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International Journal of Caring Sciences

May-August 2020 Volume 13 | Issue 2| Page 1480

www.internationaljournalofcaringsciences.org

Special Article

Knowledge Management Driven Information Systems for Improved

Services in the Social Administration Field

Dimitrios Stamoulis, BSc., MSc, PhD

Member of the Social Administration Research Laboratory, University of West Attica, Athens, Greece

Charalampos Platis, BSc., MSc, PhD

Research and Studies Officer, National School of Public Administration and Local Government

(ESDDA), Athens, Greece

Anastasios Sepetis

Research Fellow, Department of Business Administration, University of West Attica, Athens, Greece

Maria-Elisavet Psomiadi, RN, MSc

National School of Public Administration & Local Government, Healthcare Services' Administration

Specialization Department (ESDDA), Athens, Greece

George Pierrakos, PhD

Professor, University of West Attica, Athens, Greece

Correspondence: Charalampos Platis, 211 Pireos Str, 17778 Tavros, +306944602540,

charisplatis@gmail.com

Abstract

Knowledge management (KM) is mainly a social process, involving peoples’ attitudes, behaviors, opinions and

islands of information and knowledge scattered all over the public sector and the society itself. It is a paradox

that despite being a social process, KM is by and large absent from social services provision and support in the

Social Administration (SA) field of public administration. This paper tries to set the scene for using KM in the

SA field, proposes the infusion of KM into the main social services’ business processes, classifies relevant

knowledge assets in SA and identifies information technologies that could enable a paradigm shift for SA from

the top-down orientation in policy making and services provision to an knowledge based bottom-up approach, in

order to increase efficiency and effectiveness. Two short cases are discussed to propose KM as a key driver for

designing social administration’s information systems for improved social services: the social security

contribution evasion problem and the organization of a knowledge-based service desk to support social

administration’s stakeholders.

Keywords: Knowledge Management, Social Administration, Social Administration Information Systems

.

Introduction

Managing knowledge in the public sector has

been a concern since the establishment of the

field (Henry, 1974), since public administration

is a vast area of operations and practice, covering

at least the following areas: hypotheses

formulation and validation, strategy, services,

operations, infrastructures and quasi market

infrastructures. Wing (2002) emphasizes in KM

importance as an informed decision making tool

for Public Administration, mainly for effective

situation handling. Knowledge is produced in

each and every aspect of interaction among

governing bodies, public administration, private

and legal persons, while feedback is given

through governance structures to align

purposeful action with desired outcome and

expected behavior by the recipients of the public

administration decisions. In the context of public

management, knowledge management (KM) has

received quite a lot of attention, as “it is

increasingly advocated for improving novelty

and agility in policy development and service

delivery” (Pee & Kanhanhalli, 2016: 188). The

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proliferation of digital information technologies

in public administration as web-based knowledge

management systems – see for example (Savvas

& Bassiliades, 2009) – has greatly facilitated the

effectiveness and efficiencies of its operational

side, increasing the quality of service for citizens

and all the relevant stakeholders.

Social administration (SA) is part of the public

administration structure and operations, having

to do with the work, family, welfare and caring

dimensions of life, where the public sector is

expected to play a role, in view of implementing

its social justice vision (Au, 1994). Representing

horizontal policies by its nature, SA needs

particular attention in the context of KM,

because social services provision is very

interactive with the social constituencies; also

SA has direct implication on the daily life of the

involved stakeholders. Before further refining the

focus of KM on SA, it is useful to define more

explicitly the SA scope. The preferable choice

for scoping SA, is given by the definition as

provided by (Lohmann & Lohmann, 2001),

which refers and attributed to related events such

as:

•

Social Services Administration

•

Encourage, evolve and practice social

services leadership

•

Decision-making process at the political

and institutional level that influences the exercise

of social policy

•

Continuous efforts to sustain and

develop a social service system.

In this context, the study of KM becomes more

demanding, as information and knowledge flows

from multiple dimensions and pertains to all

aspects of government policies that impact

stakeholders from both the demand and the

supply side of social services. According to

Sveiby (2005), KM has to “nurture, leverage and

motivate people to improve and share their

capacity to act.” Since social services represent a

significant tool for public administration to

implement social interventions, the application of

KM principles and techniques, along with

appropriate information technologies, is a strong

prerequisite for conceiving, designing,

implementing, operating and measuring social

administration’s projects and outcomes.

Literature review reveals that KM in SA is not

yet explored in depth (excluding the healthcare

sector, which is far better researched), apart from

some case studies – for example knowledge

management at the U.S. Social Security

Administration ( Rubenstein- Montano,

Buchwalter & Liebowitz, 2001) – nor

implications

for

information

systems

requirements have been derived out of the KM

perspective. Although data are being created

during the consumption of social services as well

as through the interactions of the social

stakeholders, especially in the welfare area,

“Data Driven Management’s impact on the

provision of welfare services is still being

realized and worked out” (Pedersen

and Wilkinson, 2018: 16).

Given that one the best definitions describes KM

as a process in which knowledge conceived,

allocated, and effectively used (Davenport,

1994), this paper proposes the infusion of the

KM perspective into SA information systems

(IS) to increase their effectiveness, information

richness and accuracy, using two specific cases,

that fit very well to the above definition of KM.

Material for these two cases has been collected

as part of a research work, trying to identify

digital transformation opportunities for the SA

field in view of raising their effectiveness in

terms of their design and delivery. A preliminary

part of this research has concluded on the

appropriateness of the SSM methodology for

designing information systems (IS) for the SA

field (Stamoulis, 2019).

How KM can be applied in the SA field

First of all, why KM is needed in the SA field?

Symptoms of a KM absence in any

administrative environment include repeating

mistakes, duplicating work, poor customer

(beneficiaries) relations, good ideas being lost,

dependency on key persons rather than key ideas,

slowness in launching news services etc. To a

higher or lesser extent, SA is susceptible to all

these symptoms, partly due to the multiplicity of

agencies and institutions that design and deliver

social services, partly because of the dominant

political ideologies that drive decisions in this

area, instead of a holistic design accruing from

knowledge elicitation.

Tacit knowledge is abundant in societal systems.

Beneficiaries of the social services bear

important opinions and views that need to be

taken into account; bottom-up approaches work

much better in this field as compared to top-

down. For example, demographic incentives can

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be succinctly identified and better prioritized by

those who would like to become parents, so

eliciting this knowledge requires focus groups,

structured interviews, opinion surveys, etc.

Statistical data may not infer value-based

information about demographic incentives as

effectively as the tacit knowledge elicitation

techniques. This is one of the reasons that

strategies for social interventions have been

characterized as low effective ones (Todd, 2017)

Such knowledge must be extracted / externalized

and then get internalized by the various SA

agencies and institutions for assimilation,

processing, enrichment so that it can be rich

enough to be presented back to the various

stakeholders for validation.

Table 1. Applying KM key processes onto social services provision.

Indicative list of social services

Demographic

incentives,

family support,

childcare

Employment /

unemployment

support

Ageing,

third age

support

Anti-poverty

measures and

policies

(K

n

ow

led

ge M

an

agem

en

t) k

ey p

rocesses

Knowledge

acquisition

Opinion

surveys,

collection of

best practices,

collaboration

with family

organizations

Talent

hunting and

recruitment,

Research

&

Develpoment

, access to

technologies,

collection of

best practices

Statistical

analyses of taxable

income; input from

people in need

Knowledge

development

Policies and

measures

Networking,

training,

apprenticeships

Sharing of

knowledge

through

communities

of practice in

order to

perform pilot

actions

Dialogue with

people in

precarious

situations to

understand why

and how have

fallen in the

poverty zone

Knowledge

dissemination

and

exploitation

Inform and

communicate

target audiences

Meeting

arrangements,

formal training,

demand-supply

identification

and matching

Inform and

communicate

target

audiences

Measures and

policies for early

warning to people

at risk of poverty

Knowledge

infusion

feedback

Statistics of

effectiveness,

customer /

citizen

satisfaction,

policy goals

fulfillment

Statistics of

effectiveness,

customer /

citizen

satisfaction,

policy goals

fulfillment

Statistics of

effectiveness,

customer /

citizen

satisfaction,

policy goals

fulfillment

Statistics of

effectiveness,

customer / citizen

satisfaction, policy

goals fulfillment

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Social services need to be consumed as

designed, in order to be effective. Given that

legislation is quite often complex and

scattered into many laws and regulations,

potential beneficiaries of social services need

to have access into user friendly systems that

represent knowledge as a collection of cases

and solutions. So, it is obvious that case-

based reasoning must be applied by SA in

order to organize and present knowledge in

such a way that citizens and any kind of

stakeholders may be informed whether they

belong to potential beneficiaries of these

social services, for example.

The aforementioned examples refer to the

demand side for knowledge management in

the SA field. Let’s know have a look at the

supply side. The provision of social services

is subject to the typical public administration

decision making cycle, usually starting from

a political statement or will, getting

formulated in some sort of measures or

policies that are applied and then calibrated

through, at the best case, a continues learning

cycle that adapts to the situational

knowledge produced during policy

deployment and aligns with the social needs

on the go. In this way, SA and its

stakeholders become a community of

practice. While this is not usually the case,

the SA community of practice is the

preferable model, where stakeholders should

negotiate, communicate and coordinate with

each other directly in order to develop their

work. These collaborative actions are highly

points of importance in work practice

(Brown & Duguid, 2000). Such

knowledgeable co-evolution of stakeholders

creates an effective continuous consultation

that achieves gradually strategic alignment

and fruitful supply-demand relationships.

Which are the core business process

underpinning the main social services?

Arguably, social services cover the needs for

support for most of the phases of human life

from cradle to grave, including: demographic

incentives, support for families, childcare

facilities and financing (e.g. vouchers for

childcare), social inclusion, unemployment

aid, assistance for employment, work-life

balance, work conditions supervisory,

employer-employee relationships, pensions

and third age arrangements for active and

healthy ageing, relief for the disabled and aid

to the poor as well as combating the risk of

poverty. Along and across these business

process, horizontal and vertical social

services are designed, implemented,

deployed and applied by the SA. KM

activities must create additional value within

the core business processes to be worthwhile.

How this can be achieved in the SA field?

The following table tries to give a

preliminary set of answers:

The aforementioned table is not meant to be

an exhaustive list of KM methods applicable

to SA but aims at demonstrating the value of

injecting KM into the provision of social

services. It is worth mentioning in this vein,

that the European Commission (2020) has

adopted the Open Method of Coordination

for many of the topics that pertain social and

cultural issues, in an attempt to promote KM

practice among member – states. Reading

from the Commission’ web site: “EU

Member States have much to gain in

exchanging good practice on the way they

design policies and funding schemes. This

form of cooperation is referred to as the

"Open Method of Coordination" (OMC), and

is used in many policy areas.” It is therefore

obvious, that applying KM onto social

services design and implementation is of key

importance for the overall SA success.

In such a KM culture, all types of actors that

participate into the social services provision

and consumption process may potentially

produce knowledge assets. Looking at the

above table, sources of knowledge assets can

be easily identified and knowledge assets can

be classified into categories, such as:

-

reference assets (e.g. statistics,

procedures, models, key performance

indicators for measures and policies, etc.),

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-

informational assets (e.g. legislation

texts and interpretations, , policies,

directives, eligibility criteria, etc.)

-

operational assets (e.g. beneficiaries’

evaluation, experts’ advice, communities’

views, professional service providers’

opinions, situational knowledge, checklists,

etc.)

-

resource assets (e.g. list of agencies,

professionals, funds, sample application

forms, indicative workflows for the

provision of social services etc.)

Implications for designing KM-driven IS

for SA

IT is a key enabler in the KM domain,

because KM cannot scale up unless

appropriate information systems are used.

Different types of information systems are

used to support the various knowledge

processes, such as knowledge acquisition,

creation, organization, usage, sharing,

deployment and dissemination. So far, SA is

mainly based on statistical analyses of past

data, such as trends and projections. Tacit

knowledge, opinions, situational knowledge

etc. are lost and do not provide input into SA

policies, since there are no procedures and

information systems to capture and process

them.

Therefore, online surveys, situation

description forms, calls for specific cases

self-descriptions, platforms for collaborative

knowledge creation and sharing, open

consultation platforms for civil society

engagement are desperately needed in the

SA field.

Regarding knowledge dissemination and

exploitation, expert systems and case based

reasoning technologies need to be applied so

that when individual cases are described by

the interested parties, relevant legislation and

policies are presented to help those

concerned. Needless to say, easy to use web

sites and mobile apps are indispensable for

information collection and sharing; on-line

communities need to be built around social

services topics, through which inferences can

be made about areas of concern; online

platforms for exchanging views, opinions

and ideas are also helpful for internalizing

tacit and scattered pieces of knowledge.

Online communities have started to attract

some attention as a tool in the area of SA

(e.g. an online community to support parents

in their transition to work (Bista et. al.,

2013)), but their full potential is not yet

realized.

Since an increasing number of the social

services may be offered by the public

administration through private sector

subcontractors or private-public partnerships,

SA needs also to employ new types of

information systems, the so-called Public

Service Platforms (PSP). According to the

study (Ranerup, Zinner Henriksen &

Hedman, 2016: 6) that coined the term PSP,

“this technology supports the demand side of

the marketplace (i.e. citizens who search

among public offerings) as well as the supply

side (i.e. the public and private sectors that

provide publicly funded services in quasi-

markets)”. In this paper, social services such

as elder care, healthcare and pension have

been analyzed in terms of their value

proposition, value architecture, value

network and value finance. PSP will become

more and more the case since informed

decisions by policy makers and empowered

beneficiaries of the social services can be

better serviced though such technologies.

It is quite often the case that a significant

number of applicants fill-in application

forms to apply for social offerings; whoever,

usually some of them are rejected due to

non-compliance with the legislation criteria

for eligibility to these offerings, or

inappropriate evidence of claims. If all these

applications had to be manually, or even,

semi-automatically, checked, the time and

resources needed far exceed reasonable

processing requirements. The use of

integrated workflows and robotic process

automation technologies is the appropriate

answer for the SA field. According to

technopedia (2020), “Robotic process

automation (RPA) is the practice of

automating routine business practices with

"software robots" that perform tasks

automatically. These tasks include

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transaction processing, IT management and

automated online assistants. These software

robots could replace human beings for

common tasks. Robotic process automation

makes heavy use of artificial intelligence to

train these robots.” Using RPA technology

which exploits machine learning and AI

techniques, large amounts of applications

can be processed to allow for timely

acceptance or rejection of applications for

the social services benefits. However, each

time legislation changes, the robots need to

be retrained in order to correctly reflect

changes in eligibility criteria, evidence

needed and modifications of thresholds.

Figure-1: a generic conceptual information systems model for social services’ service desk

from a KM perspective

Finally, eligibility criteria for social services

consumption as well as social services benefits

usually aggregate information resources scattered

all over the public administration’s information

systems. Being horizontal by nature, social

services need to consume information resources,

compose benefits from various policy areas and

provide feedback to the rest of the public sector’s

IT infrastructures. To achieve such an

interoperability level, well-defined information

governance has to be in place, based on KM

rules and methodologies, in order to be effective.

Moreover, integrated workflows and federated

information technologies have to be employed to

support interorganizational and interdepartmental

information and processing flows; thus the need

for shared ontologies upon which the social

services ecosystems will be able to operate.

Designing information systems in such a way,

SA will be equipped well enough to enter into a

digital future.

Case-1: The social security contribution

evasion problem from a KM perspective

Turning our focus onto a specific problem to find

out how KM could be useful at the

organizational level, the social security

contribution evasion problem is analyzed below.

In most countries, social security is partly paid

by the employer and partly by the employee.

Some employers are susceptible to not timely

paying, or not paying at all, their due amounts to

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the social security funds. So far, manual and on-

site audits are being used for revealing evasion

cases. But prevention is better than treatment.

Proactive action could be taken; social security

funds many borrow experience already gained in

credit risk scoring by the banking and finance

sector.

Using artificial intelligence (AI) and risk scoring

models, social security funds may start collecting

information to produce knowledge about evasion

prone employers. Information from past cases

including demographic and sectoral data,

financial statements and taxation profiles, social

security funds may shape risk profiles and

continually fine-tune AI models that will be able

to predict, progressively with higher probability,

the propensity of a legal person to evade social

security contribution in order to categorize them

according to their evasion risk. Using such

taxonomy, the fund may allocate more

effectively their human audit resources, by

scheduling more often on-site audits to those

with higher risk indicator, as opposed to those

with lower ones. May also allocate more

experienced personnel to higher risk profiles,

than others. Auditors will need to evaluate the

risk score after an on-site audit, in order to

validate or contribute to calibrate the model more

effectively. Finally, evasion penalties or

favourable arrangements for delayed payment of

contributions may also be risk justified. In this

way, KM and AI will significantly reduce the

social security contributions evasion problem, by

using effectively the fund’s resources and

increase the fund’s revenue. Obviously, the use

of AI alone without a sound KM framework will

only incur costs without any benefits for the fund

and any such application will soon be

abandoned. This case reveals that cross-sectoral

transfer of knowledge to the SA field is

important, since there is no need to re-invent the

wheel, when successful implementations of KM

applications already exist in other sectors.

Our field investigation has shown that currently,

social pension funds’ auditors are usually relying

mainly on max five factors in order to determine

whom to re-visit for auditing; namely: (1)

previous offenses, (2) type of business activity,

(3) number of employees, (4) location, and (5)

frequency of audits over the past five years.

Using a KM perspective, the number of

parameters to be taken into account in order to

determine the risk factor can easily increase to

twenty. Knowledge elicitation through interviews

and focus groups with such auditors revealed the

following factors, in addition to the

aforementioned five, that can play a role and

should be used as inputs to an AI system to

calculate much more accurately the risk factor:

1.

Type of business activity

2.

Type of business sector

3.

Types of contracts with employees

4.

Mix of types of contracts

5.

Nationality of employees

6.

Number of employees per type of

contract

7.

Balance sheet of the company

8.

Type of management

9.

Corporate governance model

10.

Current financial standing of the

employer

11.

Location of business activity

12.

Mean value of wages and salaries

13.

Mean number of years of employees

with the same employer (loyalty)

14.

Employee selection process

15.

Employee compensation scheme

Employing a neural network as an AI engine

requires the initialization of weights for each

parameter before start calibrating it to produce

precise predictions. For the initialization,

parameters can be categorized as of low, medium

and high importance, with 0.25, 0.50 and 0.75

weights respectively. Running the model with

these weights, auditors will review the results

and calibrate further the model using past data

from the previous decade. A focus group with

experienced auditors may easily classify these 20

parameters in high, medium and low importance

groups to allow for the initial setting of the

neural network.

The effectiveness of using a neural-network

based AI engine with 20 parameters against the

empirical, simple data-base five parameters

model currently used is incomparable and will

increase geometrically the return of effort of the

audits.

Case-2: KM-based service desk for social

services

The variety of social services and the

complexity of their eligibility criteria make the

task of running effectively a service desk to

answer questions of potential beneficiaries a

rather challenging task. As shown above,

knowledge assets can be classified in many types

of assets with several subtypes. Moreover, the

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horizontal nature of offerings that cross-cut many

governmental functional areas, such as Ministry

of Tax and Finance, Housing, Education, Work

and Social affairs, Family, Welfare, etc. as well

as the volume of knowledge assets that are

produced by a variety of actors such as agencies

of the public and private sector, professionals

and experts in the area, require a sound

knowledge management governance model. In

such a set-up, a well-organized and effective

service desk to facilitate effective support and

assistance plays a dominant role.

Gartner Group (2018) has identified six

knowledge management types in relation to

customer service: agent knowledge, corporate

knowledge, social knowledge, search knowledge,

community knowledge and partner knowledge.

A generic conceptual model for social services

information systems from a KM perspective that

encompasses these six KM types identified by

Gartner Group, is depicted below:

This conceptual model shows on the left-hand

side the various types of interfaces for the social

services demand: contact (phone/ email/ video)

center agents, intelligent search engines, natural

language queries (e.g. using chatbots, alerting

mechanisms), organization and hosting of

thematic groups for listening to “the voice of the

customer”, etc. All these interfaces synthesize

and produce knowledge that can be derived out

of the knowledge base whose assets are

constantly being updated not only from content

editors (e.g. public and private social

administration agencies), but also from crowd

sourcing techniques and contributions of all

relevant stakeholders.

The evolution of such a conceptual model is

obviously on-line communities where knowledge

is produced and exchanged, allowing “new

participatory environments and spaces and the

new relationships among the classic service

professionals, the data analytics, the (middle)

managers and the citizens as end-users”

(Pederson & Wilkinson, 2018:13) that may lead

to “the provision of welfare services may become

an arena for negotiation of a new future model of

the provision of welfare services to citizens”

(ibid).

Discussion

Social administration demonstrates some

interesting characteristics among other public

administration areas that makes it a strong

candidate for adopting the knowledge

management discipline. In this paper, the need

and the importance for infusing knowledge

management theory and practice into social

services is demonstrated. Adopting a KM

approach into the business processes of the social

administration fields, has direct implications in

the selection and usage of new information

technologies that must be applied to the SA area,

if social services are to be effective and efficient,

both at the SA area horizontally and within SA

organizations. The benefits of designing KM-

driven information systems for the SA field are

exemplified by two short cases; one is about the

use of KM-based AI engines to tackle the social

security contribution evasion problem and the

other is about the organization of a KM-driven

service desk for SA, which can pave the way

towards on-line communities. Knowledge-driven

on-line communities is a promising KM tool for

SA service design and provision. According to

Colineau, Paris & Dennett (2011) results from

group interviews with welfare recipients have

shown the usefulness of establishing a

government-mediated online community that

would help them in making the transition from

welfare support to work.

Reinventing SA from the KM perspective is a

prerequisite for the public administration, and the

social administration more specifically, to follow

the governance paradigm, which is the

considered the next in public sector’s business

model reengineering (Osborne, 2006 & 2010).

Other implication implications of the governance

paradigm for social work administration are

researched by Frahm & Martin (2009).

Further steps of this research include definition

of the business processes to be designed around

each knowledge asset of the SA field as

identified above, as well as the construction of a

reference blueprint for a KM-driven conceptual

model of information systems for social services.

Place where the work was carried out :

UNIVERSITY OF WEST ATTICA,

ANCIENT OLIVE GROVE, 250 Thivon & P.

Ralli Str., Egaleo, Postal code 12241, Athens

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