The Art and Science of Innovation Systems Inquiry - Applications to Sub Saharan African Agriculture - 2009

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Technology in Society 31 (2009) 399–405 Contents lists available at ScienceDirect Technology in Society journal homepage: www.elsevier.com/locate/techsoc The art and science of innovation systems inquiry: Applications to Sub-Saharan African agriculture David J. Spielman a, Javier Ekboir b, Kristin Davis a, * a b International Food Policy Research Institute, PO Box 5689, Addis Ababa, Ethiopia International Food Policy Research Institute, IICA, P.O. Box 55-2200, San Jose, Costa Rica a b s t r
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  The art and science of innovation systems inquiry:Applications to Sub-Saharan African agriculture David J. Spielman a , Javier Ekboir b , Kristin Davis a , * a International Food Policy Research Institute, PO Box 5689, Addis Ababa, Ethiopia b International Food Policy Research Institute, IICA, P.O. Box 55-2200, San Jose, Costa RicaKeywords: AfricaAgricultureGame-theory modelingInnovation systemsMethodologyModelingPovertyRural developmentSocial network analysisSub-Saharan Africa a b s t r a c t Agricultural education, research, and extension can contribute substantially to reducingrural poverty in the developing world. However, evidence suggests that their contributionsare falling short in Sub-Saharan Africa. The entry of new actors, technologies, and marketforces, when combined with new economic and demographic pressures, suggests the needfor more innovative and less linear approaches to promoting a technological trans-formation of smallholder agriculture. This paper explores methodologies that can helpimprove the study of agricultural innovation processes and their role in transformingagriculture. We examine methods that address three key issues: (a) how agents interact inthe production, exchange, and use of knowledge and information; (b) how agents respondindividually and collectively to technological, institutional, or organizational opportunitiesand constraints; and (c) how policy changes can enhance the welfare effects of theseinteractions and responses. Methods include social network analysis, innovation histories,cross-country comparisons, and game-theory modeling.   2009 Elsevier Ltd. All rights reserved. 1. Introduction Agricultural education, research, and extension can contribute substantially to enhancing agricultural production andreducing rural poverty in the many parts of the developing world. However, evidence suggests that their contributions arefallingshortofexpectationswhenitcomestoSub-SaharanAfrica,whereagriculture continuesastheregion’sprimarysourceoflivelihood.Theentryofnewactors,technologies,andmarketforces,whencombinedwithneweconomicanddemographicpressures,suggests the need for moreinnovative and less linearapproaches to exploiting new opportunities and overcomingconstraints.Recent attention given to these issues has focused on innovation systems, an increasingly popular concept in the study of howsocietiesgenerate,exchange,anduseknowledge.Aninnovationsystemisbroadlydefinedasthesetofagentsinvolvedinan innovation process, their actions and interactions, and the socioeconomic institutions that condition their practices andbehaviors [1,2]. The framework embeds technological change within a larger, more complex system of interactions amongdiverse actors, organizational cultures and practices, learning behaviors and cycles, and rules and norms.More important, an innovation system framework shifts the analytical emphasis from a conventional linear model of knowledge and technology transfers (from researcher to extension agent to farmer) to a more complex, process-basedsystems approach. This shift is appropriate for the study of agriculture in Sub-Saharan Africa given that the sector’s growth *  Corresponding author. Tel.:  þ 251 11 617 2506/1 650 833 6696; fax:  þ 251 11 646 2927. E-mail address:  k.davis@cgiar.org (K. Davis). Contents lists available at ScienceDirect Technology in Society journal homepage: www.elsevier.com/locate/techsoc 0160-791X/$ – see front matter    2009 Elsevier Ltd. All rights reserved.doi:10.1016/j.techsoc.2009.10.004 Technology in Society 31 (2009) 399–405  and development is increasingly influenced by complex interactions among public, private, and civil society actors, and byrapidly changing market and policy regimes that affect knowledge flows, technological opportunities, and innovationprocesses.But early applications of the innovation system framework to developing-country agriculture suggest that opportunitiesexist for more intensive and extensive analysis. There is scope for empirical studies to utilize more diverse methodologies,both qualitative and quantitative, than are being used at present. These will help to improve the relevance of empiricalinnovation studies to an analysis of poverty reduction and economic growth.In this paper we explore conceptsand methods that can help improve the studyof agricultural innovation in Sub-SaharanAfrica.Weexaminemethodsthataddressthreespecificissues:(a)howagentsinteractintheproduction,exchange,anduseof knowledge and information within a system; (b) how agents respond individually and collectively to technological, insti-tutional, ororganizationalopportunitiesandconstraints;and (c)howpolicychangescanenhancethewelfareeffects of theseinteractions and responses.Thepaperproceedsasfollows.Section2setsforthaconceptualframeworkbasedontheoriesofcomplexadaptivesystemsand innovationwithin thesesystems.Section 3discusses methodologicalchallenges forinnovation systemsstudies, followedby a discussion of alternative methodologies in Section 4. Conclusions are given in Section 5. 2. Conceptual background  2.1. Complex systems Thebasisforanytypeofdevelopmentistheabilityofindividuals,organizations,andsocietiestoimproveonwhattheyarecurrently doing, that is, to improve their individual and collective capabilities. But such improvements are contingent on theenvironment within which innovation occurs. Individuals and their environments form complex systems characterized bya large number of actors, diverse interactions and relationships, and constantly changing influences emerging from tech-nological, market, policy, cultural, and other socioeconomic factors.Recognizingdevelopmentasacomplexprocesscanhavemajorconsequencesforthedesignandimplementationofpublicpolicy, but such recognition remains relatively uncommon. Public policies still draw on conventional analyses based onmodernist metaphors of hydraulics, machinery, and factory production d metaphors that exert a profound influence on thedesign of organizations, institutions, and policies. As a result, scientists and policymakers still emphasize control andpredictability, and still design interventions that are built from the top down and expected to be implemented throughpassive, subordinated structures [3].Complexitytheories,ontheotherhand,emphasizetheimportanceofself-organization,whichresultsfromthediversityof agentsandthedecentralizednatureofcomplexsystems.Eventhoughsomeagentshavemoreinfluencethanothers,noagentor group of agents can totally control a system. Thus, policies in a complex system do not seek to ‘‘manage’’ the system butto operate on the probability of events, to increase the odds of desired outcomes, and to reduce the chances of undesiredresults [4].We define a complex system as one whose properties cannot be analyzed by studying its components separately. Whilethere are several types of complex systems, the most relevant for the study of innovation processes are termed ‘‘complexadaptive systems’’ (CAS), or systems formed by many agents of different types, where each defines his/her strategy, reacts tothe actions of other agents and to changes in the environment, and tries to modify the environment in ways that fit his/hergoals[5].Behaviorpatternsinthistypeofsystemoftenemergefromindependent,spontaneous,orunintendedprocessesthatrender conventional, mechanistic modes of analysis quite useless.CAS evolve through the combination of initial conditions, multiple interactions, trends, and random variations in agentsand their interactions. The strength of the trends and of the random effects changes along an evolutionary path. When thetrends are strong, the CAS is more or less predictable, and the probability that a random variation results in a minor event ishigh. When variations occur close to unstable configurations, the probability of catastrophic events is high. As the systemevolves and new actors and interactions emerge, the CAS becomes less stable. Eventually, the random component maybecome more important than the trends, and at a certain bifurcation point, the system may become random and unpre-dictable [6].Systemsdo notnecessarily tendtoward chaos,buttoasituationthatisinherently unstableandunpredictable.Atany given moment, random variations occur with varying consequences and varying degrees of predictability.  2.2. Variation, selection, and innovation An important evolutionary force in CAS is the interaction between variation and selection, concepts borrowed from theevolutionary biology literature and characterized as follows. First, while new actors and strategies constantly emerge ina system, not all of them are adapted to the environment; selection enables ‘‘survival of the fittest.’’ Second, changes inefficiency within these systems are discrete, interrupted by long periods of relative stability. Third, such changes do not stopin periods of stability but continue at least at the same rate as in the periods of adaptive innovations [7].This leads to the notion of innovation. We define an innovation as anything new successfully introduced intoan economicor social process. In other words, an innovation is not just trying something new but successfully integrating a new idea orproduct into a process that includes technical, economic, and social components. D.J. Spielman et al. / Technology in Society 31 (2009) 399–405 400  This definition stresses three important features. First, innovation is the creative use of different types of knowledge inresponse to social or economic needs and opportunities [8]. Second, a trial only becomes an innovationwhen it is adopted aspart of a process; manyagents try new things, but fewof these trials yield practices or products that improve what is alreadyin use. Third, innovations are accepted as such in specific social and economic environments [9].In the terminology of complexity theory, innovation results not just from variation (trying something new), but also fromselection (finding things better thanwhat is currently used) and incorporation into long, complex processes [10]. Innovationcanhave animportant socioeconomicimpactonly whenitispartofsustainedprocesses involving manyactors withdifferentcapabilitiesandresources.Thereasonisthatifaninnovationimprovessubstantively,say,production,itmustbeaccompaniedby new managerial and marketing innovations to handle and sell the extra output.  2.3. Networks Since individuals and organizationsdo not typically possess all the requisitecapabilities and resources, theyintegrateintonetworks with other actors who can contribute resources and expertise they lack [11,12]. Thus, a successful innovationprocess is determined by the extent to which networks gather sufficient variation in capabilities and resources from diverseagents. Integration into these networks is difficult, however, because of problems with implementing collective action: thedifficulties of agreeing on rules, implementing common procedures, creating trust, and monitoring opportunistic behavior.Thus, networks form on the basis of relationships that evolve among agents and the institutional context within which theyform.Network structure and dynamics depend on the complexity and maturity of their innovations. In the case of simple ormature innovations, networks are loose. Because the economic and technical features of the innovations are relatively wellknown, members can relate to each other through formal contracts or markets. For complexor new innovations, actors havetointeractoften and informally toresolve unexpected problems and the technical and market uncertainties derived fromtheinnovation [11,12].Network effectiveness depends on the collective capacity to facilitate exchanges of information and resources. In theterminology of network analysis, this capacity is known as the network’s ‘‘navigability,’’ and depends on the existence of central actors (i.e., well-connected actors) interacting among themselves [13] and on the environment (i.e., laws or markets)in which the networks operate. Network effectiveness also depends on the ability of networks to search for and use existinginformationand,andwhenitisnotavailable,togenerateit.Thisisinturninfluencedbythenetworks’abilitytodeveloptheirorganizational capabilities, or the individuals, technologies, shared norms, and organizational routines needed to commu-nicate information and coordinate resources [9,14,15,16].In the context of developing-country agriculture, CAS can be used to describe a system of public extension agents, publicresearchers, market traders, and farmers as well as public policies on science, technology, agriculture, education, andinvestment. One of the main hurdles that diminish small farmers’ innovative capabilities is their inability to integrate intonavigable networks comprised of such agents, therebygaining access totechnical and commercial information, markets, andfinancing. Often,small farmersdo nothave adequatehuman and social resources to integrateintothese networks, or theydonot operate in an institutional environment where such networks easily form. 3. Innovation systems and agricultural development  3.1. The contribution of systems-based approaches Systems-based approaches such as those described above are not new in the agricultural development literature. Thestudy of technological change in agriculture has always been concerned with systems, as illustrated by applications of thenational agricultural research system (NARS) and the agricultural knowledge and information system (AKIS) approaches.However, the innovation systems literature, with its foundations in complexity theory, is a major epistemologicaldeparture from the traditional, neoclassical studies of technological change that are often used in NARS- and AKIS-drivenresearch. The NARS and AKIS approaches, for example, emphasize the role of public-sector research, extension, and educa-tional organizations in generating and disseminating new technologies. Interventions based on these approaches tradi-tionally focused on investing in public organizations to improve the supply of new technologies. A shortcoming of thisapproach is that the main restriction to the use of technical information is not just supply or availability but also the limitedability of innovative agents to absorb it [17]. Even though technical information may be free and freely accessible, innovatingagents have to invest heavily to develop the ability to use the information [18].WhileboththeNARSandAKISframeworksmadecritical contributionstothestudyof technologicalchangein agriculture,they are now challenged by the changing and increasingly globalized context in which Sub-Saharan African agriculture isevolving [19,20]. This includes trends, such as the rapid growth of markets, as the main drivers of technological change; newdemographic and agro-ecological pressures; new economic regimes such as trade liberalization and regional trade inte-gration; the growth of private investment in and ownership of knowledge, information, and technology; and expansion of informationandcommunications technologyasa meansofrapidlyexchangingknowledgeand information.Thereis need foramore flexibleframeworktostudyinnovationprocessesindeveloping-countryagriculture d aframeworkthathighlightsthe D.J. Spielman et al. / Technology in Society 31 (2009) 399–405  401  complex relationships between old and new actors, the nature of organizational learning processes, and the socioeconomicinstitutions that influence these relationships and processes.This brings us to the agricultural innovation system (AIS) framework. The AIS framework makes use of individual andcollective absorptive capabilities to translate information and knowledge into a useful social or economic activity in agri-culture. The framework requires an understanding of how individual and collective capabilities are strengthened, and howthese capabilities are applied to agriculture. This suggests the need to focus far less on the supply of information (e.g., brick-and-mortar research organizations, universities) and more on systemic practices and behaviors that affect organizationallearning and change. The approach essentially unpacks systemic structures into processes as a means of strengthening theirdevelopment and evolution.  3.2. Applications of the AIS approach The innovation systems approach is still nascent in the study of developing-country agriculture. Biggs and Clay [21] andBiggs [22] offer an early foray into the approach by introducing several keyconcepts d institutional learning and change, andthe relationship between innovation and the institutional milieu in which innovation occurs d that become central to laterinnovation systems studies on developing-country agriculture.LaterstudiesbyHallandClark[23],Halletal.[24,28,29],JohnsonandSegura-Bonilla[25],Clark[26],andArocenaandSutz [27] introduce the innovation systems approach to the study of developing-country agriculture and agricultural researchsystems. Regional and national applications of the innovation systems approach include Sumberg [30], Roseboom [31], Chema, Gilbert, and Roseboom [32], Peterson, Gijsbers, and Wilks [33], and Hall and Yoganand [34] for Sub-Saharan Africa; Vieira and Hartwich [35] for Latin America; and Hall et al. [24] for India. Several studies focus on the institutional arrangements in research and innovation, for example, Hall et al. [28] on public-private interactions in agricultural research in India; Porter and Phillips-Howard [36] on contract farming in South Africa; orHall et al. [24], Allegri [37], and Kangasniemi [38] on producers’ associations in South Asia and Sub-Saharan Africa. Other studies focus on technological opportunities, such as Ekboir and Parellada [39] on zero-tillage cultivation. A recent study bythe World Bank [19] contributes further to the development of the AIS approach with both conceptual and empiricalevidence.These studies are distinguished from many other works on agricultural research and development because they embedanalysesofinnovationwithinthewidercontextoforganizationalandinstitutionalchangeprocesses.Further,theyoffersomeanswers to certain research questions that the conventional literature is often unable to address. For example, Ekboir andParellada [39] offer a detailed look into the social and economic changes that encouraged the diffusion of zero-tillagecultivation in Argentina, a process that resulted from a complex series of events and interactions among farmers, farmers’organizations, public researchers, and private firms. Hall et al. [28] provide an in-depth study of the institutional andorganizationallearningprocessesthatstimulatedthediversificationofagriculturalresearchfinancinginIndiatoincludenewactors (e.g., medium-sized firms and producer cooperatives) and new modalities (e.g., contract research, public-privatepartnerships). Clark et al. [40] unlock the mysteries of a successful donor-funded project in post-harvest packaging for smallfarmers in Himachal Pradesh, India, by studying the institutional learning and change processes that were incorporated intothe project design.  3.3. Methodological limitations of the AIS approach However, the AIS framework does have several methodological limitations in its application to developing-countryagriculture [41,42].   First, while the conventional innovation systems approach relies on a diversity of rigorous qualitative and quantitativemethods in studies of industrialized countries, the methodological toolkit employed in the study of developing-countryagriculture remains fairly limited. Currently, the favored methodology is the descriptive case study, typically drawn froman action research or stakeholderanalysis exercise [34]. More often than not,studies are simply ex post   descriptions of thedynamics and complexities of some technological or institutional innovation. Powerful tools that are systematic, repli-cable, and consistent methods of analysis that could be used to enhance this descriptive work include in-depth social andeconomic histories; policy benchmarking, cross-country comparisons and best practices; statistical and econometricanalysis; systems and network analysis; and empirical applications of game theory, to name a few [43]. This methodo-logical diversity and rigor could bring greater credibility and strength to the study of innovation systems in developing-country agriculture.   Second,theAISapproachhasnotyetmaturedtoapointwhereitcangeneratepolicyindeveloping-countryagricultureforspecific interventions needed to enhance the potential for innovation and improve the distribution of gains from inno-vation[41,42].Althoughexceptionsexist,thelinkbetweenempiricalanalysisandpolicyrecommendationsremainseithernascentor weak in the application of the innovation systemsframework to developing-countryagriculture. With so manycase studies conducted and so many lessons learned, researchers should be well positioned to advise governments on D.J. Spielman et al. / Technology in Society 31 (2009) 399–405 402
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