Regional Environmental Change

, Volume 18, Issue 2, pp 561–571 | Cite as

Adapting to climate change: the role of organisational personalities in natural resource management

  • Alistair J. Hobday
  • Veronica A. J. Doerr
  • Nadine A. Marshall
  • Christopher Cvitanovic
  • Lilly Lim-Camacho
Original Article


Preparing for climate change represents a significant challenge to environmental managers and is influenced by their ability to access and use the latest information. However, communicating and delivering adaption science across diverse stakeholder groups remain a significant challenge. We explore the utility of concepts from personality research to improve understanding of stakeholder capacity. Specifically, we defined eight potential climate-related personality ‘axes’ for natural resource management (NRM) organisations. We surveyed 80% of Australia’s 56 regional NRM organisations to characterise their traits in relation to these axes. Through cluster analysis and trait mapping, we defined six NRM ‘personality types’. These types were unrelated to external factors such as geographic location or land use activities. Rather, five organisational personality axes were important in defining personality type: where information is sourced, strategic skill sets for learning and reorganising, perceptions of risk and the ability to manage for uncertainty, perceptions of the role of NRM groups, and strategies for engagement. Identifying NRM personality type allows organisations to identify and capitalise on their strengths to target their adaptation efforts to maximise success. Organisations can also recognise what they might find most challenging and deliberately collaborate with other personalities with strengths in those areas. Finally, information providers can better understand how to tailor information delivery for improved knowledge exchange between research providers and organisations responsible for sustainability of natural resources, which enables stronger relationships and facilitates evidence-based decision-making.


Adaptive capacity Capacity-building Decision-making processes Climate adaptation Knowledge exchange Knowledge transfer 


Adaptation to climate change will make a major difference to how climate impacts are experienced by a range of sectors (Berkes and Jolly 2001; Nelson et al. 2007; Howden et al. 2007; Hodgkinson et al. 2014). Accordingly, climate adaptation planning is an emerging priority for managers, planners, and policy-makers charged with the protection of the environment (Lawler et al. 2008; Preston et al. 2011). It is to these audiences that much of the climate adaptation research is targeted (Bardsley and Sweeney 2010). Yet, climate adaptation processes are very much in their infancy despite the wealth of knowledge that is emerging (Roberts and Pannell 2009; Marshall and Smajgl 2013; Marshall et al. 2014). This has been attributed to a range of factors, including environmental organisations being geographically isolated from major centres, poorly resourced, and lacking capacity to uptake new technologies and research knowledge (Dovers 2001; Schusler et al. 2003). Thus, while new research knowledge is extremely useful and supportive for decision-making processes, it can also be an additional and unwelcome demand on already overstretched entities (Schusler et al. 2003).

Internal factors such as practices or ‘cultures’ of organisations are also recognised as important influences on their ability to access and use emerging knowledge to aid adaptation (Armitage 2004; Armitage 2005). Humans have long been known to have a range of behavioural and attitudinal types, which are included within the term ‘culture’ (Harris and Harper 2000, Bond et al. 2004). The range of views, attitudes, and interactions with other people are all encompassed within the notion of cultural type (Pannell et al. 2006, Adger et al. 2013). However, common interpretations of work on organisational culture focus on the fact that some organisational cultures are good and others are bad (e.g. a ‘supportive’ or ‘secretive’ culture) (Park et al. 2004; Ismail Al-Alawi et al. 2007). Yet, climate adaptation is complex, and it is likely that organisations with different internal traits will simply have different strengths in their approaches. In addition, creating a sense that an organisation’s culture is ‘bad’ is counter-productive as it can disempower staff which can lead to reduced productivity and organisational success. Thus, we argue there is value in using a different framing concept to understand variation in organisational approaches to adaptation and ultimately enable organisational learning and adaptation success.

Organisational cultures, or personalities, manifest in part as an emergent property of the collective personalities of staff which can result in different practices and outcomes from apparently similar organisations (Allan et al. 2008; Dibrell et al. 2015). For example, in developed countries, a thriving industry specialises in personality testing as part of workplace management courses to help people better understand themselves and their co-workers, and ultimately to increase the likelihood that desired outcomes can be achieved (del Prado et al. 2007). The concept of personality has also been applied to organisations within the environmental sector, though largely in academic discourse rather than in a practical, applied context (Schneider 1987; Brown 2010). The practical application of organisational personalises in relation to climate change and adaption, however, may be important for the enhancement of adaptive capacity among decision-making bodies.

The overarching objective of this study is to explore whether organisational personalities could be identified and characterised in an applied context, to help reveal the diversity of strengths and challenges that environmental organisations might bring to the problem of climate adaptation. To our knowledge, the influence of organisational culture, let alone personality, is seldom considered at the organisational level in the environmental sector (but see Berkhout et al. 2006). One potential advantage of this framing is that the applied work on individual personalities is non-judgemental—all personalities have strengths, and no one personality is better than another across the spectrum of workplace activities. The potential benefits of recognising organisational personalities are threefold. First, organisations themselves could more readily identify and capitalise on their strengths to target their adaptation efforts where they might have most success. Second, organisations could recognise what they might find most challenging and deliberately collaborate with other organisations with strengths in those areas. Finally, adaptation scientists could better understand how to tailor information delivery so that better knowledge exchange can occur between research providers and organisations charged with the sustainability of natural resources (Cvitanovic et al. 2015a). Improving this nexus can be critical for achieving conservation outcomes and planning for climate change by facilitating evidence-based decision-making (Bellamy et al. 2001, Buchy and Race 2001; Lacey et al. 2015).

To address this knowledge gap, this study set out to characterise the personality types of Australian natural resource management (NRM) organisations in the context of climate adaptation. We then sought to identify key strengths and challenges for each ‘personality’ type in tackling climate adaptation and use the resulting knowledge to identify how to improve knowledge exchange among adaptation scientists and decision-makers for each personality type. Specifically, our approach was to survey NRM organisations with respect to their current levels of capacity to consider research knowledge that may support their climate adaptation planning processes (adaptive capacity), as well as their key attitudes, information needs, and knowledge management processes, with a focus on the process of knowledge acquisition and use, rather than an evaluation of knowledge per se (knowledge need). We then used a combination of quantitative and qualitative analysis techniques similar to those used in individual personality research to cluster NRM organisations based on their survey responses and define six personality types.


Natural resource management organisations in Australia

We focused on Australian NRM organisations. As the sixth largest country in the world, Australia contains a diverse variety of habitats including tropical rainforests in the north, snow-covered mountain ranges in the south, and a dry desert in the centre. Australia is also one of the most biologically diverse countries on the planet, and is home to more than one million species of plants and animals, many of which are endemic (Hobday and McDonald 2014). As such, Australian NRM organisations represent an ideal focal group given the range of land management issues they encounter.

In 2013, when this research began, the Australian Government, in association with state and territory governments, recognised 56 NRM regions across Australia based on catchments or bioregions (Robins and Dovers 2007). Each of these 56 regions has a representative NRM organisation that is charged with developing visions and plans that can guide the activities of managers in each region. With rapidly changing environments as a result of climate change and other land use pressures, including water extraction, agriculture, and development, the organisations charged with managing these regions are facing considerable capacity challenges in preparing strategic plans for future activities. In 2012, these NRM organisations were grouped by the Australian Government into eight clusters, with each cluster comprised of 4–11 NRM organisations (Appendix, Fig. A1 ), to deliver research funding to develop new regionally relevant information on climate change impacts and adaptation. The clusters largely correspond to the broad-scale climate and biophysical regions of Australia, but are still diverse in their history, population, resource base, geography, and climate.

Developing organisational personality axes

Personality axes are spectrums of traits related to a single aspect of personality where the end points of a spectrum represent relatively mutually-exclusive traits. Two-ended or dichotomous spectrums feature in the Myers-Briggs personality inventory (e.g. introversion and extroversion as the end points, (e.g. del Prado et al. 2007) and in research on personality types in animals (Sinn et al. 2010; Patrick et al. 2013).

To develop personality types for NRM organisations in a climate change context, we first identified potential personality axes. We drew on information on NRM organisations sourced from our own collective knowledge from working with them across Australia and from the published literature (e.g. Nelson et al. 2007; Robins and Dovers 2007) as well as a synthesis of the key challenges involved in climate adaptation for NRM organisations (Rissik et al. 2014). We initially identified 12 potential axes of organisational personality. We then evaluated these axes against several a priori criteria to identify the final personality axes we would explore using empirical data. Our a priori criteria for inclusion were: (1) known variation among NRM groups, (2) ability to define end points of the spectrum (at least two but could be more), and (3) rationale for why different traits might impact on the ability of the organisation to adapt to climate change.

Our final eight personality axes that met these inclusion criteria are shown in Table 1. Four axes were related to the information paradigm of the organisation (INFO)—the ways in which technical and stakeholder information is sourced as well as the perceived need for different types of information based on the perceived role of NRM organisations and the utility of general principles and extrapolation compared to region-specific information. The four remaining axes were related to the adaptive capacity of organisations (AC)—the skill sets and resources they can draw on to support change (Marshall and Marshall 2007; Marshall et al. 2012). These axes were related to (i) perceptions of risk and ability to manage for uncertainty, (ii) strategic skill sets for planning, learning, experimenting, and reorganising, (iii) the extent that the impacts of change can be absorbed (financially, using networks), and (iv) the level of interest in adaptation planning.
Table 1

Potential personality axes along which different natural resource management (NRM) organisations might exhibit different specific traits that influence their ability to adapt to climate change. Note that some axes could have more than two end points, but we illustrate two here

Axis name

Personality axis description (and example of contrasting end points)

Survey questions related to each axis (see Appendix 1)

Information paradigm (INFO)


Main source of climate adaptation information (networks/experts)

3, 4, 5, 6


Preference for region-specific information versus general principles (specific/general)

1, 2


Perceived role of NRM groups (land management/biodiversity management)

10, 11


Engagement strategies (proactive/reactive)

13, 15

Adaptive capacity (AC)


Perceptions of risk and the ability to manage for uncertainty (limited/extensive)

8, 9


Strategic skill sets for planning, learning, experimenting, and reorganising (limited/diverse)

7, 12, 14, 16, 17, 18, 19, 21, 22, 23, 26, 28


Extent that the impacts of change can be absorbed (financially, using networks) (high/low)

20, 27


Level of interest in adaptation planning (high/low)

24, 25

Survey design

The practicalities of eliciting information from NRM organisations across widely distributed agencies and geographies suggested that a structured survey would provide the best way to capture potential personality traits along these personality axes (Cvitanovic et al. 2015b). In this case, other methods such as focus group work or workshop techniques were not practical given geographic constraints and the risk of over-engaging NRM practitioners, given the context of the broader program of research that they were already involved in as part of the NRM Fund. We therefore designed a quantitative survey to characterise the traits of NRM organisations according to our eight potential personality axes.

Multiple survey questions were developed to elicit traits for each personality axis, particularly where the axis was considered to have more than two end points. Survey questions were written as statements and designed to elicit an opinion, attitude, or stance from each respondent. Respondents were asked to rate how strongly they agreed or disagreed with each statement using a six-point Likert scale (strongly disagree, disagree, slightly disagree, slightly agree, agree, strongly agree) (Bryman 2012). Respondents were not offered a mid-point in order to increase the interpretability of the data (a score of 3 indicated slight disagreement with the statement while 4 indicated slight agreement). However, respondents had the option to leave a response blank if desired. An initial version of the survey was pilot-tested for readability, ambiguity, and variability in responses, and refined accordingly. A final copy of the survey questions (with notes about which axis each question related to, which were not provided to participants) is provided in Appendix 1.

Survey administration

Participants for the survey were NRM planners with primary responsibility for updating their plans to better address climate adaptation. The survey was initially administered through an online forum in 2013, with invitations to participate sent via email to a relevant single contact point within each NRM group. This contact point was typically the senior NRM planner within the group who had a strong knowledge of the culture and staff. To ensure that survey responses were reflective of the group and not of an individual, each contact was asked to engage with other NRM planners within their respective organisation and presents a collective response, which was later verified through a workshop (detailed under ‘data analysis’). This initial effort yielded 26 responses (< 50% response rate). To obtain responses from the remaining NRM organisations, we converted the online forum questions into an online survey using Survey Monkey™. Invitations to participate in the survey were emailed to the primary planning contacts of each NRM organisation that had not completed the online forum several weeks later. These remaining NRM organisations were given 3 weeks within which to respond, at which time they were again reminded and an additional week was granted where necessary. Using both approaches covering the 56 NRM organisations, 44 (80%) completed the survey.

Data analysis

The scores for each question from each NRM participant were analysed using hierarchical cluster analysis based on Euclidean distances to identify groupings of NRM organisations based on similarity, and then principal component analysis (PCA) used to identify which questions (and thus personality axes) were most informative in defining the groupings. Missing responses (only 2 out of 1232 possible responses) were filled with the average for the question to permit analysis (full matrices are required for PCA and cluster analysis). Correlation analysis was also performed to check that responses to questions were not highly correlated, which can bias results in a cluster analysis. Analyses were completed with custom software written in Matlab ® (Mathworks).

The number of clusters was not specified a priori, although we considered natural branching that resulted in between four and eight clusters, before settling on the final number of clusters. To define these clusters (hereafter, types), we inspected the dendrogram, identified natural groupings, and specified a y-axis cut-off that captured those groupings. Each grouping represented a different personality type. The most informative questions in defining personality type were considered to be those with the highest loadings (> 0.3) in the PCA analysis for the first four principal components (which collectively explained ~ 45% of the total variation in responses). The most informative personality axes were considered to be those associated with the greatest number and the highest loadings of informative questions.

To complete defining organisational personality types, we used a qualitative trait-mapping approach to characterise the traits of each personality type for the most informative personality axes. For each axis, we examined the survey responses for the most informative questions to identify how the NRM organisations within each personality type were similar to each other and how they were different from the NRM organisations in other personality types. We also explored whether exogenous factors (such as geography or cluster identity) were ‘traits’ that mapped onto the dendrogram patterns and helped characterise personality types. Each personality type was then characterised by this set of trait descriptions and given a descriptive name. The interpretation and qualitative description of each type was regarded as important both for rigour of approach and for ensuring we remained true to the spirit of personality assessment—that no personality type is ‘better’ than another.

We also undertook qualitative validation of the identified personality types. This was achieved by having experts review the list of personality types and suggest where they would place NRM organisations with which they were most familiar. We defined experts as researchers who had at least 1 year of experience working with various NRM groups around Australia in relation to their adaption planning efforts. We also asked four NRM planners who had taken the survey to read the list of types and identify which type characterised their organisation. We then examined whether there were any discrepancies between the responses of experts and NRM planners and the personality types assigned to organisations using our analyses.

Finally, to explore the practical utility of personality types for regional NRM organisations, we introduced our personality types at a national NRM group workshop on climate adaptation. We asked NRM group representatives to identify which one or two personality types they thought best characterised their organisations. We then ran workshop discussion sessions first within and then among personality types. We asked participants whether this approach helped them better identify their strengths and challenges or find new collaborators.


The 44 survey respondents used the full range of scoring options (range 1–6), although positive responses (scores > 3) to questions were more common. Most of the questions were informative in that they revealed a spread of answers by the respondents (Appendix, Fig. A2). Responses varied between NRM groups, and correlation between questions was generally low (r ≤ 0.5). A total of 49 correlations were significant out of 378 possible correlations (13%) (Appendix Fig. A3) showing that the majority of questions addressed different elements of organisational personality and were retained for analysis. Cluster analysis of the responses based on all questions suggested six groups at a cut-off of ~ 9 units in the dendrogram (Fig. 1). These types had between 2 and 13 members.
Fig. 1

Dendrogram for the 44 natural resource management organisation responses to 28 survey questions. Numbers represent the NRM organisation, and the resulting types are indicated by brackets at the endpoints of the tree

Mapping the NRM type shows the six personality types were not geographically aggregated (Fig. 2). This suggests that exogenous factors associated with geography, including climate and dominant land uses, are not responsible for the types, reinforcing the interpretation that the six types represent endogenous organisational ‘personalities’.
Fig. 2

a Distribution of eight Australian natural resource management (NRM) groups according to the 2012 government-allocated clusters. b Distribution of the six NRM personality types based on analysis of 28 survey questions for 44 participating groups (NRM’s that did not participate in the study are coloured white). In Western Australia, the Rangelands NRM was split into two sub-regions (Kimberly and Rangelands; Fig. 1), which were allocated to two different NRM clusters. Thus, the cluster for the Rangelands sub-region (Cluster 2) should also contain the Kimberly sub-region, which is coloured blue in this figure

The PCA showed which questions were particularly informative in distinguishing among the six personality types, based on loadings > 0.30 in the first four principal components (Table A1). In all, 12 questions representing five of the personality axes were particularly useful. The most informative personality axes were based on the main source of climate information (INFO1, based on Q3 and 4) and strategic skills sets for planning, learning, experimenting, and reorganising (AC2, based on Q7, 14, 17, 19, 22, and 23). Perceptions of risk and the ability to manage for uncertainty (AC1, based on Q8 and to a lesser extent Q9), perceived role of NRM organisations (INFO3, based on Q10), and engagement strategy (INFO4, based on Q15) were also useful in describing the types. The resulting qualitative interpretations of distinct similarities within and differences between the six personality types based on responses to these questions are shown in Table 2. These types were each named to reflect the traits and strengths of each. For example, the Generalist NRM type took information from a wide range of sources, drew on a wide network, and used a range of decision-making approaches to cover a wide range of issues. Responders were more single-issue-driven, and followed the direction set by their stakeholders or management boards. A strength of this approach is that management plans were likely to be accepted by the stakeholders.
Table 2

The six natural resource management (NRM) organisation personality types and their traits, strengths, and challenges based on the personality axes







Generalists (n = 11)

Use all types of information

Have a broad perceived role

Network widely

Use many decision-making approaches

May be better able to recognise and avoid cross-domain perverse outcomes

‘Do everything’ approach may become overwhelming when trying to add adaptation


Naturalists (n = 7)

Source most information from expert opinion

Focus on biodiversity

Strong networks with landowners

Accessing adaptation expert opinion is an efficient way to quickly find and incorporate new information

Limiting focus to biodiversity domain may mean limited skill set for understanding broad cross-domain adaptation concepts


Classicists (n = 13)

Domain-focused planning and most info from domain experts

Preference for formal decision-making processes

May be more rigorous when incorporating adaptation into decisions

Formality can inhibit flexibility and experimentation


Explorers (n = 7)

Domain-based planning but willing to explore new horizons

Use of adaptation expert opinion and some flexibility in planning

Take a measured approach to new issues

Domain focus may lead to perverse outcomes and reduced innovation


Engagers (n = 2)

Highly innovative with flexible planning approaches—often do planning differently

Engage with stakeholders early and keep little information in house

Possess an ability to be innovative about the planning process

May struggle to network with other NRMs


Responders (n = 4)

Tend to follow the lead set by strong stakeholders and/or management boards

Responsive to ‘social licence to operate’ concerns and rarely keep knowledge in house

Plans will have widespread support from key stakeholders

May struggle to address new issues quickly and be innovative

In the qualitative validation, the only discrepancies between personality types assigned to NRM organisations independently by experts and planners and the types assigned in our analysis of the survey data were situations where the expert or planner narrowed their choice down to two personality types but was unable to pick just one. In these cases, the two types chosen included the one assigned to that NRM group in our analysis.

Personality types were agreed to be useful to NRM organisations during focus group discussions at a national NRM workshop. In particular, NRM planners indicated that the personality types helped them consider how to capitalise on their strengths in climate adaptation and find new collaborators. Some planners concentrated on developing collaborators with similar personality types who they might not have had prior interaction with due to significant geographical separation. These collaborations resulted in new sharing of information and tools. Other planners concentrated on collaboration with organisations that had different personality types to take advantage of complementary strengths. These planners reported that the typology of personality types helped them better focus their conversations with existing collaborators to get greater benefit from the relationships.


Preparing for climate change represents a significant challenge to environmental managers, and any opportunities to facilitate the process are likely to be critical. NRM organisations, such as the Australian examples studied here, have the responsibility of helping their respective regions prepare for and adapt to climate change. Their ability to access the latest climate adaptation information and convert it into meaningful strategies for their region may mean the difference between experiencing severe climate change impacts and making the most of associated opportunities (Meinke et al. 2009), and is somewhat dependent on the organisation personalities of individual NRM organisations. Through this study, we have demonstrated that it is possible to characterise the personality types of NRM organisations in the context of climate adaptation (Allan et al. 2008; Dibrell et al. 2015). We also find that doing so can provide important insights into the diversity of strengths and challenges that organisations bring in addressing climate adaptation.

By developing an assessment of organisational personalities, this study identified six different ‘types’ of NRM organisations across Australia that differ in their adaptive capacities and information requirements (Table 2). While only one survey response was used to class each NRM organisation, the respondent was selected based on their involvement and experience across the organisation. Sampling more individuals within each NRM would be useful in the future, particularly if there were significant personnel changes within each NRM. The resulting “personality types” are not a result of geographic location or similarities in land uses, although most scientific knowledge for climate adaptation in Australia appears to be targeted at regional clusters (Robins and Dovers 2007; Similarly, such regional approaches are also favoured by governments who typically design and implement NRM policies and programs, and thus allocate funding and support infrastructure, according to these regional classifications (Jennings and Moore 2000; Robins and Dovers 2007). This may, in part, provide explanation for why climate adaptation processes in Australia remain limited (Roberts and Pannell 2009; Marshall and Smajgl 2013; Marshall et al. 2014) and highlights the importance of understanding organisational personalities among regional environmental groups for building adaptive capacity to climate change via strengthening the relationship between adaption science and natural resource management.

The typology of Australian NRM groups presented here shows that the different types of NRM groups have various strengths with regard to approaching climate change adaptation. The recognition of key strengths in this manner is important in that organisations can easily identify areas on which they can capitalise to build adaptive capacity most efficiently. Perhaps more importantly, we demonstrate that understanding organisational personalities allows for the identification of the key challenges experienced in different organisational types. This is important as it allows organisations to develop and implement tailored strategies to build capacity where it is most needed. This may be achieved via a range of internal strategies, such as providing training for staff in areas where challenges exist. Alternatively, building capacity can also be achieved by deliberately collaborating with other organisations that have ‘personality’ strengths in areas of weakness. For example, our results suggest that Generalist NRM groups could foster collaboration with Naturalist NRM groups. This is because Generalists could share approaches for checking for and avoiding cross-domain perverse outcomes while the Naturalists could source and synthesise biodiversity adaptation information for both groups, reducing some of the breadth of responsibility for the Generalists (Table 2). Similarly, ‘Engager’ NRM groups that have difficulty networking with other NRM organisations would benefit from focusing their engagement efforts towards “Generalist’ NRM groups, who have the ability to network widely. Doing so would allow ‘Engagers’ to access information and from the full suite of NRM organisational types with little effort, thus providing an efficient organisational learning strategy (Table 2).

Underpinning successful collaboration among different organisational types to build capacity in areas of weakness is the presence of strong social networks among NRM groups (Crona and Bodin 2006; Bodin and Crona 2009). Indeed, an extensive body of literature has identified the existence of strong social networks as an important factor for different stakeholders coming together to effectively deal with natural resources problems, including in response to climate change (e.g. Westerhoff et al. 2011; Yun et al. 2013). This is because social networks facilitate the acquisition and diffusion of knowledge among various actors, overcoming many of the traditional barriers associated with knowledge sharing (Alexander et al. 2016). For example, the diffusion of new knowledge via social networks allows for messages to be tailored according to individual or organisational worldviews naturally as information passes through the network. Thus, while assessing the social networks among the NRM groups of Australia was outside of the scope of our study, we suggest that future assessments of organisational personalities would benefit from simultaneously mapping the social networks among various NRM groups (e.g. via social network analysis) to understand the network dynamics and identify opportunities to strengthen collaboration and knowledge sharing among complementary organisational types (Mills et al. 2014). Governance arrangements to best support enhanced collaboration should be identified. Collectively, these actions will also allow for the identification of the most trusted and influential actors within the social network (e.g. Cunningham et al. 2016), which can be further utilised to enhance the communication and delivery of adaption science among different organisational types, as well as developing strategies that promote collaboration.

Recognising the diversity of organisational personalities is also particularly important for researchers trying to communicate and deliver adaptation science to NRM groups and decision-makers (Nisbet 2009; Moser 2010) within or across geographic clusters. For example, while scientific knowledge is often presented in an explicit form (e.g. written reports or oral presentations), the information being presented is interpreted by individuals who make sense from that information based on their own worldviews and personality types (Pannell et al. 2006; Kahan et al. 2012; Leviston et al. 2013). At the same time, organisational norms and cultures can adversely influence the uptake of knowledge which further hinders the uptake and implementation of new adaption science. To our knowledge, however, the influence of personalities is seldom considered at the organisational level in the environmental sector (but see Berkhout et al. 2006). Understanding the ‘personalities’ of organisations, as undertaken here, might lead to improved outcomes from a range of interactions and can assist researchers in the delivery of more appropriate, targeted information and in tackling complex problems like climate adaptation and suggest different information needs and pathways (Wise et al. 2014; Marshall et al. 2014).

Specifically, these findings suggest that attempts to encourage broad uptake of scientific information based on geographic regions may be difficult given the vast range of personality types among NRM organisations, and the various strengths and challenges of each group. As such, traditional linear approaches to communication and knowledge delivery typically employed by research organisations and governments across national or regional levels are likely to have constrained impact (Stocklmayer 2013). Rather, the diversity of personality types among Australian NRM groups suggests that tailored engagement strategies that facilitate deeper engagement and knowledge exchange, particularly in areas of ‘weakness’, would maximise the uptake of adaption science into decision-making processes. This could be achieved in a number of ways.

Firstly, the literature on knowledge exchange advocates for the implementation of participatory research approaches to better align information delivery with end-user needs and capacities (Reed 2008). Including NRM organisations in the generation of adaption science in this manner is expected to increase the extent to which the information is considered salient, legitimate, and credible by each group (Cash et al. 2003), thus increasing its uptake into decision-making processes. At the same time, participatory research approaches are thought to facilitate the integration of local and experiential knowledge into decision-making processes. Previous research suggests that the incorporation of scientific evidence with other forms of knowledge in this way also increases the extent to which decision-makers trust, and therefore use, the science at hand (Cvitanovic et al. 2014). Finally, participatory research approaches foster learning among participants, and thus can help build organisational capacity where weaknesses may have previously existed (Dougill et al. 2006). However, while participatory research approaches are effective across localised regions and small scale projects, their applicability across broad geographic regions will be logistically difficult and resource intensive (Cvitanovic et al. 2016), and their usefulness in this regard warrants additional investigation.

Alternatively, improving the communication and delivery of adaption science across a range of organisational personalities could be achieved via the use of an intermediary, such as a knowledge broker (Meyer 2010) or boundary organisation (Guston 2001). As reviewed by Cvitanovic et al. (2015a), the key feature of these intermediaries is to facilitate the exchange of knowledge between and among various stakeholders. To do so effectively, intermediaries develop an in-depth understanding of the science that they are to communicate, and also a strong understanding of the stakeholders they engage with. This includes their operational environment (e.g. strengths and weaknesses of each organisation personality) and the most appropriate avenues to influence the research and how it is conducted (Michaels 2009). Indeed, when implemented effectively, evidence suggests that intermediaries can facilitate organisational change by removing barriers to knowledge exchange to promote a culture that values the use of science in decision-making processes (Cvitanovic et al. 2017; Marshall et al. 2017).

The findings reported here also have important implications for planning future funding for adaptation planning. Specifically, we find that there is little relationship between geographic location and personality type, although regional approaches to resource allocation are favoured by governments who typically design and implement NRM policies and programs according to geographical groups (Jennings and Moore 2000; Robins and Dovers 2007). Rather, our findings suggest that resources could instead be allocated to groups based on similar or contrasting organisational personalities. This could build capacity among NRM organisations where weaknesses currently exist, while simultaneously capitalising in areas of strength. This survey exercise could be repeated at regular intervals to test the stability of these types over time, or at least, ahead of major funding allocations that involve building groups of NRM organisations. Thus, even if the same types described here are not permanent, the approach can now be used to rapidly create new types to guide projects and investment.


Adaptation to climate change will make a major difference to the degree of impact resulting from climate change (Stokes and Howden 2010). The specific challenge faced by NRM organisations around Australia and elsewhere includes access to latest scientific research to support them in their endeavours to build the productivity and profitability of landholders within their region to cope and adapt to the impacts of climate change. For example, preparing for climate-related changes will not only mean preparing for the worst; in some cases, it may also mean preparing to take advantage of new conditions (Fankhauser et al. 1999). However, climate adaptation processes are proving to be less straightforward, as some resource-users appear better able to cope and adapt than others, and many NRM organisations are struggling to keep abreast of the latest developments in the climate adaptation space. Through recognising the range of capacities of NRM organisations and their different information needs, we anticipate that better progress might be achieved towards supporting NRM organisations and achieving adaptation to climate change in Australia and elsewhere.



Data collection was undertaken in accordance with Human Research Ethics procedures CSSHREC: No. 049/13. This activity was funded by an Australia Government Initiative through the National NRM Impacts and Adaptation project ( The views expressed herein are not necessarily the views of the Commonwealth of Australia, and the Commonwealth does not accept responsibility for any information or advice contained herein. We appreciate the involvement of all the survey respondents, and logistical support from Paul Ryan, Talia Jeanneret, Barton Loechel, and Petina Pert. We also thank two anonymous reviewers and the journal editor for constructive comments that improved this manuscript.

Supplementary material

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  1. Adger WN, Barnett J, Brown K, Marshall NA, O’Brien K (2013) Cultural dimensions of climate change impacts and adaptation. Nat Clim Chang 3:112–117. CrossRefGoogle Scholar
  2. Alexander SM, Andrachuk M, Armitage D (2016) Navigating governance networks for community-based conservation. Front Ecol Environ:155–164.
  3. Allan C, Curtis A, Stankey G, Shindler B (2008) Adaptive management and watersheds: a social science perspective. J Am Water Resour Assoc 44:166–174. CrossRefGoogle Scholar
  4. Armitage D (2004) Nature-society dynamics, policy narratives, and ecosystem management: integrating perspectives on upland change and complexity in Central Sulawesi, Indonesia. Ecosystems 7:717–728. CrossRefGoogle Scholar
  5. Armitage D (2005) Adaptive capacity and community-based natural resource management. Environ Manag 35:703–715. CrossRefGoogle Scholar
  6. Bardsley DK, Sweeney SM (2010) Guiding climate change adaptation within vulnerable natural resource management systems. Environ Manag 45:1127–1141. CrossRefGoogle Scholar
  7. Bellamy JA, Walker DH, McDonald GT, Syme GJ (2001) A systems approach to the evaluation of natural resource management initiatives. J Environ Manag 63:407–423. CrossRefGoogle Scholar
  8. Berkes F, Jolly D (2001) Adapting to climate change: social-ecological resilience in a Canadian Western Arctic community. Ecol Soc 5(2):18 [online] URL: Google Scholar
  9. Berkhout F, Hertin J, Gann DM (2006) Learning to adapt: organisational adaptation to climate change impacts. Clim Chang 78:135–156. CrossRefGoogle Scholar
  10. Bodin O, Crona BI (2009) The role of social networks in natural resource governance: what relational patterns make a difference? Glob Environ Chang 19:366–374. CrossRefGoogle Scholar
  11. Bond MH, Leung K, Au A, Tong KK, de Carrasquel SR, Murakami F, Yamaguchi S, Bierbrauer G, Singelis TM, Broer M, Boen F, Lambert SM, Ferreira MC, Noels KA, van Bavel J, Safdar S, Zhang JX, Chen LN, Solcova I, Stetovska I, Niit T, Niit KK, Hurme H, Boling M, Franchi V, Magradze G, Javakhishvili N, Boehnke K, Klinger E, Huang X, Fulop M, Berkics M, Panagiotopoulou P, Sriram S, Chaudhary N, Ghosh A, Vohra N, Iqbal DF, Kurman J, Thein RD, Comunian AL, Son KA, Austers I, Harb C, Odusanya JOT, Ahmed ZA, Ismail R, van de Vijver F, Ward C, Mogaji A, Sam DL, Khan MJZ, Cabanillas WE, Sycip L, Neto F, Cabecinhas R, Xavier P, Dinca M, Lebedeva N, Viskochil A, Ponomareva O, Burgess SM, Oceja L, Campo S, Hwang KK, D'Souza JB, Ataca B, Furnham A, Lewis JR (2004) Culture-level dimensions of social axioms and their correlates across 41 cultures. J Cross-Cult Psychol 35:548–570. CrossRefGoogle Scholar
  12. Brown M (2010) Revisiting organisational personality: organisations as functional and metaphysical entities. Philos Manag 9:31–46. CrossRefGoogle Scholar
  13. Bryman A (2012) Social research methods. Oxford University Press, OxfordGoogle Scholar
  14. Buchy M, Race D (2001) The twists and turns of community participation in natural resource management in Australia: what is missing? J Environ Plan Manag 44:293–308. CrossRefGoogle Scholar
  15. Cash DW, Clark WC, Alcock F, Dickson NM, Eckley N, Guston DH, Jager J, Mitchell RB (2003) Knowledge systems for sustainable development. Proceedings of the national Academy of Science 100, 8086–8091Google Scholar
  16. Crona BI, Bodin O (2006) What you know is who you know? Communication patterns among resources extractors as a prerequisite for co-management. Ecol Soc 11:7 [online] URL: CrossRefGoogle Scholar
  17. Cunningham R, Cvitanovic C, Measham T, Jacobs B, Dowd AM, Harman B (2016) Engaging communities in climate adaptation: the potential of social networks. Clim Pol 16:894–908. CrossRefGoogle Scholar
  18. Cvitanovic C, Cunningham R, Dowd AM, Howden SM, van Putten EI (2017) Using social network analysis to monitor and assess the effectiveness of knowledge brokers at connecting scientists and decision-makers: as Australian case study. Environ Policy Gov.
  19. Cvitanovic C, Marshall NA, Wilson SK, Dobbs K, Hobday AJ (2014) Perceptions of Australian marine protected area managers regarding the role, importance and achievability of adaptation for managing the risks of climate change. Ecol Soc 19(4):33. CrossRefGoogle Scholar
  20. Cvitanovic C, Hobday AJ, van Kerkoff L, Wilson SK, Dobbs K, Marshall NA (2015a) Improving knowledge exchange among scientists and decision-makers to facilitate the adaptive governance of marine resources: a review of knowledge and research needs. Ocean Coast Manag 112:25–35. CrossRefGoogle Scholar
  21. Cvitanovic C, Hobday AJ, van Kerkoff L, Marshall NA (2015b) Overcoming barriers to knowledge exchange for adaptive resource management; the perspectives of Australian marine scientists. Mar Policy 52:38–44. CrossRefGoogle Scholar
  22. Cvitanovic C, McDonald J, Hobday AJ (2016) From science to action: principles for undertaking environmental research that enables knowledge exchange and evidence-based decision-making. J Environ Manag 183:864–874. CrossRefGoogle Scholar
  23. del Prado AM, Church T, Katigbak MS, Miramontes LG, Whitty MT, Curtis GJ, Vargas-Flores JD, Ibanez-Reyes J, Ortiz FA, Reyes JAS (2007) Culture, method and the content of self-concepts: testing trait, individual-self-primacy, and cultural psychology perspectives. J Res Pers 41:1119–1160. CrossRefGoogle Scholar
  24. Dibrell C, Craig JB, Kim J, Johnson AJ (2015) Establishing how natural environmental competency, organisational social consciousness, and innovativeness relate. J Bus Ethics 127:591–605. CrossRefGoogle Scholar
  25. Dougill AJ, Fraser EDG, Holden J, Hubacek K, Prell C, Reed MS, Stagl S, Strigner LC (2006) Learning from doing participatory rural research: lessons from the Peak District National Park. J Agric Econ 57:259–275. CrossRefGoogle Scholar
  26. Dovers S (2001) Institutional barriers and opportunities: processes and arrangements for natural resource management in Australia. Water Sci Technol 43:215–226Google Scholar
  27. Fankhauser S, Smith JB, Tol RSJ (1999) Weathering climate change: some simple rules to guide adaptation decisions. Ecol Econ 30, 67–78Google Scholar
  28. Guston DH (2001) Boundary organizations in environmental policy and science: an introduction. Sci Technol Hum Values 26:339–408. CrossRefGoogle Scholar
  29. Harris SG, Harper BL (2000) Using eco-cultural dependency webs in risk assessment and characterization of risks to tribal health and cultures. Environ Sci Pollut Res 2:91–100Google Scholar
  30. Hobday AJ, McDonald J (2014) Environmental issues in Australia. Annu Rev Environ Resour 39:16.11–16.28.
  31. Hodgkinson JA, Hobday AJ, Pinkard EA (2014) Climate adaptation in Australia’s resource-extraction industries: ready or not? Reg Environ Chang 14(4):1663–1678. CrossRefGoogle Scholar
  32. Howden SM, Soussana J, Tubiello FN, Chhetri N, Dunlop M, Meinke H (2007) Adapting agriculture to climate change. Proc Natl Acad Sci USA 104:19691–19696. CrossRefGoogle Scholar
  33. Ismail Al-Alawi A, Yousif Al-Marzooqi N, Fraidoon Mohammed Y (2007) Organizational culture and knowledge sharing: critical success factors. J Knowl Manag 11(2):22–42. CrossRefGoogle Scholar
  34. Jennings S, Moore S (2000) The rhetoric behind regionalization in Australian natural resource management: myth, reality and moving forward. J Environ Policy Plan 2(3):177–191. CrossRefGoogle Scholar
  35. Kahan DM, Peters E, Wittlin M, Slovic P, Ouellette LL, Braman D, Mandel G (2012) The polarizing impact of science literacy and numeracy on perceived climate change risks. Nat Clim Chang 2:732–735. CrossRefGoogle Scholar
  36. Lacey J, Howden SM, Cvitanovic C, Dowd AM (2015) Informed adaptation: ethical considerations for adaptation researchers and decision-makers. Glob Environ Chang 32:200–210. CrossRefGoogle Scholar
  37. Lawler JJ, Tear TH, Pyke C, Shaw MR, Gonzalez P, Kareiva P, Hansen L, Hannah L, Klausmeyer K, Aldous A (2008) Resource management in a changing and uncertain climate. Front Ecol Environ 8:35–43. CrossRefGoogle Scholar
  38. Leviston Z, Walker I, Morwinski S (2013) Your opinion on climate change might not be as common as you think. Nat Clim Chang 3:334–337. CrossRefGoogle Scholar
  39. Marshall NA, Smajgl A (2013) Understanding variability in adaptive capacity on rangelands. Rangel Ecol Manag 66:88–94. CrossRefGoogle Scholar
  40. Marshall NA, Marshall PA (2007) Conceptualizing and operationalizing social resilience within commercial fisheries in Northern Australia. Ecol Soc 12(1):1 [online] URL: CrossRefGoogle Scholar
  41. Marshall NA, Stokes CJ, Webb NP, Marshall PA, Lankester AJ (2014) Social vulnerability to climate change in primary producers: a typology approach. Agric Ecosyst Environ 186:86–93. CrossRefGoogle Scholar
  42. Marshall NA, Park SE, Adger WN, Brown K, Howden SM (2012) Transformational capacity and the influence of place and identity. Environ Res Lett 7:034022 (9pp). CrossRefGoogle Scholar
  43. Meinke H, Howden SM, Struik PC, Nelson R, Rodriguez D, Chapman SC (2009) Adaptation science for agriculture and natural resource management-urgency and theoretical basis. Curr Opin Environ Sustain 1:69–76. CrossRefGoogle Scholar
  44. Marshall NA, Adger N, Attwood S, Brown K, Crissman C, Cvitanovic C, De Young C, Gooch M, James C, Jessen S, Johnson D, Marshall P, Park S, Wachenfeld D, Wrigley D (2017) Empirically derived guidance for social scientists to influence environmental policy. PLoS One 12(3):e0171950. CrossRefGoogle Scholar
  45. Meyer M (2010) The rise of the knowledge broker. Sci Commun 32:118–127. CrossRefGoogle Scholar
  46. Michaels S (2009) Matching knowledge brokering strategies to environmental policy problems and settings. Environ Sci Pol I2:994–1011. CrossRefGoogle Scholar
  47. Mills M, Alvarez-Romero JG, Vance-Borland K, Cohen P, Pressey RL, Guerrero AM, Ernstson H (2014) Linking regional planning and local action: towards using social network analysis in systematic conservation planning. Biol Conserv 169:6–13. CrossRefGoogle Scholar
  48. Moser S (2010) Communicating climate change: history, challenges, processes and future directions. WIREs Clim Chang 1:31–53. CrossRefGoogle Scholar
  49. Nelson DR, Adger WN, Brown K (2007) Adaptation to environmental change: contributions of a resilience framework. Annu Rev Environ Resour 32:395–419. CrossRefGoogle Scholar
  50. Nisbet MC (2009) Communicating climate change: why frames matter for public engagement. Environ Sci Policy Sustain Dev 51(2):12–23. CrossRefGoogle Scholar
  51. Pannell DJ, Marshall GR, Barr N, Curtis A, Vanclay F, Wilkinson R (2006) Understanding and promoting adoption of conservation practices by rural landholders. Aust J Exp Agric 46:1407–1424. CrossRefGoogle Scholar
  52. Park H, Ribière V, Schulte WD Jr (2004) Critical attributes of organizational culture that promote knowledge management technology implementation success. J Knowl Manag 8(3):106–117. CrossRefGoogle Scholar
  53. Patrick S, Charmantier A, Weimerskirch H (2013) Differences in boldness are repeatable and heritable in a long-lived marine predator. Ecol Evol.
  54. Preston B, Westaway R, Yuen E (2011) Climate adaptation planning in practice: an evaluation of adaptation plans from three developed nations. Mitig Adapt Strat Glob 16:407–438. CrossRefGoogle Scholar
  55. Reed M (2008) Stakeholder participation for environmental management: a literature review. Biol Conserv 141, 2417–2431Google Scholar
  56. Rissik D, Boulter S, Doerr V, Marshall N, Hobday A, Lim-Camacho L (2014) The NRM Adaptation Checklist: supporting climate adaptation planning and decision-making for regional NRM. CSIRO and NCCARF, Australia (Accessible from
  57. Roberts AM, Pannell DJ (2009) Piloting a systematic framework for public investment in regional natural resource management: dryland salinity in Australia. Land Use Policy 26:1001–1010. CrossRefGoogle Scholar
  58. Robins L, Dovers S (2007) NRM regions in Australia: the ‘haves’ and the ‘have nots’. Geogr Res 45(3):273–290. CrossRefGoogle Scholar
  59. Schneider B (1987) The people make the place. Pers Psychol 40:437–453. CrossRefGoogle Scholar
  60. Schusler TM, Decker DJ, Pfeffer MJ (2003) Social learning for collaborative natural resource management. Soc Nat Resour 16:309–326. CrossRefGoogle Scholar
  61. Sinn DL, Moltschaniwskyj NA, Wapstra E, Dall SRX (2010) Are behavioral syndromes invariant? Spatiotemporal variation in shy/bold behavior in squid. Behav Ecol Sociobiol 64:693–702. CrossRefGoogle Scholar
  62. Stocklmayer S (2013) Engagement with science: models of science communication. In: Gilbert JK, Stocklmayer S (eds) Communication and engagement with science and technology: issues and dilemmas. Routledge, New YorkGoogle Scholar
  63. Stokes C, Howden M (2010) Adapting agriculture to climate change: preparing Australian agriculture, forestry and fisheries for the future. CSIRO Publishing, AustraliaGoogle Scholar
  64. Westerhoff L, Keskitalo ECH, Juhola S (2011) Capacities across scales: local to national adaptation policy in four European countries. Clim Pol 11:1071–1085. CrossRefGoogle Scholar
  65. Wise RM, Fazey I, Stafford Smith M, Park SE, Eakin HC, Archer Van Garderen ERM, Campbell B (2014) Reconceptualising adaptation to climate change as part of pathways of change and response. Glob Environ Chang 28:325–336. CrossRefGoogle Scholar
  66. Yun SJ, Ku D, Han JY (2013) Climate policy networks in South Korea: alliances and conflicts. Clim Pol 14:283–301. CrossRefGoogle Scholar

Copyright information

© Springer-Verlag GmbH Germany 2017

Authors and Affiliations

  • Alistair J. Hobday
    • 1
    • 2
  • Veronica A. J. Doerr
    • 3
  • Nadine A. Marshall
    • 4
  • Christopher Cvitanovic
    • 2
  • Lilly Lim-Camacho
    • 5
  1. 1.CSIRO Oceans and AtmosphereHobartAustralia
  2. 2.Centre for Marine SocioecologyUniversity of TasmaniaHobartAustralia
  3. 3.CSIRO Land and WaterCanberraAustralia
  4. 4.CSIRO Land and Water, ATSIP Building #145James Cook UniversityTownsvilleAustralia
  5. 5.CSIRO Land and WaterPullenvaleAustralia

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