The When and Why of Abandonment: The Effect of Organizational Incentives on Technology Lifecycles

Abstract

Although the adoption of new technology has received significant attention in management research, the study of abandonment has lagged. While abandonment often occurs as a dual to the adoption of a superior technology, technology use may also decline in light of questionable efficacy. Arguing that organizational incentives which are aligned during periods of adoption may become misaligned during periods of abandonment, we investigate how economic and non-economic organizational incentives moderate the rate of technological abandonment. The focal technology in our study is the use of stents for the treatment of stable coronary arterial disease. Using a census of 1.4 million patients admitted to Florida hospitals during times of technological regime change, results indicate that organizations respond more slowly when driven by pecuniary incentives alone, but accelerate abandonment when pecuniary incentives are coupled with adherence to the norms of science as a key organizational value. Importantly, we find that organizational factors dominate physician differences, underscoring the role of organizational norms in shaping individuals’ decisions.

Key Words: technology abandonment, organizational incentives, norms of science, financial incentives, healthcare, medical

devices, medical guidelines, econometric analysis

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Introduction

The adoption of technology has persisted as a central theme of scholarly literature for decades, and has been

examined at the societal (Gort and Klepper 1982, Rogers 1995), organizational (Franco et al. 2009, Kapoor

and Furr 2014), and individual level (Agarwal 2000, Franco et al. 2009, Venkatesh et al. 2003). However,

despite the existence of a robust body of research on technology adoption (Abrahamson and Rosenkopf

1993, Angst et al. 2010, Davis 1985, Edmondson et al. 2001, Kennedy and Fiss 2009, Venkatesh et al. 2003)

the dual and arguably equally consequential process of technology abandonment is less studied. Extant

research suggests that the need to abandon previously adopted technologies could arise for at least two

reasons (Kennedy 2011). First, the abandonment of a technology may be necessitated by the emergence of a

superior technology when new discoveries are made, because failure to stay at the cutting edge of practice

may yield negative outcomes by putting the firm at a competitive disadvantage (Mitchell 1991, Tripsas 2009).

Intuitively, abandonment in this case should become the “twin” of adoption, because the utilization of the

emerging technology cannibalizes the use of the antiquated technology. Alternatively, new information about

the efficacy of a technology may be discovered, thereby necessitating its abandonment, in which case the rate

of abandonment cannot be related to the rate of adoption of an alternative technology. To the degree that the

limited literature examining technology abandonment does so primarily in the former context (where superior

technologies have emerged (Finkelstein and Gilbert 1985)), we suggest that a comprehensive investigation of

organizational response to technological regime change is warranted.

In this work, we seek to unpack the relationship between the drivers of organizational adoption and

abandonment of technology, thereby providing a more nuanced understanding of the two processes. We do

so by posing the following research questions: are the factors that predict organizational abandonment of

technology the same as those that influence organizational technology adoption? Further, how do varying

organizational incentives moderate the rate of technological abandonment? We approach these puzzles by

juxtaposing the effects of financial incentives and social norms in a context of significant economic and

societal importance: the adoption and abandonment of medical treatments in hospitals. That organizations

are motivated to adopt technologies that confer economic benefits, and those with stronger financial

incentives will do so faster, is intuitive and well known. However, what is less understood is the extent to

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which social norms for science (Merton 1973) influence rates of technology abandonment that are triggered

by different underlying causes, and how they interplay with financial incentives.

While important for a wide variety of organizations, these questions are notably salient in the case of

hospitals for two significant reasons. First, significant variation exists in both the degree to which

organizations seek profits, as well as the significance they accord remaining at the cutting edge of medical

practice. Second, innovation, discovery, and the development of new technologies are quintessential

characteristics of the practice of medicine, which can be traced back over centuries (Bynum and Porter 2013),

e.g. the use of ether for surgical anesthesia in 1846, the discovery of penicillin in 1928, the use of computed

tomography (CT) scan technology in 1971. Thus, an analysis of the interaction of innovation adoption and

abandonment and varying organizational norms is particularly apposite in the context of hospitals. The focus

of this paper is on one specific innovation – the utilization of coronary stents (or percutaneous coronary

interventions (PCI)) for the treatment of stable coronary arterial disease (SCAD). Medical treatment of SCAD

has evolved over the years through a number of technology cycles. Due to the wide variety in the severity of

SCAD, multiple treatment options have emerged; ranging from coronary artery bypass grafts (an invasive

surgical procedure where the clogged section of artery is physically replaced) to pharmacological treatment

coupled with lifestyle changes.

To investigate how financial and social incentives influence the evolution of the practice of stenting

we leverage a longitudinal data set spanning from 1995-2007 which captures a census of stenting decisions in

hospitals in the state of Florida. These data afford us a unique opportunity to observe three discrete and

distinct changes in the technological regimes surrounding the use of stents as a treatment for SCAD. The

first is the FDA-approved introduction of bare metal stents in 1995 which presented a revolutionary advance

in previous approaches to treating SCAD. The second regime change occurred in 2002 with the development

of drug eluting stents, an innovation that represented a significant improvement in the base technology for

stents (i.e. the emergence of a superior technology). The third regime change results from a watershed

medical guideline released jointly by the American Heart Association (AHA) and the American College of

Cardiology (ACC) in December 2005 (Smith et al. 2006). This guideline questioned the efficacy of stents for

treating low severity SCAD, recommending explicitly that they not be used for the treatment of low severity

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heart disease patients. Empirically, the use of stenting as a research setting offers us several benefits. First, for

the purpose of identification, the release of bare metal stents, drug eluting stents, and the AHA/ACC

guideline are exogenous to the studied physicians. Second, because we are able to study both forms of

abandonment within serial generations of the same technology, we are able to exploit a within-subjects

estimate and mitigate the effect of unobserved contextual heterogeneity that may arise when examining

different technologies being abandoned for different reasons in varying contexts, a significant step forward

from existing research studying adoption and abandonment.

Our empirical analysis provides several insights into the role of financial incentives and social norms

in driving the technology adoption and abandonment decisions of hospitals. Findings suggest significant

differences in organizational responses across the two reasons for abandonment. We observe that

organizations with social incentives to adhere to scientific norms, i.e. teaching hospitals affiliated with

academic medical centers (AMCs) where significant original research is produced, adopt the use of stent

technology significantly faster than all other organizations. Further, these hospitals abandon the use of stents

faster than other organizations, both when there is a superior technology available and when its efficacy for

low severity SCAD patients is called into question. Interestingly, among for profit hospitals, i.e. hospitals with

less pressure to adhere to the norms of science but have significant financial incentives, we find considerable

variation in the rate of abandonment in response to the two reasons identified here. Relative to not-for-profit

hospitals, for-profit hospitals abandon the old technology much faster in the presence of a new and superior

technology. However, when the efficacy of stenting is questioned, for profit hospitals abandon the use of

stents slower than all other types of hospitals. Strikingly, our robustness tests suggest that these differences in

adoption and abandonment are a result of organizational level factors and not simply a result of individual

level (i.e. physician) response. We see no differences in adoption and abandonment behavior among either

research active faculty physicians or those with clinical appointments across organizations. Further, physicians

who split their practice across multiple hospitals (i.e. freelance physicians (Huckman and Pisano 2006)) also

vary their behavior to conform to the dominant norms of the organization where the specific procedures are

performed, i.e. the hospital setting matters even across the same physician. These results suggest that in spite

of the significant agency possessed by physicians, their behavior is consistent with the identity of the

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organization where they practice (Tripsas 2009).

Our study contributes to ongoing research in technology adoption and abandonment in at least four

ways. First, we study an important organizational process – technology abandonment – which has received

limited attention in prior work (Burns and Wholey 1993, Howard and Shen 2012), and show that rates of

abandonment may differ across organizations based on the impetus for that abandonment (superior

technology vs. questioning of efficacy of current practice). Second, in exploring how distinct organizational

incentives, both financial and social, affect processes of adoption and abandonment simultaneously, we

demonstrate the salience and dominance of the norms of science, where hospitals trade-off economic

benefits in order to remain on the cutting edge of practice. These findings offer new insight into how

different organizations react to technological regime change. Third, to the extent that our individual level

analysis reveals that decision makers known for significant agency nonetheless conform to the dominant

practice of their organizations, results suggest that organizational identity and incentives can overshadow

individual ones in determining rates of abandonment. Finally, our results offer one plausible explanation for

mixed findings that have characterized the study of organizational abandonment to date, organizational

incentives. To the degree that many researchers, in both management (Burns and Wholey 1993, Finkelstein

and Gilbert 1985) and medicine (Greer 1981, Howard and Shen 2012), have found divergent results regarding

the abandonment of technology and practices, our study suggests that these may be the result of underlying

heterogeneity in organizational incentives and impetus for technology abandonment which, to date, have not

been explicitly considered.

Theory and Hypotheses

The adoption and diffusion of new technology and practices has remained an important field of study in

management research for decades (Abrahamson and Rosenkopf 1993, Angst et al. 2010, Davis 1985,

Edmondson et al. 2001, Gort and Klepper 1982, Kapoor and Furr 2014, Kennedy and Fiss 2009, Venkatesh

et al. 2003); see Agarwal and Tripsas (2008) and Venkatesh (2006) for recent summaries. However, perhaps as

a result of a predominant emphasis on innovation and the adoption of emerging technologies, research on

technology abandonment has lagged (Howard and Shen 2012). In Table 1, we summarize recent

representative studies examining the abandonment of technology at the organizational and individual level.

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Our review leads to three conclusions. First, compared to technology adoption, there is a significantly smaller

body of work examining technology abandonment as a phenomenon. Second, studies examining

abandonment of technology and practices (Adner and Levinthal 2004, Greve 1995, Rao et al. 2001) have

abstracted away from a systematic comparison of drivers of adoption and abandonment. In particular, it is

not clear if abandonment is always a dual, accompanying the adoption of a superior technology, or whether

factors that are not relevant in the adoption decision, such as de-legitimation of the technology (Kennedy,

2011), may cause differences in rates of abandonment. Finally, we note that the findings of these studies vary

widely. While most agree on when abandonment should occur, viz. when the organization or individual is

placed at a competitive disadvantage by pursuing its present course of action, this does not always occur.

Greve (1995), for example, finds that the abandonment of radio station formats spreads quickly after the

herding away from “easy listening” began. Conversely, Burns and Wholey (1993) find little correlation

between local abandonment of managerial structures and the decision of hospitals to abandon their own

organizational structures.

The equivocal results surrounding the abandonment of technology and practices have also been

found in medicine, where the benefits and drawbacks of each potential treatment are rigorously codified

before the treatment is available for use. Some treatments, such as gastric freezing, are abandoned well before

the medical evidence outlining their flaws was released to the public (Greer 1981). Others such as

pharmaceutical prescription (Finkelstein and Gilbert 1985), experience some change when an emerging drug

offers a relative edge over established drugs, but the abandonment patterns remain erratic. Still further,

treatments like episiotomies continue to be used frequently despite extensive medical evidence of the serious

harm they can do (Howard and Shen 2012, Lede et al. 1996). Together, these findings beg the following

questions: When may the rates of adoption and abandonment of technologies be the same? If they do differ,

what are the organizational factors that may cause rates of adoption and abandonment to differ?

We posit that one plausible explanation for the mixed findings to date could be the presence of

salient and influential organizational incentives. Although incentives have been explored extensively in the

context of technology adoption, ranging from the pecuniary benefits the firm can reap from utilizing

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emerging technologies (Kapoor and Furr 2014) to the legitimizing effect of exploiting nascent innovations

(Abrahamson 1991), their role in technology abandonment has been largely overlooked. Further, while

traditional economic theory would predict that all organizations would be incentivized to adopt technologies

(such as stents) which confer economic benefit in the form of enhanced revenues and potentially limit

competitive disadvantage, less is known about the response of organizations whose identities are also

intrinsically constructed around norms of research and science, in addition to the presence of economic

benefits. We describe this interplay between social norms and economic incentives in the context of

technology adoption and abandonment in more detail as we propose our research hypotheses next.

Financial Incentives, Social Norms, and Rates of Technology Adoption and Abandonment

There are several reasons why organizations with incentives to profit maximize will rapidly adopt emerging

technologies. First and foremost is the pecuniary benefit which can result from direct utilization of the

technology in the specific function or process it is designed for (Venkatesh et al. 2003). Second, leveraging

the technology may increase the economic efficiency of knowledge and assets which are currently held by the

firm through complementarities, thereby enhancing the firm’s economic value (Kapoor and Furr 2014).

Third, exploitation of the new technology may open new markets for the firm. Similar to the mechanism of

asset recombination, leveraging the emerging technology helps increase either the scope of products offered

or increase the reach of the firm; thereby allowing it to penetrate previously untapped markets (Moeen 2013).

These financial incentives are similarly influential within the healthcare context. However, significant

differences exist in the objectives of for-profit and not-for-profit hospitals. While both types of hospitals are

subject to many of the same regulations when treating patients, e.g. the Stark Law (Wales 2003), the anti-

kickback statute (Bales et al. 2014), and the Emergency Medical Treatment & Labor Act (EMTALA) (Lee

2004), there are differences in the regulatory structure that apply to not-for-profit hospitals. For example, not-

for-profit community hospitals are required to perform triennial community assessments in order to maintain

their 501(c)(3) tax exempt status1 (Bales et al. 2014), and are directly accountable to the communities they

1 The purpose of the triennial assessment is to gather information about the needs of the community and to ensure that the hospital is presently meeting the needs of “low income, minority, and medically underserved populations.”

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serve (FHGPA 2011). In contrast, for-profit hospitals are often governed by corporate boards and are

permitted to occupy strategic market niches which the board believes will increase margin and return on

investment (FHGPA 2011). As a result, unsurprisingly, for-profit hospitals are far more likely to offer

revenue enhancing services like stenting (Horwitz 2005) when compared to their non-profit counterparts.

While economic incentives for the adoption of technology may drive the decision to leverage

emerging technologies for many organizations, scholars have also suggested that social incentives to adhere to

prevailing organizational norms will play a strong role in the adoption of emerging technologies (Kennedy

and Fiss 2009). Broadly speaking, extant literature offers two views on why social incentives may influence

organizations to adopt technology at a faster rate: reputational benefits in the form of organizational prestige and

Mertonian norms of science (1973).

From the perspective of organizational prestige, the expedited adoption of new technologies or

practices may enhance the long-term viability of the organization by ensuring that it is operating on the

cutting edge of practice, thereby demonstrating to stakeholders (e.g. investors, customers) that the

organization is a reputational trend setter (Abrahamson 1996). In this case, the adoption of technology is a

market signal of innovation, thereby legitimizing the organization. Moreover, being an trend setter can help

the organization attract and retain human capital, both in terms of appeal to high prestige practitioners and

increasing the loyalty of current employees (Lee 1969).

From the perspective of Mertonian norms, it is equally likely that organizations that view themselves

as part of the broader scientific community will adopt emerging technologies faster. Following Merton’s

(1973) “institutional imperatives” we may expect this expedited adoption for three reasons. First, emerging

technologies in medicine are subject to rigorous critical scrutiny in the form of medical trials, thereby

conforming to the norms of Skepticism and Originality. Second, there is a strong norm of Communalism in

the medical context; insofar as no one medical facility has the exclusive right to use any single treatment.

Finally, the utilization of emerging medical technologies provides common benefit to both patients and the

scientific community (thereby adhering to the norm of Disinterestedness).

Much like the arguments regarding financial incentives, significant evidence exists suggesting that

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certain hospitals will be strongly incentivized to adhere to scientific social norms and stay abreast of cutting

edge medical treatments; namely teaching hospitals associated with academic medical centers (AMCs). To the

extent that AMCs are responsible for training the next generation of physicians (Aaron 2000), their success in

attracting top quality students is dependent on their ability to teach students how to use emerging

technologies and perform advanced procedures. Moreover, because the production of original research is an

organizational imperative for AMCs (Wartman 2008, 2010), these hospitals are incentivized to ensure their

treatments are at the vanguard of medicine to prevent their research from becoming outmoded. Burns and

Wholey (1993), for example, find that hospitals with superior research reputations are far more likely to adopt

new and innovative organizational structures. Furthermore, Angst et al. (2010) find that celebrity hospitals are

more likely to both adopt emerging medical technologies and influence their local competitors to do the

same. As a result, it is unsurprising that AMCs are often “finely tuned” to avant-garde medical research

(Wartman 2010) and often, have better clinical outcomes than their peers (Jha et al. 2005). Drawing on these

arguments we propose the following baseline hypotheses:

Hypothesis 1a (H1a): Organizations with stronger financial incentives to maximize profits will adopt emerging technologies faster than organizations without these incentives.

Hypothesis 1b (H1b): Organizations adhering strongly to the social norms of science will adopt emerging technologies faster than organizations with weaker adherence to social norms of science.

While our arguments have focused on the adoption of new, superior technologies, there is an implicit

corollary from the first set of hypotheses regarding the abandonment of an antiquated technology. To the

extent that the adoption of new and emerging technologies will require the utilization of the obsolete

technology to decline, it would follow that the same organizational incentives which lead for-profit hospitals

and AMCs to adopt such technologies faster, would also cause them to abandon older technologies in the

same manner. As discussed previously, this would result in abandonment becoming the “twin” of adoption

when a new, superior, technology emerges. Therefore, we propose the following additional hypotheses that

specifically pertain to abandonment of antiquated technologies in the presence of a superior technology:

Hypothesis 2a (H2a): Organizations with stronger financial incentives to maximize profits will abandon antiquated technologies faster than organizations without these incentives.

Hypothesis 2b (H2b): Organizations adhering strongly to the social norms of science will abandon antiquated technologies faster

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than organizations with weaker adherence to social norms of science.

Technology Abandonment under Questioned Efficacy

Thus far, we have theorized how firms with different organizational incentives, viz. profit maximization and

social norms, will react when new and superior technologies emerge. We next explore how organizations are

likely to respond when the efficacy of the technology they are using is called into question. In our context,

this second form of abandonment is triggered by the December 2005 release of a new AHA/ACC stenting

guideline (Smith et al. 2006). Issued to significant anticipation, this guideline drastically changed the “rules”

for stenting by explicitly stating that stents should no longer be used as a treatment for low severity SCAD

patients. It is important to note that although the guidelines released by medical societies are non-binding,

insofar as physicians are not legally liable for not conforming to them, the existence of the guideline

significantly undermines the legitimacy of performing a stenting procedure in these circumstances.

While it is possible that hospitals, regardless of social and financial motives, will abandon the use of

technology quickly (especially if the incumbent technology delivers a competitive disadvantage), this may not

occur when the efficacy of the technology is questioned, but the utilization of the technology may continue2.

From an organizational learning perspective, it is plausible that the organization may fall prey to

organizational pathologies, e.g. competency traps (Levinthal and March 1993), which stymie the full use of

the new information. As a result, the organization may choose to ignore the new information questioning the

efficacy of the technologies they leverage because the source of the information is distant, resulting in it being

discounted. More simply, because the organization itself did not discover the flaws in stenting, the finding

may be perceived as less credible (Levinthal and March 1993). Or, the new information may be ignored

because extensive experience using the technology leads to both the organization, and the actors within it,

falling prey to a confirmation bias (Nickerson 1998), thereby discounting the validity of the new information.

Indeed, in healthcare, ceasing to use the questioned technology may be viewed as a tacit admission that the

best possible care was not given. Studies in medical settings affirm that slow adherence to medical guidelines

does occur (Grimshaw and Russell 1993, Mittman et al. 1992), both in terms of updating clinical behavior

2 Although the efficacy of stents is questioned by the 2005 AHA/ACC Guideline they are still seen as a viable technology for high severity SCAD patients. As such, hospitals can continue to use them without risking malpractice liability.

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(Choudhry et al. 2005) as well as altering the utilization of technology (Letourneau and Minnesota 2004); both

of which persist as causes for concern among researchers and policy makers (Smith 2000).

From a financial perspective, it is similarly plausible that organizations with strong financial

incentives will be unwilling to accept the loss of the sunk cost associated with initially mitigating the barriers

to adoption of the incumbent technology (Fichman and Kemerer 1997). This is notably problematic in

contexts like medicine, where hospitals face significant financial perils (Aaron 2000)3. As the implementation

of new technology is rarely costless, the organization may resist abandoning the technology for even a sub-

segment of the market if it fears that it will not recoup its startup costs4. Even in the focal context, where

stenting is discouraged only for part of the market (low severity SCAD patients), this concern persists because

the replacement technology (pharmacological intervention) is far less profitable5. Furthermore, because

organizational stakeholders may actively demand sufficient return on investment, it is plausible that the

cannibalization of the revenue stream is not economically feasible. It is important to note that significant

empirical and anecdotal evidence exists of physician non-adherence to medical guidelines. Not only has this

issue garnered significant attention in the popular press and among professional societies (AssociatedPress

2012, Bristow et al. 2013), many groups, ranging from the American Medical Association (AMA)6 and the

American College of Cardiology (the society which released the focal guideline in this study) to the National

Physicians Alliances’ Choosing Wisely Campaign7, are attempting to call attention to the issue.

Countervailing the question of how for-profit hospitals may react is the question of how hospitals

with a taste for science, i.e. AMCs, will respond to the recommendation to abandon the use of stenting for

low-severity patients. The literature discussing Mertonian norms (1973) offers at least two reasons for why

these organizations will likely abandon the use of questionable technologies faster, even at a financial cost of

3 In 2008, hospitals in Florida had an average operating margin of 0.7% (FHGPA 2011) 4 Personal communication with a large-scale healthcare technology vendor indicates that the cost of setting up a fully functional, state of the art, cardio catheterization lab can run between $1.3 million and $2.0 million as of May 2014. 5 Currently the reimbursement difference to the hospital is greater than $17,000 per stent - http://www.bostonscientific.com/. Discussions with physicians suggests that the cocktail of pharmaceuticals (e.g. anti-clotting agents, nitrates, anti-angina medications) which a patient is prescribed after the stenting procedure is similar to that used when treating SCAD pharmacologically. In each case, the pharmacy filling these prescriptions will be the primacy financial beneficiary, not the hospital or physician who prescribed them. 6 http://www.ama-assn.org/ama/pub/news/news/2013/2013-07-10-strategies-to-minimize-overuse.page 7 http://www.choosingwisely.org/choosing-wisely-continues-conversation-about-unnescessary-care-with-release-of-new-lists-in-2014/

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reduced revenues. From the perspective of rigorous critical scrutiny, continuing to use the technology after it

has been found wanting is inconsistent with the inherent Skepticism required of the scientific community.

Continued utilization will also violate the norm of Disinterestedness because the organization will be acting

for financial gain instead of promoting the welfare of the “common scientific enterprise” (Merton 1973).

Extant literature on organizational prestige further corroborates these arguments. To the degree that the

organization elects to act purely on short term financial incentives, it risks losing its status as a reputational

trend setter because it is no longer operating on the cutting edge of practice (Abrahamson 1996). Moreover, if

it violates the norms of science, the hospital may lose the human capital it attracted by adopting cutting edge

technology in the first place (Lee 1969), thereby placing the organization at a long term strategic disadvantage.

Domain-specific work in medicine discussing the mission of AMCs offers further insights into why

these organizations may adhere to scientific norms in lieu of garnering greater profits in the short term. First,

the mission of AMCs is a “synergistic mix” of research, education, and patient care (Wartman 2010).

Although the use of slightly outmoded treatments may effectively meet the needs of most patients, it

inherently violates the educational goal of the institution because students will not be trained on the state of

the art methods of treatment. Second, as previously discussed, because the production of innovative and

original research is a vital component of the AMC mission (Aaron 2000), the use of procedures which are

even modestly antiquated may cast doubt on both the validity and legitimacy of research which emerges from

these organizations (Kennedy and Fiss 2009). We thus expect organizations with strong social incentives to

remain on the cutting edge of medicine to forgo the short-term economic gain associated with continued

technology use after the perceived efficacy of the technology is questioned. Formally, we test:

Hypothesis 3a (H3a): Organizations with stronger financial incentives to maximize profits will abandon technologies that are revenue enhancing but whose efficacy is questioned slower than organizations without these incentives

Hypothesis 3b (H3b): Organizations incentivized to adhere to the social norms of science will abandon technologies that are revenue enhancing but whose efficacy is questioned faster than organizations without these incentives.

Data and Methodology

The empirical context of our study is the utilization of coronary stents for the treatment of stable coronary

arterial disease (SCAD). We trace the life-cycle of stents from their introduction in 1995 through 2007,

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permitting the analysis of organizational rates of adoption and abandonment in the face of two subsequent

“shocks” to the technology. The first, bare metal stents, were introduced in 1995 and replaced in 2002 by the

development of drug eluting stents; an innovation that represented a significant improvement in the base

technology for stents (i.e. the emergence of a superior technology). The second, the release of a new medical

guideline by the American Heart Association (AHA) and the American College of Cardiology (ACC) (Smith

et al. 2006), delegitimized the practice of stenting for certain patients. This guideline questioned the efficacy

of stents for treating low severity SCAD, recommending explicitly that they no longer be used. Thus, the

context allows us to study the same technology’s life cycle under different reasons for abandonment,

permitting us to isolate effects resulting from unobserved heterogeneity across different technologies.

Data

To test our hypotheses, we draw data from multiple sources to create a longitudinal sample of decisions for

the treatment of SCAD over the 13-year time period of the study. The primary data source is the Florida

Agency for Healthcare Administration (AHCA), used extensively in prior research (Burke et al. 2003, Burke et

al. 2007, Greenwood and Agarwal 2013). These data capture a census of patients admitted to hospitals in the

state of Florida as well as their diagnosis, co-morbidities (i.e. ICD-9 codes), the attending physician, and the

hospital where they were admitted. We merge the AHCA data with information from the Council of

Teaching Hospitals (COTH) to identify hospitals in Florida associated with academic medical centers.

We note two limitations of this dataset due to patient privacy considerations. First, we are unable to

track patients over time. Although this introduces a form of unobserved patient heterogeneity into the dataset

there is no reason, a priori, to believe that there is a difference in patient re-admittance which is correlated

with the changes to the technology and knowledge regarding stenting (discussed below). Second, the patient

data is aggregated at the quarter level. However, as we are studying the change in stenting over time, this

simply precludes us from aggregating the stenting measure to a more granular time window. Before

conducting our analysis we apply two restrictions to the datasets. First, we drop all patients who suffer an

acute myocardial infarction, i.e. heart attack, because their condition is by definition unstable coronary arterial

disease (as opposed to stable) (361,211 patients). Second, we drop all patients who have received a coronary

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artery bypass graft, a far more invasive procedure rendering stents irrelevant (130,234 patients).

Identification Strategy

Our identification strategy focuses on three exogenous changes to the technological regimes surrounding the

treatment of SCAD between 1995 and 2007. The first is the approval of bare metal stents (BMS) by the FDA

in the second quarter of 1995. The second is the introduction of drug eluting stents (DES) into the market in

2002. The third is the previously discussed release of the AHA/ACC Guideline in late December of 2005

which recommends that physicians should no longer implant stents for patients with low severity SCAD (i.e.

Canadian Cardiovascular Society (CCS) Class I and II Coronary Arterial Disease)8. The introduction of BMS

and DES present hospitals with an opportunity to adopt new technology, the release of DES represents an

opportunity to abandon the inferior BMS, and the release of the guideline is a trigger for technology

abandonment when efficacy is questioned. Figure 1 presents a visual representation of the stenting rate, i.e.

percent of SCAD patients in Florida treated with a stent as opposed to pharmacologically, over time (as well

as the periods of study surrounding the three changes in technology).

We justify that these changes in stenting technology are exogenous for the following reasons. The

first two, the approval of BMS and DES stents by the FDA, offer the opportunity to observe the utilization

of both technologies from genesis. Although there is no control group for these two shocks we can observe

the increase in utilization of each type of stent for each type of hospital compared to its base utilization point

of zero. We argue that the third and final shock, the release of the AHA/ACC guideline, is exogenous for the

following reasons. First, although the fact that a panel had been assembled to release a stenting guideline was

public information, the contents of the guideline were unknown until release. For many legal and practical

reasons, guidelines are constructed under strict confidentiality agreements and penalties for violating them

can range from professional censure to the physician’s loss of license to practice. In this regard, the release of

the guideline is similar to the release of an earnings statement by a firm. Although it is known that the firm

will be making an earnings announcement the contents are not known until they are made publically available,

at which time they can be acted upon. Second, although the contents of the guideline are kept secret until

8 See Appendix A for a detailed description of SCAD and CCS classification

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release, the eventual announcement is accompanied by significant publicity and is carefully observed by both

the popular press and the practicing community. In addition to press releases and numerous information aids

produced by the AHA and ACC, many hospitals created summaries of the guidelines and disseminate them

to practicing physicians. Third, the focal guideline is built on Class C evidence that represents a synthesis of

expert opinion as opposed to the result of clinical trials. As the clinical trial which supported the contents of

the guideline was released later, i.e. the COURAGE trial (Boden et al. 2007), this further increases the

ambiguity surrounding the contents of the guideline. Finally, we see an almost immediate reaction by the

physician community in our data with no other known concomitant exogenous shock. As can be seen in

Figure 1, there is a striking change in the stenting rate after the release of the guideline (Period 3). High

severity SCAD patients serve as the control group for this shock.

Variable Definitions

Dependent Variable: To examine how hospitals react to these three shocks we construct three different

sample frames (summarized below) with four different indicators of the 0/1 stenting decision by physicians

affiliated with a hospital. For the first set of analyses (the release of bare metal stents and their adoption), the

dependent variable is the 0/1 decision to implant a BMS in the focal patient. For the second set of analyses

(the adoption of DES), the dependent variable is the 0/1 decision to implant a DES in the focal patient. For

the third set of analysis (the abandonment of BMS after the introduction of the superior DES technology),

the dependent variable is the 0/1 decision to implant a BMS in the focal patient. For the fourth set of

analyses (the release of the AHA/ACC Guideline as the trigger for abandonment in the presence of

questioned efficacy) the dependent variable is the 0/1 decision to implant a stent of any kind in a low severity

SCAD patient. 0 in each of these cases represents the decision to treat the patient without a stent, i.e.

pharmacologically, and 1 represents the decision to implant a stent at the time of treatment.

We conduct our analysis at the bed level for each time quarter, consistent with extant management

work in cardiology (Huckman and Pisano 2006). While we are interested in variations across organizations,

the analysis at the bed-level permits us to control for the underlying patient level heterogeneity such as age,

race, gender, and severity of SCAD (as determined by the patient’s ICD-9 codes). Further, it also allows us to

15

control for unobserved physician heterogeneity, such as the propensity to stent, through physician fixed

effects. Finally, it reduces a significant aggregation bias in the form of a Yule-Simpson effect (Simpson 1951).

Independent Variables: Hospital Type: The key independent variable in our analysis is the type of

hospital, operationalized as a set of indicators for the hospital’s organizational mission: Not-For-Profit, For-

Profit, and AMCs, each of which is mutually exclusive and coded dichotomously. For-Profit hospitals represent

organizations with significant financial incentives for revenue maximization. Consistent with prior research

(Aaron 2000, Wartman 2008, 2010), AMCs proxy organizations with both revenue incentives, due to their

need to financially support the associated medical school, and incentives adhere to the social norms of

science. The indicator of association with an AMC is retrieved directly from the COTH website9. Indicators

of hospital for-profit / not-for-profit status are retrieved directly from the AHCA dataset. Not-for-profit

hospitals represent organizations with the weakest financial incentives and serve as the base case in our

analysis, unless otherwise noted.

Time: The next set of independent variables are a series of linear splines that capture the variation in the

stenting rate over time after each of the exogenous changes in the technological regime, i.e. the release of bare

metal stents, the release of drug eluting stents, and the guideline which questions the efficacy of stents. We

address the potential complication of the linearity constraint that splines enforce on the change in utilization

through tests using second and third order splines. These results indicate that there is no significant

curvilinear shift in the relationship10. We further mitigate this concern by replicating our analysis using

quarter dummies and graphing the results.

Control Variables: To control for the effect of other forms of patient heterogeneity influencing the rate of

stent implantation we include a robust series of controls. These include 104 dummies for the age of the

patient (ages 3-108), dummies for the race of the patient (e.g. African American, Caucasian, Latino, etc.), the

Gender of the patient, and, finally, fixed effects for the type of SCAD the patient has been diagnosed with.

Furthermore, to decrease the effect of unobserved physician and hospital level heterogeneity we include

9 https://www.aamc.org/members/coth/ 10 Results available upon request

16

hospital and physician fixed effects. As these fixed effects often perfectly predict the independent variables of

interest we introduce the fixed effects sequentially into our econometric specifications to increase the

interpretability of the results. Summary statistics for the three datasets are available in Table 2.

Empirical Strategy

The primary econometric specification we rely on for the estimation of the change in stenting over time is a

fixed effect linear probability model (LPM) with the 0/1 stenting decision as the dependent variable. We use

the LPM, in lieu of a logit or probit model, for several reasons. First, as noted by King and Zeng (2001), the

rarity of stent implantation (often less than 5% of the time in our sample) can lead to a biased estimation of

the standard errors. Second, the interpretation of interaction terms (the primary coefficients of interest in our

estimations) is exceedingly difficult and requires the simulation of the marginal effects post estimation

because the effect of changes in the independent variable of interest is dependent upon the values of the

other covariates in the model (Ai and Norton 2003, Zelner 2009). While this is not a pressing concern in

models with a single interaction term and a reasonable number of covariates, it poses a considerable challenge

for our analysis because the statistical tools designed both by Zelner (2009) and Ai and Norton (2003) have

been developed for only a single interaction term to be analyzed (thereby ignoring concomitant changes in

the other interaction terms of the model). Further, the available tools are subject to matrix size constraints

which prevent their use in our estimations (due to the thousands of physician, hospital, and patient-

characteristic fixed effects). Finally, because of the both the large number of observations (hundreds of

thousands in each model) and covariates there are significant convergence problems with using logistic

regression as the primary statistical model.

While non-linear estimators like logistic regression pose significant challenges, the LPM is also not

without its flaws as it introduces heteroscedasticity into the model and may yield predicted values that reside

outside the [0..1] interval. To mitigate the first concern we leverage heteroscedastic consistent Huber-White

standard errors. Second, a post estimation inspection of the data reveals that the predicted probability of

stenting is consistently within the [0..1] bound.

Formally, we model the probability that a patient receives a stent as:

17

𝑦 = 𝛽1𝑠1 + 𝛾1𝑠2 +𝑀′𝜃1 + 𝑋′𝛿1 + 𝜈 + 휀 (1)

Where y is an indicator equal to one if a physician implants a stent and zero otherwise. The variable s1 is the

hospital’s AMC status and s2 is the hospital’s for-profit status. M is the vector of spline values (time and time

interacted with hospital characteristics) while X is the vector of patient characteristics. The terms {β1,γ1,δ1,θ1}

are parameters to be estimated and ν represents the constant. As discussed, after the estimation of these initial

regressions we re-estimate the equation with hospital fixed effects (which results in {β1,γ1} being dropped

from the model) and then with hospital and physician fixed effects. For each set of analyses, we constrain the

time of the investigation to a single quarter pre-change, and six quarters post-change (18 months) to limit the

effect of omitted variable bias, i.e. other confounding incidents in the market. A change in this specification

refers to the three focal events - the introduction of the bare metal stent, the introduction of the drug eluting

stent, and the release of the medical guideline. Results are available in Table 3.

Results

We first consider results relating to hypotheses H1a and H1b concerning technology adoption (Columns 1-6

of Table 3). Interestingly, we see that For-Profit hospitals adopt the use of bare metal stents (BMS) significantly

faster than Not-For-Profit hospitals, as indicated by the positive and significant interactions For-Profit and Time

in Columns 1-3. Furthermore, we see that these hospitals adopt the use of drug eluting stents (DES)

significantly faster than Not-For-Profit hospitals, shown by the positive and significant interactions between

For-Profit and Time in Columns 4-6. Each of these coefficients suggest strong statistical support for H1a.

Furthermore, as indicated by Columns 3 and 6 we see that for both bare metal stents and drug eluting stents,

AMCs adopt the use of the new treatment option faster that For-Profit hospitals (an average increase of 1.09%

v. 0.59% per quarter for BMS and an average increase of 2.14% v. 1.54% for DES). Taken together, these

estimates offer strong statistical support for H1b. These results are further corroborated by the graphical

output of our time dummy estimations. As seen in Figure 2 and 3 the adoption of stents by AMCs are the

fastest, followed by For-Profit hospitals, and finally Not-For-Profit hospitals are the slowest.

We next consider our hypotheses regarding technology abandonment in the presence of an emerging

superior technology (H2a and H2b). As expected, we see that each of the hospital types (For-Profit, Not-for-Profit,

18

and AMCs) abandon the use of bare metal stents when drug eluting stents are released into the market

(Column 9 of Table 3). Interestingly, we note that in this scenario, abandonment is indeed the “twin” of

adoption; insofar as AMCs are the fastest to abandon the antiquated technology and For-Profits are the second

fastest to abandon the antiquated technology (recall that AMCs and For-Profits also adopted DES stents

significantly faster than Not-for-Profits). Each of these estimates provide strong support for Hypotheses H2a

and H2b. Graphical representations further confirm the output of the spline estimations (Figure 4). As can

be seen, AMCs abandon the use of bare metal stents the fastest after the introduction of drug eluting stents,

followed by For-Profits and then Not-For-Profits.

Regarding our hypotheses pertaining to technology abandonment when the efficacy of stenting is

questioned (H3a and H3b), an equally interesting story emerges (Columns 9-12 of Table 3). We see that all

hospitals abandon the use of stents in low SCAD patients, despite the fact that the guideline is non-binding.

However, as indicated by the interaction between For-Profit and Time in Column 12, and consistent with H3a,

we see that For-Profit hospitals abandon the use of stents significantly slower than all other hospitals.

Furthermore, and consistent with H3b, we see that AMCs abandon the use of stents significantly faster than

all other hospitals (as witnessed by the interaction between AMC and Time in Column 12). The graphs shown

in Figure 5 further corroborate these results.

To summarize, both the regressions and graphical interpretations provide support for the salience of

financial incentives and science-based social norms in adoption and abandonment responses triggered by

changes in technology regimes. A striking finding is the dominant role of an organization’s taste for science

in both the adoption and abandonment of technology. Those organizations whose identity is predicated on

being a producer of original research at the leading edge of science seize new technologies swiftly but are

equally willing to forfeit financial gain and abandon the technology with speed when it violates the core

principles of science.

Robustness Tests

Severe SCAD Patients Post Guideline

To mitigate the possibility of alternative explanations driving the results we conduct a series of robustness

19

tests. As discussed previously, the control group for the third shock, the release of the AHA/ACC Stenting

Guideline, is formed of patients who have been diagnosed with Severe SCAD. As the guideline does not

recommend any substantive change to how these patients should be treated, there should be no significant

change to their stenting rate post guideline. We note that, although the traditional method for ensuring that

this control group is valid would be to run a difference in difference estimation, this method is inappropriate

because the control and treatment group have a different ex ante trend in propensity to receive a stent,

therefore violating the assumptions of the difference in difference model (Angrist and Pischke 2008). We

therefore construct a new sample which includes only Severe SCAD patients and re-execute our analysis for

the third shock. Results are available in Table 4. As can be seen, once physician and hospital heterogeneity is

accounted for, there is no significant change in the stenting rate for this group post guideline release,

establishing that the application of the guideline is indeed the driving force for abandonment in the case of

low SCAD patients.

Physicians with a Taste for Science: Faculty Members

Our theoretical arguments relating to the influence of social norms and financial incentives on technology

adoption and abandonment are constructed at the organizational level. To the extent that medicine is a

discipline where practitioners possess a high degree of agency, and to mitigate the concern that results are

being driven by the heterogeneity among physicians, we next explore incentives at the individual level. To that

end, we introduce a new indicator of physician incentive to remain on the cutting edge of practice into our

empirical estimations, Faculty placement. As faculty members are required both to teach and produce original

research our expectation is that, on the margin, these physicians will be more sensitive to changes in the

scientific norms surrounding treatment than physicians without faculty positions. Empirically, Faculty is a

dichotomous variable indicating whether or not the physician has received a faculty appointment at any of the

universities in the state of Florida11. We interact this variable with the time splines and the indicators of

hospital incentives (AMC and For-Profit status). Results are available in Table 5 and graphical output from the

11 In further robustness tests here, we eliminate clinical faculty (e.g. instructors, adjuncts, preceptors, etc.) from the sample. The results remain consistent and are available from the authors upon request.

20

dummy variable time regressions are available in Figures 6-9.

Results from Table 5 provide further confirmatory evidence for our hypotheses about organizational

incentives. In each adoption case we see that Not-For-Profit hospitals adopt the nascent technology

significantly slower than both AMCs and For-Profit hospitals (Columns 1-6 of Table 5). Moreover, results

suggest that both AMCs and For-Profit hospitals abandon the use of bare metal stents significantly faster than

Not-for-Profit hospitals when drug eluting stents become available (Columns 7-9). Finally, we find that when

the efficacy of stents is questioned AMCs abandon the use of stents significantly faster across physician types,

while For-Profit hospitals abandon the use of stents significantly slower than other hospitals across physician

type. Our results indicate that being a member of the Faculty does not strongly influence the change in the

stenting rate after each of the shocks (although there is some evidence that Faculty members implant fewer

stents before the release of the guidelines (Column 11)). For each shock the change in the marginal stenting

rate for Faculty and non-Faculty members is statistically indistinguishable within hospital type12. Graphical

representations of the results confirm these findings (Figure 6-9), where the difference between Faculty

members and non-Faculty members is largely insignificant (insofar as the lines are almost on top of each

other). Overall, these results suggest that while social incentives to forgo short-term economic benefit in

favor of upholding the principles of science dominate at the organizational level, there is limited evidence that

this is an individual level phenomenon.

Physicians with Weaker Organizational Affiliation: Freelancers

While our regressions regarding the reaction of Faculty members to changes in the knowledge regarding

stenting provide preliminary evidence that organizational, as opposed to individual, incentives are driving the

change in stent utilization, other plausible explanations exist. For example, it is possible physicians are self-

selecting into these organizations. To the extent that there may be heterogeneity in the taste for scientific

norms among faculty members, this would suggest that the change in the stenting rate could still be a result of

individual level decision making because physicians will sort themselves into the organizations which reflect

their preferences (Agarwal and Ohyama 2013). To mitigate this potential confound we replicate our analysis

12 The lack of a significant difference is confirmed using a Chow’s test.

21

using freelance physicians (Huckman and Pisano 2006), i.e. physicians simultaneously practicing at multiple

different types of hospitals. To the degree that separating these physicians will indicate whether or not they

adhere to the institutional norms of the organization, as opposed to maintaining consistent behavior across

institutional settings, these regressions should mitigate the potential selection confound.

To isolate Freelancer status we identify all physicians who have treated at least one SCAD patient in

two different hospitals, of different types, in the same quarter. For example, a physician who treats patients at

both Jacksonville Memorial Hospital and Baptist Medical Center in Jacksonville, in the same quarter, would

fit the definition because one institution is for-profit and the other is not. Conversely, a physician who treats

patients only at Tampa General Hospital and UF Health Shands in Gainesville would be excluded, because

both hospitals are associated with AMCs. After excluding all non-Freelancers we replicate our analysis. Results

are available in Table 6. Corroborating previous estimations we see that Freelance physicians practicing at

AMCs and For-Profit hospitals adopt the use of both BMS and DES significantly faster than they do when

practicing at Not-For-Profit hospitals (thereby providing further support for H1a and H1b). Moreover, during

periods of abandonment catalyzed by the release of a superior technology (Columns 6-9 of Table 6) we find

that Freelancers in AMCs and For-Profits abandon the use of stents significantly faster (H2a and H2b). Finally,

these same Freelance physicians, when reacting to the guideline questioning the efficacy of stents (Columns 10-

12), abandon the use of stents significantly slower at For-Profit hospitals (H3b) and significantly faster at

AMCs (H3a). A graphical interpretation of the results (Figures 10-13) confirms these findings and lends

further support to our hypotheses.

Discussion

Our study sought to examine the following research questions: Are the factors that predict organizational

abandonment of technology the same as those that influence organizational technology adoption? And, how

do varying organizational incentives moderate the rate of technological abandonment? By studying a

technology from its inception through two different regime changes, and examining the role of financial

incentives and scientific social norms in influencing organizational decision making, we hypothesized and

found that rates of abandonment differ based on why the abandonment was occurring. Specifically, in the

22

presence of a superior technology, organizational rates of abandonment mirror their rates of adoption (of

both the original and emerging technology). Academic medical centers adopt and abandon new technologies

at the fastest rates, followed by for-profit hospitals, and then not-for-profit hospitals. However, when the

dominant technology’s efficacy is called into question and the practice is delegitimized, there are sharp

differences in the observed patterns. Academic medical center response is similar under both circumstances,

but for-profit hospital abandonment is slower, rather than faster, than the not-for-profit hospital rate. As a

result, we show that underlying organizational incentives play a key role: scientific social norms impact both

adoption and abandonment decisions symmetrically, but financial incentives accelerate the adoption and

retard the abandonment of revenue generating technologies. Importantly, results suggest that these patterns

cannot be explained by differences in individual level incentives; insofar as both faculty and freelancing

physicians adhere to the norms of the organization they are practicing at.

Our claim about the salience of financial incentives and scientific norms was based on extensive prior

work. To qualitatively ascertain what specific differences in norms yield our findings we conducted post-hoc

interviews with practicing attending physicians. The practitioners underscored differences across hospitals as

they relate to a “culture of science”, indicating the presence of strong pressure to stay abreast with the latest

technologies, given peer recognition, and hospital status. This is exemplified by the following comment:

"I think [the] hypothesis that teaching institutions are most “in tune” with the social norms of science is true. In the setting of being responsible for teaching new doctors, it is very common that the trainees keep their teachers on their toes by challenging them and making them aware of the “latest and greatest” medical trials and information. It’s nearly impossible to pass off “old thinking” on new trainees because they are always reading and keeping up with the latest info."

Likewise, the lack of individual level variation and the dominance of organizational effects were corroborated

by comments indicating that technology adoption and abandonment decisions were made at the institutional

level. Consider, for example, the following regarding differences in freelancers’ behavior across hospitals:

“They are immensely busy practitioners who have little excess time to involve themselves in the often intricate and difficult institutional decision-making which governs the purchase of or abandonment of technologies, especially at more than one hospital. Therefore, being pragmatic problem-solvers, they probably “go with the flow” most of the time in order to provide their patients with the best of what is available at each hospital to whose staff they belong.”

These, and other similar comments offer additional support for our results, i.e. the importance of scientific

norms for organizations whose identity is constructed around original research and innovation, and the

dominant role that organizational incentives play in influencing the behavior of high agency decision makers.

23

Our study makes several contributions to extant literature. First, we investigate an understudied yet

critical organizational process: the abandonment of technology, and shed further light on equivocal findings

in extant literature (Burns and Wholey 1993, Finkelstein and Gilbert 1985, Greve 1995, Keil 1995, Rao et al.

2001). It is widely understood in the management and economics literature that technology innovation occurs

with regularity, thereby necessitating organizations to adopt new technology on an on-going basis or face the

risk of competitive disadvantage. However, arguably, the need to discard old technology when its efficacy is

called into question is an equally important organizational imperative. Our analysis of the adoption and

abandonment of three distinct changes in the technological regime of stents allows us to illuminate the drivers

of these critical organizational processes in a richer and more detailed way than prior research, while

simultaneously mitigating the concerns of unobserved heterogeneity that can be introduced when

investigating abandonment across contexts.

Second, results reveal interesting nuances to extant knowledge regarding firm specific human capital

in medicine. While prior research in this space, e.g. Huckman and Pisano (2006), has shown little correlation

in the performance of physicians across organizations, our results indicate that this may be a result of

pursuing different treatment options across organizational settings. To the extent that our analysis of

freelancers suggests that these physicians’ treatment choices vary widely based on what type of hospital they

are practicing at, it is plausible that their performance is uncorrelated due to a different population of patients

being selected for treatment at each of the individual hospitals. Furthermore, this finding highlights the

importance of future work devoted not only to how physicians choose their intended treatment, based on

organizational factors, but also to how these treatment choices influence patient care outcomes.

Third, our work presents several insights into the effect Mertonian norms (1973) have on the

decision making of physicians. To the extent that it has been argued that the norms of science will not be

bounded geographically (Gittelman 2007), because labor markets of science operate through a geographically

unbounded cosmopolitan network of colleagues (Murray 2004), our results provide important cautionary

evidence against generalizing findings from biotechnology (Gittelman 2007, Murray 2004) and other contexts

into the fields like medicine. Insofar as practitioners in our sample appear to significantly change their

24

prescribing behaviors based on the location where they are practicing, results suggest that adherence to the

norms of science does require the organization to value (i.e. provide non-pecuniary award) such behaviors

(Agarwal and Ohyama 2013). Moreover, to the degree that these networks of practicing physicians are

geographically proximate, results suggest that the social penalty for deviating from scientific norms may

manifest only when violations occur within an insulated group that actively endorses such norms.

Finally, this study identifies several potential pitfalls policy makers will face during the transformation

of the United States healthcare system as envisioned in the Patient Protection and Affordable Care Act of

2010. To the extent that comparative effectiveness is one of the cornerstones of this legislation, in an effort

to curb the ballooning costs of medical treatment in the United States (Agarwal et al. 2010, Iglehart 1999),

many scholars have highlighted barriers to effectively implementing these protocols (Timbie et al. 2012). Our

results suggest that the creation of coherent social incentives is one viable way to mitigate these issues.

We note several limitations of this study, which also offer fruitful avenues for future research. First,

although we are able to see how the release of new medical treatments and guidelines affects the treatment

choices made by physicians, our data do not offer substantive insight into how changes in medical practice

diffuse across and within these organizations. Although the exact mechanism by which information diffuses is

beyond the scope of our study, this is a limitation which prevents further comment on an important field of

study (Burke et al. 2007). Second, although our results provide insight into how different organizations react

to their social and financial incentives, we cannot isolate the exact mechanism by which hospital

administrators enforce that the directives to change behavior are followed, given the scale of the analysis and

the data available. Third, we are unable to rule out agency on the part of the patient in demanding stenting as

a treatment over pharmacological intervention. Although there is no reason, a priori, to believe that

admittance of these patients is correlated with the type of hospital, it is hard to account for this effect directly

in our empirical tests. A hospital-level field study, as a follow-up, would be able to tease out these effects

more directly. Finally, while results indicate that physician behavior does conform to the identity of the

organizations where they practice, we do not capture the within-organization heterogeneity in the reaction of

physicians (both in terms of adoption and abandonment). To the extent that research indicates that the

25

training and expertise of the individual physicians in these hospitals (Greenwood et al. 2013) as well as their

teams (Edmonson et al. 2001) influences their reaction to issues of technology adoption and abandonment,

we believe more research is needed here to establish these effects unequivocally.

In conclusion, prior research has emphasized the importance of technological regime changes as

important opportunities for the firm to both expand its economic reach and increase its profitability.

However, while investigations of technology adoption have been far reaching, research on the dual and

arguably equally consequential process of technology abandonment has lagged. The contribution of this work

is to further refine understanding of organizational incentives and how they influence the reaction of

organizations during these pivotal periods of change by considering not simply the reason for abandonment,

but how organizational identity influences the response to the need to abandon. Finally, this work sheds

significant light on how change can be affected in the healthcare sector, an area of vital societal importance

where the utilization of appropriate technologies may represent a difference between life and death. We

encourage future research to more fully explore the construct and processes underlying technological

abandonment at the societal, organizational, and individual levels, as well as consider how these varying

perspectives play an increasingly important role in the ever-evolving field of medicine and health.

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Table 1: Literature Discussing Firm and Individual Abandonment

Paper Finding Context

Finkelstein and Gilbert

(1985)

Abandonment is driven by absolute disadvantage of treatment options Pharmaceuticals

Burns and Wholey

(1993)

Antecedents of adoption have little effect on abandonment Management Practices

Greve (1995) Strategy abandonment is contagious in highly uncertain environments Radio Station Format

Rao et al. (2001)

Abandonment occurs as a result of performance disappointment and

mimetic behavior Securities Analysts

Adner and Levinthal

(2004)

Abandonment occurs when options are unlikely to yield performance. The

larger the potential later discovery of the option's value the harder it is to

abandon.

Real Options (Theory)

Ewusi-Mensah and

Przasnyski (1991)

Abandonment occurs when projects are unlikely to yield expected

performance or cause political strife in the organization Project management

Keil (1995) Rational abandonment can be stymied by escalation of commitment Project management

Greer (1981)

Abandonment does not have the same strong determinants as adoption.

Sometimes it precedes new information and sometimes it never occurs Medical Technology

Howard and Shen (2012) Evidence of abandonment is limited when it undermines profits Stenting

Choudhry et al. (2005)

Physicians are less likely to abandon established practices as tenure

increases

Review of Medical

Practice

29

Table 2: Summary Statistics and Correlation Matrices Table 2a – Bare Metal Stents Shock Dataset

Table 2b – Drug Eluting Shock Dataset Table 2c – AHA / ACC Guideline Release Dataset

Table 2a

N - 515,447

Variable Mean Std. Dev. (1) (2) (3) (4) (5) (6)

(1) Bare Metal Stent 0.045 0.206

(2) Teaching 0.050 0.219 0.032

(3) For Profit 0.550 0.498 0.036 -0.255

(4) Severe SCAD 0.280 0.449 0.120 0.014 -0.013

(5) Year 1996 0.648 0.041 0.062 -0.017 -0.016

(6) Age 71.454 12.132 -0.120 -0.099 -0.050 -0.153 0.007

(7) Gender 0.559 0.496 0.049 0.008 0.029 0.014 -0.003 -0.167

Table 2b

N - 705,697

Variable Mean Std. Dev. (1) (2) (3) (4) (5) (6) (7)

(1) Drug Eluting 0.042 0.202

(2) Bare Metal Stent 0.092 0.289 -0.002

(3) Teaching 0.069 0.254 0.018 0.006

(4) For Profit 0.547 0.498 0.025 0.034 -0.300

(5) Severe SCAD 0.148 0.355 0.093 0.118 0.002 0.009

(6) Year 2002 0.647 0.154 -0.077 -0.003 0.002 -0.023

(7) Age 71.106 12.939 -0.090 -0.112 -0.097 -0.029 -0.114 0.012

(8) Gender 0.558 0.497 0.041 0.065 0.012 0.033 0.021 0.008 -0.151

Table 2c

N - 709,045

Variable Mean Std. Dev. (1) (2) (3) (4) (5)

(1) Stent 0.108 0.311

(2) Teaching 0.073 0.260 0.021

(3) For Profit 0.547 0.498 0.025 -0.308

(4) Year 2006 0.621 -0.029 -0.009 -0.002

(5) Age 71.713 13.063 -0.145 -0.093 -0.025 0.014

(6) Gender 0.559 0.496 0.074 0.020 0.021 -0.003 -0.137

30

Table 3: LPM Estimation of Change in Stenting Over Time Comparison of AMC, For-Profit, and Not For-Profit Hospitals

Hospital, Age, Race, and SCAD Diagnosis Omitted*

* Omitted variables indicate that the variables have been included in the estimations but the coefficients have not been

displayed in the interest of space. Complete output from the estimations is available upon request

(1) (2) (3) (4) (5) (6) (7) (8) (9) (10) (11) (12)

Phenomenon

DV BMS BMS BMS DES DES DES BMS BMS BMS Stent Stent Stent

Sample

AMC 0.00990*** -0.00907*** 0.0311*** 0.0308***

(0.00274) (0.00174) (0.00251) (0.00267)

For-Profit 0.0126*** -0.00803*** 0.0296*** 0.0146***

(0.00109) (0.000892) (0.00129) (0.00140)

Time 0.00397*** 0.00419*** 0.00427*** 0.0154*** 0.0155*** 0.0155*** -0.0102*** -0.0101*** -0.0107*** -0.00408*** -0.00351*** -0.00152***

(0.000220) (0.000222) (0.000223) (0.000187) (0.000184) (0.000185) (0.000270) (0.000262) (0.000254) (0.000294) (0.000284) (0.000256)

AMC * Time 0.00547*** 0.00626*** 0.00671*** 0.00864*** 0.00861*** 0.00993*** -0.00642*** -0.00669*** -0.00754*** -0.00217*** -0.00245*** -0.00186***

(0.000652) (0.000719) (0.000743) (0.000474) (0.000466) (0.000480) (0.000683) (0.000664) (0.000660) (0.000735) (0.000709) (0.000658)

For-Profit * Time 0.000880*** 0.00196*** 0.00168*** 0.00580*** 0.00554*** 0.00601*** -0.00462*** -0.00489*** -0.00490*** 5.11e-05 -5.81e-05 0.000833**

(0.000289) (0.000294) (0.000293) (0.000244) (0.000240) (0.000242) (0.000351) (0.000342) (0.000332) (0.000383) (0.000370) (0.000334)

Constant -0.0471 -0.0354 0.0452 0.00737 -0.102 -0.0434 -0.00606 0.0302 0.0252 -0.246** -0.693*** 0.0477

(0.0556) (0.0548) (0.0875) (0.138) (0.163) (0.205) (0.199) (0.204) (0.258) (0.107) (0.183) (0.271)

Hospital Fixed Effects No Yes Yes No Yes Yes No Yes Yes No Yes Yes

Physician Fixed Effects No No Yes No No Yes No No Yes No No Yes

Observations 515,447 515,447 515,447 705,697 705,697 705,697 705,697 705,697 705,697 709,045 709,045 709,045

R-squared 0.041 0.072 0.175 0.068 0.103 0.19 0.055 0.111 0.254 0.06 0.129 0.376

Adoption Abandonment

*** p<0.01, ** p<0.05, * p<0.1

Release of BMS Release of DESAbandon of BMS After Release of

DES

Abandon of Stents After Release of

Guideline for Low Severity SCAD

Standard errors in parentheses

31

Table 4: LPM Estimation of Change in Stenting Over Time High Severity SCAD Patients Post Guideline AMC, For-Profit, and Not For-Profit Hospitals

Hospital, Age, Race, and SCAD Diagnosis Omitted*

* Omitted variables indicate that the variables have been included in the estimations but the coefficients have not been

displayed in the interest of space. Complete output from the estimations is available upon request

(1) (2) (3)

DV Stent Stent Stent

AMC 0.0300**

(0.0124)

For-Profit 0.0548***

(0.00613)

Time -0.00403*** -0.00192 -0.000711

(0.00135) (0.00125) (0.00128)

Time * AMC -0.000102 0.000782 -0.000934

(0.00351) (0.00326) (0.00343)

Time * For-Profit -0.00167 -0.00242 0.00111

(0.00174) (0.00162) (0.00166)

Constant -0.352 -0.149 0.161

(0.454) (0.419) (0.417)

Hospital Fixed Effects No Yes Yes

Physician Fixed Effects No No Yes

Observations 709,045 709,045 709,045

R-squared 0.06 0.129 0.376

Standard errors in parentheses

*** p<0.01, ** p<0.05, * p<0.1

32

Table 5: LPM Estimation of Change in Stenting Over Time For Faculty Members Comparison of Faculty * [AMC, For-Profit, and Not For-Profit Hospitals]

Hospital, Age, Race, and SCAD Diagnosis Omitted*

* Omitted variables indicate that the variables have been included in the estimations but the coefficients have not been

displayed in the interest of space. Complete output from the estimations is available upon request

(1) (2) (3) (4) (5) (6) (7) (8) (9) (10) (11) (12)

Phenomenon

DV BMS BMS BMS DES DES DES BMS BMS BMS Stent Stent Stent

Sample

AMC 0.0106*** -0.0126*** 0.0369*** 0.0542***

(0.00381) (0.00225) (0.00325) (0.00346)

For-Profit 0.0108*** -0.00738*** 0.0259*** 0.0104***

(0.00117) (0.000960) (0.00139) (0.00150)

Faculty -0.000620 -0.000573 0.00343* 0.00100 -0.00135 -0.00232 -0.0101*** -0.00964***

(0.00250) (0.00248) (0.00208) (0.00206) (0.00300) (0.00294) (0.00338) (0.00328)

Time 0.00395*** 0.00419*** 0.00435*** 0.0155*** 0.0156*** 0.0156*** -0.0100*** -0.00999*** -0.0105*** -0.00393*** -0.00336*** -0.00152***

(0.000235) (0.000236) (0.000238) (0.000199) (0.000196) (0.000196) (0.000287) (0.000279) (0.000270) (0.000312) (0.000302) (0.000272)

Time * For-Profit 0.000871*** 0.00202*** 0.00169*** 0.00511*** 0.00489*** 0.00542*** -0.00421*** -0.00439*** -0.00446*** 0.000129 -6.49e-06 0.000950***

(0.000312) (0.000316) (0.000316) (0.000262) (0.000258) (0.000260) (0.000378) (0.000368) (0.000357) (0.000409) (0.000395) (0.000357)

Time * AMC 0.00802*** 0.00801*** 0.00864*** 0.0135*** 0.0133*** 0.0149*** -0.00741*** -0.00800*** -0.0105*** -0.00183* -0.00268*** -0.00169*

(0.000891) (0.000949) (0.00102) (0.000617) (0.000607) (0.000623) (0.000891) (0.000866) (0.000857) (0.000962) (0.000928) (0.000864)

Time * Faculty 0.000160 3.93e-05 -0.000697 -0.00119** -0.000792 -0.000546 -0.00189** -0.00105 -0.00147* -0.00128 -0.00131 -4.74e-06

(0.000664) (0.000654) (0.000667) (0.000576) (0.000567) (0.000576) (0.000832) (0.000809) (0.000793) (0.000923) (0.000890) (0.000802)

Faculty * AMC -4.39e-05 -0.0269*** -0.0467*** 0.00507 0.0163*** 0.0344*** -0.0123** -0.0184*** -0.00642 -0.0454*** -0.0223*** -0.0125

(0.00577) (0.00599) (0.00868) (0.00381) (0.00391) (0.00667) (0.00550) (0.00558) (0.00917) (0.00594) (0.00592) (0.00920)

Faculty * AMC * Time -0.00582*** -0.00315** -0.00338** -0.0101*** -0.00994*** -0.0109*** 0.00365** 0.00372** 0.00773*** 0.000666 0.00154 -0.000344

(0.00140) (0.00140) (0.00154) (0.00105) (0.00103) (0.00106) (0.00151) (0.00147) (0.00146) (0.00164) (0.00158) (0.00146)

Faculty * For-Profit 0.0110*** 0.00685** -0.00122 -0.00505* -0.00854*** -0.00566 0.0233*** 0.0179*** 0.00348 0.0304*** 0.0256*** -0.000760

(0.00315) (0.00313) (0.00490) (0.00261) (0.00259) (0.00394) (0.00376) (0.00369) (0.00541) (0.00423) (0.00412) (0.00572)

Faculty * For-Profit * Time 7.20e-05 -0.000370 8.46e-05 0.00474*** 0.00438*** 0.00391*** -0.00207** -0.00287*** -0.00235** -0.000113 2.01e-05 -0.000844

(0.000837) (0.000825) (0.000838) (0.000719) (0.000707) (0.000717) (0.00104) (0.00101) (0.000986) (0.00116) (0.00112) (0.00101)

Constant -0.0455 -0.0718 0.0505 -0.0271 -0.136 -0.0577 -0.00236 -0.0476 0.0993 -0.243** -0.690*** 0.0563

(0.0556) (0.151) (0.158) (0.138) (0.163) (0.205) (0.199) (0.232) (0.282) (0.107) (0.183) (0.271)

Hospital Fixed Effects No Yes Yes No Yes Yes No Yes Yes No Yes Yes

Physician Fixed Effects No No Yes No No Yes No No Yes No No Yes

Observations 515,447 515,447 515,447 705,697 705,697 705,697 705,697 705,697 705,697 709,045 709,045 709,045

R-squared 0.041 0.073 0.175 0.065 0.100 0.187 0.055 0.111 0.254 0.061 0.129 0.376

*** p<0.01, ** p<0.05, * p<0.1

Adoption Abandonment

Release of BMS Release of DES Abandon of BMS After Release of DESAbandon of Stents After Release of

Guideline for Low Severity SCAD

Standard errors in parentheses

33

Table 6: LPM Estimation of Change in Stenting Over Time For Freelance Physicians Comparison of AMC, For-Profit, and Not For-Profit Hospitals

Hospital, Age, Race, and SCAD Diagnosis Omitted*

* Omitted variables indicate that the variables have been included in the estimations but the coefficients have not been

displayed in the interest of space. Complete output from the estimations is available upon request

(1) (2) (3) (4) (5) (6) (7) (8) (9) (10) (11) (12)

Phenomenon

DV BMS BMS BMS DES DES DES BMS BMS BMS Stent Stent Stent

Sample

AMC 0.0103*** -0.0141*** 0.0412*** 0.0682***

(0.00394) (0.00232) (0.00335) (0.00388)

For-Profit 0.0124*** -0.00632*** 0.00991*** -0.000820

(0.00146) (0.00108) (0.00156) (0.00173)

Time 0.00404*** 0.00448*** 0.00445*** 0.0164*** 0.0165*** 0.0162*** -0.0115*** -0.0114*** -0.0118*** -0.00440*** -0.00404*** -0.00174***

(0.000284) (0.000288) (0.000299) (0.000221) (0.000218) (0.000222) (0.000320) (0.000312) (0.000310) (0.000352) (0.000340) (0.000321)

AMC * Time 0.00762*** 0.00789*** 0.00929*** 0.0148*** 0.0148*** 0.0175*** -0.00884*** -0.00954*** -0.0113*** -0.00335*** -0.00446*** -0.00249**

(0.000929) (0.00102) (0.00105) (0.000634) (0.000625) (0.000654) (0.000917) (0.000893) (0.000913) (0.00107) (0.00103) (0.000991)

For-Profit * Time 0.000467 0.00135*** 0.00212*** 0.00347*** 0.00339*** 0.00327*** -0.00241*** -0.00237*** -0.00222*** 0.00113** 0.00132*** 0.00141***

(0.000379) (0.000387) (0.000399) (0.000295) (0.000291) (0.000298) (0.000427) (0.000415) (0.000416) (0.000473) (0.000455) (0.000433)

Constant -0.0316 -0.0201 -0.0740 -0.0512 -0.139 -0.165 0.0181 -0.0573 0.154 -0.314 -0.867*** -0.336

(0.106) (0.105) (0.207) (0.193) (0.190) (0.187) (0.280) (0.271) (0.261) (0.295) (0.284) (0.357)

Hospital Fixed Effects No Yes Yes No Yes Yes No Yes Yes No Yes Yes

Physician Fixed Effects No No Yes No No Yes No No Yes No No Yes

Observations 314,782 314,782 314,782 456,204 456,204 456,204 456,204 456,204 456,204 426,553 426,553 426,553

R-squared 0.041 0.077 0.166 0.065 0.101 0.183 0.054 0.114 0.233 0.058 0.136 0.336

*** p<0.01, ** p<0.05, * p<0.1

Adoption Abandonment

Release of BMS Release of DES Abandon of BMS After Release of DESAbandon of Stents After Release of

Guideline for Low Severity SCAD

Standard errors in parentheses

34

Figure 1: Stenting Rate Over Time - X Axis: Time / Y Axis: Percent Stents Implanted

Figure 2: Change in BMS Utilization X Axis: Time / Y Axis: Percent Stents Implanted Period 0 – First Period After Approval of BMS

Figure 3: Change in DES Utilization X Axis: Time / Y Axis: Percent Stents Implanted Period 0 – First Period After Approval of DES

0.0000

0.0100

0.0200

0.0300

0.0400

0.0500

0.0600

0.0700

-1 0 1 2 3 4 5

NFP FP AMC

35

Figure 4: Change in BMS Utilization X Axis: Time / Y Axis: Percent Stents Implanted Period 0 – First Period After Approval of DES

Figure 6: Change in BMS Utilization X Axis: Time / Y Axis: Percent Stents Implanted Period 0 – First Period After Approval of BMS

Figure 5: Change in Stent (DES & BMS) Utilization X Axis: Time / Y Axis: Percent Stents Implanted

Period 0 – First Period After AHA/ACC Guideline Release

Figure 7: Change in DES Utilization X Axis: Time / Y Axis: Percent Stents Implanted Period 0 – First Period After Approval of DES

36

Figure 8: Change in BMS Utilization X Axis: Time / Y Axis: Percent Stents Implanted Period 0 – First Period After Approval of DES

Figure 10: Change in BMS Utilization (Freelancers) X Axis: Time / Y Axis: Percent Stents Implanted Period 0 – First Period After Approval of BMS

Figure 9: Change in Stent (DES & BMS) Utilization X Axis: Time / Y Axis: Percent Stents Implanted

Period 0 – First Period After AHA/ACC Guideline Release

Figure 11: Change in DES Utilization (Freelancers) X Axis: Time / Y Axis: Percent Stents Implanted Period 0 – First Period After Approval of DES

37

Figure 12: Change in BMS Utilization (Freelancers) X Axis: Time / Y Axis: Percent Stents Implanted Period 0 – First Period After Approval of DES

Figure 13: Change in Stent (DES & BMS) Utilization (Freelancer) X Axis: Time / Y Axis: Percent Stents Implanted

Period 0 – First Period After AHA/ACC Guideline Release

38

Appendix A

Coronary arterial disease is a condition where plaque builds up in a patients’ arteries causing a restriction of

blood to the heart, thereby reducing the amount of oxygen the muscle receives. Left untreated, arterial disease

can lead to a variety of negative patient care outcomes; ranging from a reduced ability to perform everyday

tasks as a result of angina, i.e. chest pain, to death as a result of acute myocardial infarction, i.e. heart attack.

At present, it is the leading cause of death in the United States, affecting roughly 40% amount of the

American population suffering some form of the disease over the course of their lifetime (Rosamond 2007).

Unsurprisingly, given the length of time required for the plaque buildup to become life threatening,

many classifications of the disease have been developed with varying medical treatments existing at each

degree of severity. At present, the dominant classification comes from the Canadian Cardiovascular Society

(CCS), a representation of which from Cassar et al. (2009) is available in Appendix Table 1, which is

referenced explicitly in the 2005 AHA/ACC Guideline (Smith et al. 2006). According to the newly released

guideline, stents should no longer be used as a treatment for CCS Class I and CCS Class II arterial disease.

We therefore classify patients suffering from the following medical conditions as Severe SCAD

patients based on their ICD-9 codes: intermediate coronary syndrome, an acute coronary occlusion without

myocardial infarction, or angina decubitus13. Acute coronary occlusion without myocardial infarction is a

complete blockage of one of the arteries that supplies the heart with blood, thereby making it severe by

definition. Angina decubitus is CCS Class III based on the descriptions in Table 1, because it is resting chest

pain. Finally, intermediate coronary syndrome is severe SCAD according to the ICD-9 description.

Source: Cassar et al. (2009)

13 Recall that all patients suffering from a heart attack are dropped from the sample.