Overcoming knowledge stickiness in scientific knowledge transfer

Abstract

This paper explores the transfer and dissemination of knowledge between scientists, the volunteers who collect the knowledge and the communities which learn from it in order to implement change. The role of knowledge “stickiness” in the reduction of knowledge transfer is outlined. The characteristics of the knowledge and the situation combine to develop a range of factors, “stickiness predictors,” which can deter knowledge transfer. These stickiness predictors are used to analyse data gathered from three qualitative cases, which were developed from both participant observation and semi-structured interviews studying the interactions between the scientists, volunteers and organisations. A reconsideration of the way that knowledge and knowledge transfer are being conceptualised by scientists is proposed, in order to enable “stickiness” factors to be recognised and managed, thereby increasing the potential for scientific literacy. A move towards a more broadly constituted community of practice is proposed.

Keywords

knowledge stickiness knowledge transfer scientific literacy volunteers

1. Introduction

This paper examines how volunteers contribute, both financially and practically, to worldwide, scientifically based research projects (Benson, 2005; Clifton and Benson, 2006). There are two streams of literature outlining why volunteers engage in such projects. First, Wearing (2001) argues that it is about “making a difference” with volunteers acting for altruistic motives. Second, some volunteers use the experience for self-development and skill building in order to develop a more rounded curriculum vitae (Blackman and Benson, 2010; Lyons and Wearing, 2008). Volunteers generally choose their projects according to their ecosystem of preference, e.g. a rain forest. In order to fulfil these motivations, organisations including Operation Wallacea, Coral Cay, Earthwatch and Frontier bring volunteers together with the scientists who lead the research projects.¹ Such projects are developed, managed and, in their view, owned by the scientists who approach the organisation for funding and support; such projects are usually funded for at least two years but applications must be lodged annually. The organisations make such provision dependent upon using volunteers and try to ensure that the volunteers have a meaningful experience; future funding will, in theory, be subject to this.

Whilst all the organisations offer a similar service, the range of research projects available to volunteers is both complex and diverse; organisations may be differentiated by their portfolio of projects based around the destination (frequently remote), the type of ecosystem in which the project takes place (marine, terrestrial, rainforest) and the actual activity or purpose that the project is based around (e.g. diving coral reefs, related to wildlife i.e. polar bears/turtles). In most cases the organisational objective is to support projects and scientists in order to further the development of scientific understanding and enable conservation, land management, strategic development aid and environmental progress. For example Earthwatch claims its mission is to engage “people worldwide in scientific field research and education to promote the understanding and action necessary for a sustainable environment. We believe that teaching and promoting scientific literacy is the best way to systematically approach and solve the many complex environmental and social issues facing society today” (Earthwatch, n.d.).

There is an increasing concern that there is not enough general scientific literacy, nor enough desire on the part of scientists to share their knowledge and that these factors are reducing the possibility of effecting change more broadly (COPUS, 2009). Lederman (2008) argues that there must be an increase in the capacity of ordinary people to cope with science and technology if society is to benefit from the findings of science. It is argued that such capacity would be increased through the use of volunteers who would then disseminate such knowledge to their peers and into the wider community. However, we will argue that such capacity building is not currently widely occurring because of the model of scientific research that the volunteers are involved with. There are different models of knowledge and research emerging such as community science (Tebes, 2005; Wandersman, 2003) or participatory action research (Whyte, 1989) but we are reporting upon the model being used in the majority of these volunteer projects which are much more traditional and researcher led.

This paper considers the creation, transfer and dissemination of knowledge between scientists and the volunteers who help them. Initially, the paper will outline why effective knowledge transfer is so important, summarising reasons why there may be problems with knowledge transfer in general, and between scientists and the volunteers in particular. The “knowledge stickiness” model of Szulanski (2000) is used to analyse qualitative data collected from case studies of research tourism companies which support scientists undertaking research by recruiting research volunteers (tourists) who pay to travel to certain research sites and collect data for the principal investigators. The data demonstrate knowledge transfer problems and the paper then discusses the implications, making tentative recommendations to reduce the current knowledge stickiness problems, thereby increasing the possibility of effective dialogue between scientists, volunteers, organisations and, potentially, the wider community (Office of Science and Technology and the Wellcome Trust, 2001).

2. Knowledge transfer – why does it matter?

Knowledge and how well it is developed, stored and utilised, some would say “managed,” is recognised as being critical for the global knowledge economy, in terms of not only sustaining competitive advantage and profitability (Cook and Brown, 1999; Elwyn et al., 2007) but also nurturing innovation, thereby enabling long-term value adding in all areas of human achievement (Cutler, 2008). The development of new knowledge is crucial as it is the basis for any improvement in understanding, which in turn forms the underpinning for change (Blackman, 2005). Consequently, it is important to support processes that enable knowledge creation, are able to recognise the new knowledge and then ensure that it is shared and transferred appropriately in order to facilitate its use in new and innovative ways.

In terms of the context of scientific projects that use volunteers, the objective is to acquire new insights into animal behaviour, habitats and land care via biological and social science expeditions which operate in remote locations across the world. These expeditions are designed to promote the understanding and action necessary for sustainable environments. The aims will not be met if only the scientists are aware of the issues as, in many cases, real solutions will only be possible if all the stakeholders involved in a habitat recognise the need for change (Knox and Gruar, 2007; Koontz, 2005). Consequently, the new knowledge will need to be disseminated in a way that leads to a shared understanding of the changes that must occur. This has implications for the forms of knowledge being developed.

The knowledge in this context is being developed and added to over time; the majority is empirically derived from scientific experimentation (Doyal and Harris, 1986; Tebes, 2005), but there will also be some that is constructed, being created through interactions between agents in social contexts (Horn and Wilburn, 2005; Tebes, 2005). In the former there will be scientific experimentation and data collection utilised to better understand, for example, certain flora, fauna and anthropological phenomena. Concerning constructed knowledge, it is held that social facts are not imposed onto individuals from the outside; instead they are constituted in the practical and contextualised interactions of the individuals who accomplish them (Plane, 2000; Tebes, 2005). If ideas are shared, transferred and interpreted, a collective understanding can be developed which becomes the source of action. If knowledge is not being shared, either during its construction or after it has been recognised, then there will be less knowledge transfer and a reduction in the effectiveness of changes that the new knowledge could drive.

3. Knowledge transfer – why does it stall?

Cook and Brown (1999) argued that in order for there to be new knowledge creation or application, it needed to be moved from one location, form or source to another, i.e. it needed to be transferred. However, knowledge creation and transfer is complex, messy and unpredictable, because any new knowledge emerges as a result of complex, cultural and interactive processes (Carlile and Rebentisch, 2003; Tsoukas and Vladimirou, 2001). The fact that there is movement means that, unless the transfer system is perfect there will be losses or alterations during the process (Brown and Duguid, 2001; Szulanksi and Jensen, 2006). One such loss emerges through “stickiness” within the system (Jensen and Szulanski, 2004) where knowledge stickiness constitutes inhibitors to the transfer of knowledge within the process (Szulanski, 1995, 2000; von Hippel, 1994). In this paper we are using “knowledge stickiness” as an analytical framework, since there is a potential mismatch between the knowledge creation views of the different knowledge creating parties. The scientists might not see the volunteers as an actual part of the process and this would affect the likely knowledge outcomes. In other models of knowledge creation and transfer the focus is upon how to transfer (see for example Nonaka et al., 2000; Cook and Brown, 1999); an explanation was needed why the knowledge might have stalled. What matters is that knowledge transfer is a process and stickiness inhibits the process. According to Szulanski (1995, 2000), the characteristics of the knowledge and the situation combine to develop a range of nine “stickiness predictors” (see Table 1).

Table 1.

Predictors of stickiness.

Stickiness predictor	Description of the predictor
Causal ambiguity	Where the features of the new context become unclear because it is not clear whether the context is replicable
Unproven knowledge	How solid is the proof that something is useful to an organisation
Source lacks motivation	How clearly does the knowledge source see the benefit in transferring some form of knowledge
Credibility of source	When the source is not perceived as being reliable, trustworthy or knowledgeable and is thus open to challenge
Recipient lacks motivation	How clearly does the knowledge recipient see the benefit in receiving some form of knowledge
Recipient lacks absorptive capacity	Whether the recipient possesses the language, extant knowledge, readiness, or skills to be able to implement the potential transfer
Recipient lacks retentive capacity	Where the knowledge transferred must be retained and used but the recipient is not capable of “institutionalising” the knowledge and developing new practice
Arduous relationship	Is communicating between the source and the recipient easy or not
The “freedom to” possibilities for learning are not recognised	Opportunities for novelty or change are not recognised or perceived by either the source, the receiver or both

Source: Adapted by the authors from Szulanski (2000: 19–20).

Stickiness does not have to be linear, it can affect any knowledge creation process. In this case, we argue that because of the nature of the science project development there may be “stickiness” tensions between the scientists, who view the findings as empirical, capable of being transferred as a fully replicated whole to other interested parties and “theirs,” and the volunteers who are more likely to develop constructed knowledge as a result of a range of interactions with the scientists, other volunteers, members of the community or other parties such as the media (Christidou et al., 2004). Elwyn et al. (2007) argue that transfer “stickiness” is normal and that the key is to identify the potential sources of stickiness in a given context and actively manage the process. Thus the aim of the research was to establish what stickiness factors were present (if any) within the cases, and what were the implications of this for the effective implementation of the knowledge being developed by the scientists.

4. Methodology

As research volunteer tourism is an emerging niche with little current research about the organisations, the scientists or the volunteers who take part (Brown and Lehto, 2005) we undertook a qualitative approach that would enable the knowledge creation voices to be heard (Creswell, 2003; Leedy and Ormrod, 2005). The qualitative methods were designed in order to understand the interactions and relationships between the organisations, the scientists and the volunteers with the purpose of exploring the transfer and dissemination of knowledge. The organisations were researched by using a case study approach as it enabled an in-depth investigation into a specific set of circumstances in a particular context (Yin, 2003a, 2003b). The investigation of phenomena within a case is supported by Yin (2003b) who argues that case studies are particularly appropriate where the observer has access to a novel, previously unexplained phenomenon. In total, three case studies were undertaken, with three different UK based organisations which operate research volunteer programmes throughout the world. The methods for each case consisted of participant observation, semi-structured interviews and documentary analysis of organisational materials such as final accounts, volunteer briefing documents, publicity articles and company websites. Participant observation was undertaken with the researcher’s identity being revealed to all respondents (Gill and Johnson, 2002). Descriptive observation (Robson, 2002) notes were kept which consisted of recorded observations of participants, project activities, team, staff and organisational meetings, team briefings and lectures. For example, the extent to which learning took place, the extent to which volunteers engaged in activities, interactions between scientists and volunteers, and how these stakeholders engaged with the organisations were recorded. In this study, primary and secondary observation techniques were used (Delbridge and Kirkpatrick, 1994); consequently, recording consisted of keeping a diary (primary) and recording statements of what happened or was said (secondary).

The other qualitative method used consisted of individual, semi-structured, face-to-face interviews, thus allowing flexibility during the actual interview process. In total, eighty-one interviews were conducted, which consisted of three with the owners/CEOs of the three case study organisations; two with the principal scientists on two of the projects; twenty-one initial interviews with volunteers in Indonesia, which enabled key themes to be developed; a further fifty-five interviews with volunteers were then undertaken in the field (Table 2 illustrates the profile of each case).

Table 2.

Profile of case studies.

Location of project	No of volunteer interviews	Length of project	Volunteer gender	Predominant age range	Predominant nationality of volunteers
Case A
Indonesia	28	4–6 weeks	9 male	19–29 years	100% Great Britain and Channel Islands
			19 female
Case B
Malaysia	11	2 weeks	8 male	30–55 years	No predominant country evident
			3 female
Case C
Madagascar	16	4 weeks	8 male	19–39 years but widest range from 17–63 years	64% British and a range of participants from all continents
			8 female

In case study A purposive sampling was used and the number of interviews was dictated by the premise that volunteers would be interviewed until there was data saturation and no new information was forthcoming. The point of data saturation is still a moot one in the literature (Guest et al., 2006) but once all data emerging were repeating those already heard it was considered that saturation was reached. In both case study B and case study C, the number of interviews was governed by the number of participants on the project, consequently, all participants in both projects were interviewed. Interestingly, very few new codes were generated by cases B and C after A had been analysed.

Each data collection phase lasted between 3 and 6 weeks; however, in each phase no interviews took place during the first week, thereby enabling a period of “settling in” for the volunteers, the researcher and the project team. All of the interviews were recorded and transcribed. NVivo was used as the vehicle for working through the transcripts, thus allowing unstructured data to be analysed and interrogated for instances of “stickiness” in the knowledge transfer between the scientists, the volunteers, the organisations and the wider publics, to identify the “stickiness” predictors present within the cases.

5. Applied “stickiness” predictors

Interviews with the case study managers, scientists and volunteers demonstrated that there were concerns that, whilst the volunteers were very interested in the topics, they often did not learn about the outcomes of the research: this was particularly clear in cases where individuals had volunteered on more than one occasion.

Here we are only seeing part of this whole project. I’ve only gotten from one – no, two of the projects that I’ve been on, I’ve gotten the actual papers from the research. (Erin had participated in 10 projects, case study B)

Concerns were also expressed that, not only were the volunteers not learning about the outcomes, nor were the wider public; after all how could there be change in behaviours (for example in habitat destruction) unless the real impacts of the behaviours were understood? The organisational leaders wished to receive knowledge of the projects and their science outcomes in order to be able to demonstrate their role to others, enable the dissemination to a wider range of publics and to develop “joined up science” by identifying areas of overlap and developing projects that could work in a more holistic way. There are already strong moves in the direction of community education and involvement by several of the organising companies, but there is still a lot of knowledge not being widely disseminated.

It seems tremendously important because we just don’t do that and I think it’s incumbent upon [organisational name] to make sure that the education programme which they started this year, not only continues in its own right, but also tells the Indonesian peoples in the [local] region about exactly the science that we are doing and why we are here. (James, Scientist, case study A)

The volunteers argued that they wished to learn so that they could understand their role in the process, gain closure in the area where they are researching and be able to disseminate their knowledge to friends and contacts through raising awareness. Unfortunately the scientists involved, who were administratively independent from the companies that supported them financially, did not have a strong motivation to disseminate their findings to the case organisations or volunteers and this is where the stickiness began, leading to seven of the nine stickiness indicators being present within the data in one form or another.

Causal ambiguity

This was not present. As these are scientific projects there were clear contexts which were seen to be appropriate and well understood.

Unproven knowledge

This was present. Scientists recognised the need to transfer knowledge but thought only the final outcomes were important, whereas the volunteers and local communities were interested in the state of the project as it was occurring:

I feel I am contributing something. The cool thing too about this is that never before in my life have I ever contributed to raw data collection for pure science. I am finding that actually very interesting … (Connie, case study C)

In the observed team briefings, scientists often promised updates but these were not always forthcoming and this was a source of disappointment. If the volunteers are seen as a conduit to the wider public, keeping them informed enables greater dissemination at all times and, although a project is not complete, it will sustain public awareness and enable the science to become a more widely accepted issue. Moreover, neither the scientists nor the organisations actively managed disseminating findings or updates to the volunteers. In case study C, blogging was being facilitated by the organisation which asked the scientist and the volunteers of a team to contribute. However, this was being used more as a marketing tool than as a source of scientific knowledge transfer.

The stages of the knowledge transfer process are relevant here. Szulanski (2000) argues that there are four distinct stages: initiation, implementation, ramp-up and integration. The scientists, if they considered the problem at all, would probably assume that the volunteers did not need the full transfer of knowledge and it is likely that they would not initiate a process that could lead to full routinised, integration of any ideas that were not directly related to current data collection. Thus, in addition to the already considered forms of stickiness there may also be initiation stickiness, whereby there is difficulty in recognising opportunities for transfer and acting upon them (Elwyn et al., 2007). In theory as soon as any form of knowledge is recognised an opportunity for transfer is formed, but often transfer will only be triggered by a perception of an actual gap (Klimecki and Lassleben, 1999; Szulanski, 2000). In the case of volunteers, a gap in data collection ability will trigger knowledge sharing, but a gap that is merely “wanting to know” may not be perceived as important or appropriate enough by the scientists to warrant a full process of knowledge transfer; unless the “usefulness” of the knowledge to the volunteers or other communities is perceived by the scientists as relevant in terms of their desired outcomes, transfer initiation is unlikely.

Source lacks motivation

This was present and links to the problems of unproven knowledge above. Scientists see the dissemination as important in terms of academic papers and theses developed at the end of the project. However, they are not motivated to share the results earlier, nor share such academic outcomes with the volunteers or case companies. Evidence suggests that although there has been an increase in the total numbers of papers produced, there has been very little communication of results via mass media and no real change in the communication patterns (Suleski and Ibaraki, 2010). This may be, in part, a direct result of the way the funding and rewards structures are currently set up within academia as it is a well known maxim that “we get what we measure” (Blackman, 2006). When a research bid is applied for, dissemination plans are a part of the application process; however, this is usually laid out as journal papers and conference attendance with a minimal amount of public engagement. In terms of academic promotions and peer recognition it comes from journal articles and not how well has the public been kept informed. This may be changing in the UK as new impact factors are being developed (Davies et al., 2005) possibly leading to an increase in motivation, but at present there is little extrinsic motivation to share.

Moreover, owing to the time lags between the data collection, the research completion and the journal publication, scientists have forgotten the role of the volunteers and the possibility that they would be interested in the outcomes. If scientists reconceptualised the public and volunteers as a core element of their potential for success, their motivation might change.

An example of successful stakeholder involvement emerged from observations of organisational meetings and community interactions over a period of six weeks. The problem related to the fact that each year the size of the sea cucumbers being collected by a local community was getting smaller. It became apparent that the smaller sea cucumbers were too young to breed and that through harvesting smaller ones, because they were available and the larger ones had already been taken, the population was being decimated. For a couple of years there was data that showed the population was declining and the cucumbers being taken were smaller. However, this had not alerted the organisation or the community to make changes because the data had been collected by volunteers and, although published in dissertations, it was not acted upon. Time lags, data lacking as not all dissertations were made available to the organisation, and a lack of knowledge transfer between volunteers and then from the volunteers to the scientists led to the problem not being addressed at once. When it was noted the message was initially about numbers.

The change occurred when the message changed to be about the impact, not of the current numbers of sea cucumbers, but of breeding possibilities and long-term sustainability. To ensure reproduction prior to harvesting, the critical size of a sea cucumber was identified, an agreement was reached to collect only larger ones and a measurement gauge developed to enable all fishermen to be sure of the suitability of the sea cucumbers. In this case the community was a very clear stakeholder. However, unless the scientists can see the volunteers or other less involved publics as stakeholders there may not be the same effort to transfer the knowledge; this lack of engagement delayed the opportunity for change.

Credibility of source

This was not present, the scientists were seen as knowledgeable experts in their field and their ideas were not challenged.

Recipient lacks motivation

The recipients were motivated but the sources used for transfer, i.e. academic papers or theses, were not appropriate mechanisms, nor made easily available. Even where the formal outputs and papers could be understood, what volunteers said they would prefer were newsletters or updates via the web and internet designed as an ongoing process informing them of developments. In particular, more mature participants in case study B said they wished to receive some form of less formalised feedback. Often the time lag between the project and the dissemination of the final results meant that the impetus for change had been lost; they were arguing for interim feedback and some form of continued volunteer engagement with the project.

There were two forms of volunteer being interviewed: those who were purely volunteering and collecting data for the scientist and those who were collecting their own data as well for dissertation projects; this was particularly so for case study A. The motivation of the latter was clearly different and their level of scientific understanding much higher; however their problem was that it was hard for them to collect enough data to have a publishable project. This led to the scientists taking their projects less seriously and not believing in the strength of their motivation to learn. The dissertations were not always seen as “real research”; in particular when the volunteer was an undergraduate. The higher the qualification, the more seriously the scientist viewed the data.

This reluctance by the scientists to engage with the research findings of some of the volunteers implied the existence of barriers to becoming a part of a larger community of practice that was in existence. A community of practice has been identified as a means whereby knowledge is held, transferred and created (Brown and Duguid, 1991; Roberts, 2006; Wenger, 2000). Over time, shared understandings are developed which are used to enable faster, potentially more effective solutions to problems or to develop new ideas and innovations. The understandings are developed through ongoing, willing engagement with others within the community, which develops a feeling of belonging and common purpose (Bate and Robert, 2002).

What matters for this paper, in terms of how it affects the potential transfer of knowledge, is that the shared discourse reflects and sustains a certain perspective of the world; if only natural scientists are within the community it will lead to a self-referential confirmation that only “real” research and science matters. It should be noted that there was observable difference between the natural scientists and the social scientists: the social scientists were more likely to engage and publish with the volunteers which may reflect their conceptualisations of how knowledge is created within their separate communities of practice. This is discussed later.

Recipient lacks absorptive capacity

This was present as the format of the knowledge transfer was often not easily accessible to volunteers and other publics in terms of the language and the complexity of the ideas being expressed. Some of the technical elements of science may not be well understood and so the level of write up needs to be considered, as well as the forms of communication and transfer used.

Part of the problem was that often the volunteers would need to be trained before they were able to help and their ultimate abilities might not be recognised.

If the volunteers are in the right place at the right time, then yes, they get briefed. Whether they listen is another issue. (Jane, Scientist, case study B)

Furthermore, the volunteers themselves were not always sure they were capable and this would act as a confirmation for the scientists.

I hope that my time and energies will be useful in terms of scientific data that goes out. I would not want to just come here and not pass any of the tests and not be able to help in terms of that. I will get a sense of satisfaction if I am able to contribute to pulling the data together. (Anna, case study C)

The ability of volunteers to learn to make measurements accurately in order to ensure all collected data are valid is crucial and so the volunteers have to have some absorptive capacity during the project. The question is whether it is greater than merely an ability to measure effectively, but to be able to disseminate the essence of the project and its implications more widely. Observation showed that on all projects, to some extent, the scientists were actively educating the volunteers, undertaking lectures and discussion groups and, where there were longer periods of less formal interaction there was more knowledge transfer. However, this was about engaging them in the “now” so they would be functional volunteers; there was no feel about a longer term view.

Recipient lacks retentive capacity

As with concerns about absorptive capacity, scientists are liable to assume that the different stakeholders are neither interested in, nor capable of, involvement in knowledge transfer. However, where education programmes have been developed with local communities there have been successes in long-term behaviour change (Benson and Clifton, 2004). Nevertheless, it can be difficult where, for example, the local community is predominantly subsistence farmers whose level of concentration wanes over time. One scientist talked of needing to rotate local volunteers regularly to maintain their interest and the problems in terms of retention of knowledge that this created.

However, there were positive stories too whereby the volunteers not only absorbed and retained the knowledge but actually created something new:

On the education we often get really genuine contributions, people designing things and making things. The fieldwork is very volunteer driven and varies enormously from individual to individual as to what they do and what kind of contribution they are making. (Jane, Scientist, case study B)

For both “stickiness” factors regarding absorption and retention, the problem was a difference in perception between the volunteers and the scientists.

Arduous relationship

This was an issue as scientists saw transferring knowledge to the volunteers as adding to their role (in a way that did not advance science) as the forms of transfer needed to be different from those usually adopted.

When you go on a [organisation name] project basically you’re in sole charge, so the PI [principal scientist] is basically responsible, not just for the science but for the whole conga, both social and intellectual of the project. (James, Scientist, case study A)

Usually how I run that is to get an educational officer every year and initially it was an English woman who has been a lot. In the last two years they have been Chinese, Malaysian, and they have come for two to three months and I usually have a set project for them. I vaguely have the materials and they implement it but, yes, it does take up a lot of time and you know, in the end, you could just do that full time and no research, so finding the balance is quite hard. (Jane, Scientist, case study B)

In some cases the scientists have to take care of the volunteers outside the confines of the science (organising trips, logistics etc.) and this is seen as a chore. Moreover, in many cases the volunteers perceived that the projects did not always need volunteers to achieve the actual outcomes. This was particularly the case with case study B where there were predominantly more mature volunteers who had clearer expectations; however, there were concerns across all the cases.

Keeping people feeling like they are making a genuine contribution and they are not just a dollar sign is the hardest thing with using volunteers to support science. (Jane, Scientist, case study B)

I really thought that I would be a volunteer and would really mean something to [the name of project leader]. … it is like: okay, they don’t really need us but they need the money – no problem. (Hytham, case study B)

… to contribute something, and up to now it’s our – we are now secondary, don’t feel like contributing anything up to now. I hope I will have the feeling when I leave the site. (Regular, case study C)

These tensions made managing the projects difficult and reduced the effectiveness of the transfer process. Moreover, in some cases the scientists talked of difficulties with age, group dynamics and expectation issues, all of which reduced their propensity to share and the disposition of the volunteers to listen. In summary the patterns of knowledge development and transfer within the projects could be drawn as in Figure 1.

Figure 1.

Current knowledge development flows.

The “freedom to” possibilities for learning are not recognised

The scientists and the research organisations all want change but do not necessarily recognise the advantages of alternative dissemination systems. As indicated, there is an increase in the range of educational opportunities being offered but the full potential of creating, not just some training or a short-term knowledge opportunity, but a way of increasing scientific literacy is not being embraced. Indeed, despite the improved educational opportunities many volunteers still felt underutilised and unclear:

I will probably be able to see it clearer when I leave – but at the moment I can only see the local benefits like the staff that they employ. (Louise, case study A)

6. Discussion and implications

It was clear that there was knowledge “stickiness” present in one form or another in all of the research projects and organisations, the question is what are the consequences? What has been demonstrated is that if there is to be greater knowledge transfer and an increased, faster impact of the scientific outcomes upon members of the public, especially those in the local community, then there will need to be a reconsideration of how knowledge is shared. It should be stressed that not all of these predictors exist at every stage of knowledge creation and transfer so not all can be managed in every case. Nevertheless, some could be by providing the option for interactions to occur, which would change the current mindset of at least one of those involved in the knowledge transfer. This then leads to the questions, firstly, whose mindsets need to be, or could be, changed and how? Secondly, who should be responsible for initiating, supporting and managing the dissemination and knowledge transfer?

As indicated earlier, whilst dissemination and education are elements of research proposals and there is increasing focus upon these as part of the debate on impact, according to our research the concept of keeping volunteers and other stakeholders updated and involved is not well addressed, yet this could clearly be an element of dissemination mandated by the funding organisations. Part of the problem may be that dissemination is not the same as impact and wider dissemination may not increase knowledge transfer; as Davies et al. argue: “evaluations of social science research should therefore look beyond dissemination to capture evidence of application by research users” (2005: 22). Definitions of dissemination include: promulgate extensively, broadcast and disperse, all implying the distribution of something. However, there is also the concept of opening a subject to widespread discussion and debate and it is this form of dissemination that needs to be actively sought if there is to be increased change consequent to knowledge transfer. It can be argued that there needs to be change in the way that knowledge transfer is developed in order to enable scientific change (Figure 2).

Figure 2.

Volunteers and scientists co-creating new understandings.

In Figure 2 there is actively encouraged dialogue between volunteers and scientists. However, the same model can be considered for any of the relevant stakeholders. The recognition of the need to facilitate and support knowledge transfer would lead to a reconceptualisation of the roles of both the scientist who is overtly in the model and the organisation which is an implicit part of both the context and the driver for change. On some case study projects the volunteers drove some interactions after the project, but this was usually as a result of intimate links between the scientists and the volunteers (resulting from, issues such as small teams, a longer, more intense period away or more maturity in the team) leading to an informal maintenance of the relationship. This more prolonged relationship might lead to enhanced knowledge transfer but there is no certainty and so, unless there is some management of this, it will not be enough to trigger or maintain the knowledge transfer as shown in Figure 2.

One of the reasons why Figure 2 is not emergent may be the conceptualisation of knowledge held by natural scientists. If there is an assumption that knowledge is a structural commodity, an absolute truth that can be tested for, then the anticipation of how it can be transferred will be very different from a conception of knowledge as a processual, constructed entity (Newell et al., 2002). Dissemination as dispersal implies the ability to transfer objective, potentially static, ideas from one party or constituency to another and has strongly commodified tendencies; it is assumed that the knowledge will not differ between the sender and the recipient and, in terms of the process transfer and movement, all that needs to occur is a move from one individual to another. However, knowledge transfer to develop debate would imply that there is a dynamic process of co-construction that would enable the ideas to be developed and shared to encourage new practice, which would lead to new knowledge creation in conjunction with the new communities implementing it. In the latter system of dissemination the act of knowing becomes as important as the knowledge itself, as the knowledge becomes situated, contextual and applied (see Figure 2).

Whilst there is much literature already in place which discusses these ideas (Brown and Duguid, 2001; Cook and Brown, 1999; Newell et al., 2002; Sorensen et al., 2006), how they relate to knowledge “stickiness” and the consequent implications for the development of scientific literacy has not been widely discussed. Davies et al. highlight that there are different ways of conceptualising research impact processes: two common ones are hierarchies and networks with networks being increasingly favoured. They state that networks reflect:

current understandings about communities of practice, which emphasise the importance of situated knowledge: knowledge is not an object that can be disconnected from the community within which it develops. Once we move towards models of knowledge co-production, the idea of research impact cannot be captured by phrases such as knowledge transfer. At the very least we need to think in terms of knowledge translation, knowledge mediation or knowledge interaction. (2005: 16)

If there is to be a change in the way that impact is assessed then this should drive the types and quantities of knowledge to be shared; but we would argue that, for real change to occur knowledge transfer must be reconceptualised as a stakeholder activity. One way to do this may be to redefine who is involved in the community of practice around certain knowledge (Brown and Duguid, 2001; Lave and Wenger, 1991). Orr (1996) suggested that a community of practice develops effectively because they hold and share knowledge as a collective and extend it through their practice. Thus, if a network of community is to develop enabling reduced stickiness and increased knowledge transfer and utilisation, then it can be held that all relevant stakeholders need to be seen as a core part of that community.

At present the scientists see themselves as a separate community of practice and thus, when they disseminate they see it as a distribution through a hierarchy or network in order to transfer it as a given whole. They do not often see it as an activity of practice sharing or knowledge creation. In the example given earlier about sea cucumbers, it can be argued that the community of practice was extended to include the local community and that this is why the knowledge transfer was effective. Partly, the developments occur when the question “Why” which is a processual construct is asked as well as “What” which is structural. In terms of the sea cucumbers, practice changed when the “Why is this a problem for us?” was asked and answered rather than merely reporting “What were the statistics” and claiming this as a problem. It had been assumed that numbers of sea cucumbers were the boundary object of translation between the scientific and local communities (Star and Griesmer, 1989), but in fact it was a recognition of the importance of an inability to breed that enabled knowledge transfer. It should be stressed that whilst the onus of knowledge transfer would appear to be with the scientists (as per our earlier analysis) there should also be a recognised role for all members of the community to develop reasons why there needs to be transfer and what the new practices might look like. However, it is inevitable that the initial conversations must be driven by the scientific community (Christidou et al., 2004) as the other potentially interested parties need to know that there is a conversation to be had.

7. Conclusion

This paper considered if and why there was knowledge stickiness present in the scientific knowledge transfer between research volunteers, scientists and the organisations that bring them together. We posit that there is stickiness as a consequence of a lack of consideration about the complexity of the knowledge development process and where it can become stuck. We have discussed knowledge stickiness between scientists and volunteers in particular, and scientists and other communities more widely, arguing that the way that knowledge is being conceptualised leads to a structural process of dissemination whereby final project outputs are transferred as a fixed object to others. We suggest that knowledge transfer is re-envisaged as emerging through practice within more inclusive communities of practice, enabling dialogue between all key stakeholders including volunteers. In metaphorical terms it moves away from being delivered as a parcel by scientists to interested parties to a more multifaceted, co-created approach. It is unlikely to be a cooperative approach as the expertise remains with the scientists, but it can be seen more as a medical operation where, unless all those involved understand the objectives, their own role and the role of others, there will be problems. Not everyone needs to be able to operate, but they all need to know why and what will happen; surgeons may be vital to the success but they are not able to work alone and need to recognise they are leading a complex set of interactive practices within which novelty may grow.

Footnotes

Notes

Author Biographies

Deborah Blackman’s interests focus upon the areas of organisational learning and knowledge management, particularly with reference to why claims made for such systems fail to deliver their promises. Recent publications and Australian Research Council Grant awards review how inadequate links between knowledge management and organisational governance reduce public sector effectiveness.

Angela M. Benson’s research has led to a number of publications in the areas of volunteer tourism, best value, sustainability and research methods. She has given several keynote addresses on volunteer tourism and is the Founding Chair of the Association for Tourism and Leisure Education (ATLAS) Volunteer Tourism Research Group and was elected a Fellow of the Royal Geographical Society in 2007.

References

Bate

Robert

(2002) Knowledge management and communities of practice in the private sector: Lessons for modernizing the National Health Service in England and Wales. Public Administration 80(4): 643–663.

Benson

(2005) Research tourism: Professional travelling versus useful discovery. In: Novelli

(ed.) Niche Tourism: Contemporary Issues, Trends and Cases. Oxford: Butterworth, Heinemann Elsevier, 133–142.

Benson

Clifton

(2004) Assessing tourism’s impacts using local communities’ attitudes towards the environment. In: Brebbia

Mugica

(eds) Sustainable Tourism. Southampton: WIT Press, 3–12.

Blackman

(2005) Knowledge creation and the learning organisation. In: Murray

Poole

Jones

(eds) Contemporary Management Issues in Management and Organisational Behaviour. Melbourne: Thomson, 246–273.

Blackman

(2006) How measuring learning may limit new knowledge creation. Journal of Knowledge Management Practice 7(3), URL (consulted September 2009): http://www.tlainc.com/articl117.htm

Blackman

Benson

(2010) The role of the psychological contract in managing research volunteer tourism. Journal of Travel and Tourism Marketing 27(3): 221–235.

Brown

Duguid

(1991) Organizational learning and communities of practice: Towards a unified view of working, learning and innovation. Organization Science 2: 40–57.

Brown

Duguid

(2001) Knowledge and organization: A social-practice perspective. Organization Science 12(2): 198–213.

Brown

Lehto

(2005) Travelling with a purpose: Understanding the motives and benefits of volunteer vacationers. Current Issues in Tourism 8(6): 479–496.

10.

Carlile

Rebentisch

(2003) Into the black box: The knowledge transformation cycle. Management Science 49(9): 1180–1195.

11.

Christidou

Dimopoulos

Kouladis

(2004) Constructing social representations of science and technology: The role of metaphors in the press and popular science magazines. Public Understanding of Science 13: 347–362.

12.

Clifton

Benson

(2006) Planning for sustainable ecotourism: The case of research ecotourism in developing country destinations. Journal of Sustainable Tourism 14(3): 238–254.

13.

Cook

Brown

(1999) Bridging epistemologies: The generative dance between organizational knowledge and organizational knowing. Organization Science 10(4): 381–400.

14.

COPUS (2009) Coalition on the public understanding of science. URL (consulted September 2009): http://www.copusproject.org/about.php

15.

Creswell

(2003) Qualitative, Quantitative, and Mixed Methods Approaches. London: Sage.

16.

Cutler

(2008) Venturous Australia. URL (consulted September 2009): http://www.innovation.gov.au/innovationreview/Pages/home.aspx

17.

Davies

Nutley

Walter

(2005) Approaches to Assessing the Non-academic Impact of Social Science Research. Report of the ESRC symposium on Assessing the Non-academic Impact of Research, 12–13 May, URL (consulted September 2009): http://www.esrc.ac.uk/ESRCInfoCentre/Images/non-academic_impact_symposium_report_tcm6-16593.pdf

18.

Delbridge

Kirkpatrick

(1994) Theory and practice of participant observation. In: Wass

Wells

(eds) Principles and Practice in Business and Management Research. Aldershot: Dartmouth, 35–62.

19.

Doyal

Harris

(1986) Empiricism, Explanation and Rationality. London: Routledge & Kegan Paul.

20.

Earthwatch (n.d.) What we do. URL (consulted November 2009): www.earthwatch.org/aboutus/whatwedo/

21.

Elwyn

Taubert

Kowalczuk

(2007) Sticky knowledge: A possible model for investigating implementation in healthcare contexts. Implementation Science 2(44), URL (consulted September 2009): http://www.implementationscience.com/content/2/1/44

22.

Gill

Johnson

(2002) Research Methods for Managers, 3rd edn. London: Sage.

23.

Guest

Bunce

Johnson

(2006) How many interviews are enough? An experiment with data saturation and variability. Field Methods 18(1): 59–82.

24.

Horn

Wilburn

(2005) The embodiment of learning. Educational Philosophy and Theory 37(5): 745–760.

25.

Jensen

Szulanski

(2004) Stickiness and the adaptation of organizational practices in cross-border knowledge transfers. Journal of International Business Studies 35(6): 508–523.

26.

Klimecki

Lassleben

(1999) What causes organizations to learn? In: 3rd International Conference of Organizational Learning, June, Lancaster University. URL: http://notes.lancs.ac.uk/pub/ol3.nsf

27.

Knox

Gruar

(2007) The application of stakeholder theory to relationship marketing strategy development in a non-profit organization. Journal of Business Ethics 75(2): 115–135.

28.

Koontz

(2005) We finished the plan, so now what? Impacts of collaborative stakeholder participation on land use policy. Policy Studies Journal 33(3): 459–481.

29.

Lave

Wenger

(1991) Situated Learning: Legitimate Peripheral Participation. New York: Cambridge University Press.

30.

Lederman

(2008) Science news 2008-05-10. In: Science for the Public. URL (consulted 7 September 2009): http://scienceforthepublic.org/

31.

Leedy

Ormrod

(2005) Practical Research: Planning and Design, 8th edn. Upper Saddle River, NY: Merrill Prentice Hall.

32.

Lyons

Wearing

(2008) Volunteer tourism as alternative tourism: Journeys beyond otherness. In: Lyons

Wearing

(eds) Journeys of Discovery in Volunteer Tourism. Wallingford: CABI, 3–11.

33.

Newell

Robertson

Scarbrough

Swan

(2002) Managing Knowledge Work. Basingstoke: Palgrave.

34.

Nonaka

Toyama

Konno

(2000) SECI, ba and leadership: A unified model of dynamic knowledge creation. Long Range Planning 33(1): 5–34.

35.

Office of Science and Technology and the Wellcome Trust (2001) Science and the public: A review of science communication and public attitudes to science in Britain. Public Understanding of Science 10: 315–330.

36.

Orr

(1996) Talking about Machines: An Ethnography of a Modern Job. Ithaca, NY: IRL Press.

37.

Plane

J-M

(2000) The ethnomethodological approach of management: A new perspective on constructivist research. Journal of Business Ethics 26(3): 233–243.

38.

Roberts

(2006) Limits to communities of practice. Journal of Management Studies 43(3): 623–639.

39.

Robson

(2002) Real World Research: A Resource for Social Scientists and Practitioner-Researchers. Oxford: Blackwell.

40.

Sorensen

Rivkin

Fleming

(2006) Complexity, networks and knowledge flow. Research Policy 35: 994–1017.

41.

Star

Griesmer

(1989) Institutional ecology, “translations” and boundary objects: Amateurs and professionals in Berkeley’s Museum of Vertebrate Zoology 1907–39. Social Studies of Science 19: 387–420.

42.

Suleski

Ibaraki

(2010) Scientists are talking, but mostly to each other: A quantitative analysis of research represented in mass media. Public Understanding of Science 19(1): 115–125.

43.

Szulanski

(1995) Unpacking stickiness: An empirical investigation of the barriers to transfer best practice inside the firm. Academy of Management Proceedings 437–441.

44.

Szulanski

(2000) The process of knowledge transfer: A diachronic analysis of stickiness. Organizational Behavior and Human Decision Processes 82(1): 9–27.

45.

Szulanski

Jensen

(2006) Presumptive adaptation and the effectiveness of knowledge transfer. Strategic Management Journal 27: 937–957.

46.

Tebes

(2005) Community science, philosophy of science and the practice of research. American Journal of Community Psychology 35(3&4): 213–230.

47.

Tsoukas

Vladimirou

(2001) What is organisational knowledge? Journal of Management Studies 38(7): 973–993.

48.

von Hippel

(1994) “Sticky information” and the locus of problem solving: Implications for innovation. Management Science 40(4): 429–439.

49.

Wandersman

(2003) Community science: Bridging the gap between science and practice with community-centred models. American Journal of Community Psychology 31(3&4): 227–242.

50.

Wearing

(2001) Volunteer Tourism: Experiences that Make a Difference. Wallingford: CABI.

51.

Wenger

(2000) Communities of practice and social learning systems. Organization 7(2): 225–246.

52.

Whyte

(1989) Advancing scientific knowledge through participatory action research. Sociological Forum 4(3): 367–385.

53.

Yin

(2003a) Application of Case Study Research, 2nd edn. Thousand Oaks, CA: Sage.

54.

Yin

(2003b) Case Study Research: Design and Methods, 3rd edn. Thousand Oaks, CA: Sage.