presented in this paper can also serve as a method to test for preference consistency. Originality/value – This study is the first using an online survey to assess values associated with dolphin excursion. It describes the benefit of involving online tools to enhance modeling and interpretation of consumer behavior. Applications of internet-based surveys on household consumer products are abundant (such as food and electronics) but this study offers a much less discussed application in environmental service.

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33

Al-Omari, Basem, and Peter McMeekin. "Patients’ Preferences Regarding Osteoarthritis Medications: An Adaptive Choice-Based Conjoint Analysis Study." Patient Preference and Adherence Volume 14 (December 2020): 2501–15. http://dx.doi.org/10.2147/ppa.s283922.

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34

Ben-Akiva, Moshe, Daniel McFadden, and Kenneth Train. "Foundations of Stated Preference Elicitation: Consumer Behavior and Choice-based Conjoint Analysis." Foundations and Trends® in Econometrics 10, no. 1-2 (2019): 1–144. http://dx.doi.org/10.1561/0800000036.

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35

Fazeli, Soudabeh, Yesenia Covarrubias, Shirin Bassirian, Jordan Cuevas, Kathryn Fowler, Irine Vodkin, Yuko Kono, et al. "Eliciting Patient Preferences for Hepatocellular Carcinoma Screening: A Choice-Based Conjoint Analysis." Journal of the American College of Radiology 19, no. 4 (April 2022): 502–12. http://dx.doi.org/10.1016/j.jacr.2022.01.015.

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36

Szűcs, V., L. Guerrero, A. Claret, M. Tarcea, E. Szabó, and D. Bánáti. "Food additives and consumer preferences: A cross-cultural choice based conjoint analysis." Acta Alimentaria 43, Supplement 1 (November 2014): 180–87. http://dx.doi.org/10.1556/aalim.43.2014.suppl.25.

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37

김태훈 and 김보영. "Choice-based Conjoint Analysis of Consumer Preferences for Health Food Attributes FocusedonVitaminCSupplements." Journal of Distribution Science 13, no. 3 (March 2015): 79–91. http://dx.doi.org/10.15722/jds.13.3.201503.79.

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38

Toubia, Olivier, John Hauser, and Rosanna Garcia. "Probabilistic Polyhedral Methods for Adaptive Choice-Based Conjoint Analysis: Theory and Application." Marketing Science 26, no. 5 (September 2007): 596–610. http://dx.doi.org/10.1287/mksc.1060.0257.

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39

Gilbride, Timothy J., Peter J. Lenk, and Jeff D. Brazell. "Market Share Constraints and the Loss Function in Choice-Based Conjoint Analysis." Marketing Science 27, no. 6 (November 2008): 995–1011. http://dx.doi.org/10.1287/mksc.1080.0369.

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40

Yegoryan, Narine, Daniel Guhl, and Daniel Klapper. "Inferring attribute non-attendance using eye tracking in choice-based conjoint analysis." Journal of Business Research 111 (April 2020): 290–304. http://dx.doi.org/10.1016/j.jbusres.2019.01.061.

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41

Voleti, Sudhir, V. Srinivasan, and Pulak Ghosh. "An approach to improve the predictive power of choice-based conjoint analysis." International Journal of Research in Marketing 34, no. 2 (June 2017): 325–35. http://dx.doi.org/10.1016/j.ijresmar.2016.08.007.

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42

Gao, Zhihua, Zhangjian Wu, Feng Gao, and Jun Zhang. "Using Choice-Based Conjoint Analysis in the China Market of Imported Beer." Journal of Industrial Distribution & Business 9, no. 6 (June 30, 2018): 57–64. http://dx.doi.org/10.13106/ijidb.2018.vol9.no6.57.

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43

Sigurdsson, Valdimar, R. G. Vishnu Menon, and Asle Fagerstrøm. "Online Healthy Food Experiments: Capturing Complexity by Using Choice-Based Conjoint Analysis." Behavior Analyst 40, no. 2 (October 2, 2017): 373–91. http://dx.doi.org/10.1007/s40614-017-0114-9.

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44

Al-Omari, Basem, Julius Sim, Peter Croft, and Martin Frisher. "PATIENT PREFERENCES FOR THE PHARMACOLOGICAL TREATMENT OF OSTEOARTHRITIS: A FEASIBILITY STUDY USING ADAPTIVE CHOICE-BASED CONJOINT ANALYSIS (ACBCA)." European Journal for Person Centered Healthcare 3, no. 2 (June 3, 2015): 253. http://dx.doi.org/10.5750/ejpch.v3i2.975.

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Rationale, aims and objectives Patient preferences are an important part of optimizing the pharmacological treatment of osteoarthritis (OA). Recent choice experiments have explored this issue using two types of conjoint analysis: choice-based conjoint analysis (CBCA) and adaptive conjoint analysis (ACA). The aim of this study was to examine the feasibility of using adaptive choice-based conjoint analysis (ACBCA) methods to determine patient preferences for pharmacological treatment of OA. The specific outcomes were patient evaluations of a) eight attributes in an ACBCA task, b) the computer skills required to complete the task, and c) the perceived utility of the results. Method Participants were drawn from members of a Research Users’ Group (RUG) who had been diagnosed with osteoarthritis. Participants took part in two feasibility studies. In the first feasibility study, four RUG members critically examined the implementation of a computerized ACBCA task. In the second feasibility study, 11 RUG members completed an ACBCA task on medication preferences for osteoarthritis. The ACBCA task was evaluated by a set of self-completed questions and through semi-structured interviews. ResultsThe first feasibility study helped to shape the design and contents of the ACBCA task. In the second feasibility study, no participants reported the ACBCA task to be hard to read or understand. Most participants agreed that the task was adjusting appropriately as the session proceeded and that it helped them in making decisions about preferences. Older patients and patients with little computer experience appeared to find no substantial challenges in using this interactive computer-based technique. ConclusionsThese studies indicate that, with the involvement of patients, face and content validity of an ACBCA task can be achieved through a developmental process taking account of participants’ requirements.

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Ellickson, Paul B., Mitchell J. Lovett, and Bhoomija Ranjan. "Product Launches with New Attributes: A Hybrid Conjoint–Consumer Panel Technique for Estimating Demand." Journal of Marketing Research 56, no. 5 (July 9, 2019): 709–31. http://dx.doi.org/10.1177/0022243719843132.

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The authors propose and empirically evaluate a new hybrid estimation approach that integrates choice-based conjoint with repeated purchase data for a dense consumer panel, and they show that it increases the accuracy of conjoint predictions for actual purchases observed months later. The key innovation lies in combining conjoint data with a long and detailed panel of actual choices for a random sample of the target population. By linking the actual purchase and conjoint data, researchers can estimate preferences for attributes not yet present in the marketplace, while also addressing many of the key limitations of conjoint analysis, including sample selection and contextual differences. Counterfactual product and pricing exercises illustrate the managerial relevance of the approach.

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Rao, Vithala R., and Luis Eduardo Pilli. "Conjoint Analysis para Pesquisa de Marketing no Brasil." Revista Brasileira de Marketing 13, no. 4 (September 11, 2014): 25–38. http://dx.doi.org/10.5585/remark.v13i4.2707.

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Este artigo oferece uma reviso, de 1971 at a atualidade, dos mtodos de conjoint analysis que so abordagens de coleta de dados baseadas em preferncias ou escolhas declaradas pelos consumidores. Milhares de estudos foram realizados com o uso de conjoint analysis, desde a introduo do mtodo no incio da dcada de 70. Este conjunto de mtodos permite que os pesquisadores de mercado estudem trade-off entre os atributos de novos produtos, sendo til para vrias decises de marketing com design de produto, apreamento e segmentao de mercado. O conjunto atual de opes de conjoint analysis composto pela abordagem tradicional de preferncia declarada, pelas tcnicas de escolhas discretas (CBCA ou choice based conjoint analysis) que se baseiam em escolhas declaradas, pela abordagem autoexplicativa que usa elicitao direta de importncia de atributos e avaliao dos nveis dos atributos e pela abordagem adaptativa (ACA ou adaptive conjoint analysis) que implica em coleta de dados por etapas e adaptativa. Este artigo resume estes mtodos e seus desenvolvimentos recentes e apresenta uma aplicao no Mercado brasileiro. Dada a versatilidade do mtodo, existe um enorme potencial para a pesquisa de marketing no Brasil. Essencialmente, esta metodologia est viva e crescendo.

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Odsuren, T., and N. Nyamaa. "Conjoint analysis on Mongolian meat consumption." Mongolian Journal of Agricultural Sciences 11, no. 2 (November 25, 2014): 133–37. http://dx.doi.org/10.5564/mjas.v11i2.234.

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The diet in Mongolia is essentially based on animal products: meat, considered as "red food", and dairy products, "white food". Related to urbanization and changes of lifestyle, Mongolian food consumption structure is changing. But, meat is still important food for Mongolians. Therefore, Improvement of meat quality and meat safety issues is core issues of meat production and meat consumption. The study applied conjoint analysis methodology for consumer choice of meat. In result of Conjoint Analysis, Mongolians prefer good price and high fat content meat and safety meat. In future, the Mongolian Government should be oriented meat production and supply with trade mark and increase a production of Mongolian Brand meat. DOI: http://dx.doi.org/10.5564/mjas.v11i2.234 Mongolian Journal of Agricultural Sciences Vol.11(2) 2013 pp.133-137

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Allenby, Greg M., Neeraj Arora, and James L. Ginter. "Incorporating Prior Knowledge into the Analysis of Conjoint Studies." Journal of Marketing Research 32, no. 2 (May 1995): 152–62. http://dx.doi.org/10.1177/002224379503200203.

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The authors use conjoint analysis to provide interval-level estimates of part-worths allowing tradeoffs among attribute levels to be examined. Researchers often possess prior information about the part-worths, such as the order and range restrictions of product attribute levels. It is known, for example, that consumers would rather pay less for a specific product given that all other product attribute levels are unchanged. The authors present a Bayesian approach to incorporate prior ordinal information about these part-worths into the analysis of conjoint studies. Their method results in parameter estimates with greater face validity and predictive performance than estimates that do not utilize prior information or those that use traditional methods such as LINMAP. Unlike existing methods, the authors’ methods apply to both rating and choice-based conjoint studies.

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Zunaidi, Rizqa Amelia, and Sinta Dewi. "EVALUASI PRODUK SEPATU WANITA BAGI UKM KLASTIK FOOTWEAR MENGGUNAKAN METODE CHOICE BASED CONJOINT." JURNAL REKAYASA SISTEM INDUSTRI 5, no. 1 (November 15, 2019): 1. http://dx.doi.org/10.33884/jrsi.v5i1.1399.

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In Indonesia, there are three industrial sectors with the largest number of Small Medium Enterprises (SMEs) from 2010 to 2014: food, clothing, and wood. Klastik Footwear is one of the SMEs in Indonesia that focuses on the footwear industry, especially women's shoes. Klastik Footwear brings classic and ethnic themes to its products. In order to be well received by the market, Klastik Footwear wants to identify potential consumer groups formed from market segmentation and assess the preferences of each group on Klastik Footwear products. In this study, the choice based conjoint analysis method is used to determine potential consumer preferences for Klastik Footwear products. From the results of the conjoint analysis, the importance and part-worth level of each attribute will be known. Next, a cluster and market simulator analysis is performed to see the profile of the formed segments. The results showed that there are 3 groups of potential consumers. After conducting market simulation on the three groups, it is predicted that the most ordered Klastik Footwear products by consumers are Galuh, Ara, Srikandi, and Gendhis 2 (in group 1); Brastagi, Telaga, and Gendhis 2 (in group 2); and Rinjani, Galuh, and Soko (in group 3).

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Sato, Kazuo, Hiroyuki Iwamoto, and Katsuhiko Demura. "Using Choice Based Conjoint Analysis to Assess Competitiveness of Chemical-free Hokkaido Rice." Journal of Rural Problems 37, no. 1 (2001): 37–49. http://dx.doi.org/10.7310/arfe1965.37.37.

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