Application Of Conjoint Analysis Research Articles

Research on the Application of Conjoint Analysis in Carbon Tax Pricing for the Sustainable Development Process of China

This study aims to evaluate the preferences of Shanghai residents for four key attributes of carbon tax policies via the choice-based conjoint analysis method, with the goal of understanding the potential application of carbon tax policies in China. The findings indicate that the most critical concern for Shanghai residents is the annual cost of the ca trbonax (48.87%), followed by policy transparency (24.72%), revenue allocation (16.68%), and policy implementers (9.73%). The average willingness to pay is CNY 1435.28 annually, indicating a relatively high acceptance of carbon tax policies. Furthermore, increasing public awareness of carbon tax policies significantly enhances willingness to pay, which in turn helps increase tax revenue and promotes the development of low-carbon technologies. This study provides valuable insights for policymakers on carbon tax implementation, contributing to China’s transition to a low-carbon economy.

A paradigm shift in Precambrian research driven by big data

The enigmatic nature of the Precambrian era poses ongoing challenges for researchers seeking to uncover its secrets. With the exponential growth of scientific data, there exists a vast repository of information through which these mysteries can be explored. However, this data is often derived from fragmented investigations of geological phenomena within specific disciplinary domains and limited spatiotemporal boundaries, resulting in untapped potential for extracting undiscovered knowledge. To address this limitation, the field of big data science provides a foundation for multidisciplinary research on the geological evolution of the Precambrian. By harnessing the power of large-scale data integration and analysis, valuable insights into this pivotal era in Earth's history can be revealed. This paper offers a comprehensive overview of big data types in geoscience and elucidates common analysis methods. Through a case study focused on the North China Craton (NCC), we demonstrate the application of conjoint analysis to a dataset comprising rock and mineral geochemistry data. Employing local singularity analysis and wavelet analysis on zircon age frequency and Hf isotopic time series, we reveal a persistent long-term periodicity of 800–500 million years since 3.5 billion years ago. This finding indicates a co-evolution between the NCC and the global supercontinent cycle dating back to the Archean period. To investigate the formation of the NCC, we employ machine learning-based crustal thickness evolution reconstruction, which highlights arc formation during subduction as the primary factor, with regional mantle activities playing a secondary role in the convergence. Combining spatio-temporal evolution analysis of magmatic intensity and εHf(t) values, we infer that the development of the NCC primarily resulted from a prolonged process of accretion targeting the Eastern Block (the primary continent nuclei) through the incorporation of diverse arc massifs. This examination of the NCC serves as an example of how data collection, processing, and utilization can enhance our understanding of formational and evolutionary processes, signaling a paradigm shift in Precambrian research driven by the integration of big data.

One-stage product-line design heuristics: an empirical comparison

Selecting or adjusting attribute-levels (e.g. components, equipments, flavors, ingredients, prices, tastes) for multiple new and/or status quo products is an important task for a focal firm in a dynamic market. Usually, the goal is to maximize expected overall buyers’ welfare based on consumers’ partworths or expected revenue, market share, and profit under given assumptions. However, in general, these so-called product-line design problems cannot be solved exactly in acceptable computing time. Therefore, heuristics have been proposed: Two-stage heuristics select promising candidates for single products and evaluate sets of them as product-lines. One-stage heuristics directly search for multiple attribute-level combinations. In this paper, Ant Colony Optimization, Genetic Algorithms, Particle Swarm Optimization, Simulated Annealing and, firstly, Cluster-based Genetic Algorithm and Max-Min Ant Systems are applied to 78 small- to large-size product-line design problem instances. In contrast to former comparisons, data is generated according to a large sample of commercial conjoint analysis applications (n = 2,089). The results are promising: The firstly applied heuristics outperform the established ones.

Application of Conjoint Analysis and 5Ps Marketing Mix in Identifying Customer Preference of Alcohol Consumption among Youths in the Philippines.

Alcoholic beverages have long been one of the most popular commodities. They have been mass-produced worldwide because of their popularity and demand. On a survey by Statista in 2021, alcohol consumption is projected to increase in the Philippines. Thus, the popularity of numerous alcoholic beverages piqued the curiosity of consumers and researchers alike. This study used conjoint analysis that aims to have a better understanding of consumer preferences for alcohol consumption among youths. Additionally, the study considered the following attributes: (1) type of alcoholic beverage, (2) oral sensation, (3) flavor, (4) origin, (5) color, and (6) price. Results revealed that the product's price is the most crucial attribute influencing consumer preferences by 26.311%, followed by the type of alcoholic beverage with an importance score of 26.237%. The least considered attribute is the product's color, having an importance score of 7.790%. These were supported by two statistical tests, the Pearson's Correlation Test and Kendall's Tau Test, which both obtained a value higher than 0.8. Managerial implications were presented in the study to help provide strategies and development for alcoholic beverage distribution in the Philippines based on the findings of this study, which relate to young consumers.

An approach to targeted promotion of HPV vaccination based on parental preferences for social media content

PurposeEffectiveness of message tactics in social marketing projects often varies across groups of individuals, which suggests the importance of tailoring communication approaches to maximize the success of promotional strategies. This study aims to contribute in this direction by using an innovative approach to promote targeted human papillomavirus vaccination, applying conjoint analysis to understand parental preferences for social media content features.Design/methodology/approachAn online purpose-built quantitative survey was administered to a group of parents meeting eligibility criteria. The survey questions were designed based on inputs from formative qualitative research conducted in a previous phase of the study.FindingsIn the overall sample of 285 parents, responses show that image was the most important feature of social media posts overall, followed by source and text. Cluster analysis identified eight segments in the sample based on parental preferences for content features. Significant differences across segments were identified in terms of need for cognition, vaccine hesitancy, parental gender, concerns around side effects, trust in medical providers, information sharing behaviors on social media and information seeking online.Originality/valueThe application of conjoint analysis to promotional content allows to assess which features of the content are most important in persuading different individuals and provide insights on how people process the information, ultimately to inform targeted promotion based on preferences. Conjoint analysis has been widely used in consumer research to explore audience preferences for products or services, but only a few applications of conjoint analysis to the design and testing of promotional content are found in the literature.

Multiple Hypothesis Testing in Conjoint Analysis

AbstractConjoint analysis is widely used for estimating the effects of a large number of treatments on multidimensional decision-making. However, it is this substantive advantage that leads to a statistically undesirable property, multiple hypothesis testing. Existing applications of conjoint analysis except for a few do not correct for the number of hypotheses to be tested, and empirical guidance on the choice of multiple testing correction methods has not been provided. This paper first shows that even when none of the treatments has any effect, the standard analysis pipeline produces at least one statistically significant estimate of average marginal component effects in more than 90% of experimental trials. Then, we conduct a simulation study to compare three well-known methods for multiple testing correction, the Bonferroni correction, the Benjamini–Hochberg procedure, and the adaptive shrinkage (Ash). All three methods are more accurate in recovering the truth than the conventional analysis without correction. Moreover, the Ash method outperforms in avoiding false negatives, while reducing false positives similarly to the other methods. Finally, we show how conclusions drawn from empirical analysis may differ with and without correction by reanalyzing applications on public attitudes toward immigration and partner countries of trade agreements.

Investors’ Preference for Creating Impact: (A study to understand impact investors’ preference while making investment in India)

Purpose: The main aim of this article is to analyse the preference of impact investors when it comes to the selection of ventures for investment. The study has been conducted with the sole objective of trying to evaluate the choices of impact investors in the Indian market. In addition to this, the present study strives to find out how a certain venture turns out to be the most preferred area of investment and what instrument is preferred to invest in for impact investments, and the criteria that guide investors to arrive at their choice of investment portfolios in Indian impact investment market. Design/Methodology/Approach: A choice-based conjoint experiment has been done in which respondents have to rank the different options which were generated by orthogonal arrays based on their preference. The preference function was computed by using OLS regression. Findings: It has been revealed through this study that while deciding to invest for impact, first preference is accorded to stage of investment in terms of highest importance value which was ensued by their behavioural penchant to do impact investment. Impact investors are inclined to invest in social ventures having the most innovative ideas in response to addressing social problems. The most preferred area of investment is the healthcare sector which is ensued by agriculture and education. Research Limitations: The real behaviour of an individual cannot be predicted through an online survey, which restricts the generalization of the study. Practical Implications: This study presents a simple yet empirical tool that will undoubtedly aid social entrepreneurs, impact investors and other investors to understand the leanings of impact investors and allows them to understand in a better way, how investors’ behaviour differs while making investment decisions in India. The findings will also help policymakers to create a more conducive environment for impact investing in India. Originality/Value: The article is unique in the sense that it endeavours to analyse the impact investors’ preferences and choices with the application of conjoint analysis in the Indian perspective.

Motivations and barriers to using high-speed rail: An application of conjoint analysis – Insights from Vietnam

Background: Nowadays, a gradual change in customers’ attitudes towards transport service makes it more challenging to understand the reasons behind customers’ travel decisions. High-speed rail (HSR) has been mentioned recently and is expected as the best and most modern transport option in long-distance trips in Vietnam. However, research studies have paid scant regard to how HSR’s attributes may affect potential users, and therefore the motivations and barriers to adopting HSR are still unknown.Objectives: This study aimed at examining motivations and barriers to take-up HSR for considering customers’ preference on the proper attributes and levels of HSR.Method: This study drew on a nationwide survey and conjoint analysis to investigate customers’ behaviour.Results: In Vietnam’s context, HSR ticket was found to be the principal barrier to adoption, whereas the speed of HSR was identified as the least important behavioural driver amongst potential HSR users. The results show that HSR design and planning should provide a combination of minimum check-in and waiting time, a 20-min frequency, average speed of 250 km/h, all add-on services and facilities, ticketing of approximately VND 500–700 thousand per 300–500 km and nearby all-day parking.Conclusion: This investigation has demonstrated the value of conjoint analysis to compare a wide range of attributes associated with consumers’ decision to use HSR. The findings indicate that in countries such as Vietnam, in particular, where train usage is low, policymakers and transportation agencies seeking to boost the use of HSR must take attributes other than fare into consideration.

How dish components influence older consumers’ evaluation? – A study with application of conjoint analysis and eye tracking technology

A visually appetising and vegetable-rich dish can be an alternative to the traditional dish for well-functioning older consumers. The aim of this study was to investigate older people’s evaluations towards dishes by incorporating dish attributes (main course, potatoes, vegetables, and dish label) with novel dish components. During the test, a total of 100 participants aged from 60 to 91 were invited to verbally rate their willingness to try, perceived familiarity and healthiness towards eight dish combinations presented randomly in the form of pictures. In the meanwhile, 31 participants’ eye motions for each dish picture were successfully recorded by an eye-tracker. Through conjoint analysis, main course and vegetables were found to have the highest influence on willingness to try, perceived familiarity, perceived healthiness, and total eye fixation time towards the dish. A labelled dish with meatballs, broccolis and plain potatoes was perceived as the most familiar dish. A dish with veggie balls, mixed vegetables and decorated potatoes was perceived as the healthiest. Older participants were most willing to try the dish consisting of mixed vegetables, veggie balls and decorated potatoes when the dish label was added. Females, who emphasised healthy eating and cared about price, variety, organic and health value of a dish were more willing to try veggie balls than meatballs. Regarding the eye metrics, most participants looked at the main course area first, and had the longest total eye fixation time on the dish valued as the healthiest. In the future, to meet older consumers’ demand for daily diet, appropriate selection of dish components with frequent food communication will be indispensable.

Effects of urban environmental attributes on graduate job preferences in Northeastern China: an application of conjoint analysis and big data methods

A constant supply of novel ideas and contributions from all economic sectors is required to further the sustainable development of cities. Therefore, there is a growing need for well-educated graduates to enter metropolitan job markets. As urban environments and culture have been shown to affect a graduates’ eventual carrier choice and trajectory, governments often seek to change their local environments to attract graduates who can help efficiently allocate and utilize a city’s often-limited environmental budgets. In this study, the conjoint analysis (CA) method was employed to explore the effects of four environmental attributes (water pollution, air pollution, littering, and green area) on graduate employment preferences in northeast China. Water pollution was shown to have the greatest effect on graduate preferences (43.6%), followed by air pollution (34.1%), littering (20.7%), and green area (1.6%). According to this ranking of importance, cities could improve their environmental attributes to maximize the attraction of Northeast graduates. Moreover, this study applied the Baidu index (a big data sharing platform) to improve the attribute selection process of the CA method. The improvement reduced the cost of the CA method and enhanced its objectivity.

Mental health service preferences of patients and providers: a scoping review of conjoint analysis and discrete choice experiments from global public health literature over the last 20\u2009years (1999\u20132019)

BackgroundIn designing, adapting, and integrating mental health interventions, it is pertinent to understand patients’ needs and their own perceptions and values in receiving care. Conjoint analysis (CA) and discrete choice experiments (DCEs) are survey-based preference-elicitation approaches that, when applied to healthcare settings, offer opportunities to quantify and rank the healthcare-related choices of patients, providers, and other stakeholders. However, a knowledge gap exists in characterizing the extent to which DCEs/CA have been used in designing mental health services for patients and providers.MethodsWe performed a scoping review from the past 20 years (2009–2019) to identify and describe applications of conjoint analysis and discrete choice experiments. We searched the following electronic databases: Pubmed, CINAHL, PsychInfo, Embase, Cochrane, and Web of Science to identify stakehold,er preferences for mental health services using Mesh terms. Studies were categorized according to pertaining to patients, providers and parents or caregivers.ResultsAmong the 30 studies we reviewed, most were published after 2010 (24/30, 80%), the majority were conducted in the United States (11/30, 37%) or Canada (10/30, 33%), and all were conducted in high-income settings. Studies more frequently elicited preferences from patients or potential patients (21/30, 70%) as opposed to providers. About half of the studies used CA while the others utilized DCEs. Nearly half of the studies sought preferences for mental health services in general (14/30, 47%) while a quarter specifically evaluated preferences for unipolar depression services (8/30, 27%). Most of the studies sought stakeholder preferences for attributes of mental health care and treatment services (17/30, 57%).ConclusionsOverall, preference elicitation approaches have been increasingly applied to mental health services globally in the past 20 years. To date, these methods have been exclusively applied to populations within the field of mental health in high-income countries. Prioritizing patients’ needs and preferences is a vital component of patient-centered care – one of the six domains of health care quality. Identifying patient preferences for mental health services may improve quality of care and, ultimately, increase acceptability and uptake of services among patients. Rigorous preference-elicitation approaches should be considered, especially in settings where mental health resources are scarce, to illuminate resource allocation toward preferred service characteristics especially within low-income settings.

Application of Discrete Choice Experiment in Health Care: A Bibliometric Analysis.

Background: Discrete choice experiment (DCE) as a tool that can measure medical stakeholders' preferences especially patients recently has been increasingly applied in health care.Objective: The aim of this study was to examine the hotspots and trends of the application of DCE in health care and to provide reference and direction for further development of DCE in the future.Method: A bibliometric method was implemented using the Web of Science (WoS) Core Collection for the period from the database established to December 8, 2020. The data files are imported into CiteSpace and Excel to analyze and visualize the annual volume of productive, authors, countries, cited journals, cited articles, and keywords.Results: A total of 1,811 articles were retrieved, then we read the abstract of each paper one by one, and 1,562 articles were included after screening, with an exponential increase in publication volume. John F. P. Bridges contributed to 40 publications and ranked first, followed by F. Reed Johnson (n = 37), Julie Ratcliffe (n = 36). The majority of the papers were conducted in the United States (n = 513) and the United Kingdom (n = 433). The top three cited journals were “Health Economics” (n = 981), “Value in Health” (n = 893), and “Pharmaceutical Economics” (n = 774), and the top three articles were “Constructing experimental designs for discrete-choice experiments: report of the ISPOR Conjoint Analysis Experimental Design Good Research Practices Task Force,” “Conjoint analysis applications in health-a checklist: a report of the ISPOR Good Research Practices for Conjoint Analysis Task Force,” and “Discrete choice experiments in health economics: a review of the literature.” The research hotspots and trends included “health technology assessment,” “survival,” “preference based measure,” and “health state valuation.”Conclusion: The size of the literature about DCE studies in health care showed a noticeable increase in the past decade. The application of DCE in health care remains in an early growth phase, and “health technology assessment,” “survival,” “preference based measure,” and “health state valuation” reflected the latest research hotpots and future trends.

Optimization of Perilla, Roselle and Zanthoxylum Levels in Pork Patties by Application of Conjoint Analysis

Background: Perilla, roselle and zanthoxylum are spices which are commonly used in preparation of meat dishes. They provide unique and distinctive flavour and taste which improves the sensory properties of the products to which they are added. In addition, they also possess desirable functional properties like antioxidant and antimicrobial properties of natural origin. The objective of the study was to optimize the inclusion levels in combination with the most acceptable sensory quality and to study the storage stability of pork patties at refrigerated (4±1oC) temperature. Methods: Three different levels of perilla 0.5, 0.7 and 0.9%, roselle 0.2, 0.3 and 0.4% and zanthoxylum 0.1, 0.15 and 0.2% were used in nine (9) different combinations designated as C1, C2, C3, C4, C5, C6, C7, C8, and C9 in the preparation of pork patties, to determine the most preferred combination according to physicochemical and sensory analysis. The patties were analyzed for pH, emulsion stability, colour and sensory evaluation. Sensory quality of patties was conducted and rating score for each profile, contribution of each attribute to the respondent’s preference was calculated by using conjoint analysis procedure. Result: The results of conjoint analysis revealed that the combination with perilla 0.5%, roselle 0.2% and zanthoxylum 0.1% was the most preferred combination as compared to all other combinations. Treated patties show lower oxidative and microbial parameters values and higher sensory scores over a period of 21 days.

Entrepreneurship education as an arena for career reflection: the shift of students' career preferences after a business planning course

PurposeThe purpose of this paper is to examine the impact of entrepreneurship education (EE) in the form of a business planning course on students' career intentions and preferences. While there is extensive research in which traditional survey scales have been applied to study students' entrepreneurial intentions, this study takes a novel approach by extending the construct of entrepreneurial intention to include preferences for intrapreneurship and team entrepreneurship. Furthermore, the use of conjoint analysis captures students' unconscious decision-making processes when presented with different career opportunity scenarios, thereby overcoming many of the limitations of self-reported survey measures.Design/methodology/approachThe study uses a quasi-experimental design with a novel application of conjoint analysis in EE research. A two-part survey combining a traditional questionnaire with conjoint analysis was distributed to students enrolled in a business planning course at two campuses of a Norwegian university, resulting in 99 matched pre- and post-test responses.FindingsTwo main findings arise from the study. First, there is a significant decrease in entrepreneurial intention among students in the EE course. Second, the conjoint analysis contributes to a better understanding of this decrease by illustrating how students shift their career preferences from entrepreneurship to employment during the EE course. This suggests that EE provides a space for students' career reflections where they can explore, commit to and reconsider entrepreneurship as a career.Research limitations/implicationsA limitation of the study is that it focuses on a small sample of undergraduate students from two campus locations in Norway. Thus, further investigation is still necessary to establish whether the findings are valid in other contexts. The research has implications for higher educational institutions, policymakers and researchers in the field of EE.Practical implicationsThe study contributes with a novel perspective on EE as a trigger for career reflection, a perspective that is important for educators teaching EE courses, as well as for higher education institutions who decide to implement EE in study programmes.Originality/valueBy focusing on the development of students' career preferences through conjoint analysis, the study expands knowledge on the impact of EE on students' careers, while also accentuating the value of the application of conjoint analysis in research on EE.

Zastosowanie conjoint analysis w badaniu preferencji konsumentów wina

W artykule przedstawiono wyniki badań preferencji konsumentów wina przy wykorzystaniu metody conjoint analysis, czyli addytywnego pomiaru łącznego. Podstawę pomiaru preferencji stanowi tu funkcja użyteczności, która umożliwia przypisanie charakterystyki liczbowej każdemu z dostępnych wariantów będących przedmiotem wyboru. Conjoint analysis jest jedną z metod wielowymiarowej analizy statystycznej. Opiera się ona na prezentacji respondentom zbioru obiektów (profilów produktów lub usług) opisanych wybranymi atrybutami, z których każdy przyjmuje określone wartości (poziomy), w celu uzyskania informacji o całkowitych preferencjach odnośnie tych obiektów. Celem opracowania było rozpoznanie preferencji konsumentów wina, w szczególności określenie, jakimi kryteriami kierują się oni, podejmując decyzję o zakupie wina. Przeprowadzono badania ankietowe przy wykorzystaniu kwestionariusza ankiety wśród 248 wybranych w sposób przypadkowy osób deklarujących spożywanie wina, w podziale na kobiety i mężczyzn oraz trzy grupy wiekowe (18–35 lat, 36–55 lat oraz 56 lat lub więcej). W wyniku analizy stwierdzono, że czynnikiem decydującym o wyborze wina jest dla konsumentów przede wszystkim cena. W dalszej kolejności konsumenci uwzględniają wytrawność wina i jego barwę. Najmniej ważnymi czynnikami okazały się rodzaj wina (spokojne, musujące) oraz to, czy wino było laureatem nagród lub wyróżnień.

Application of conjoint analysis in the study of the wine consumers’ preferences

W artykule przedstawiono wyniki badań preferencji konsumentów wina przy wykorzystaniu metody conjoint analysis, czyli addytywnego pomiaru łącznego. Podstawę pomiaru preferencji stanowi tu funkcja użyteczności, która umożliwia przypisanie charakterystyki liczbowej każdemu z dostępnych wariantów będących przedmiotem wyboru. Conjoint analysis jest jedną z metod wielowymiarowej analizy statystycznej. Opiera się ona na prezentacji respondentom zbioru obiektów (profilów produktów lub usług) opisanych wybranymi atrybutami, z których każdy przyjmuje określone wartości (poziomy), w celu uzyskania informacji o całkowitych preferencjach odnośnie tych obiektów. Celem opracowania było rozpoznanie preferencji konsumentów wina, w szczególności określenie, jakimi kryteriami kierują się oni, podejmując decyzję o zakupie wina. Przeprowadzono badania ankietowe przy wykorzystaniu kwestionariusza ankiety wśród 248 wybranych w sposób przypadkowy osób deklarujących spożywanie wina, w podziale na kobiety i mężczyzn oraz trzy grupy wiekowe (18–35 lat, 36–55 lat oraz 56 lat lub więcej). W wyniku analizy stwierdzono, że czynnikiem decydującym o wyborze wina jest dla konsumentów przede wszystkim cena. W dalszej kolejności konsumenci uwzględniają wytrawność wina i jego barwę. Najmniej ważnymi czynnikami okazały się rodzaj wina (spokojne, musujące) oraz to, czy wino było laureatem nagród lub wyróżnień.

コンジョイント分析のICT商品企画への応用(プロダクトデザインI,研究発表,芸術工学会2009年度秋期大会(神戸))

コンジョイント分析のICT商品企画への応用(プロダクトデザインI,研究発表,芸術工学会2009年度秋期大会(神戸))

Engaging multiple weighting approaches and Conjoint Analysis to extend results acceptance of life cycle assessment in biological wastewater treatment technologies

Environmental impacts of biological wastewater treatment technologies (BWTTs) can be evaluated by life cycle assessment (LCA). However, very few efforts have been made to expand the ranges of results acceptance and promote stakeholders to participate in the results analysis. To facilitate the evaluation reaching more wide and deep understanding, this study proposed to employ multiple weighting methods and the Conjoint Analysis. To investigate the feasibility, an illustrative case of a bioaugmented constructed wetland was carried out. Weighting results indicated that appropriate improvement strategies could be obtained from synthesizing the similarities and differences of LCA results due to different weighting methods employed. Meanwhile, application of Conjoint Analysis was conducive to the communication between LCA practitioners and BWTTs stakeholders. In a simulated decision-situation, this study found that the decision-making process of stakeholders could be clearly derived to indicate how stakeholders would take trade-offs and make choices based on analyzing LCA outcome.

Development of a nobel and weighted scale for measuring burden of dementia care -application of conjoint analysis-

Development of a nobel and weighted scale for measuring burden of dementia care -application of conjoint analysis-

Application of Conjoint Analysis to Improve Reliability of Dietician Consultation in Pediatric Celiac Disease.

Celiac disease (CD) management involves lifelong adherence to a gluten-free diet, making the dietician a key member in CD care. However, our institution lacked a standardized process for dietary consultation in newly diagnosed CD. To understand provider CD care preferences, a 24-1 fractional factorial conjoint analysis was performed. Attributes studied (2 levels each) included type of initial follow-up gastroenterology (GI) provider, interval from diagnosis to follow-up, concurrence of initial dietary consultation with gastroenterology visit, and on-going follow-up GI provider. CD care was standardized in July 2014 to facilitate concurrent visits with the clinician and dietician during the same clinical session. Changes to mean time of dietary consultation and reliability of dietary consultation were monitored using an individual-control and G-control chart, respectively. Standard control chart rules were followed. Conjoint analysis identified shorter time to initial follow-up visit and concurrent GI/dietician visits as more important attributes in newly diagnosed CD subjects' care. Types of follow-up provider during first or subsequent visits were identified as less important attributes. After initiation of a standardized follow-up process, a special cause was identified in December 2015 with a decrease in the mean time to dietary consultation from 30 to 20 days. In addition, standardized follow-up resulted in a more reliable process as evident by a special cause on the G-control chart in February 2015. Conjoint analysis identified attributes thought to be important in CD follow-up care. After redesign of our care process, a decrease in time to dietary consultation with improved reliability was observed.