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Health insurance, financial investments, telecom service plans — consumers frequently find it harder to make choice decisions in these exemplar domains. Such domains are more susceptible to exhibiting greater complexity: details, many and technical, to account for, multiple options difficult to differentiate and to choose from, and unclear consequences. In products, we may refer in particular to those involving digital technology and computer-based software that some consumers are likely to find more cumbersome to navigate and operate. When consumers are struggling to make any choice, they develop a stronger tendency to delay or avoid the decision at all. They need assistance or guidance in making their way towards a choice that more closely matches their needs or goals and preferences.

Handel and Schwartzstein (2018) are distinguishing between two mechanism types that obstruct or interfere with making rational decisions: frictions and mental gaps.

Frictions reflect costs in acquiring and processing information. They are likely to occur in earlier stages of a decision process when consumers are encountering difficulties in searching for and sorting through relevant information (e.g., what options are more suitable, what attributes and values to look at), and they have to invest time and effort in tracing the information and organising it. Furthermore, frictions may include the case when consumers fail to see in advance or anticipate the benefits from an available alternative  (e.g., consider the difficulty of older people to realise the benefits they may gain from smartphones).

Mental gaps are likely to make an impact at a more advanced stage: the consumer already has the relevant information set in front of him or her but misinterprets its meanings or does not understand correctly the implications and consequences of any given option (e.g., failing to map correctly the relation between insurance premium and coverage). Mental gaps pertain to “psychological distortions” that generally may occur during information-gathering,  attention and processing, but their significance is primarily in comprehension of the information obtained. In summary, it is “a gap between what people think and what they should rationally think given costs.”

In practice, it is difficult to identify which type of mechanism is acting as an obstacle on the way of consumers to a rational decision.  Research techniques are not necessarily successful in separating between a friction and a mental gap as sources of misinformed choices (e.g., choosing a dominated option instead of a dominating one apparent to the rational decision-maker). Notwithstanding, Handel and Schwartzstein are critical of research practices that focus on a single mechanism and ignore alternative explanations. In their view, disregard to the distinction between mechanisms can lead to spurious conclusions. They suggest using counterfactual approaches that test a certain mechanism, or a combination of explanations, and then argue against it with a ‘better’ prospective mechanism explanation. They also refer to survey-based and experimental research methods for distinguishing frictions and mental gaps. The aim of these methods is to track the sources of misinformed decisions.

Consumers often run into difficulty with financial investments and saving plans. In some countries policy makers are challenged with driving consumers-employees towards saving for retirement during the working years. Persuasion per se turns out to be ineffective and other approaches for directing or nudging consumers into saving are designed and implemented (e.g., encouraging people to “roll into saving” through a scheme known as ‘Save More Tomorrow’ by Thaler and Sunstein).

Confronting employees with a long list of saving plans or pension funds may deter them from duly attending to the alternatives in order to make a decision, and even risks their aborting the mission. When consumers-employees have a hard time to recognise differences between the plans or funds (e.g., terms of deposit, assets invested in, returns), they are likely to turn to heuristics that brutally cut through the list. Crucially, even if information on key parameters is available for each option, decision-makers may use only a small part of it. Similar difficulties in choosing between options may arise in financial investments, for instance when choosing between equity and index funds or bond funds. One may be assisted by suggesting a default plan (preferably, recommending a personally customised plan) or sorting and grouping the proposed plans and funds into classes (e.g., by risk level or time horizon). However, it should be acknowledged that consumer responses as described above may harbour frictions as well as mental gaps, and it could help to identify which mechanism has the greater weight in the decision process.

A key issue with health insurance concerns the mapping of relationship between an insurance premium and the level of deductibles or cost-sharing between the insurer and the insured. For example, consumers fall into a trap of accepting an insurance policy offered with a lower premium while not noticing a higher deductible they would have to pay in a future claim. An additional issue consumers have to attend to is the coverage provided for different medical procedures such as treatments and surgeries (given also the deductible level or rate). Consumers may stumble in their decision process while studying health insurance plans as well as while evaluating them.

  • Public HMOs (‘Kupot Holim’) in Israel offer expanded and premium health insurance plans as supplementary to what consumers are entitled to by the State Health Insurance Act. Yet in recent years insurance companies are prompting consumers to get an additional private health insurance plan from them — their argument is that following changes over the years in the HMOs’ plans and reforms by the government, those plans do not offer adequate coverage, or none at all, for more expensive treatments and surgeries. The coverage of private insurance plans is indeed more generous, but so are the much higher premiums , affordable to many only if paid for by the employer.

In addressing other aspects of healthcare, Handel and Schwartzstein raise the issue of consumer preference for a branded medication (non-prescription) over an equivalent and less costly generic or store-branded medication (e.g., buying Advil rather than a store-branded medication that contains the same active ingredient [ibuprofen] for pain relief as in Advil). Another vital issue concerns the tendency of patients to underweight the benefits of treatment by medications prescribed to them, and consequently do not take up medications satisfactorily as instructed to them by their physicians (e.g., patients with a heart condition, especially after a heart attack, who do not adhere as required to the medication regime administered to them).

Customers repeatedly get into feuds with their telecom service providers — mobile and landline phone communication , TV and Internet. Customers of mobile communications (‘cellular’), for example, often complain that the service plan they  had agreed to did not match their actual usage patterns or they did not understand properly the terms of the service contract they signed to. As a result, they have to pay excessive charges (e.g., for minutes beyond quota), or they are paying superfluous fixed costs.

With the advancement of technology the structure of mobile service plans has changed several times in the past twenty years. Mobile telecom companies today usually offer ‘global’ plans for smartphones that include first of all larger volumes of data (5GB, 10GB, 15GB etc.), and then practically an infinite or outright unlimited use of outgoing talking minutes and SMSs. While appealing at first, customers end up paying a fixed inclusive monthly payment that is too high relative to the traffic volume they actually make use of. On the one hand customers refrain from keeping track of their usage patterns because it is costly (a friction). On the other hand, customers fail in estimating their actual usage needs that will match the plan assigned to them (a mental gap). In fact, information on actual usage volumes is more available now (e.g., on invoices) but is not always easily accessible (e.g., more detailed usage patterns). It should be noted, however, that companies are not quick to replace a plan, not to mention voluntarily notifying customers of a mismatch that calls for upgrading or downgrading the plan.

A final example is dedicated here to housing compounds of assisted living for seniors. As people enter their retirement years (e.g., past 70) they may look for comfortable accommodation that will relieve them from the worries and troubles of maintaining their home apartment or house and will also provide them a safe and supportive environment. Housing compounds of assisted living offer residence units, usually of one or two rooms of moderate space, with an envelope of services: maintenance, medical supervision and aid, social and recreational activities (e.g., sports, games, course lectures on various topics). The terms for entering into assisted living housing can be nevertheless consequential and demanding. The costs involve mainly a leasing payment for the chosen residence and monthly maintenance fee payments.

Making the decision can be stressing and confusing. First, many elderly people cannot afford taking residence in such housing projects without selling their current home or possibly renting it (e.g., to cover a loan). In addition the value of the residence is depreciated over the years. Second, the maintenance fee is usually much higher than normal costs of living at home. Hence residents may need generous savings plus rental income in order to finance the luxury and comfort of assisted living. Except for the frictions that are likely to occur while looking for an appropriate and affordable housing compound, the prospect residents are highly likely to be affected by mental gaps in correctly understanding the consequences of moving into assisted living (and even their adult children may find the decision task challenging).

Methods of intervention from different approaches attempt to lead consumers to make decisions that better match their needs and provide them greater benefits or value. Handel and Schwartzstein distinguish between allocation policies that aim to direct or guide consumers to a recommended choice without looking into reasons or sources of the misinformed decisions (e.g., nudging techniques), and mechanism policies that attempt to resolve a misguided or misinformed choice decision by tackling a specific reason causing it, such as originating from a mechanism of friction or mental gap. From a perspective of welfare economics, the goal of an intervention policy of either type is to narrow down a wedge between the value consumers obtain from actual choices subject to frictions and mental gaps, and the value obtainable from a choice conditional on being free of frictions and mental gaps (i.e., assuming a rational decision). (Technical note: The wedge is depicted as a gap in value between a ‘demand curve’ and a ‘welfare curve’, respectively.)

Policies and methods of either approach have their advantages and disadvantages. An allocation policy has a potential for greater impact, that is, it can get farther in closing the welfare wedge.  Yet, it may be too blunt and excessive: while creating a welfare gain for some consumers, it may produce an undesirable welfare loss to consumers for whom the intervention is unfitting. Without knowing the source of error consumers make, it is argued that a nudging-type method (e.g., simplifying the structure of information display of options) could be insufficient or inappropriate to fix the real consumer mistake. A fault of allocation policies could particularly be, according to the authors, that they ignore heterogeneity in consumer preferences. Furthermore, and perhaps as a consequence, such policies overlook the presence of informed consumers who may contribute by leading to the introduction of far better products at lower prices.

Mechanism policies can in principle be more precise and effective while targeting specific causes of consumers’ mistakes, and hence correcting the costs of misinformed decisions without generating unnecessary losses to some of them. The impact could be more limited in magnitude, yet it would be measured. But achieving this outcome in practice, the authors acknowledge, can be difficult and complicated, requiring the application of some costly research methods or complex modelling approaches. They suggest that “[as] data depth and scope improve, empirically entangling mechanisms in a given context will become increasingly viable”.

The analysis by Handel and Schwarztsein of the effects of intervention policies — mechanism versus allocation — could come as too theoretical, building on familiar concepts of economic theory and models, furthermore being difficult and complicated to implement. Importantly, however, the authors open up a door for us to a wider view on sources of mistakes consumers make in decision-making and the differences between approaches aimed at improving the outcomes of their decisions. First, they clarify a distinction between mechanisms of frictions and mental gaps. Second, they contrast allocation policies (e.g., nudging) versus mechanism policies which they advocate. Third, to those less accustomed to the concepts of economic analysis, they demonstrate their ideas with practical real-world examples. Handel and Scwharzstein present a perspective well deserving to learn from.

Ron Ventura, Ph.D. (Marketing)

Reference:

Frictions or Mental Gaps: What’s Behind the Information We (Don’t) Use and When Do We Care?; Benjamin Handel and Joshua Schwartzsetein, 2018; Journal of Economic Perspectives, Vol. 32 (1 – Winter), pp. 155-178. (doi: 10.1257 / jep.32.1.155)

 

 

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Competition in health-related industries (i.e., health care services, pharmaceutical, biotechnology) has been increasing continuously in the past two to three decades. The health business has also become more complex and multilayered with public and private institutions, individual doctors and patients, as players. Consequently, decision processes on medical treatment may become more complicated or variable, being more difficult to predict which treatment or medication will be administered to patients. For example:

  • For many medical conditions there are likely to exist a few alternative brands or versions of the same type of prescribed medication. Depending on the health systems in different countries, and on additional situational factors, it may be decided by a physician, a health care provider and/or insurer, or a pharmacist what particular brand of medication a patient would use. In some cases the patient may be allowed to choose between a more expensive brand and an economic brand (e.g., original and generic brands, subsidised and non-subsidised brands).
  •  There are plenty of over-the-counter (OTC) medications, formulae and devices that patients can buy at their own discretion, possibly with a recommendation of a physician or pharmacist.
  • Public and private medical centers and clinics offer various clinical tests and treatments (e.g., prostate screening, MRI scanning, [virtual] colonoscopy), often going above the heads of general/family physicians of the concerned patients.
  • In more complex or serious conditions, a patient may choose between having a surgery at a public hospital or at a private hospital, depending on the coverage of his or her health insurance.

In the late 1990s, professionals, executives and researchers in health-related areas have developed an interest in methods for measuring preferences that would allow them to better understand how decisions are made by their prospect customers, especially doctors and patients (“end-consumers”). This knowledge serves (a) to address more closely the preferences of patients or requirements of physicians, and (b) to channel planning, product development or marketing efforts more effectively. In particular, they have become interested in methods of conjoint analysis and choice-based conjoint that have already been prevalent in marketing research for measuring and analysing preferences. Conjoint methods are based on two key principles: (a) making trade-offs between decision criteria, and (b) decomposition of stated preferences with respect to whole product concepts (e.g., a medication) by means of statistical techniques into utility values for levels of each attribute or criterion describing the product (e.g., administering 2 vs. 4 times in 24 hours). The methods differ, some argue quite distinctly, in terms of the form in which preferences are expressed (i.e., ranking or rating versus choice) and in the statistical models applied (e.g., choice-based conjoint is often identified by its application of discrete choice modelling). An important benefit for pharmaceutical companies, for example, is gained in learning what characteristics of a medication (e.g., anti-depressant) contribute more to convincing physicians to prescribe it, versus factors like risks or side-effects that lead them to avoid a medication.

The product concepts presented are hypothetical in the sense that they are specified by using controlled experimental techniques and do not necessarily match existing products at the time of study. This property is essential for deriving utility values for the various levels of product attributes studied, and to allow prediction by simulation of shares of preference (“market shares”) for future products. The forecasting power of conjoint models is considered their major appeal from a managerial perspective. In addition, conjoint data can be used for segmenting patients and designing refined targeted marketing strategies.

Interest in application of conjoint methods in a health context has grown in the past decade. According to a review research of conjoint studies reported in 79 articles published between 2005 and 2008, the number of studies nearly doubled from 16 in 2005 to 29 in 2007. The researchers estimated that by the end of 2008 the number of published studies would reach 40. The most frequent areas of application have been cancer (15%) and respiratory disorder (12%)(1). However, applications of conjoint techniques can be found also for guiding policy making and the design of health plans in a broader context of health-care services provided to patients (e.g., by HMOs).

Most conjoint studies in health (71%) apply choice experiments and modelling, becoming the dominant approach (close to 80%) particularly in 2008. A typical study includes 5 or 6 attributes with 2 or 3 levels for each attribute. Most studies in a choice-based approach involve 7 to 8 scenarios (choice sets) but studies with 10-11 or 14-15 scenarios are also frequent (2). A choice scenario normally includes 3 to 5 concepts from which a respondent has to choose a single most prefered concept.

Interpretation of conjoint studies among medical doctors needs a special qualification to be distinguished from studies of patients or consumers. That is because the physicians make professional judgements about the most appropriate treatment option for their patients.  Therefore, it is less appropriate to relate to personal preferences in this context. It is more sensible and suitable to talk about decision criteria that physicians apply, their priorities (i.e., represented by importance weights), and requirements of physicians from pharmaceutical or other treatment alternatives available in the market.

Including monetary cost in conjoint studies on products and services in health-care may be subject to several complications and limitations. That may be the reason for the relatively low proportion of articles on conjoint studies in health that were found to include prices (40%)  (3). For instance, doctors do not take money out of their own pockets to pay for the medications they prescribe, so it is generally less relevant to include price in their studies. It may be sensible, however, to include cost in cases where doctors are allowed to purchase and hold a readily available  inventory of medications for their visiting patients in their private clinics (e.g., Switzerland). It may still be useful to examine how sensitive doctors are to the cost of medication that their patients will have to incur when prescribing them. However, this practice may be additionally complicated because the actual price patients pay for a specific medication is likely to change according to the coverage of their health plan or insurance. It is appropriate and recommended to include price in studies on OTC medications or health-related devices (e.g., for measuring blood pressure). Aspects of cost can be included in studies on health plans such as the percentage of discounts provided on medications and other types of clinical tests and treatments in the plan’s coverage.

An Example for a Conjoint Study on Health-Care Plans:

A choice-based conjoint study was conducted to help a health-care coverage provider assess the potential for a new modified heath plan it was considering to launch. Researchers Gates, McDaniel and Braunsberger (4)  designed a study with 11 attributes including provider names (the client and two competitors), network of physicians accessible, payment per doctor visit, prescription coverage, doctor quality, hospital choice, monthly premium, and additional attributes. Each respondent was introduced to 10 choice sets where in each set he or she had to choose one out of four plans. This setting was elected so that in subsequent simulations the researchers could more accurately test scenarios with existing plans of the three providers plus a new plan by the client-provider. The study was conducted among residents in a specific US region by mail. Yet beforehand a qualitative study (focus group discussions) and a telephone survey have been carried out to define, screen and refine the set of attributes to be included in the conjoint study. 506 health-care patients returned the mail questionnaire (71% response rate out of those in the phone survey who agreed to participate in the next phase).

The estimated (aggregate) utility function suggested to the researchers that the attributes could be divided into two classes of importance: primary criteria for choosing a health plan and secondary considerations. The primary criteria focused on access allowed to doctors in the region of residence and cost associated with the plan, representing the more immediate concerns to target consumers in the market in choosing a health-care plan by a HMO. It was mainly confirmed in the study that consumers are less concerned by narrowing the network of doctors they may visit, as long as they can keep their current family physician and are not forced to replace him or her with another on the list. Respondents appeared to rely less on reported quality ratings of doctors and hospitals. Vision tests and dental coverage were among the secondary considerations. Managers could thereby examine candidate modifications to their health plan and estimate their impact on market shares.

The conjoint methods offer professionals and managers in health-related organizations research tools for gaining valuable insights into patient preferences or criteria governing the clinical decisions of doctors on medications and other treatments. These methods can be particularly helpful in guiding the development of pharmaceutical products or instruments for performing clinical tests and treatments when issues of marketing and promoting them to decision makers come into play. As illustrated in the example, findings from conjoint studies can be useful in policy making on health-care services and designing attractive health plans to patients. This kind of research-based knowledge is acknowledged more widely as a key to success in the highly competitive environs of health-care.

Ron Ventura, Ph.D. (Marketing)

Notes:

(1)  Conjoint Analysis Applications in Health – How Are Studies Being Designed and Reported? An Update of Current Practice in the Published Literature Between 2005 and 2008, D. Marshall, J.F.P. Bridges, B. Hauber, R. Cameron, L. Donnalley, K. Fyie, and F.R. Johnson, 2010, The Patient: Patient-Centered Outcomes Research, 3 (4), 249-256

(2) Ibid. 1.

(3) Ibid. 1.

(4) Modeling Consumer Health Plan Choice Behavior to Improve Customer Value and Health Plan Market Share, Roger Gates, Carl McDaniel, and Karin Braunsberger, 2000, Journal of Business Research, 48, pp. 247-257 (The research was executed by DSS Research to which Gates is affiliated).

Additional sources:

A special report on conducting conjoint studies in health was prpared in 2011 by a task force of the International Society for Pharmaeconomics and Outcomes Research. The authors provide methodological recommendations for guiding the planning, design, and analysis and reporting conjoint studies in health-related domains.

Conjoint Analysis Applications in Health – A Checklist: A Report of the ISPOR Good Research Practices for Conjoint Analysis Task Force, John F.P. Bridges, and A. Brett Hauber et al., 2011, Value in Health, 14, pp. 403-413

http://www.ispor.org/taskforces/documents/ISPOR-CA-in-Health-TF-Report-Checklist.pdf

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