Translating Theory Into Practice_ 4 of 4 Discussions

Research paper

How can toast be radical? Perceptions of innovations in healthcare Richard Adams Senior Research Fellow, University of Exeter Business School, Exeter, UK

David Tranfield Emeritus Professor of Management

David Denyer Professor of Organisational Change

Cranfield School of Management, Cranfield, UK

Introduction

Innovation is a priority issue in the UK NHS. In 2009,

Lord Darzi, then Health Minister in a Labour admin-

istration, announced a £220 million fund specifically to encourage innovation (www.dh.gov.uk/en/Media

Centre/Pressreleasesarchive/DH_098579, accessed April

2010). Alongside this new investment came a legal

requirement for England’s strategic health authorities to

support the diffusion of innovations throughout the

health service (NHS, 2008).

For the NHS, the real value of innovations comes

not from singular adoptions, but from their wide- spread diffusion and adoption. However, innovations

differ one from another, and are rarely readily

transplantable from one context to another. Even the

same innovation can have different implications across multiple locales in complex organisations such as the

NHS. This makes the diffusion of innovations across the

NHS a challenging task. It is important, then, to

understand not only what drives the adoption of

innovations in healthcare, but also what might hinder

their adoption.

Previous research has shown adoption to be influ-

enced by a variety of factors (Adams and Bessant, 2008). Among the most important of these are the attributes of

ABSTRACT

Background: Innovation is a priority in the NHS. Yet innovations differ one from another. They can

mean different things for different individuals

and so adoption and diffusion present a series of challenges. Innovations in healthcare are complex

phenomena and warrant a suitably sensitive con-

ceptualisation to promote the generation of new

insights and better understanding.

Aims: Diffusion of innovation theory argues that perceptions of both innovations (innovation attri-

butes) and context, significantly affect adoption deci-

sions. Synthesising this theoretical heritage with empirical findings from four case studies of success-

ful innovation in the NHS, this paper proposes a

descriptive framework of innovation attributes that

captures the perceptions of innovators in healthcare.

Methods: Data are collected on four cases of suc- cessful innovation in the NHS using semi-

structured interview, repertory grid technique and

a variety of secondary sources. These data are

analysed using the constant comparison method.

Results: Innovations that are complex and risky, whose benefits may not immediately be clear and

may be disruptive, are evidently adopted in the NHS.

This view, captured in the developed framework,

extends diffusion of innovation theory’s notion of

the readily adopted innovation and offers a heuristic

for thinking about wider diffusion of innovations.

Conclusion: The proposed framework addresses limitations identified in other conceptualisations, offers the potential for a tradition of cross-case and

cumulative research and stimulates new avenues for

further research.

Keywords: adoption and diffusion, innovation, social perceptions

The International Journal of Clinical Leadership 2011;17:37–48 # 2011 Radcliffe Publishing

R Adams, D Tranfield and D Denyer38

the innovation (how potential adopters perceive the

innovation) and the adoption context, that concepts

are drawn from Diffusion of Innovation (DoI) theory

(Rogers, 2003). Although there is a large literature

from a variety of perspectives addressing the topic of

healthcare innovation, only a small proportion of this literature has utilised DoI theory to address the

adoption and diffusion challenge.

In this study, drawing on cases of successful inno-

vation implementation, we report on an investigation

to outline a descriptive framework of attributes relevant

to innovation in healthcare. Our principal contri-

bution is to propose a conceptual tool that combats

the limitations of previous approaches and facilitates cross-case and cumulative research.

Diffusion of Innovation theory: attributes and context

According to DoI theory, a variety of factors affect the

rate of adoption including individual innovativeness, supplier/promoter characteristics, process character-

istics, innovation attributes and contextual factors

(Rogers, 2003). The latter two are generally regarded

as the most influential in explaining the adoption

decision.

Attributes are those descriptive or cognitive prop-

erties that reflect how an innovation is perceived by

potential adopters. In his seminal Diffusion of Inno- vations, Rogers (2003) proposed that five innovation

attributes affect adoption rates. Four were argued to

be positively related to adoption, compatibility, observ-

ability, relative advantage and trialability, whereas the

fifth factor, complexity, is generally negatively correlated

with adoption rates. Up to 87% of the variance in

adoption rates has been explained by configurations

of these perceptual variables (Rogers, 2003). Simply stated, the more positive an individual’s perceptions

of an innovation, the greater the likelihood of adoption.

However, this framework has a number of limi-

tations. First, Rogers and Shoemaker (1971) described

the framework as empirically indefensible, in that

although attributes are conceptually distinct, they fre-

quently overlap in empirical studies. Second, and

relatedly, attributes are not objective features of inno- vations. They are not stable for what may be complex

for one potential adopter may be straightforward for

another. Third, the framework was originally devel-

oped in the context of agriculture and education and

although it has subsequently been operationalised in a

diversity of other contexts, it has not satisfactorily

been found to be universally applicable. Finally, as

illustrated in Appendix 1, the framework has been applied rather uncritically and case studies of single

innovations predominate, thereby restricting oppor-

tunities for the cumulation of research findings.

Taken together, these limitations have prompted

some researchers to develop and extend Rogers’ original

framework to take account of the particular exigencies

of innovation and context (Moore and Benbasat, 1991;

Dearing and Meyer, 1994; Meyer et al, 1997). Context

is an interacting element in the diffusion process (Dopson and Fitzgerald, 2006). Because of the inter-

action between innovation and context, for example,

actions by one adopter may change the context for

others. In the circumstances diffusion in healthcare

systems becomes a non-linear process (Atun et al,

2007). The importance of context is underlined by the

small number of studies that have examined attributes

in healthcare innovations. Appendix 1 shows that, although the core framework remains reasonably

consistent throughout studies (i.e. drawn from Rogers’

work), there is little consistency among findings. Some

studies find general support for Rogers’ formulation,

others only for single attributes and others still note

variance in perceptions across adopter populations.

Consequently, in their extensive systematic review of

diffusion in healthcare, the strongest conclusion that Greenhalgh et al (2005) are prepared to draw is that

overall, three of Rogers’ six attributes of innovations

(Greenhalgh et al include re-invention as an attribute)

came out as influencing adoption in organisational

settings. These were namely relative advantage, com-

patibility and complexity.

Clearly, attributes are important in adoption de-

cisions, but research to date gives limited guidance as to which are important in which circumstances. Even

within the context of healthcare, a constant formu-

lation of attributes that can be applied across inno-

vations has yet to be identified. The typical approach

appears to be to start with Rogers’ formulation,

possibly excluding trialability and observability, and

possibly supplementing these on what often appears

to be little more than a whimsical basis. Together, the variability of attributes across adop-

tion scenarios, coupled with the importance of con-

text and the absence of a framework generalisable to

healthcare, raises an important question about whether

or not a consistent and coherent set of attributes can

be applied across multiple healthcare settings. Is it that

each instance is unique, or is there some configuration

of attributes that describes the adoptability of innova- tions? Is there a stable set of attributes that holds across

innovations and contexts?

In a previous study, Adams et al (2002) presented a

descriptive framework which, they argued, had poten-

tial for application across the range of healthcare

innovations. In the remainder of this paper we build

on this framework, providing case study evidence to

support its composition, and consider each dimen- sion of the framework and its connection to healthcare

innovations. The paper concludes with an assessment

Perceptions of innovations in healthcare 39

of further work needed to develop the framework and

an evaluation of its practical application potential.

Developing the framework of attributes

To address the question ‘Which attributes collectively

constitute a representation of innovators’ perceptions

of healthcare innovations?’, we concurrently under-

took four in-depth case studies of successful health-

care innovation in the UK (outlined below), and a

review of the literature. Innovation case histories were

developed from data collected by semi-structured interview (n = 23), repertory grid technique (RGT)

and from secondary sources.

Repertory grid technique is predicated on Kelly’s

(1955) theory of personal constructs, which suggests

that individuals construe and make sense of the world

through the constant formulation and testing of

hypotheses about it. RGT constitutes a mechanism for

both the elicitation and the representation of cognitive models, emphasising the idiographic characteristics of

personal construct systems. The technique takes the

form of a conversation that is structured by concept-

ualisations of the subject under discussion. In order to

uncover the ways in which people think about the

phenomenon of interest, they are forced to compare

and contrast different manifestations of the phenom-

enon and describe the ways in which they are similar to and different from each other (Goffin, 2002).

Repertory grid technique terms these manifes-

tations ‘elements’. The elements in this research

were innovations in healthcare with which team

members were familiar. The name of each element is

written on a numbered card, each card having been

pre-numbered in a random sequence. Once all the elements have been annotated onto separate cards, the

informant is presented with a set of three cards, which,

in the terminology of RGT, is called a ‘triad’. The

informant is then asked, ‘In what way are two of these

innovations similar to each other and different from

the third?’. A typical response – termed a ‘construct’ –

could be that two innovations are ‘simple’ and the

third is ‘not simple’. The construct forms a bipolar scale (in this example ‘simple–complex’) and inform-

ants are asked to rate each innovation on a five-point

scale against this construct and poles. These ratings are

recorded on a pro forma (see Table 1). Further

differently constituted triads are then presented and

the process continued until meaningful ways of dis-

criminating between elements cease. In this way,

respondents contributed, on average, slightly more than six discrete attributes each.

Often, the process induces reflection, informants

‘think aloud’ and explore different dimensions of

elements. This reflection is a valuable source of con-

textualising data.

Attributes were also derived from an inductive

study of the literature, and the two datasets were

integrated into a single framework through the pro- cess of constant comparison to develop theoretical

properties of those categories (Partington, 2000) and

was facilitated by the use of NVivo software (NVivo,

Table 1 Illustrative repertory grid pro forma and innovation ratings

Tape reference: xxx Elements by card

number

Construct (1) 1 2 3 4 5 6 7 8 9 Pole (5) triad

Simple 3 5 3 2 4 5 2 3 4 Complex 123

People 3 4 3 1 2 5 2 2 1 Project 456

Beliefs 3 4 4 4 2 3 4 2 1 Action 789

Staff’s expressed wishes 1 2 2 1 5 3 1 4 3 Nutrition team’s wants

245

Low combinatorial newness 2 3 4 1 2 5 1 2 2 High combinatorial newness

369

Focused 5 5 4 1 4 5 3 5 5 Trust-wide 234

Small numbers of staff

required

3 2 4 2 5 5 2 5 5 Large numbers required

157

Note: Numbers in bold indicate the presentation of triads

R Adams, D Tranfield and D Denyer40

2000). The process was highly iterative and continued

until saturation was reached. This point was reached

during the fourth case study. The process resulted in

the development of a 13-item framework of inno-

vation attributes (detailed below).

Synopsis of four cases of successful innovation

Case A

A core team of three senior clinical and management

personnel was responsible for the development and

implementation of palliative care service redesign in

one English county. Over approximately two and a half years the concept of palliative care in the county

was reconfigured, ultimately achieving Beacon Status

within the NHS.

Case B

A hospital rebuilding programme provided the con-

text in which a radical review of healthcare was under-

taken, allowing an emergent multidisciplinary, multilevel modernisation team to address entrenched problems

of increasing numbers of emergencies, demand for

elective surgery, pressure to reduce costs, too many

visits for patients and an inequitable and costly oper-

ations booking system.

At the core of the innovation was a research and

development project to redesign the patient journey,

from GP referral to operation to discharge with supporting client/server ITC systems. The team devel-

oped a modified form of business process re-engin-

eering as the technique for exploring new dimensions

of hospital service. The innovations proved significant,

enabling first the modernisation team and subse-

quently the wider hospital community to conceive

that fundamental redesign was a possibility within a

large NHS trust.

Case C

Tackled concerns over the nutritional intake of

inpatients at an NHS trust hospital of 550 beds. The

team consisted of a core membership of medical/

nutrition, dietetic and catering specialists but drew,

also, on the expertise of external colleagues in medical,

catering and nutritional specialisms. The team made significant improvements in nutrition awareness and

screening. Historically perceived as providers of ‘hotel-

type’ services, catering had become dislocated from

the caring roles. The renewed focus on patient benefit

was instrumental in enabling catering to be reconceived

by users and managers as part of the care infrastruc-

ture.

Case D

A small multidisciplinary service delivery unit, re-

sponsible for significant patient-focused innovations,

including an early example of nurse-led pre-assess-

ment, one consequence being that patients are seen

quicker, are better educated about their contact with

the hospital (easing levels of distress), patient flow is

improved and a substantial reduction in cancellations

on the day of operation achieved; establishing the acute pain service, a significant departure from con-

ventional pain management techniques; and a novel

technique for patient-control epidurals. For the pain

service work, the team has received commendation

from the Audit Commission and is active in diffusing

its experience to other quarters of the NHS.

A framework of attributes

Novelty

Novelty captures the notion of degree of change from

a pre-existing state, the difference between the ‘before’

and ‘after’. Commonly, novelty has been dichotomised

between radical and incremental innovations. In pre-

vious studies in fields other than healthcare, novelty

has been identified as a significant construct in inno-

vation research, but results are equivocal and different

levels of novelty have been shown both to hinder and facilitate adoption.

A radical innovation is one that breaks new ground,

is original, requires new skills to implement and

operate, may cause significant departure from pre-

vious practice and may entail some risk. But, because

novelty is a relative rather than absolute concept,

the picture is further confounded and reinforces the

importance of context. The innovation in case study C was hailed as radical. However, this perplexed a

number of the nurses involved, who traced nutritional

care for patients back to Florence Nightingale: ‘After

all’ one remarked, ‘how can toast be radical?’

So, in a context in which there is familiarity with

this sort of change, radicalness may not be as disrup-

tive as in less familiar contexts. When a hospital or

other organisational unit is already accustomed to and accomplished in change, adoption of an innovation

widely considered to be ‘radical’ may, in fact, be an

instance of adaptation through marginal adjustments

to its processes (Wilson et al, 1999).

Departure

More radical innovations have been depicted as gen-

erating significant departure from existing practices and are those that produce fundamental changes in

Perceptions of innovations in healthcare 41

the activities of the organisation. Incremental inno-

vations, by contrast, result in a lesser degree of depar-

ture from existing practices (Damanpour, 1996).

Departure is the extent to which the innovation results

in changes in prevailing practices in the context of

implementation. Whereas radicalness is an indication of general newness, departure points to the impact of

adoption – the extent to which change in the status

quo would be likely to result (West and Anderson,

1996, p. 686). Clearly, some changes mostly associated

with incremental innovation are small enough to be

made with minimal disruption, but others can be

pervasive, as one respondent in Case A noted: ‘Even

though the scale was not large, conceptually this is massively challenging, massively. Like, you know, it

contradicts so many cultural things about the way in

which the NHS has worked...’.

Disruption

A team’s departure from existing ways of behaving can

occur in more or less disruptive ways. Disruption is

conceived as the extent to which the departure from prevailing practice occurred in a disruptive manner.

More radical innovations have been associated with

greater levels of disruption.

Each of the studied innovations demonstrated their

capacity to affect a range of stakeholders, implying

some degree of social, organisational or structural

displacement. Although some disruption did occur

in Case C, it was comparatively small and local. However, Cases A and B were more disruptive. For

example: ‘... and so we changed the whole clinic

structure and this is quite a big thing to have consult-

ants change their clinic structure particularly by

nurses. It is rather like board directors having their

working practices changed by assembly line workers’.

Evidently, it is possible to be highly disruptive yet still

be adopted and successful.

Risk

The consideration of risk is an important element in

adoption decisions and risks of many sorts are mani-

fest in the NHS. Perhaps the most evident type is the

desire to avoid unnecessary risk to patients, but there

are also risks to careers, to organisational reputations,

entrenched positions, established ways of working, personal credibility, risks to the organisational status

quo and so forth, a number of which were picked up in

the four case studies.

Consequently, risk is conceived as the extent to

which the innovation is inherently risky or poses a risk

to individuals, the institution or user-base. The degree

to which individuals perceive there to be risks associated

with adoption has generally been found to have a

negative relationship with adoption (Meyer et al,

1997), though not consistently so (e.g. Duguay et al,

2004).

A significant proportion of the innovation

literature suggests that adopters can be discriminated

on the basis of risk tolerance and it has become taken for granted that earlier adopters are more inclined to

be risk tolerant and later adopters risk averse. A recent

study has, however, suggested an alternative interpret-

ation, that early adopters act because they see the risks

associated with adopting as lower than their non-

adopter counterparts, partly because they see the risks

as more manageable (Panzano and Roth, 2006).

Their argument is consistent with West and Farr’s (1990) view that individuals are more likely to take the

risk of proposing new and improved ways of working

in a climate which they perceive as personally non-

threatening and supportive. In circumstances of threat

or insecurity, risk-taking is likely to be diminished and

the failure to make people feel safe in their jobs or

experimentation can lead to a tendency to avoid risk-

taking or experimentalism and so militate against innovating.

Ideation

Successful innovation can have its roots in creativity,

but originality, in the sense of ‘new-to-the-world’, is

not a necessary antecedent. At the heart of innovation

are combinations of new knowledge or re-combinations

of existing knowledge (Nonaka, 1990), so innovation

can be conceived as embodying different configur- ations of new and existing knowledge from within or

outside the innovating group. These configurations of

knowledge can be conceived in terms of different

points of origin. Ideation is distinguished from new-

ness in that it is concerned with the source of the ideas

and knowledge that feed newness, as opposed to a

relative measure of the novelty of an innovation.

Different points of origin exist: ‘original’ (developed entirely in-house and wholly original), ‘borrowed’

(copied from outside with no modification) and

‘adapted’ (prior solutions identified and modified to

fit the local context). The point of origin of new

knowledge appears critically important in healthcare

innovation, particularly where the clinicians involved

in the process give weight to two primary consider-

ations in their decision making: the evidence base (science) and what peers in the field say. The evidence

of the empirical work corroborates this view of a range

of origins both for ideas and for the material sources of

new knowledge components that make up the inno-

vation artefact that form a significant part of the

perception of it.

However, even innovations with apparently impec-

cable provenance can face adoption challenges: ‘... I

R Adams, D Tranfield and D Denyer42

mean, when you say innovation ... you go back to

Florence Nightingale, that is what they used to do ...

but it still feels like we have got an uphill battle to

maintain where we are’.

Uncertainty

Uncertainty has an equivocal relationship with inno-

vation. Compared with uncertainties affecting organ- isational behaviour that originate from other sources,

the literature has had relatively little to say specifically

about uncertainties relating to innovations, yet in-

formants across the case studies strongly indicated

levels of uncertainty about their innovations. One

respondent from Case D noted: ‘The whole concept

was really difficult to get your head around, and

sometimes I struggled as well ... but ... I can hang on to the fact that at the end of the day, whatever it is we

are trying to do now is going to make a difference to

patients, then I can hang in there’. Also, the timing of

adoptions can be strongly influenced by innovation-

related uncertainties (Farzin et al, 1998).

The excerpt illustrates a level of conceptual uncer-

tainty about how precisely the innovation, in this case

the establishment of an acute pain service, would fit within the existing model of care. Past research has

characterised this innovation-related uncertainty (as

opposed to political, market, resource, environmental

uncertainties, etc.) as perceived technological uncer-

tainty (Song and Montoya-Weiss, 2001). Because valid

knowledge and experience are minimal, technological

uncertainties are likely to be at their greatest as the

innovation first begins to emerge. However, the pass- ing of time does not necessarily provide a smooth

process of clarification because successive modifications

and adaptations in the context of adoption can con-

tinue to contribute degrees of uncertainty. Where these

uncertainties are confronted by understood and em-

bedded practices, it may be easier for potential adopters

to retain the status quo – particularly where there is a

range of alternative innovations each with associated uncertainties (Meijer et al, 2005).

Scope

The fundamental notion underpinning the scope of an

innovation is the nature of the linkage between an

innovation and its environment. That is, to what extent

can the innovation stand alone and be pursued inde-

pendently or does its introduction require changes elsewhere in the system? Innovations may affect only a

single functional area and not other functions or they

may cause wider change in a range of functions. That

is, what proportion of behaviours within the organis-

ation is expected to be affected by the innovation? For

example, Case B’s innovation shows wide-ranging

repercussions beyond the context of the immediate

group: ‘... we designed a slightly different day surgery:

we have got a pre-assessment unit, we have got different

people working in different ways and we have got a

whole different process for our patients’.

Complexity

The notion of complexity articulates innovators’ views

on the ease or difficulty of making use of the inno-

vation. Innovations might be perceived as complex

based on their origins (emanating from culturally

different contexts), due to high levels of component

parts, customisation and interconnectivity between

parts, or because co-ordination of many stakeholder groups is required. Our data suggest social, organ-

isational and co-ordination complexity. On the face of

it, nutritional care for patients may not appear com-

plex, but Case C ‘require[d] components of lots of

individual people to achieve, i.e. menu review,

detailed discussions with dieticians, head of kitchen

and chef team, suppliers, etc., making compromises,

checking out the menu clerks office, call it popularity of dishes, changes to food provisions, lots of parts to

that, costings, financial implications, quality impli-

cations, the delivery time implications, food hygiene

education...’.

Complexity can therefore be considered to be a

function of the nature, quantity and magnitude of the

units involved in its development and implemen-

tation and of the component parts of the innovation, rendering it difficult to understand or use. Complexity

is negatively associated with adoption because it places

extra demands on the learning capacity of adopters as

they are required to develop and apply new knowledge

and skills to assimilate the innovation effectively (Rogers,

2003).

Adaptability

Adaptability is the degree to which the innovation can

be modified to fit with local needs. The easier they are

to adapt to local conditions, the greater their chance of

successful implementation. In Case A, adaptability

was an important consideration: ‘... and we have

decided to ... buy into the health service [database

system] and adapt it to suit ourselves ... It is not a

perfect solution ... but it is the nearest we are going to get and, from a financial point of view we are going to

save ourselves £100,000 a year on software – and that is

a lot of coffee mornings!’.

Other analysts may have been tempted to code this

category ‘compatibility’ – the degree to which an

innovation is perceived as consistent with existing

values, past experiences and the needs of adopters –

one of three attributes of Rogers’ (2003) framework

Perceptions of innovations in healthcare 43

most frequently associated with adoption (Tornatzky

and Klein, 1982; Greenhalgh et al, 2005). In the con-

text of healthcare, other researchers have operation-

alised both adaptability and compatibility (e.g. Meyer

et al, 1997), but although there were identifiable

instances of compatibility in the cases, it failed to satisfy criteria for inclusion in the framework. Although

there are similarities between the two, adaptability is

different from compatibility in the implications of

active modification as opposed to passive fit, and the

degree to which users are able to refine to fit their need

is key to adoption (Leonard-Barton and Sinha, 1993).

Actual Operation and Relative Advantage

Innovations are typically adopted with certain pur-

poses in mind and they must be perceived to fulfil

these intended purposes better relative to the status

quo if they are to be adopted. The construct ‘Actual

Operation’ relates to the extent to which the inno- vation is perceived to be likely to satisfy these objec-

tives. ‘Relative Advantage’, the extent to which an

innovation is perceived as being better than the idea it

supersedes, on the face of it appears to be similar. After

all, if the innovation satisfies the objectives originally

set for it (Actual Operation) then, ipso facto, it would

satisfy the criteria of Relative Advantage of being

better than the idea it supersedes (Holloway, 1997). However, the difference between them lies in their

relationship with the original objectives for the inno-

vation. An innovation may not have achieved all that

was planned for it in addressing and solving the problem

that triggered the process (its actual operation) although

it may be a considerable improvement on what came

before even though it bears little resemblance to initial

terms of reference (relative advantage). The difference appears finely nuanced, but the data clearly distinguish

between achieving original objectives and being in a

better place post innovation.

The following excerpt from Case C describes an

unplanned, but nonetheless significantly beneficial,

outcome from the project. It is a clear articulation of

the outcome being better than that which preceded it

but not necessarily in the manner in which the benefit was envisaged at the start of the innovating process. ‘...

But I think the other huge plus that I think we feel that

we have achieved is that we have re-established re-

lations between the nurses and the catering depart-

ment. That we have actually re-established a dialogue

and we feel that we are working with the catering

department which before we weren’t. You can’t put it

down as a specific ‘‘we did this in order to re-establish the relationship’’, it is a consequence, it was a conse-

quence of the improvements we sought to make.’

Profile

Profile is positively associated with innovation adop-

tion. Innovations may be pursued for the sake of

enhancing the social status of adopters (Moore and

Benbasat, 1991; Agarwal and Prasad, 1997), or be motivated by the desire for prestige and professional

status, sometimes at the cost of organisational goals

(Mohr, 1969). There is moderate indication in our

data of personal aggrandisement, otherwise it mostly

points toward the benefits of profile accruing to the

team or a larger institutional entity.

The importance of social status and the fact that it is

part of the brain’s (e)valuation processes when mak- ing decisions have been demonstrated by recent work

utilising functional magnetic resonance imaging map-

ping brainwave activity. Izuma et al (2008) provide

neural evidence that perceiving one’s good reputation

formed by others activates the striatum, the brain’s

reward system, in a similar manner to monetary

reward.

Observability

Observability is the extent to which the results of an

innovation are observable by others and partially

echoes profile, but the two are differentiated by their

focus: profile relates to individuals, the group or the

organisation, whereas observability is concerned only

with the visibility of the innovation itself, i.e. is object

focused. If the positive outcomes of innovation adop- tion are easily observable by important stakeholders,

the greater is the likelihood for adoption.

Discussion

Adoption and diffusion is not a straightforward pro- cess, even for demonstrably beneficial innovations.

How actors perceive the innovation is an important

factor in facilitating adoption, for it is on the basis of

perceptions that individuals decide behaviours

(Rogers, 2003). As adopters differ in how they perceive

innovations, determining how perceived attributes

affect individuals, contexts and innovations can pro-

vide useful insights for managing the processes of adoption and diffusion.

In this paper, we explore how innovations are

perceived. Findings are in line with existing theory

to the extent that specific innovation characteristics

are associated with adoption. Rogers’ (2003) original

framework consisted of five attributes and our analysis

finds support for three of these, complexity, observ-

ability and relative advantage, but not for trialability and compatibility. The absence of trialability may be

R Adams, D Tranfield and D Denyer44

explained by the fact that as each of the innovations

studied was generated by the adopting team, they were

neither imported, nor were they imposed. So, teams

did not need to trial in the commonly accepted sense.

Innovations brought in from outside may have their

adoption fate partially determined by trialability. The absence of compatibility is a little harder to explain,

although the explanation might be similar to that

proffered for trialability in that, because these were

developed in-house, compatibility could be taken for

granted.

Intuitively, a number of the attributes detailed in

the framework would appear to be closely related. For

example, risk, uncertainty and complexity may be characterised by an absence, to a greater or lesser extent,

of information regarding, among other things, resource

availability and future benefits. In previous studies,

risk and complexity have loaded together (Brown et al,

2003). Yet, at least according to our analysis, informants

discriminated between each of these factors. Future

research will need to test the discreteness of these

factors in a more diverse sample. Similarly, in this exploratory study we have treated

attributes as discrete and independent. A small number

of previous studies have suggested interdependencies

or stepwise relations between attributes. For example,

Vollink et al (2002), in a study of the adoption of

energy conservation initiatives, found support for the

idea that decision making about an innovation is a

stepwise process in which relative advantage is the first critical attribute for continuing or discontinuing the

assessment of an innovation. Only when advantage is

considered sufficiently high does evaluation on the

basis of other attributes proceed. Again, the proposed

framework will need to be tested against this in future

research.

Whilst the analysis identifies a set of attributes

synthesised from innovators’ narratives and the pre- vious literature, it makes no objective assessment

regarding their weight or degree of influence. DoI

theory suggests that a readily adoptable innovation is

characterised by high levels of compatibility, observ-

ability, relative advantage and trialability, and low

levels of complexity. The cases have illustrated a set

of innovations that are perceived to be risky and

complex, where the benefits are not immediately clear for all to see and challenge the taken-for-granted ways

of doing things and yet are evidently adopted. This

raises a question about whether further types of

innovation, in addition to the readily adopted inno-

vation, might be identifiable in different configur-

ations of attributes, whose adoption process would be

better described by some other adverb. It seems likely,

though future research would have to confirm this, that the apparently high levels of risk, novelty, depar-

ture and disruption which characterised, to some

extent, each of the cases would indicate challenging

adoption processes. Additionally, if a set of different

types of innovation based on configurations of attri-

butes can be identified, what are the implications for

innovation diffusion in healthcare? Are the different

types, for example, characterised by different sets of

underpinning processes? It is reasonable to assume that different innovations

will present different profiles, reflecting different con-

figurations of the presence or absence of each of the 13

attributes that make up the framework. Potentially,

this could lead to 8192 (2 13

) different configurations –

more if degree is included in the analysis. However,

this potential variety is limited by attributes’ tendency

to fall into coherent patterns, the upshot of which is that just a fraction of the theoretically conceivable

configurations are viable and likely to be observed

empirically (Meyer et al, 1993). Therefore, one as-

sumption for future research is that it is an inherent

quality of attributes to configure into manageable and

coherent patterns, thereby generating the opportunity

for a polythetic classification, which, by its multi-

variate nature, is more sensitive to innovation hetero- geneity. This polythetic approach allows the researcher

to attend to different kinds of innovation without

making any one attribute the sole determining factor

for inclusion in a class and helps clinicians and man-

agers become more aware of the ways in which

innovations are perceived, thus facilitating greater

understanding and easing the processes of adoption.

If such innovation types can be discovered, it would significantly contribute to cross-disciplinary learning

within healthcare. For example, departments confronted

with the challenge of adopting innovations typified by,

say, complexity and uncertainty may have more to

learn from each other than departments adopting the

same, say, technological innovation which for one

department is highly complex and uncertain but for

the other is perceived to be quite straightforward and highly adaptable.

The research has other implications for innovation

in healthcare. First, for the wider diffusion of inno-

vations across the NHS, not just singular adoptions in

individual locales, it is important to establish how the

innovation is perceived by potential adopters, perhaps

through trial adoptions, to allow promoters to de-

velop support mechanisms addressing individuals’ perceptions. With a clearer understanding of the par-

ticular factors that potential adopters take into con-

sideration prior to adoption, promoters of innovations

will be able to develop appropriate communications

for each particular context. The framework should

also permit remedial communications strategies to be

developed after an innovation has been implemented

to reinforce the positive and address the negative (Pankratz et al, 2002). Finally, this analysis confirms

the view that the same innovation can mean different

things for individuals in different contexts. For diffusion

Perceptions of innovations in healthcare 45

to be successful, active management is required and by

managers who understand in detail the nuances of local

contexts and who are informed and aware of national

and wider priorities and pressures (Dopson and

Fitzgerald, 2006).

Conclusion

This paper takes as its starting point the dual con-

siderations that innovations differ one from another

on account of potential adopters’ perceptions that are

partly shaped by context. Because of this heterogen-

eity, there is need for a parsimonious representation so

that adoption lessons can be generalised. This research does not present a generalisable model, but begins the

process of moving towards one. Taking the NHS as the

context for the study and the pragmatic objective of

finding mechanisms to help more widely and rapidly

diffuse innovations through the system, this paper has

focused on innovation attributes which are among the

most significant of factors influencing adoption deci-

sions. Through an iterative process cycling between the-

ory and empirical research, we have extended work

undertaken in other fields to develop a framework

consisting of attributes specifically applicable to

healthcare. None of the attributes is previously un-

known, although they differ one from another in

terms of the attention accorded them in the literature.

The advantage of a framework based on attributes is that it permits the exploration of innovation types

based on configurations and so the comparison of

previously incommensurable innovations becomes

enabled.

However, our contribution is to extend previous

work by developing a framework that reflects inno-

vators’ cognitive frames in the context of healthcare.

Owing to the nature of the methodology, the general- isability of the findings is limited. Although the context

of the study was the NHS with four very different

healthcare innovations investigated, it is not possible

to generalise into the NHS beyond these cases, let alone

into other areas of innovative activity. That must be a

challenge left for future research.

ACKNOWLEDGEMENTS

The author(s) gratefully acknowledge the contri-

butions of all those who have given of their time to

inform this research. This research was supported by the UK’s Engineering and Physical Sciences Research

Council, grant numbers M72869 and M74092.

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ADDRESS FOR CORRESPONDENCE

Dr Richard Adams, University of Exeter Business

School, Streatham Court, Rennes Drive, Exeter EX4

4PU, UK. Email: [email protected]

Accepted 9 November 2010

Perceptions of innovations in healthcare 47

Appendix 1 Studies of innovation attributes in healthcare

Study Number of

innovations studied

Attributes Basis for selection

of attributes

Findings

Meyer and Goes,

1988

12 – Medical

innovations into

community

hospitals

Risk, Skill,

Observability

Sought a set of

invariant attributes

Assimilation of a

new technology is

highly dependent

upon attributes

Hebert and

Benbasat, 1994

1 – IT in healthcare

– bedside computer

terminals for

record keeping

Image, Relative

advantage,

Compatibility, Ease

of use,

Result

demonstrability, Voluntariness

Adapted from

Moore and

Benbasat

Approximately

77% of the variance

in intent to use the

technology was

explained by three

attitude variables: Relative advantage,

Compatibility and

Result

demonstrability

Parcel et al, 1995 1 – Tobacco

prevention

programme

Relative advantage,

Compatibility,

Complexity

Draws on Rogers Relative advantage

predictive of

adoption

Johnson et al, 1998 4 – IT innovations

in the Cancer

Information Service

Relative advantage,

Risk,

Compatibility,

Complexity,

Trialability,

Observability,

Adaptability,

Acceptance, Computer

knowledge

Selects from

previous studies

Organisational

members rate

contrasting

dimensions of an

innovation

differentially.

Generally supports

Rogers’ model

Landrum, 1998 1 – Wound treatment protocol

Compatibility, Complexity,

Observability,

Relative advantage,

Trialability

Draws on Rogers Generally supports Rogers’ model

Wilson et al, 2009 68 – Medical

imaging

technologies

Radicalness,

Relative advantage

Relevance and

provenance

Attributes are not

invariant

Pankratz et al, 2002 1 – Diffusion of drug prevention

policy

Relative advantage, Compatibility,

Complexity,

Observability

Draws on Rogers Commenced with Rogers’ framework,

but factor analysis

revealed three, not

five significant

attributes

Al-Qirim, 2003 1 –

Teledermatology

Relative advantage,

Complexity,

Compatibility,

Trialability,

Observability, Cost,

Image

Draws on and

extends Rogers

Users and patients

perceive the

innovation

differently

R Adams, D Tranfield and D Denyer48

Appendix 1 Continued

Study Number of

innovations studied

Attributes Basis for selection

of attributes

Findings

Armstrong et al,

2003

1 – Genetic

screening test

Compatibility,

Complexity,

Relative advantage

Adapted from

Moore and

Benbasat

Adoption only

associated with

compatibility

Duguay et al, 2004 1 – Transgenic

biopharmaceuticals

Complexity,

Relative advantage,

Compatibility

Draws from

Tornatzky and

Klein meta-analysis

Attributes need to

be developed

specifically for each

context

Helitzer et al, 2003 1 – Telemedicine Compatibility,

Complexity,

Observability,

Relative advantage,

Trialability

Rogers’ model most

closely matched

findings of their

grounded theory

approach

Attributes are

useful in

identifying barriers

to adoption

Lee, 2004 1 - Computerised

healthcare plan

Compatibility,

Complexity,

Observability,

Relative advantage,

Trialability

Draws on Rogers Generally supports

Rogers’ model

Greenhalgh et al,

2008

1 – Centrally

stored, shared

electronic patient record

Relative advantage,

Simplicity,

Compatibility, Trialability,

Observability,

Potential for

reinvention

Previous systematic

review

To be successfully

and widely

adopted, a technology must be

seen by potential

adopters as having

these attributes

Rahimi et al, 2009 1 – Computerized

provider order

entry systems

Relative advantage,

Compatibility,

Complexity

Draws on Rogers Helpful in

identifying barriers

to adoption

Note: The table excludes the nine studies included in Greenhalgh et al’s (2004) systematic review. In eight of the nine studies, only single innovations were reported on but represent a more diverse set of attributes than those reported above. Across the studies, wide variance of impact of attributes was noted.

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