Authors: Ph.D.Stefan Kovacs INCDPM
”Alexandru Darabont”
ABSTRACT
Social networks on Internet are at this
time not just a mode but a way to cluster youngsters from the entire world
around various subjects of discussion. One of these subjects could be staff
competency in the assurance of better conditions at workplace and better work,
safety-oriented improvement of the quality of human factors and so on.
Moreover, in cases of emergency, messages sent through the social networks
could reach more easily the receiver and could disseminate more quickly the
desired information.
The paper presents some results from a
research started in 2008 and dedicated to the safety utility of using social
networks. It shows our opening models regarding the two way research- a free
social network connection regarding safety problems and a scenario oriented
experiment together with the results obtained using 2 main Romanian net forums,
Face book and Twitter. We found in our research that social networks could be a
good support to improve the safety conscience, to develop safety capabilities
and attitudes and to quickly disseminate and communicate aspects that could
lead to an imminent risk. Social networks could be easy to use- on handheld dispositive and could attract attention more
than normal communication ways.
KNOWLEDGE,
COMMON INTELLIGENCE, SAFETY AND SOCIAL NETWORKS-THE BASIS OF OUR RESEARCH
In the emerging economy, knowledge is the primary
resource for individuals and for the economy overall; land, labour, and
capital. Improving front-line worker productivity together with its confort at
work and implicit safety is the greatest challenge of the 21st century. [1].
Knowledge is the main engine of economic development.But
what is knowledge ?
Davenport and Prusak [2] view knowledge as a fluid mix of framed experience, values, contextual
information, and expert insight that provides a framework for evaluating and
incorporating new experiences and information. It originates in
the minds of knowers. In organizations, it often becomes
embedded not only in documents or repositories but also in organizational
routines, processes, practices, and norms. Although there is no common definition of the
term knowledge, there is a wide agreement that knowledge
is social in nature. Many researchers emphasise the social, collective and
distributed aspect of knowledge.
Knowledge could and would be useful just in a community,
not alone. Wenger [3]
points out that knowledge does not exist either in a
world of its own or in individual minds but is an aspect of participation in
cultural practices.
He uses the term participation to describe the social
experience of living in the world in terms of membership in social communities
and active involvement in social enterprises. Participation in this sense is
both personal and social. It is a complex process that combines doing, talking,
thinking, feeling, and belonging. Siemens [4] defines a community as
the clustering of similar areas of interest that allows for interaction,
sharing, dialoguing, and thinking together. The accent is put on the term
„areas of interest” which imply the sharing process, even if this sharing
refers to thinking. Wenger [5] points
out that
community does not imply necessarily co- presence, a
well-defined, identifiable group or
socially visible boundaries. It does imply participation in an activity system
about which participants share understanding concerning what they are doing and
what that means in their lives and for their communities. The concept of
community is very close to the concept of social network. A network is an
important source of labour for the formation of a collective subject,where
there is no common opinion allready in place (for example, emerging risks). It
is possible to define a social network as a complex, dynamic
system in which, at any given time, various knowledge exists or could be
developed in different instantiations.
The heterogenous nature of a social network gives a plus value because various
kinds of experience could be mixed in order to obtain a certain knowledge. For
example, beyond engineers and risk specialists, in the safety knowledge domain
a network could mobilize doctors, psychologues and also various other
specialities which could elaborate a more complex and well sustained point of
view. The network is the power of many regarding the development of new
knowledge. As a special type of community inside a network , Wenger introduces
the concept of communities of practice (CoP).
Wenger defines CoP as groups of people who share a
concern or a passion for something they do and learn how to do it better as
they interact regularly. According to Wenger, a CoP is characterised by:
(a) The domain; a CoP has an identity defined by a shared domain of interest. Membership therefore
implies a commitment to the domain, and therefore
a shared competence that distinguishes members from other
people.
(b) The community; in pursuing their interest in their
domain, members engage in
joint activities and discussions, help each other, and
share information. They build relationships that enable them to learn from each
other. A website in itself is not a community of practice.
(c) The practice; members of a community of practice are
practitioners. They develop a shared repertoire of resources: experiences,
stories, tools,
ways of addressing recurring problems, in short a shared
practice. This takes time and sustained interaction. To differentiate between
CoP and network, it could be said that CoP or a „cloud of friends” as these
structures are nominated in this paper are included in the social network .
Zager [6] explores a collaboration configuration called a coalition and notes that coalitions
are temporary collaborative groups where shared concerns and interests
connectconstituent individuals and teams.
This coalition
leads inevitably to the idea of collective intelligence. Jenkins et al. [7] define collective intelligence as the ability to pool
knowledge and compare notes with others towards a common goal. Levy [8] sees
collective intelligence as an important source of power in knowledge
communities to confront problems of greater scale and complexity than any
single person might be able to handle. He argues that everyone knows something,
nobody knows everything, and what any one person knows can be tapped by the
group as a whole.
THE RESEARCH
Taking into account the above mentioned aspects, our
research, started in 2008, has as a main goal the response to the following
question „Could social networks like facebook, Twitter, hi5 and so on be used
in order to promote and improve safety among young workers ?”. In Romania
statistics show that young workers are the most accident prone group. They are
also the most significant participants in the social networks- so perhaps there
is a possibility to make them conscient of the risks at which they are exposed,
to imprint some basic safety rules through the social networks and also to make
them interact or act directly in the way to mantain and improve safety.
First we have developed a model, presented in the
following paragraph. Then, on the basis
of this model we have developed two experiments:
-a „static experiment” based on facebook in which young
workers were informed about an accident occured some days earlier, accident in
which five young born died because of an electrical instalation fault. Our
experiment subjects were asked to interact with the facebook page „Safety now!”
in order to express their opinions through the message system, search for
specific knowledge using the links given on page and also participate at
various pro-bono safety trainings performed at the Romanian National
Occupational Safety Research Institute (INCDPM)
-a „dinamic experiment” based on Twitter in which the
young workers were exposed to a simulated event (an accidental crude oil spill)
and were asked to help and to contribute as much as possible to the stoppage of
the spill.
We have used two significant statistical lots, one of 200
persons for the first experiment and one of 500 persons for the second
experiment.
The methods used for our research were of two types:
A.the first
category (methods used for network data collection) aims to provide a data set
that helps study the effects of the interaction with a safety subject on
different aspects of social activities.
To achieve
this aim, the following methods are used:
-Socio-centric:
to examine sets of relationships between actors that are regarded for
analytical purposes as bounded social collectives.
-Ego-centric:
to select focal actors (egos), and identify the nodes they are connected to.
B.The second
category (methods used for network data visualisation and analysis ) aims to
render data in easily understood formats, thus making complex information
usable and interpretable.
To achieve
this aim, the following are used:
- graphs: to
visualise relationships among members of the social networks used in the experiment
- matrices:
to visualise large or dense „clouds of friends”
- maps: to
manage a wide amount of data and information;
THE
MODEL
In order to perform the experiments we
have developed a model of interaction and learning in social networks, model
that has as a basis [9] and other research papers that will be also mentioned.
Our model
starts with a canonical model of social
learning which comprises a set of agents I , a finite set of actions A, a set of states
of context Ω, and a common
payoff function U(a,ω), where a is the action chosen and ω is the state of context. Each agent i receives a private
signal σi(ω), a function of the state of
context ω, and uses this
private information to identify
a payoff-maximizing action.The
set of agents is not homogeneous and
could have the following strategies at the social network interaction:
-do nothing;
-interact weakly (access the
subject but nothing more)
-interact strongly (access the
subject and participate at the interaction with its own contributions);
-act; the agent has the freedom of decision- in the
same time he must maximize its payoff or , at last, not be the looser.
This set of agents is comprised
in the „cloud of friends” developed inside a
social network like facebook or
Twitter.
For each such a network some results could be ennounced, like:
• Uniformity of behavior. Initially, diversity of
private information leads to diversity
of actions. Over
time, as agents learn by observing the actions of their neighbors,
some convergence of beliefs is inevitable. A central
question is whether agents can
rationally choose different actions forever.We postulate
that each agent has its own power of
decision and would choose the best strategy that fits him.For example, if he is
an expert in oil spills he could offer his help- acting; if not he could choose
to express himself on a strong interaction – like joining the call to boycot
the institution seen as guilty of the spill. Here, this uniformity takes the form of herd behavior.Smith and Sørensen [10] have
shown that, in the SSLM, herd behavior arises in finite time with probability
one. Once the proportion of agents choosing a particular action is large
enough, the public information in favor of this action outweighs the private
information of any single agent.So each subsequent agent “ignores” his own
signal and “follows the herd.”
• Optimality. It is interesting whether the common
action chosen asymptotically is
optimal, in the sense that the same action would be
chosen if all the signals were public
information. In special cases, it could be shown show
that asymptotically the optimal action is
chosen
but, in general, there is no reason why this should be the case.
There
are some specific social network principles, the most interesting for our model
being the following:
-the welfare improvement principle.
Agents have perfect recall, so expected utility is non-decreasing over time. Welfare-
as seen fit by the specfic social
network- is continously improving. If a subject- for example safety- is
considered of interest by the social network- its agents are promoting inside
the network (here we speak about an informal promotion) by developing the
subject.
- the imitation principle. If agent i can observe the
actions of agent j , the simplest strategy available to
him is to imitate whatever j does. Since i and j have different
information sets, their conditional payoffs under this strategy may be
different. However, on average, i must do as well as j . If safety is considered „cool” by a critical mass of
agents, it would be cool for every other agent which wants to get his position
in the social network.
A social network is represented by a
family of sets [11] {Ni : i = 1, . . . ,n}, where
Ni = {1, . . . , i − 1, i + 1, . . . ,n}.For each agent i , Ni
denotes the set of agents j _= i who can be observed by agent i. We can think of Ni
as representing the “cloud of friends .”. The social network
determines the information flow in the proximity of the „cloud of friends”
given a specific context and also the existence of self- control objectives
inside the network . Agent i can observe the action of agent
j
if and only if j is included in Ni . Agents have perfect recall so their information set at each date includes
the actions they have observed at every previous date.
Utility and Usability.
If the
social network has no immediate utility, it could imply:
-a status
utility: agent m rises his status among its friends included in
the „cloud of friends” by belonging to the social network. In this respect, the
competent authorities (like HSE or the Romanian Labour Inspection) could
contribute by revarding the agent which is involved in a weak interaction, a
strong interaction or takes an action by himself (like announcing the
authorities at the signs of an imminent danger) with honorific labels or badges
which could increase the social status among the network members.
-an emergent
learning process- agent m lis prepared to learn about something that
could be offered by the network.
If a social
network agent could not use the information he gets, then the social network is
also useless. So, the social network could:
-inform on the current level of understanding about a
subject;
-imprint
some subjects into the network present and future topics;
-facilitate
a learning process;
-give some”user”
feedback regarding possible hazardous manifestations at the workplace or in the
vicinity.
The figure 1
gives a global image of the model.
Figure 1 The
global image of the model
THE
FACEBOOK EXPERIMENT
Description of the experiment
The facebook experiment regarding safety
was centred on the idea that this social network is the perfect way to
disseminate safety around not homogenous groups of youngsters which are, or
could be interested, in safety problems.
In our experiment, facebook was conceived
as a disseminator for various safety events and happenings, taking into account
that the facts- presented partly in the Romanian mass-media- were not safety
related.
We have found a statistical significant
lot of 200”friends”, young workers in the main functional enterprises in
Bucharest and also in the government agencies.
The statistical significant lot was
chosen taking into account their common facebook affiliation, the
non-homogeneity of the group (otherwise the experiment could be biased by the
common organisational and safety culture); their apparent un-interest in
traditional safety classes and also their desire to act for status significant
causes like environment protection.
The facebook account”Safety now” was
built specially for this experiment. Using the above presented model and given
a subject of interest- a recent occupational accident in which five infant
babies died because their Intensive Care unit took fire because the lack of surveillance
and safety control-we have tried to establish how many from the statistical
significant lot would interact weakly, how many would interact strong and how
many would act effectively- the action proposed being a request for specialised
electricians to check their electrical installations, especially the air
conditioning ones.
The experiment was done between mid of
2008 and mid of 2010, totally 24 months.
The figure 2 gives the schema of the
experiment.
Figure 2 Schema of the facebook experiment
Figure 3 Links on the facebook page
Figure 4 Closed spaces course invitation
on the facebook page
RESULTS
The table 1 shows the results of the
experiment
Table 1- Results of facebook experiment
|
Attribute
taken into account
|
Value
|
|
Statistical
significant lot
|
200 workers,
80 male, 120 female, average age:25 years, average education: college, all
having facebook accounts
|
|
Participants
at events developed for the experiment
|
150
|
|
Participants
at pro-bono safety training at class
|
80 (the
training was focused on the confined spaces subject and was about 2 hours
long)
|
|
Interacting
weakly (by accessing links)
|
120
|
|
Interacting
strongly (by sending messages of support, compassion or with various
solutions to the problem that led to the accident)
|
170 (initially
we intended to use a tracer to differentiate every participant. Taking into
account facebook characteristics this was not possible- so there is a
probability that the weak interactions also interact strongly)
|
|
Accessing the
safety forums
|
40
|
Conclusions of the facebook
experiment
The facebook experiment shows that
participants at a social network like facebook, with a not homogenous group are
interested in safety problems having a trigger event (the fire in which the five
babies died). Counters were used in order to number the participants at various
interaction forms. Also, a password allowed working just with the statistical
significant lot and not with strangers.
We found an interested”cloud of friends”
which acted as a group without a visible reward. The apartenence at this safety
cantered group was enough for them.
Also, we found that facebook could be a
very valuable link to safety knowledge on-line, if this knowledge is
essentially needed and is available in a friendly format. Accessing the Safety
now! Facebook profile, those in need could access easily the desired knowledge.
We have computed an involvement coefficient (IC) as follows:
Ic=Number
of persons that interact/Total number of the statistical significant lot (1)
As we could not differentiate among the
participants we choose the larger number so
Ic= 170/200= 0.85. So, 85% of the
participants were interacting somehow (we consider it strong interaction as we
received 170 different messages from the”cloud of friends”).In our simulation
of the above mentioned model we have estimated an Ic of 25…35% so the results
were well above our expectations.
THE
TWITTER EXPERIMENT
Description of the experiment
In this event we simulated an undesired
event, a spill in one of the pipes that are transporting crude from the Constanta
port to the Pitesti refinery. The event was placed spatially in a natural
reservation zone, with a lot of damage that an oil spill can do.
Here we have chosen a statistical
significant lot of 500 Twitter members located mostly in the supposed spill
region. A very brief description of the simulated event was twitted expecting
the possible responses. The lot of twitter members was following the initial
tweet, retwitting it 350 times. The main
actions after the twitt are summarised in the Table 2
Table 2 Actions determined by the
Twitter experiment
|
Type of action
|
Number of
subjects
|
|
Announce of
the Competent Authority (in our case, the Inspectorate for Emergency
Situations)
|
120
|
|
Give solutions
to stop or mitigate the spill
|
60
|
|
Offers to
volunteer to clean the environment after the spill
|
300
|
|
Gives bad
ratings on various forums for the company supposed guilty for the spill
|
375
|
|
Call to
boycott the fictive company that was
supposed guilty for the spill
|
220
|
Figure 5 summarizes the Twitter
experiment
Figure 5 The Twitter experiment
CONCLUSIONS
Our research has shown that social
networks- facebook and others- are a possible way to imprint safety concepts
and rules among young workers. The classic way of safety training generally
implies little interaction, being an expositive one. The worker- and young
workers generally- are more pro-active, willing to imply them into action. The
desire to be cool and to gain the support for the group or “cloud of friends”
could lead towards good safety behaviour- if this is properly supervised.
An integrative solution would be to open
and maintain facebook safety nodes, with each node of the network centred on a
specific topic. Each node should develop its own “cloud of friends” and
propagate the ideas of safety around the network.
We found that, properly presented,
safety could be cool for youngsters that are socializing through such a
network. On the other side, specialization could be a bit difficult, given the
extreme diversity of network participants.
In the wake of this research we are
preparing a proposal on the next year FP7 calls in which we want to research heterogeneous-
context depending learning, using social networks and being centred on the
individual status improvement- inside the network and also usability outside
it.
One new possibility that we tried since the initial writing of this paper was the blog. Our blog received in 10 months more than 5000 visitors- we found this a good result considering the specificity of our domain.
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