Individual Knowledge as a Bridge between Human and Customer
Juan G. Cegarra - Navarro
(University Polytechnic of Cartagena, Spain
Beatriz Rodrigo - Moya
(University National Education of Distance, Madrid, Spain
Abstract: This paper will study the influence of three components
of human capital focusing on operative personnel under a dynamic perspective.
It considers learning flows and the knowledge stocks that the employees
of the organization generate because of the relationships that they maintain
with their clients. The influence of individual knowledge in these learning
flows will be examined. These being components such as: learning capacities;
automatic and conscious knowledge, on the flows of the relational learning
process including transfer, transformation and harvesting phases of knowledge.
In order to study the relative importance of the individual knowledge components
in each phase of the relational process, the scale established by [Kohli
and Jaworski 1990] will be used in this research. The paper is structured
in four parts. In the first, a theoretical reference on individual knowledge
on the relational learning process will be established. In the second part,
some hypothesis and the necessary methodology will be proposed. In the
third part the results will be shown and finally, in the conclusions some
interesting aspects on the role of individual knowledge in the process
described will be shown. Conclusions are based on a study of eighty-four
organizations. This investigation establishes important conclusions on
the role of individual knowledge in the generation of the customer capital.
Concretely, the explicit knowledge of the employees is the most meaningful
in the relational learning process, although it is also true that the tacit
knowledge and individual learning capacities have a special importance
in the harvesting phase of knowledge.
Keywords: Individual knowledge, explicit and collective; customer
capital; intuition processes; interpretation; and integration.
Categories: J.4, J.5
This article identifies the relationships among human components of
customer capital and the flows of the relational learning process. For
this aim, it has been considered that context will be different depending
on the analysed post. In this sense, because the operative personnel are
the key element that insure that the transfer of the knowledge provided
by customers at an individual level in the relational learning process
takes place, the present investigation focuses on operative personnel who
are in touch with end customers such as sales representatives, sales people
and people in contact with customers, but not on administrative and executive
1A short version of this article was
presented at I-Know '03, (Graz, Austria, July 2-4, 2003).
The individual learning within the organizational context, is a topic
which has been studied mainly within a framework of strict psychology and
has not received due attention from the strategic organizational literature.
Thus, [Kim 1993] defines the individual learning:
"as increasing one's to take effective action" (p. 38). Other
authors such as [Swieringa and Wierdsma 1992] understand
it as the behavioural change in order to reach a form of conduct that is
better suited to the goals of those who are going to learn. These two selected
quotes are illustrative of the absence of concern for the individual level
in the strategic organizational literature.
However, the individual level has been frequently used as an illustrative
metaphor of some problems that are identifiable in organizational learning.
For example, concerning the classic differentiation between the exposed
action theory and the action theory in use [Argyris
and Schön 1978], [Argyris, 1994], or as far
as it refers to the classic hierarchy of learning at different levels according
to the depth of the behaviour and or cognitive changes involved (single
loop learning or adaptive rational system where individuals basically learn
from experience; double loop learning or generative learning occurs when
individual mental models become incorporated into the organization through
a shared mental model; as well as triple loop learning or Deutero learning
that is the acquisition of these learning capabilities) [Swieringa
and Wierdsma 1992], [Argyris 1994].
Therefore, [Kim 1993] offers a relatively exhaustive
structured explanation about the dynamics of the individual learning level
in the strategic organizational context. In the model proposed by Kim the
core of the individual learning is constituted by the cycle OADI (observe-assess-design-implement).
This cycle interacts with the individual mental models2, constituted at
the same time by two components: frameworks and routines. According to
this model, assess (reflect on observations) and design (form abstract
concepts) integrate conceptual learning or know-why, that is to say, 'the
ability to articulate a conceptual comprehension of an experience'. On
the other hand, implement (test concepts) and observe (concrete experience)
form as named by [Kim 1993] operational learning or
know-how, that is to say, "the physical ability of producing some
action" (p. 38).
According to [Kim 1993], operational learning represents
that stage of learning at the habitual procedure level, where one learns
the steps in order to complete a particular task. This know-how is captured
as routines. On the other hand, conceptual learning has to do with thinking
about why things are done in the first place, sometimes challenging the
very nature or existence of prevailing conditions, procedures, or conceptions
and leading to a new framework in the mental model. Thus, the reference
frameworks are the cognitive component of mental models, while the routines
constitute the operative part.
The individual learning is a process through which the individual generates
knowledge such as the interpretation, assimilation and implementation of
tacit and explicit information.
2According to [Kim 1993]:
"Mental models represent a person's view of the world including explicit
and implicit understandings, mental model provide the context in which
to view and interpret new material, and they determine how stored information
is relevant to a given situation" (p. 39).
Taking the following definition of individual learning, provided by
[Murga 1984]: "assimilation and elaboration of
new conscience contents, of life knowledge and of experience, as well as
of individual behaviour patterns" (p. 23), we can say that: individual
learning can be understood as a personal phenomenon in which cognitive
aspects, such as behaviour and experience acquire a highly relevant role
in generating routines and frame works. This definition associates learning
with the generation of individual knowledge and serves as a starting point
for the model that we present in the following paragraph.
2 Classification of Individual Knowledge
Despite all that has been said in the previous paragraph, organizations
have not given time to evaluate which part of the relational learning process
has improved more by using individual knowledge. For this aim, the previous
consideration is to establish a classification of this individual knowledge,
which forms the human capital of the company (i.e. the set of skills which
an employee acquires on the job, through training and experience, and which
increase that employee's value in the marketplace). To classify the individual
knowledge, this research will take into consideration the classification
proposed by [Spender 1996]. According to Spender
knowledge can be classified according to its explicit or tacit character,
as well as its individual or social character. In this sense, Spender distinguishes
individual knowledge as automatic or conscious according to the possible
combinations between its tacit and explicit characteristics. A short explanation
of each one of these types would be the following:
- Automatic knowledge is the individual and tacit knowledge that includes
abilities acquired by experience. Automatic knowledge takes into account
the attitudes and behaviours that individuals maintain within their organization
and includes aspects which are closely related to their emotional reactions
and motivations, which are relevant to how people integrate within the
- Conscious knowledge is that which is individual and explicit and that
therefore can be articulated or codified, and consequently it is susceptible
to being shared by the rest of the organization. Conscious knowledge includes
the aptitudes and abilities that human resources possess, and refers to
the external capacities of the individuals that are put to the service
of the organization.
This classification of individual knowledge can be improved introducing
the concept (present / future) as a new evaluation. This perspective allows
us to distinguish the value of the individual knowledge components at a
given time and its potential for future development. This new perspective
considers inherent capacity as the ability for a resources group to accomplish
some task or activity.
The tangible and intangible resources nourish the company's capacities
and these capacities are the principal sources that provide the company
with its competitive advantages. In this sense, the automatic and conscious
knowledge represent a set of human resources in the present. The individual
learning capacities (conceptual and operational learning) represent those
abilities that make it possible to move from a given situation to another
desired situation of individual knowledge. But the capacities will not
be limited solely to organizing and co-ordinating a set of present resources.
The individual capacities incorporate complex interactions between individuals
to other individuals and the resources that belong to the company. Here
we have the concept of organizational routines in the sense given by [Nelson
1991] or [Nelson and Winter 1982]. [Grant
1991] suggests that a capacity is in essence a routine, or a number
of routines that interact with each other (p. 122).
Bearing in mind previous considerations, table 1
establishes a classification of the individual knowledge components. The
array of aspects that could be included is very wide, therefore the proposal
outlined in table 1 is to show those human resources
which are more relevant. We have considered that these resources with the
exception of academic development and professional formation are non-defendable
due to the difficulty of codifying and systematising the concept of individual
knowledge. We understand that training can be substantiated because it
can be understood and analysed with greater clarity. With respect to the
protection criterion of the knowledge, [Fernández
et al. 1998] assert that it is accurate to clarify that though the
training is not actually defensible in the sense in which this criterion
has previously been defined it is nonetheless included as such under the
supposition of the fact that it can be defended in employment contracts
Present Future Automatic knowledge Conscious knowledge Automatic and
conscious capacities Not defensible Intuition Motivation Attitude Loyalty
Cognitive map Leadership Language Conversation and dialogue Team work Polyvalence
Social relationships Learning capabilities Defensible Academic formation
Future internal or external training
Table 1: Classification of human capital components
2.1 Automatic Knowledge
In Spender's classification, automatic knowledge refers to an individual's
implicit (i.e., tacit subconscious skills, e.g., riding a bicycle). Automatic
knowledge represents that knowledge which is difficult to express in words.
This is the kind of knowledge that individuals perceive, but cannot describe
in words. Automatic knowledge allows expert sellers not to have to think
consciously about their actions with customers. Having been in the same,
or similar, situations and recognising the pattern, the expert knows, almost
spontaneously, what to do. Expressed simply automatic knowledge can be
thought of as an unconscious recollection of experiences. This helps explain
why automatic knowledge is so hard to transfer from one person to another.
However, although difficult to put into words, experiences, images and
metaphors can be used to describe some sensations, intuitions, motivations
and attitudes. Individuals use metaphors to he1p explain their automatic
knowledge to themselves and to share it with others. As [Tsoukas
1991] suggests, "metaphors involve the transfer of information
from a relatively familiar domain to a new and relatively unknown domain"
(p. 568). According to [Crossan et al. 1999], metaphors
constitute an economical way of relaying primarily experiential information
in a vivid manner, and they can be used as variety reduction mechanism
in situations where experience cannot be segmented and imparted through
Even the most sceptical person would have to admit that the feeling
denoting emotional experience has objective correlations such as psycho
physiological responses, expressive gestures, and overt motor actions.
For example, when in fear, a person is likely to exhibit increased heart
rate, to show specific facial grimaces, and to try hard to get away from
the situation. However, this set of facts can be interpreted in different
ways. An example of automatic knowledge is that which is carried out through
processes such as the observation and imitation of clients. We find in
expert sellers, that they are capable of interpreting given corporal and
facial (i.e. eyes, mouth) expressions of the clients, thanks to the fact
that they are expressions or images that the clients have previously used
to transmit an idea. These sensations have developed into a way of sharing
personal knowledge and they create a common place without needing to use
a codified language.
To illustrate the transformation from sensations or intuitions to images
or metaphors, [Bowers 1984] invoked the contrasting
approaches of a hard-nosed experimental psychologist and an optometrist
in deciding the eye-sight of a person in need of glasses. Certainly, above-chance
forced-choice discrimination between Os and Ws or Qs and Ds would provide
evidence that the potential lens-bearer would be able to discriminate much
smaller letters on the eye chart than revealed when the optometrist merely
asks what the subject sees. Nevertheless, the phenomenological based criterion
is much more appropriate for the patient's need (i.e. lenses that make
the world seeable rather than discriminable).
2.2 Conscious Knowledge
Once automatic knowledge is named, in experiences, images or metaphors,
then individuals can make more explicit connections among them. In Spender's
classification, conscious knowledge refers to an individual's explicit
knowledge (i.e. knowing the spelling of words or syntax of a computer programming
language). In the context of this work, we have considered conscious knowledge
in a wide sense, that is to say including all the knowledge that the individual
can codify, such as: language; conversation and dialogue; team work; polyvalence;
and social relationships.
According to [Tolman 1948] a cognitive map is a
global representation of the environment, it is a personal mind map or
a mapping of the thoughts an individual has about a particular situation
or problem of interest. The cognitive map represents an interpretative
framework of the world (automatic knowledge), which, it is argued, exists
in the human mind and affects actions and decisions as well as knowledge
structures. The cognitive map is not only influenced by the domain or environment
but also guides what is interpreted from that domain. Language plays a
pivotal role in the development of these maps, since it enables individuals
to name and begin to explain what were once simply feelings, hunches, or
Just as language plays a pivotal role in enabling individuals to develop
their cognitive maps, it is also pivotal in enabling individuals to develop
a sense of shared understanding through conversation and dialogue. These
methods can be used not only to convey established meaning but also to
evolve new meaning. In this sense, teamwork is a social activity that creates
and refines conversation and dialogue creating common language, clarifies
images, and creates shared meaning and understanding. As [Isaacs
1993] explains some of the most powerful forms of co-ordination may
come through participation in different tasks (p. 25). Polyvalence allows
individuals to unfold meaning, which might even be perceived differently
by different people.
2.3 Automatic and Conscious Learning Capacities
There is a large difference between the human mind and the ape mind.
Our conjecture is that besides the larger brain, there is one qualitative
difference of our consciousness of our learning capacity. The evolutionary
step consisted of making more of the brain state itself OADI (observe-assess-design-implement)
than was possible for our ape-like ancestors. The consequence was that
we could learn procedures that take into account the state of the brain
(automatic and conscious knowledge), e.g. previous observations, knowledge
or lack of it, etc. Individual's capacities have much in common with conceptual
learning [Kim 1993]. Automatic and conscious knowledge
represent a person's view of the world, including explicit and implicit
understanding. This knowledge provides the context in which individuals
view and interpret new material and decide what information is relevant
in a given situation. Through the individual's learning capacities people
understand and apply automatic and conscious knowledge and individuals
develop cognitive maps (routines, diagnostic systems, rules and procedures)
about the various domains in which they operate and use automatic and conscious
knowledge [Huff, 1990]. Therefore, individual's learning
capacities are essential to understanding work. Without a clear understanding
of automatic and conscious knowledge and the role they play, the practice
itself cannot be well understood, engendered (through training) or enhanced
As a result of individual capacities, individuals will interpret the
same stimulus differently, based on their established attitudes, improvement
learning capabilities or improvement in competence. The same stimulus can
evoke a different or equivocal meaning for different people [Hambrick
and Mason 1984]. Individual learning capacities have do with thinking
about why things are done in the first place, sometimes challenging the
very nature or existence of prevailing automatic and conscious knowledge
and leading to new knowledge. This new knowledge in turn, can open up opportunities
for connected steps of improvement by reframing a problem in radically
different ways. For this reason, in the following epigraph, a relational
learning model will be presented, in which every level of learning (individual,
group, and organizational) will be studied, information exchange and beginning
with explicit customer knowledge, distributed and used to create customer
capital (i.e. the value of the knowledge created because of the relationship
that an organization maintains with its customers).
3 Relational Learning Process
From the relational learning point of view, the human factor is the
key, and the organizational learning from these customers quite simply
the fact of exchanging information with them [García
et al. 1999], and it is precisely sales representatives who have direct
contact with customers. However, the information given by the customer
is one thing and the knowledge used by the company is another.
On the other hand, and since much of the individuals knowledge is of
a tacit nature, it is necessary to impulse its conversion to explicit knowledge
thus increasing its formality. In consequence the individual knowledge
is converted into an organizational knowledge and certain mechanisms on
knowledge service are needed that help to create, combine, group and integrate
the knowledge that comes from the various individuals that interact on
a daily basis in the organization, and conversely, the combination and
knowledge transformation throughout the net.
Bearing in mind these considerations, the relational learning process
represents the mechanism by which the organization transforms the tacit
and explicit knowledge of the client into customer capital. Such as is
presented in figure 1, it is understood as a process which is structured
in four phases (customer selection, transfer, transformation and harvesting
of the knowledge). This division is more pedagogic than structural. That
is to say, the variables are neither independent nor autonomous, but interacting
permanently. For example, a specific perception of an individual on a topic,
influences on the degree of motivation that he has for a related learning.
According to [Crossan et al. 1999] relational
learning is a "dynamic process" (p. 532). Not only does learning
occur over time and across levels, but it also creates a tension between
assimilating new learning (feed forward) and exploiting or using what has
already been learned (feedback). Through feed forward processes, new ideas
and actions flow from the individual to the group to the organization levels.
At the same time, what has already been learned feeds back from the organization
to group and individual levels, affecting how people act and think. The
concurrent nature of the feed forward and feedback processes creates a
tension, which can be understood by arraying the transfer, transformation
and harvesting phases against one another, as shown in figure 1.
Figure 1: Relational Learning Process
Moving from transfer to transformation and harvesting phases (feed forward)
requires a shift from individual learning to learning among individuals
or groups. It entails taking personally constructed cognitive maps and
integrating them in a way that develops a shared understanding among the
On the other hand, moving from harvesting to transformation and transfer
phases (feedback). Harvesting of knowledge can easily drive transfer and
transformation phases. The tension between acquiring new learning (feed
forward) and using what has already been learned (feedback) arises because
the organizational memory (what has already been learned) impedes the assimilation
of new learning. Especially, because individuals and groups learn within
organizations using theirs organizational memory, in this sense, companies
with a high degree of routines learning requires what [Schumpeter
1949] refers to as "creative destruction" destroying, or
at least setting aside, the institutional order to enact variations that
allow intuitive insights and actions to surface and be pursued. There are
many factors that could facilitate and inhibit these phases of the relational
learning process, some of which are part of the organizational memory or
institutionalised learning itself (e.g. reward systems, information systems,
resource allocation systems, strategic planning systems, and structure),
and others are consequence of human components (i.e. individual capacities,
automatic and conscious knowledge). In order to understand better the roll
of individual knowledge on the relational learning process, these phases
and some hypotheses are shown below.
3.1 Customer Selection
The first phase, 'customer selection' represents a first step to apply
the process itself and it is due to a company strategy process, whereby
after differing, identifying and classifying all the customers, it is possible
to establish which of the customers are interesting to the company. To
establish an indiscriminate relationship with every customer is not profitable.
Those organizations that have tried to be all things to all people have
ended up being nothing for anyone [Day 2000], [Kaplan
and Norton 2000]. For these reasons, the first phase in a learning
process must be to choose those initial customers to learn from. In this
aim, many academic studies conducted in demonstrating the importance of
focusing on profitability with individual customers, they have offered
arguments, heuristics, and methodologies for determining segment profitability
[Reicliheld 1996]. A very simple classification
of the clients is the one proposed by [Sherden 1994],
starting of with the fact that 20% of the better clients produce 80% of
the earnings, while 30% of the worse clients are being subsidized and reduce
the earnings to half.
According to Sherden´s contributions, the top left of figure 1would
represent the universe of clients. That is to say potential and existing
clients. From a relational learning consideration we should classify them
according to the necessary effort to reach them. Or put another way potential
clients would be located in a distant circle and considerable effort would
be required to learn from them. Closer to the organization would be those
clients with whom it would be less difficult to form a business relationship
and who are in addition of value to the organization, [Sherden
1994] asserted that these last were not representing more than a 10%
of the total customers. The next step is identified in terms of profitability
existing and potential customers. For existing customers the accounting
approach of activities based on costing (ABC) is useful, in many cases
the allocation of service costs to customers is the best way. On the other
hand, the organizations have to understand, to record, and to store the
costs associated with each customer in their customer files [Zeithaml
et al. 1999].
Once they classified the clients and considering that all relationship
can be improved, the degree of learning will be increased from within to
outside. That is to say, beginning by the interior circle, and starting
with the existing clients who are providing a greater profitability to
the organization. The following step would be to learn to convert, or loose
the rest of the current clients, and to capture new allies, widening the
circle in the measure and to the speed that the resources of the organization
permit. It is convenient to have a realistic vision of what the organization
can encompass in each moment maintaining the quality of the learning and
thus the transfer of knowledge starts.
3.2 Transfer of Knowledge
The second phase, 'transfer' represents the individual learning level.
It is this level which is the key to acquiring knowledge, the sales representatives
in touch with the clients starting off with an information exchange, and
beginning by a literal harvesting of the explicit knowledge of the client.
All this 'know how' is internalized by the individual who materializes
it in the form of experiences and mental models. Furthermore, this knowledge
which is internalised by the individual in the form of tacit and explicit
knowledge will represent an important part of the human capital of the
Information from customers may be acquired by workers from direct experience
with customers, the experiences of other agents (e.g. customers, banks,
suppliers, competitors, employees, shareholders, etc.) or organizational
memory. Learning from others is called by [March 1991]
"exploration". This process encompasses common practices, such
as benchmarking, forming joint ventures, networking, and making strategic
alliances. Learning from others also includes providing continuing education
or training. The knowledge transfer provided by other agents of the organization
and the organizational memory, will be studied in the transformation and
harvesting phases respectively.
In this paragraph we focus on knowledge acquired by the direct experience
of sellers working with profitable customers, who both recognize strength
before the rest of the market and are motivated to find solutions to those
needs [Webster 1994]. This process shows the clearest
illustration of acquiring knowledge from internally focused experience
and the effect of cumulative hours and user experience on sellers in relationships
with customers, this learning process, is called "exploitation"
by [March 1991]. Organizations encourage information
sharing with customers by creating external communities of practice, where
customers and employees interacting and interdependent working together
for the achievement of a particular objective. These communities, not only
stimulate real-time information sharing, but they also generally increase
the quality of the information gathered, for example, to drive new products
concept to launch more rapidly and with fewer mistakes.
In all these methods to acquire knowledge from customers, it is necessary
that the organization promote an organizational context shared between
the clients and the senders (the shared organizational context is referred
to joint elements related to the environment provided by the organization,
so that the desired vision exchanges and opinions that facilitate the individual
learning can take place). However, we must consider that the knowledge
assimilation in the individual learning implies internal processes to the
persons such as reflection, intuition, or interpretation.
This is something, which makes the previous satisfaction existence or
human components of the individual indispensable, since an organization
will find it difficult to achieve client satisfaction if previously employee
satisfaction has not been achieved [Fornell 2000].
Therefore, in this part of the learning process, the automatic and conscious
knowledge are critical to understanding of how people come to discern and
comprehend something new, for which there was no prior explanation. Therefore,
at this phase those variables that have been studied for their influence
on the individual learning (i.e. perceptions, attitudes, values, abilities,
motivation, and conduct) will have to be presented. These considerations
lead us to frame the first hypothesis of the work.
[Nonaka and Takeuchi 1995] suggest that individual
knowledge may guide the actions of the individual to acquire knowledge,
but automatic knowledge is difficult to share with others. However the
outputs of transfer phase, imagery sometimes called 'visions' or 'metaphors'
aid the individual in his or her interpretation of the insight and in communicating
it to others. Therefore, the organization will need all this individual
knowledge to be shared between all the members of the organization and
thus the third phase of our model starts.
3.3 Transformation of Knowledge
The group and organizational learning levels represent the third phase,
'transformation of knowledge' which constitutes the process through which;
tacit knowledge is converted into explicit knowledge. In our learning process,
we take for granted that in the process of creation of knowledge, individual
knowledge is generated and expanded as a consequence of the interaction
between tacit and explicit knowledge. It should be borne in mind that this
conversion is due to a social process between groups and individuals. The
result of these externalisation and combination processes will be the structural
capital, in the form of shared explicit knowledge. This could be seen in
its broader context by all organizational players who might use or be affected
by it and who are able to feedback questions, amplifications or modifications
that provide new insights to the senders.
This phase of the relational learning process is enhanced by the development
of conscious knowledge (i.e. language, conversation and dialogue, teamwork,
polyvalence, and social relationships). This encourages open sharing of
information and removes constraints on information and communication flows
[Woodman et al. 1993]. It is through the continuing
conversation among members of the community or group and through shared
practice [Brown and Duguid 1998] that shared understanding
or collective mind [Weick and Roberts 1993] develops
and mutual adjustment and negotiated action take place [Simons
Therefore, to ensure that all information is considered, organizations
must provide forums for information exchange and discussion. This communication
may occur through liaison positions, integrator roles, face to face contact
in meetings and on task forces, or utilization of information technology
to create organizational bulletin boards on topics such as competitive
activity or technology development. In all these circumstances, the evolution
of conscious knowledge extends the process of interpreting to interactions
In other words, language developed through conversation and dialogue
allows the evolution of shared meaning for the group. However, when organizations
remove the functional barriers that impede the flow of information from
development to manufacturing to sales and marketing, they improve the organization's
ability to make rapid decisions and execute them effectively. Under these
circumstances, organizations must support tasks, such as: decision-making,
solution of conflicts, leadership, motivation and attitude that is to say
that the automatic knowledge is influencing in the transformation process
too. These aspects also are studied in our investigation in the second
hypothesis of the work:
H2: The automatic and conscious knowledge has a significant affect
on the transformation of knowledge.
However, organizations are more than simply a collection of individuals;
organizational learning is different from the simple sum of the learning
of its members. Although individuals may come and go, what they have learned
as individuals or in groups does not necessarily leave with them, thus
starts the fourth phase of our model.
3.4 Harvesting of Knowledge
The fourth phase will internalise and use the knowledge acquired in
the previous phases. The result of such a process will be that tacit and
explicit knowledge on the clients are stored in a shared organizational
memory and then used by the members of the organization. If it were not
for organizational memory, learning would have a relatively short half-life
because or 'employees' turnover and the passage of time [Levitt
and March 1988]. Organizational memory is particularly important in
this era of restructuring and reliance on temporary or contract workers.
It is essential that important knowledge be recodified or recorded in information
systems, operating procedures, white papers, routines, diagnostic systems,
rules, mission statements and procedures.
Organizational memory will be used by the members of the organization:
beginning a new learning cycle and facilitating the one which in the transfer
and transformation 'phases' new learning is also acquired. This new knowledge
is included in the learning process as 'customer capital' [March
1991]. Therefore, harvesting phase is a means for organizations to
convey the learning of the individual and group members in transfer and
transformation phases to customer capital. In this sense, structures, systems,
and procedures provide a context for interactions.
Over time, spontaneous individual and group learning become less
prevalent, as, the prior learning becomes embedded in the organization
and begins to guide the actions and learning of organizational
members. Organizations outgrow their ability to exclusively use
spontaneous interactions to interpret, integrate, and take coherent
action. In this aim, relationships become formalized and coherent
action is achieved with the he1p of plans, training programs and other
formal systems. If these formal systems produce favourable outcomes,
then the actions deemed to be consistent with the plan become
routines, this is the role for what [Simons
1991], [Simons 1994] calls "diagnostic
systems". In other words, if individuals are capable of taking
advantage of these routines then, they will influence in the
harvesting phase of the knowledge.
However these memories may contain outdated information
or even encourage ineffective learning if they focus the organization
inappropriately. In other words, new procedures or capabilities may be
more effective than old ones; it runs the risk of becoming irrelevant
and may even obstruct feed forward learning flows.
Because learning that has become institutionalised at the organization
level is often difficult to change, the organization must promote active
unlearning and motivate its employees to take risk [Schein
1992]. But, changes in systems, structures, and routines occur relatively
infrequently in organizations; as a result, equivocal situations are often
resolved through a group interpretive process [Weick
and Van Orden 1990]. According to [Daft and Weick
1984], mistakes are reduced through interpreting by "shared observations
and discussion until a common language and course of action can be agreed
upon" (p. 291). Under this framework the hypothesis that we propose
H3: The automatic and conscious knowledge has a significant affect
on the harvesting of knowledge.
H4: Automatic and conscious capacities have a significant affect on the
harvesting of knowledge.
4 Methodology and Results
Once the importance of individual knowledge in every phase of the relational
learning has been justified this work is going to develop the methodology
to test the hypothesis. In this aim, the population of the most important
companies of the optician and optometrist sector from Spain were considered.
In this sense, attending to the criterion of the European Union from 1996,
the research considered as population the small and medium companies with
more than three employees; the information-collecting period lasted about
a month, from early March to April 2002.
The information collected was done through an electronic letter sent
by e-mail to the manager or general director of the SMEs who had to indicate
the position of their companies with respect to their competitors on a
scale Likert of seven points (1= strong down and 7= strong up). In order
to contrast the five hypotheses, a unique measure is necessary to show
us a reference point about individual knowledge, for this aim, we used
Intelect model developed by [Bueno 1998]. This decision
is found to be justified due to the success and usefulness of this model
among Spanish companies. In table 2 are shown the 9
articles used to measure the individual knowledge: 1-3 automatic, 4-6 conscious
and 7-9 capabilities. On the other hand, such as it is presented in table
2, to measure relational learning process articles proposed by [Kohli
and Jaworski 1990], [Jaworski and Kohli 1993]
were used: 10-12 transfer, 13-15 transformation, and 16-18 harvesting.
In contrasting each hypothesis only those cases that had answered all
the relevant questions were considered. Finally, on a sample of 108 companies,
the total of surveys that were carried out was of 84 companies which gives
a response rate of the 77.77% of the total, with a factor of error of 5.1%
for p=q=50% and a level of reliability of the 95.5%. According to [Hair
et al. 1999] the size of the sample was considered sufficient, since
it is greater than ten times the number of predictors from the indicators
on the most complex formative construct or antecedent construct leading
to a endogenous construct.
Table 2: Construct summary, confirmatory factor analysis
and scale reliability
The evaluation of psychometric properties in each of the measurement
scales used for different constructs is based on methodological suggestions
developed by [Churchill 1979] and was validated for
convergence and discrimination [Anderson and Gerbing
1988], [Lehmann et al. 1999]. Results of the confirmatory
factor analysis and reliability of the scale are shown in table
2. The standard coefficient regression between the set of explanatory
variables of scales and their corresponding variables of saturation are
significant, confirming the existence of three inherent dimensions to measure
each of the proposed variables. In all cases the coefficients of reliability
exceed the minimal level of 0.6 recommended by [Bagozzi
and Yi 1988] confirming the reliability of each construct. The standardized
parameters (>0.5) indicate that there is convergent validity and that
they are significant at the level of reliability of 99%. Discriminate validity
is guaranteed between each pair of dimensions because the interval of reliability
in their correlations does not include unity [Anderson
and Gerbing 1988].
The confirmation statistics of the hypothesis have been accomplished
using the statistical technique of regression analysis. This decision is
considered to be justified, due to the quantitative nature of the dependent
and independent variables. Within this technique we opted for the hierarchic
method, which permits the introduction of the independent variables in
different blocks. Through these equations, the degree of explanation of
the variance in the dependent variables is studied. For this, we designed
some standardized coefficients of the independent variables. Table
3 shows the path coefficients we got using this technique.
In model first, the effect of the learning capabilities, automatic and
conscious knowledge in the transfer phase was studied. Table
3 shows that though three variables incorporated in the model had a
positive influence, the automatic and conscious components had a greater
and significant influence with beta coefficients of 0.333 (p<0.05) for
the automatic, and 0.356 (p<0.01) for the conscious component. Among
the three variables (R2=17.7%) of the variable 'transfer of
knowledge' was explained. Considering these results, we can assert that
learning capabilities, automatic and conscious knowledge have a positive
influence on the transfer of the knowledge.
The second model analysed how the independent variables, individual
capacities, automatic and conscious knowledge were influencing the dependent
variable transformation of knowledge. Though all the three variables incorporated
in the model had a positive influence, only automatic knowledge with a
beta coefficient of 0.291 a level of (p<0.1) and the conscious knowledge
with a beta coefficient of 0.308 and a level of (p<0.05) resulted significant.
The independent variables included in this model explained (R2=12.8%)
of the 'transformation of knowledge'. Because of this, we can assert that
the automatic and conscious components of the individual knowledge are
good predictors of transformation of the knowledge.
Finally, the third model analysed how the independent variables individual
capacities, automatic and conscious knowledge were influencing the dependent
variable harvesting of the knowledge. It is important to emphasize that
the three indepeese ndent variables had a positive and significant influence
on the harvesting phase of the knowledge. However, the automatic and conscious
components had a greater influence with beta coefficients of 0.332 for
the automatic, and 0.525 for the conscious component, and levels of (p<0.01).
Nevertheless, the individual capacities also were significant 0.300 (p<0.01).
Among the three variables (R2=36.1%) of the variable 'harvesting
of knowledge' was explained. Considering thresults, we can assert that
learning capabilities, automatic and conscious knowledge have a positive
influence on the harvesting phase of the knowledge.
Table 3: Result for Correlation Path coefficients
The present investigation presents a starting point for the discussion
on the relative importance of each one of the components of the individual
knowledge in each phase of the relational learning processes (transfer,
transformation and harvesting of the knowledge). This work has demonstrated
that the relational learning process is influenced by the sum of all these
human assets (knowledge and capacities). Using data of the optical sector
from Spain, the contributions of [Bueno 1998] and
the scale of market orientation proposed by [Kohli and
Jaworski 1990] and [Jaworski and Kohli 1993],
the objectives have been: a) to classify by order of importance the different
components that compose the individual knowledge in the relational learning
process; and b) to justified that individual knowledge which is not present
in tangible data is indeed a source of competitive advantage for the organizations.
Among all individual knowledge, automatic and conscious knowledge have
been elected as of primary importance, while the individual learning capacity
has been in second place. With respect to the influence of these components
in the relational learning process, it is observed that in spite of the
fact that it is the conscious component which is the area with more influences
in the relational learning processes; the automatic knowledge has a decisive
roll in the transfer and transformation phases of the knowledge, while,
in the harvesting phase of the knowledge the individual learning capacity
is fundamental. The results support all hypothesis, this implies, a positive
influence between individual knowledge (learning capacities, automatic
and conscious knowledge) and the relational learning process.
These findings are significant, since they call into question the traditional
focus of organizational learning research and management practice on learning
at the individual level. Specifically, the current results indicate that
automatic and conscious knowledge are more closely related than individual
learning capacity to transfer and transformation phases. This suggests
that companies may be in these phases over investing in the development
of individual learning capabilities, and under investing in mechanisms
to facilitate the automatic and conscious knowledge. However, harvesting
phase of the learning process is influenced by the individual learning
capacity, this suggests that companies may be in the harvesting phase under
investing in mechanisms to develop individual learning capabilities.
We are conscious of the limitations that to accomplish this type of
analysis for one sector located in a geographical zone can have; in fact,
some of the results reached are influenced directly by characteristics
of the companies of our population. Mainly the fact that all companies
are Small and Medium Size enterprises (SMEs) and the types of products
and services that they sell.
A more limiting factor regarding the generalizing this research deals
with national cultural issues For instance, the nature of organizational
learning may be different in different cultures. One other limitation of
our approach in this paper needs to be acknowledged. We have tried to define
our constructs, as precisely as possible by drawing on relevant literature,
to articulate clearly our conceptual framework, and to then closely link
our measures to these theoretical underpinnings through a careful process
of item generation and refinement. Nonetheless, the measurement items that
we use here can realistically be thought of as only proxies for an underlying,
latent phenomenon that is itself not fully measurable. Finally, taking
into account the limitations of the current study, in future this research
study will take place across other sectors and with others items, in order
to generalize results.
This research would not have been possible without the translation support
we received from D. Tomás Jiménez.
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