Running Head: INNOVATION DIFFUSION: A PROCESS OF DECISION .
Running Head: INNOVATION DIFFUSION: A PROCESS OF DECISION-MAKINGInnovation Diffusion: A Process of Decision-MakingThe Case of NAQCJonathan E. Beagles, M.S.Ph.D. Candidate520-975-1224; jbeagles@email.arizona.eduSchool of Government and Public PolicyUniversity of ArizonaKeith G. Provan, Ph.D.McClelland Professor of Management & OrganizationsEller College of Management and School of Government and Public PolicyUniversity of ArizonaScott F. Leischow, Ph.D.Professor, Family and Community MedicineArizona Cancer CenterUniversity of ArizonaWork on this paper was funded by a grant from the National Cancer Institute (R01CA12863801A11) and an Arizona Cancer Center Support Grant (CCSG - CA 023074)1
INNOVATION DIFFUSION: A PROCESS OF DECISION-MAKING2AbstractThis research examines the effect of both information sharing ties and internal decisionmaking factors to understand the innovation implementation process among organizations withinthe North American Quitline Consortium (NAQC). NAQC is a large, publicly funded “wholenetwork,” spanning both Canada and the U.S., working to get people to quit smoking. BringingSimon‟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 internaldecision-making factors likely to be influential at various stages in the innovation diffusionprocess. Using negative binomial regression to model three distinct stages in the implementationprocess (Awareness, Adoption/Rejection, Implementation), the findings provide evidencesupporting the argument that different types of ties are likely to be important at different stagesin the innovation implementation process and the importance of these ties varies depending onthe role an organization plays as well as internal decision-making factors.
INNOVATION DIFFUSION: A PROCESS OF DECISION-MAKING3Collaboration among networks of public and private organizations has been an especiallyimportant 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 smokingcessation. In particular, networks have become important mechanisms for building capacity torecognize complex health and social problems, systematically planning for how such problemsmight best be addressed, mobilizing and leveraging scarce resources, facilitating research on theproblem, 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 theorganizations 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 effectiveneeds to be disseminated, not only from those who create knowledge about these practices tothose who utilize them, but also among those who utilize the practices (Ferlie et al., 2005). Inthis regard, network ties have been found to be essential for the dissemination of knowledgeleading 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 longbeen recognized (Coleman, 1966), more recent research suggests networks matter more thansimply as a means of transferring information (Brass et al, 2004). In addition to the literature onnetworks and information transfer (Hansen 1999, 2002; Reagans & McEvily, 2003) networkshave been shown to serve as conduits of social influence either through direct influence by socialrelations (Galaskiewicz & Wasserman, 1989; Rao, Davis & Ward, 2000) or through similaritiesin network positions leading structurally equivalent actors to adopt similar opinions and
INNOVATION DIFFUSION: A PROCESS OF DECISION-MAKING4behaviors (Galaskiewicz & Burt, 1991). This research has contributed significantly to ourunderstanding of networks. However, additional questions have been left unanswered.Specifically, while evidence suggests the types of ties, their strength, and who they are with areimportant for knowledge transfer and the diffusion of innovations, fewer studies have looked athow different characteristics of network ties may impact the diffusion process differently or howthe relative importance of these ties may vary across stages in an organization‟s innovationimplementation decision. These questions are especially important with regard to the literatureon „whole networks‟ (Provan, Fish & Sydow, 2007) where the structure of network ties impactsnot 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 individualdecision-making framework (Simon, 1997) to derive hypotheses regarding the relativeimportance of network ties and internal decision-making factors across the distinct stages of theinnovation decision process (Rogers, 2003). We test these hypotheses across organizationswithin the North American Quitline Consortium (NAQC); a network of public and privateorganizations within the U.S. and Canada involved in the provision of telephone-basedcounseling and related services to people trying to quit smoking.Research SettingThe North American Quitline Consortium (NAQC) is an example of the increasingnumber of networks established to help address complex health and social problems (Bazzoli etal., 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 controlcommunity, that wide variation existed among emerging quitlines with respect to the practicesbeing adopted and implemented. In response to this perception, one of the primary purposes of
INNOVATION DIFFUSION: A PROCESS OF DECISION-MAKING5NAQC was to increase communication among the quitlines in order to reduce this variationthrough 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 andCanada; each quitline consisting of at least one funder and a one service provider. Typically, thesole 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), vendorsserve multiple states/provinces. This leads to a unique network structure within NAQC comparedto 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 privateservice providers (c.f. Provan & Milward, 1995; Provan, Huang & Milward, 2009), withinNAQC, private service providers are often the most central actors spanning numerous politicalboundaries to provide services to multiple public funders. At the time of our data collection, thelargest service provider was a for-profit entity contracting with 18 state quitlines. While thepublic funders maintain ultimate accountability for the success of the quitlines, the providersplay an important yet varying role in decision-making regarding the services provided withineach quitline.In addition to funders and venders, other organizations and individuals participated in thenetwork such as national funders and researchers. In 2006, this diversity of roles and interests ledto the creation of an independent network administrative organization (NAO) to serve as the fulltime coordinator and neutral broker for the network (Provan, Beagles & Leischow, 2011). Figure1 provides a depiction of the network using the NetDraw function in UCINET 6 (Borgatti,Everett, & Freeman, 2002).
INNOVATION DIFFUSION: A PROCESS OF gure 1--------------------------------Literature Review and HypothesesTwo frameworks form the basis for developing the hypotheses in this study: Rogers‟diffusion of innovation framework (2003) and Simon‟s bounded rationality (1997). While thetwo frameworks provide important contributions in their respective fields, there has been littleconversation between them. This lack of conversation was noted by Valente (2010) when hesuggested more diffusion studies try to understand how their “postulates influence individualdecision-making” (p. 194).An important distinction between the two frameworks has to do with the perspectivefrom which they enter the decision-making process. Specifically, research on innovationdiffusion begins with specific innovations of interest and tries to understand how theseinnovations move through the stages of the implementation process: knowledge, persuasion,decision, implementation and confirmation (Figure 2). On the other hand, Simon (1997) andthose developing a decision-making framework study how information, search, evaluation andcapacity (Figure 2) come together in an iterative process around a perceived problem. For thosefrom a diffusion of innovation perspective a pro-innovation bias assumes the new innovation willsolve a perceived need and make its way through all phases in each organization while thosefrom a bounded rationality perspective try to understand how a perceived need is solved throughthe coming together of these decision-making factors and any particular innovation is one ofmany alternatives being evaluated.----------------------------Figure 2-----------------------------
INNOVATION DIFFUSION: A PROCESS OF DECISION-MAKING7Diffusion of Innovation FrameworkIn his comprehensive review of the innovation diffusion literature, Rogers (2003) outlinesa decision-making process developed by researchers over 60 years, beginning with the firstdiffusion 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 ofknowledge and moving through what are termed the persuasion, decision, implementation andconfirmation stages. At each stage various types of communication channels have beensuggested to be more or less important along with distinct characteristics of the decision-makerand the innovation itself (Wejnert, 2002).In the knowledge stage, decision-makers become aware of new innovations and begin togain knowledge of how they function. The persuasion stage refers to a process by whichdecision-makers develop opinions regarding an innovation culminating in an explicit decisionwhether or not to adopt or reject the innovation based on the values, goals and other criteria usedby a decision-maker to evaluate the innovation.If a decision is made to adopt an innovation, it then passes through to the implementationstage of the process, where research suggests reinvention takes place (Rogers, 2003). Similar tothe persuasion stage, where information is manipulated in order to make sense within a particularvalue system and goal structure, in the implementation stage the innovation itself is manipulatedto 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 theconfirmation stage where all dissonance between the adoption/rejection decision and the currentoperating environment is removed. While researchers have found it useful to think of this inlinear terms, it is accepted by many that this may result in an iterative process and information
INNOVATION DIFFUSION: A PROCESS OF DECISION-MAKING8gathering is necessary at all stages of the process albeit the types of information necessary maydiffer.Bounded Rationality FrameworkIn his study of Administrative Behavior, Simon (1997) laid out the framework for a studyof organization behavior based on an understanding of individual decision-making. From thisperspective, organization decision-making and action is seen as the result of an interactionbetween four key components: information, search, valuation, and capacity. Specifically, it isargued decision-makers do what is perceived to be in their best interest based on their unique setof goals and preferences. However, decision-makers are limited in two ways. First, they may belimited in the amount and quality of information they possess regarding their availablealternatives. Second, they may be limited in their capacity to implement an alternative even if itis preferred. Thus organizational behavior regarding the adoption and implementation ofinnovations is expected to vary based on differences across these components. First, if goals andvalues differ across organizations, behavior is expected to differ regardless of whether theypossess 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 behaviordue to differences in capacities. For any single organization, decision-making is seen as aprocess of adjusting each of these components until an alternative is identified consistent with allthree (Barnard, 1938).A SynthesisDespite differences in terminology, the overlap in the frameworks is apparent. It is notdifficult to sense similarities between awareness and information; and the factors that increase
INNOVATION DIFFUSION: A PROCESS OF DECISION-MAKING9the amount of information a decision-maker possesses are also likely to increase its awareness ofany particular innovation. Likewise, the emphasis on goals and values as the criteria used toevaluate alternatives overlaps neatly with the persuasion and decision stages in the diffusionliterature. Finally, while the diffusion literature highlights the importance of innovationadaptation and dissonance removal as important aspects of the implementation and confirmationphases, other research highlights the importance of capacity in an organization‟s ability to utilizenew information (Tsai, 2001). Bringing these two frameworks together allows us to generatehypotheses regarding which network and decision-making factors are likely to be most importantat each stage in an organization‟s decision whether or not to adopt and implement a newinnovation. Specifically, factors leading to increased information are likely to be most importantfor awareness. Factors impacting values, goals and evaluative criteria in general are most likelyto be influential at the decision stage and factors increasing organizational capacity are likely tobe most important for implementation.Information, Search and AwarenessThe importance of networks for gathering information is well documented (Ahuja, 2001;Burt, 2004; Tsai, Hansen, 1999 & 2002; Owen-Smith & Powell, 2004; Powell, Koput & SmithDoerr 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 tiesare more likely to provide an actor with non-redundant information. Burt (1992) modified theargument suggesting weak ties are important not because they are weak but because they oftenspan structural holes which leads to nonredundent information. However, Hansen (1999, 2002)add to the discussion by arguing that complex knowledge, such as information regarding thecosts and benefits of new innovations, is more easily transmitted across strong ties. In their study
INNOVATION DIFFUSION: A PROCESS OF DECISION-MAKING10of knowledge transfer within a contract R&D firm, Reagans and McEvily (2003) articulate theconcept of knowledge pools, suggesting specific types of information are located in differentareas of a network based on the roles and functions of those actors. Thus rather than havingrelationships spanning structural holes between individuals, they argue tapping into diverseknowledge pools is what is truly important and having strong ties to these knowledge pools isbeneficial 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 asan independent network administrative organization (NAO) (Provan & Kenis, 2008) which wasestablished to coordinate activities and information sharing among these other participants. Eachof these groups plays an important role in the network and is perceived by the NAO to contributea unique set of resources and perspectives to the network (Provan et al., 2011). While it seemsreasonable each group of organizations can and does contribute unique knowledge to thenetwork and can be the source of new innovations, the role of researchers stands out as anexceptionally likely source of information regarding evidence based practices. Also, because therole of the NAO is to gather and disseminate knowledge we suspect ties to the NAO willincrease the likelihood of an organization being aware of evidence based practices . Based onthis logic, we propose the following hypotheses:Hypothesis 1a: The greater the number of connections an organization has to others inthe network (especially researchers), the more likely it will be aware of innovativepractices.Hypothesis 1b: Organizations connected to the network administrative organization willbe more likely to be aware of innovative practices.
INNOVATION DIFFUSION: A PROCESS OF DECISION-MAKING11In addition to network ties, research in both the innovation diffusion and decision-makingliteratures has identified search behavior as an important factor influencing a decision-makersawareness of information (March & Simon, 1958; Rogers, 2003). Both lines of research havenoted decision-makers with a felt need are likely to be more active in seeking out solutions whilethose without a perceived need may be more passive in receiving information from their socialcontacts or simply mimic the behavior of others (DiMaggio & Powell, 1983). Being activelyinvolved in decision-making may be one factor leading to more active search behavior. Forexample, if an organization perceives itself to be in a role with significant decision-makingresponsibility it may feel a need to be more informed regarding information affecting thosedecisions. However, if an organization shares its decision-making responsibilities with others, itmay 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 tohave, the greater the number of innovative practices it will be aware of.Values, Norms and Decision-MakingMore than a means of information sharing, research suggests networks are important fortransmitting social norms (Galaskiewicz & Wasserman, 1989; Galaskiewicz & Burt, 1991)which lead to the adoption of behaviors above and beyond what would be expected by rationalprocesses. Often these forces come from central or powerful organizations in the environmentsuch as national policy or funding organizations (Fligstein, 1990) or central networkcoordinating organizations (Owen-Smith & Powell, 2004).If this is indeed the case, we could expect ties to the NAO and to national policy andfunding organizations to serve more than just an information sharing function. In addition toinformation sharing, we would suspect ties with these powerful organizations to influence a
INNOVATION DIFFUSION: A PROCESS OF DECISION-MAKING12decision-makers valuation criteria. Specifically, in the context of our study, we would suspectties to the NAO and these national organizations, more than ties to other organizations, toincrease the likelihood of an organization adopting evidence based
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.
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