Management Buy-in and User Acceptance in an ELN Project

Contributor: John Trigg
Posted: 09/12/2011  12:00:00 AM EDT

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Management buy-in and user acceptance are two factors regularly identified as critical to the successful outcome of a project.  It’s been encouraging to observe, over recent years, that most case studies presented on the deployment of Electronic Laboratory Notebooks (ELNs) that these are two factors that are given more and more consideration in project planning.  It goes without saying that management buy-in is essential for an ELN project since the decision to migrate from paper to electronic has far reaching implications beyond the laboratory. Legal, regulatory and IP concerns, not to mention the long-term preservation of electronic records, are all considerations that require management commitment to initiate an ELN project. 

But the management commitment is also required throughout the implementation phases. Getting the balance right between hands-off and hands-on is critical; too much hands-off, and the project team and users get concerned whether management cares; too much hands-on and it can feel like interference and a lack of trust. The ‘management’ role is more a case of leadership; being visible when needed, reinforcing the project team, communicating the right messages and generally providing authoritative support to the project, which in turn will prove to be beneficial to user acceptance. The key to this is the recognition that people are more likely to comply with a request when:

• A reason is provided
• There is give and take
• They see others complying
• The request comes from someone they respect or like
• The request comes from a legitimate source of authority

The strategy adopted for addressing user acceptance of ELNs has almost become a de facto standard, and ‘crossing the chasm’ is the mantra preached by ELN project managers.  The term derives from Geoffrey Moore’s book ‘Crossing the Chasm’ (1), which outlines a strategic approach to technology marketing that recognises a normal distribution of attitudes towards technology, originally reported by Everett Rogers in his book ‘The Diffusion of Innovations’ (2), but identifies a ‘chasm’ between the early adopters and the mainstream market.

Figure 1: Crossing the Chasm
 

The early adopters are a relatively easy market. Targeting them initially is important, but the next phase of the marketing strategy must address the conservative and pragmatic majority.  The early adopters can play a central role in this.  Since the electronic laboratory notebook deployment team is likely to be formed from the early adopters, they can play a pivotal role not only in specifying and selecting a solution, but in articulating the rationale for the project, and providing training and first level support to the conservative and pragmatic majority. Many organisations have reported good user acceptance when they have adopted this strategy. However, if we look a bit deeper at the factors that influence our attitude towards technology, are there other lessons to be learned?

The Technology Acceptance Model (3) is an information systems theory that illustrates how users come to accept and use a technology.

Figure 2: Technology Adoption Model

The model suggests that when users are presented with a new software system, a number of factors influence their decision about how and when they will use it. The main ones are: 

• Perceived usefulness (PU) - "the degree to which a person believes that using a particular system would enhance his or her job performance".
• Perceived ease-of-use (EOU) "the degree to which a person believes that using a particular system would be free from effort”.

The technology acceptance model assumes that when someone forms an intention to act, that they will be free to act without limitation. In the real world there will be many constraints (external variables), such as limited ability, time constraints, previous experience, influence of peers, environmental or organisational limits, or unconscious habits which will limit the freedom to act.  Concentration on the positive aspects of ‘usefulness’, both to the organisation and to the individual, and ‘ease of use’ will help users develop a positive attitude.  It is in this area that the early adopters can have a powerful influence of their conservative and pragmatic peers by articulating the rationale for the project and its potential benefits.  It also throws the challenge to the system designers to ensure that the user interface really does meet ease-of-use criteria. But it’s the ‘external variables’ that we need to be wary of, and there’s no simple answer to breaking down these barriers as they may be quite complex and irrational, and this is where the management role can be significant in articulating the benefits not only for the organisation, but also for the user.

The question of ‘perceived usefulness’ raises some interesting points. It’s a common criticism of OTS software that ‘it doesn’t fully meet our needs’. The problem here can be often attributed to replacing a number of individual, carefully honed processes for managing laboratory data with a new, commercial one-size-fits-all system. This, of course, makes financial sense, but can have a negative effect on user acceptance, and although users may well appreciate the business case, the end result may be a sullen and unenthusiastic acceptance of the new solution. But does the one-size-fits-all strategy really deliver results?

David Snowden, Founder of the Cynefin Centre, wrote an article entitled ‘Multi-ontology sense making – a new simplicity in decision making’ (4).  In it, he raises interesting questions about business processes and the extent to which they are fit for purpose in different domains.  The model is generic, but can readily be applied to the laboratory where it provides a framework for understanding a number of aspects the laboratory landscape.

Figure 3: The landscape of management

For example, the lower left-hand box (process engineering) relates to laboratory functions that fit an ordered and rule-based environment, typically a routine, QA, highly automated laboratory dominated by a systematic workflow.  On the other hand, the top right-hand box (social complexity) relates to a classical research/innovation function based on complexity, chaos, creativity and innovation.  The other two boxes represent hybrid environments, one in which emerging rules can be applied to complex or chaotic environments (biology?) and one in which an order, well-planned approach may require innovative thinking to develop understanding (chemistry?).

An underlying concern about process engineering is the extent to which it can de-humanise laboratory activities and reduce the demand for intellectual input, or indeed, any fundamental knowledge about the science and technology processes that is in use. Nevertheless, there’s little argument against the productivity and efficiency benefits of automation and process improvements (Lean, Six-Sigma) in routine and QA laboratories. It remains to be seen whether there are other long-term consequences of increased automation in making some laboratory work less attractive.  However, Snowden’s argument is that the success of process improvement techniques in one domain raises the temptation to apply them in other domains to which they may not be suited. So, for example, in the ‘social complexity’ domain, where freethinking and creativity are important qualities, rigid and systematic processes may well prove to be a constraint. This seems an obvious observation, but there are cases where the temptation has proved to be too great, with disappointing consequences!  The other two domains, ‘mathematical complexity’ and ‘systems dynamics’ both have an intellectual component, but contain sub-processes that may lend themselves to engineered interventions.  The overall conclusion is that different management styles, processes and systems are required for each of the four domains, and that it doesn’t follow that what works in one domain will necessarily work in another.

In a convoluted way, this can explain why the early ELN market was sub-divided into different solutions for Chemistry, Biology and QA, where the tools were specifically designed to address user-centric functionality. The risk for a multi-disciplinary laboratory looking to implement an ELN would be to adopt a one-size-fits-all approach and force-fit a chemistry system into a biology lab, for example, generatinga certain amount of disaffection amongst users. However, the current ELN market is migrating towards more modular solutions, which have a generic core, and optional discipline-specific modules. This creates a better opportunity to find a single-source solution. The shared functions such as document authoring, approval/witnessing, file and document management, and legal and regulatory compliance, all of which fall into the ‘bureaucratic’ category and which more readily lend themselves to process improvement opportunities, are separated from the scientific functions which are closer to the heart and soul of the scientist’s laboratory work.  It is still a one-size-fits-all approach, but designed to accommodate the requirements of multi-disciplinary laboratories, and to standardise and improve common sub-processes, rather than making compromises.

Ideally, using an electronic lab notebook should not be perceived as an intrusive bureaucratic process, but something that facilitates the scientific method and doesn’t intrude on the social and intellectual processes that are essential to the science. Achieving this objective is essential to joined-up science and to user acceptance, and is a responsibility that falls within the remit of management throughout the lifecycle of the project. It requires a sympathetic view of the requirements of the different disciplines and the way in which these functions are managed and provided for, even when organisational demands strive for increased uniformity and consistency.

References

1. Crossing The Chasm,  G.A.Moore, Capstone Publishing
2. Diffusion of Innovations, Everett M. Rogers, The Free Press. New York
3. Bagozzi, R. P., Davis, F. D., & Warshaw, P. R. (1992). Development and test of a theory of technological learning and usage. Human Relations, 45(7), 660-686.
4. Multi-Ontology Sense Making,  David Snowden, www.cynefin.net

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