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1. Management of emerging
technologies for economic and
social impact: an introduction
Dimitris G. Assimakopoulos, Ilan Oshri and
Krsto Pandza
PREAMBLE
From February 2010 to January 2014, the European Union (EU)-
funded Management of Emerging Technologies for Economic Impact
(ManETEI)– Marie Curie Initial Training Network, brought together a
diverse group of leading European business schools, research institutes
and industrial partners to investigate the challenges associated with the
management of emerging technologies for economic and social impact.
Individual research projects progressed broadly in two directions. The
first group of researchers embraced the notion that technological and
organizational innovation unfolds in complex settings where a myriad of
actors (companies, governments, universities and wider public) collabo-
rate and interact in order to translate technological advances into solutions
with impact of economic progress and social well- being. They followed the
already recognized notion that innovation frequently depends on interplay
and networking of many organizations and communities across different
industries. Such ecosystems of interdependent organizations and scientific
and technological communities are characterized by the wide dispersion of
adequate knowledge that needs to be integrated and coordinated through
collaboration and networking. This group could be further divided into
researchers who investigated strategic challenges for policy makers in
shaping technology development and those who centred their research
on exploring collaborative and networking practices in technological and
regional innovation networks.
The second group zoomed in and investigated how individual companies
organize their innovation processes and identified what constitutes organi-
zational capabilities that help organizations to capture value from emer-
gent technologies. This group consists of researchers who investigated the
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2 Managing emerging technologies for socio- economic impact
foundations of organizational capabilities for technological innovation
and those who explored how emerging technologies (mostly information
and communication technology- enabled) help companies to strengthen
their innovation capabilities.
The book is organized in four parts. Each part includes four chapters.
Part I explores foundations of organizational capabilities for technologi-
cal innovation. Part II explores collaboration and networking for shaping
the emergence and progression of technologies. This is followed by Part
III, addressing strategic challenges for policy makers that influence the
sustainable and responsible development of technology. Part IV is less
concerned with the technology dynamics; it investigates how emerging
technology supports organizational capabilities and how novel technolo-
gies affect work and communication practices.
In the rest of this introductory chapter we discuss, in four sections: the
changing conceptions of managing innovation and emerging technolo-
gies; the role of collaboration networks and communities in the process of
emergence of emerging technologies; and then in the last two sections we
discuss how emerging technologies relate to broader discussions on open
innovation and changing work practices in organizations, with a particu-
lar reference to ICT innovations.
THE CHANGING NATURE OF MANAGING
INNOVATION
Innovation is a word on everybody’s lips. However, it often means differ-
ent things to different people. Policy makers responsible for channelling
governmental funds into research have recently rebranded science and
technology policy into innovation policy. This should remind academic
scientists and technologists that their inventions require transformations
into something with value and impact. The wider public mostly experi-
ence innovation through improvements in products, new gadgets, and by
consuming news about technology (mostly internet) entrepreneurs and
their journeys from a garage to riches. For scientists and engineers, inno-
vation is essentially a scientific discovery or engineering problem- solving.
For managers in companies, innovation stands for new products and
services that support the competitive advantage of their companies. They
represent a group most likely sensitive to the fact that innovation requires
strategic intent, appropriate structures, coordination and incentives, yet
they still mostly believe that innovation is about a link between investment
into research and development (R&D) and the number of new products
and services introduced to market. For economists, innovation is about
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An introduction 3
studying the relations between measurable inputs into a national innova-
tion system and quantitative outputs captured in conventional national
accounting. This suggests that different constituencies are united in seeing
innovation as a set of inputs and outputs.
Innovation, however, is much better portrayed as a process: a journey
(Van de Ven et al., 2008) characterized by temporal and relational com-
plexity as well as extreme uncertainty. It encompasses the emergence of
novelty and patterns among activities and events that enable the transfor-
mation of novel ideas into new products, services, organizational forms or
industry that create value for customers and, more broadly, for society.
This process resists stabilization and continuously develops in directions
that challenge established theories and the very practice of innovators. In
order to make sense of complex innovation processes Garud et al. (2013)
divide the process into phases and levels. Innovation progresses from
invention through development towards implementation and could be
analysed on a level of a firm, multi- organizational networks and wider
communities.
This multifaceted and multilayered nature of innovation is even more
profound if the process that connects the emergence of technology and
grand societal challenges is concerned. The twenty- first century poses
major challenges to protect and enhance the quality of life, economic
wealth and social stability (Pandza and Ellwood, 2013). It is deemed that
advances in emergent technology fields offer opportunities to address
the challenges in areas such as health, energy, environment, agriculture,
transport, security and education, and that these in turn create business
opportunities. Two influential reports were published at the beginning of
the twenty- first century to guide policy makers and research funders when
making research policy decisions.
A report sponsored by the USA National Science Foundation and
edited by Roco and Bainbridge (2002) introduces nanotechnology, bio-
technology, information technology and cognitive technologies (NBIC)
as four general- purpose technologies that, through convergence, enable
improvement in human potential. The Nordmann (2004) report that was
supported by the European Commission (EC) focuses on nanotechnol-
ogy, biotechnology and information communication technology (ICT).
Nanotechnology is the understanding and control of matter at dimen-
sions of roughly 1–100 nm, where unique phenomena potentially enable
a huge variety of novel applications. Biotechnology explores and exploits
chemical and physical processes and structures in living systems that
are traced to their material basis in cellular and genetic organization;
it has applications in health, medicine, agriculture, food and energy.
ICT is the study, design, implementation, support or management of
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4 Managing emerging technologies for socio- economic impact
computer- based information systems, particularly software applications
and computer hardware.
Schmidt (2007) highlights some major differences between both
reports, especially in respect to understanding interdisciplinarity. The
NBIC report argues for a holistic view of technology based on trans-
formative tools, the mathematics of complex systems, and unified
cause- and- effect understanding. By contrast, the European approach
to emergent technologies assumes that nanotechnology, biotechnology
and ICT are not the only enabling technologies1 capable of support-
ing each other (Nordmann, 2004: 39). It recognizes and supports the
contribution of the social sciences to emergent technologies. The report
explicitly suggests integrating social science research into emergent tech-
nologies, which could then be promoted alongside science and technol-
ogy research. By recognizing the social and dynamic nature of emergent
technology- driven innovation, the report implicitly calls for management
and business studies research that uncovers insights into the socially
complex processes that lie between investment into emerging technolo-
gies and social impact.
The business and management studies literature has already contrib-
uted significantly to the understanding of technological and organiza-
tional innovation and its impact on firms’ competitive positions. There is a
general understanding that changing technological paradigms create envi-
ronments of uncertainty and instability. It is also argued that the higher
the complexity of innovation, the bigger the influence of non- technical
factors on the adoption of technology (Anderson and Tushman, 1990).
In the context of emerging technologies, this implies that non- technical,
socio- economic factors warrant special consideration from the policy,
practice and social science points of view. The knowledge about emerging
technologies is also widely dispersed across disciplinary, sectoral, insti-
tutional and national borders. Knowledge about technology is therefore
integrated, diffused and utilized in a complex technology innovation
system (Carlsson et al., 2002), which can include individuals, firms, univer-
sities, research institutions, venture capitalists and public policy agencies
(or parts or groups of each).
Building on the tenets of the sociology of technology (Knorr- Cetina,
1999), Garud and Karnøe (2003) emphasize the importance of distributed
agency in the wider social construction of technology progression. From
this perspective, it is impossible to distinguish between the social and tech-
nical spheres; technology itself, understood as a complex mix of social and
technical elements, becomes a level of analysis. New technology is viewed
as a driver for the dynamic emergence of new industrial architectures,
disintegration of established industries, and changes in relations between
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An introduction 5
co- specialized firms (Jacobides et al., 2006). Nanotechnology poten-
tially enables applications that will spread through different industries
(Rothaermel and Thursby, 2007) and may prove fruitful for studying the
influence of emergent technology on emergent and established industrial
architectures.
Literature on technology dynamics has traditionally been concerned
with competitive dynamics between incumbent and new firms. The notion
of disruptive technology (Christensen and Rosenbloom, 1995) emphasizes
the difficulties incumbent firms face when emergent technology threatens
to make their core capabilities obsolete. The emergence of ICT certainly
had disruptive effects on areas such as music and media distribution and
publishing. On the other hand, biotechnology shows that interfirm com-
petitive dynamics could take the form of productive co- existence between
different firms of the kind characterizing biotechnology firms and the
pharmaceutical industry. Interfirm competitive dynamics introduce two
additional perspectives on emergent technology innovation. Competitive
interactions in the context of emergent technologies often switch into a
collaborative dynamic, in which members of innovation networks, orches-
trators of innovation ecosystems (Dhanaraj and Parkhe, 2006), strive to
integrate and share the knowledge to foster innovation. The notion of
open innovation (Chesbrough, 2003) highlights the relevance of externally
available knowledge, but also creates challenges to establish appropriate
business models to capture value from collaborative endeavours for large
global firms.
The networked nature of technological and organizational innovation
and dispersion of relevant knowledge challenges firms’ dynamic capabili-
ties (Eisenhardt and Martin, 2000). Established firms face the challenge of
absorbing relevant external knowledge to accelerate their knowledge tra-
jectories or create new knowledge paths in high- velocity industries. On the
other hand, new technology- driven firms, which have established an iden-
tity based on their competency in a particular aspect of emergent technol-
ogy, need transformative capacity (Garud and Nayyar, 1994) to transfer
emergent knowledge into a potentially immense variety of applications.
Last but not least, individual agency is deemed to be instrumental for
the process of technological innovation. Opportunity- seeking behav-
iour is a critical element of technology entrepreneurship (Shane and
Vankataraman, 2000) and managerial agency is essential for competi-
tive survival in the context of high uncertainty. This resonates with the
recent call to understand better the role of managerial agency in creating
new organizational capabilities (Barney and Felin, 2013; Gavetti, 2005).
Moreover, entrepreneurial behaviour is not solely contained in entrepre-
neurial and established firms, but is also instrumental for initiating action
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6 Managing emerging technologies for socio- economic impact
and creating novelty in non- profit organizations such as universities,
research policy institutions and community supported organizations.
COMMUNITIES AND NETWORKS IN EMERGING
TECHNOLOGIES
Emerging technologies addressing grand societal challenges increasingly
stem from neither any single company, nor a unique organizational
setting. They stem from collaboration and networking within and across
diverse communities, networks and regions bringing together and integrat-
ing knowledge and expertise in Europe and worldwide (Chesbrough et al.,
2011; EC, 2012; Curley and Salmelin, 2013). Emerging technologies have
progressively engaged a large number of organizations and people, which
do not only compete, but also collaborate, forming diverse networks
across all kinds of boundaries: geographical, institutional, demographic
and disciplinary (Assimakopoulos, 2007; Schweer et al., 2012; Boudreau
and Lakhani, 2013). In their process of emergence, emerging technologies
also raise several challenges – technical, social and economic – that open
up tremendous opportunities for solving the most complex and challeng-
ing problems of our era, while at the same time they create formidable bar-
riers in understanding and managing precisely this process of emergence.
Students of emerging technologies need to acquire an in- depth under-
standing of an array of complex knowledge bases underlying the origins
of emerging technologies vested in multiple scientific and technologi-
cal communities (Constant, 2002). Such an understanding seems a vital
requirement before any meaningful policy or strategy intervention is put
in place for managing their emergence. As scientific and technological
subject areas have become vastly complex, knowledge is accumulated in
huge databases of publications and patents (Leydesdorff et al., 2013). The
organization of collective knowledge in subject categories, patent clas-
sifications and the like seems a necessary evil for bounding disciplines and
associated communities of experts in silos of vested interests. Experts and
knowledge workers need first to absorb ‘disciplinary’ knowledge before
they start transcending boundaries, collaborating for innovation and
emergent technology across boundaries. Recent research has also shown
that experts are rewarded if they broaden their expertise in several knowl-
edge domains, and both publish and patent their knowledge in an intricate
interplay of scientific communication and intellectual property protec-
tion (Murray, 2002; Meyer, 2006; Breschi and Catalini, 2010). As a result
they have to make choices about whether they socialize and engage in
multiple relevant communities involved in the social shaping of emerging
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An introduction 7
technology, maintaining multiple memberships and appreciating differ-
ent professional values and beliefs in the process of engaging with these
communities.
Nowadays a cross- or multidisciplinary understanding of emerging
technology is challenging to achieve on an individual basis. This is often
grasped at the collective level, by both pre- existing and new emerg-
ing technological communities, professional associations, committees
setting standards (Rosenkopf and Tushman, 1998), and securing both
continuity and change in terms of pre- existing and emerging technologies
(Assimakopoulos, 2000). Membership in multiple communities raises its
own challenges as each subject area and scientific or technological com-
munity has accumulated a vast body of knowledge that unavoidably
creates cognitive dissonance to outsiders. It also fosters patterns of col-
laborative behaviour that often embed and to some extent lock in experts
to networks and communities where they talk the same language and share
the same interests, values and beliefs (Bechky, 2003).
Nonetheless key actors and institutions in an array of communities try
to shape the technological evolution of emerging technologies and socially
construct their deployment and use. A myriad of actor networks (Latour,
2005) have tried for decades to create and take stock of necessary research
infrastructures, testing scientific discoveries, and carrying out trials for
new solutions in terms of diagnosis and treatment (Rosenberg, 2009). For
example, in developing and diffusing new therapies for cancer there is a
basic requirement for carrying out trials that they may last five to ten years
minimum for establishing mortality rates or/and quality of life after treat-
ment, before government administrations approve them for adoption and
use with ‘real’ patients.
Key enabling technologies (KETs) also call for smart public policies and
specialized clusters (EC, 2012; Wilkins et al., 2013), where orchestrators
build interorganizational networks with small and large ‘anchor’ firms,
leading universities and public research laboratories, as well as related
supporting innovation services. The distributed nature for emerging
technologies therefore requires the development of business models at the
ecosystem rather than the organizational level of analysis, highlighting
the interdependencies among emerging technologies, co- evolutionary eco-
system dynamics and the performance of key firms (Adner and Kapoor,
2010; Agouridas and Assimakopoulos, 2014). In a sense, key actors within
such ecosystems need to develop dynamic capabilities and manage dis-
persed industrial architectures, global supply chains and distant markets,
and co- create with lead customers.
Last but not least, as emerging technologies are diffusing across differ-
ent geographies and cultures, they are reinvented from a broad range of
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8 Managing emerging technologies for socio- economic impact
actor networks who give situated meaning to such technologies based on
different local interests, traditions and scientific and technological prac-
tices. Historians of science and technology as well as the field of social
studies of science and engineering have highlighted the social construction
of large technological systems for the past few decades (Hughes, 1983;
Geels, 2002). We endeavour here to unravel the socio- economic impact
of such emerging technologies for all kinds of stakeholders in order to
manage them more effectively in their early critical days.
One of the main findings that cuts across several chapters of the book is
the need to create, share and manage knowledge and research infrastruc-
tures at the network and community levels of analysis. This is character-
istic of much European research and innovation since the early 1980s,
when the first collaborative EU research and technological development
framework programme was funded by the European Commission (EC)
in the area of ICT (Georghiou, 1999; Assimakopoulos et al., 2004). No
one organization or discipline on its own can solve these highly complex
problems arising from practice (clinical or otherwise). ‘Mode 1’ knowledge
production is rather limited scientific knowledge triggered and produced
within disciplinary boundaries and scientific paradigms. ‘Mode 2’ knowl-
edge production in the ‘triple helix’ of university–industry–government
relationships has rather been the norm for more than 20 years (Gibbons
et al., 1994). ‘Mode 3’ knowledge production in the ‘quadruple helix’ of
innovation systems, including non- profit, non- governmental community-
supported organizations, is the latest non- linear network innovation
model, appearing in the early 2010s (Carayannis and Campbell, 2012).
EMERGING TECHNOLOGIES AND OPEN
INNOVATION IN ORGANIZATIONS
The focus on innovation networks for managing emergent technology
exposes intriguing questions of how organizations organize internally to
search for adequate knowledge dispersed through wider networks. The
openness (Chesbrough, 2003) suggests that organizations need to attach
equal importance to knowledge generated outside the organization and
knowledge produced within organizational borders. Managers within
organizations are well advised to tap into the rich pool of externally avail-
able knowledge, at the same time paying serious attention to internally
generated knowledge that resists commercialization through existing busi-
ness models or current market channels. This focus on collaboration and
the open nature of innovation has exposed questions of: (1) how organi-
zations organize internally to search for adequate knowledge dispersed
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An introduction 9
throughout an innovation network; (2) which strategies are required for
capturing value in open innovation contexts; and (3) how new internal
organizational structures emerge as a result of the need to participate in
and benefit from innovation networks.
Many innovation networks are driven by grand societal challenges (for
example, sustainability, environment, health and ageing), which increases
their institutional diversity. Transnational institutions, national govern-
ments, local authorities and professional associations accompanied by
organized members of the public play a powerful role in shaping the inno-
vation efforts that aim to translate social challenges into growth opportu-
nities. The changing nature of innovation in such issue- driven innovation
networks requires the investigation of very complex interactions between
agglomerations of organizations, groups and individuals. This further
challenges the internal organizational design of innovating firms, their
strategies and organizational capabilities. The societal issue- driven and
open nature of innovation in emergent technology innovation networks
radically shifts the logic and understanding of firms’ innovation strategies
and the organization of innovation processes.
Openness and the resulting knowledge complexity, interdependency
and dispersion alter the logic of how innovation processes within a firm
are strategically managed. These characteristics of emergent technology-
driven innovation networks demand a simultaneous search for technologi-
cal knowledge, development of organizational capabilities and creation
of new business models. The openness of the innovation process suggests
that knowledge and solution- providing competency is extremely widely
distributed. Innovative organizations are, therefore, challenged to organ-
ize their search processes for the integration and recombination of distant
(less familiar technologies and markets) and often very fragmented knowl-
edge domains (Afuah and Tucci, 2012). New organizational structures
are emerging that enable coordination across organizational borders, yet
those that support open innovation processes are not sufficiently studied.
The search for highly distributed knowledge is additionally complicated,
because firms are not only searching for external knowledge that supports
the development of their products or services. Many technology- enabled
new products or services are themselves complex knowledge architectures
(for example, cloud computing) with numerous firms contributing their
competency only at a component level. If firms are facing architectural
innovation (Henderson and Clarke, 1990) – components change only
incrementally, but interdependencies among them alter radically – then
search, no matter how distant, cannot only be focused on finding knowl-
edge that supports improvement of a component. It also needs to provide
adequate knowledge to understand the innovation on the level of the
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10 Managing emerging technologies for socio- economic impact
entire system. Architectural innovation changes competitive dynamics,
division of capabilities and industrial structure, yet it is less studied how
firms should organize their search activities in order to successfully shape
emerging architectural innovation. In such circumstances it is unlikely that
a network of contributing organizations will form a well- structured supply
chain. Networks of organizations that attempt to shape complex architec-
tures of new products or services are more akin to ecosystems that do not
always have a clearly defined central actor. This suggests that firms need
to organize their search activities not only to acquire adequate techno-
logical knowledge, but also to develop new organizational capabilities and
business models in order to influence the direction of architectural innova-
tion, differentiate themselves within the complex product or service, and
capture adequate value from within the emerging industrial architecture.
It is intriguing that emerging research insights from the ManETEI
project suggest that many technology- driven companies are reluctant to
label collaboration with external partners as ‘open’ if new knowledge sup-
ports the existing core products. Regardless of whom they engage with or
how uncertain or radically different the relevant technology is, collabora-
tion is rarely described as open if it directly contributes to improvements
of core products or technological competency. It is indicative, however,
that companies use open innovation strategies when exploring integration
of their products into radically new product or service architectures. Here,
technological advances in products are of secondary importance to how
a product fits into emerging product or service architectures. This is even
more pronounced if the emerging industry architecture lacks a central
firm to orchestrate integration efforts or when collaboration is needed
between companies from different sectors to create a complex new product
or service architecture. Companies have to develop new organizational
capabilities to navigate emergent innovation networks that aspire to create
complex architectural innovation from a variety of innovative products
(for example, smart cities). In such an architectural innovation context it
is unproductive to distingush between product, process, business model
(Teece, 2010) and organizational innovation (Birkinshaw et al., 2008).
Organizations often experiment with all these innovations simultaneously.
The institutional diversity of societal issue- driven innovation networks
increases the number and variety of constituencies involved in innovation
and changes the ways innovative firms organize their innovation processes
to navigate multiple institutional logics (Thornton et al., 2012). The insti-
tutional diversity of the innovation process is recognized in the innova-
tion management literature, inspired by insights from the sociology of
technology (Garud and Gehman, 2012). Configurations of interdependent
actors include not only firms and public research institutions, but also
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An introduction 11
governments, regulators, professional associations, organized public and
end- users. Such institutionally complex innovation networks are theoreti-
cally conceptualized as organizational fields that bring together various
constituencies with often disparate purposes and institutional logics. It is
often characteristic for such organizational fields to be driven by a central
issue (for example, sustainability: smart cities) and not necessarily by a
particular technology or market (Hoffman, 1999). Societal issues that
drive institutionally diverse innovation ecosystems challenge firms’ exist-
ing capabilities, yet it is rarely studied how developments at the level of an
organizational field impact upon innovation strategies and capabilities at
the level of an individual firm. It is even more scarcely investigated how an
innovative firm organizes in order to shape and influence developments in
an issue- driven innovation ecosystem, where the interactions are not only
limited to interfirm or industry–university collaboration, but also include
close interactions with governments, regulators and the wider public.
ManETEI researchers have identified the emergence of new organiza-
tional forms (for example, technology platforms supported by the EC) as
typical meta- organizations (Gulati et al., 2012) that combine elements of
interorganizational networks with structures and hierarchies as character-
istics for single organizations. The ManETEI focus on exploring interac-
tions between an individual firm and members of the wider organizational
field is timely, because innovation managers with strategic responsibilities
often participate in initiatives such as setting standards and collabora-
tive technology development (for example, SEMATECH consortia),
that bring together institutionally diverse constituencies. Moreover, they
interact with governments, municipalities and the wider public in order to
gain information, generate social and reputational resources and influence
the direction of innovation. It is indicative that such interactions between
institutionally diverse members often happen within new organizational
forms such as European Technology Platforms (European Commission,
2004) and private–public partnerships that require adequate managerial
skills and organizational capabilities in order to influence the agenda
and support the strategic intent of a company that participates in such
meta- organizations.
If innovation ecosystems are characterized by different degrees of
institutional diversity and complexity, interdependency and dispersion
of adequate knowledge, it is possible to argue that different capabilities
and different internal organizational structures are needed within a single
organization. This suggests that heterogeneity of external innovation
ecosystems could be accompanied by internal heterogeneity of innovation
structures. ManETEI researchers have identified that technology- driven
companies are themselves becoming complex networks of internally
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12 Managing emerging technologies for socio- economic impact
distributed innovation initiatives. These decentralized groups are becom-
ing increasingly autonomous in collaborating with external partners such
as universities and technology- intensive small and medium- sized compa-
nies. ManETEI research has identified two intriguing and interrelated con-
sequences. Firstly, complex organizations introduce new organizational
units that specialize in supporting autonomous innovation initiatives
undertaken in collaboration with external partners. Secondly, innovation
management becomes a new profession distinct from R&D activities. This
very intriguing trend enables the study of new organizational interde-
pendencies (Siggelkow, 2011) and professional identities that influence the
emergence of autonomous actions within complex organizations involved
in innovating with emergent technologies.
WORK PRACTICES AND EMERGING ICT
For as long as technology has existed, it has affected the way people per-
formed their work. In the beginning of the twentieth century, technology
helped to automate manufacturing lines, resulting in the specialization of
the workforce and efficiencies for car manufacturers. One hundred years
later, ICT has emerged to enable collaboration and coordination between
dispersed teams, redefining the grounds upon which distributed work can
be carried out. However, such advances in ICT were not free of challenges,
and indeed were at the centre of the ManETEI project. Various aspects
such as geographical distance, time zone and cultural differences associ-
ated with global distribution have been examined as causing problems for
globally distributed software teams in achieving successful collaboration.
Indeed, a growing number of studies have reported problems regarding
collaboration in distributed work, such as coordination breakdowns, lack
of understanding of a counterpart’s context (Cramton, 2001) and different
language competencies across remote sites. Other studies have argued that
globally distributed work may exacerbate the chance of misunderstand-
ings, lack of trust (Jarvenpaa and Leidner, 1999), asymmetry in distribu-
tion of information among sites (Carmel, 1999), difficulty in collaborating
due to different skills and training, and mismatches in information tech-
nology (IT) infrastructure.
By and large, the practices proposed in the literature have focused on
two possible routes to ensure effective and efficient collaboration and
coordination between remote sites. One stream of research has advocated
for the division of work that minimizes the need for intersite coordina-
tion and, therefore, communication and synchronization (Herbsleb and
Mockus, 2003). To achieve this, it was recommended that tightly coupled
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An introduction 13
work items that require frequent coordination and synchronization should
be performed within one site. Clearly, such a view highlights the impor-
tance of ICT as supporting the integration of components across sites.
Another stream of research has promoted the idea of rich and intensive
interactions between various sites, supported by ICT and face- to- face
meetings in which counterparts develop social and cultural contextual
knowledge of their remote peers as part of enabling coordination and
collaboration. This view, in contract to the former one, advocates the use
of ICT as a communication tool that enriches coordinative activities via
contextual knowledge, thus supporting distributed work.
The contrasting views presented here offer new avenues to under-
stand the links between ICT and work practices. Arguably, firms cannot
eliminate coordinative efforts through the practice of division of tasks
between remote sites. While excessively investing in coordination activi-
ties may erode the advantages that distributed work may offer, such as
low- cost locations and the utilization of time zones to speed up product
development.
There are, therefore, two possible explanations through which the rela-
tionships between work practices and ICT can be further explored. First,
understanding how socialization takes shape in such settings. Second,
exploring the notion of ‘expert systems’ and how codification plays a role
in such systems.
ICT and Socialization
Collaboration and team performance depends, to some extent, on the
socialization of dispersed team members (Govindarajan and Gupta,
2001). Socialization refers to the process by which individuals acquire
the behaviours, attitudes and knowledge necessary for participation in an
organization (Ahuja and Galvin, 2003). Through socialization, the norms,
identity and cohesion between team members develop, enabling team
members to effectively communicate and perform (Hinds and Weisband,
2003). Most studies refer to organizational socialization as a process that
is based on interactions between a newcomer and members of the organi-
zation (for example, colleagues, superiors or subordinates). Through such
interactions an employee is taught and learns what behaviours and views
are customary and desirable at their workplace, and becomes aware of
those that are not, as well as acquiring the knowledge and skills needed
to perform their job. While such a socialization practice is supported by
human interactions when team members are co- located, distributed teams
may face spatial, cultural and time zone challenges that may prohibit them
from socializing.
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14 Managing emerging technologies for socio- economic impact
Socialization in globally distributed teams, therefore, may take place
through two key mechanisms. One is the application of ICT and the
other is through face- to- face interactions. In terms of the application
of ICT, various electronic media will be needed to support connectivity
between remote sites and facilitate socialization. Additionally, generic col-
laborative technologies (for example, Groupware) will be needed to enable
remote counterparts to connect and communicate. The most commonly
suggested collaborative technologies are social media, e- mail, chat (for
example, instant messaging), phone / teleconferencing, videoconferencing,
intranet, group calendar, discussion lists and electronic meeting systems.
More recent studies have focused on integrating collaborative technolo-
gies into an integrated development environment in order to offer solu-
tions that deal with breakdowns in communication among developers in
dispersed software development teams.
However, while ICT plays a role in socializing, face- to- face meetings
are also important for the development of distributed teams through the
establishment of interpersonal relationships. Furthermore, such meetings
were found to positively affect team collaboration and team performance,
mainly through the enhancement of interactions between team members.
However, such face- to- face meetings are sporadic, short, selective and
formal, therefore suggesting that their impact on socializing remote coun-
terparts is likely to be minimal.
Recent studies have indeed acknowledged the difficulties in socializing
through human interaction in distributed settings and therefore proposed
the need to ‘reacquire’ norms, attitudes and contextual knowledge by com-
bining social media with short- term visits, a term coined ‘re- socializing’
(Oshri et al., 2007). Re- socializing in distributed contexts takes several
steps in which both human interactions and the application of ICT play
an equal role in moving the distributed team from introducing each other
through social media means, meeting up for a preliminary meeting, but
also renewing social ties via short visits and interactive social media
platforms.
Transactive Memory and ICT
Socialization may increase remote counterparts’ awareness of the islands
of knowledge existing within the team; however, this may not suffice to
effectively transfer knowledge between the sites. Indeed, studies have dem-
onstrated repeatedly that, despite advances in technologies, ICTs do not
prevent breakdowns in the transfer of knowledge across distributed sites
(Cramton, 2001). While ICTs are critical for knowledge transfer processes
in distributed teams, a neighbouring stream of studies has considered
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An introduction 15
human- related factors, such as trust and interpersonal ties, which may act
as facilitators for knowledge transfer between remote counterparts. But
collaboration also requires remote counterparts to be aware of their peers’
expertise and their mastery. In this regard, ICT are unlikely to support
developing awareness for mastery, but rather assist in encoding, storing
and retrieving information from and for experts when needed (Wegner,
1995).
Indeed, a transactive memory system (TMS) has been defined as the
combination of individual memory systems and communications (also
referred to as ‘transactions’) between individuals. The group- level TMS
is constituted by individuals using each other as a memory source.
Transactions between individuals link their memory systems; through a
series of processes (that is, encoding, storing and retrieving), knowledge
is exchanged. Individuals encode information for storing and retrieval,
similar to a librarian entering details of a new book in the particular
library system before putting it on the shelves. Through encoding, knowl-
edge is categorized (that is, assigned labels that reflect the subjects of the
knowledge) for systematically storing the location of the knowledge, but
not the knowledge itself. Then, individuals store this information intern-
ally (building their own memory), or externally (storing it in ICT or
indirectly in other people’s memories). And lastly, information about the
location of the knowledge or expertise is retrieved when someone else asks
for it (Nevo and Wand, 2005).
Retrieval thus consists of two interconnected sub- processes: person A
asks person B for information; person B retrieves the information. As
Nevo and Wand (2005: 551) put it simply: ‘knowledge is encoded, stored
and retrieved through various transactions between individuals’. Wegner
(1995) explains that for a TMS to work, three corresponding aspects to
encoding, storing and retrieving should be considered. Creating a TMS
that supports collaboration between team members is perceived to be
less challenging than in distributed contexts (Oshri et al., 2008). Globally
distributed teams often experience changes in membership that negatively
affect the long- term development of a TMS. Furthermore, in many distrib-
uted settings, team members do not have any prior experience of working
together. Their distributed mode of operation decreases communications
and increases the possibilities for conflict, misunderstanding and break-
downs in communication. In teams that do not carry out joint training or
arrange face- to- face meetings, the development of shared understanding
is even more challenging because members of such teams do not stand on
common ground. How then can a TMS benefit from ICT, thus enhancing
collaborative practices in distributed modes?
Studies suggest that, similarly to socialization, there is interaction
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16 Managing emerging technologies for socio- economic impact
between human interaction and artefacts which creates the conditions for
the development of a TMS in distributed contexts. Indeed, both person-
alized and codified directories are created during memory processes in a
TMS which supports the encoding, storing and retrieving of information
when remote counterparts exchange knowledge (Oshri et al., 2008). ICT
and digital artefacts provide standard templates to capture information
about individuals involved in knowledge exchanges, and central project
repositories serve as storing artefacts to ensure the ability to retrieve infor-
mation upon request.
Furthermore, we find that the three transactive memory processes – that
is, encoding, storing and retrieving – play different roles in shaping a work
practice such as knowledge transfer. Firstly, the development of collective
expertise – that is, encoding – acts as a process for defining the proce-
dure through which knowledge will be transferred. During the encoding,
parties negotiate the meaning of knowledge (that is, the subject and loca-
tion of the knowledge) following either a codified, standardized approach
or by relying on an embedded routine developed within the organization.
Secondly, the management of expertise – that is, storing – creates a pointer
to the location where the knowledge is stored and from which it can later
be transferred. In this regard, creating a pointer involves the actual storing
activity during which remote counterparts attach particular labels to the
knowledge stored within them or in a digital artefact. These labels, includ-
ing for instance contextual information, make it possible to negotiate and
clarify the meaning of this information, and its subsequent retrieval from
its place of storage. And thirdly, the coordination of expertise – that is,
retrieval – concerns the integration of knowledge by bringing together
experts through search mechanisms and interpersonal contacts. For
knowledge transfer to take place, teams rely, on the one hand, on the
procedures and shared meanings established through encoding processes;
and on the other hand, on interpretation and the use of labels attached to
the transferred knowledge during the storing process. The coordination of
expertise – and thus knowledge transfer – can be supported by relying on
either the codified or the personalized directories, or both.
The question that we may pose at this juncture is: to what extent do
personalized or codified directories matter to a certain practice such as
knowledge transfer? Indeed, it would be wrong for either of these memory
systems to be perceived as ‘better’ or ‘worse’. In line with Cook and
Brown’s (1999) observation on epistemologies of knowledge, codified and
personalized directories are best seen as two complementary, rather than
competing, memory systems. Absence of the codified or digital directories
would deprive the teams of shared methods for encoding, storing and
retrieving information, which may strain the personalized directories
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An introduction 17
beyond feasibility. Leaving out the personalized directories, on the other
hand – for instance, due to high personnel turnover rates – would leave
the project with independently working individuals who would find it dif-
ficult to agree on collaboration standards. In this regard, groups develop
meta- routines that interlink the two types of directories. Further, there is
a ‘generative dance’ (Cook and Brown, 1999) between these two memory
systems that contributes to the transfer of knowledge between remote
counterparts. The codified directories depend on interpersonal ‘norming’
processes for defining standards, templates and procedures. The personal-
ized directories extend the codified system by offering additional avenues
in cases when documents provide incomplete knowledge about a task. In
these cases, individuals know who to contact and how to retrieve informa-
tion. Evidently, the development and use of a TMS may change over time.
During initial phases of the project, rudimentary parameters of transac-
tive memory are defined (for example, which sites and individuals are
responsible for which tasks and knowledge domains). These are extended
and refined when people work together over prolonged periods of time,
renegotiating meanings and regenerating learning around the knowledge
transfer process.
The two explanations offered here about the relationships between
work practices and emerging ICTs have been captured in the ManETEI
network. Cloud services, as a recent example, pose serious challenges to
firms’ business models, in particular those that rely on a product business
model, requiring such firms to reorganize their operations to consider a
service- based business model. Similarly, the digital games generation, who
bring gamification skills to their work, are shaping the way they interact
with work artefacts and what would motivate them to perform. In this
regard, the research carried out by ManETEI researchers is advancing our
understanding of the complex relationships between technology and work
practices, and how these two evolve over time and recursively shape each
other.
NOTE
1. The report coined the term ‘Nano- Bio- Info- Cogno- Socio- Anthro- Philo- Geo- Eco-
Urbo- Orbo- Macro- Micro- Nano’.
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18 Managing emerging technologies for socio- economic impact
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