research articles on conjoint analysis

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Systematic Review of Studies Using Conjoint Analysis Techniques to Investigate Patients’ Preferences Regarding Osteoarthritis Treatment

Basem al-omari.

1 College of Medicine and Health Sciences, Khalifa University, Abu Dhabi, United Arab Emirates

Peter McMeekin

2 School of Health and Life Science, University of Northumbria, Newcastle-Upon-Tyne, UK

Angela Bate

The use of conjoint analysis (CA) to elicit patients’ preferences for osteoarthritis (OA) treatment has the potential to contribute to tailoring treatments and enhancing patients’ compliance and adherence. This review's main aim was to identify and summarise the evidence that used conjoint analysis techniques to quantify patient preferences for OA treatments.

A comprehensive search strategy was conducted using electronic databases and hand reference checks. Databases were searched from their inception until 10th June 2019. All OA and CA related terms were used to conduct the search. The authors reviewed the papers and used the International Society of Pharmacoeconomics and Outcomes Research (ISPOR) checklist to assess the quality of the included studies.

The search identified 534 records. Sixteen records were selected for full-text review and quality assessment and all were included in the narrative data synthesis. All included studies suggested that the severity of symptoms influenced the patients’ preference for OA treatment. All included studies recognised CA as a useful method to investigate patients’ preferences concerning OA treatment.

Patients preference for OA treatment is driven by the severity of patients’ symptoms and the desire to avoid treatment side effects and CA is a useful tool to investigate patients’ preferences for OA treatment.

Osteoarthritis (OA) is the most common form of arthritis. 1 It is a long-term chronic disabling degenerative joint disease that causes pain and limitation of movement. 2 , 3 Pain associated with OA substantially reduces the patient’s mobility and quality of life. 4 Treatments primarily target joint pain to maintain and improve joint mobility. 5 Options include surgery, pharmacological and non-pharmacological treatments. 6 , 7 However, alternative treatments differ in terms of the risks and benefits offered. Preferences for alternative treatments vary across individuals and depend on how they value the benefits relative to the associated risks. 8 , 9

It has increasingly become the goal of healthcare systems to promote patient involvement, 10 especially that the discordant patient and healthcare provider preferences for different attributes of healthcare interventions are common. 11 In the United Kingdom (UK), the Health and Social Care Act 2012 made clear the duties of the national health service (NHS) to involve patients in the decisions about their treatment. 12 The use of stated preference techniques to elicit and understand patients’ preferences and values for health services and treatments to then inform treatment decisions is an accepted method of promoting patient-centred care 13–15 and its use has grown dramatically. 16–18 Specifically, identifying patients’ preferences for OA treatment offers a potential method for tailoring treatments, enhancing compliance, and improving patients’ satisfaction. 19

One of the commonly used stated preference methods is conjoint analysis (CA) 20 , 21 which is a popular analytical technique for eliciting preferences. 22 The idea behind CA is that it closely resembles the decisions that individuals make daily when choosing between multi-attribute alternatives. 23 The popularity of CA in health care is growing and it has gained increasing attention in health services research. 24 , 25 It is used as a method to measure patient preferences for health care and medicine, and as a means to identify and evaluate the relative importance of aspects of health outcomes and healthcare services. 26 , 27 CA methods and particularly discrete-choice experiments (DCEs) have become the most frequently applied approach in health care in recent years. 28 A review of published studies using DCEs to quantify preferences in healthcare reported that their use increased from fewer than 20 per year on average in the 1990s to over 60 published per year between January 2013 and December 2017. 29 Whilst DCEs are not the only conjoint analysis method, they make up the majority of published stated preference studies in healthcare. 29 Other CA techniques include traditional choice based conjoint (CBC), best-worst scaling (BWS), adaptive conjoint analysis (ACA) and adaptive choice-based conjoint (ACBC). All techniques require participants to compare and make trade-offs between a set of attributes and levels that define the health service or treatment under evaluation, and the trade-offs that participants make between these. 30

Alongside the increasing use of CA techniques, increased attention has been paid to their methodological quality. In 2011 and prior to the Health and Social Care Act of (2012), the International Society of Pharmacoeconomics and Outcomes Research (ISPOR) published a checklist for good research practices for CA studies, highlighting the items to be considered for best practice for CA applications in healthcare studies. 26

This systematic review aims to identify, summarise, and assess the methodological quality of the evidence that used CA techniques to quantify patient preferences for OA treatments and identify common approaches and methods employed and attributes considered important in eliciting patients’ preferences regarding OA treatment.

Search Strategy

A comprehensive search strategy was developed by the lead author. The Cochrane Library, PubMed (MEDLINE), CINHAL, EMBASE, and web of science were electronically searched from their inception until 10th June 2019. Medical Subject Headings (MeSH) and search terms were used to interrogate the databases. OA and CA related terms were used to conduct the search. No restrictions on publication language were used in the search strategy ( appendix 1 shows an example of a MEDLINE search). In addition, electronic searching of Google, hand searching through an examination of the reference list of the published articles and contact with experts were also used to identify additional publications.

Three authors reviewed the titles and abstracts and evaluated all records against the inclusion/exclusion criteria.

Inclusion Criteria

Studies included in the review fulfilled the following criteria: 1) used any conjoint analysis methodology to elicit patient preferences including Conjoint Value Analysis (CVA), Choice-Based Conjoint (CBC), Discrete Choice Experiments (DCE), Best-Worst Scale (BWS), Adaptive Conjoint Analysis (ACA) and Adaptive Choice-Based Conjoint (ACBC); 2) focussed on patients diagnosed with OA irrespective of their age, gender, illness severity or joint of the body affected; 3) considered any form of OA intervention treatment.

Exclusion Criteria

Studies were excluded from the review if 1) participants were clinicians or healthcare workers (ie, not patients); 2) the focus was on the economic evaluation or willingness to pay (WTP) of a service or intervention; 3) the evaluation was restricted to quality rather than effectiveness or patient preference; 4) the focus was on the priority of treatment allocation, such as prioritising patients on the waiting list.

Quality Assessment and Data Extraction

The included papers were quality assessed and the data were extracted by the three authors. The ISPOR checklist for CA 26 was adopted to review and assess the methodological quality of studies included in this review. In the absence of a validated tool for quality assessment of CA studies, we considered the use of ISPOR checklist to guide this process. The checklist contains 10 main questions, each has 3 sub-questions, which adds up to 30 items in total. 26 Studies were assigned a score of “1” for each item of the ISPOR checklist if they were considered to meet at least one aspect of this item and “0” if not. A total score for each study was calculated by summing the item scores. The maximum possible final score was 30.

A data extraction form was developed by two authors. Key data elements included: study aims, population characteristics (country, number, age, and gender), sampling method, response rate, CA method, inclusion criteria, treatment, attributes, levels, and scenarios, statistical analysis, main results, and authors’ conclusion.

The included papers were independently assessed and scored by at least two of the three authors. Where there was a conflict of interest or potential reviewer bias, the reviewer in question was not involved in the assessment of scoring or the data extraction. Disagreements were resolved by discussion and consensus between all authors. A narrative data synthesis approach was used to analyse and report the results from the studies reviewed.

Studies Identified

The search identified 534 records. Three hundred and sixteen records remained after removing duplicates. Based on the titles/abstracts review, a total of 297 records were deemed irrelevant and excluded as they did not meet one or more of the inclusion criteria. A further three records were excluded as they were published as conferences proceeding abstracts and the full reports were not published and not available from the authors. The remaining sixteen records were selected for full-text review and quality assessment. The PRISMA flowchart illustrating this process (see Figure 1 ).

The PRISMA flowchart.

Quality Assessment

Sixteen studies were included in the review. The quality assessment scores of studies ranged between 19/30 and 29/30. This indicates that these studies fulfil at least 19 of the 30 best practice criteria in the ISPOR checklist. Across the 16 studies, there was low variation in total and individual item scores. Furthermore, the checklist did not provide emphasis to the themes that may have not been considered in the studies, which resulted in a high level of subjectivity in relation to the judgments made regarding the individual and total scores. Therefore, we are unable to make judgments on the quality of the studies or discriminate based these scores.

Study Population, Sample Size and Recruitment

All the included studies expressed a clearly defined research aim and conducted original research to examine patients’ preferences comparing OA treatments (exercise, drug, or surgery), and presented testable hypotheses (see Table 1 ).

Type of OA Treatment, Aims, and Findings for All Reviewed Studies

Fifteen studies were conducted in a single country site – one in Australia, five in the UK, and nine in the United States of America (USA). One study was conducted across multiple countries – Australia, Canada, the UK, and the USA. Sample sizes for the studies ranged from 11 in the pilot study 31 to 3895 the multi-site study. 32 Justifications for the sample sizes were based on the study type (eg, whether it was a pilot study or part of a larger trial) and the sampling strategies employed. Most studies recruited patient participants from clinical lists directly using letters, telephone interviews or face-to-face methods. Four studies sampled members of the general population via emails through market research databases to recruit participants who self-identified as living with OA. One study recruited participants from both clinical lists – the patient sample; and a random public sample (identified through random-digit telephone dialling). 23 One study recruited participants from a clinical trial as part of the evaluation 33 (see Table 2 ).

Sampling for All Reviewed Studies

All studies included participants with OA, mean age 55 years or more, and reported higher numbers of females to males. One study included a public sample of people age 20 and over. 23 One study did not report the gender of their population. 19 The response rates (RR) reported varying from 7.6% 32 to 100% 9 , 31 , 34 , 35 in the included studies, population and sampling features are presented in Table 2 . The methods of data collection used in the studies also vary, reporting mostly either computer-based questionnaire, 9 , 31 , 34–40 or online web-based questionnaires 32 , 40 , 41 (see Table 2 ).

Conjoint Analysis Method

A range of CA methods was used in the included studies. One study used Conjoint Value Analysis (CVA), three studies used Choice-Based Conjoint (CBC), three studies used Discrete Choice Experiments (DCE), three studies used Adaptive Choice-Based Conjoint (ACBC), and six studies used Adaptive Conjoint Analysis (ACA) (see Table 3 ). The number of attributes and levels identified in the studies ranged from 4 attributes with 12 levels 35 to 9 attributes with 29 levels 41 (see Table 3 ). The attributes tended to define the features of the OA symptoms, OA treatment such as the benefits and the risks, and cost of treatment (for all attributes and levels of the included studies see appendix 2 ).

The CA Methods’ Characteristics for All Reviewed Studies

Statistical Analysis

In all types of CA, regression analysis techniques are generally used to study the patient’s preference. The choice of regression analysis type in CA depends on the type of the main outcome under study (eg, binary outcome, continuous outcome, etc.). More recent studies have adopted Hierarchical Bayesian (HB) models to investigate participants’ preferences at both the group “average” level as well as at the individual level 31 , 35 , 41 (see Table 3 ).

Treatment Preferences

The review included studies investigating pharmaceutical, non-pharmaceutical, and surgical treatment for OA (see Table 1 ).

NSAID and Other Medication Treatment

The majority of studies investigated the side effects and other features of nonsteroidal anti-inflammatory drugs (NSAIDs) and other medications such as disease-modifying drugs and supplements (glucosamine) on patients’ preferences for treatment of OA. 9 , 32 , 33 , 35–39 , 41–43

The relative importance of the risks of side effects; both rare and common were rated more important than the benefits associated with the treatment, time to benefit, out-of-pocket monthly cost, route of administration, and the product label. 36–38 One study found that relatively the most important attribute was the route of administration (cream, pills, injections into the knee and exercise) (relative importance of 24%), followed by the risk of dyspepsia and risk of bleeding ulcer, with the least important being decrease in pain and improved strength (relative importance of approximately 14%). 42 Similarly, a study investigating the long-term evaluation of glucosamine sulphate, found that relatively the most important attributes were the side effects of high blood pressure, heart/liver/kidney problems followed by cost. 33 The authors concluded that in their study, preferences to continue with OA treatments were influenced by side effects first and foremost and treatment efficacy did not significantly influence patient choice. 33 Again, a study 31 investigating 8 medication attributes, found that relatively the risks of side effects were the most important (combined their relative importance accounted for 66% of the treatment decision) and effectiveness of the medication only accounted for 8% of the treatment decision.

Exercise Treatment

One study examined patients’ preferences for exercise in the context of other available treatment options (excluding surgery). 42 The authors found that patients prefer exercise over pharmacological treatment for; risk of dyspepsia and bleeding ulcer combined accounted for the relative importance of 41.3% compared to 28.9% relative importance for both decrease pain and improve strength attributes. 42 Another study investigated individual preferences for physical activity attributes (with no comparison to other types of OA treatment). 40 This study found that “health benefits” (26%) and “enjoyment” (24%) attributes were considered by patients to be relatively the most important.

Surgical Treatment

Three studies investigated patients’ preferences for surgical treatment of OA. One study investigated the relative preferences for 9 different surgical related procedure attributes and simulated how patients may have responded to real-world knee OA procedures based on their preferences. 41 They found that patient preferences for surgical interventions were influenced by “the amount of cutting and removal of existing bone required” (relative importance of 18.7%), followed by “chance of additional surgery” (relative importance of 14.1), “amount of pain relief” (relative importance of 12.7%), with the least important attributes being “limits or complicates any future treatment need on the knee” and” length of hospital stay” with a relative importance of 7.3% each. 41

Similarly, in the study comparing patient preferences for surgery for patients with a hand OA diagnosis, 44 the authors found that “the need for future surgery” (relative importance=19%) and “recovery time” (relative importance=3%) were the least important factors influencing surgical preferences, while “joint stiffness” (relative importance=32%) and “grip strength” (relative importance=29%) were the most important. This supports the results from the earlier study that explored preferences for surgery versus medical treatment of knee OA, 23 which found that the severity of OA symptoms, directly and indirectly, influenced the patients’ choice of OA treatment, even in the presence of cultural differences in attitudes towards particular treatments.

To the best of our knowledge, this is the first review to investigate and summarise the use of CA techniques to value patients’ preferences for OA treatment. In addition, the search strategy was comprehensive, including the search of many databases, contacting authors and experts in the field, and searching the reference lists of published studies.

One of the limitations of this review is the lack of a validated quality assessment tool for CA studies. The use of the ISPOR checklist to score studies may be subjective to the examiner’s opinion. We tried to assess the methodological quality of these studies using the ISPOR Conjoint Analysis Experimental Design Good Research Practices Checklist. We were unable to make an objective decision regarding the minimum acceptable evidence required to award the scores. For example, question 2 “was the choice of attributes and levels supported by evidence?” we were unable to determine the quality and quantity of evidence required. This caused lengthy subjective disputes between the reviewers. Furthermore, the total scores for the studies indicated that CA studies published post the publication of the ISPOR checklist scored higher than those published pre-2011. This would be expected as most of these studies referenced ISPOR in their papers, meaning that we are assessing their quality against the same or similar criteria they used to design their studies, which was not available for studies published before 2011. It is not clear if this improvement in the scores is correlated with the publication of the ISPOR checklist or is simply reflecting an improvement in reporting. We agree with Webb and colleagues that the ISPOR checklist should not be used as a quality assessment tool for conjoint studies in its current format, as it was not originally developed for this purpose. 45

The studies have a high degree of heterogeneity in study design, study population, and treatment choice. The included papers incorporated studies using both rating/ranking and choice-based methods to investigate different options of treatment for OA (exercise, medication, and surgery) in the UK, Australia, Canada, and the USA. All included studies had homogeneous samples in terms of suffering from OA. Thus, the studies sample may represent the OA population. However, the healthcare systems differ between the countries within which the studies were conducted; therefore, the generalisability of the results could be limited.

Variations in the sample sizes between included studies (n = 11 to 3895) may indicate that there is still no consensus on the appropriate or agreed sample size calculation method for CA studies, as it depends on many factors such as the number of questions and scenarios in the conjoint task. It has been suggested that the sample size for a CA study should be at least 300 in one sample group. 46 However, the traditional calculations for sample size determination cannot readily be applied to CA 43 and are rarely applied for practical reasons. 47 Furthermore, it has been argued that collecting more data from each respondent by designing high-quality conjoint tasks may reduce sampling and measurement error. 46 Using similar CA methods to those in the review 36–38 , 42 in a study of patient preferences for acute pain treatment researchers attempted to reduce the limitation of a small sample (50 participants) by interviewing their respondents 4 times at 4 different stages of pain treatment. 48 Limitations around sample size in CA studies may be overcome in the design of the conjoint task and data collection.

The variation in the RR (7.6% to 100%) in the studies is potentially a reflection of the robustness of the methods of recruitment and methods of data collection. The included studies used a variety of methods of data collection. Methods reporting face-to-face interviewing or questionnaires targeted a specific population of interest tended to have higher response rates. Studies using telephone interviewing or emails, predominantly in a general population, had a lower response rate. These studies with low RR recognised the limitations of using an untargeted strategy and suggested response rates could be improved in future research by pre-screening participants in order to target the full survey to those who report a diagnosis or other study characteristic of interest. 32

All included studies recognised the value in utilising CA method to investigate patients’ preferences for OA treatment, but there was no consensus on which CA approach is the most appropriate. Both rating/ranking and choice-based methods were used to examine patients’ preferences for the treatment of OA. Recent academic and practical research applications have tended to favour choice-based approaches as opposed to rating/ranking. 49 However, the rating/ranking approach has also been used and recommended by many researchers to study patients’ preferences for OA treatment 36–38 , 42 as well as treatment preferences in rheumatoid arthritis (RA), 50 chronic pain, 51 and abdominal surgery 48 because it allows the inclusion of a large number of attributes and levels, which reflect the outcomes/concerns of patients with OA. The main advantage of ACA is that it is adaptive and therefore allows a large number of characteristics to be evaluated without resulting in information overload or respondent fatigue, and minimises interviewer, product, and brand bias. Nevertheless, there are still practical limitations associated with ACA, with researchers reporting that not all treatment characteristics could be included in an ACA task. 36–38

In this review, studies that used the choice-based approach reported that the use of the discrete choice method allowed them to identify attributes significantly influencing patients’ preferences for OA treatment. 43 Furthermore, a very low number of inconsistent responses were found, and participants reported that the questions were easy or very easy to answer. 23 Those studies that used ACBC 9 , 31 , 34 argued that the approach can capture more individual-level data and precise estimates than through a traditional CBC approach and that it can yield similar group-level standard errors using up to 38% fewer participants. 39 , 40 Furthermore, it has been reported that the ACBC method is more user friendly and engaging than alternative CA methods 31 , 34 , 52 , 53 and it can be used to elicit individual patients’ preferences. 9

Overwhelmingly the results of the studies in this review indicated that patient preferences for OA medications were driven by the desire to avoid both common and rare side effects, especially those with more serious drug-related toxic effects and that the effectiveness of the OA medication had very little impact on patients’ preferences. However, where investigated, studies suggested that preferences for side effects were affected by patient characteristics such as age and symptoms severity. Older respondents were more willing than younger respondents to trade-off an increased risk in the side effects 36–38 , 43 for an improvement in the symptoms of OA. The side effects associated with NSAIDs had a greater negative influence on the preferences of patients with milder OA than those in more severe OA states. 32 Even when exercise was compared to OA medications, patients were still more concerned about the side effects of the treatment than the benefits. 42 However, patients with more knee pain were more reluctant to choose exercise.

Patients generally attached greater importance to reducing or eliminating adverse events than reducing pain, but one study investigated the level of treatment-related risks patients were willing to accept in exchange for various improvements in pain. 39 The investigators found that participants’ “risk tolerance” varied according to their pain level at baseline and type of symptom relief – participants were willing to accept greater risks for improvements in ambulatory pain than in resting pain. 39 Similarly, a study of treatment options for disease-modifying drugs found that sub-groups of participants were willing to trade-off the risks of side-effects for improvements in a benefit. 35 In relation to surgical treatment for OA, it was reported that younger patients and those who reported the highest pain thresholds, and the greatest functional limitations were more likely to opt for surgical intervention. 41 Furthermore, the severity of the patients underlying symptoms proved to be the main driver influencing their preferences for surgery. 44

Where the severity of OA symptoms was measured alongside the conjoint task, all included studies suggested that the severity of symptoms influenced the patients’ preference of treatment, and consequently the relative importance of treatment characteristics. However, it is not clear whether these differences are a result of symptom severity or artefacts of the CA methods, attributes used, or treatments being assessed.

The severity of OA symptoms and the side effects of treatment have a significant influence on patients’ preferences for OA treatment. Both rating/ranking and choice-based CA methods are recommended in investigating patients’ preferences for OA treatment, but there is no consensus on which CA approach is the most appropriate.

Abbreviations

ACA, Adaptive Conjoint Analysis; ACBC, Adaptive Choice-Based Conjoint; BWS, Best–Worst Scaling; CBC, Choice-Based Conjoint; CA, Conjoint Analysis; CVA, Conjoint Value Analysis; DCEs, Discrete Choice Experiments; HB, Hierarchical Bayesian; ISPOR, International Society of Pharmacoeconomics and Outcomes Research; MeSH, Medical Subject Headings; NHS, National Health Service; NSAIDs, Nonsteroidal Anti-Inflammatory Drugs; OA, Osteoarthritis; RR, Response Rates; RA, Rheumatoid Arthritis; UK, United Kingdom; USA, United States of America; WTP, Willingness to Pay.

Data Sharing Statement

The datasets used and/or analysed during the current study are available from the corresponding author on reasonable request.

Author Contributions

All authors made substantial contributions to conception and design, acquisition of data, or analysis and interpretation of data; took part in drafting the article or revising it critically for important intellectual content; agreed to submit to the current journal; gave final approval of the version to be published; and agree to be accountable for all aspects of the work.

The authors report no conflicts of interest in this work.

• Open access
• Published: 09 September 2022

Conjoint analyses of patients’ preferences for primary care: a systematic review

• Audrey Huili Lim   ORCID: orcid.org/0000-0001-6721-1505 1 ,
• Sock Wen Ng   ORCID: orcid.org/0000-0002-1727-3043 1 ,
• Xin Rou Teh   ORCID: orcid.org/0000-0003-3969-1745 1 ,
• Su Miin Ong   ORCID: orcid.org/0000-0002-5430-5040 1 ,
• Sheamini Sivasampu   ORCID: orcid.org/0000-0003-2314-6048 1 &
• Ka Keat Lim   ORCID: orcid.org/0000-0002-2340-4097 2 , 3  

BMC Primary Care volume  23 , Article number:  234 ( 2022 ) Cite this article

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While patients’ preferences in primary care have been examined in numerous conjoint analyses, there has been little systematic effort to synthesise the findings. This review aimed to identify, to organise and to assess the strength of evidence for the attributes and factors associated with preference heterogeneity in conjoint analyses for primary care outpatient visits.

We searched five bibliographic databases (PubMed, Embase, PsycINFO, Econlit and Scopus) from inception until 15 December 2021, complemented by hand-searching. We included conjoint analyses for primary care outpatient visits. Two reviewers independently screened papers for inclusion and assessed the quality of all included studies using the checklist by ISPOR Task Force for Conjoint Analysis. We categorized the attributes of primary care based on Primary Care Monitoring System framework and factors based on Andersen’s Behavioural Model of Health Services Use. We then assessed the strength of evidence and direction of preference for the attributes of primary care, and factors affecting preference heterogeneity based on study quality and consistency in findings.

Of 35 included studies, most (82.4%) were performed in high-income countries. Each study examined 3–8 attributes, mainly identified through literature reviews ( n  = 25). Only six examined visits for chronic conditions, with the rest on acute or non-specific / other conditions. Process attributes were more commonly examined than structure or outcome attributes. The three most commonly examined attributes were waiting time for appointment, out-of-pocket costs and ability to choose the providers they see. We identified 24/58 attributes with strong or moderate evidence of association with primary care uptake (e.g., various waiting times, out-of-pocket costs) and 4/43 factors with strong evidence of affecting preference heterogeneity (e.g., age, gender).

Conclusions

We found 35 conjoint analyses examining 58 attributes of primary care and 43 factors that potentially affect the preference of these attributes. The attributes and factors, stratified into evidence levels based on study quality and consistency, can guide the design of research or policies to improve patients’ uptake of primary care. We recommend future conjoint analyses to specify the types of visits and to define their attributes clearly, to facilitate consistent understanding among respondents and the design of interventions targeting them.

Word Count: 346/350 words.

Trial registration

On Open Science Framework: https://osf.io/m7ts9

Peer Review reports

Introduction

Primary care, defined as the first contact a person has with the health system, encompasses a broad range of health services, including preventive, curative and rehabilitative services, that addresses both acute and chronic conditions [ 1 , 2 , 3 ]. Internationally, better access to primary care has been associated with better health outcomes and lower total healthcare costs [ 4 ]. Thus, not only can primary care meet a broad range of the people’s health needs, it can also provide quality health services to people without resulting in financial hardship [ 5 , 6 ].

To better address the changing health needs due to ageing population and rising prevalence of chronic conditions, many countries worldwide, including the low and middle-income countries (LMICs) have undertaken initiatives to reform their delivery of primary care [ 7 , 8 ]. A central idea behind many such reforms is person-centred care that emphasises the value of patients’ views in co-designing and in delivering health care [ 9 , 10 ]. To co-design and to deliver person-centred care at primary care settings require policy makers and primary care service providers to understand patients’ preferences for health services delivered at primary care.

Conjoint analysis is a stated-preference method that derives the implicit values for an attribute of a product or a service using surveys [ 11 ]. In a conjoint analysis survey, respondents are presented hypothetical alternatives of a product or a service characterised (conjointly) by two or more attributes, each over a range of levels, alternatives which they are asked to rank, rate, or choose; a choice-based conjoint analysis where respondents are asked to choose between two or more alternatives is also known as “discrete choice experiment (DCE). Based on how the rankings, ratings or choices differ between the shortlisted attributes or between the alternatives of primary care services characterised by the shortlisted attributes, one could estimate preferences associated with the attributes [ 11 ] and use the preferences to predict uptake of the primary care service. Conjoint analyses can also elucidate preference heterogeneity by examining factors (e.g., patient characteristics) that modify the preference (and by extension, the uptake of the primary care service), which would provide insight on how to tailor the service to the characteristics of the target population.

Given its usefulness, numerous conjoint analyses on patients’ preference in primary care have been performed among patients visiting primary care facilities or among public members who are potential users of primary care. The only review of conjoint analyses on patients’ preference in primary care thus far found 18 DCEs (including two on out-of-hour service) performed between 2006 and 2015. The review [ 12 ] summarised a list of the attributes examined, organised into three general categories of structure, process and outcome attributes. However, it did not synthesise the direction of preference and the strengths of evidence of the attributes. The review also did not examine factors affecting preference heterogeneity. A synthesis of evidence for primary care attributes and factors affecting preference heterogeneity would advise which attributes or factors should be considered in future research and policy decisions in providing person-centred care at primary care settings.

To address these gaps, our review aims (1) to update the list of primary care attributes and to provide a list of factors affecting preference heterogeneity, focusing on outpatient visits based on all studies since the inception of the databases (2) to categorise the attributes based on a framework developed to describe primary care system [ 13 , 14 ], and the factors based on a framework of health services utilisation [ 15 ], and (3) to synthesise the direction and the strength of evidence of the attributes and the factors affecting preference heterogeneity.

This systematic review was prospectively registered on Open Science Framework ( https://osf.io/m7ts9 ) and is reported according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guideline (Appendix 1 ).

Search strategy

We conducted systematic searches in five databases (PubMed, Embase, PsycINFO, Econlit and Scopus) from inception until 15 December 2021 using terms related to “primary care” and “preferences”, “conjoint analyses” or “DCE” (Appendix 2 ); these terms were adapted from the previous review on the same topic [ 12 ], as well as other systematic reviews in primary care [ 16 , 17 , 18 , 19 ] and systematic reviews of discrete choice experiments in healthcare [ 20 , 21 , 22 ]. To identify studies that may have been missed from database searches, we also hand-searched Google, included studies from previous review [ 12 ], and the reference lists of included studies.

Inclusion and exclusion criteria

All articles from the database searches were downloaded into EndNote for de-duplication, before being screened for eligibility by two independent reviewers (AHL, SWN) based on titles and abstracts and subsequently, based on full text. Any disagreements were reconciled via consensus and if necessary, involving a third reviewer (XRT or KKL). In cases of no access to full text, we contacted the corresponding authors of the studies and the journals multiple times. If we did not receive any response from the corresponding authors and the journals by the time the manuscript draft was complete, the studies were excluded.

We included studies that used DCEs or conjoint analyses to survey the patients or the general public on preferences for primary care outpatient visits.

We excluded studies that examined preferences on specific treatment (e.g., anti-diabetics), specific services in a clinic (e.g., pharmacy services), services in hospital outpatient clinics or out-of-hour services. Studies on out-of-hours service were excluded because they have evolved in some settings to be delivered over the phone or in tandem with hospital emergency departments, hence cater to patients with perceived urgent problems who are different from the general population who use primary care [ 23 ]. The inclusion and exclusion criteria are also summarised in Appendix 3 .

Data extraction

We created a data extraction form and a data dictionary using Microsoft Excel to extract data on study settings (publication year, continent, country’s income level, sources of funding), study design (recruitment setting and methods of survey administration), questionnaire design (the choice contexts, the types of primary care visits, the attributes, methods to identify the attributes and level, the factors affecting preference heterogeneity, methods to generate choice set and whether the study reported design efficiency), study samples (sample size, response rate, age, gender) and analyses (statistical model) from eligible articles. We also extracted the direction of association and statistical significance at p  < 0.05 for the attributes and the factors affecting preference heterogeneity. Factors affecting preference heterogeneity were identified from study sample characteristics that are associated with latent class memberships (among studies that performed latent class analysis) or characteristics that moderated the associations between attributes and primary care uptake (among studies that performed logit or probit regression analyses). The data extraction form and the data dictionary were pre-tested with two studies by AHL and SWN and feedback was obtained to update the form before use.

Quality appraisal

The quality of the included studies was appraised using the checklist by ISPOR Task Force for Conjoint Analysis [ 24 ]. The checklist is made up of 10 items, each comprising 3 criteria. Each criterion was first evaluated “Yes”, “Partial” or “No” by two independent reviewers (AHL with SMO, or SWN). Based on the extent to which the three criteria were met, each item was then rated “Yes”, “Partial” or “No”. Any disagreements between them were reconciled via consensus, and if necessary, involving a third reviewer (LKK).

Data analyses

To provide an overview, we tabulated, in numbers and percentages, the study and sample characteristics, including the contexts of the choice questions (hereafter “choice contexts”), the types of primary care visits, the attributes and the factors affecting preference heterogeneity. The choice contexts were categorised based on for whom the primary care services were chosen (self, friend or relative) and if specified, the hypothetical reason the choices were required (e.g., current primary care clinic closes). The types of visits were categorised into visits for major acute, minor acute, chronic, or non-specific / other conditions based on data that emerged from the included studies. “Minor acute” conditions included influenza, urinary tract infections and upper respiratory tract infections while “major acute” conditions included severe lower back pain, “new urgent symptoms”, and perceived severe disease. Meanwhile, “non-specific / other conditions” referred to routine check-ups or conditions that were not explicitly stated and thus unable to be categorised into acute or chronic.

Meanwhile, the attributes were categorised into three levels (structure, process, or outcome). Each level was broken down into dimensions and features, based on the Primary Care Monitoring System (PC Monitor) framework. The framework describes primary care systems in three levels of structure, process, and outcome, each further divided into dimensions and features, with a total of 11 dimensions and 57 features. For example, the structure level comprises three dimensions: (a) governance, (b) economic conditions, and (c) workforce development. The governance dimension, for instance, includes the use of appropriate technology, decentralisation, ownership, etc. as its features. Meanwhile, the process level comprises four dimensions: (a) access, (b) continuity of care, (c) coordination of care. and (c) comprehensiveness of care; the outcome level comprises three dimensions: (a) quality of care; (b) efficiency of care; and (c) equity in health [ 13 , 14 ] (Fig. 1 ).

Finally, the factors affecting preference heterogeneity were categorised based on Andersen’s Behavioural Model of Health Services Use [ 15 ] into predisposing, enabling, health behaviour or need factors.

In the absence of gold standard on what constitutes “high quality”, we considered studies rated either “Yes” or “Partial” across all 10 items as high quality in main analysis and studies rated “Yes” in ≥ 5 out of 10 items as high quality in sensitivity analysis [ 24 ].

To synthesise the evidence level, we stratified each attribute and each factor into strong, moderate, limited, conflicting or inconclusive based on study quality and consistency of findings across ≥ 75% studies [ 25 , 26 , 27 ]. As illustrated in Fig. 2 , an attribute (or a factor) had “strong evidence” if it had been examined ≥ 2 times in studies of high quality, of which ≥ 75% produced consistent findings. If an attribute had been examined once in a high-quality study and ≥ 2 times in low-quality studies with consistent findings, it would be assigned “moderate evidence”. If an attribute had only been examined once in a high- and a low-quality study each or produced consistent findings ≥ 3 times in low-quality studies, it would be assigned “limited evidence”. If an attribute had been examined < 3 times in low-quality studies, the level of evidence would be deemed “inconclusive”. If < 75% of the findings were consistent, the evidence level would be deemed “conflicting” regardless of the study quality. For attributes that were binary (yes/no), ordinal or continuous, consistency accounted for the direction of association (positive, negative, none) as well as statistical significance (at p  < 0.05) whereas for attributes that were nominal (e.g., choice of providers), consistency accounted for statistical significance; similarly for factors affecting preference heterogeneity. We were unable to account for consistency in the direction for binary (yes/no), ordinal or continuous factors affecting preference heterogeneity due to small number of studies examining the interaction terms of the same factor with the same attribute. This approach of evidence synthesis is commonly used in systematic reviews where meta-analyses are not feasible due to heterogeneity among the included studies. While it has been applied to synthesise evidence levels in systematic reviews of prognostic factors of clinical conditions [ 25 , 26 , 27 ], we are not aware of any attempt to apply the approach to synthesise the evidence levels for attributes and / or factors affecting preference heterogeneity in systematic review of conjoint analyses.

All analyses were performed on Microsoft Excel or R version 4.0.5 (The R Foundation for Statistical Computing, Vienna).

Study selection

The search strategy identified 18,980 articles (Fig. 3 ), of which 17,233 were unique. After screening their titles and abstracts, 166 were retrieved for full text screening, from which 132 were excluded because they were not DCEs ( n  = 53), were not on primary care ( n  = 45), examined specific treatment ( n  = 20), not English ( n  = 8), examined preferences for out-of-hours treatment ( n  = 5), or conference abstract ( n  = 1). One additional article [ 28 ] was retrieved from the previous review [ 12 ]. For one abstract that may be eligible based on title and abstract [ 29 ], we had to contact the author and the journal via their contact emails and ResearchGate accounts for the full-text but did not receive a reply despite five attempts over a span of nine months. This gave 35 eligible articles for extraction, of which two were rating-based conjoint analyses, and the rest choice-based conjoint analysis or DCEs.

Study and sample characteristics

Table 1 summarises the study and sample characteristics, with details for each study in Appendix 4 . The studies were mostly published after 2010 (60.0%), in Europe (65.7%), from high-income countries (82.9%). Among studies that reported funding sources (71.4%), government funding dominated (45.7%). Study samples were recruited from primary care facilities (54.3%) or the community (42.9%), most of whom self-completed the questionnaires (62.9%). These studies recruited on average 881.8 respondents, with 62.8% response rates. The respondents, with 51.6 years-old mean age, comprised of 41.9% men.

The studies examined minor acute (54.3%), non-specific / other (45.7%), chronic (17.1%) and / or major acute (11.4%) conditions. They more frequently used process (94.3%) or outcome (91.4) than structure attributes (51.4%), predominantly identified through literature review (71.4%). Among the 16 studies that investigated factors affecting preference heterogeneity, they most investigated predisposing characteristics (28.6%), followed by enabling resources (25.7%), needs (14.3%) and health behaviour (5.7%). As for statistical analysis, logit model (74.5%) was the most widely used.

Study quality was determined based on the number of items rated “Yes” for each study. Including one study that received only “Yes” ratings, 29/35 studies had “Yes” or “Partial” across all 10 items; these studies were considered high quality in main analysis. Meanwhile, 25/35 studies received ≥ 5 “Yes” ratings and were considered high quality in sensitivity analysis.

Only 4/10 items received at least one “No” – “choice of attributes and levels supported by evidence” (3/35 studies were rated “No”), “choice of experimental design justified and evaluated” (2/35 “No”), “appropriate statistical analyses and model estimations” (2/35 “No”) and “appropriate design of data collection instrument” (1/35 “No”) (Appendix 5 ).

Attributes of primary care

Overall, the 35 included studies examined 58 unique primary care attributes 183 times (average 5.2 attributes per study). These attributes fell into 3 levels, 9 dimensions and 19 features of primary care of the PC Monitor framework (Fig. 1 , Appendix 6 ).

Among the 3 levels of primary care, process had the largest number of unique attributes (34) across 4 dimensions (access, comprehensiveness, continuity, and coordination) and 12 features; outcome had 19 unique attributes across 2 dimensions (quality, efficiency) and 3 features; structure had 5 unique attributes across 3 dimensions (governance, workforce, others) and 4 features. Relational continuity of care was the most examined feature within the process level, efficiency in the performance of primary care workforce was the most examined feature within the outcome level, whereas profile of workforce was the most examined feature within the structure level (Fig. 1 ).

Across all levels, dimensions, and features of primary care, the ten most frequently examined attributes were waiting time for appointment (20 studies), out-of-pocket cost (15 studies), ability to choose the providers they see (15 studies), length of consultation time (12 studies), waiting time at clinic (10 studies) involvement in decision making (10 studies), amount of information received during consultation (8 studies), quality of the physical exam (7 studies), depth of the explanation (6 studies), and convenience of appointment time (5 studies) (Appendix 7 ).

Based on all 35 included studies regardless of type of visits, of the 58 attributes, none had inconclusive or conflicting evidence, but 21 had strong, 3 had moderate and 34 had limited strength of evidence (Table 2 a). Most of the attributes, listed in Table 3 , either positively or negatively influenced preference for primary care. For example, higher experience of care providers, availability of a convenient appointment time, better communication skills, better drug availability, longer consultation time, extended opening hours, amount of information received are associated with higher preference of primary care, whereas longer distance, higher out-of-pocket cost and longer waiting time are associated with lower preference; these attributes have strong or moderate strength of evidence in the main analyses and retained their strengths of evidence in the sensitivity analyses, except for drug availability for which the strength of evidence became limited. On the other hand, some attributes in the main analyses have limited strength of evidence of positively influencing preference (e.g., clinic managed by the government, availability of home visits, opening at lunch time or more days in a week, multidisciplinary care) or negatively influencing preference (e.g., clinics seeking voluntary contribution in addition to out-of-pocket cost, waiting time for referral). Finally, a minority of attributes, for instance, amount of billing problems, facility size, and provision of preventive care by the facility were found to have no association with a preference of primary care, although their evidence are also of limited strength.

The number of attributes with strong or moderate evidence decreased when the evidence was stratified by the type of visits, with some attributes becoming inconclusive (Table 2 a). The full list of attributes is available in Appendix 7 , including how their strengths of evidence varied with the type of visits.

Factors affecting preference heterogeneity of primary care

The 16 studies examined 43 unique factors affecting preference heterogeneity (Table 2 b) 196 times (average 12.3 factors per study) – enabling resources (22 factors), needs factors (12 factors), predisposing characteristics (7 factors), and health behaviour (2 factors). Of these, only 4 had strong evidence of affecting preference heterogeneity of primary care (Table 4 ), i.e., age, gender, employment status, and income; all retained their strength of evidence in sensitivity analysis. Older respondents preferred lower out-of-pocket cost [ 30 , 31 ] and to choose their own healthcare provider [ 32 , 33 , 34 ] while younger respondents preferred shorter waiting times [ 31 , 35 ]. Meanwhile, female respondents preferred to choose their own healthcare provider [ 33 , 34 , 36 ] and better quality physical examination [ 31 ]. Patients who are employed were more willing to pay higher out-of-pocket cost [ 30 ] but preferred shorter waiting times [ 34 ], likewise for those with higher incomes [ 37 ]. The remaining factors had limited ( n = 31), inconclusive ( n = 5) or conflicting ( n  = 3) evidence of affecting preference heterogeneity of primary care. The full list of factors is available in Appendix 8 , including how their strengths of evidence varied with the type of visits.

To provide person-centred care, primary care provision should align with patients’ preferences. The preferences of patients as well as public members who could be patients have been examined in numerous conjoint analyses. However, no systematic effort has been undertaken to synthesise their findings. To address this gap, our systematic review identified, organised, and assessed the evidence level of the attributes examined for patients’ preferences in primary care as well as the factors affecting these preferences. The 35 included conjoint analyses had similar characteristics – most were published in the last decade (since 2010), by high-income countries in Europe based on samples recruited from primary care facilities seeking to elicit preferences on visits for acute or non-specific / other conditions. Thus, it may not be surprising that despite spanning diverse levels, dimensions, and features of primary care, none of the 58 attributes was found to have conflicting evidence. Instead, 24 had strong or moderate evidence of an association with preference for primary care, while the remaining 34 attributes had limited evidence of an association or no association. Similarly for the factors affecting preference heterogeneity, albeit with smaller number of studies and only 4 factors found to have strong or moderate evidence.

Process of care, which had the highest number of unique attributes (vs structure and outcomes), was the most studied level of primary care. As no single unique attribute dominated the list, this indicates more varied priorities in selecting process attributes. Conversely, the lack of interest on structure of care (the lowest number of unique attributes) may be due to structural attributes being less observable by the public and less amenable by the policy makers in the short-term.

Meanwhile, the absence of attributes with conflicting evidence from our syntheses implies that patients or public members generally have consistent preference, at least within the contexts examined by the included studies. The consistency suggests the feasibility to improve primary care uptake by changing the attributes in the direction associated with a higher preference. Based on our review, examples of such attributes may be the providers’ communication skills (strong evidence for all visits except that for chronic conditions), quality of the physical examinations (strong evidence for minor acute conditions) and opening hours in the weekend (strong evidence for other / non-specific visits). On the other hand, our review also found some studies reporting attributes with subjective or unclear definition e.g., “best care” in one of the included studies [ 38 ]. Such attributes are likely challenging to operationalise and to target in policy interventions, as they may be understood differently by different respondents. To facilitate consistent understanding and the design of policy interventions, [ 39 , 40 ], we recommend future studies to clearly define and present their attributes (e.g. as a table in Wang et al. [ 41 ]).

As few studies examined factors affecting preference heterogeneity, most factors had either limited or inconclusive evidence. Out of the 43 unique factors, only four were examined across enough studies to have strong evidence affecting preference heterogeneity (age, gender, employment status, and income). Younger respondents and those with higher incomes may have lower preference for long waiting times for acute conditions [ 35 ] due to perceived lower value of a visit [ 42 ], while older respondents prefer lower out-of-pocket costs [ 30 , 37 ] possibly due to growing financial constraints [ 43 ] or healthcare expenditure with age [ 44 ]. Meanwhile, women respondents may prefer to choose their own providers [ 33 ], as they are likely to trust female physicians more [ 45 ] and are more comfortable with female physicians [ 46 , 47 ]. On the other hand, three factors were found to have conflicting evidence (education level, health status, and chronic disease status), which may be due to the same factor interacting differently with different attributes. For instance, those with chronic diseases were found to prefer more information on their condition but also less involvement in their treatment [ 48 ]. Hence, unlike that for attributes, we could not examine the direction of association for the factors affecting preference heterogeneity, which should be explored further in future conjoint analyses.

Comparison with existing literature

The only other review [ 12 ] on patients’ preferences in primary care encompassed three databases between 2006 and 2015, compared to five databases without date restriction (until 15 December 2021) in our review. This gives us more eligible studies (35 vs 18) and unique attributes (58 vs 30). Of the 18 studies from the previous review [ 12 ], 16 were included in our current review (15 of which appeared on our database searches); the remaining two [ 49 , 50 ] were excluded as they examined out-of-hour service. In terms of findings, the earlier review [ 12 ] found structure attributes to be the most common whereas our review found process attributes to be predominant. This difference in findings is due to both reviews using different approaches to definitions in categorising the attributes, the earlier review [ 12 ] followed the definitions in Donabedian’s model for quality of health care [ 51 ] whereas we followed that in the PC Monitor framework [ 13 , 14 ] which was specifically designed for primary care and allowed us to sub-categorise each attribute into dimensions and features. This resulted in some attributes e.g., opening hours, cost and distance that were “structure” in the earlier review [ 12 ] but were considered “process” in our review.

In addition to a list of attributes, our review also generates additional insights by (1) examining the factors affecting heterogeneity, (2) appraising the quality of included studies and (3) synthesising, based on study quality and consistency in findings, the evidence levels of the attributes and the factors affecting preference heterogeneity overall, and by the types of visits. Our findings on the attributes, their evidence level and direction of association largely corroborate findings from other quantitative or qualitative studies on barriers and facilitators on access to primary care that found higher preference for shorter travel distance to health facility [ 52 ], shorter waiting time [ 53 , 54 ], lower out-of-pocket costs [ 55 ], being treated with respect and having their own choice of healthcare provider [ 56 ]. Similarly for our findings on the factors affecting preference heterogeneity where female respondents preferred to choose their healthcare provider who they were more comfortable with [ 46 , 47 ], while older respondents preferred to choose healthcare provider but placed higher emphasis on the doctor making decisions [ 57 ]. Those with higher incomes were also willing to pay more for treatment than respondents with lower incomes [ 57 ].

Strengths and limitations

Our findings should be interpreted alongside several limitations. First, the categories of attributes are based on the PC Monitor framework, which may have different definitions than other frameworks for primary care services [ 13 ]. However, as the framework was developed based on systematic review [ 13 , 14 ], it increases the generalisability of our findings to other settings. Second, some attributes may fit under > 1 category. For instance, “quality of the physical exam” reported in Cheraghi-Sohi et al. [ 58 ] and Kruk et al. [ 31 ] was categorised in “treatment and follow-up of diagnosis” feature of primary care (Appendix 6 ), although it may also fit into “quality of diagnosis and treatment in primary care”. However, we categorised each attribute only to one level, one domain and one feature, for ease of interpretation. Next, as we synthesised evidence only from published literature, our findings on the evidence levels may be susceptible to publication bias. In addition, as we extracted findings only from the final model, our findings on the evidence levels may also be sensitive to model selection by the respective studies. Besides that, the small number of studies that examined factors affecting preference heterogeneity only allowed us to synthesise the overall evidence levels of these factors, rather than based on how they interact with different attributes, which can be explored in future conjoint analyses or future reviews. Finally, we only included conjoint analyses examining primary care outpatient visits. Hence, our findings may not generalise to other services that may be considered primary care e.g., antenatal care [ 59 , 60 ] or pharmacy services [ 61 ].

Despite the limitations, the syntheses of evidence levels for the attributes and the factors affecting preference heterogeneity are our main strengths. To our knowledge, this has only been done on systematic reviews of prognostic factors [ 25 , 26 , 27 ] but not by any systematic review of DCEs.

Implications for research and/or practice

For research, our findings may advise the choice of attributes and factors affecting preference heterogeneity in future conjoint analyses. For instance, future conjoint analyses may focus on attributes with limited or inconclusive evidence, or attributes in levels, dimensions or features of primary care that have been less studied. We also found a paucity of evidence for chronic conditions or in LMICs apart from China, despite the importance of primary care in meeting the preventive and curative care needs of patients in chronic conditions including in LMICs. In addressing these gaps, we recommend future conjoint analyses to specify the types of visits, as our findings suggest patients’ preferences may differ for different types of primary care visits.

For policy, our findings provide an evidence-based list of attributes to design primary care services for optimal uptake, at the local, regional, and national levels. At the local level, the attributes with strong or moderate evidence suggest that extending opening hours as well as allowing patients to choose their own providers or see a provider they are familiar with would improve the uptake of primary care services. Similarly, proactive management of the waiting time to get an appointment or waiting time at the clinic may also help. Healthcare providers may also be provided with trainings on communication skill, including how to get patients involved in their treatment decisions. At the regional or the national level, new primary care facilities should ideally be built in a location within reasonable distance travel time from nearby community, with services available at reasonable out-of-pocket cost. It will be up to the policy makers to determine which attributes should be prioritised first based on local context, whether as part of an ongoing changes or part of a larger reform.

Our review found 35 studies that examined 58 attributes and 43 factors that potentially affect patients’ preference in primary care, which we categorised based on PC Monitor framework and synthesised the strength of evidence based on study quality and consistency of study findings across studies. The lists of attributes and factors with their evidence levels can guide policies to improve patients’ uptake of primary care and future DCE studies in this area. Due to the lack of conjoint analyses performed in LMICs or examining visits for chronic conditions, we recommend future DCEs to look into these. In addressing any research gaps on preference for primary care outpatient visits, they should specify the types of visits and define their attributes clearly, to facilitate the design of interventions to target these attributes.

Number of studies examining each level, dimension and feature of the Primary Care (PC) Monitor Framework

Graphical presentation of the algorithm used to assign evidence level for each attribute and each factor

PRISMA flow diagram

Availability of data and materials

All data presented in the manuscript or additional files are extracted from published papers, hence are publicly available.

Abbreviations

Discrete choice experiment

Preferred Reporting items for systematic reviews and meta-analyses

Low and middle-income countries

International society for pharmacoeconomics and outcomes research

Primary care monitoring system

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Acknowledgements

We would like to thank the Director General of Health Malaysia for his permission to publish this article. We would like to acknowledge Dr. Azreena Che Abdullah for performing and downloading the initial search hits from the bibliographic databases

The study did not receive any funding.

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Audrey Huili Lim, Sock Wen Ng, Xin Rou Teh, Su Miin Ong & Sheamini Sivasampu

School of Life Course & Population Sciences, Faculty of Life Sciences & Medicine, King’s College London, London, UK

Ka Keat Lim

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Sivasampu S (SS) and KK Lim (KKL) conceptualized and designed the study. AH Lim (AHL) prepared the search strategies and performed the searches. AHL and SW Ng (SWN) screened the abstracts and full texts. XR Teh (XRT), AHL and SWN prepared and piloted the data extraction tables. AHL and SWN extracted and crosschecked the data. AHL, SWN and SM Ong (SMO) assessed the methodological quality of the included papers and discussed any ratings that could not be agreed. AHL and KKL cleaned the data and performed the analyses based on input from SS, SWN and SMO. AHL and KKL prepared the first draft of the manuscript. SS, XRT, SWN, SMO and KKL critically reviewed drafts of the manuscript for important intellectual content. SS sought for and obtained the funding for open access publication. All authors approve the final draft and agreed to the final submission.

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Additional file 1:.

Appendix 1. PRISMA checklist. Appendix 2. Search strategies. Appendix 3. List of inclusion and exclusion criteria. Appendix 4. Detailed characteristics of included studies (include quality rating for each paper). Appendix 5. Methodological quality ratings of included studies, based on ISPOR Task Force for Conjoint Analysis checklist. Appendix 6. Number of studies that examined attributes within various levels, dimensions, and features of primary care according to the types of visits. Appendix 7. Full list of attributes according to evidence levels, overall and by types of visits (main analyses). Appendix 8. Full list of factors affecting preference heterogeneity according to evidence levels, overall and by types of visits (main analyses).

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Lim, A.H., Ng, S.W., Teh, X.R. et al. Conjoint analyses of patients’ preferences for primary care: a systematic review. BMC Prim. Care 23 , 234 (2022). https://doi.org/10.1186/s12875-022-01822-8

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DOI : https://doi.org/10.1186/s12875-022-01822-8

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Conjoint analysis: a research method to study patients’ preferences and personalize care.

1. Introduction

3. conjoint analysis trend over the past 70 years, 4. the conjoint analysis study process, 4.1. identifying the relevant attributes, 4.2. assigning levels, 4.3. choosing scenarios, 4.4. establishing preference, 4.5. analyzing data, 5. conjoint analysis in healthcare, 6. validity of conjoint analysis data.

• External validity is the ability of the CA tool to predict what people would choose in real life. This can be achieved by asking the question “did people choose what CA predicted?”. For example, in a conjoint study estimating the market share for an American multinational telecommunications corporation, various trial simulations were implemented hypothesizing that several product features had to be changed in order to attain desired sales (8% of the total market share) [ 56 ]. Four years after launching this product, the actual share was just under 8% [ 56 ], concluding that CA contributes towards the identification of people-desired choices and the estimation of the actual preference behavior. Investigating external validity for CA methods is a challenging task that requires the researcher to follow the participants to examine if they did what the CA tool predicted in terms of buying a product, taking a treatment, attending a particular doctor’s clinic, etc.
• Internal consistency validity is the main validity criterion that has been studied in recent years for strengthening the reliability and applicability of CA. To test the internal validity, the holdouts’ choices are used [ 84 ]. The holdouts are choices that are similar to those selected by the participants in real life but are “held out” of the conjoint approximation by not being part of the final estimation. The internal validity of the conjoint task is examined by comparing how well conjoint utilities predict choices from the holdout tasks. Therefore, the holdout tasks are not used in the estimation of part-worths, but they are presumed to represent respondent choices in the real world [ 85 ]. In a review evaluating CA as a method of estimating consumers’ preferences, Green and Srinivasan reported that several studies have demonstrated the consistency of conjoint models in terms of reproducing current market conditions [ 39 ]. Furthermore, a study offering four topical antibiotics to treat acne confirmed CA consistency and validity when patients’ preferences assessment, the simulated product rankings, and the results of the traditional questionnaire were matched [ 86 ].

7. Strengths and Limitations of Conjoint Analysis

8. strengths and limitations of this study, 9. conclusions, author contributions, institutional review board statement, informed consent statement, data availability statement, acknowledgments, conflicts of interest.

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Al-Omari, B.; Farhat, J.; Ershaid, M. Conjoint Analysis: A Research Method to Study Patients’ Preferences and Personalize Care. J. Pers. Med. 2022 , 12 , 274. https://doi.org/10.3390/jpm12020274

Al-Omari B, Farhat J, Ershaid M. Conjoint Analysis: A Research Method to Study Patients’ Preferences and Personalize Care. Journal of Personalized Medicine . 2022; 12(2):274. https://doi.org/10.3390/jpm12020274

Al-Omari, Basem, Joviana Farhat, and Mai Ershaid. 2022. "Conjoint Analysis: A Research Method to Study Patients’ Preferences and Personalize Care" Journal of Personalized Medicine 12, no. 2: 274. https://doi.org/10.3390/jpm12020274

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Conjoint Analysis: A Research Method to Study Patients’ Preferences and Personalize Care

• February 2022
• Journal of Personalized Medicine 12(2):274

• Khalifa University

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International Journal of Quality & Reliability Management

ISSN : 0265-671X

Article publication date: 28 December 2021

Issue publication date: 25 January 2023

Since the inception of the conjoint analysis technique in the year 1971, papers addressing the epistemological aspects of conjoint analysis are scant. Hence, this paper attempts to address the vacuum of qualitative discourse addressing the epistemological and methodological aspects of conjoint analysis including different issues, challenges, probable solutions, limitations and future direction of conjoint analysis in the recent decade.

Design/methodology/approach

For exploring the methodological and epistemological aspects of conjoint analysis, the seminal papers on conjoint analysis were reviewed. Moreover, the authors' experience for the state-of-art review was also taken into consideration.

The findings suggest that conjoint analysis that roots back since 1971 has not seen much exploration in Asian regions and is mainly used for new product development in the field of marketing or allied areas. Moreover, the reliability and validity of conjoint analysis is always a matter of concern for the researchers that hinders this technique's wider adaptability. Thus, the paper presents some probable solutions to address the focal issues useful for improved reliability and validity of the conjoint analysis technique.

Research limitations/implications

This paper attempts to familiarize the researchers with epistemological and methodological aspects of conjoint analysis with certain solutions to evolve beyond existing conjoint analysis dimensions in terms of improved validity, reliability, epistemological and methodological aspects of conjoint analysis (CA). Moreover, it acts as a call for research in different research domains, especially in the Asian continent.

Originality/value

There exist certain seminal research papers on epistemological aspects of conjoint analysis. However, there is a dearth of such attempt in the recent decade addressing the application issues of conjoint analysis incorporating the recent issues as well. Therefore, this paper is an attempt to usher the future researcher to understand the methodological aspects of conjoint analysis. It may prevent them from violating the basic assumptions and methodological threshold. This research technique is preferred equally by academicians and practitioners, thus making it imperative to have clarity beforehand for improved research rigor.

• Conjoint analysis
• Consumer preference
• Methodology
• Epistemology
• Reliability

Acknowledgements

The authors would like to thank the editor of the journal Associate Professor Ton van der Wiele and the anonymous reviewers are gratefully acknowledged for their comments and suggestions.

Kulshreshtha, K. , Sharma, G. and Bajpai, N. (2023), "Conjoint analysis: the assumptions, applications, concerns, remedies and future research direction", International Journal of Quality & Reliability Management , Vol. 40 No. 2, pp. 607-627. https://doi.org/10.1108/IJQRM-07-2021-0199

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• > Journals
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• > Causal Inference in Conjoint Analysis: Understanding...

Article contents

Causal inference in conjoint analysis: understanding multidimensional choices via stated preference experiments.

Published online by Cambridge University Press:  04 January 2017

• Supplementary materials

Survey experiments are a core tool for causal inference. Yet, the design of classical survey experiments prevents them from identifying which components of a multidimensional treatment are influential. Here, we show how conjoint analysis , an experimental design yet to be widely applied in political science, enables researchers to estimate the causal effects of multiple treatment components and assess several causal hypotheses simultaneously. In conjoint analysis, respondents score a set of alternatives, where each has randomly varied attributes. Here, we undertake a formal identification analysis to integrate conjoint analysis with the potential outcomes framework for causal inference. We propose a new causal estimand and show that it can be nonparametrically identified and easily estimated from conjoint data using a fully randomized design. The analysis enables us to propose diagnostic checks for the identification assumptions. We then demonstrate the value of these techniques through empirical applications to voter decision making and attitudes toward immigrants.

Authors' note: We gratefully acknowledge the recommendations of Political Analysis editors Michael Alvarez and Jonathan Katz as well as the anonymous reviewers. We further thank Justin Grimmer, Kosuke Imai, and seminar participants at MIT, Harvard University, Georgetown University, and Rochester University for their helpful comments and suggestions. We are also grateful to Anton Strezhnev for excellent research assistance. An earlier version of this article was presented at the 2012 Annual Summer Meeting of the Society for Political Methodology and the 2013 Annual Meeting of the American Political Science Association. Example scripts that illustrate the estimators and companion software to embed a conjoint analysis in Web-based survey instruments are available on the authors' websites. Replication materials are available online as Hainmueller, Hopkins, and Yamamoto (2013). Supplementary materials for this article are available on the Political Analysis Web site.

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• Volume 22, Issue 1
• Jens Hainmueller (a1) , Daniel J. Hopkins (a2) and Teppei Yamamoto (a3)
• DOI: https://doi.org/10.1093/pan/mpt024

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Conjoint Analysis in Consumer Research: Issues and Outlook

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Paul E. Green, V. Srinivasan, Conjoint Analysis in Consumer Research: Issues and Outlook, Journal of Consumer Research , Volume 5, Issue 2, September 1978, Pages 103–123, https://doi.org/10.1086/208721

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Since 1971 conjoint analysis has been applied to a wide variety of problems in consumer research. This paper discusses various issues involved in implementing conjoint analysis and describes some new technical developments and application areas for the methodology.

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What Is Conjoint Analysis & How Can You Use It?

• 18 Dec 2020

For a business to run effectively, its leadership needs a firm understanding of the value its products or services bring to consumers. This understanding allows for a more informed strategy across the board—from long-term planning to pricing and sales.

In today’s business environment, most products and services include multiple features and functions by default. So, how do businesses go about learning which ones their customers value most? Is it possible to assign a specific value to each feature a product offers?

This is where conjoint analysis becomes an essential tool.

Here’s an overview of conjoint analysis, why it’s important, and steps you can take to analyze your products or services.

Access your free e-book today.

What Is Conjoint Analysis?

Conjoint analysis is a form of statistical analysis that firms use in market research to understand how customers value different components or features of their products or services. It’s based on the principle that any product can be broken down into a set of attributes that ultimately impact users’ perceived value of an item or service.

Conjoint analysis is typically conducted via a specialized survey that asks consumers to rank the importance of the specific features in question. Analyzing the results allows the firm to then assign a value to each one.

Learn about conjoint analysis in the video below, and subscribe to our YouTube channel for more explainer content!

Types of Conjoint Analysis

Conjoint analysis can take various forms. Some of the most common include:

• Choice-Based Conjoint (CBC) Analysis: This is one of the most common forms of conjoint analysis and is used to identify how a respondent values combinations of features.
• Adaptive Conjoint Analysis (ACA): This form of analysis customizes each respondent's survey experience based on their answers to early questions. It’s often leveraged in studies where several features or attributes are being evaluated to streamline the process and extract the most valuable insights from each respondent.
• Full-Profile Conjoint Analysis: This form of analysis presents the respondent with a series of full product descriptions and asks them to select the one they’d be most inclined to buy.
• MaxDiff Conjoint Analysis: This form of analysis presents multiple options to the respondent, which they’re asked to organize on a scale of “best” to “worst” (or “most likely to buy” to “least likely to buy”).

The type of conjoint analysis a company uses is determined by the goals driving its analysis (i.e., what does it hope to learn?) and, potentially, the type of product or service being evaluated. It’s possible to combine multiple conjoint analysis types into “hybrid models” to take advantage of the benefits of each.

What Is Conjoint Analysis Used For?

The insights a company gleans from conjoint analysis of its product features can be leveraged in several ways. Most often, conjoint analysis impacts pricing strategy, sales and marketing efforts, and research and development plans.

Conjoint Analysis in Pricing

Conjoint analysis works by asking users to directly compare different features to determine how they value each one. When a company understands how its customers value its products or services’ features, it can use the information to develop its pricing strategy.

For example, a software company hoping to take advantage of network effects to scale its business might pursue a “freemium” model wherein its users access its product at no charge. If the company determines through conjoint analysis that its users highly value one feature above the others, it might choose to place that feature behind a paywall.

As such, conjoint analysis is an excellent means of understanding what product attributes determine a customer’s willingness to pay . It’s a method of learning what features a customer is willing to pay for and whether they’d be willing to pay more.

Conjoint Analysis in Sales & Marketing

Conjoint analysis can inform more than just a company’s pricing strategy; it can also inform how it markets and sells its offerings. When a company knows which features its customers value most, it can lean into them in its advertisements, marketing copy, and promotions.

On the other hand, a company may find that its customers aren’t uniform in assigning value to different features. In such a case, conjoint analysis can be a powerful means of segmenting customers based on their interests and how they value features—allowing for more targeted communication.

For example, an online store selling chocolate may find through conjoint analysis that its customers primarily value two features: Quality and the fact that a portion of each sale goes toward funding environmental sustainability efforts. The company can then use that information to send different messaging and appeal to each segment's specific value.

Conjoint Analysis in Research & Development

Conjoint analysis can also inform a company’s research and development pipeline. The insights gleaned can help determine which new features are added to its products or services, along with whether there’s enough market demand for an entirely new product.

For example, consider a smartphone manufacturer that conducts a conjoint analysis and discovers its customers value larger screens over all other features. With this information, the company might logically conclude that the best use of its product development budget and resources would be to develop larger screens. If, however, future analyses reveal that customer value has shifted to a different feature—for example, audio quality—the company may use that information to pivot its product development plans.

Additionally, a company may use conjoint analysis to narrow down its product or service’s features. Returning to the smartphone example: There’s only so much space within a smartphone for components. How a phone manufacturer’s customers value different features can inform which components make it into the end product—and which are cut.

One example is Apple’s 2016 decision to remove the headphone jack from the iPhone to free up space for other components. It’s reasonable to assume this decision was reached after analysis revealed that customers valued other features above a headphone jack.

Leveraging Conjoint Analysis for Your Business

Conjoint analysis is an incredibly useful tool you can leverage at your company. By using it to understand which product or service features your customers value over others, you can make more informed decisions about pricing, product development, and sales and marketing activities.

Are you interested in learning more about how customers perceive and realize value from the products they buy, and how you can use that information to better inform your business? Explore Economics for Managers — one of our online strategy courses —and download our free e-book on how to formulate a successful business strategy.

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What is Conjoint Analysis?

Conjoint analysis is a popular method of product and pricing research that uncovers consumers’ preferences, which is useful when a company wants to:

• Select product features.
• Assess consumers’ sensitivity to price changes.
• Forecast its volumes and market share.
• Predict adoption of new products or services.

Conjoint analysis is frequently used across different industries for all types of products, such as consumer goods, electrical goods, life insurance plans, retirement housing, luxury goods, and air travel. It is applicable in various instances that centre around discovering what type of product consumers are likely to buy and what consumers value the most (and least) about a product. As such, it is a familiar tool for marketers, product managers, and pricing specialists.

Businesses of all sizes can benefit from conjoint analysis, including even local grocery stores and restaurants — and its scope is not just limited to consumer contexts, for example, charities can use conjoint analysis’ techniques to find out donor preferences, while HR departments can use it to build optimal compensation packages .

How does conjoint analysis work?

Conjoint analysis works by breaking a product or service down into its components ( attributes and levels ) and testing different combinations of these components to identify consumer preferences .

For example, consider a conjoint study on smartphones. The smartphone is broken down into four attributes which are each assigned different possible variations to create levels:

Each choice task then presents a respondent with different possible smartphones, each created by combining different levels for each attribute:

Going further than simply asking respondents what they like in a product, or what features they find most important, conjoint analysis employs a more realistic approach: asking each respondent to choose between potential product concepts (or alternatives) formed through the combination of attributes and levels. These combinations are carefully assembled into choice sets (or questions). Each respondent is typically presented with 8 to 12 questions . The process of assembling attributes and levels into product concepts and then into choice sets is called experimental design and requires extensive statistical and mathematical analysis (done automatically by Conjointly or manually by researchers).

Using survey results, it is possible to calculate a numerical value that measures how much each attribute and level influenced the respondent’s choices. Each of these values is called a “ preference score ” (AKA “partworth utility” or “utility score”). The below example shows preference scores for attributes and levels of a mobile phone plan.

Preference scores are used to build simulators that forecast market shares for a set of different products offered to the market. By using the simulator to model (i.e. simulate ) respondents’ decisions, we can identify the specific features and pricing that balance value to the customer with cost to the company and forecast potential demand in a competitive market situation. The below example shows how different data amounts in a mobile plan will affect a company’s market share.

Consider you are launching a new product and wish to address several research questions. Through the below example, we demonstrate how various outputs from your Conjointly survey report can be used to gain insights.

• It is also possible to perform clustering based on raw conjoint utilities .

Why do conjoint analysis with Conjointly?

Conjointly automates the often complicated experimental design process using state-of-the-art methodology. This gives you control over specific settings , such as the number of concepts per choice set and the number of choice sets per respondent when you set up a conjoint analysis experiment. Respondents then complete the choice tasks within the conjoint survey – this typically requires a few hundred responses but may vary depending on the complexity of the study.

Once we’ve gathered the recommended sample size of respondents, Conjointly produces a survey report which contains several in-depth outputs. The outputs of Brand Specific Conjoint , Generic Conjoint , and Brand-Price Trade-Off include estimates of respondents’ preferences, overall sample profile, segmentation and interactive simulations. Conjointly estimates and charts preference shares, revenue projections, and price elasticity using simulators.

There are many types/flavours of conjoint analysis , classified by response type, questioning approach, design type, and adaptivity of the design. All flavours of conjoint analysis have the same basics but not all are as effective as others. That’s why Conjointly offers two key conjoint designs, called generic and brand-specific, and uses the most tested, developed, and theoretically sound response type – choice-based conjoint analysis (CBC). CBC’s predictive power far surpasses its alternatives , such as SIMALTO and self-explicated conjoint, making it the ideal choice for your next experiment.

Don’t have a large marketing budget or the scope to conduct conjoint analysis? That’s OK: Conjointly does full conjoint analysis for you, affordably . Unlike desktop software tools, Conjointly does not require you to deep dive into the advanced methodology of conjoint analysis. Your business can rely on the full functionality of the software to deliver high-quality analysis and powerfully accurate results. Conjointly embodies an agile approach that puts you in control of the research process without the need.

Conjointly is made unique by the following characteristics:

We are the home of conjoint analysis. Conjointly offers complete set of outputs and features through an accessible interface.

Quick to set up. Setting up your experiment is fast and hassle-free with a simple wizard, which helps you choose appropriate settings and suggests your minimum sample size. You won’t need to customise or test any survey – the system does that for you. Conjointly can send participants invites on your behalf or generate a shareable link for you.

Easy on respondents. Experiment participants only need a few minutes to complete a survey and can answer questions with ease on their mobile phone, tablet, or computer.

Smart analytics done for you. Behind the scenes, Conjointly uses state-of-the-art analytics to crunch the numbers, and check validity of reporting. Outputs are ready for any application of conjoint analysis (pricing, feature selection, product testing, new market entry, cannibalisation analysis, etc.) in any industry (telecommunications, SaaS, FMCG, automotive, financial services, HR, etc.).

Our market research experts are always ready to support your studies. Schedule a consultation if you need any assistance.

What is the difference between conjoint and discrete choice experiments?

Conjoint analysis is a survey-based quantitative research technique of presenting respondents with several options (each described in terms of feature and price levels) and measuring their response to these options.

When the measured response is their choice between these options (rather than ranking or rating each of these options), it is called choice-based conjoint (which is the most commonly-used type of discrete choice experiments).

Discrete choice analysis is examination of datasets that contain choices made by people from among several alternatives. Commonly, we want to understand what drove people to make these choices. For example, how does weather affect people’s choice of eating out, ordering food delivery, or cooking at home. Discrete choice analysis can be done on historical data (e.g. sales data) or from experiments (including survey-based experiments).

Choice-based conjoint is an example of discrete choice experimentation.

History of conjoint analysis

Conjoint analysis has its roots in academic research from the 1960s and has been used commercially since the 1970s. In 1964, two mathematicians, Duncan Luce and John Tukey published a rather indigestible (by modern standards) article called ‘Simultaneous conjoint measurement: A new type of fundamental measurement’ . In abstract terms, they sketched the idea of “measuring the intrinsic goodness of certain characteristics of objects by measuring the goodness of an object as a whole”.

The article did not mention data collection, products, features, prices, or other elements that we associate with conjoint analysis today, but it spurred academic interest in the topic and perhaps gave rise to the name “conjoint”. It not only kick-started the topic but also set the tone for future developments in the area. Over time, it has become technical to the point of inaccessibility to most people, led by American academics with a strong emphasis on the statistical workings of survey research.

Green and Srinivasin (1978) agree that the theory of conjoint measurement was developed in Luce and Tukey’s paper but that “the first detailed, consumer-orientated” approach was Green and Rao’s (1971) ‘Conjoint Measurement for Quantifying Judgmental Data’ . In 1974, Professor Paul E. Green penned ‘On the Design of Choice Experiments Involving Multifactor Alternatives’ , cementing the impact of conjoint analysis in market research.

Over the next few decades, conjoint analysis became an increasingly popular method across the globe with notable studies in the 1980s and 90s highlighting its growing adoption and development during this time (Wittink & Cattin 1989; Wittink, Vriens, and Burhenne 1994 cited in Green, Kreiger & Wind 2001) .

Conjoint surveys are continuously developing on a range of software platforms, through which many different flavours of conjoint analysis can be enjoyed. Today, conjoint analysis thrives as a widespread tool built on a robust methodology and is used by market researchers daily as an indispensable tool for understanding consumer trade-offs.

Example outputs of Generic Conjoint on ice-cream

This is a simple conjoint analysis report for a Generic Conjoint test on ice-cream. You can also take this survey yourself . We tested three features:

• Flavour (Fudge, Vanilla, Strawberry, and Mango)
• Size (from 120g to 200g)
• Price (from $1.95 to $3.50)

We collected over 1,500 good quality responses in this test (even though this report would be robust enough with a hundred complete answers). It turns out that variation of price was a more important driver of people’s decision-making than differences in both flavour and size of the cone combined:

Unsurprisingly, people preferred larger and cheaper cones. Fudge and vanilla were the two top flavours:

But when we look at confidence intervals, we notice that we are much less certain about average preferences for flavours than for size or price:

It is probably because if we simulate preference shares for four concepts with varied flavours but fixed price and size, we observe that the distribution of people who pick different options is not extremely skewed towards one flavour:

But when we do simulation analysis with different price points, we clearly see that more people prefer to pay a lower price. Even though some still stick with a higher price, probably due to price-quality inference.

Another useful output of the study is marginal willingness to pay , which shows the equivalent amount of money for upgrade from the less preferred to the more preferred features:

If you want to pick the topmost preferred combination of product features, you can take a look at the following ranking as well:

It looks like a large dollop of modestly-priced Frosty Vanilla is the winner today.

A simple conjoint analysis example in Excel

To further your understanding, you can download a conjoint analysis example in Excel , also available on Google Sheets (which you can copy to edit). This example covers:

• Inputs for a conjoint study
• Questions presented to respondents
• Calculations of preference scores (relative preferences and importance scores of attributes)

This example is limited to:

• Ten choice-based responses (in real conjoint tests, we collect ~12 choices from 100 to 2,000 respondents);
• Four attributes with two levels each (in real conjoint tests, we can have up to a dozen attributes and up to several dozen levels);
• A multiple linear regression (in real conjoint tests, we use hierarchical Bayesian multinomial logit );
• A fractional factorial design .

The best way to learn more about conjoint analysis is to set up your own study, which you can do when you sign up . You can also read about:

• Alternatives to conjoint (such as MaxDiff and Claims Test )
• Common mistakes and practical tips for setting up conjoint studies
• Key takeaways from our Conjoint Analysis 101 webinar

Which one are you?

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What is a conjoint analysis conjoint types & when to use them.

11 min read Conjoint analysis is a popular market research approach for measuring the value that consumers place on individual and packages of features of a product.

Conjoint analysis explained

Conjoint analysis can be defined as a popular survey-based statistical technique used in market research. It is the optimal approach for measuring the value that consumers place on features of a product or service. This commonly used approach combines real-life scenarios and statistical techniques with the modeling of actual market decisions.

Product testing and employee benefits packages are examples of where conjoint analysis is commonly used. Conjoint surveys will show respondents a series of packages where feature variables are different to better understand which features drive purchase decisions.

Note: For an in-depth guide to conjoint analysis, download our free eBook:   12 Business Decisions you can Optimize with Conjoint Analysis

Menu-based conjoint analysis

Menu-based conjoint analysis is an analysis technique that is fast gaining momentum in the marketing world. One reason is that menu-based conjoint analysis allows each respondent to package their own product or service.

Conjoint studies can help you determine pricing, product features, product configurations, bundling packages, or all of the above. Conjoint is helpful because it simulates real-world buying situations that ask respondents to trade one option for another.

For example, in a survey, the respondent is shown a list of features with associated prices. The respondent then chooses what they want in their ideal product while keeping price as a factor in their decision. For the person conducting the market research , key information can be gained by analyzing what was selected and what was left out. If feature A for $100 was included in the menu question but feature B for $100 was not, it can be assumed that this respondent prefers feature A over feature B.

The outcome of menu-based conjoint analysis is that we can identify the trade-offs consumers are willing to make. We can discover trends indicating must-have features versus luxury features.

Add in the fact that menu-based conjoint analysis is a more engaging and interactive process for the survey taker, and one can see why menu-based conjoint analysis is becoming an increasingly popular way to evaluate the utility of features.

The advanced functionality of Qualtrics allows for the perfect conjoint survey – built with the exact look and feel needed to provide a reliable, easy to understand experience for the respondent. This means better quality data for you.

  There are numerous conjoint methodologies available from Qualtrics.

• Full-Profile Conjoint Analysis
• Choice-Based/Discrete-Choice Conjoint Analysis
• Adaptive Conjoint Analysis
• Max-Diff Conjoint Analysis

To provide a sense of these options, the following discussion provides an overview of conjoint analysis methods.

Two-attribute tradeoff analysis

Perhaps the earliest conjoint data collection method involved presented a series of attribute-by-attribute (two attributes at a time) tradeoff tables where respondents ranked their preferences for the different combinations of the attribute levels. For example, if two attributes each had three levels, the table would have nine cells and the respondents would rank their tradeoff preferences from 1 to 9.

The two-factor-at-a-time approach makes few cognitive demands of the respondent and is simple to follow but it is both time-consuming and tedious. Moreover, respondents often lose their place in the table or develop some stylized pattern just to get the job done. Most importantly, however, the task is unrealistic in that real alternatives do not present themselves for evaluation two attributes at a time.

Full-profile conjoint analysis

Full-profile conjoint analysis takes the approach of displaying a large number of full product descriptions to the respondent. The evaluation of these packages yields large amounts of information for each customer/respondent. Full-profile conjoint analysis has been a popular approach to measure attribute utilities. In the full-profile conjoint task, different product descriptions (or even different actual products) are developed and presented to the respondent for acceptability or preference evaluations.

Each product profile represents a part of a fractional factorial experimental design that evenly matches the occurrence of each attribute with all other attributes. By controlling the attribute pairings, the researcher can correlate attributes with profile preferences and estimate the respondent’s utility for each level of each attribute tested. In the rating task, the respondent gives their preference or likelihood of purchase. While many features and levels may be studied, this type of conjoint is best used where a moderate number of profiles are presented, thereby minimizing respondent fatigue. The advanced functionality of Qualtrics employs experimental designs to reduce the number of evaluation requests within the survey. The output and analysis accumulated from full-profile conjoint surveys is similar to that of other conjoint models.

Adaptive conjoint analysis

Adaptive conjoint analysis varies the choice sets presented to respondents based on their preference. This adaption targets the respondent’s most preferred feature and levels, thereby making the conjoint exercise more efficient, wasting no questions on levels with little or no appeal. Every package shown is more competitive and will yield ‘smarter’ data.

Adaptive conjoint analysis is often more engaging to the survey-taker and thus can produce more relevant data. It reduces the survey length without diminishing the power of the conjoint analysis metrics or simulations. There are multiple ways to adapt the conjoint scenarios to the respondent. Most commonly the design is based on the most important feature levels. As each package is presented for evaluation, the survey accounts for the choice and then makes the next question more efficient. A combination of full profile and feature evaluation methods can be utilized and is referred to as Hybrid Conjoint Analysis.

Choice-based conjoint

The Choice-based conjoint analysis (CBC) (also known as discrete-choice conjoint analysis) is the most common form of conjoint analysis. Choice-based conjoint requires the respondent to choose their most preferred full-profile concept. This choice is made repeatedly from sets of 3–5 full profile concepts.

This choice activity is thought to simulate an actual buying situation, thereby mimicking actual shopping behavior. The importance and preference for the attribute features and levels can be mathematically deduced from the trade-offs made when selecting one (or none) of the available choices. Choice-based conjoint designs are contingent on the number of features and levels. Often, that number is large and an experimental design is implemented to avoid respondent fatigue. Qualtrics provides extreme flexibility in utilizing experimental designs within the conjoint survey.

The output of a Choice-based conjoint analysis provides excellent estimates of the importance of the features, especially in regards to pricing. Results can estimate the value of each level and the combinations that make up optimal products. Simulators report the preference and value of a selected package and the expected choice share (surrogate for market share).

Self-explicated conjoint analysis

Self-explicated conjoint analysis offers a simple but surprisingly robust approach that is easy to implement and does not require the development of full-profile concepts. Self-explicated conjoint analysis is a hybrid approach that focuses on the evaluation of various attributes of a product. This conjoint analysis model asks explicitly about the preference for each feature level rather than the preference for a bundle of features.

Although the approach is different, the outcome is still the same in that it produces high-quality estimates of preference utilities.

• First, like ACA, factors and levels are presented to respondents for elimination if they are not acceptable in products under any condition
• For each feature, the respondent selects the levels they most and least prefer
• Next, the remaining levels of each feature are rated in relation to the most preferred and least preferred levels
• Finally, we measure how important the overall feature is in their preference. The relative importance of the most preferred level of each attribute is measured using a constant sum scale (allocate 100 points between the most desirable levels of each attribute).
• The attribute level desirability scores are then weighted by the attribute importance to provide utility values for each attribute level.

Self-explicated conjoint analysis does not require the statistical analysis or the heuristic logic required in many other conjoint approaches. This approach has been shown to provide results equal or superior to full-profile approaches, and places fewer demands on the respondent. There are some limitations to self-explicated conjoint analysis, including an inability to trade off price with other attribute bundles. In this situation, the respondent always prefers the lowest price, and other conjoint analysis models are more appropriate.

Max-diff conjoint analysis

Max-Diff conjoint analysis presents an assortment of packages to be selected under best/most preferred and worst/least preferred scenarios. Respondents can quickly indicate the best and worst items in a list, but often struggle to decipher their feelings for the ‘middle ground’. Max-Diff is often an easier task to undertake because consumers are well trained at making comparative judgments.

Max-Diff conjoint analysis is an ideal methodology when the decision task is to evaluate product choice. An experimental design is employed to balance and properly represent the sets of items. There are several approaches that can be taken with analyzing Max-Diff studies including: Hierarchical Bayes conjoint modeling to derive utility score estimations, best/worst counting analysis and TURF analysis.

Hierarchical Bayes analysis (HB)

Hierarchical Bayes Analysis (HB) is similarly used to estimate attribute level utilities from choice data. HB is particularly useful in situations where the data collection task is so large that the respondent cannot reasonably provide preference evaluations for all attribute levels. As part of the procedure to estimate attribute level utilities for each individual, hierarchical Bayes focuses individual respondent measurement on highly variable attributes and uses the sample’s attribute level averages when attribute-level variability is smaller. This approach again allows more attributes and levels to be estimated with smaller amounts of data collected from each individual respondent.

Conjoint is a highly effective analysis technique

Conjoint analysis methodology has withstood intense scrutiny from both academics and professional researchers for more than 30 years. It is widely used in consumer products, durable goods, pharmaceutical, transportation, and service industries, and ought to be a staple in your research toolkit.

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Conjoint analysis in pharmaceutical marketing research

Conjoint analysis is a technique that evaluates the importance of a product’s attributes to consumers. This article details how to use conjoint analysis in pharmaceutical marketing research, including design, data analysis, validation, simulating market share and limitations of the technique.

Editor’s note: Yilian Yuan is director, marketing analytics, IMS Health, a Plymouth Meeting, Pa., health care information firm. Gang Xu is associate professor and project director, Jefferson Medical College of Thomas Jefferson University, Philadelphia.

Conjoint analysis is a technique that evaluates the importance of a product’s attributes to consumers. For a pharmaceutical product such as a drug, its attributes may include price, dosing, efficacy, and side effects, among others. Conjoint analysis is used to examine how consumers’ perceptions of these attributes influence their preference of the products. In a marketing research study for the pharmaceutical industry, “consumers” usually refers to physicians and patients. As we know, when consumers are making decisions on either prescribing a drug (e.g., physicians) or buying a drug (e.g., patients), they are comparing the drug with other drugs on the market. They are evaluating almost all the attributes of the drugs, the pros and cons, simultaneously. A drug may have a low price, but if it requires three daily dosages, it may be a less convenient option. Similarly, a drug with a high efficacy may also have many side effects. In this regard, consumers are making trade-offs: they set the priorities in terms of importance of these attributes to the patients. Consequently, their preference to a product depends on these priorities. One uniqueness of conjoint analysis is that it assesses a consumer’s preferences more accurately than other traditional methods by examining these trade-offs. For this reason, conjoint analysis studies are also sometimes referred to as a multi-attribute trade-off studies.

Conjoint analysis is perhaps the most widely used technique in quantitative marketing research. Since its first introduction in the early 1970s (Green and Rao, 1971; Green and Wind, 1975), it has been applied in many different areas ranging from cars (Johnson, 1974) to air travel (Green and Wind, 1975), job application (Norman 1980), the arts (Currium, 1981), health plans (Acito and Jain, 1980), and medicine (Graf et al, 1993). It is perhaps the most documented applied statistical techniques in quantitative marketing research (Louviere, 1988).

Practically, a conjoint study answers the following three key research questions:

1. How important are a product’s attributes to consumers? In a pharmaceutical marketing research study, for instance, we ask how physicians perceive drug X’s attributes such as efficacy, dosing, and cost. The feedback provided by physicians can be used in concept testing, designing clinical protocols, making marketing decisions and enhancing marketing efforts.

2. What profile, or combination of attributes, is deemed most attractive to consumers? In a pharmaceutical marketing research study, for instance, the question would be: Do physicians consider a drug with fewer side effects, lower efficacy, higher cost, and more difficulty of administering more attractive than a drug with more side effects, higher efficacy, lower cost and ease of administration?

3. What is the share of preference for the new product? For instance, a pharmaceutical company intends to introduce a new drug in an already crowded therapeutic area. It wants to assess how the new drug will perform in comparison with the existing drugs and how share of preference of the existing drug changes as a result of the launch of the new drug.

The design is the first and crucial step in developing and implementing a conjoint study. In this article, we’ll focus on the general perspectives of the design rather than elaborate on the details of the design issues. (See Kuhfeld, et al., 1994 for more discussion on this issue.) There are largely three steps in designing a conjoint study: (1) determine the number of attributes and attribute levels; (2) determine the number of profiles; and (3) calculate the sample size.

1. Determine the number of attributes and attribute levels For evaluating a product, the first thing is to decide the number of attributes and levels of each attribute. A level here refers to the value of the attribute. For instance, an attribute of “price” could have three levels: $10 per day, $15 per day and $20 per day. Table 1 shows a profile of conjoint card with five attributes.

A common approach to selecting attributes and attribute levels is through a focus group. For a conjoint study in the pharmaceutical industry, a focus group usually consists of a panel of experts in the study areas or consumers of products (e.g., physicians or patients) from whom the list of attributes and attribute levels are elicited. This phase of qualitative marketing research is crucial not only for generating the appropriate list of attributes and attribute levels, but also for helping determine whether a sufficient amount of information has been included so that physicians or patients can respond to the profiles in a meaningful way.

The guideline for selecting attribute and attribute levels is straightforward: They must be unambiguous and actionable. In other words, they should be clear and precise in expression and meaning, and can be implemented in practice and reality. Unimportant attributes and unrealistic attribute levels should be identified and eliminated with caution.

2. Determine the number of profiles Before we determine the number of profiles, let’s first briefly review the concept of full-profile design.

Full-profile design Once the attributes and attribute levels have been determined, we start to generate a variety of combinations of the attribute levels, each different from another. For instance, for an attribute of “price” with three levels ($10, $15 and $20) and an attribute of dosing with three levels (BID, QID, and QD), we have a total number of nine combinations (3 2 =9: the base is the number of levels and the exponent is the number of attributes). In a conjoint study, each of the combinations is named “profile” (also termed “task” or “run”) and thus a design including all combinations is called full factorial design. For four attributes with three levels each and additional one attribute of two levels, we have a total of full-factorial 162 profiles (3 4 x 2 1 = 162). In a full factorial design, all main effects of attributes and the interactions among them can be estimated.

In a typical conjoint analysis, a consumer (e.g., a physician) is asked to rate the likelihood of purchasing the product (e.g., prescribing the drug) upon seeing each profile. Obviously, there is quite a cognitive burden on consumers to rate each of 162 profiles. It has been well documented that a consumer should rate no more than 30 profiles at any given time. A product may have more than five attributes and in today’s competitive market, the number of attributes in a marketing research study could be well above 10. As a result, the total number of profiles based on a full factorial design is usually too large. As such, a fractional factorial design is usually used.

A fractional factorial design selects only a subset of the profiles based on a full factorial design so that the number of profiles can be handled relatively easily by respondents, while each attribute and the attribute level can also be assessed adequately. Thanks to the advance of computer technology, most statistical packages such as SAS, SPSS, and Sawtooth (CVA, 2000) can generate fairly quickly a fractional factorial design. Two criteria that are frequently mentioned in literature in evaluating a fractional factorial design are orthogonality and balance. The former refers to a design where the effect of each attribute can be evaluated independently. This is important because only by an orthogonal design will the effects will be uncorrelated to each other, thus avoiding possible confounding problems. The balanced design refers to the design in which levels of attributes are equally represented in the design, so that the effects are also uncorrelated with the intercept and the design becomes more efficient.

The number of profiles How many profiles do we need to have from a fractional factorial design? There is no absolute number to follow; the answer depends on the number of attributes and attribute levels, as well as the level of efficiency for the design. It is generally perceived that if there are n attributes with an average of k levels, we need to have n (k - 1) + 1 parameters and the total number of profiles equals to about 1.5 times of the number of parameters. With five attributes having three levels of each, for instance, there would be 11 parameters (5 (3 - 1) + 1) and thus about 16 tasks to complete.

Here, we give a rough range of the number of profiles rather than a concrete number to follow. This is because when a fractional factorial design is implemented, the choice of the final number of profiles depends on other factors such as the efficiency of the design itself. Briefly, the efficiencies refer to the measure of design goodness. For those who are interested in knowing about the efficiencies, please see the paper by Kuhfeld et al (1994).

The number of profiles increases for a segmental experiment. For instance, in evaluating the effects of a headache treatment, the three types of patients (tension, migraine, and cluster) may be identified, and they differ in the types of treatments received. If we want to hold their treatment type constant across the attributes and attribute levels, we would need three times more of the number of profiles we have generated.

The numbers of levels of the attributes should be about equal. Consumers may place high value on the importance of an attribute with many levels. Therefore, an attribute with more levels may be weighted more important than an attribute with fewer levels. (Wittink, Krishnamurthi and Reibstein, 1989). The importance of an attribute with more levels will be inflated.

Rating and ranking methods There are many different types of methods for assessing consumers’ preferences for a certain product. Among these, rating and ranking are frequently used with conjoint studies.

Rating methods, as noted by marketing researchers (Green, and Tull, 1978), are some of the most popular and easily applied data collection methods. In a conjoint analysis, for instance, a physician is asked to indicate the likelihood of prescribing that product upon seeing each profile. The rating scale ranges from 1 (definitely no) to 7 (definitely yes). Other rating scales, from 1 to 5 or from 1 to 9 are also used as well. The ranking method, on the other hand, requires physicians to order each of the profiles based on the likelihood of prescribing the product. If we have a total of nine profiles, all profiles will be ranked from 1 to 9, with a lower number usually indicating the product most likely to be prescribed by the physicians. Usually no ties are allowed.

3. Sample size The sample size required for the conjoint analysis is debatable because there is no definite rule to follow.

Before we calculate the sample size for a conjoint analysis, we need to calculate the number of parameters. As mentioned earlier, usually the total number of parameters is equal to the total number of levels (all levels for all attributes combined) minus the total number of attributes plus one. For example, say we have four attributes with three levels for each and one attribute of two levels (see the example in Table 1). We would have a total of 14 (3 x 4 + 2) levels. We then have 10 parameters (14 – 5 + 1).

The rule of thumb for the ratio of the number of parameters to the number of respondents is between five and 10. In other words, if we have four attributes with three levels each and one attribute of two levels, we then need at least 50 physicians (10 parameters x 5) to complete the study. If we have 10 attributes with three levels each, we then have 21 parameters (30 – 10 + 1) and need at least a sample size of 105. Most researchers in conjoint analysis agree that we need probably at least 70 to 100 respondents to make the results stable.

Data analysis

Utilities In a conjoint analysis, a consumer’s preference (rating or ranking) is the dependent variable and product attribute levels are the independent variables. Note here that the dependent variable can also be a binary preference intention (e.g., yes versus no) or constant sum (e.g., For the next 10 patients you are going to treat, how many patients you are to prescribe this drug?). The coefficients in the regression model are the estimated part-worth utilities. As in a regression analysis, the R-square gives an indication on how the data fit the model. The R-square tells the proportion of the variance of the consumer’s preference that is explained by the combination of the independent variables (attributes and attribute levels). While its values range between 0 to 1, a high value of R-square would indicate the data fit the model well. On the other hand, if the R-square is low, there is an indication that data may not fit the model well, either because there are some errors in the data collection or some inconsistency while consumers perform their rating or ranking tasks.

Relative importance of an attribute The relative importance value shows how important an attribute is in affecting consumers’ preference for a product. It is derived from the part-worth utilities for each attribute. First, the range of the attribute is computed for each attribute by subtracting the smallest part-worth utility from the largest one; second, the total range is computed by adding the ranges for all attributes together; third, the relative importance value is computed by dividing the range of the attribute by the total range.

Two methods that are frequently mentioned in literature are metric and non-metric. The key difference between metric and non-metric conjoint analysis lies in how the dependent variable (rating or ranking) is transformed. For metric conjoint analysis, a linear transformation is performed and the original rating or ranking data is unchanged. For non-metric analysis, a monotone transformation is conducted. In this monotone transformation, the order of the rating/ranking is preserved but the data have been transformed to make the model fit better. For these reasons, the R 2 in non-metric conjoint analysis is always higher than that of metric analysis; however the former is also less stable than the latter. In general, metric conjoint analysis is used more often than non-metric conjoint analysis.

In quantitative marketing research, we often need to validate the model. In a common regression model, for instance, we may develop a model based on the first group of 100 consumers. We then apply the regression coefficients derived from the model to the second group of consumers. The high correlation between predicted value and actual values for the second group will indicate that the model has good predictive power. Similarly, we can also do it in conjoint analysis by using “holdout observations.”

In conjoint analysis, the observations used for developing a model are commonly referred as “actual observations,” which is different from “holdouts.”

Holdout observations are used to validate the model and to calibrate the simulator. The profile for validation is a hypothetical product that is rated or ranked by consumers but is not used in the estimation of utility values in the model. The purpose of having holdout observations is to determine internal validity of the model by examining the associations between the actual and predicted ratings for these observations. Usually, the number of holdouts is small because these profiles, though not used in estimating the model, add to the burden placed on respondents. We should not increase unnecessarily the burden on consumers when they are performing tasks. These holdout observations are normally derived from those that are included in a full-factorial design but not in fractional factorial design. In the example we referred to earlier, there are 81 profiles for a full-factorial design with four attributes with three levels each. If the fractional factorial design only has the 24 profiles selected from the 81, the holdout observations will be derived from the 57 remaining profiles that are left out.

The validations assessed through the holdout observations are calculated based on the magnitudes of correlation coefficients between the predicted values of these holdout observations and their actual values. The ranges of correlation coefficients are between –1 to +1. In the evaluation of the validity of model, a positive moderate magnitude of correlation coefficient is usually needed. Note that the magnitude of the correlation coefficient depends on many other factors such as sample size (in this case the number of holdout observations and the number of profiles) and variation of the predicted and actual values. Another indicator for the assessment of the model would be the p-value associated with the level of the significance (e.g., .05 or .01).

Simulating market share

As we have indicated earlier, one of the objectives in using conjoint analysis is to simulate market share. In other words, after the model is developed, we want to know how many times a consumer will purchase the new product, or in the example cited earlier, how many times a physician will prescribe the drug. Note that a simulation of market share can be performed both for a new product and for an existing product.

In a conjoint study, the overall utility score associated with each drug is calculated based on the utility values of each attribute level for that drug. One way to simulate the market share is to assume that physicians will prescribe the drug with the highest utility value. It is particularly useful for a hypothetical product because the simulator predicts how physicians will react to changes in certain attribute levels based on the utility values estimated from the model.

Limitations

1. Conjoint analysis applies a regression model which uses attributes and attribute levels to predict the likelihood of purchasing a product. In the model, we have to decide the attributes and attribute levels that are identifiable and important to the consumers. In a research project for a pharmaceutical manufacturer, for instance, we identify those attributes such as price, efficacy, side effects, etc., that are important in physicians’ decisions to prescribing drugs. However, in defining the attributes and levels, we may not be as inclusive as we should.

2. Conjoint analysis is mainly concerned with main effects. It ignores the possible interaction effects among the attributes and levels. For instance, price and brand interaction (i.e., different brands may have different price sensitivities) is difficult to estimate in a traditional conjoint study.

3. When the number of attributes and attribute levels gets very large, it is difficult to justify the use of traditional conjoint analysis. In such cases, we either have to use a special type of conjoint analysis such as adaptive conjoint (Sawtooth, 2000) or we have to increase the sample size so that the rating or ranking tasks can be divided among the respondents. See Johnson (1991) for more on adaptive conjoint analysis.

4. Conjoint analysis usually requires that the number of levels for each attribute is about equal. For instance, if one attribute has two levels and the other six levels, the range of utility values for the six-level one will likely be higher than the two-level one simply because one attribute has more levels than another.

5. Conjoint analysis usually dictates that the values of levels are the same across the attributes. For instance, one of the requirements in using conjoint analysis is that the levels of price are the same across the drugs, which is sometimes not the case. For this reason, a discrete choice modeling is suggested, which we will discuss in another article.

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Research looks at the role of pharmacists as retail and pharma continue to change Related Categories: Health Care (Healthcare), Pharmaceutical Products, Health Care (Healthcare) Research Health Care (Healthcare), Pharmaceutical Products, Health Care (Healthcare) Research, Pharmacies/Drug Stores, Pharmacists, Research Industry, Research Industry – COVID-19, Patients , Retailing

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Using conjoint to gauge physicians’ views of a diabetes test Related Categories: Health Care (Healthcare), Conjoint Analysis/Trade-Off Analysis, Health Care (Healthcare) Research Health Care (Healthcare), Conjoint Analysis/Trade-Off Analysis, Health Care (Healthcare) Research, Physicians, Research Industry, Data Quality, Survey Research

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Conjoint Analysis, Related Modeling, and Applications

Cite this chapter.

• John R. Hauser 3 &
• Vithala R. Rao 3  

Part of the book series: International Series in Quantitative Marketing ((ISQM,volume 14))

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Conjoint analysis has as its roots the need to solve important academic and industry problems. Paul Green’s work on conjoint analysis grew out of his contributions to the theory and practice of multidimensional scaling (MDS) to address marketing problems, as discussed in Chapter 3. MDS offered the ability to represent consumer multidimensional perceptions and consumer preferences relative to an existing set of products.

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An interdisciplinary review of research in conjoint analysis: recent developments and directions for future research.

Choice-Based Conjoint Analysis

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Hauser, J.R., Rao, V.R. (2004). Conjoint Analysis, Related Modeling, and Applications. In: Wind, Y., Green, P.E. (eds) Marketing Research and Modeling: Progress and Prospects. International Series in Quantitative Marketing, vol 14. Springer, Boston, MA. https://doi.org/10.1007/978-0-387-28692-1_7

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Value-Based Pricing Research: What Is Conjoint Analysis?

By Bernd Grosserohde, Director, Strategic Solutions Operations, Product & Pricing Practice Area Lead at GLG

Read Time: 5 Minutes

Drive product success by finding what your customers consider valuable with GLG Conjoint Analysis.

Conjoint analysis is one of the most versatile methods in market research today. One of the most powerful forms of value-based pricing research, conjoint analysis is most often used for new product and feature pricing, along with determining the best feature and benefit mix. It can also be employed for existing products and services that need to be adapted to changing customer needs.

Along with commercial market research, the method is also often used in academic, psychology, or life sciences research. Over the past 10 years, conjoint analyses have been accepted as scientific evidence in U.S. court cases (like in the legal fight between Apple and Samsung over software patents).

What Is Conjoint Analysis?

When you run a conjoint analysis for pricing research, you break a product into its parts, like a Lego house made from many bricks. But in a conjoint case, the parts of that house would not be the bricks, but the walls, roofs, doors, and so on. In a conjoint survey, respondents choose among houses, and from these choices, you can derive the perceived value of each part. You can then build hypothetical new concepts and simulate preference among customers.

The purpose of conjoint analysis is not to understand choice among products as they are today. There are likely better data sources for that. Rather, it’s to learn what customers would choose in hypothetical new situations. The whole point is to be experimental and forward-looking. In a conjoint context, we are also not really asking about price acceptance or willingness to pay directly. Price is only one of many attributes. Respondents will never know a survey is about pricing. They will also not know that we are interested in one brand.

Value-Based Pricing Research: An Example

Here’s an example. Suppose you want to run a conjoint study on laptops. Respondents would see three laptop concepts and be asked to choose the one they’re most likely to buy. The survey would include 10 to 15 questions. From the choices across many respondents, you can derive which parts have the most value to them.

Webcast: Using Conjoint Methods to Create and Capture Customer Value

This GLG Applied webcast was recorded on May 19, 2021

Let’s say you are interested in how a laptop that costs $999 competes with eight other products. A market simulator would calculate a preference share for this laptop at 15.8%, meaning that percentage of respondents in the sample would choose this product. If you changed the price of the laptop to $899, it’d gain 1.2 percentage points in share. At $799, add another 2.5 percentage points. With these simulations, you can create a price sensitivity curve, as well as a demand curve and elasticity values for the product and each of the price increments. You also learn which other products lose out.

Determining Willingness to Pay

Conjoint simulations are also great if you want to measure customer willingness to pay for a single feature. For example, if you add extra memory to the survey laptop, the share goes up to 18.8% because the product is now worth more. If you compare the laptop with one that has more memory and costs $90 more, you can surmise that the added value of extra memory is $90. This insight is useful for many business decisions, such as which features to prioritize when developing the next generation of products and how to price them.

The true value of conjoint analysis is that with only a few questions, you get an unrivaled amount of information about preferences and perceived value.

Businesses benefit from this especially when they want to create and/or capture value. Developing added-value products is necessary if you want to win new customers or retain existing ones. Conjoint analysis helps in this context by showing exactly what constitutes value from a customer’s point of view. When they want to catch added value, conjoint analyses show how price changes will impact demand for a product in the market by revealing what prices customers will accept.

Creating and capturing value is relevant for all businesses, but most of all, for those with ambitious growth goals in a dynamic environment. Why? Businesses need to adapt quickly to changing customer needs, and they may follow different strategies to this goal. They may acquire new products and services, or innovate with their own R&D capabilities. In either case, they need to know how to maximize customers’ perceived value of those assets.

On the other hand, many companies are struggling with customers who constantly bargain for lower prices, and it is helpful to know which costs could be reduced and which features could be omitted without compromising the perceived value of the brand. Pressure is exerted not only internally but also by competitors that cut prices and innovate. Understanding the options you have to respond and knowing what the impact your responses to these competitive pressures will have on market performance is critical. Conjoint analysis is popular because it addresses these issues and reduces the risk of making the wrong business decision.

It can guide the product and price strategy and provide fundamental insights into the needs and preferences of customers. The results can then be used to prioritize features or product benefits or to get a sense of customers’ price sensitivity.

Conjoint analysis can give you the data and the customer insights to allow you to plan for profitable growth. It allows data to be turned into informed opinion about the way forward in pricing and innovation management. That can concern the management of prices and innovation, how to transition efficiently into the next product development stage, and how to involve and guide internal stakeholders and external partners.

For many decision makers, all of these insights will be a huge help in staying competitive in the market.

Read our other article in the GLG Applied Value-Based Pricing Research Series: Getting Value-Based Pricing Right Is Difficult, But Worth It

Bernd Grosserohde leads GLG’s offer for new product development and pricing research. His focus is on building company and brand value through innovation and optimization. With over 20 years of marketing research experience, Bernd has worked with many global companies on building stronger, more profitable products. Before joining GLG, Bernd was Global Head of Pricing and Portfolio Management at Kantar.

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Flange Bolt Failure Analysis

• Wright State University

Research output : Contribution to journal › Article › peer-review

Failure cause investigation of flange bolts from the bi-axial shaft fatigue test rig was carried out and crack growth rates in combined bending and torsion conditions were determined. Flange bolts are used in gas turbine engines that are pre-torqued exerting bending and torsion fatigue situation arising from high centrifugal stresses. The fracture surface features were characteristic of bending fatigue (BF), where striations were observed, a distinct area within which conjoint bending and torsion fatigue (CBTF) features were documented, and a region between the BF and CBTF, a region of overloading (OL), where ductile dimples were observed. Striations dominated within BF and CBTF areas. The CBTF fracture comprised predominantly 80 percent or more of the total fracture surface area. However, as these two modes progress from opposite sides, the region between them experiences overloading and fails as a result. These features were documented for each case. Fatigue striations were counted for CBTF mode and crack growth rate as a function of crack length and crack length as a function of cumulative striations were presented. Since the failure occurred with the application of 60,000 minor cycles, cumulative striation counts were 1/5 of this life. The fatigue crack nucleation stage was affected since the part contained burr marks.

• Bending fatigue
• conjoint bending torsion fatigue
• ductile dimples
• overloading

Disciplines

• Biomedical Engineering and Bioengineering
• Engineering
• Industrial Engineering
• Operations Research, Systems Engineering and Industrial Engineering

Access to Document

• 10.1515/JMBM.1999.10.4.205 License: Unspecified
• Flange Bolt Failure Analysis Final published version, 1.87 MB License: Unspecified

Other files and links

• Link to repository

T1 - Flange Bolt Failure Analysis

AU - Goswami, Tarun

PY - 1999/1/1

Y1 - 1999/1/1

N2 - Failure cause investigation of flange bolts from the bi-axial shaft fatigue test rig was carried out and crack growth rates in combined bending and torsion conditions were determined. Flange bolts are used in gas turbine engines that are pre-torqued exerting bending and torsion fatigue situation arising from high centrifugal stresses. The fracture surface features were characteristic of bending fatigue (BF), where striations were observed, a distinct area within which conjoint bending and torsion fatigue (CBTF) features were documented, and a region between the BF and CBTF, a region of overloading (OL), where ductile dimples were observed. Striations dominated within BF and CBTF areas. The CBTF fracture comprised predominantly 80 percent or more of the total fracture surface area. However, as these two modes progress from opposite sides, the region between them experiences overloading and fails as a result. These features were documented for each case. Fatigue striations were counted for CBTF mode and crack growth rate as a function of crack length and crack length as a function of cumulative striations were presented. Since the failure occurred with the application of 60,000 minor cycles, cumulative striation counts were 1/5 of this life. The fatigue crack nucleation stage was affected since the part contained burr marks.

AB - Failure cause investigation of flange bolts from the bi-axial shaft fatigue test rig was carried out and crack growth rates in combined bending and torsion conditions were determined. Flange bolts are used in gas turbine engines that are pre-torqued exerting bending and torsion fatigue situation arising from high centrifugal stresses. The fracture surface features were characteristic of bending fatigue (BF), where striations were observed, a distinct area within which conjoint bending and torsion fatigue (CBTF) features were documented, and a region between the BF and CBTF, a region of overloading (OL), where ductile dimples were observed. Striations dominated within BF and CBTF areas. The CBTF fracture comprised predominantly 80 percent or more of the total fracture surface area. However, as these two modes progress from opposite sides, the region between them experiences overloading and fails as a result. These features were documented for each case. Fatigue striations were counted for CBTF mode and crack growth rate as a function of crack length and crack length as a function of cumulative striations were presented. Since the failure occurred with the application of 60,000 minor cycles, cumulative striation counts were 1/5 of this life. The fatigue crack nucleation stage was affected since the part contained burr marks.

KW - Bending fatigue

KW - cleave

KW - conjoint bending torsion fatigue

KW - ductile dimples

KW - overloading

KW - striation

UR - https://corescholar.libraries.wright.edu/bie/181

U2 - 10.1515/JMBM.1999.10.4.205

DO - 10.1515/JMBM.1999.10.4.205

M3 - Article

JO - Journal of the Mechanical Behavior of Materials

JF - Journal of the Mechanical Behavior of Materials