Emerging Marketing Research on Healthcare and Medical Decision Making: Toward a Consumer-Centric and Pluralistic Methodological Perspective

Abstract

Next article FreeHealthcare and Medical Decision MakingEmerging Marketing Research on Healthcare and Medical Decision Making: Toward a Consumer-Centric and Pluralistic Methodological PerspectiveMeng Zhu, Dipankar Chakravarti, and Jian NiMeng Zhu Search for more articles by this author , Dipankar Chakravarti Search for more articles by this author , and Jian Ni Search for more articles by this author PDFPDF PLUSFull Text Add to favoritesDownload CitationTrack CitationsPermissionsReprints Share onFacebookTwitterLinked InRedditEmailQR Code SectionsMoreThe healthcare market has been changing rapidly since the new millennium, creating a need for a new, integrated perspective on consumer relevant healthcare topics through the lens of psychology, marketing, and economics (Wood 2018; Iacobucci 2019). Even though marketing and consumer researchers with both quantitative and qualitative orientations have recently joined forces to tackle these emerging topics, healthcare and medical decision making remain understudied substantive areas. The goal of the current special issue was to stimulate high-quality scholarly articles focusing on contemporary issues in healthcare and medical decision making from both consumer research and marketing science perspectives in order to advance our understanding of consumer, firm, and regulatory choices and their interactive impact on healthcare markets and relevant public policy.In this editorial, we first review the evolution of healthcare ecosystems, followed by a summary of extant marketing literature addressing healthcare issues. We then propose a consumer-centric and pluralistic methodological approach that we hope will advance the corpus on research in marketing that examines healthcare and medical decision making. Next, we summarize the nine articles included in this special issue and highlight the novel insights that they contribute. We conclude with a discussion of future directions and priorities in healthcare marketing and decision-making research.The Evolution of Healthcare EcosystemsThe twenty-first century healthcare system has shifted focus away from the traditional disease-oriented model to a patient-centered approach toward care and support (Frist 2005). This patient-centered healthcare model is grounded in the goals, values, priorities, and experiences of individual patients and encourages shared decision making in which patients and providers together design and manage customized care plans (Epstein 2000; NEJM Catalyst 2017). At the same time, technological innovation and transdisciplinary advances have helped double the volume of available healthcare, biomedical, and social research data every 12–14 months (Dinov 2016). Both the velocity (speed of data collection and accessibility) and variety (i.e., different types of data such as video, audio, text or log files) of healthcare data reflect this exponential growth (Dash et al. 2019). Moreover, the COVID-19 pandemic has accelerated the evolution of a healthcare ecosystem that is both patient-centered and data-driven (Singhal et al. 2020). Figure 1 graphically represents this rapidly evolving ecosystem (adapted from Singhal et al. 2020).Figure 1. The evolving healthcare ecosystem.View Large ImageDownload PowerPointSpecifically, the patients are at the center of the advanced analytics driven health system. On one hand, patients have access to traditional care modalities (e.g., pharmacy, hospital, clinic, assisted living), support systems (e.g., community, family, transportation, financing), as well as digital tracking (e.g., of nutrition and fitness) and virtual care (e.g., home health, compliance, adherence and monitoring tools). On the other hand, advanced analytics (e.g., natural language processing-, machine learning- and deep learning-empowered artificial intelligence, and big data analytics) uses provider- and patient-generated clinical as well as health and wellness data, along with financial and social structure data, helps enable personalized care and precision public health (Singhal et al. 2020; Velmovitsky et al. 2021), while guarding patient privacy and data security.Marketing Research on Healthcare and Medical Decision MakingA recent comprehensive literature review (Iacobucci 2019) shows that an average of 1.59, 3.24, 4.8, 4.37, 3.07, 7.05, and 1.34 healthcare-related articles have been published annually in Journal of Marketing (JM, N=132, since 1936), Journal of Marketing Research (JMR, N=178, since 1964), Journal of Consumer Research (JCR, N=216, since 1974), Journal of Consumer Psychology (JCP, N=118, since 1992), Marketing Science (N=92, since 1989), Journal of Public Policy and Marketing (JPPM, N=282, since 1979), and International Journal of Research in Marketing (IJRM, N=47, since 1984), respectively. With JPPM leading the trend in the 1990s, research on healthcare topics started receiving more space in these publications mainly after 2010. Prior to this special issue, JACR published a special issue on addictive and maladaptive (Reimann and Jain 2021), as well as other important articles (e.g., patient responses to healthcare innovations; Wood and Schulman 2019). More recently, Marketing Science has featured a special issue on health in 2020 (see Ailawadi et al. 2020), and a special issue on marketing in the healthcare sector is under preparation at JM.Spanning both firm and consumer behavior, a significant proportion of these articles cover choice and/or consumption issues in substantive areas such as food (e.g., Wood 2010; Block 2013), cigarettes (e.g., Gordon and Sun 2015; Wang, Lewis, and Singh 2016), drugs (e.g., Ching et al. 2016), medical treatments (e.g., Botti, Orfali, and Iyengar 2009), and health insurance (e.g., Mehta et al. 2017). Another significant body of work addresses the impact of marketer-controlled variables such as drug detailing (Chintagunta, Goettler, and Kim 2012), cigarette advertising (Pechman and Knight 2002; Keller 2006); surcharges and health labels (Shah et al. 2014); nutrition labels (e.g., Moorman 1990, 1996), nudges (Cadario and Chandon 2020), portion size (e.g., Scott et al. 2008; Chandon and Ordabayeva 2009), and related policy issues. Key health-related constructs that were examined in these articles include risk perception (e.g., Raghubir and Menon 1998), self-control (e.g., Wertenbroch 1998; Mukhopadhyay et al. 2008), and adherence to testing and treatment regimens (Kahn and Luce 2003), among others.Toward a Consumer-Centric and Pluralistic Methodological PerspectiveWe propose a substantive consumer-centric perspective in marketing research on healthcare topics aimed at building a corpus of work that embraces a pluralistic methodological stance (Chakravarti and Crabbe 2019). Our proposed approach is consistent with the two defining characteristics of the evolving healthcare ecosystem, that is, it is both patient centered and data driven (Singhal et al. 2020). The approach is also consistent with the anticipated future trajectories of healthcare marketing research highlighted in the Iacobucci (2019) article. Substantively, such research would focus at the micro-level on the heath-related behaviors of individual patients or patient segments, and at the macro-level on the broader healthcare system. At each level, the work would feature appropriate single or mixed methods, drawing on both qualitative and experimental methods, as well as traditional econometric and cutting-edge analytics tools for examining secondary and survey data.Specifically, we suggest four broad categories of focal dependent variables. These include health awareness and perception outcomes (i.e., individuals’ objective knowledge and subjective beliefs regarding their own health status; Henchoz et al. 2008); preventive care (i.e., services focusing on health evaluation and disease prevention, e.g., routine check-ups, screening tests and immunizations typically provided to asymptomatic individuals); diagnostic care (services that are typically provided when an individual shows symptoms or is at risk, e.g., tests or procedures to help diagnose, monitor or treat a medical issue or health condition); and wellness promotion (advocating the six dimensions of wellness: emotional, occupational, physical, social, intellectual, and spiritual; National Wellness Institute 2020).We also suggest four categories of independent variables as focal determinants of downstream healthcare outcomes. These include contextual influences (i.e., characteristics of local decision environments, e.g., information type, presentation format and textual structure), structural determinants (i.e., macro-level factors at the healthcare system level such as health coverage, market structure, and the socioeconomic environment; Mhasawade, Zhao, and Chunara 2021; Sawatzky et al. 2021); antecedent individual differences (e.g., in knowledge, attitudes, skills, goals, and values), and consumer segments or clusters (i.e., meaningfully differentiated consumer groups that share similar health needs, beliefs, attitudes, preferences, and sociocultural norms).In terms of methodology, healthcare research in marketing can benefit from more qualitative methods that allow deeper insights into the lived experiences of individuals and communities of patients and treatment providers as they confront the challenges of ill health and addiction. Traditional experimental approaches help identify and isolate factors that may serve as barriers as well as facilitators of ameliorating interventions. Similarly, traditional econometric and newer AI-based can extract insights from large aggregates of secondary and survey data that can inform the practice of personalized medicine and precision public health. Recent JACR issues on addiction and maladaptive consumption (Jain and Reimann 2021) and the COVID-19 pandemic (Goldsmith and Lee 2022) as well as a set of articles in the present issue exemplify the potential of such multi-method research perspectives.Importantly, the data collection, analytics, and reporting of such research must reflect due regard for individual privacy and data security. They must be transparent, shared, and developed through collaborative networks, and disseminated so as to be accessible to all entities in the healthcare ecosystem (Dinov 2016; Sullivan, DeHaven, and Mellor 2019). Researchers must strive for greater diversity and inclusivity in data, eliminate data and algorithmic bias, and enhance accountability through ongoing replication.Summary of the Articles in This IssueTable 1 provides a summary of the nine articles included in this special issue on Healthcare and Medical Decision Making. We draw on our framework to group these articles into three clusters: contextual influences on health perceptions; structural determinants of preventive and diagnostic care; and individual/group level correlates of wellness promotion.Table 1. Overview: Special Issue on Healthcare and Medical Decision Making Health perception, preventive care, diagnostic care, and wellness promotionContextual influencesArticle 1: Machine Learning Models for Predicting, Understanding, and Influencing Health Perception (Aka and Bhatia)▪ Machine learning: Sentence and word embedding models▪ National Health Service Data and Surveys done with Prolific samples (data and models available at https://osf.io/peym7/)Article 2: Empowering Consumers to Engage with Health Decisions: Making Medical Choices Feel Easy Increases Patient Participation (Steffel, Williams, and Carney)▪ Three experiments▪ MTurk and Prolific samples (data available at https://osf.io/4p9he/?view_only=970f5d02de8248cca67b9e92b7d8d613)Article 3: Psychological Causes of Medical Signs Decrease Perceived Severity, Support for Care, and Donations (Goksel, Faro, and Puntoni)▪ Experiments (three out of three preregistered)▪ Prolific samples (data and preregistrations available at https://osf.io/xm2jw/?view_only=696bfe4e6b8b43f3a19dc01badee21df)Structural determinantsArticle 4: Quantifying the Zero-Price Effect in the Field: Evidence from Swedish Prescription Drug Choices (Ching, Granlund, and Sundström)▪ Quasi-regression discontinuity design▪ Regional administrative data on consumer drug choices from SwedenArticle 5: Motivated Inferences of Price and Quality in Healthcare Decisions (Prinsloo, Barasz, and Ubel)▪ Five experiments ( https://aspredicted.org/blind.php?x=cc9iy6)▪ MTurk samplesArticle 6: Consumer Health in the Digital Age (Liu, Inman, Li, Wong, and Yang)▪ Conceptual article introducing a consumer-centric framework for studying consumer health in the digital ageIndividual characteristics and consumer clustersArticle 7: Advance Care Plans: Planning for Critical Healthcare Decisions (Gurdamar-Okutur, Botti, and Morwitz)▪ Survey and experiments▪ Records from an ACP platform and MTurk samples (data and materials available at https://osf.io/wdb5a/?view_only=397426d95640458594ef8808c5275e68)Article 8: The Enthusiasts and the Reluctants of COVID-19 Vaccine Uptake: A Cluster Analysis (Lee, Wang, Böckenholt, Lee, Ohme, Reykowska, and Yeung)▪ Two surveys (https://aspredicted.org/ZIS_DJW)▪ Cloudresearch.com samplesArticle 9: Is Religiosity a Barrier to Organ Donations? Examining the Role of Religiosity and the Salience of a Religious Context on Organ-Donation Decisions (Harel, Mayorga, Slovic, and Kogut)▪ Experiments▪ Christian and Jewish samples View Table Image Contextual Influences on Health PerceptionsThe importance of predicting, understanding, and influencing how individuals judge the severity of a health issue and their own vulnerability has been made abundantly clear by the population’s response to the COVID-19 pandemic (Van Bavel et al. 2020). However, the significance of these issues extends further, affecting not only individual and group health outcomes but also overall societal welfare. There is also a collateral impact on research support with second order and spillover effects.Aka and Bhatia (2022) investigate the contextual influences of textual descriptions on health perceptions. They combined the text explanations and discussions of 777 unique health states from the National Health Service (NHS) website with large-scale survey data. They use the database to train a machine learning model that employs sentence and word embedding to predict lay health perceptions (data and models at https://osf.io/peym7/). The resultant accuracy rates are higher than those obtained from other predictors such as number of deaths, disability adjusted life years, and search frequency. Their work showcases how integrating cutting-edge data science technologies with consumer health behavior research can help develop scalable tools that can be easily adopted by researchers and policy makers to predict and influence lay perceptions of medical conditions and treatments.Steffel, Williams, and Carney (2022) add new insights on the contextual influences on health perceptions on health behaviors. They report three experiments with Amazon Mechanical Turk (MTurk) and Prolific samples (data at https://osf.io/4p9he/?view_only=970f5d02de8248cca67b9e92b7d8d613) showing that presenting medical treatment decisions in a fluent (tabular, plain language) versus a disfluent (paragraph, medical jargon laden) format enhances subjective comprehension. This makes people believe that they understand the choice better. Hence, they feel more confident in their ability to choose. In turn, higher perceived comprehension and confidence, in turn, empower individuals to participate in their own medical treatment decisions, instead of delegating to medical professionals. The implications for healthcare professionals and policy makers are clear: simplify health communications, and develop and incentivize accessibility standards.Goksel, Faro, and Puntoni (2022) study how the origin of a health problem, psychological versus physical, affects lay perceptions of its severity. In three experiments with Prolific samples (data at https://osf.io/xm2jw/?view_only=696bfe4e6b8b43f3a19dc01badee21df), they show that people perceive the same medical symptom (e.g., a cough) as less severe (e.g., less harsh and scratchy) when it originates from psychological (e.g., anxiety) versus physical (e.g., drinking contaminated tap water) cause. This perception of lower severity lowers the likelihood of care recommendation and prioritization. Moreover, it also decreases the level of financial support for scientific research for developing cures. These findings suggest an explanation for why mental health issues are often overlooked and research is underfunded (Holmes et al. 2020).Structural Determinants of Preventative Care and Diagnostic CareAs illustrated in figure 1, the evolving healthcare ecosystem is centered on patients, their needs, and care journeys, including but beyond the clinic itself (Singhal et al. 2020). The number of healthcare touchpoints include traditional care modalities as well as the broader macro-level structural elements, such as payment schemes, benefits/insurance coverage, digital devices, and virtual platforms. The current issue includes three articles that form a second cluster which examines how preventative as well as diagnostic care are affected by structural determinants in the healthcare ecosystem, such as zero pricing, insurance coverages, and digitalization.Ching, Granlund, and Sundström (2022) employ a large-scale data set containing 2,981,745 observations of prescription drug fills by adult inhabitants of a Swedish county to examine whether zero-price triggers a large, discontinuous jump in demand (the zero-price effect). The health benefit scheme in Sweden provides a unique quasi-random environment˙ that allows for the estimation of the zero-price effect using a regression discontinuity approach. The study provides the first documentation of the zero-price effect in the real-world setting. The findings also highlight a potential downside of health systems where care is free to all: extremely long waits for medical care and severe overcrowding in emergency rooms.Prinsloo, Barasz, and Ubel (2022) study situations where care is not free and examine how individual consumers’ insurance coverages might affect the choices made by their providers. Across five experiments conducted with MTurk samples, they find that consumers whose insurance plans provide lower (vs. higher) coverage are more likely to choose lower-priced providers. This occurs because low-coverage consumers find it more difficult to justify the choice of higher-priced providers. Instead, they are motivated to perceive a smaller quality gap between providers, rating lower-priced providers as higher in quality. These findings suggest that price transparency policies may exacerbate inequalities in healthcare.Liu and colleagues (2022) coauthor a thoughtful conceptual article that presents a framework for studying consumer health in the digital age. Specifically, they develop a matrix that delineates how three key digitalization affordances (personalization, interactivity, and information transparency) would collectively shape healthcare across three stages of the patient journey (preclinic, in-clinic, and postclinic). Their discussion not only anticipates the positives that could stem from these affordances but also highlights the key tensions to resolve and the barriers to overcome. These include privacy concerns, lack of consideration of consumer uniqueness (Longoni et al. 2019), information overload, and the escalation of worries.Wellness Promotion: Individual- and Group-Level CorrelatesThe third and final cluster includes three articles that identify individual- and group-level correlates of three unique medical decisions that promote general wellness. These are advance care planning (i.e., learning about medical decisions that may be made when one is unable to speak for oneself, considering these decisions ahead of time, and communicating preferences to family and health care providers; NIH 2018); vaccination (protecting against harmful disease before actual contact by training the body’s natural defense to create antibodies and build resistance to specific infections; WHO 2021); and organ donation (giving an organ or a part of an organ for transplant into another person; HRSA 2021).Gurdamar-Okutur, Botti, and Morwitz (2022) explore why few people worldwide have advanced care plans (ACPs) despite their obvious benefits (e.g., preventing costly unwanted treatments and ensuring preferred end-of-life treatments). The authors use records from an ACP platform to identify four clusters of ACP holders. Their MTurk studies (data at https://osf.io/wdb5a/?view_only=397426d95640458594ef8808c5275e68) find that ACP holders and nonholders share some commonalities (e.g., a preference for being able to care for self and avoiding prolonged end-of-life medical interventions among older adults). However, those with (or intending to create) ACP show a higher preference for a peaceful end of life avoiding invasive life-sustaining treatments. Two interventions highlighting values and preferences for the self and similar others show inconsistent effects on ACP adoption. The authors call for future research on structural interventions targeting government agencies, healthcare providers and insurance companies.Lee and colleagues (2022) identify psychological and behavioral correlates for COVID-19 vaccine uptake, another important medical decision. The authors report two multi-wave studies that help identify US consumer segments that differ in perceptions, attitudes, concerns, and behaviors related to COVID-19 pandemic. While lack of trust in the vaccine and concern about side effects emerge across segments as common barriers to vaccine uptake, the segments also differ on other implicit drivers. Thus, concern for others and isolation anxiety relates to the vaccine decisions of segments labeled vigilant enthusiasts (70% vaccinated) and affluent receptives (80% vaccinated), respectively, whereas income and financial vulnerability relates to vaccination status for segments of skeptical reluctants (28% vaccinated) and vulnerable hesitants (52% vaccinated). These findings highlight the importance of targeted interventions versus an one-size-fits-all approach to understanding and mitigating COVID-19 vaccine hesitancy.Harel and colleagues (2022) take a closer look at the role of religiosity in individuals’ organ-donation decisions. Across three experiments conducted with Christian and Jewish samples, they find that religiosity decreases willingness to donate organs. Making religion salient at the time of the decision lowers willingness to support a transition to an opt-in policy. The authors note that only extrinsic (vs. intrinsic) religious attitudes correlate with donation intention; and that salience of religion could increase organ-donation intent for secular people (perhaps by raising the salience and accessibility of their own moral standards). The results help reconcile previous mixed findings on the impact of religiosity on organ donation. They also highlight the importance of culture-sensitive health communication (Betsch et al. 2016) and why medical professionals should attend carefully to donors’ religious orientations when eliciting organ donor registrations.Future Research DirectionsThe contributions made by the articles in this special issue highlight the wealth of future research opportunities that await marketing scholars interested in focusing on healthcare and medical decision-making topics. Several research issues emerge as key priorities.• What are the key differences among markets for healthcare versus other goods and services?• What are the key psychological, sociocultural, and economic factors that shape consumer decisions regarding health maintenance activities, physician and treatment choices, insurance coverage, adoption of innovative drugs, and personalized medicine?• What encourages shared decision making in patient-physician interactions? What qualitative information, data, and metrics facilitate high quality healthcare choices?• What key factors drive the micro-level choices made by various agents in the healthcare market (e.g., patients, physicians, providers, payers, institutions, and manufacturers)? How are these choices linked across agents and sequenced over time?• How do the micro-level drivers translate into macro-level (aggregate) outcomes at the individual and societal level? What is the role of public policy and regulation in facilitating this translation?• What micro-level deficiencies and systemic imperfections provoke public debates (e.g., cost inflation, lopsided medical research and development [R&D], disparate access to care, moral hazards, and adverse selection)? What are some of the leading indicators of these problems?• How is (should) social welfare being (be) conceptualized and measured in healthcare markets? What antecedent role does (should) health play in well-being (ill-being)?• What are the fundamental micro- and macro-level differences between healthcare markets in the developing versus developed economies (Chakravarti 2006)? How do these differences influence medical and healthcare decision making at the individual consumer, firm, and policy levels?• How do (should) one address fairness concerns (e.g., gender and ethnicity biases) with medical algorithms and other ethical issues in healthcare interactions (Obermeyer et al. 2019)?• What are potential consumer level concerns regarding privacy, security, and personal safety in healthcare and medical contexts? How can these be mitigated?We believe that employing a substantive consumer-centric focus and adopting a pluralistic methodological perspective would allow marketing and consumer researchers to generate insights that add value beyond core medical knowledge. 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