LEDDRA theoretical framework

2012-06-20T12:49:23+00:00

Authors: Geoff Wilson and Claire Kelly

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1. Introduction

The theoretical framework is a core component that sits within the broader LEDDRA conceptual framework described and discussed in Chapter 2. The conceptual framework situates responses to LEDD in the CAS paradigm by highlighting the non-linear links between the human and natural systems, and the wider institutional context. The theoretical framework draws on that contextual basis to explain the 'why' in each SES, and the broader mode of production-linked 'why' in each land theme; i.e. why do these LEDD issues arise and cause these specific impacts, why have these specific responses to LEDD been adopted and implemented, and why have these particular ways of implementing responses to LEDD in this particular land theme and SES under study been chosen and have caused specific impacts?

The theoretical framework will guide the LEDDRA study site partners in addressing the specific research questions outlined below and will also contribute to addressing the wider suite of LEDDRA research questions. Building on the land theme and study site-specific theoretical frameworks, WP4 will develop a ‘theory of responses to LEDD’ which will be elaborated in the final WP4 joint Deliverable Reports.

2. Aim and purpose of the theoretical framework

The purpose of the general theoretical framework is to guide Work Package leaders and study site teams to address the broad WP4 research question: Which are the main determinants of responses to LEDD and what are their relationships? In other words, why do people in complex adaptive socio-ecological systems choose to ‘respond’ to LEDD in the way that they do in particular situations, and under particular circumstances? Based on the general theoretical framework which has been developed to date, and is discussed in the joint Deliverable Reports D111 (cropland); D211 (grazing land) and D311 (forests/shrubland), land theme and study site-specific theoretical frameworks are being developed and will be summarized in the next set of WP4 joint Deliverable Reports. Using the CAS approach, these specific theoretical frameworks will reflect the way in which study sites with similar or different modes of production and situated in similar or differing biophysical, economic and social environments respond to LEDD issues.

The theory of responses to LEDD, developed as a result of the broader LEDDRA research process, will make important contributions to existing theories to improve understanding of actual response assemblages to LEDD in a number of ways:

By providing a better understanding of the role and importance of relationships between the biophysical and the human system components of a socio-ecological system (SES) as determinants of both LEDD issues and of human responses to LEDD.
By examining the livelihoods of stakeholders affected by LEDD, the theory of responses will contribute towards existing debates about constraints and opportunities for LEDD alleviation at multiple spatial levels.
Shedding light on the roles played by social memory and learning pathways associated with responses to LEDD, including how communication about LEDD issues is passed on between and within LEDD-affected stakeholder groups.
Highlighting the importance of path dependencies and lock-in effects as constraining the effective alleviation of LEDD problems.

3. Background to the development of the LEDDRA theoretical framework

LEDD issues are complex, multi-dimensional problems which cut across spatial levels and timescales. Understanding how and why people respond to LEDD issues, therefore, requires an approach which can take spatial and temporal complexities and interdependencies into account. The ecosystem approach and the Complex Adaptive Systems (CAS) paradigm offer such an approach because they recognise that people and environment are bound together in complex relationships which are unpredictable and dynamic. In addition, each case of LEDD and each response to LEDD is situated in its own unique environmental, social and economic context – in other words, each of the LEDDRA study sites will have its own ‘storyline’ with regard to LEDD histories, issues and responses to LEDD which LEDDRA aims to unravel. At any point in time, a LEDD-affected area is, therefore, the product of multiple complex interactions between biophysical, socio-economic, cultural and institutional factors, which may have no direct or explicit relationship with land or resource use. As a result, the LEDD problems which such an area faces and the responses to these LEDD problems are not pre-determined but are emergent and may be unpredictable; they are the product of the complex interplay between these factors.

As section 4.2 below will highlight, past history matters. The current state of a socio-ecological system is directly related to the interactions among its components, the system’s evolutionary dynamics and, in the case of the human system, of decisions made and knowledge or skills acquired in the past. The role of knowledge, and the pathways and mechanisms used to transfer knowledge, therefore, also play a critical role in how human systems respond to LEDD. Sustainable land management practices and responses to LEDD which are based on a synthesis of embodied local knowledge (knowledge drawn from past personal and/or shared experience, often referred to as local environmental knowledge or LEK) and scientific and technical expertise are likely to be better adapted and more readily adopted than responses which have been devised at higher spatial/organizational levels with little or no understanding of, or respect for, local social, environmental or economic conditions. If a comprehensive theory of responses to LEDD is to be developed, it must, therefore, take these complex interrelationships into account (LEDDRA Partners 2010).

In developing the theory of responses to LEDD, the LEDDRA theoretical framework builds on the work of existing approaches, such as the Drylands Desertification Paradigm (DDP) (Stafford-Smith and Reynolds 2002; Reynolds et al. 2007), because such approaches recognise the interdependence between the biophysical and human dimensions of LEDD problems at multiple spatial scales, and their resultant impacts on human welfare. Drawing on both the Panarchy model (Gunderson and Holling 2002) and critical debates in the field of socio-ecological resilience (Blaikie and Brookfield 1987; Adger 2000; Wilson 2012), the DDP also recognises that interrelationships in coupled human-environment systems are characterised by non-linear processes which operate at different spatial and temporal scales, and highlights the importance of identifying the key variables which contribute to understanding the causes of LEDD issues, rather than just focusing on the effects of LEDD issues.

Allied to understanding the complex and multidimensional processes which cause LEDD issues and the human responses to these LEDD issues is the need to understand how to respond effectively to them. The LEDDRA project adopts the notion of ‘optimal response assemblages’, defined as arrays of responses to LEDD which are ‘well adapted to the biophysical, socio-economic, cultural and institutional conditions prevailing in a region’ and that ‘the criterion of fit will be the preservation of the socio-ecological resilience’ (Briassoulis 2011; LEDDRIS 2011). Many authors have suggested that socio-ecological resilience (and at lower spatial scales, community resilience) are closely associated with the way that three broad types of ‘capital’ (natural, economic and social) are developed and interact (Adger 2000; Western et al. 2005; Abel et al. 2006; Kinzig et al. 2006; Magis 2010; Wilson 2012). The Resilience Alliance, for example, in its Workbook for Scientists (2007b), suggests that ‘a useful way to envision the system’s resilience and adaptability […] is to consider the levels of and changes in the ‘pools’ of various capitals’. The three capitals (the definitions and components of which are discussed in more detail in section 3.4.1 below) and the relationships between them, therefore, play a critical role in the way that socio-ecological systems are impacted by LEDD issues, and in how responses to those issues are formulated and implemented.

There are some limitations to theories such as Panarchy, in that concepts which originate in biophysical contexts may need to be adapted in order to adequately capture the nuances and subtlety of human structure and agency in responding to LEDD issues (see, for example, Adger 2000; Davidson 2010). In addition, previous work on understanding LEDD has failed to adequately reflect the importance of social capital in driving and in responding to LEDD (Wilson 2012). In developing the LEDDRA theoretical frameworks and, eventually, the theory of responses to LEDD, the intention is to begin to address these shortcomings by developing an integrated research approach to develop a suite of context-specific theories which can help to explain the actual response assemblage (ARA) found in each study site.

The first step in developing land-theme and study-site theoretical frameworks was, therefore, to outline the broad characteristics of each land theme at a range of spatial scales, to identify the key LEDD issues occurring in each and the broad suite of drivers which generate such issues, and to analyse their subsequent impacts on socio-ecological systems. From this understanding, and in combination with detailed study site descriptions which encompass biophysical and human factors (see Deliverable Reports D131, D231 and D331), a suite of land theme and study site-specific theoretical frameworks have begun to be formulated which will hypothesise the roles of the various ‘capitals’ and, more importantly, the links and relationships between them, in developing and implementing responses to LEDD.

4. Overview of the LEDDRA theoretical framework

As discussed above, the LEDDRA theoretical framework is based on a variety of key assumptions that underpin human responses to LEDD in complex adaptive systems. First, human responses to LEDD occur at various scales, forming a nested hierarchy ranging from individual and community-level responses to regional, national and supra-national responses (Wilson 2012). Agents within and between levels interact in complex ways and, therefore, responses to LEDD at one level can only be fully understood in the context of interactions occurring at other levels. Second, complex socio-ecological systems affected by LEDD are ‘open’ systems shaped by both endogenous and exogenous drivers of change. Endogenous drivers include, in particular, social, cultural, political and institutional processes that influence both causes and responses to LEDD in complex ways (Wilson and Juntti 2005; Imeson 2012). Exogenous drivers are largely linked to natural factors (e.g. climate change leading to drought) and higher-level drivers such as national and supranational political regimes, policies, cultural processes and knowledge transfer processes. Social, economic and natural capitals play an important role in shaping human responses to LEDD, as do social memory, path dependencies and lock-in effects associated with LEDD pathways. Sections 4.1 and 4.2 introduce and discuss in more detail how these complex processes will be integrated within the LEDDRA theoretical framework. The discussions in the following sections represent the first steps in refining and developing the general and context-specific theoretical frameworks. Further and more detailed discussions will be included in subsequent Deliverable Reports.

4.1 The role of social, economic and natural capital in responding to LEDD

At local and regional levels, responses to LEDD can be conceptualised as the interplay between different processes associated with the social, economic and natural capitals that a SES possesses. Using the CAS approach and building on existing theories of the relationship between capitals, resilience, and responses to LEDD, the proposed LEDDRA theoretical framework, therefore, posits that societies in which LEDD response assemblages are appropriate and effective are likely to exhibit well developed and closely interlinked components of economic, social and natural capital (Rigg 2006; Parnwell 2007; Bunce et al. 2009; Wilson 2012). Conversely, societies that have focused almost entirely on developing components of one or two capitals, at the expense of other capitals, are likely to be more vulnerable to LEDD . So, for example, societies that have focused almost entirely on the maintenance of natural capital, perhaps by reducing agricultural output and aiming at local self-sufficiency at the expense of developing economic (and social) capital may be less able to implement effective responses to LEDD. Socio-ecological and community resilience can, therefore, be seen as representing a well developed and effective relationship, or ‘balance’ between the components of economic, natural and social capitals. The definitions, key components of each capital and their critical functions in terms of responses to LEDD are discussed in Chapter 4 and APPENDIX C. However, for ease of reference, brief definitions are also included below.

For the purposes of the LEDDRA project, natural capital is defined as the natural environment from which emanates the goods and services that sustain life (IISD 2008). Economic capital refers to the durable stock or assets that provide flows of services over time. Social capital is used here in two ways; to refer to a multidimensional, aggregate concept and to refer to a specific component.

Social capital as an aggregate: Bourdieu (1983) and Coleman (1988) popularised the notion of ‘social capital’, arguing that social capital can be seen as a resource which is used to hold communities (at whatever scale) together. Subsequently, other commentators have broadened out notions of social capital, referring also to human capital (often used in the context of the skills and knowledge available in a society), political capital (the inclusiveness of the political process and/or the extent of democratic processes), institutional capital (organisational ability, institutions, trust in institutions and processes) and cultural capital (society’s historical memory and experience, the arts, or ideological standpoints of a society (Ostrom 1990; Berkes and Folke 1998; Bryant 2005). Although there is much debate about the components of social capital, and about the hierarchies and interdependencies between social, political, institutional and cultural capitals, the boundaries between them are blurred. In order to encompass the various dimensions of social capital within the LEDDRA approach, a multidimensional concept of the term is used. Social capital is therefore comprised of complex social processes, political arenas, institutions, regulations and cultural factors (Ostrom 1990; Adger 2000; Parnwell 2007; Cutter et al. 2008; Bunce et al. 2009).

Social capital as a component: This more narrow aspect defines social capital as a resource to collective action that concerns the ability and willingness of community members to participate in actions directed to community objectives and to processes of engagement. Its key features are social norms, networks and trust. Social norms are informal rules that condition behavior in various circumstances. Social networks are interconnected groups of people who usually have an attribute in common. Trust is the level of confidence people have that others will act or are expected to act as they say, or that what they say is reliable.

If specific attributes of the components of all three capitals are well developed, the theoretical framework suggests that communities/regions will respond effectively to land degradation and will deal effectively with future development options and unexpected events. As Deliverables D111, 211 and 311 highlighted, the interplay between different capitals and their underlying properties is highly complex, contingent on cultural interpretations of change, and predicated on complicated power structures and networks (Davidson, 2010). This emphasizes that the relationships among the components of the three capitals matters more than the components themselves, while the high degree of interdependence between the three capitals means that any disruption in one capital can cause a ‘ripple effect’ that affects other capitals, thereby modifying opportunities for successful responses to LEDD (e.g. the loss of the productive capacity of soils also affects the components of economic and social capitals).

With this caveat in mind, our starting point is that natural capital in regions affected by land degradation is often of poor quality (i.e. soil degradation, salinisation) or poorly managed (for example a history of protective vegetation removal, water pollution, poorly maintained protective terraces, etc.). Building on critical discussions about complex adaptive systems and land degradation (Blaikie and Brookfield 1987; Walker and Salt 2006; The Resilience Alliance 2007a; The Resilience Alliance 2007b; Davidson 2010) only communities with relatively strong social and economic capital are able to cope with, and at times reverse, land degradation processes.

Economic capital is the key foundation of financial and economic well-being of a society. Magis (2010, 406) has highlighted the links between economic and social capitals, noting that economic capital ‘refers to the financial resources available to be invested in the community for business development, civic and social enterprise, and wealth accumulation’. Economic capital, thus, includes not only forms of mercantile transactions, but also all human processes associated with the use and generation of monetary capital (e.g. the monetary value of the built environment in a community) (Bourdieu 1993; Magis 2010; Wilson 2010). Factors such as availability of funding, high levels of community or household income, well developed community infrastructure, or well established trade flows, are usually associated with strong economic capital (Bardhan 2006).

Common characteristics of social capital emerge that will find resonance as indicators for LEDD issues in most societies around the globe (O'Brien et al. 2005; Magis 2010). Social capital is based on strong local embeddedness (Coleman 1988; Western et al. 2005), self-regulating moral codes, or, as Chaskin (Chaskin 2008, p.68) suggested, as ‘the nature of social ties and interaction … and the context of trust and norms of reciprocity within which these relationships inhere’. This suggests that close interaction between people through tight-knit communities, the ability to rely on neighbors in times of crisis, and good communication between stakeholder groups, are generally seen as signs of well developed social capital (see for example Dorfman et al. 2009), while the ‘graying’ of communities through outmigration of young people would usually be accepted as a sign of poorly developed (and further weakening) social capital (e.g. Zhao 1999; Bell 2004; Rigg et al. 2008; Ye and He 2008; Forbes et al. 2009). Similarly, there is little debate about the importance of availability of skills training and educational opportunities as an indicator of well developed (or strengthening) social capital (Gahin et al. 2003), or of the availability of good health and sanitation systems as positive indicators (Chaskin 2008).

Lack of leadership, weak governance structures, high levels of corruption (low moral and ethical standards, self-centredness), poorly managed public spaces (i.e. dirty, poorly maintained, ‘nobody cares’ attitude), or lack of control over the destiny of future development pathways are also signs of poorly developed social capital at both local and regional levels (Lebel et al. 2006; Smit and Wandel 2006). Usually, transparent and clear land ownership rights will also enhance social capital as they increase local control over land resources (Curry and Koczberski 2009), while high levels of tenant/dependent landholders may indicate poorly developed social capital (Cumming et al. 2005). Gender relations and the status of ethnic minority groups are also key indicators for well or poorly developed social capital (Lebel et al. 2006; Magis 2010) although these may be some of the most culturally-dependent indicators (Janssens 2010). Most would agree that empowerment of women or ethnic minorities with regard to decision-making opportunities and land and resource ownership issues should be seen as a sign of well developed social capital, leading to more inclusive decision-making which is better able to address land degradation issues (Cumming et al. 2005; Chaskin 2008).

Investigation of the components of social capital, and the relationships between this and other types of capital at community and regional levels, therefore, forms a crucial component of LEDDRA, as social capital is an important driver of LEDD, is impacted by LEDD problems, and is critical in understanding why particular responses to LEDD have or have not been implemented. LEDDRA will, therefore, make important contributions towards an improved understanding of the complex interlinkages between social capital, LEDD, and responses to LEDD.

4.2 Social memory and LEDD

The LEDDRA conceptual and theoretical frameworks place specific emphasis on the context within which decision-making affecting land degradation takes place. By taking the temporal dimension into account, LEDDRA acknowledges the role of history and path dependencies within which decision-making is embedded. In this context, the notion of ‘social memory’, and how it affects land degradation processes and responses, is particularly important. Social memory implies that any system – human, natural, or socio-ecological – is imbued with a ‘memory’ that relates the system to past events, i.e. that land degradation processes and responses to LEDD do not occur in a vacuum but are embedded in often complex antecedent histories. It implies that knowledge, experience and accumulated wisdom are passed on within a society and the point at which a society finds itself is the product of that history of decision-making (Stump 2010). In other words a land management system carries with it the memory or, in a more negative sense, the ‘baggage’ of previous decision-making trajectories (this is discussed further in section 4.3 below). Human systems are anticipatory and non-deterministic, and social memory is a crucial transitional element which may lead to a learning and adjustment phase based on past experience (Wilson 2007).

There is a close link between social memory and path dependency that shapes the nature and pace of LEDD processes and responses (Rotmans et al. 2002). Key is how social memory of human systems is shaped by, and in turn shapes, institutionalized forms of learning, communication, knowledge transfer and institutional thickness, and the fact that personal choices can be self-reinforcing and, therefore, often self-fulfilling (Davidson 2010). In other words, means may become ends and alternative pathways may not even be considered. This shows that social memory can be both a good and bad thing, as it may ‘lock-in’ areas on pathways but may also propel societies onto pathways that may, ultimately, lead to their disappearance (Diamond 2005).

The critical literature on social memory suggests that there are three human processes most closely associated with social memory: social learning; tradition; and historical stakeholder networks, all of which will be investigated at the community and regional levels within LEDDRA. A community or region’s adaptive capacity will largely depend on past and present learning processes or social learning described as ‘the diversity of adaptations, and the promotion of strong local social cohesion and mechanisms for collective action’ (Adger et al. 2005, p.1038). Learning is a complex process that involves interpretations of information, reflections on previous experience (social memory), group discussions, and established practices (rites). It is, therefore, intricately intertwined with communication processes and how knowledge about LEDD is passed on at inter- and intra-generational levels. Davidson (2010) has added the importance of human imagination and anticipation as crucial aspects of learning. Learning is often non-linear, uncertain and unpredictable and depends on specific spatio-temporal processes and histories. New knowledge gained through this ‘adaptive resilience’ process (e.g. learning from having experienced soil degradation) can both influence antecedent conditions and enhance the potential for LEDD alleviation in the future through the implementation of new strategies (Wilson 2012).

Key is the quality of learning processes. Cutter et al. (2008) have highlighted the need to distinguish between positive learning (in the context of the ‘adaptive resilience’ process), and lessons learned from the past (i.e. learning from mistakes). While the former can be seen as proactive learning in which stakeholders learn to anticipate disturbance based on improved risk awareness and associated preparedness (e.g. policy change linked to land degradation), the latter is reactive in that the learning process occurs after a disturbance has taken place. This, in turn, will affect how LEDD issues are communicated within and between affected stakeholder groups and whether knowledge transfer leads to more effective LEDD management.

As highlighted above, issues of scale are important in social learning and are closely associated with power relations (Allen 2003). Although it may be possible for all individuals in small communities to be involved in preparing for disturbances, in most communities and regions a scale-dependent compartmentalization of social learning is likely to take place. This will be based on expertise and how this expertise can best be put into practice. Many studies highlight, however, that the most successful social learning takes place when the entire community/region is given opportunity to take part in joint learning efforts to tackle LEDD (Gale 1996). The notions of social memory and learning highlight particularly the importance of the regional level of analysis, as small communities by themselves are often not in a position to guarantee that acquired social memory (e.g. the best ways to tackle LEDD in the area) can effectively be passed on to other communities or regional stakeholders (e.g. linked to issues of power, politics and the psychology of historical community interactions). Indeed, evidence suggests that social memory, especially as regards land management, is seldom shared among the people of a small community; instead, it is shared by all those experiencing similar environmental conditions on a larger scale. This means that the stronger the bonds with other communities in the regional SES, the higher the capacity will be to retain and use memory effectively (positively or negatively).

How learning processes function, and how knowledge about how to tackle disturbances is passed on to individuals, is another crucial step for understanding communication and knowledge transfer at local and regional levels. Some argue that social learning is most successful when beneficial actions linked to environmental management are put into formal policy or informal customary laws/rights (e.g. oral tradition) for handling future events (Keen et al. 2005). It is this ‘encoding’ of learning that is seen to be particularly important by sociologists, as individual memory can be subject to decay over time (Stump 2010). The most successful learning processes are, therefore, those that encode knowledge in a way that is available to stakeholders over several generations (referred to by Ostrom (1990) as ‘rules-in-use’). Individual ‘encoding’ of knowledge can, however, be selective and may lead to ruptures in social memory, for example through the outmigration or death of knowledgeable community members. Social learning is also shaped by power structures. Who is learning to cope with LEDD and who benefits most? In every society, power is unevenly distributed, with some actors or stakeholder groups having disproportionate access to information or influence (Nadasdy 1999; Shortall 2008; Janssens 2010; Van Assche et al. 2011). For example, those stakeholders with access to finance and technology may be in a better position than others to implement adaptive responses needed to cope with LEDD. In some cases, powerful elites may be able to capitalize on disturbances or catastrophes through the weakening of their political or economic opponents. This suggests that the period of LEDD readjustment and recovery may not benefit all stakeholders equally, and that the ‘new’ trajectory after a transitional rupture is almost always qualitatively different from preceding structures with regard to shifts in power structures and networks of decision-making. These aspects of social memory, and the broader concept of socio-ecological memory, will be further investigated and developed during the next phase of the project.

4.3 Path dependency, lock-ins and LEDD

The notion of path dependency is intricately connected with social memory. A pathway can be seen as a process where ‘memory’ (i.e. knowledge, experience, accumulated wisdom or genetic advantage) can be passed on from generation to generation or from individual to individual (Stark 1992). Path dependency is based on the assumption that specific ‘pathways’ of change can be identified over space and time at community/regional level, and that often these pathways are associated with positive (improving LEDD management) or negative feedbacks (leading to worsening LEDD problems) (Figure 3.1). A pathway should be understood as a simplified version of complex processes and represents the sum total of cumulative actions at individual and stakeholder group levels. These pathways are rarely static and usually fluctuate based on various factors of change. Path dependency is, therefore, defined as the general direction of the pathway trajectory, taking into account upward and downward qualitative shifts in decision-making over time. As Figure 3.1 highlights, the non-linearity of pathways is emphasised by the fact that seemingly linear pathways are often disrupted by ‘transitional ruptures’ that may lead to a sudden shift in the ‘quality’ of a LEDD system (upwards or downwards shifts). How communities and regions tackle such sudden ruptures (e.g. droughts, policy change, and economic recessions) will be at the heart of the LEDDRA project.

As Stark (1992) and others have emphasized, path dependency is a process in which the next steps or nodes of change are determined by the previous ones. The starting point for each nodal section carries with it the memory of LEDD issues. In human systems, in particular, history and memory matter, and path dependency means that system trajectory is a function of past states and dependent on previous (and subsequent) probabilities of change (Stump 2010). However, path dependency is not pre-determined, and a general loss of ability to address LEDD can be reversed (as long as natural capital is not irretrievably eroded) if appropriate positive action is taken (adaptive capacity) and carried by a sufficiently large number of stakeholders (see change in pathway ‘quality’ highlighted in Figure 1).

Four key issues are important when investigating notions of path dependency in LEDD contexts. First, pathway changes are often associated with changing ‘learning pathways’ which streamline transitional processes but which, at the same time, can be difficult to change (Dahle 2007) (the notion of ‘transitional corridors’ in Figure 1). Second, the notion of path dependency often simplifies what are usually complex stakeholder interactions based on intricate power structures within communities and regions (Allen 2003). This means that at both local and regional levels there are multiple stakeholder pathways with multiple and often overlapping path dependencies, or with some stakeholder groups attempting to implement ‘radical’ pathways that differ substantially from established ideologies or norms (e.g. pathways b1-3 and c1-3 in Figure 1). Rotmans et al. (2002, 4), therefore, argued that pathways are ‘a mélange of fast and slow dynamics, the tempo and direction of which are ultimately constrained by the slowest processes’ – in other words, multiple abilities to respond to LEDD may emerge in what are often non-linear processes. Third, due to its close association with conservatism, lethargy and a lack of willingness to change, path dependency is often associated with an inability to address LEDD problems (Scheffer et al. 2003). The LEDDRA research project will highlight that such ‘lock-in’ effects (structural, economic, political, psychological), closely associated with path dependency, often result in negative processes that, as the term implies, lock stakeholder groups, communities and regions into pathways from which it may not be easy to ‘escape’.

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Theory of responses

LEDDRA theoretical framework

D712**

D713**

D40 Theory of Responses and Knowledge Transfer: A Synthesis**