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Although there is evidence that the efforts of pharmaceutical marketing exert a positive and significant influence on physician prescription decisions, the specific mechanisms by which this impact occurs remain uncertain. To address this issue, this study aims to investigate whether the physician’s habit persistence may explain the relationship between marketing efforts such as brand attributes and drug promotion and prescribing behavior. This study also explores whether there was any significant difference between specialists and general practitioners (GPs) regarding the effect of brand attributes and drug promotion on physician’s habit persistence, which influences prescribing behavior.

A survey instrument was administered to physicians, encompassing specialists in a particular field and GPs who provide primary care services. A nonparametric partial least squares multigroup analysis was used to analyze 393 valid responses using partial least squares-structural equation modeling.

The results indicate that the decision to prescribe the drug may be affected by the physician’s habit persistence in the presence of vital brand attributes and drug promotion. The relationship between physician’s habit persistence and prescribing behavior is significant in GPs and specialist groups. The findings also reveal that brand attributes are the most influential and significant determinant of physician’s habit persistence in the GPs group. In addition, physician specialists are more aware of the influence of drug promotion initiatives than GPs. Furthermore, this study discovered that the relationship between drug promotion and physician’s habit persistence indicates a significant difference between specialists and GPs.

Although several marketing scholars discuss the factors influencing prescribing behavior, a few studies have shown the role of brand attributes and drug promotion and their effect on physician’s habit persistence. This study will specifically contribute by examining the mediating role of physician’s habit persistence between marketing efforts and prescribing behavior. In addition, so far, no studies have effectively made a comparative analysis across physicians regarding the effect of marketing efforts on physician’s habit persistence and prescribing behavior.

The purpose of this paper is to use results from a comprehensive analysis of a physician‐prescribing model to draw guidelines on how to promote a new drug in the presence of competing older drugs, in a chronic therapeutic state.

The authors use an extensive database from SDI Health, LLC, and a second data set from IMS Health. They calibrate their model using logarithmic regression methodology. The dependent variable in the model is number of new prescriptions and the explanatory variables are physician and patient characteristics, and marketing variables.

The authors' estimates imply that heavy prescribers are likely to be specialists, be in solo practice, have more experience, receive more sales rep traffic, have more HMO affiliations, have a higher proportion of patients in HMOs, write more prescriptions across all therapeutic categories, see a higher number of patients, receive more free samples from the sales reps, have more rep intensity in their offices, and allow longer visits by sales reps.

This model has novel implications for drug manufacturers on the effect of time‐in‐the‐market. Accordingly, new drug makers are well‐advised to wait until a drug gets established in the community for it to be prescribed more heavily by specialists and target physicians in solo practice and newer physicians to speed up the adoption process. Furthermore, for newer drugs traditional forms of detailing via a live sales rep are not as effective as for older more established drugs – the new‐drug manufacturer can try other means such as e‐detailing, social media, direct‐to‐consumer advertising, and word‐of‐mouth/mouse to initiate market share.

Launching a new product successfully in a multi-brand portfolio is one of the major challenges a pharmaceutical marketer faces. This study aims to examine the role of detailing of new brands on physicians’ prescription behaviour as compared to established brands. Further, the study explores mediating role of detailing priority and detailing time on the relationship between detailing of new versus established brands and physician’s prescription behaviour.

This study was conducted as a real-world observational study involving field research. In total, 338 physicians, 90 PSRs and 44 field managers participated in this study. A serial mediation model (Hayes, Model 6) was used to examine the relationship. Regression analysis with bootstrapping was used to test the hypotheses.

Detailing of new versus established brands has a differential effect on physicians’ prescription behaviour. In addition, this relationship is serially mediated by detailing priority and detailing time.

Results suggest that detailing priority and detailing time positively and significantly alter the relationship between the detailing of new brands and physicians’ prescription behaviour as compared to established brands. While, in the absence of mediators, established brands generate higher prescriptions than new brands, the serial mediating effect helps new brands to generate more prescriptions as compared to established brands.

This research highlights the importance of detailing priority and detailing time for the successful launch of the new products. It presents compelling evidence for practicing managers to effectively use a “predetermined detailing plan” vis-à-vis “individualized detailing strategy” during the launch of a new brand.

To the best of the authors’ knowledge, this is the first study to evaluate the role of detailing priority and detailing time as mediators between the relationship of detailing and physicians’ prescription behaviour. This is also one of the rare studies to use real-world observational study methodology for conducting research.

This paper aims to explore whether and how consumers perceive the impact of pharmaceutical marketing on their own doctor’s prescribing behaviors, and subsequent responses toward their doctor’s advice.

Three experimental studies were conducted. Studies 1 and 2 are based on text-based manipulations and undergraduate student research participants. Study 3 uses image-based manipulations and average adult consumers.

Study 1 demonstrates that consumers can be quite skeptical about their doctor’s motives for prescribing certain brand-name drugs; in particular, consumers can construe doctors as agents of persuasion for prescribed brands. Study 2 shows that this can result not only in choosing generic drugs over prescribed brands but also in opting out of pharmaceuticals altogether by choosing alternatives like natural remedies. Study 3 further demonstrates that these effects can be easily triggered by visual cues in a non-student sample.

This research builds on the existing literature on pharmaceutical marketing communications, and extends the theory of persuasion knowledge into healthcare settings.

The purpose of this paper is to propose data mining techniques to model the return on investment from various types of promotional spending to market a drug and then use the model to draw conclusions on how the pharmaceutical industry might go about allocating promotion expenditures in a more efficient manner, potentially reducing costs to the consumer. The main contributions of the paper are two-fold. First, it demonstrates how to undertake a promotion mix optimization process in the pharmaceutical context and carry it through from the beginning to the end. Second, the paper proposes using directed acyclic graphs (DAGs) to help unravel the direct and indirect effects of various promotional media on sales volume.

A synthetic data set was constructed to prototype proposed data mining techniques and two analyses approaches were investigated.

The two methods were found to yield insights into the problem of the promotion mix in the context of the healthcare industry. First, a factor analysis followed by a regression analysis and an optimization algorithm applied to the resulting equation were used. Second, DAG was used to unravel direct and indirect effects of promotional expenditures on new prescriptions.

The data are synthetic and do not incorporate any time autocorrelations.

The promotion mix optimization process is demonstrated from the beginning to the end, and the issue of negative coefficient in promotion mix models are addressed. In addition, a method is proposed to identify direct and indirect effects on new prescriptions.

A better allocation of promotional expenditures has the potential for reducing the cost of healthcare to consumers.

The contributions of the paper are two-fold: for the first time in the literature (to the best of the authors’ knowledge), the authors have undertaken a promotion mix optimization process and have carried it through from the beginning to the end Second, the authors propose the use of DAGs to help unravel the effects of various promotion media on sales volume, notably direct and indirect effects.

In pharmaceutical marketing, salespeople require promotional instruments to satisfy and retain their customers (physicians), but companies ignore the need because of the costs involved. It appears that the importance of promotional support is understood differently by managers and sales representatives. This study aims at developing a new construct called “promotional support”, as a resource support provided to salespeople to help them satisfy customer needs and achieve sales goals.

A detailed review of literature is followed by in-depth interviews to create items for the construct “promotional support”. Substantive validity is performed for identification of items measuring the construct. To initially validate the measures, a vignette study is performed. Formative index development procedure is followed. Data from a sample of 124 pharmaceutical sales representatives are collected and analyzed.

A formative construct is developed which possesses convergent (redundancy analysis) and nomological validity. The results of post hoc validation tests like confirmatory tetrad analysis and inter-item correlation are satisfactory and confirm the authors’ conceptualization.

The scale requires external validation by testing it with different samples such as managers of pharmaceutical firms.

This study will help managers of pharmaceutical firms gain insights into the role and importance of promotional inputs given to their sales representatives.

This study uses the formative scale development procedure to develop promotional support. This construct can be used to understand the effect of promotional support given to pharmaceutical sales representatives on other constructs, which in turn helps in theory-building.

The quality and reliability of medical communication for branded drug adoption is extremely critical, not only for safeguarding patient interests but also for ensuring successful investments by multinational pharmaceutical firms. This paper predicts doctors’ prescribing intentions based on communication relationship among factors for late entrant branded drugs, compared with pioneering brand choice, for treating chronic diseases such as hypertension.

The constructs were validated with structural equation model for a sample set of 151 doctors from private hospitals in the National Capital Region of India.

This research reveals communication drivers and draws on theory to suggest that the doctor’s behavioural prescription intentions, subject to social influence from their colleagues, leads to lower adoption responses.

Given that limitations on sample size are often unavoidable, this study reveals that, due to the availability of substituting brands, alternate therapeutic routes and lack of availability of a practical guide for prescription, a communication model needs to be developed and validated.

Furthermore, managers of pharmaceutical firms should differentiate between the effects of direct and indirect communication–integration efforts for minimizing uncertainty in drug adoption in the context of the fragmented and unpredictable Indian market.

A late entrant may lose its dominant market share to alternate brands from other suppliers due to communication gaps in an unstructured market, leading to low adoption intentions. The study provides business theorists, drug marketers and health-care professionals with unique insights into specific communication drivers of prescribing decisions, aimed at ensuring reliable and appropriate drug adoption in Indian markets.

Reusing existing data sets of health information for public health or medical research has much to recommend it. Much data repurposing in medical or public health research or practice involves information that has been stripped of individual identifiers but some does not. In some cases, there may have been consent to the reuse but in other cases consent may be absent and people may be entirely unaware of how the data about them are being used. Data sets are also being combined and may contain information with very different sources, consent histories, and individual identifiers. Much of the ethical and policy discussion about the permissibility of data reuse has centered on two questions: for identifiable data, the scope of the original consent and whether the reuse is permissible in light of that scope, and for de-identified data, whether there are unacceptable risks that the data will be reidentified in a manner that is harmful to any data subjects. Prioritizing these questions rests on a picture of the ethics of data use as primarily about respecting the choices of the data subject. We contend that this picture is mistaken; data repurposing, especially when data sets are combined, raises novel questions about the impacts of research on groups and their implications for individuals regarded as falling within these groups. These impacts suggest that the controversies about de-identification or reconsent for reuse are to some extent beside the point. Serious ethical questions are also raised by the inferences that may be drawn about individuals from the research and resulting risks of stigmatization. These risks may arise even when individuals were not part of the original data set being repurposed. Data reuse, repurposing, and recombination may have damaging effects on others not included within the original data sets. These issues of justice for individuals who might be regarded as indirect subjects of research are not even raised by approaches that consider only the implications for or agreement of the original data subject. This chapter argues that health information should be available for reuse, information should be available for use, but in a way that does not yield unexpected surprises, produce direct harm to individuals, or violate warranted trust.

To explain how technology will replace a great deal of human labor in knowledge markets using a theory of reasoned action applied to demand and theories of procedural rationality, cost structure and system dynamics applied to supply.

Two illustrative scenarios are presented. The first is a third-party Best Treatments site, and its effect on the expert advice pharmaceutical representatives provide doctors. The second scenario is an online higher education business course module with embedded AI.

Both scenarios demonstrate the advantages of online expertise and teaching platforms over the in-person alternative in variable and marginal cost, ease and convenience of use, quality conformance, scalability, knowledge reach and depth and most importantly, speed of evolutionary adaptability. Despite such overwhelming advantages, a number of reasons why the substitution might be slowed are presented, and some strategies firms might adopt are discussed. Opportunities for service scholars to confirm, challenge and extend the conclusions are presented throughout the paper.

Increasing cost structure and adaptability advantages of online technology and AI over in-person delivery of expertise and training services are demonstrated. It is also demonstrated that the innovation-imitation cycle is accelerating because of exogenous innovation in knowledge access and online influence networks and an endogenous effect where imitators accelerate their innovation that drives innovators to accelerate their innovation, which drives imitators to further accelerate their imitation.

The aim of this paper is to identify factors (i.e. age, gender, ethnicity, type of medical facility, geographical location, etc.) associated with physicians' prescribing behavior when treating influenza in the USA. The study aims to examine why the number of antiviral prescriptions remains substandard.

Data were obtained from the National Ambulatory Medical Care Survey for each influenza season between the years of 2005-2008. Bivariate analyses and two models of multivariate logistic regression analyses (one with no fixed effect and the other including year as a fixed effect) were used to analyze the data.

The results from this study revealed that among family practice physicians, 40.5 percent prescribed antiviral medications to patients presenting with influenza while 59.5 percent prescribed another form of medication. Antibiotics comprised 41.3 percent of the prescriptions for treatment of influenza. Multivariable logistic regression analyses revealed that race (White; p=0.023), type of health setting (private solo/group practice; p=0.041), employment status (owner; p=0.046), and metropolitan location (metropolitan statistical area; p=0.032) were all significantly associated with prescribing antivirals. Patients' expected source of payment (private insurance) and geographical location (Midwest) of health facility were marginally associated with prescribing antivirals.

By identifying factors associated with physicians' prescribing practices of antiviral medications, a more timely diagnosis and treatment of influenza can occur. Efforts should be targeted to improve physician education and awareness of the illness. Interventions may be implemented to improve the prescribing of antiviral medications and potentially inappropriate prescribing.