Running Head: INNOVATION DIFFUSION: A … Head: INNOVATION DIFFUSION: A PROCESS OF DECISION-MAKING 1 Innovation Diffusion: A Process of Decision-Making …

Running Head: INNOVATION DIFFUSION: A PROCESS OF DECISION-MAKING 1

Innovation Diffusion: A Process of Decision-Making

The Case of NAQC

Jonathan E. Beagles, M.S.

Ph.D. Candidate

520-975-1224; [email protected]

School of Government and Public Policy

University of Arizona

Keith G. Provan, Ph.D.

McClelland Professor of Management & Organizations

Eller College of Management and School of Government and Public Policy


Scott F. Leischow, Ph.D.

Professor, Family and Community Medicine

Arizona Cancer Center


Work on this paper was funded by a grant from the National Cancer Institute (R01CA128638-

01A11) and an Arizona Cancer Center Support Grant (CCSG - CA 023074)

INNOVATION DIFFUSION: A PROCESS OF DECISION-MAKING 2

Abstract

This research examines the effect of both information sharing ties and internal decision-

making factors to understand the innovation implementation process among organizations within

the North American Quitline Consortium (NAQC). NAQC is a large, publicly funded “whole

network,” spanning both Canada and the U.S., working to get people to quit smoking. Bringing

Simon‟s (1997) decision-making framework together with a framework of innovation diffusion

(Rogers, 2003) we develop and test hypotheses regarding the types of network ties and internal

decision-making factors likely to be influential at various stages in the innovation diffusion

process. Using negative binomial regression to model three distinct stages in the implementation

process (Awareness, Adoption/Rejection, Implementation), the findings provide evidence

supporting the argument that different types of ties are likely to be important at different stages

in the innovation implementation process and the importance of these ties varies depending on

the role an organization plays as well as internal decision-making factors.


Collaboration among networks of public and private organizations has been an especially

important strategy for addressing the public‟s most pressing health and human services needs,

such as mental health, diabetes and obesity, homelessness, child and youth health, and smoking

cessation. In particular, networks have become important mechanisms for building capacity to

recognize complex health and social problems, systematically planning for how such problems

might best be addressed, mobilizing and leveraging scarce resources, facilitating research on the

problem, and delivering needed services (Provan and Milward, 1995; Chaskin et al., 2001;

Lasker, Weiss and Miller, 2001; Bazzoli et al., 2003; Leischow et al., 2010; Luke et al., 2010).

In order to achieve these gains, critical information must flow between and among the

organizations involved in the network. For instance, when addressing complicated health needs,

it has been suggested that information about new practices that appear to be especially effective

needs to be disseminated, not only from those who create knowledge about these practices to

those who utilize them, but also among those who utilize the practices (Ferlie et al., 2005). In

this regard, network ties have been found to be essential for the dissemination of knowledge

leading to adoption of innovative practices (c.f. Greenhalgh et al., 2004; Rogers, 2003; Valente,

2010).

While the association between network ties and the diffusion of innovations has long

been recognized (Coleman, 1966), more recent research suggests networks matter more than

simply as a means of transferring information (Brass et al, 2004). In addition to the literature on

networks and information transfer (Hansen 1999, 2002; Reagans & McEvily, 2003) networks

have been shown to serve as conduits of social influence either through direct influence by social

relations (Galaskiewicz & Wasserman, 1989; Rao, Davis & Ward, 2000) or through similarities

in network positions leading structurally equivalent actors to adopt similar opinions and


behaviors (Galaskiewicz & Burt, 1991). This research has contributed significantly to our

understanding of networks. However, additional questions have been left unanswered.

Specifically, while evidence suggests the types of ties, their strength, and who they are with are

important for knowledge transfer and the diffusion of innovations, fewer studies have looked at

how different characteristics of network ties may impact the diffusion process differently or how

the relative importance of these ties may vary across stages in an organization‟s innovation

implementation decision. These questions are especially important with regard to the literature

on „whole networks‟ (Provan, Fish & Sydow, 2007) where the structure of network ties impacts

not only each individual organization but also the network as whole (Provan & Milward, 1995).

In an attempt to address this gap in the literature, this study utilizes an individual

decision-making framework (Simon, 1997) to derive hypotheses regarding the relative

importance of network ties and internal decision-making factors across the distinct stages of the

innovation decision process (Rogers, 2003). We test these hypotheses across organizations

within the North American Quitline Consortium (NAQC); a network of public and private

organizations within the U.S. and Canada involved in the provision of telephone-based

counseling and related services to people trying to quit smoking.

Research Setting

The North American Quitline Consortium (NAQC) is an example of the increasing

number of networks established to help address complex health and social problems (Bazzoli et

al., 2003; Chaskin et al., 2001; Lasker, Weiss and Miller, 2001; Provan and Milward, 1995).

NAQC was established in 2004 in response to a perception, among those in the tobacco control

community, that wide variation existed among emerging quitlines with respect to the practices

being adopted and implemented. In response to this perception, one of the primary purposes of


NAQC was to increase communication among the quitlines in order to reduce this variation

through the promotion of evidence based practices (Anderson & Zhu, 2007).

In the summer of 2009, when the study began, there were 63 quitlines within the US and

Canada; each quitline consisting of at least one funder and a one service provider. Typically, the

sole or dominant quitline funding organization is the state/provincial public health department,

which then contracts with a vender to provide the actual array of quitline services. In some cases

(n=13), vendors provide services for a single state/province while in other cases (n=7), vendors

serve multiple states/provinces. This leads to a unique network structure within NAQC compared

to the majority of public/private networks previously reported in the literature (Provan, Fish &

Sydow, 2007). Rather than there being a central public funder working with numerous private

service providers (c.f. Provan & Milward, 1995; Provan, Huang & Milward, 2009), within

NAQC, private service providers are often the most central actors spanning numerous political

boundaries to provide services to multiple public funders. At the time of our data collection, the

largest service provider was a for-profit entity contracting with 18 state quitlines. While the

public funders maintain ultimate accountability for the success of the quitlines, the providers

play an important yet varying role in decision-making regarding the services provided within

each quitline.

In addition to funders and venders, other organizations and individuals participated in the

network such as national funders and researchers. In 2006, this diversity of roles and interests led

to the creation of an independent network administrative organization (NAO) to serve as the full-

time coordinator and neutral broker for the network (Provan, Beagles & Leischow, 2011). Figure

1 provides a depiction of the network using the NetDraw function in UCINET 6 (Borgatti,

Everett, & Freeman, 2002).


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Figure 1

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Literature Review and Hypotheses

Two frameworks form the basis for developing the hypotheses in this study: Rogers‟

diffusion of innovation framework (2003) and Simon‟s bounded rationality (1997). While the

two frameworks provide important contributions in their respective fields, there has been little

conversation between them. This lack of conversation was noted by Valente (2010) when he

suggested more diffusion studies try to understand how their “postulates influence individual

decision-making” (p. 194).

An important distinction between the two frameworks has to do with the perspective

from which they enter the decision-making process. Specifically, research on innovation

diffusion begins with specific innovations of interest and tries to understand how these

innovations move through the stages of the implementation process: knowledge, persuasion,

decision, implementation and confirmation (Figure 2). On the other hand, Simon (1997) and

those developing a decision-making framework study how information, search, evaluation and

capacity (Figure 2) come together in an iterative process around a perceived problem. For those

from a diffusion of innovation perspective a pro-innovation bias assumes the new innovation will

solve a perceived need and make its way through all phases in each organization while those

from a bounded rationality perspective try to understand how a perceived need is solved through

the coming together of these decision-making factors and any particular innovation is one of

many alternatives being evaluated.

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Figure 2

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Diffusion of Innovation Framework

In his comprehensive review of the innovation diffusion literature, Rogers (2003) outlines

a decision-making process developed by researchers over 60 years, beginning with the first

diffusion studies of seed adoption by Iowa Farmers (Ryan & Gross, 1943). During this time,

researchers have outlined a five stage diffusion process beginning with the attainment of

knowledge and moving through what are termed the persuasion, decision, implementation and

confirmation stages. At each stage various types of communication channels have been

suggested to be more or less important along with distinct characteristics of the decision-maker

and the innovation itself (Wejnert, 2002).

In the knowledge stage, decision-makers become aware of new innovations and begin to

gain knowledge of how they function. The persuasion stage refers to a process by which

decision-makers develop opinions regarding an innovation culminating in an explicit decision

whether or not to adopt or reject the innovation based on the values, goals and other criteria used

by a decision-maker to evaluate the innovation.

If a decision is made to adopt an innovation, it then passes through to the implementation

stage of the process, where research suggests reinvention takes place (Rogers, 2003). Similar to

the persuasion stage, where information is manipulated in order to make sense within a particular

value system and goal structure, in the implementation stage the innovation itself is manipulated

to fit within a particular operating environment (Westphal, Gulati & Shortell, 1997).

The adaptation of an innovation to fit the environment is a crucial process leading to the

confirmation stage where all dissonance between the adoption/rejection decision and the current

operating environment is removed. While researchers have found it useful to think of this in

linear terms, it is accepted by many that this may result in an iterative process and information


gathering is necessary at all stages of the process albeit the types of information necessary may

differ.

Bounded Rationality Framework

In his study of Administrative Behavior, Simon (1997) laid out the framework for a study

of organization behavior based on an understanding of individual decision-making. From this

perspective, organization decision-making and action is seen as the result of an interaction

between four key components: information, search, valuation, and capacity. Specifically, it is

argued decision-makers do what is perceived to be in their best interest based on their unique set

of goals and preferences. However, decision-makers are limited in two ways. First, they may be

limited in the amount and quality of information they possess regarding their available

alternatives. Second, they may be limited in their capacity to implement an alternative even if it

is preferred. Thus organizational behavior regarding the adoption and implementation of

innovations is expected to vary based on differences across these components. First, if goals and

values differ across organizations, behavior is expected to differ regardless of whether they

possess the same information and capacities. Second, with the same goals and capacities,

behavior is expected to differ if organizations have access to different information. Finally,

holding information and values/goals constant, differences are expected in organization behavior

due to differences in capacities. For any single organization, decision-making is seen as a

process of adjusting each of these components until an alternative is identified consistent with all

three (Barnard, 1938).

A Synthesis

Despite differences in terminology, the overlap in the frameworks is apparent. It is not

difficult to sense similarities between awareness and information; and the factors that increase


the amount of information a decision-maker possesses are also likely to increase its awareness of

any particular innovation. Likewise, the emphasis on goals and values as the criteria used to

evaluate alternatives overlaps neatly with the persuasion and decision stages in the diffusion

literature. Finally, while the diffusion literature highlights the importance of innovation

adaptation and dissonance removal as important aspects of the implementation and confirmation

phases, other research highlights the importance of capacity in an organization‟s ability to utilize

new information (Tsai, 2001). Bringing these two frameworks together allows us to generate

hypotheses regarding which network and decision-making factors are likely to be most important

at each stage in an organization‟s decision whether or not to adopt and implement a new

innovation. Specifically, factors leading to increased information are likely to be most important

for awareness. Factors impacting values, goals and evaluative criteria in general are most likely

to be influential at the decision stage and factors increasing organizational capacity are likely to

be most important for implementation.

Information, Search and Awareness

The importance of networks for gathering information is well documented (Ahuja, 2001;

Burt, 2004; Tsai, Hansen, 1999 & 2002; Owen-Smith & Powell, 2004; Powell, Koput & Smith-

Doerr 1996; Regeans & McEvily, 2003). However, this work shows not all ties are the same.

Early on, Granovetter (1983) suggested weak ties are better for finding jobs because these ties

are more likely to provide an actor with non-redundant information. Burt (1992) modified the

argument suggesting weak ties are important not because they are weak but because they often

span structural holes which leads to nonredundent information. However, Hansen (1999, 2002)

add to the discussion by arguing that complex knowledge, such as information regarding the

costs and benefits of new innovations, is more easily transmitted across strong ties. In their study


of knowledge transfer within a contract R&D firm, Reagans and McEvily (2003) articulate the

concept of knowledge pools, suggesting specific types of information are located in different

areas of a network based on the roles and functions of those actors. Thus rather than having

relationships spanning structural holes between individuals, they argue tapping into diverse

knowledge pools is what is truly important and having strong ties to these knowledge pools is

beneficial especially when the knowledge is complex.

Within NAQC there are at least five general „knowledge pools‟: state/provincial funders,

service providers, national tobacco policy and funding organizations, and researchers as well as

an independent network administrative organization (NAO) (Provan & Kenis, 2008) which was

established to coordinate activities and information sharing among these other participants. Each

of these groups plays an important role in the network and is perceived by the NAO to contribute

a unique set of resources and perspectives to the network (Provan et al., 2011). While it seems

reasonable each group of organizations can and does contribute unique knowledge to the

network and can be the source of new innovations, the role of researchers stands out as an

exceptionally likely source of information regarding evidence based practices. Also, because the

role of the NAO is to gather and disseminate knowledge we suspect ties to the NAO will

increase the likelihood of an organization being aware of evidence based practices . Based on

this logic, we propose the following hypotheses:

Hypothesis 1a: The greater the number of connections an organization has to others in

the network (especially researchers), the more likely it will be aware of innovative

practices.

Hypothesis 1b: Organizations connected to the network administrative organization will

be more likely to be aware of innovative practices.


In addition to network ties, research in both the innovation diffusion and decision-making

literatures has identified search behavior as an important factor influencing a decision-makers

awareness of information (March & Simon, 1958; Rogers, 2003). Both lines of research have

noted decision-makers with a felt need are likely to be more active in seeking out solutions while

those without a perceived need may be more passive in receiving information from their social

contacts or simply mimic the behavior of others (DiMaggio & Powell, 1983). Being actively

involved in decision-making may be one factor leading to more active search behavior. For

example, if an organization perceives itself to be in a role with significant decision-making

responsibility it may feel a need to be more informed regarding information affecting those

decisions. However, if an organization shares its decision-making responsibilities with others, it

may perceive less of a need to stay informed. Stated in the form of a hypothesis:

Hypothesis 2: The more control in decision-making an organization perceives itself to

have, the greater the number of innovative practices it will be aware of.

Values, Norms and Decision-Making

More than a means of information sharing, research suggests networks are important for

transmitting social norms (Galaskiewicz & Wasserman, 1989; Galaskiewicz & Burt, 1991)

which lead to the adoption of behaviors above and beyond what would be expected by rational

processes. Often these forces come from central or powerful organizations in the environment

such as national policy or funding organizations (Fligstein, 1990) or central network

coordinating organizations (Owen-Smith & Powell, 2004).

If this is indeed the case, we could expect ties to the NAO and to national policy and

funding organizations to serve more than just an information sharing function. In addition to

information sharing, we would suspect ties with these powerful organizations to influence a


decision-makers valuation criteria. Specifically, in the context of our study, we would suspect

ties to the NAO and these national organizations, more than ties to other organizations, to

increase the likelihood of an organization adopting evidence based practices while controlling for

its level of awareness. Stated in the form of hypotheses:

Hypothesis 3a: Organizations connected to the network administrative organization will

be more likely it will be to adopt innovative practices.

Hypothesis 3b: The greater the number of connections an organization has to National

Organizations, the more likely it will be to adopt innovative practices.

Internal decision-making processes such as values and goals and other evaluative criteria

are likely to have their biggest impact at the decision stage of the innovation diffusion process. It

is at this stage where diffusion researchers suggest we will see the culmination of an

organization‟s process of evaluating an innovation based on the knowledge it has gleaned.

However, to understand this evaluation process, it is important to be familiar with the evaluative

criteria organizations are likely to use. Three criteria are prevalent in the literature: efficiency,

effectiveness and prestige. Underlying the rational decision-making perspective is the idea

decision-makers will choose the alternative they perceive to be in their best interest. This concept

of best interests is commonly understood to be the most efficient (greatest benefit for least cost)

decision. Another criterion used to evaluate alternatives is by their perceived effectiveness. This

criterion differs from efficiency in that it pays less attention to the costs of an alternative. In

practice, effectiveness is often evaluated based on perceptions of consistency with an

organization‟s mission. Finally, in the innovation diffusion literature, research suggests

prestigious organizations are more likely to adopt new innovations; especially when they are

perceived as being consistent with the norms of the community (Rogers, 2003). Depending on


which criterion an organization utilizes, we can expect differences in its adoption/rejection

decisions. Specifically, organizations placing a greater emphasis on efficiency are likely to

require an innovation to meet more rigorous requirements than those placing an emphasis on

mission fit as an evaluative criterion in deciding whether or not to adopt a particular evidence

based practice. While both may see benefits in implementing a practice those focusing on

efficiency also place a great deal of concern on the cost side of the equation. Prestigious

organizations may also have less rigorous requirements for adopting new practices because of

the additional perceived benefit of maintaining their status within the network. Stated in the form

of hypotheses:

Hypothesis 4a: The greater the importance an organization places on rational

factors(efficiency), the less likely an organization will adopt new evidence based

practices.

Hypothesis 4b: The greater the importance an organization places on mission fit, the

more likely an organization will adopt new evidence based practices.

Hypothesis 4c: The greater an organization’s reputation within the network, the more

likely an organization will adopt new evidence based practices.

Capacity and Implementation

The final stage of the innovation-decision process with which we are concerned has to do

with implementation. At this stage information about the practice has been gathered and it has

been evaluated in light of the evaluative criteria of the organization. Here we suspect the capacity

of an organization will play a crucial role in determining whether or not an organization is able

to implement a practice it has decided to adopt. Along with internal capacities such as technical

expertise and finances, network and diffusion researchers have pointed to the importance of


network relations at the implementation stage. Specifically, these findings suggest the ability of

an organization to implement a new innovation effectively is enhanced when it can communicate

with others who have gone through or are going through the same process (Ducharme, et al.,

2007). A second way in which implementing organizations can gain the information they need is

through connections to the NAO; since this organization often plays a central role in the network

and is charged with network coordination and the dissemination of information. In the case of

quitlines, these connections are likely to be most important for provider organizations because of

their direct involvement in the implementation and reinvention process. Also, because

reinvention is an important part of successful implementation, the involvement of implementing

organizations in the decision-making process should enhance the effectiveness of reinvention

decisions and thus increase the likelihood of successful implementation. Stated in the form of

hypotheses:

Hypothesis 5a: The greater the number of connections a quitline’s provider organization

has with other providers the greater the number of innovative practices successfully

implemented.

Hypothesis 5b: Quitlines with provider organizations connected to the network

administrative organization will successfully implement a greater number of innovative

practices.

Hypothesis 5c: The more a quitline’s provider organization is made part of the decision-

making process, the greater the number of innovative practices it will successfully

implement.

Figure 3 provides a visualization of the hypotheses.


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Figure 3

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Data

The data for this analysis was collected during the summer of 2009. It is the first of three

rounds of data collection, which will ultimately allow for longitudinal analysis and a better

understanding of the diffusion process. The network consists of numerous individuals and

organizations filling a variety of roles. However, our focus on the adoption and implementation

of innovations guided our decision to limit the collection of data to only the organizations

directly involved in this particular decision-making process along with the network

administrative organization (NAO).

The organizations surveyed (n=95) consisted of 73 funder organizations (some quitlines

had multiple funders), 20 service providers and one organization serving in both capacities as

well as the NAQC NAO. Depending on organization size, data were collected from 1 to 6

respondents (identified beforehand as the top decision-makers regarding quitline issues) at each

organization. Primary data were collected using a web-based survey developed expressly for this

project but based on methods and measures utilized previously by Provan and colleagues

(Provan and Milward, 1995; Provan, et al., 2009). In addition, questions and methods were pre-

tested on a “working group” of key quitline members who agreed to provide initial feedback.

After extensive follow-up efforts using email and telephone, our final results included completed

surveys from 186 of 277 individual respondents (67.1% response rate), representing 85 of 94

quitline component organizations (90.4%) plus the NAQC NAO, and at least partial data (at least

one component organization) from 62 of the 63 quitlines (98.4%).


Our unit of analysis is the quitline, represented by the funder organization. We focused

on the funder for a number of reasons. First, we had complete data on network ties as well as

responses about awareness, adoption, rejection and implantation of evidence-based practices

from 60 of the 63 quitlines, but only partial data from a number of the larger, multi-quitline

provider organizations. In particular, one of these large providers did not complete the practice

questions since its management felt strongly that because the funder organization initiates the

contract and pays the bills, it is the funder who decides what practices to use. We used this logic

as well in our decision to focus on the funder. Second, many of the providers served multiple

states and provinces, making it difficult to disentangle the effects of the role of these providers

relative to one of its quitlines versus another. Each U.S. state (and territory) and each Canadian

province is represented by a quitline funder organization, each with its own separate budget and

network connections, making it possible to compare meaningfully across quitlines and thus, test

our hypotheses. Finally, while providers represent public, nonprofit, and for-profit entities, all

quitlines are predominantly funded by a public entity, allowing us to examine the impact of

public contracting on service awareness. Hence, our analytical focus is the funder organization

as the representative of each quitline.

Measures

Innovation decision stages. To gather information at each stage of implementation, we

asked respondents where they believed their quitline was in the implementation process

regarding 23 practices identified by the network NAO and „project working group‟. These

practices ranged from the provision of proactive counseling to the use of text messaging and the

referral of callers to health plans. However, for this study we excluded six practices from the

analysis: two because they pertained to US quitlines only; two because they were pharmacology


related practices; and two due to lack of evidence supporting their effectiveness. The remaining

17 practices related to behavioral therapy and related management practices consistent with the

core mission of quitlines (See Appendix A for a complete list of practices).

In completing this section of the survey, respondents were first asked to indicate „Yes‟ or

„No‟ regarding whether or not they were aware of a practice. If respondents indicated „Yes‟ they

were aware of the practice, they were then presented with a follow-up question asking them to

indicate at what level of the decision-making process their quitline was at. To answer this

question, they were provided four response options: „Have not yet discussed‟, „In discussion‟,

„Decided not to Implement‟ or „Decided to Implement.‟ If the respondent indicated a decision

had been made to implement a particular practice, they were next presented with a 5-point scale

1=No progress has been made yet to 5=Fully implemented (the practice has become part of the

quitline’s policy or standard operating procedures for all eligible callers) and asked to indicate

what level of implementation they felt their quitline had achieved regarding the practice. From

this information we created four binary variables for each practice for each quitline1. A quitline

was considered AWARE2 of a practice and received a score of 1 if at least one respondent from

the quitline marked „Yes‟ to the first question. A quitline was considered to REJECT a practice

and received a score of 1 for the practice if the majority of respondents within the quitline

indicated „Decided not to Implement‟ in the second question. Likewise, a quitline was

considered to ADOPT a practice and received a score of 1 for the practice if a majority of

respondents indicated „Decided to Implement‟ in the second question. Finally, for

1 While providers were asked to respond to these questions separately for each quitline they served, due to the

abstention from these questions by one of the large providers, we chose to analyze the funder‟s responses as the

quitline‟s response except where noted otherwise. 2 While the measure described is a count of the number of practices a quitline is aware of, analysis was done on

UNAWARE (the number of practices a quitline was unaware of) to better suit negative binomial modeling 2 While the measure described is a count of the number of practices a quitline is aware of, analysis was done on

UNAWARE (the number of practices a quitline was unaware of) to better suit negative binomial modeling


IMPLEMENT, quitlines received a 1 if the majority of respondents indicated a 4 or higher on the

5-point scale in the last question. Once these variables were constructed, a count variable was

calculated to indicate the number of practices in which a quitline received a score of 1 at each

stage. Scores could range from 0 to 17 (See Table 1 for a summary)

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Table 1

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Information sharing. Data on network relationships were collected based on receipt of

information in four areas: financial, general management, service delivery, and

promotion/outreach. Respondents were presented with a list of all quitline funders, then provider

organizations, and then other national non-quitline member organizations having a major tobacco

control focus and involvement. For each organization listed, respondents were asked to indicate

whether they received information from that organization, which of the four types of information

they received, and the level of intensity of the relationship in terms of frequency and importance

(scored on a 1 to 3 scale). Only responses scored at a high level of intensity (3) were utilized in

the final analysis.

Because some quitlines consist of multiple funders or multiple providers, we found it

necessary to aggregate these multiple responses to obtain a single funder or single provider

response for each quitline. Of the 62 quitlines from which we received at least partial data, we

received multiple funder responses from six and multiple provider responses from one. To obtain

a single funder and single provider response from each quitline, we aggregated individual

responses from the multiple organizations as if the respondents came from the same

organization. These aggregations left us with 60 funder and 17 provider responses.

Because responses were provided by individuals and the analysis for this paper is


presented at the organization level, a tie was considered to exist at the organization level if at

least one respondent from that organization reported receiving information from that

organization. We apply this rule based on the presumption that the segregation of duties within

an organization often necessitates a single individual be the primary person responsible for

maintaining a relationship with a particular organization (Broshack, 2004; Maurer & Ebers,

2006).

Using these data, a series of network variables were constructed for both the funders and

providers of each quitline. Consistent with the survey data and the hypotheses, the following

five distinct types of network variables were constructed for each funder, all based on indegree

(information received) centrality and all based on the highest level of intensity of involvement:

funder ties to the NAO (fnNAO: coded 0 or 1); the number of funder ties to other funders

(fnFUNDERS); number of funder ties to other providers (fnPROVIDERS); number of funder ties

to the 12 national organizations that were NAQC members, but which were not part of a specific

quitlines, like the RWJ foundation, CDC, American Legacy Foundation, and Health Canada

(fnNATIONAL); and the number of funder ties to the 10 most highly connected tobacco control

researchers (fnRESEARCH) (from a drop-down list of 42 tobacco control researchers previously

identified). For this last measure, each quitline respondent was allowed to list up to five

researchers but responses were weighted so no quitline organization could score more than a

single point for any one researcher and no more than five points total.

Because our hypotheses regarding the effect of providers‟ connections is based on their

ability to observe and discuss implementation related information we constructed the following

two variables: provider ties to the NAO (prNAO: coded 0, 1); provider ties to other providers


(prPROVIDERS). Ties to these two knowledge pools are suspected to be most important for

implementation and reinvention decisions.

Search. To capture an organization‟s involvement in quitline decision-making, we asked

each respondent the following question: “When deciding whether or not to implement a new

treatment practice, please indicate how decisions are usually made between your organization

and your quitline partner organization(s).” Responses were provided using a likert-type 5-point

scale with 1 = ‘Funder Decides’, 5 = ‘Service Provider Decides’, and 3 = ‘Decision is Shared

Equally’. After taking the average individual score within the organization as the organization‟s

response, we created a dummy variable, WHO, with organizations scoring a three or higher

receiving a 0 indicating the provider is heavily involved in decision-making and organizations

scoring less than 3 receiving a 1 indicating the funder dominates decision-making. Twenty-six of

the 60 funders reported the provider was heavily involved in decision-making.

Valuation criteria. In addition to the information sharing data, we asked 12 questions

regarding a quitlines‟ decision-making process (see Appendix B for a copy of the questions). The

12 items (4 items each) were designed to capture the three components of the Theory of Planned

Behavior (Ajzen, 1991): attitude toward behavior, subjective norms, and perceived behavior

control. The first 8 items, anticipated to capture the first two components, were measured using a

likert-type 5-point scale where 1 = Strongly Disagree and 5 = Strongly Agree. The final 4 items

thought to capture the last component again used a likert-type 5-point scale where 1 = Not Very

Important to 5 = Very Important. We then took the average individual response within each

organization to serve as the organization level response. If an organization did not have a

response for a particular item, we substituted the overall average response. Next we reverse

coded the responses to question 7 and ran a confirmatory factor analysis (see Appendix C for the


results). Using Varimax rotation with stata v.9 we confirmed a three factor solution. However,

the three factors were not the three factors anticipated. Rather, the four questions anticipated to

capture attitudes toward behavior split in to two factors with the first two questions regarding

evidence of effectiveness and cost loading on one factor with a scale reliability alpha of .66

while the last two items regarding the importance of mission and team consensus loading on the

third factor with a scale reliability alpha of .68. Four of the remaining eight items created a third

factor with a scale reliability alpha of .58.

Based on the factor analysis, we constructed two variables. The first variable,

RATIONAL, was constructed by taking the average of an organization‟s responses to the two

items loading on the first factor regarding effectiveness and cost. The second variable, MISSION,

was constructed by taking the average of an organization‟s responses to the two items loading on

the third factor regarding mission and consensus. Being part of the attitudes component, these

two factors correspond well with the two evaluative criteria identified in the literature: efficiency

and mission fit. Because the construct underlying the items in factor two was not apparent and

the scale reliability was low, we exclude these items from the analysis.

Reputation. Respondents were asked to identify up to five quitlines that “other than

[their] own, [they] most admire for doing an especially good job regarding tobacco quitline

activities.” Because organizations could have more than one respondent and thus nominate more

than five quitlines, the responses were first aggregated to the organization level and each

organization was given a total of five votes. Thus, if individuals belonging to the same

organization listed 10 quitlines, each of those 10 quitlines received a score of .5. Likewise, if an

organization only reported admiring a single quitline, that quitline received a score of 53. All the

3 Other methods for creating a REPUTATION variable were explored such as using the total individual responses or

total organization responses. While the results did not vary substantively, we chose this measure as a way of


organization level scores were aggregated for each quitline giving it an overall REPUTATION

score ranging from 0 to 56.19.

Controls. Finally, because the size of an organization has consistently been shown to

predict innovation and the adoption of innovation (Rogers, 2003), we control for a quitline‟s size

by including a variable, SPEND, indicating the quiltine‟s 2009 spending per smoker as

calculated by the NAQC NAO based on data reported by the quitlines in the network‟s Annual

Survey. The overall average spending per smoker was substituted for any missing data (Table 2

provides correlations and descriptive statistics for all the variables described above).

--------------------------

Table 2

--------------------------

Analysis

As discussed above, we constructed four dependent variables capturing three stages of

the innovation-decision process: awareness, decision, and implementation. Because an

organization could decide either to adopt or reject a practice it was necessary to create a variable

to capture both decisions. In this way, an organization rejecting a practice would not be modeled

as a late adopting organization but rather as a distinct type of organization perhaps more

comparable to those identified as early adopters. Because we are analyzing count variables that

do not meet the distribution requirements of a Poisson distribution we utilize negative binomial

regression for all analysis. Negative binomial regression allows us to test and correct for

oversdispersion in the data (Long & Freese, 2006). Using robust standard errors adds an

additional level of conservatism in the case of high levels of underdispersion (Winkelmann,

Signorino & King, 1995)

controlling for large organizations skewing the results. In addition if an organization admired only a single quitline,

we presumed that this admiration was much more important to the organization than those admiring several

quitlines.


For each dependent variable, we ran three models. In the first models (Table 3 – Models

1 & 4; Table 4 – Model 7 & 10), we included a single variable of the stage immediately prior as

a control for the general level of either awareness (in the case of adoption & rejection) or

decisions to adopt (in the case of implementation). Essentially, we are trying to control for the

possibility that organizations which are aware of more practices adopt or reject more practices

and those that adopt more implement more. This is especially important for the AWARE >

ADOPT > IMPLEMENT path because of the high and significant correlations between the

variables ranging from .64 to .71 (See Table 2).

In the second set of models (Table 3 – Models 2 & 5; Table 4 – Models 8 & 11) we add

all of our independent variables to each equation. These equations help us begin to ascertain the

effect of each variable at each stage of the innovation diffusion process while controlling for its

effect on previous stages. Second, because, we are faced with a modest number of observations

on which to conduct our analysis (a common problem for studies of whole-networks), we found

ourselves in the position of utilizing more degrees of freedom (df = 14 - 15) than is recommend

for a data set with only 60 observations. This makes us susceptible to overfitting the model

(Babyak, 2004). To determine whether or not our estimates were a result of overfitting, we ran a

third set of models (Table 3 –Models 3 & 6; Table 4 – Model 9 & 12) including only the

variables we found to be significant in the full models at the α = .10. Except for the effect of

funders ties to the NAO (fnNAO) on the likelihood of rejection, in each case the reduced model

confirmed the results found in the full models increasing our confidence in the findings. In

addition, the substantially smaller BIC statistics for these trimmed models indicate the trimmed

models are a better fit of the data.


---------------------------

Table 3 & 4

---------------------------

Our hypotheses were derived from our expectation that specific information sharing

relationships and internal evaluation criteria will impact the innovation-decision at different

stages in the process. Specifically, we expected a funder‟s connections to all different types of

organizations, but most specifically the NAO (fnNAO) and top researchers (fnRESEARCH), to

play an important role in a quitline‟s awareness of the 17 evidence based practices (hypotheses

1a & 1b). We also expected a funder‟s connection to the NAO (fnNAO) and to national policy

and funding organizations (fnNATIONAL) to have an impact on the decision stage independent

of their effect on awareness because of the influence they are suspected to have on the norms

and values within the network (hypothesis 3a & 3b). In only one case, were our hypotheses

supported. Specifically, only ties to researchers (fnRESEARCH, Table 3 – Model 3) increased

the likelihood of an organization being aware of more evidence based practices and neither ties

to the NAO or to national organizations significantly impacted the likelihood of a quitline

organization adopting or rejecting an additional practice. One type of network tie that appears to

increase the likelihood of an organization rejecting evidence-based practices was connections to

more provider organizations (fnPROVIDERS). However, it is unclear why this is the case. One

explanation consistent with our understanding of power and competition (Burt, 1992) could be

that funders who communicate with multiple competing providers are better able to select the

bundle of services they feel is right for them

In addition to network connections, we expected funders actively involved in quitline

decision-making (WHODECIDES = 1) to be more active in searching out innovations thus

increasing their overall awareness (hypothesis 2). However, at the implementation stage, we

expected quitlines in which providers take an active role in decision-making (dmWHODECIDES


= 0) to have a higher rate of implementation (hypothesis 5c). Both these hypotheses were

supported (Table 3 – Model 3; Table 4 – Model 12). While funders who are more engaged in

decision-making are more likely to be aware of evidence based practices, the evidence in this

data suggests that allowing service providers to take a more active role in decision-making may

result in more complete implementation of the practices. An alternative conclusion could be that

funders who are less active in decision-making don‟t have a good sense of how well practices are

being implemented by their service providers and thus are more likely to perceive

implementation is more complete than may be accurate.

We expected the three evaluative criteria to (RATIONAL, MISSION & REPUTATION)

to have their greatest influence on the decision stage of the implementation process Specifically,

we expected concerns with efficiency (RATIONAL) to reduce the likelihood of adopting

innovations because of the use of more stringent evaluative criteria compared to those concerned

with mission fit (MISSION) or prestige (REPUTATION) (hypotheses 4a, 4b & 4c). We found

none of the variables in our analysis to influence an organization‟s decision to adopt an

innovation beyond their impact on awareness (Table 4 – Model 9). However, a number of these

factors do seem to impact a quitline‟s decision to reject a practice (Table 3 – Model 6).

Specifically, the more an organization is concerned with either MISSION or REPUTATION the

less likely they are to reject an evidence based practice which they are aware of. These findings

support or general hypotheses (4b & 4c). While concern with efficiency (RATIONAL) does not

appear to significantly impact the likelihood of either adoption or rejection, we do find that it

does significantly increase the likelihood of an organization being aware of evidence-based

practices (Table 3 – Model 3). A possible explanation for this finding consistent with our search

hypothesis could be that organizations highly concerned with obtaining evidence about


innovations perform more search activity and as a result are aware of more evidence based

practices.

Our final set of hypotheses focused on the implementation stage and the impact a

providers‟ information sharing ties might have on the process (hypotheses 5a & 5b). Specifically,

we expected providers‟ connections to other providers (prPROVIDER) and the NAO (prNAO) to

play an important role in enabling them to more successfully implement new practices. Neither

of these hypotheses was supported. However, it is not to say these connections are not important.

Rather, the analysis in model 6 (Table 3) suggests these variables are important in reducing the

likelihood of a quitline organization rejecting an evidence based practice once it becomes aware

of it. There are a number of plausible explanations for these findings. However one explanation

consistent with our arguments regarding the iterative nature of the diffusion process could be that

quitlines may only reject evidence based practices once attempts to implement the practice have

proven unsuccessful. Alternatively, providers may take information regarding capacity in to

account during the decision stage thus reducing the chance adopted innovations cannot be fully

implemented. However, these hypotheses require further investigation.

Discussion and Conclusions

Overall, 5 of our 11 hypotheses were supported. While, a number of our hypotheses were

not supported, the analysis suggests it is not because these variables are unimportant, rather the

impact of these variables manifested themselves at different stages than the ones expected (See

Figure 4 for a summary of significant relationships). Specifically, while we expected concerns

with efficiency to impact an organization‟s decision to adopt or reject an innovation our analysis

suggests a concern with efficiency is likely to impact the amount of energy invested in searching

out information and alternatives to solve problems. Additionally, we expected the providers‟


information sharing ties to enhance their „how to knowledge‟ which in turn would impact their

ability to fully implement new practices. While this may be the case, we found the significance

of these ties was manifested in an increased likelihood of adopting new practices. This leads to

multiple possible interpretations. First, consistent with our initial arguments, providers not

having adequate capacity to successfully implement a practice may be more likely to reject the

practice without attempting to implement it. Alternatively, a mimetic argument could also

explain this relationship. Specifically, providers that are more embedded within the network may

feel more pressure to adopt new practices while those on the periphery do not feel as much

pressure and may be more able to reject practices inconsistent with their goals or values.

Interestingly, funders with an increased number of ties to other providers had the opposite effect.

One possible explanation for this could be that having ties with multiple potential contracting

partners could allow funders to be more selective in the practices they decided to provide to their

constituents. The complexity of these findings, especially with regard to network ties, suggests

further work is necessary to fully understand the complexity of the innovation diffusion process.

Overall, this study has implications for both theory and practice. First, this analysis

provides support for the argument that taking a decision-making approach may be a useful way

of disentangling this complexity of innovation diffusion (Valente, 2010). Specifically, network

ties appear to impact the diffusion process in multiple ways. Ties to some organizations provide

opportunities for gaining information about the existence of new practices. Other ties may

influence the adoption decision through the transmission of normative pressures or „how to

knowledge‟. Alternatively, these same ties may provide opportunities for reducing dependency

or constraint (Burt, 1992) on a particular contracting partner. What determines the effect of these


ties is likely due in some part to the role different actors play in a network and the internal goals,

values and decision-making structure of these organizations.

For public managers, NAQC provides an important example of a public/private

collaboration where public organizations are not the central actors in the network. Rather, we see

a private network coordinator (NAO) and a cluster of private service providers filling key central

roles due to the pattern of contracting across political boundaries. This analysis also suggests that

while a funder‟s ties to researchers can help them stay informed about the innovative practices

emerging in the field of tobacco control, ties to service providers and their ties to others in the

network have a significant influence on quitline decisions to either adopt or reject these

practices. For public managers operating in the „hollow state‟ (Milward & Provan, 2000),

understanding the network dynamics within their particular policy domain and taking the

initiative to maintain relationships within this domain may help improve their ability to contract

with and monitor the service providers representing the government on the ground.

Limitations & Future Steps

This study is not without its limitations. First, the cross sectional nature of the data does

not allow us to make causal inference. Second, because we are essentially performing a case

study of one network any attempts to generalize to other networks must be done with extreme

caution. Finally, with the modest number of cases and limited qualitative data, thoughts

regarding the mechanisms underlying our observations must be corroborated with further study.

Because this is the first slice of a three year study, the findings in this study will be able

to focus our attention as we test our hypotheses over two more waves of data. In addition, the

dedication and interest of a working group of managers within the network provide us with a

forum for scrutinizing our findings for face validity. Finally, although this is a single goal-direct


„whole network‟, a number of its fundamental characteristics can be found in numerous other

examples allowing us to find future forums for us to more fully develop our understanding.

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Figure 1. Network of Information Receipt within the North American Quitline Consortium

Funders Providers NAO Researchers Non-Quitline Members

Information Receipt Intensity Level High


Figure 2. Comparison of Frameworks

Figure 3. Hypotheses & Variables


Figure 4. Significant Relationships


Table 1

Coding of Dependent Variable

Q1

„No‟ „Yes‟

AWARE 0 1

Q2

Not Yet

Discussed

In

Discussion

Decided

NOT to

Implement

Decided

to

Implement

REJECT 0 0 1 0

ADOPT 0 0 0 1

Q3

No

Progress Low Medium High

Fully

Implemented

IMPLEMENT 0 0 0 1 1

Table 2

Correlations and Descriptive Statistics

Mean SD 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15

1 UNAWRE 2.40 2.34 -

2 REJECT .92 1.55 -.32 -

3 ADOPT 12 1.89 -.64 -.24 -

4 IMPLEMENT 10.56 1.99 -.50 -.12 .71 -

5 SPEND 2.73 2.26 -.23 .09 .16 .20 -

6 fnNAO .55 .50 -.25 -.14 .18 .06 .01 -

7 fnFUNDERS 1.93 3.07 -.06 .03 .11 .05 -.21 -10 -

8 fnPROVIDERS 1.30 .93 -.13 .08 .08 -.06 .06 .08 .25 -

9 fnNATIONAL 1.02 1.02 -.15 .14 .16 .11 -.03 -.15 .26 .34

10 fnRESEARCH 2.62 1.89 -.37 .17 .31 .28 -.02 .12 .27 .29 .47 -

11 WHO DECIDES .57 .50 -.33 -.03 .32 .00 .03 .02 .21 .21 .21 .11 -

12 RATIONAL 4.63 .43 -.07 .18 -.03 .07 -.13 -.03 -.11 -.06 -.15 -.16 -.16 -

13 MISSION 3.96 .77 -.07 -.18 .24 .11 .13 -.24 -.02 .14 .15 .09 .02 .08 -

14 REPUTATION 5.23 8.18 -.17 -.18 .16 .04 -.03 .33 .11 .36 .03 .26 .30 -.24 -.08 -

15 prNAO .78 .42 -.17 -.13 .19 .20 .18 .01 -.08 .13 .21 .07 .11 .01 .20 -.16 -

16 prPROVIDER 2.63 1.77 -.12 -.20 .33 .20 .03 .19 -.08 .01 .08 .25 .12 -.04 .23 .01 .31

BOLD p < .05, ITALIC p < .10


Table 3

Negative Binomial Estimation of Unawareness and Rejection

UNAWARE REJECT

Base Full Trimmed Base Full Trimmed

Model1 Model 2 Model 3 Model 4 Model 5 Model 6

coef. s.e. coef. s.e. coef. s.e. coef. s.e. coef. s.e. coef. s.e.

UNAWRE -.30 .17 -.58 .15 -.51 .09

SPEND -.09 .03 -.10 .03 .03 .04

fnNAQC -.40 .26 -.68 .37 -.58 .37

fnFUNDERS -.02 .03 -.01 .04

fnPROVIDERS .10 .16 .55 .18 .54 .23

fnNATIONAL .10 .17 .30 .19

fnRESEARCH -.28 .11 -.23 .08 -.03 .14

WHODECIDE -.74 .24 -.67 .23 -.67 .59

RATIONAL -.51 .30 -.55 .27 .23 .50

MISSION -.04 .17 -.78 .20 -.60 .20

REPUTATION .01 .02 -.13 .04 -.15 .05

prNAQC -.34 .27 -.78 .29 -.84 .42

prPROVIDER .06 .07 -.19 .08 -.18 .08

Constant 4.65 1.38 4.51 1.29 .43 .24 3.87 2.80 4.19 .75

alpha .26 .16 .32 .17 1.28 .73 .00 .00 .11 .40

Wald chi2 37.50 34.64 3.38 100.88 81.05

BIC 277.47 249.50 160.76 174.40 156.44

-2LL -123.84 -110.08 -112.47 -74.24 -56.49 -59.79

df 0 14 6 3 15 9

BOLD = p ≤ .05, Italics p ≤ .10; robust (s.e.)


Table 4

Negative Binomial Estimation of Adoption and Implementation

ADOPT IMPLEMENT

Base Full Trimmed Base Full Trimmed

Model 7 Model 8 Model 9 Model 10 Model 11 Model 12

coef. s.e. coef. s.e. coef. s.e. coef. s.e. coef. s.e. coef. s.e.

UNAWRE -.04 .00 -.04 .01 -.04 .01 .07 .01 .08 .01 .08 .01

SPEND .00 .00 .01 .01

fnNAQC .01 .03 -.04 .04

fnFUNDERS .01 .00 .00 .01

fnPROVIDERS -.02 .02 -.02 .02

fnNATIONAL .00 .02 .01 .02

fnRESEARCH .00 .01 .01 .01

dmWHO .03 .04 -.11 .04 -.10 .04

dmRATIONAL -.01 .04 .05 .05

dmMISSION .04 .03 -.03 .02

REPUTATION .00 .00 .00 .00

prNAQC -.00 .04 .06 .04

prPROVIDER .02 .01 .02 .01 -.00 .01

Constant 2.59 .02 2.41 .54 2.52 .03 1.48 .12 1.30 0.24 1.43 .13

alpha .00 .00 .00 .00 .00 .00 .00 .00 .00 .00 .00 .00

Wald chi2 45.62 176.84 59.13 50.84 82.99 53.67

BIC 278.27 325.30 281.25 271.35 317.74 274.05

-2LL -135.04 -133.99 -134.48 -131.58 -130.21 -130.88

df 2 14 3 2 14 3

BOLD = p ≤ .05, Italics, p ≤ .10; robust (s.e.)


Appendix

Appendix A. List of Practices

Innovative Practices Identified by NAQC NAO and ‘Project Working Group’

Practices Used in Analysis

Proactive (outbound) counseling

Reactive (inbound) counseling

Multiple call protocol

Conduct mass media promotions for the mainstream population

Conduct mass media promotions for targeted populations

Provide self-help materials to proxy callers

Provide self-help materials for tobacco users regardless of reason for calling

Provide self-help materials for tobacco users who receive counseling

Provide counseling immediately to all callers who request it

Conduct an evaluation of the effectiveness of the quitline

Refer callers with insurance to health plans that provide telephone counseling

Use text messaging

Integrate phone counseling with web-based programs

Fax referral programs

Re-contact relapsed smokers for re-enrollment in quitline services

Supplement quitlines services with IVR services

Train provider groups on 2A's or 3A's and refer

US Specific Practices

Serve callers without insurance coverage

Obtain Medicaid or other insurance reimbursement

Pharmacological Practices

Provide NRT without requiring counseling

Provide NRT but require counseling

Practices lacking Evidence of Effectiveness

Staff the quitline with counselors who meet or exceed Masters-level training

Integrate phone counseling with face-to-face cessation services


Appendix B. Decision-making Questions

Strongly

Disagree

Strongly

Agree

Don‟t

Know

1. Strong evidence of effectiveness was an important

consideration. 1 2 3 4 5 6

2. The overall financial cost of the quitline practices

was extremely important. 1 2 3 4 5 6

3. A critical consideration was whether or not these

quitline practices were consistent with <auto-fill

organization‟s name>‟s mission.

1 2 3 4 5 6

4. Opinions of others in <auto-fill organization‟s

name>, such as staff or other decision makers,

strongly influenced the decision to adopt or not

adopt these quitline practices.

1 2 3 4 5 6

5. Dealing with and overcoming bureaucratic

procedures (e.g., rules, red-tape, etc.) was a

significant barrier to the adoption of these quitline

practices.

1 2 3 4 5 6

6. The decision was based on the expertise of current

staff to implement the quitline practices effectively. 1 2 3 4 5 6

7. <Auto-fill organization‟s name> tries not to pay

much attention to cost when considering adopting a

new quitline practice.

1 2 3 4 5 6

8. The practices used by well-respected quitlines in

other states and provinces were important

considerations in our decision process.

1 2 3 4 5 6

Not Very

Important

Very

Important

Don‟t

Know

9. When considering the adoption of these quitline

practices, pressure or mandates from major outside

organizations, like other levels of government,

agencies such as CDC, Health Canada, national

advocacy groups, etc. were

1 2 3 4 5 6

10. Being among the first to adopt a new quitline

practice was 1 2 3 4 5 6

11. When considering the adoption of these quitline

practices, <auto-fill vendor if respondent‟s

organization is the funder; funder if respondent‟s

organization is the vendor>‟s opinion was

1 2 3 4 5 6

12. Whether most other quitlines had adopted or not

adopted these quitline practices was 1 2 3 4 5 6


Appendix C. Decision-making Factor Analysis

dmRational

dmMISSION

Variable Factor1 Factor2 Factor3 Uniqueness

q1 effective 0.6029 -0.0280 -0.0703 0.6307

q2 cost 0.7358 0.1164 0.0801 0.4387

q3 mission -0.0611 0.1969 0.6510 0.5337

q4 opinion 0.1603 -0.0413 0.6432 0.5589

q5 redtape 0.3739 -0.3129 0.1983 0.7229

q6 expertise 0.0815 0.4107 0.2480 0.7631

q7rv nocost 0.3324 -0.0831 0.0076 0.8826

q8 otherlrspct 0.1258 0.6180 0.2169 0.5552

q9 mandates -0.1236 0.3059 0.2425 0.8324

q10 first -0.3987 0.1524 -0.0848 0.8106

q11 opinion 0.0012 0.4903 -0.1220 0.7448

q12 othermny -0.0543 0.5131 0.0268 0.7331

alpha 0.6624 0.5809 0.6762 Bold – Factor loading ≤ .40

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Running Head: INNOVATION DIFFUSION: A … Head: INNOVATION DIFFUSION: A PROCESS OF DECISION-MAKING 1 Innovation Diffusion: A Process of Decision-Making …