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
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 antikickback 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, notfor-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
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
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 subsegment 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
In 2008, hospitals in Florida had an average operating margin of 0.7% (FHGPA 2011)
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-in2014/
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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
14
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, ForProfit, 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/
Results available upon request
10
15
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 patientcharacteristic 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:
16
𝑦 = 𝛽1 𝑠1 + 𝛾1 𝑠2 + 𝑀′ 𝜃1 + 𝑋 ′ 𝛿1 + 𝜈 + 𝜀
(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,
17
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
18
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
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.
11
19
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 selfselecting 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.
20
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 1012), 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
21
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.
22
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
23
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
24
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
Finkelstein and Gilbert
(1985)
Burns and Wholey
(1993)
Greve (1995)
Finding
Context
Abandonment is driven by absolute disadvantage of treatment options
Pharmaceuticals
Antecedents of adoption have little effect on abandonment
Management Practices
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
28
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
(1)
(2)
(3)
(4)
(5)
(6)
(7)
(1)
(2)
(3)
(4)
(5)
(6)
(7)
(8)
(1)
(2)
(3)
(4)
(5)
(6)
Variable
Bare Metal Stent
Teaching
For Profit
Severe SCAD
Year
Age
Gender
Variable
Drug Eluting
Bare Metal Stent
Teaching
For Profit
Severe SCAD
Year
Age
Gender
Variable
Stent
Teaching
For Profit
Year
Age
Gender
Mean
0.045
0.050
0.550
0.280
1996
71.454
0.559
Mean
0.042
0.092
0.069
0.547
0.148
2002
71.106
0.558
Mean
0.108
0.073
0.547
2006
71.713
0.559
Std. Dev.
0.206
0.219
0.498
0.449
0.648
12.132
0.496
Std. Dev.
0.202
0.289
0.254
0.498
0.355
0.647
12.939
0.497
Std. Dev.
0.311
0.260
0.498
0.621
13.063
0.496
Table 2a
N - 515,447
(1)
(2)
0.032
0.036
0.120
0.041
-0.120
0.049
-0.255
0.014
0.062
-0.099
0.008
Table 2b
N - 705,697
(1)
(2)
-0.002
0.018
0.025
0.093
0.154
-0.090
0.041
0.006
0.034
0.118
-0.077
-0.112
0.065
Table 2c
N - 709,045
(1)
(2)
0.021
0.025
-0.029
-0.145
0.074
-0.308
-0.009
-0.093
0.020
29
(3)
(4)
(5)
(6)
-0.013
-0.017
-0.050
0.029
-0.016
-0.153
0.014
0.007
-0.003
-0.167
(3)
(4)
(5)
(6)
(7)
-0.300
0.002
-0.003
-0.097
0.012
0.009
0.002
-0.029
0.033
-0.023
-0.114
0.021
0.012
0.008
-0.151
(3)
(4)
(5)
-0.002
-0.025
0.021
0.014
-0.003
-0.137
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*
Phenomenon
DV
Sample
(1)
(2)
BMS
BMS
(3)
(4)
Adoption
BMS
DES
Release of BMS
AMC
0.00990***
(0.00274)
For-Profit
0.0126***
(0.00109)
Time
0.00397***
(0.000220)
AMC * Time
0.00547***
(0.000652)
For-Profit * Time
0.000880***
(0.000289)
Constant
-0.0471
(0.0556)
Hospital Fixed Effects
No
Physician Fixed Effects
No
Observations
515,447
R-squared
0.041
0.00419***
(0.000222)
0.00626***
(0.000719)
0.00196***
(0.000294)
-0.0354
(0.0548)
Yes
No
515,447
0.072
0.00427***
(0.000223)
0.00671***
(0.000743)
0.00168***
(0.000293)
0.0452
(0.0875)
Yes
Yes
515,447
0.175
(5)
(6)
DES
DES
(7)
(8)
BMS
BMS
(9)
(10)
Abandonment
BMS
Stent
Abandon of BMS After Release of
DES
Release of DES
-0.00907***
(0.00174)
-0.00803***
(0.000892)
0.0154***
(0.000187)
0.00864***
(0.000474)
0.00580***
(0.000244)
0.00737
(0.138)
No
No
705,697
0.068
0.0311***
(0.00251)
0.0296***
(0.00129)
-0.0102***
(0.000270)
-0.00642***
(0.000683)
-0.00462***
(0.000351)
-0.00606
(0.199)
No
No
705,697
0.055
(12)
Stent
Stent
Abandon of Stents After Release of
Guideline for Low Severity SCAD
0.0308***
(0.00267)
0.0146***
(0.00140)
-0.00408***
(0.000294)
-0.00217***
(0.000735)
5.11e-05
(0.000383)
-0.246**
(0.107)
No
No
709,045
0.06
0.0155***
0.0155***
-0.0101*** -0.0107***
(0.000184) (0.000185)
(0.000262) (0.000254)
0.00861*** 0.00993***
-0.00669*** -0.00754***
(0.000466) (0.000480)
(0.000664) (0.000660)
0.00554*** 0.00601***
-0.00489*** -0.00490***
(0.000240) (0.000242)
(0.000342) (0.000332)
-0.102
-0.0434
0.0302
0.0252
(0.163)
(0.205)
(0.204)
(0.258)
Yes
Yes
Yes
Yes
No
Yes
No
Yes
705,697
705,697
705,697
705,697
0.103
0.19
0.111
0.254
Standard errors in parentheses
*** p<0.01, ** p<0.05, * p<0.1
* 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
30
(11)
-0.00351***
(0.000284)
-0.00245***
(0.000709)
-5.81e-05
(0.000370)
-0.693***
(0.183)
Yes
No
709,045
0.129
-0.00152***
(0.000256)
-0.00186***
(0.000658)
0.000833**
(0.000334)
0.0477
(0.271)
Yes
Yes
709,045
0.376
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*
DV
AMC
(1)
(2)
(3)
Stent
Stent
Stent
-0.00403***
-0.00192
-0.000711
(0.00135)
(0.00125)
(0.00128)
-0.000102
0.000782
-0.000934
(0.00351)
(0.00326)
(0.00343)
-0.00167
-0.00242
0.00111
(0.00174)
(0.00162)
(0.00166)
0.0300**
(0.0124)
For-Profit
0.0548***
(0.00613)
Time
Time * AMC
Time * For-Profit
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
709,045
709,045
709,045
0.06
0.129
0.376
Observations
R-squared
Standard errors in parentheses
*** p<0.01, ** p<0.05, * p<0.1
* 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
31
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*
Phenomenon
DV
Sample
AMC
(1)
(2)
BMS
BMS
Release of BMS
(3)
(4)
Adoption
BMS
DES
(5)
(6)
DES
DES
Release of DES
0.0106***
(0.00381)
For-Profit
0.0108***
(0.00117)
Faculty
-0.000620
(0.00250)
Time
0.00395***
(0.000235)
Time * For-Profit
0.000871***
(0.000312)
Time * AMC
0.00802***
(0.000891)
Time * Faculty
0.000160
(0.000664)
Faculty * AMC
-4.39e-05
(0.00577)
Faculty * AMC * Time
-0.00582***
(0.00140)
Faculty * For-Profit
0.0110***
(0.00315)
Faculty * For-Profit * Time
7.20e-05
(0.000837)
Constant
-0.0455
(0.0556)
Hospital Fixed Effects
No
Physician Fixed Effects
No
Observations
515,447
R-squared
0.041
(7)
(8)
(9)
(10)
(11)
(12)
Abandonment
BMS
BMS
BMS
Stent
Stent
Stent
Abandon of Stents After Release of
Abandon of BMS After Release of DES
Guideline for Low Severity SCAD
0.0369***
0.0542***
(0.00325)
(0.00346)
0.0259***
0.0104***
(0.00139)
(0.00150)
-0.00135
-0.00232
-0.0101*** -0.00964***
(0.00300) (0.00294)
(0.00338) (0.00328)
-0.0100*** -0.00999*** -0.0105*** -0.00393*** -0.00336*** -0.00152***
(0.000287) (0.000279) (0.000270) (0.000312) (0.000302) (0.000272)
-0.00421*** -0.00439*** -0.00446*** 0.000129
-6.49e-06 0.000950***
(0.000378) (0.000368) (0.000357) (0.000409) (0.000395) (0.000357)
-0.00741*** -0.00800*** -0.0105*** -0.00183* -0.00268*** -0.00169*
(0.000891) (0.000866) (0.000857) (0.000962) (0.000928) (0.000864)
-0.00189** -0.00105
-0.00147* -0.00128
-0.00131
-4.74e-06
(0.000832) (0.000809) (0.000793) (0.000923) (0.000890) (0.000802)
-0.0123** -0.0184*** -0.00642 -0.0454*** -0.0223***
-0.0125
(0.00550) (0.00558) (0.00917) (0.00594) (0.00592) (0.00920)
0.00365** 0.00372** 0.00773*** 0.000666
0.00154
-0.000344
(0.00151) (0.00147) (0.00146) (0.00164) (0.00158) (0.00146)
0.0233*** 0.0179***
0.00348
0.0304*** 0.0256*** -0.000760
(0.00376) (0.00369) (0.00541) (0.00423) (0.00412) (0.00572)
-0.00207** -0.00287*** -0.00235** -0.000113
2.01e-05
-0.000844
(0.00104) (0.00101) (0.000986) (0.00116) (0.00112) (0.00101)
-0.00236
-0.0476
0.0993
-0.243**
-0.690***
0.0563
(0.199)
(0.232)
(0.282)
(0.107)
(0.183)
(0.271)
No
Yes
Yes
No
Yes
Yes
No
No
Yes
No
No
Yes
705,697
705,697
705,697
709,045
709,045
709,045
0.055
0.111
0.254
0.061
0.129
0.376
-0.0126***
(0.00225)
-0.00738***
(0.000960)
-0.000573
0.00343*
0.00100
(0.00248)
(0.00208) (0.00206)
0.00419*** 0.00435*** 0.0155*** 0.0156*** 0.0156***
(0.000236) (0.000238) (0.000199) (0.000196) (0.000196)
0.00202*** 0.00169*** 0.00511*** 0.00489*** 0.00542***
(0.000316) (0.000316) (0.000262) (0.000258) (0.000260)
0.00801*** 0.00864*** 0.0135*** 0.0133*** 0.0149***
(0.000949) (0.00102) (0.000617) (0.000607) (0.000623)
3.93e-05
-0.000697 -0.00119** -0.000792 -0.000546
(0.000654) (0.000667) (0.000576) (0.000567) (0.000576)
-0.0269*** -0.0467*** 0.00507
0.0163*** 0.0344***
(0.00599) (0.00868) (0.00381) (0.00391) (0.00667)
-0.00315** -0.00338** -0.0101*** -0.00994*** -0.0109***
(0.00140) (0.00154) (0.00105) (0.00103) (0.00106)
0.00685** -0.00122
-0.00505* -0.00854*** -0.00566
(0.00313) (0.00490) (0.00261) (0.00259) (0.00394)
-0.000370
8.46e-05 0.00474*** 0.00438*** 0.00391***
(0.000825) (0.000838) (0.000719) (0.000707) (0.000717)
-0.0718
0.0505
-0.0271
-0.136
-0.0577
(0.151)
(0.158)
(0.138)
(0.163)
(0.205)
Yes
Yes
No
Yes
Yes
No
Yes
No
No
Yes
515,447
515,447
705,697
705,697
705,697
0.073
0.175
0.065
0.100
0.187
Standard errors in parentheses
*** p<0.01, ** p<0.05, * p<0.1
* 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
32
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*
Phenomenon
DV
(1)
(2)
BMS
BMS
For-Profit
(4)
Adoption
BMS
DES
Release of BMS
Sample
AMC
(3)
(5)
(6)
DES
DES
Release of DES
(7)
(8)
(9)
(10)
(11)
(12)
Abandonment
BMS
BMS
BMS
Stent
Stent
Stent
Abandon of Stents After Release of
Abandon of BMS After Release of DES
Guideline for Low Severity SCAD
0.0103***
-0.0141***
0.0412***
0.0682***
(0.00394)
(0.00232)
(0.00335)
(0.00388)
0.0124***
-0.00632***
0.00991***
-0.000820
(0.00146)
Time
0.00404***
(0.000284)
AMC * Time
0.00762***
(0.000929)
For-Profit * Time
0.000467
(0.000379)
Constant
-0.0316
(0.106)
Hospital Fixed Effects
No
Physician Fixed Effects
No
Observations
314,782
R-squared
0.041
(0.00108)
(0.00156)
(0.00173)
0.00448*** 0.00445*** 0.0164*** 0.0165*** 0.0162*** -0.0115*** -0.0114*** -0.0118*** -0.00440*** -0.00404*** -0.00174***
(0.000288) (0.000299) (0.000221) (0.000218) (0.000222) (0.000320) (0.000312) (0.000310) (0.000352) (0.000340) (0.000321)
0.00789*** 0.00929*** 0.0148*** 0.0148*** 0.0175*** -0.00884*** -0.00954*** -0.0113*** -0.00335*** -0.00446*** -0.00249**
(0.00102) (0.00105) (0.000634) (0.000625) (0.000654) (0.000917) (0.000893) (0.000913) (0.00107) (0.00103) (0.000991)
0.00135*** 0.00212*** 0.00347*** 0.00339*** 0.00327*** -0.00241*** -0.00237*** -0.00222*** 0.00113** 0.00132*** 0.00141***
(0.000387) (0.000399) (0.000295) (0.000291) (0.000298) (0.000427) (0.000415) (0.000416) (0.000473) (0.000455) (0.000433)
-0.0201
-0.0740
-0.0512
-0.139
-0.165
0.0181
-0.0573
0.154
-0.314
-0.867***
-0.336
(0.105)
(0.207)
(0.193)
(0.190)
(0.187)
(0.280)
(0.271)
(0.261)
(0.295)
(0.284)
(0.357)
Yes
Yes
No
Yes
Yes
No
Yes
Yes
No
Yes
Yes
No
Yes
No
No
Yes
No
No
Yes
No
No
Yes
314,782
314,782
456,204
456,204
456,204
456,204
456,204
456,204
426,553
426,553
426,553
0.077
0.166
0.065
0.101
0.183
0.054
0.114
0.233
0.058
0.136
0.336
Standard errors in parentheses
*** p<0.01, ** p<0.05, * p<0.1
* 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
33
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
0.0700
0.0600
0.0500
0.0400
0.0300
0.0200
0.0100
0.0000
-1
0
1
2
NFP
3
FP
4
5
AMC
34
Figure 4: Change in BMS Utilization
X Axis: Time / Y Axis: Percent Stents Implanted
Period 0 – First Period After Approval of DES
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 6: Change in BMS Utilization
X Axis: Time / Y Axis: Percent Stents Implanted
Period 0 – First Period After Approval of BMS
Figure 7: Change in DES Utilization
X Axis: Time / Y Axis: Percent Stents Implanted
Period 0 – First Period After Approval of DES
35
Figure 8: Change in BMS Utilization
X Axis: Time / Y Axis: Percent Stents Implanted
Period 0 – First Period After Approval of DES
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 10: Change in BMS Utilization (Freelancers)
X Axis: Time / Y Axis: Percent Stents Implanted
Period 0 – First Period After Approval of BMS
Figure 11: Change in DES Utilization (Freelancers)
X Axis: Time / Y Axis: Percent Stents Implanted
Period 0 – First Period After Approval of DES
36
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
37
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.
38