A Knowledge Infrastructure Hierarchy Model for Call-Centre Processes
(Queensland University of Technology, Australia
(Know-Center Graz, Austria
(Queensland University of Technology, Australia
(Know-Center Graz, Austria
Abstract: This paper explores a process view of call-centres
and the knowledge infrastructures that support these processes. As call-centres
grow and become more complex in their function and organisation so do the
knowledge infrastructures required to support their size and complexity.
This study suggests a knowledge-based hierarchy of 'advice-type' call-centres
and discusses associated knowledge management strategies for different
sized centres. It introduces a Knowledge Infrastructure Hierarchy model,
with which it is possible to analyze and classify call-centre knowledge
infrastructures. The model also demonstrates different types of interventions
supporting knowledge management in call-centres. Finally the paper discusses
the possibilities of applying traditional maturity model approaches in
Keywords: knowledge management, information systems, data
Category: H.1, H.2, H.4
A call-centre is an organisational unit where inbound calls are received
or outbound calls placed for the purposes of sales, support, advice and
other business transactions. This article focuses on support or advice-type
call-centres and the hierarchical nature of the organisational knowledge
infrastructures [Strohmaier 2004] that support them.
The motivation for this paper arises from research undertaken within
private and public sector call-centres [Schefe et al.
2003; Timbrell et al. 2002]. This prior research
suggests that targeted knowledge management initiatives and interventions
have an impact on the quality of advice and the efficiency and cost of
knowledge services to customers.
This paper presents a descriptive model that illustrates the relationship
between call-centre processes and knowledge infrastructure. The developmental
nature of this relationship, as call-centres become larger and more complex,
suggests a relational hierarchy of process and knowledge infrastructures.
We introduce a model for a Knowledge Infrastructure Hierarchy
for the analysis and classification of call-centre knowledge infrastructures.
Call-centres are classified by size and analyzed in relation to specific
process-oriented dimensions including maturity stages. The model also points
out the kinds of knowledge management (KM) interventions that are successfully
established in call centres. Examples of knowledge strategies for small
(Building Codes Q), medium (CC1 and CC2: names not released) and large
(Hewlett-Packard) call-centres are described based on Timbrell's and Schefe's
empirical work in these centres.
2 The Call-Centre
The following introduces a typical process in a multi-agent call-centre.
Please note this process is meant to be illustrative rather than comprehensive.
The customer dials the call-centre number and is greeted with a number
of options that include the following: 1) a recorded message followed by
the placement in a telephone queue managed by an Automated Call Distribution
System (ACD); or 2) an Integrated Voice Response (IVR) that offers the
caller different options where caller interacts with the IVR using a touch-tone
telephone or voice control; or 3) the call is immediately directed by an
ACD to an agent who manages the query. If the agent cannot personally resolve
the query they direct the call to someone who can answer the query.
To better understand the process of a call-centre that provides knowledge-based
support (responses) to queries from a customer base, the following general
theoretical schema, the Query-Response Cycle is proffered.
2.1 The Query-Response Cycle
The following model, the Query-Response Cycle [Timbrell
et al 2002] provides a knowledge-based view of a call-centre encounter.
See Figure 1.
In the call-centre context, the caller has a query but does not have
the personal (expertise) or public knowledge sources to trawl for the response
and / or their search strategies are deficient. Rather than abandoning
the query, they may consign it to the call-centre.
Figure 1: Query-Response Cycle
Das [Das 2003] suggests such problem solving as
being the process of reducing differences between the known and desired
state of knowledge of the caller. A problem is resolved when these two
states are sufficiently close to satisfy the caller. There are two other
outcomes from the process: (1) if the querist does not believe that the
query can be resolved, or the payoff from pursuing the query is not worthwhile
(e.g. in terms of time or money) then they abandon seeking a response;
(2) if the respondent cannot resolve the query but believes that another
party with superior knowledge sources and / or search strategies then they
may consign the query to a willing third party. The authors believe that
querists are continually assessing the payoff from their efforts in seeking
a response to a query. The authors believe that a positive payoff assessment
corresponds to the notion of superior technical service quality [Brogowicz
If the resolution of a query is a public sector responsibility then
efforts to provide a response may be free to the querist i.e. tax-funded
services rather than user-pays. This positively affects the payoff assessment
applied by the querist. Moreover, if resolving the query is a public service
responsibility then the caller knows that there is a high probability that
the relevant government department will make all efforts to do so. A free
advisory call-centre service reduces the chance that the querist will abandon
the query. Subsequently, it is most important for government call-centres
to provide effective and efficient service with high levels of technical
and functional service quality [Brogowicz 1990].
Conversely, a time-based or per-use user-pays call-centre service from
either the public or private sector will affect the payoff assessment of
the querist throughout the process.
Once the agent receives the query, the Query-Response Cycle starts afresh.
The agent applies relevant search strategies against their knowledge bases
that may include databases, intranet, paper-based resources, other knowledgeable
agents and staff inside the organisation, and contacts in other organisations.
The maturity of the knowledge infrastructure, discussed later in the paper,
is a key determinant in the ability of the call-centre agent to respond
to the querist. Their searches may result in a single response that will
resolve the query or an equivocal set of responses from which they must
choose. The agent will assess the response set against their own 'payoff
assessment / resolution value either (1) alone; (2) in conjunction with
the caller; and / or (3) in discussion with other staff. In some cases,
the agent will consign or refer the query to "2nd Tier" specialist
call-centre or specialist organisational unit. This escalation represents
additional cost to the organisation through cost of labour of specialists
and the diversion of these specialists from other activities [Das
2003]. Queries may be consigned several times. Each consignment starts
another Query-Response Cycle (Figure 2) until the resolution
is passed back, either directly or via the tiers, to the original querist.
Figure 2: Tiered Query-Response Cycles
The elements on the left-hand side of the Query-Response Cycle model
(Query reformulation, search strategies, use of knowledge sources, creation
of the response set) and the final offering of one or more responses will
vary according to the maturity of the knowledge infrastructure. The actions
of the respondent will vary according to the perception of service quality
of the querist [see Brogowicz 1990]. The type of
knowledge problem entering the cycle has an effect on the knowledge processing
and subsequent consignments to more expensive knowledge resources such
as expert group tiers. Zack [Zack 2001] offers a
suitable framework for the typing of such knowledge problems.
2.2 Knowledge Problems
According to [Zack 1999] different types of knowledge
problems are best processed by differing knowledge and information systems
strategies. Zack's knowledge problems are summarised in Table 1. For each
of his knowledge problems, [Zack 1999] suggests a
number of information systems strategies. For problems of 'Uncertainty',
he suggests: (1) providing central repositories to enhance the ability
to locate codified and documented information; (2) providing automated
capabilities to analyse large amounts of information; (3) configuring communication
networks in highly flexible ways to respond to unpredictable information
processing needs; (4) enabling communication regardless of geography or
time; and (5) enabling broadcast at-large requests for information and
knowledge, eliminating the need to know precisely where it is located.
For problems of 'Complexity' Zack suggests (1) auxiliary high-capacity
memory for managing and rapidly analysing complex sets of information i.e.
computer-based decision support systems, database systems, and expert systems;
(2) develop searchable online repositories of explicit knowledge to leverage
the organisation's experts; (3) develop the ability to spontaneously and
quickly locate experts; and (4) facilitate decentralised decision-making
by making local information available globally and global information available
Table 1: Summary of Zack's knowledge problems
Becker's strategy [Becker 2001] to reduce uncertainty
involves acquiring increasing amounts of information until the problem
is diminished in terms of uncertainty. He also suggests that a problem
defined as ambiguous will not be resolved by this strategy; rather it may
make it worse. The information already at hand needs to be processed in
order to reduce an ambiguous problem to a complex one.
For issues of 'Ambiguity' and 'Equivocality', Zack suggests the provision
of communication technologies to best support dialogue between a flexible
and responsive network of experts and associates. The purpose of the dialogue
is to transform problems of 'Ambiguity' and 'Equivocality' into problems
of 'Complexity' and 'Uncertainty'.
In some ways Zack's approach is similar in nature to [Hansen
et al 1999] who when describing their two knowledge strategies, codification
and personalisation, promoted the use of information systems in their codification
strategy and communication technologies in their personalisation strategy.
The nature of the problem types (queries) directed at the call-centre effects
the process employed to respond to that problem.
So, how this kind of process is executed, depends among other things
on the specific size of the call-centre. The authors suggest the following
classification of different sized call-centres: Small (to 12 agents);
Medium (12 to 50 agents); and, Large (50 - 3000 agents and
By using this classification scheme, it's possible to build up a call-centre
specific model containing a Knowledge Infrastructure (KI) Hierarchy.
3 Knowledge Infrastructure Hierarchy
The authors developed a model for a Knowledge Infrastructure (KI) Hierarchy.
In this model, call-centres are classified by size.
Four call-centre specific KM interventions are mapped within this KI-Hierarchy
model. Additionally, the model includes four process-oriented dimensions
to analyze the call-centre types in terms of their organisational knowledge
infrastructures for supporting business and knowledge processes within
In Table 2 KM interventions for call-centres (Training, Metrics, Knowledge
Policy applied, Knowledge Streaming and Use of Knowledge Sources) can be
analysed in detail using the [Strohmaier 2004] B-KIDE
framework. A special example of this is the application of a knowledge
and information policy. Such policy would dictate the use of a particular
sequence of activities for the creation, storage, transfer and application
of knowledge. In chapter 4 the mentioned KM interventions are described
in more detail.
3.1 Business Process Standardization Level
A process-oriented dimension is the Business Process Standardization
Level (BPSL). This dimension indicates the level of business process implementation,
formalization and standardization in a call-centre.
The level values we suggest as Low, Medium and High. A
Low BPSL implies that the analyzed company has not modelled or implemented
any business process yet or their occurrence is at a low level. Some decision-supporting
criteria are, for example, the existence of ISO-certifications in a call-centre
or the complexity and quality of employed process modelling tools. In a
company classified with a Medium BPSL there is a high probability
of existing ISO-certifications and modelled processes. Accordingly, a High
BPSL requires high quality modelled and implemented business processes.
The BPSL forms the basis for the further analysis in the KI-Hierarchy
Model. It influences the Knowledge Process Maturity Stage and the
Knowledge Process Scenario Type.
Table 2: KI-Hierarchy
3.2 Knowledge Process Scenario Type
An additional process-oriented dimension in the KI-Hierarchy is determined
to be a Knowledge Process Scenario Type based on the B-KIDE (Business process-oriented
Knowledge Infrastructure Development) approach from [Strohmaier
2004/2003] with which knowledge flows within
and across business processes can be identified and analyzed. Based on
this analysis key knowledge flows can be supported, business processes
can be adapted accordingly, structures for organizational memories can
be derived, and effective IT support can be designed. By using this methodology
it becomes possible to analyze and classify knowledge infrastructures in
a company, and in this case, in call-centres.
[Strohmaier 2004] uses a set of specific knowledge
activities to describe knowledge work within and between organizational
business processes. The knowledge activities used (knowledge generation,
transfer, storage and application) are based on [Heisig
2001]. The method of [Strohmaier 2004] also
implies the importance of identifying relationships between business processes.
B-KIDE tries to identify knowledge processes that span across multiple
business processes. This is a crucial factor when analyzing larger and
more complex companies.
[Strohmaier 2003] describes the knowledge flows,
such as those found in call-centres, in a formal framework of knowledge
processes. He illustrates three examples or scenarios of knowledge processes
in Figure 3.
Figure 3: Illustration of potential knowledge processes [Strohmaier
As shown in Figure 3, related business process steps (in Figure
2 represented as e.g. BP1S4 - Business process 1 step 4) are illustrated,
per knowledge domain, to give an idea, where that knowledge domain is being
generated, stored, transferred and/or applied.
Knowledge Process Scenario Type A illustrates a complete knowledge
process. All knowledge activities are supported and managed. Knowledge
Process Scenario Type B shows, that knowledge storage and transfer
are not defined and supported in any considered business process. From
this it follows that the considered knowledge flow is identified, but there
exist potential flaws in the appropriate knowledge process. Knowledge
Process Scenario Type C gives an example for the waste of knowledge
and resources. Knowledge is generated and stored but its transfer and application
are not supported or managed by the knowledge infrastructure.
These scenario types (A, B, C) can be assigned to the different size
types of call-centres.
3.3 Knowledge Process Maturity Stage
The authors propose an expansion of the classification of call-centres
by using an appropriate KM- and process-oriented maturity model.
[Paulzen et al. 2002] developed the Knowledge Process
Quality Model (KPQM). This model includes the following dimensions: maturity
stage, knowledge activity, management area and assessment
The maturity stage dimension forms the basis for an applicable model
to define Knowledge Process Maturity Stages (for call-centres).
Table 3 gives a short overview of the maturity stages
of KPQM. A special benefit of the KPQM is its process-orientation concerning
business as well as knowledge processes.
|1 - Initial
||The quality of knowledge processes is not planned and changes randomly.
This state can be best described as one of chaotic processes.
|2 - Awar
||Awareness for knowledge processes has been gained. First structures
are implemented to ensure a higher process quality.
|3 - Established
||This stage focuses on the systematic structure and definition of knowledge
processes. Processes are tailored to react to special requirements.
|4 - Quantitatively Managed
||To enhance the systematic process management, measures of performance
are used to plan and track processes.
|5 - Optimising
||The focus of this stage lies on establishing structures for continuous
improvement and self-optimisation.
Table 3: Maturity stages of KPQM [Paulzen
et al. 2002]
3.4 Knowledge Risk Level
The authors propose an additional perspective for analysis of knowledge
infrastructures in call-centres: the Knowledge Risk Level. As the
name says, this dimension handles with a special sort of critical corporate
risks, so called Knowledge Risks. These risks concern the usage
of knowledge and its possible danger for a company's success. According
to [Lindstaedt et al 2004] Knowledge Risks are risks,
which derive from a lack of knowledge and skills needed for critical actions
and decisions in business situations. For example, interferences in knowledge
(or information) flows, non existing transparency of knowledge or the change
of technology are potential knowledge risks in this context.
One of the authors (Koller) is currently working on a theoretical framework
including a method, called Knowledge@Risk, which aims to introduce
and manage Knowledge Risks. They are deduced from three perspectives: business
and knowledge processes, human know-how and the corporate organisational
business environment. The primary perspective focuses on business and knowledge
processes. This builds up the link to this KI-Hierarchy Model and
its process-oriented dimensions. Inconsistencies and gaps in knowledge
processes as shown in the Knowledge Process Scenario Type B and
C situations are the leading risk factors for the assessment of
the Knowledge Risk Level.
By applying Knowledge@Risk and its process-oriented perspective
special knowledge profiles (knowledge process-, business process- and activity
role-profiles) are analyzed. These results are used for the identification
of Knowledge Risks, which are assessed and summarized for the consolidation
with the usual business risks in further Knowledge@Risk process
steps. Figure 4 illustrates the basic elements of the Knowledge@Risk
Figure 4: Knowledge@Risk - basic elements
The BPSL is an additional Knowledge Risk Level indicator. A low
level of business process standardization offers a dangerous potential
for the inefficient usage and the loss of critical knowledge.
We suggest low (knowledge infrastructure works sufficiently;
no critical gaps in knowledge processes identified), medium (knowledge
infrastructure is deficient; control actions should be taken) and high
(knowledge infrastructure brings out a critical risk for the company's
success; control actions should be taken immediately) as tentative values
of the Knowledge Risk Level. Table 2 shows that the Knowledge
Risk Level grows inversely proportional to the BPSL values.
4 Application of the model
The following chapters demonstrate how KM interventions in different
Call-centre size-types are adopted and how the KI-Hierarchy Model can be
applied. The authors give a practical example for a small Call-centre.
4.1 Small Call-Centre
A small call-centre providing support services e.g. an internal information
technology help-desk, might have a number of agents who provide analogous
support services. They each manage customer queries and no knowledge specialisation
strategy is in place. New agents acquire their skills on the job with assistance
provided by their colleagues and supervisors. Calls are distributed using
an ACD or manually by a reception agent. Support issues are not recorded
or formally disseminated to the group. Agents use some standard information
sources and build up their own materials over time. Mentoring and on-the-job
learning are the principal training mechanisms. They gain an increased
knowledge of common queries over time making them more efficient and able
to provide greater service quality.
Using [Strohmaier's 2004] B-KIDE framework the
knowledge processes in a small call-centre would generally coincide with
his scenario B whereby knowledge storage or transfer is not defined in
any considered business process. Informal storage by individual agents
[Markus 2001] discusses this phenomenon in her
work on knowledge re-use. Transfer can also occur independently of formal
storage media through personal conversation rather than the passing of
documentation but no formal business process is in place to facilitate
4.1.1 Example: Building Codes Q (BCQ)
Building Codes Queensland (BCQ) is a state government call-centre that
has 12 agents. The centre advises building inspectors on the application
of building codes, regulations and legislation. Agents are all experienced
tradespersons, builders or building inspectors. New callers are routed
to agents by a receptionist. Some callers dial direct to their favoured
BCQ staff, knowing them to be experienced and knowledgeable or because
they have a personal relationship with them built over years of advice.
BCQ agents use personal information materials and public information
made available through the organisation's intranet. New agents learn on-the-job.
Advice is given either on the phone, via email, or in more formal circumstances
by letter. Prior to 1999, there was little sharing of advice given by agents
to clients because there were no formal organisational storage and transfer
knowledge processes. The consequences were that rework of research into
issues was common and smarter clients could 'shop around' for advice i.e.
try several agents until they got the advice that would best suit their
In 1999, BCQ trialled an advice-recording system. Agents would identify
that advice had already been given on the matter to the caller and refer
the caller back to the initial adviser ensuring consistency of advice.
In effect, the system recorded that a 'transfer process' had already taken
place. The system could also identify agents that had unresolved or 'open
cases' i.e. the agent was still researching the case offline and would
re-contact the caller when they had resolved the issue. Agents with a high
'open' case load could be taken off the support desk to let them clear
their load (create knowledge). In conjunction with the advice-recording
system, a search facility was implemented to data-mine past advice letters
to reduce rework (transfer ? application).
The take-up of these systems was poor. Agents did not think that the
additional work to record issues and advice would reduce their work in
the long term. One reason cited was that the changing nature of codes and
regulations meant that re-using past advice without first checking its
currency and validity could lead to the provision of flawed recommendations.
4.2 Medium Sized Call-Centres
A medium sized call-centre requires greater attention to knowledge re-use.
One would expect to find in a medium-sized call-centre some central database
system to which agents can refer when advising their callers. This database
reference systems fall into two types: pre-recorded information prepared
for the call-centre agents by third parties and recorded queries and responses
recorded by the agents themselves. The use of such databases with call-centres
correspond to Knowledge Process Scenario Type A situation when agents
can make full use of knowledge processes whereby knowledge is generated,
stored, transferred and applied within the call-centre's intended business
Yet, research has demonstrated that inadequate training and lack of
knowledge of the intended business processes within call-centres due to
high turnover of staff can lead to ineffective use of the organisational
knowledge infrastructure [Timbrell et al. 2003]. A
Knowledge Process Scenario Type B situation can arise where knowledge
is stored in central repositories by agents but ineffectively transferred
to other agents. Agents may use one or both of these types of knowledge
bases. ACD management tends to be more sophisticated often including a
monitor within the call-centre that records real-time statistics on call
rates and caller queues. While call throughput is important so is advice
quality. However, advice quality is often secondary in the traditional
management of call-centres. This is quite apparent when one reviews the
traditional measures of call-centre management within the literature [see
Feinberg et al. 2000].
The determinants of advice quality [Brogowicz 1990]
are able to be empirically defined and include tangibles, reliability,
responsiveness, assurance and empathy. Because of the greater potential
for knowledge variance within a larger call-centre, and the propensity
for larger call-centres to be dealing with a larger number of callers with
greater varying advisory needs, a more structured knowledge infrastructure
is required to meet these service quality and efficiency challenges. Some
examples of knowledge strategies employed towards this end are: 1) Information
and Knowledge Policy that determines the extent of advice given by agents
2) Cells of agents specialise in particular knowledge domains and operate
as a community of practice stewarding the knowledge for that specific domain.
This is called Vertical Knowledge Streaming. 3) Unresolved caller queries
are referred to a knowledge broker (perhaps a supervisor) who re-assigns
the query within the call-centre or refers it to staff within the organisation
proper. 4) An 'all points bulletin' is sent to all call-centre agents who
respond with suggested responses or suggested experts who may be able to
help. [Markus 2001] and [Davenport
1998] both exhort the importance of access to experts and expertise
as the hallmark of effective knowledge re-use.
4.2.1 Medium-Sized Call-Centre Case Study: CC1
CC1 operates within a government department whose principal business
is to maximise the economic potential for agricultural industries on a
sustainable basis. Assistance is provided to both domestic and commercial
clients, with some services being on a fee for service basis. Agents in
CC1 address queries on a wide range of scientifically based topics. Any
caller can contact CC1 for the cost of a local call either to a direct
number or via a 13 prefixed number.
Management implemented a policy not to use IVR technology to route calls.
The policy that people, and not information systems, would handle call
diversions is designed to heighten callers' perception of functional service
quality [Brogowicz 1990]. The most significant investment
in technology is the infrastructure used to manage call-centre operations
and statistics. Continuous displays of calls in queue and average time
in queue monitor centre performance in terms of call management. A range
of databases and Intranet based information sets are used to support the
agents. Scientists are encouraged to provide information to the call-centre
to allow them to address hot issues.
The department which hosts CC1 makes a conscious effort to maintain
its Internet site with the most current information. However, due to issues
such as lack of access to the World Wide Web and degree of comfort with
the technologies by the department's principal constituents (the agricultural
sector), querists prefer to access the call-centre for advice and information.
Scientists provide Tier 2 support mostly from research stations outside
the central business district.
Although responses to callers are not scripted, agents are instructed
not to give advice but only to provide information sourced from their standard
information systems. They are supported by a range of fact sheets and databases
populated by the scientists. These continually evolve through interaction
between the scientists and the call-centre agents. Agents have responsibility
for meeting with scientists and business groups to identify future events;
report on issues being addressed by the call-centre; satisfaction levels
with the call-centre; and, to request additional or updated fact sheets
from scientists and other informants within the organisation.
Due to the technical and scientific nature of calls, substantial effort
is placed on recruitment and training. The key selection criteria for call-centre
agents contain a requirement to achieve superior functional service quality
attributes by using techniques such as active listening; patience; and,
when and how to speak [Thompson et al 2001]. Superior
technical knowledge of the department's business minimises the time to
reach acceptable levels of competence (typically 6-8 months). Maintenance
of staff's capabilities is achieved via weekly meetings (operations close
down) that allow discussion of important issues (able to be identified
from records of the calls), future events and other issues of concern.
The focus of the call-centre is on client satisfaction and speed of
service (80% of calls answered within 20 seconds). Normal days involve
each operator taking approximately 80 calls. However in peak times caused
by staff shortages or increased activity from emergent events, CC1 can
cope with up to 200 calls per operator per day. All calls are recorded,
with a library of calls dating back several years.
Performance measures which are collected and used in reporting include
response rates (grade of service and occupancy statistics are most used
in reporting and decision making), quality issues (the team leader "hot
links" to an operator in order to gauge quality of responses via a
sampling process) and staff satisfaction. Currently 60% of calls are resolved
in CC1 while the vision for the future is to have 80% of all calls completed
by the call-centre staff (80% knowledge self-sufficiency). Only 20% of
calls will be consigned to 2nd tier support groups.
4.2.2 Medium-Sized Call-Centre Case Study: CC2
CC2 is a call-centre in a government department that provides consumer
advice. CC2 is an integral component of a Customer Service Centre (CSC)
which also includes counter staff both in the central business district
and at regional centres. Calls to the widely publicised 1300 number (free)
are routed to the nearest available reception point; either a regional
office or the call-centre located in the central business district. The
IVR system then routes the call based on whether it is a general query
or complaint (more complex) or transaction relating to the regulation of
business. Transactional functions are performed in real time while client
issues may involve further research, be consigned to 2nd tier
experts or investigations officers where breaches of legislation have occurred.
Prior to a reorganisation in October 2002, the CSC comprised of approximately
60 Brisbane based staff. Regional staff generally processed over the counter
enquiries or took calls directly. The CSC consisted of teams with clearly
defined roles separating transaction processing (lower level of staff),
complaints via the phones, written complaints and other business services
such as data entry and bank reconciliation of payments. The situation was
characterised by very little communication between teams, limited written
procedures and no ability to load balance during peak times. Training was
minimal, on the job and ad-hoc.
After October 2002, the organisation corrected the (both real and perceived)
inequities in work through the formation of work clusters and multi-skilling
of all staff in the CSC. The supporting information systems in the regional
offices were integrated with the Brisbane systems and staff performing
customer service roles in the regions were included into the CSC division.
The PABX system commenced routing callers to the nearest available service
staff to both decrease communications costs, but also to provide local
recognition for services of the department. Strategies included rotation
of staff, production of procedure manuals, targeted training (including
regular meetings) and other technology upgrades.
The CSC consists of front counter staff who predominantly serve face-to-face
customers, a call-centre taking IVR directed calls and the business process
area. This model is consistent with that of other service industries [Graumann
et al 2003]. The CSC established a matrix of teams across functional
areas and regional offices. These teams are encouraged to share knowledge
and discuss decisions across these lines of responsibility.
Management sees technology as the best option to drive efficiency while
still providing advice and management registers to support the State's
businesses. All CSC staff have access to the departmental Intranet. This
includes staff contact lists, self publishing, threaded discussion and
event driven capabilities. Specific work groups have also created sub-webs
for their specific use.
Transaction systems include registers for management of business details
and history of client complaints. These systems are accessible to all CSC
staff. A static database has been developed in house to hold hierarchical
lists of information, categorised by functional area to allow fast access
to the information. The information is placed in this database by staff
with a degree of expertise in that particular area and only contains statements
of fact - no interpretive information is provided.
Reported performance measures include:
- response rates (including statistics on number of calls by type, time
in queue and dropout rates. Approximately 1/2 million calls per year are
- quality issues ( mystery shopping exercises have been performed to
test a range of criteria including consistency and accuracy of advice and
service focus) and
- staff satisfaction ( no measures available for this but at Dec 2002,
the longest serving employee in one team had 4 months experience).
An attempt to embed continuous performance improvement through training
has led to the appointment of a dedicated training officer. The role includes
the normal formal training sessions, but also includes responsibility for
maintenance of the information database. Other strategies include regular
team meetings and externally facilitated workshops to build trust and shared
vision through identification of improvement strategies by the teams.
Action plans developed and reviewed 6 monthly include identification
of knowledge gaps and processes to address these. New staff "hot link"
with an experienced operator for 2 weeks prior to going solo while each
operator has a buddy nominated by management to match experienced with
new staff for mentoring and support.
CC2 calls are not recorded. However, analysis of calls via "hot
link" (where a researcher tracks the IVR path of a caller) suggests
the IVR system effectively directs callers with "uncertain" problem
types to agents with expertise from experience and others with predominantly
"complex" (mainly due to legislative interpretation) and "ambiguous"
problems to agents who need to do further research before the problem is
4.2.3 Comparison of CC1 and CC2
CC1 has developed from a planned strategy to provide accurate and timely
information as part of an integrated approach to client management. Based
on a personalised service and supported by training and coaching, the approach
is to protect clients from the technology driving the call-centre. CC2
has evolved from business pressure to cope with increasing demand for timely
and accurate advice. Efficiency continues to be the major business driver
in CC2. Table 2 summarises the strategies employed in each of the call-centres.
Use of IVR/Call Routing
80 calls per person per day
Up to 200 calls per person per day
Information recorded on calls
Minimal - for reporting and return contact purposes
Minimal - for reporting and return contact purposes
Updating of information repository
Meetings with scientists / 2nd Tier operatives
All information available
Limited information available
Consignment if query cannot be resolved with standard information
Research performed by agent prior to consignment
Table 4: Comparative Strategies of two medium-sized call-centres
4.3 Large Call-Centre
Invariably these call-centres introduce the complicating elements of
multi-sited agents, usually multi-country and possibly a multi-lingual
customer base. Advanced ACD or IVR are the normal entry path diverting
customers to either general or specialist groups of agents depending on
customer choice within the IVR pathways and / or general agent call handling
procedures. Call agents use both pre-recorded and recorded knowledge repositories.
A large call-centre, such as that found in Hewlett-Packard, corresponds
to Knowledge Process Scenario Type A.
Typical knowledge infrastructure strategies include: 1) Multi-layered
call-centres where general agents handle the majority of initial responses
and have a selection of specialist groups to which more difficult queries
are referred. 2) A subset of the Intranet based reference materials is
made available to customers to reduce the call pressure on the centres.
Customers are referred to this repository by agents thereby encouraging
knowledge self-serve. 3) Customers with more complex knowledge needs are
assigned specialist knowledge brokers and call-centre agents. They may
also have access to a greater subset of the internal call-centre knowledge
reference repositories to alleviate call rates (specialist knowledge self-serve).
4) Some advanced call-centres use knowledge 'self-sufficiency' measures
[Timbrell et al. 2002]. Knowledge self-sufficiency
is the ratio of queries resolved to the total queries within a call-centre
(or group within a call-centre responsible for a specialist knowledge domain).
Unresolved queries referred to more specialist groups represent the percentage
balance of this ratio. 5) Measurement of query incidence and the proactive
knowledge transfer responses to specific knowledge deficiencies in the
customer base e.g. email bulletins, seminar series, product re-configuration
and agent/customer education strategies.
Over the past years maturity models were successfully developed and
applied in research and industry. They are primarily used to analyze systems
(e.g. companies) in terms of their KM readiness and to assist in developing
KM strategies. The effects of applying maturity models on call-centre Knowledge
Infrastructure analysis are not explored sufficiently so far.
Is an adjusted maturity model, like the one introduced by the authors,
able to assist managers in developing and improving KM and quality management
strategies? The authors suggest that maturity stages provide a guide towards
an optimal state for knowledge process management.
Another open issue concerns the correlations between call-centre
size-type, BPSL value and Knowledge Process Maturity Stage.
Our future work focuses on the refinement of the KI-Hierarchy Model (in
a broader context beyond call-centres) and its validation through empirical
Table 5 shows a comparison of the four case studies using the Knowledge
Infrastructure Hierarchy and Knowledge Process Maturity model
for call-centres. Whilst certain facets of the model do not necessarily
apply to the differing sizes of the call-centres this may be due to the
background and experience of the call-centre management and the evolutionary
growth of each call-centre.
Building Codes Q
|Business Process Standardization Level
Low - Medium
|Knowledge Process Scenario Type
B - C
|Knowledge Process Maturity Type
|Knowledge Risk Level
Full preparatory training
Full preparatory training
|Use of knowledge sources
|Knowledge policy applied
Information use policy
|Knowledge streaming strategy
Horizontal and vertical
Horizontal and vertical
Call throughput/service quality
Service quality and knowledge self-sufficiency
Table 5: Comparison of the case studies
As call-centres get larger, the knowledge processes become more formal
to ensure consistency of advice and efficiency. This paper suggests a Knowledge
Infrastructure Hierarchy and discusses a Knowledge Process Maturity
model for call-centres to advance and frame future discussion of these
knowledge intensive environments.
Call-centres will benefit from taking a knowledge process and infrastructure
perspective; it informs them in determining knowledge interventions that
improve service quality and efficiency. A classification by Business
Process Standardization Level, Knowledge Process Maturity Stage
and Knowledge Risk Level helps to build a funded basis for further
The four case studies presented are a first step towards the application
of these generic models. Further research is required to advance and confirm
The Know-Center is a Competence Centre funded within the Austrian Competence
Centre program K plus under the auspices of the Austrian Ministry of Transport,
Innovation and Technology (www.kplus.at).
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