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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 University of Arizona Scott F. Leischow, Ph.D. Professor, Family and Community Medicine Arizona Cancer Center University of Arizona 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)

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  • 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

    University of Arizona

    Scott F. Leischow, Ph.D.

    Professor, Family and Community Medicine

    Arizona Cancer Center

    University of Arizona

    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

    Simons (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.

  • INNOVATION DIFFUSION: A PROCESS OF DECISION-MAKING 3

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

    important strategy for addressing the publics 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

  • INNOVATION DIFFUSION: A PROCESS OF DECISION-MAKING 4

    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 organizations 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

  • INNOVATION DIFFUSION: A PROCESS OF DECISION-MAKING 5

    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).

  • INNOVATION DIFFUSION: A PROCESS OF DECISION-MAKING 6

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

    Figure 1

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

    Literature Review and Hypotheses

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

    diffusion of innovation framework (2003) and Simons 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.

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

    Figure 2

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

  • INNOVATION DIFFUSION: A PROCESS OF DECISION-MAKING 7

    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

  • INNOVATION DIFFUSION: A PROCESS OF DECISION-MAKING 8

    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

  • INNOVATION DIFFUSION: A PROCESS OF DECISION-MAKING 9

    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 organizations 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 organizations 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

  • INNOVATION DIFFUSION: A PROCESS OF DECISION-MAKING 10

    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.

  • INNOVATION DIFFUSION: A PROCESS OF DECISION-MAKING 11

    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

  • INNOVATION DIFFUSION: A PROCESS OF DECISION-MAKING 12

    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

    organizations 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

    organizations 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

  • INNOVATION DIFFUSION: A PROCESS OF DECISION-MAKING 13

    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 organizations 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

  • INNOVATION DIFFUSION: A PROCESS OF DECISION-MAKING 14

    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 quitlines 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 quitlines 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.

  • INNOVATION DIFFUSION: A PROCESS OF DECISION-MAKING 15

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

    Figure 3

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

    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%).

  • INNOVATION DIFFUSION: A PROCESS OF DECISION-MAKING 16

    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

  • INNOVATION DIFFUSION: A PROCESS OF DECISION-MAKING 17

    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

    quitlines 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 funders responses as the

    quitlines 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

  • INNOVATION DIFFUSION: A PROCESS OF DECISION-MAKING 18

    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)

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

    Table 1

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

    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

  • INNOVATION DIFFUSION: A PROCESS OF DECISION-MAKING 19

    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

  • INNOVATION DIFFUSION: A PROCESS OF DECISION-MAKING 20

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

    implementation and reinvention decisions.

    Search. To capture an organizations 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 organizations

    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

  • INNOVATION DIFFUSION: A PROCESS OF DECISION-MAKING 21

    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 organizations 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 organizations 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

  • INNOVATION DIFFUSION: A PROCESS OF DECISION-MAKING 22

    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 quitlines size

    by including a variable, SPEND, indicating the quiltines 2009 spending per smoker as

    calculated by the NAQC NAO based on data reported by the quitlines in the networks 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.

  • INNOVATION DIFFUSION: A PROCESS OF DECISION-MAKING 23

    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 = .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.

  • INNOVATION DIFFUSION: A PROCESS OF DECISION-MAKING 24

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

    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 funders connections to all different types of

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

    play an important role in a quitlines awareness of the 17 evidence based practices (hypotheses

    1a & 1b). We also expected a funders 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

  • INNOVATION DIFFUSION: A PROCESS OF DECISION-MAKING 25

    = 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 dont 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 organizations 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 quitlines 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

  • INNOVATION DIFFUSION: A PROCESS OF DECISION-MAKING 26

    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 organizations 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

  • INNOVATION DIFFUSION: A PROCESS OF DECISION-MAKING 27

    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

  • INNOVATION DIFFUSION: A PROCESS OF DECISION-MAKING 28

    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 funders 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

  • INNOVATION DIFFUSION: A PROCESS OF DECISION-MAKING 29

    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

  • INNOVATION DIFFUSION: A PROCESS OF DECISION-MAKING 35

    Figure 2. Comparison of Frameworks

    Figure 3. Hypotheses & Variables

  • INNOVATION DIFFUSION: A PROCESS OF DECISION-MAKING 36

    Figure 4. Significant Relationships

  • INNOVATION DIFFUSION: A PROCESS OF DECISION-MAKING 37

    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

  • INNOVATION DIFFUSION: A PROCESS OF DECISION-MAKING 38

    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.)

  • INNOVATION DIFFUSION: A PROCESS OF DECISION-MAKING 39

    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.)

  • INNOVATION DIFFUSION: A PROCESS OF DECISION-MAKING 40

    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

  • INNOVATION DIFFUSION: A PROCESS OF DECISION-MAKING 41

    Appendix B. Decision-making Questions

    Strongly

    Disagree

    Strongly

    Agree

    Dont

    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 s mission.

    1 2 3 4 5 6

    4. Opinions of others in , 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. 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

    Dont

    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, 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

  • INNOVATION DIFFUSION: A PROCESS OF DECISION-MAKING 42

    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