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What difference does a living lab make? Comparing two health technology innovation projects

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Abstract

Living laboratories are increasingly common and promising arrangements in collaborative design. Their strength lies in being real life, open ended, sustained and complex coproduction arrangements, but these characteristics also make it hard to research what difference a living lab collaboration would make – after all the project within a living lab should be quite different to one conducted without it. This paper reports on a rare opportunity to compare two unusually similar innovation projects, one of which relied on a living lab and the other that did not. Contrary to what one might have predicted, the living lab collaboration did not make the development paths very different, and the key challenges regarding design collaboration remained closely similar. Extensive redesign in pilot use, an extended learning period between developers and users, consciously built collaboration arrangements, effective boundary spanners and investment in conflict resolution were equally paramount to success in both cases. The living laboratory did make meeting these challenges quicker, and lessened the strain that redesigns caused to customer relations.
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What difference does a living lab make?
Comparing two health technology
innovation projects
Sampsa Hyysaloa & Louna Hakkarainena
a Department of Design, School of Art, Design and Architecture,
Aalto University, Helsinki, Finland
Published online: 03 Dec 2014.
To cite this article: Sampsa Hyysalo & Louna Hakkarainen (2014) What difference does a living lab
make? Comparing two health technology innovation projects, CoDesign: International Journal of
CoCreation in Design and the Arts, 10:3-4, 191-208, DOI: 10.1080/15710882.2014.983936
To link to this article: http://dx.doi.org/10.1080/15710882.2014.983936
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What difference does a living lab make? Comparing two health
technology innovation projects
Sampsa Hyysalo*and Louna Hakkarainen
Department of Design, School of Art, Design and Architecture, Aalto University, Helsinki, Finland
(Received 7 March 2014; accepted 31 October 2014)
Living laboratories are increasingly common and promising arrangements in
collaborative design. Their strength lies in being real life, open ended, sustained and
complex coproduction arrangements, but these characteristics also make it hard to
research what difference a living lab collaboration would make after all the project
within a living lab should be quite different to one conducted without it. This paper
reports on a rare opportunity to compare two unusually similar innovation projects, one
of which relied on a living lab and the other that did not. Contrary to what one might
have predicted, the living lab collaboration did not make the development paths very
different, and the key challenges regarding design collaboration remained closely
similar. Extensive redesign in pilot use, an extended learning period between
developers and users, consciously built collaboration arrangements, effective boundary
spanners and investment in conflict resolution were equally paramount to success in
both cases. The living laboratory did make meeting these challenges quicker, and
lessened the strain that redesigns caused to customer relations.
Keywords: living lab; collaborative design; case study comparison; health technology;
innovation
Introduction
The field of collaborative design has grown to feature a wide range of approaches by which
designers and users can collaborate in the creation of new technologies and services.
Further, it has become salient that in more demanding contexts any one-time measure is
unlikely to be sufficient. Most codesign approaches rely on some form of iterative
development, but many now argue that design collaboration needs to continue also after
the initial launch. The full potential of an innovation and its eventual best shape becomes
visible only after being explored in its real-life settings by both users and designers (Voss
et al. 2009; Hess and Pipek 2012; Simonsen and Hertzum 2012; Botero and Hyysalo
2013).
This is where many see promise in real-life exploratory settings such as living
laboratories. Defined as ‘a real-life test and experimentation environment where users and
producers co-create innovations’ (ENoLL [European Network of Living Labs] website
2014), living labs are seen as an opportunity to give shape to new technology in real-life
contexts and turn end users to active coproducers (Ballon, Pierson, and Delaere 2005;
Hillgren, Seravalli, and Emilson 2011; Manzini and Rizzo 2011); embed complex product
ideas and prototypes in everyday life; and to enrich the description and the evolution of
q2014 Taylor & Francis
*Corresponding author. Email: sampsa.hyysalo@aalto.fi
CoDesign, 2014
Vol. 10, Nos. 3 4, 191–208, http://dx.doi.org/10.1080/15710882.2014.983936
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behaviour, motives, attitudes and knowledge of the persons involved (Pierson and Lievens
2005). Living labs have been further endorsed as offering a governance and structure to
user involvement and user contributions, helping sense user insights, providing solutions
to the user input filtering problem, creating societal involvement and promoting user
entrepreneurship (Almirall and Wareham 2008). By now, over 340 living labs worldwide
are listed by ENoLL website (2014).
Yet, to our knowledge, there is little detailed empirical assessment of the merits of
living labs as settings for collaboration in innovation projects. As is typical to the early
years of a research area, most of the over 200 key research papers we have identified on
living labs focus on what can or potentially could be done in them and how it should
happen. The papers that seek to assess living labs or practices therein combine practitioner
reflection and conceptual comparisons with other collaborative design settings and means,
or have compared differences between various living labs (e.g. Mulder and Stappers 2009;
Almirall, Lee, and Wareham 2012; Leminen, Westerlund, and Nystro
¨m2012).
The lack of empirical assessments is likely owed to it being considerably more difficult
to undertake such assessment than it may first appear. Unlike relatively simple and short
codesign techniques, such as card sorting or collaborative walkthroughs (Bødker, Kensing,
and Simonsen 2004), the effects of living labs are hard to assess in experimental set-ups or
through comparing project experiences. This is because a living lab is an open ended,
sustained and complex coproduction arrangement that brings together technology
providers, users, researchers and other social actors such as cities. By definition, living
labs are not just test beds; they turn users to active co-creators and explorers of emerging
ideas, scenarios and innovative concepts (Manzini and Rizzo 2011; Leminen, Westerlund,
and Nystro
¨m2012). Research on innovation processes has shown how such exploratory
projects tend to be affected by tens or even hundreds of significant events and decisions
made by partisan actors as well as external stakeholders (Van de Ven et al. 1999). The
resulting project trajectories are highly particular, and it is rare that one can sensibly
compare high contingency processes with regard to the relative merits of this or that
complex arrangement (Russell and Williams 2002; Garud and Gehman 2012).
In the course of running a 15-year research programme of longitudinal studies on
designer user collaboration in innovation projects, however, we gained access to two
health care information and communication technology (ICT) projects that appear to
provide grounds for sensible comparison of the merits of a living lab. These two projects
wrist monitoring and floor monitoring system for elderly care used roughly similar basic
technology, had a technology-driven start-up history, originated in the same city, were
targeted at the same users and use contexts, had struggled similarly to succeed but are both
up and running, although without as yet making it very big. One project evolved within the
ENoLL listed Helsinki living lab, the other did not.
The wrist monitoring system refers to a device worn on the wrist, which collects data
about elderly user’s physical activity. In addition to regular nurse call feature, the system is
able to automatically call for help when the user is unconscious. The floor monitoring
system is based on a sensor network that is installed under the flooring material and it is
used primarily in elderly care institutions. The system allows the monitoring of user’s
motion and position on the floor, and it can inform nurses, e.g. when an elderly person is
fallen down, getting out of bed or spending unusually long time in the toilet. The alarms
are received through cell phones in both technologies.
The floor monitoring innovation that evolved in a living lab matches, as an emblematic
case, how living lab collaboration has been envisioned: it evolved in a living lab that is
formally listed among ENoLL living labs, and we selected it from among several projects
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therein because it exemplified the most in-depth co-creation between developers, users
and third parties in a real-life context to develop both the new technology and its
applications. Indeed, upon starting to follow this project, our hypothesis was that its
development path would be strikingly different than that of the wrist monitoring project
we had studied before. The two paths continued, however, to resemble each other, and
particularly the challenges they faced in collaborative design appeared roughly similar.
Assessing the merits of living lab for health technology innovation projects
In-depth case studies have become the state of the art for researching innovation projects,
which tend to be complex and contingent; their outcomes are a result of many events,
decisions and responses to the particularities of the then current situation. A given event in
collaboration tends to be part of the configuration of other events that together have effects
on the next steps. Some events may become negated later; for instance, the results
emerging from user collaboration may be disregarded amidst other concerns. Thus, tens of
interviews, rich document materials and observations are typically needed to form a mesh
of observational units that covers the analysis units sufficiently (Van de Ven et al. 1999;
Russell and Williams 2002;Ho
¨yssa
¨and Hyysalo 2009; Garud and Gehman 2012).
Both of our cases were studied using the biography of technologies and practices
approach (Pollock and Williams 2008; Hyysalo 2010; Johnson et al. 2014; Pollock &
Hyysalo, 2014). The approach means deploying long-term investigation into the
biography of an innovation by following the development of technology as well as the
practices of both developers and users related to it, as well as the influences of other
stakeholders insofar as they are relevant. With regard to the development project, the
changes in the material make up, visions of its future states and the business models are
charted as a changing nexus throughout its development. The organisation of the design
activities, collaborative network, knowledge base, company organisation and size are
mapped and linked to the biography of the project. Regarding user practices, the
development paths of key user-organisations are investigated both prior to and after
implementation. The evolution of use of the technology is then enquired for an extended
period of time, in both studies reported here, encompassing from earliest ideas to more
than one version of the technology being deployed. Other stakeholders’ are investigated
insofar as they play a major role, but are not given as much attention than developers and
users, which form the key parties in the coproductive arrangement.
Longitudinal follow-up research has been realised by combining different research
materials. The main data types were semi-structured interviews, documents and field
observations. In both projects discussed in this paper, interviews were utilised to
reconstruct the course of the innovation project prior to our entry as well as to make
periodical updates on events and actor perspectives. In both cases, we also had access to
rich documentary material both prior to and after our entry. Field observations were
substantial in the wrist monitoring case, but remained as supportive data in the floor
monitoring case. In both cases, the authors have been impartial outside researchers.
In the document analysis, we followed the principles of historiographic source
criticism (Tosh 1991). Open coding of content was used to sort interviews. In the wrist
monitoring study, we used ATLAS.ti, which led to 758 entries in 132 categories. In the
floor monitoring case, the interviews were coded manually (Glaser and Strauss 1967). The
source criticism of documents and the initial interview analyses were complemented by
data triangulation and across-method triangulation (Denzin 1989). Interview data, such as
informants’ accounts of the development process, and document sets, such as the series of
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business plans, were compared and cross-validated to complement one another. The case
analyses and methods are reported in detail in prior articles and book length reports: on
wrist monitoring Hyysalo 2003,2006,2010; on floor monitoring Hakkarainen 2013;
Hakkarainen and Hyysalo 2013.
The above research provides us with fair confidence on the processes of design
collaboration within both cases. Because of the same research approach and similarities in
the project contexts, it also became sensible to seek further comparison (Russell and
Williams 2002; Hyysalo 2010). This additional comparative analysis was conducted for
the study presented here. It rests on coding and comparing key events and interactions
following Van de Ven et al.’s (1999) event mapping technique of the innovation journey.
Key ideas, key outcomes, changes in people or technology, key interactions between
designers and users, and issues about markets and in the contexts of the two innovations
were mapped and then compared. Both authors read the detailed case descriptions and then
sought to identify the events to be compared. After the initial mappings, 69 key points for
comparison were found. These could be consolidated into 52 points of comparison that
were directly relevant for understanding the role of the living lab for designer user
relations and are examined in Figures 1 – 5. Data-based discussion between the authors was
then used to evaluate the degree of difference or resemblance of each event.
In the following we first recount the floor monitoring project in five project stages,
followed by the wrist monitoring case. In doing so we provide, in brackets, numbers
related to events we compare for resemblance/difference in Figures 1 5. As an example, a
marking (1, 3, 11) would point to three comparison events in Figure 1.
Case overviews: floor monitoring and wrist monitoring
Floor monitoring case
Initiation stage
The first innovation project, which we call ‘floor monitoring’, has its roots at Helsinki
School of Technology, where a motion tracking technique was discovered in the late
1990s. The suggestion to advance the techniques from intelligent environment
demonstrations (Kyma
¨la
¨inen 2015) into a gerontechnological device came from a
manager of a large public nursing home (2). Because of this impetus, a group of
researchers and students began to develop a system for detecting residents’ falls in a
nursing home environment (1, 3, 11). The students won a business idea competition with
their concept in 2005, and set up a company around it with the prize money.
The nursing home manager was disappointed in the quality of elderly care
technologies on the market and wanted to bring living lab activities to the nursing home in
order to achieve better, more reliable and more ethical care technologies. She developed
Data type/case Floor monitoring Wrist monitoring
Semi-structured Interviews 16 95
Internal documents (memos,
plans, project descriptions,
correspondence, etc.)
90 meeting memos and plans,
reports, etc. Approx. 150
documents altogether
Approx. 400 pages
Field observation Several site visits 120 site visits between
1999 and 2007
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the nursing home into a living lab with the help of a municipal innovation fund and
partnered with technology companies, one of which was the floor monitoring start-up. The
nursing home living lab was established in 2006 as part of Helsinki Living Lab, an early
and active living lab in the ENoLL. Within the nursing home living lab, four innovation
projects were started, all of which aimed at developing new care arrangements and
improving treatments along with technical testing and further technological development.
These projects were a telecare remote rehabilitation service, a novel music service for
elders, a safety monitoring carpet and the safety floor project examined in this paper. Out
of the four projects, the safety floor project saw the most extensive collaboration between
developers and users as well as the greatest technological development and expansion in
the business case during the living lab. The safety floor thus represents a project done
within the ENoLL living lab context formally, and within it it forms an emblematic case
sample (Gobo 2004; Flick 2008).
The first version of the ‘safety floor’ was developed prior to the living lab based on the
designers’ implicit assumptions about the users and the context of use; the system would
inform the nurses when a resident had fallen down, so that they could come to pick them
up (4). The developers drew on their previous experience from surveillance technologies,
and no formal market or user studies were carried out (5). The early effort was targeted at
technical development and the system seemed to work well in the university laboratory
where it had been thoroughly tested (6, 7, 8). The product had already been sold to a couple
of institutions. The living lab development started in 2006 and the municipal innovation
fund allowed the care actors to hire project workers to support the implementation in the
user site and to organise collaboration (9). Whilst the start-up company mostly had only
technical expertise, the health care side had expertise in the development and assessment
of care practices.
Realities faced in early use: leading to redesign
The safety floor experienced real-life problems soon after the living lab collaboration
started in 2006. In the university laboratory test, subjects had been lying on the floor,
whereas in reality the elderly persons rarely ended up in that position as they grabbed the
back of a chair or bedside rail when falling down (15). Also, the nurses behaved in
unexpected ways; they, for example, placed dirty laundry piles on the floor, which the
system identified as a person (21). In turn, the technology meant a new kind of monitoring
of nurses work, for instance, placing laundry on the floor was against the nursing home’s
hygiene regulations (39). In general, the residents were in weaker shape and the care work
was more laborious than the engineers had expected (16).
In creating the first version of the system, the developers had invested large amounts of
time in creating unneeded technical features based on their assumptions about the nurses’
work (13). One example of this was a floor plan function in the user interface. Based on
their previous experience with surveillance technologies, the engineers assumed that it
would be useful to monitor movements of the residents from the computer screen (13).
In reality, the nurses neither had the time to sit in the office nor were they interested in the
movements of the residents. If the nurses wanted to find out what was going on in the
rooms, they would pay a visit. In spite of this, the users were active in coming up with
unexpected ways to use the technology. When a resident fell down in her room, the nurses
would use the data recorded by the floor plan function to analyse events that had led to the
accident so as to prevent future falls from happening (14). In general nurses did not need a
fall detector, but rather something else (14).
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When the floor monitoring system was put to use in three units, it was possible to assess
how the system affected daily care practices, which had to be redesigned together with the
system. In addition, the system had to be integrated to the units’ existing equipment (17, 18)
and the nursing home building, e.g. fire safety regulations had to be taken into consideration
in the installation phase. Moreover, IT officials from the municipal social and health care
office had demands for the components, especially with regard to information security.
In all, the installation and repair turned out to be more demanding and costly than anticipated
in, for instance, the internationalisation plans (19, 20,22).
Early developer user collaboration
The original goal of the project was to discover sensible ways to utilise the system in the
nursing home. Several project workers were hired by the user site to organise the collaboration
and implementation of the safety floor. The project plan was loose and the project workers
could, to a large extent, work as they saw best (24). Pilot costs were thus shared between the
public and private partners; the project workers in charge of the pilot were hired by the nursing
home, and the technical development was financed by the company (26).
From the user perspective, the initial system was at best a prototype, whereas the
company saw their product as more or less ready and was in a hurry to get to the market (8).
Developing the system further and quickly getting it to work reliably became the new
objective of the project (10, 23, 12, 27), albeit tension remained between the nursing home’s
wish to have a tailored system and the company’s wish to have a generic and profitable
product (25). The care professionals were also displeased with the initial interface (28).
False alarms, technical bugs and integration problems began to frustrate the nurses and
project workers, who went as far as to exclaim that the safety floor was a raw prototype,
not a product (29). The developers were perceived as arrogant with respect to the
problems, which made the collaboration even more complicated (30). Eventually, the
project workers’ wishes turned into demands, and the user side refused to continue with
the implementation until their requirements were met (31). The situation finally became so
inflamed that the project coordinator, one project worker and the CEO of the company quit
over a period of six months (32).
Maturing of collaboration and concept
Version 2.0 of the user interface was launched a year after the beginning of the original
implementation (6). The care professionals were pleased, since their ideas and concerns
had now been taken into consideration (33). A new kind of project coordinator was looked
for: a negotiator rather than an advocate, someone capable of mediating between the
participants, albeit one having a nursing background rather than being a neutral outside
facilitator (34, 35). After the staff changes, the functioning form of collaboration started to
develop and the new project coordinator started to actively observe problems and to seek
new development ideas (36).
The reason for project workers becoming responsible in ideation and problem spotting by
observing use lie in many nurses’ reluctance to do so. Some went as far as boycotting the system
by ‘accidentally’ forgetting to carry the phone with them during the work shifts. The nursing
home management decided to make the use of the system and participation in the feedback
meetings obligatory; not using the system was declared to be a mistreatment (37, 38).
When use became more widespread, the company got to more profoundly understand
the system’s impact on work processes and its key benefits. The night shift seemed to be
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the biggest beneficiary: the nurse on-call did not have to go around checking the residents
all night as had previously been the case, because she was informed if someone got up
during the night (40). The sleeping elderly were not disturbed by the checks, bedside rails
were not needed and they no longer needed to communicate to the nurses that they wanted
to get up. Floor monitoring allowed the nurses to help the residents when they put their feet
on the ground and an alarm was sent. A new kind of care and work practices started to take
shape, and it was because of the care professionals that the system had evolved from a fall
detection system to a fall prevention system, which allowed more flexible ‘just-in-time’
care rather than rigid routines and support for the night shift, and its reliability had been
worked on by both parties (41, 42, 43).
Extending from pilots
During the project the start-up company merged with an established electronics company
(47), and after the project the merged company started to gain new customers (44). The
firm hired the project coordinator; her new job was to train new users and act as a link
between company and customers.
With new customers, new contextual challenges arose, which required some redesign.
There were minor differences in the work practices of different institutions. Due to heavy
installation costs, sales were limited to new rest homes (45), albeit newer buildings created
new kinds of technical problems, e.g. the concrete had more humidity in the newer
buildings, which initially messed up the algorithms of the system (49, 50).
The company adopted a tailoring strategy, and the system was fitted to each customer
institutions’ needs, which meant, e.g. integration to existing equipment (48). After a while,
this was found to be unviable and a more generic product was needed (22, 51). Hence, the
company sought to repackage its offering as a more standard product with servicing (46)
and developed an installation floor version (52). By 2014 the floor had been introduced in
over 2000 apartments and it was a stable product in the market.
Wrist monitoring case
Initiation stage
The second technology, we here call ‘wrist monitoring’, was equally a gerontechnology
project that departed from new technical possibilities in monitoring elderly users. This
concept took shape during the years 1992 1994 and a start-up company made up of
engineers was founded to develop it. The idea arose from its inventor’s experience with the
engineering and marketing of safety phones and alarm-systems (1). The technology was
designed to monitor users’ physiological state from their wrist movement, temperature and
electroconductivity sensing, and thereby to generate an automatic alarm in case of medical
emergency. It included a manual alarm-button and a receiver unit. Alarms were relayed to
a predetermined end, for example, to a nurse on call, an alarm centre or to relatives. This
person then made the decision on the appropriate action, for instance, calling the user, her
neighbours, maintenance or an ambulance.
After the project initiation, there were internal and external studies that assisted in
defining the concept: technical feasibility and monitoring were studied within the
technical research centre, European markets were investigated in two small marketing
researches and the concept was ‘test-marketed’ in interviews with the inventor’s elderly
relatives. All of these indicated demand for this kind of technology (2, 3, 5). During the
years 1995 1997, the prime concern for product development was finding the right
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sensors, ways of measurement and adequate algorithms for quick detection of illness
attacks and for proactive measures. Further insight about users was generated in a design
and usability study that was conducted during 1995 – 1996. This had hardly any immediate
effects, even though it warned against some of the core assumptions made about the use of
the device (4). The designers had already proceeded far with the design, and believed in it,
albeit a technical setback made them lose a year (6, 7). The product was launched and first
pilots started in early 1998 (6). The product developers regarded the device to be a success
in technical terms and an achievement in terms of getting to market launch with just the
personal assets of the company founder (8, 9, 10).
Realities faced in early use: leading to redesign
The first pilot uses revealed an unexpected number of false alarms that had to be worked
on, along with other technical bugs. To work flawlessly, the device required specific
procedures in wearing, removing and storing the device; cancelling false alarms, cleaning,
et cetera. These instructions grew from 7 to 25 pages during the two first years of use. Even
though some users were happy with the device, many had problems (11, 12, 20, 21).
In institutions, much of the reliability of the device was on the shoulders of nurses. The
system required them to be readily awaiting for and reacting to the information provided
by the system; yet this was a poor fit with their work practices and existing
instrumentations, whilst their care rounds also gave them a fair understanding of the
elderly residents’ condition (13, 17, 18).
Between 1998 and 1999, the company made numerous adjustments and new
developments, ranging from adjusting the algorithms and reducing features, to user-training
(14). The product was expanded to include diagnostic software for alarms, which was soon
complemented with online graphical monitoring, following a suggestion from the users (13).
Use of wrist monitoring in rest homes was augmented by developing an integrated system
with a number of receiver units and wrist devices, in part due to difficulties in integrating the
wrist monitoring devices to extant infrastructure and device stock both in rest homes and in
alarm centres that received home sector emergency calls (17, 18, 19). During this period,
experiencefrom usage led to a questioning of manyof the previousassumptions,such as who
the users and clients were, how they worked the technology, how the technology fit the
infrastructure and how the condition of the elderly could be monitored, given they were in
fluctuating health and more frail than was assumed in the initial algorithms and
instrumentation (15, 16, 18, 19). In the midst of struggling to fix and improve the technology,
the company sold about 1000 devices and won both domestic and international innovation
awards, received positive press coverage and attracted new investments.
Early developer user collaboration
The pilots were set so as to only verify the technical feasibility and benefits derived from a
technology along with fixing small remaining bugs. The developers and elderly care actors
both expected a readily functioning technology (23). There were few preparations in place
for handling the piloting phase such as what to do with continued technical problems (24).
After the first pilot study, the sites were now paying-customers and both parties resented
allotting time and money to techical bug fixing. The developers wanted to concentrate on
marketing, internationalisation of business and development of the next product version,
even though they became forced to create different versions and additions to the product in
order to close deals with institutions (25). Elderly care actors ended up spending time on
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complaints and working around the early system that had frustrating interfaces and
generated false alarms, albeit they did not formally provide resourcing or funding for the
pilots (26, 28, 29). The company’s first approach to the situation was to seek to train the
elderly care staff to operate the technology better, but gradually they realised they needed to
start fitting the technology to nursing work and increase its reliability to soothe the rising
pressure from pilot sites. There were also a growing number of redesign wishes (30, 31) and
the position of the mediating personnel between R&D and customers was difficult to bear:
over a period of five years, five people quit this position regardless of how the position was
defined (e.g. as product manager, marketing manager and customer manager (32)).
Maturing of collaboration and concept
During the years 1999 –2002 the company built, tested and iterated the second version of the
product (33). Attention was paid to the appeal and usability of both the wrist device and the
monitoring software. Partnerships were developed with several user-organisations and they
began to be used explicitly for testing and gaining ideas for improving the design (36).
Strategies were changed with regard to how the technology was presented in marketing,
user-training and in dealing with the medical community. Reliability was emphasised along
the user-identified key value points in diagnosing and monitoring of elderly patients and
restructuring care work particularly in the night shift. A key value point emerged from users
finding care-work use for the ‘activity curve’ illustration, which the designers had originally
created as a gimmick for a fair to visualise what their device monitored (39, 40, 41, 42, 43).
User-organisations, in turn, began to charge rent for the device irrespective of whether
it was in use (37, 38). Inside the company, all installations, user-training and feedback to
R&D were placed under a single person who had extensive experience with safety phone
systems in elderly care (34, 35). The change in strategy in relating to users enabled the
company to improve all aspects of the product system, particularly its control-software,
which was a key feature for users to recover from false alarms, and overcome difficulties
in fitting work practices into different rest homes and alarm centres (22).
Extending from pilots
The 2.0 version increased company sales to several thousands of units (44). In 2003, the
nature of the user partnerships changed, as the company sought to build locally configurable
but generic product packages to improve economic viability. As part of this, slowness and
complex steps in public sector purchacing cycles became evident, along with difficulties of
selling equipment beyond new nursing homes under construction (45, 46, 48, 49, 50). The
company had to seek repeated rounds of further funding (47). The company still sought and
received information from the key user sites, but ceased to alter the existing design, and
channeled the improvements into the next release 3.0 (/2.0), which they launched in 2007
(50, 51, 52). At this point, there was was a stable and profitable product in the market.
Comparing the key project items in developer user interaction
The comparative mapping of key events clarifies the resemblances between the two projects
(Figures 1 5). As abbreviations we use OUT for outcome, TECH for technology, INT for
interaction, CTXT for contextual event, MKT for market, PPL for people, ID for ideas.
Of the 11 comparison items in the initiation stage (Figure 1), three bear a strong
resemblance and four a moderate resemblance, mostly resulting from the engineering
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starting point of both projects. Two of the three differences result from the fact that floor
monitoring gained the idea of viability from elderly care actors, whilst wrist monitoring
relied on the developers’ own assessment, verified by market studies. With floor
monitoring the user side also ended funding of the initial development, which gave them
more say over the project in the ensuing stages.
Figure 1. Resemblances and differences in the key events in the initiation stage.
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Figure 2. Resemblances and differences in the key events in the redesign stage.
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Strong similarities become evident when the projects move from technical development
to first deployment at the user site (Figure 2). In both cases, this led to major redesigns and
many early assumptions about use and system features becoming questioned. In 10 data
items, the only difference was that the floor monitoring project evolved within the living lab,
whilst for wrist monitoring the pilot sites were also paying-customers. The extent of
continued development in use was an equal surprise in both projects.
The earliest designer user collaboration happened in pilots in both cases. Here the
differences induced by the living lab are visible through the collaboration arrangement and
plan as well as in sharing costs (Figure 3). The strain caused by the redesigns and
Figure 3. Resemblances and differences in the key events related to early collaboration.
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reorientation of the project also affected the users in the living lab setting as user side
mediating personnel quit, and not only staff within the company. However, in light of
claims made in living lab literature, one would expect greater differences between the two
projects already here.
The maturing of collaboration is where one would, at the latest, expect a decisive
difference between the projects (Figure 4), but out of 11 events six bear close resemblance,
Figure 4. Resemblances and differences in the key events in the stage of matured collaboration.
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Figure 5. Resemblances and differences in the key events after the living lab / piloting phase of the
project.
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two a moderateresemblance and the only differing ones concern theissue of use and feedback
becoming mandatory within the living lab. The explanation for resemblances appears to be
that the wrist monitoring project had to, in effect, establish the similar kind of real-world
partnering arrangements that the living lab development helped to build for floor monitoring.
It is worth noting that in both cases it was users that ideated the new key value points for the
project and that both projectsended up with somewhat similar benefits more proactivecare
given by the nursing staff (not through automation as it was originally envisioned in both).
The road from the pilot stages bears close resemblance in both projects (Figure 5).
In both, it became evident that the amount of customisation and partnering was unfeasible
as a long-term business strategy, and they suffered funding shortages. In both cases, the
company opted for a mix of occasional collaborations and more arm’s length user
relations. Also, in both, the company sought to have a generic product with a ready set of
accumulated functionality that it then could just configure to each setting, and in both these
configurationality needs were higher than expected after the pilot years.
When comparing the overall trajectories of these two projects, 63% of events have
close resemblance, 19% feature some resemblance, 12% have moderate difference and 6%
strong difference. Two things stand out as particularly salient as explanations for these
high resemblances. First, the challenges in making monitoring technology work reliably in
care homes were equally formidable as was the need to reiterate the working principles
and value points of the technology. Second, the direction of events appears not to have
been ‘the living lab is not that different’ but rather that the wrist monitoring, in fact, had to
revert to establishing similar collaboration affording arrangements; in other words,
collaboration that resembled the living lab was required to succeed.
Conclusions
Living lab advocates and research literature alike stress how these real-life environments
for design collaboration offer a unique environment for exploratory collaboration between
developers, users and third parties, seen as vital for improving the success of innovation
(Niitamo et al. 2006; Almirall and Wareham 2008). Our study of two technology-driven
health projects underscores that such collaboration, indeed, is vital for the success of these
kinds of innovation projects. In both projects, it was hard for developers to grasp the health
care context and to reiterate the concept and its material realisation sufficiently. Interaction
and learning between developers and users was paramount for changes and for achieving a
well-received product in the market. In neither project did collaboration emerge without
high levels of frustration and conflicts of interests, purposeful efforts to build the
collaboration arrangements and intermediary actors to champion it. These are all facets
that research and practice on participatory design has stressed for a long time (e.g. Schuler
and Namioka 1993; Bødker, Kensing, and Simonsen 2004). The literature would add that
for both projects, more intensive collaboration at the very outset might have been
beneficial.
The extended living lab collaboration appears to have speeded up the redesign process
that both projects had to suffer. The living lab also spread some of the ensuing costs to
users and mitigated the strain on early customer relations in the company. The eventual
difference appears, however, to be of degree rather than kind in the shape of the innovation
trajectory. As noted above, this is explained not so much by the failure of the living lab
development, but by the necessity of the wrist monitoring case to move to a similar kind of
collaboration arrangement in the course of the project. This interpretation finds support
from the other similarly detailed case studies of Finnish health ICTs (diabetes software,
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brain imaging technology, e-grocery service for elderly, information infrastructure for
elderly): both developer and user visions of the eventual working technology have been
questioned, and only those projects where the visions and material form of the technology
have been altered collaboratively have survived (Hasu 2001; Hyysalo and Lehenkari 2003;
Hyppo
¨nen 2007; Hyysalo 2010; Botero and Hyysalo 2013). The case comparison can thus
be taken to question the uniqueness of the effects of the living lab as a collaborative
setting, but highlights the importance of this kind of collaborative setting and co-creation
between developers and users.
To cap our analysis, through this comparison we argue that extensive collaboration
between designers and users is paramount for the success of complex new health technology
projects, but this can be achieved without a formal living lab arrangement, albeit such
arrangement does appear to help in achieving it. The metaphor of the ‘quadruple helix’ is
often used in living lab discussions, and conveys an image of a (genetic) formula for
effortless and joyful multiparty collaboration. When the collaboration is examined in depth,
as in the case here, the nature of collaboration is not effortless or automatic. A living lab, as
such, appears to be no panacea for collaborative design efforts between designers and users.
Rather, the question is whether the parties engaged in living lab collaboration are willing to
go through all the work needed to create the specific and particular relationships by which
the relevant information can be made visible and transferred to the other party. A living lab
arrangement appears to offer a legitimate rationale for trying such engagement and the
resources it requires. Perhaps creating a living lab may be best seen as shorthand for the
collaboration processes, in which the partners in innovation processes have to partake in
real-life settings in order to aide project success.
In terms of further research, the present study exemplifies the state-of-the-art
innovation process research comparison on projects conducted in living labs. Living labs
are open ended, sustained and complex coproduction arrangements, which typically affect
even more complex, multi-causally formed and long-term innovation journeys.
As Van de Ven et al. (1999), Garud and Gehman (2012) and Russell and Williams
(2002) have shown, these characteristics limit the valid types of comparative research.
Operating on variance epistemology and ontology is ill-suited for such complex process
research and comparison. Less process-oriented and coarser-level comparisons can,
however, be used to contextualise and generalise the findings from the present study (Gobo
2004). Our findings are most generalisable to innovative health care technologies, to
projects in publicly hosted living labs (Leminen, Westerlund and Nystro
¨m2012), to
projects where co-creation is extensive (and not just testing) and to engineer-driven start-
up technology companies. The further the distance from these primary contextual
characteristics of these currently investigated projects, the lesser the likelihood that the
patterns observed here would be found or play out similarly (Gobo 2004).
The findings indicate four recommendations for practitioners. First, at least in health
technology innovation projects it is imperative to invest in creating a real-life
collaboration setting with or without formal living lab. Second, even if living lab setting is
used, targeted action needs to be taken to build up the collaboration and reconciling
different interests of participants. Third, it is advisable to retain relatively open agreement
on what the collaborative relationship may hold, but inform all parties realistically of the
uncertainties and development needs both in technology and in user practices. Fourth, it is
advisable to prepare for changes in collaboration as the innovation process evolves; the
need for collaboration between developers and user will not disappear with ending of
living lab collaboration, but the topics and forms will change when the product becomes
sold to wide clientele.
S. Hyysalo and L. Hakkarainen206
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Funding
The authors have received funding from Academy of Finland project User Innovation Pathways to
Utility [grant number 138187].
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... A recent study by Alavi et al. (2020) Although living labs have received a great deal of attention in HCI literature and as a method, the research is argued to be characterized by recommendations rather than examinations (Kanstrup, 2017). Hyysalo & Hakkarainen (2014) argue that there is little detailed empirical research of the merits of living labs with a focus on how things should happen. Rather, the focus is more toward what can or potentially could happen. ...
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Thesis
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