Nonprofit Organizational Assessment

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Do Not Follow Where the Path May Lead;

Go Instead Where There is No Path,

and Leave a Trail!

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The Advocacy Foundation, Inc.

Helping Individuals, Organizations & Communities

Achieve Their Full Potential

Since its founding in 2003, The Advocacy Foundation has become recognized as an effective

provider of support to those who receive our services, having real impact within the communities

we serve. We are currently engaged in community and faith-based collaborative initiatives,

having the overall objective of eradicating all forms of youth violence and correcting injustices

everywhere. In carrying-out these initiatives, we have adopted the evidence-based strategic

framework developed and implemented by the Office of Juvenile Justice & Delinquency

Prevention (OJJDP).

The stated objectives are:

1. Community Mobilization;

2. Social Intervention;

3. Provision of Opportunities;

4. Organizational Change and Development;

5. Suppression [of illegal activities].

Moreover, it is our most fundamental belief that in order to be effective, prevention and

intervention strategies must be Community Specific, Culturally Relevant, Evidence-Based, and

Collaborative. The Violence Prevention and Intervention programming we employ in

implementing this community-enhancing framework include the programs further described

throughout our publications, programs and special projects both domestically and

internationally.

www.TheAdvocacy.Foundation

ISBN: ......... ../2017

......... Printed in the USA

Advocacy Foundation Publishers

Philadelphia, PA

(878) 222-0450 | Voice | Data | SMS

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Dedication

______

Every publication in our many series’ is dedicated to everyone, absolutely everyone, who by

virtue of their calling and by Divine inspiration, direction and guidance, is on the battlefield dayafter-day

striving to follow God’s will and purpose for their lives. And this is with particular affinity

for those Spiritual warriors who are being transformed into excellence through daily academic,

professional, familial, and other challenges.

We pray that you will bear in mind:

Matthew 19:26 (NLT)

Jesus looked at them intently and said, “Humanly speaking, it is impossible.

But with God everything is possible.” (Emphasis added)

To all of us who daily look past our circumstances, and naysayers, to what the Lord says we will

accomplish:

Blessings!!

- The Advocacy Foundation, Inc.

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The Transformative Justice Project

Eradicating Juvenile Delinquency Requires a Multi-Disciplinary Approach

The Juvenile Justice system is incredibly

overloaded, and Solutions-Based programs are

woefully underfunded. Our precious children,

therefore, particularly young people of color, often

get the “swift” version of justice whenever they

come into contact with the law.

Decisions to build prison facilities are often based

on elementary school test results, and our country

incarcerates more of its young than any other

nation on earth. So we at The Foundation labor to

pull our young people out of the “school to prison”

pipeline, and we then coordinate the efforts of the

legal, psychological, governmental and

educational professionals needed to bring an end

to delinquency.

We also educate families, police, local businesses,

elected officials, clergy, and schools and other

stakeholders about transforming whole communities, and we labor to change their

thinking about the causes of delinquency with the goal of helping them embrace the

idea of restoration for the young people in our care who demonstrate repentance for

their

mistakes.

The way we accomplish all this is a follows:

1. We vigorously advocate for charges reductions, wherever possible, in the

adjudicatory (court) process, with the ultimate goal of expungement or pardon, in order

to maximize the chances for our clients to graduate high school and progress into

college, military service or the workforce without the stigma of a criminal record;

2. We then enroll each young person into an Evidence-Based, Data-Driven

Restorative Justice program designed to facilitate their rehabilitation and subsequent

reintegration back into the community;

3. While those projects are operating, we conduct a wide variety of ComeUnity-

ReEngineering seminars and workshops on topics ranging from Juvenile Justice to

Parental Rights, to Domestic issues to Police friendly contacts, to mental health

intervention, to CBO and FBO accountability and compliance;

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4. Throughout the process, we encourage and maintain frequent personal contact

between all parties;

5 Throughout the process we conduct a continuum of events and fundraisers

designed to facilitate collaboration among professionals and community stakeholders;

and finally

6. 1 We disseminate Quarterly publications, like our e-Advocate series Newsletter

and our e-Advocate Quarterly electronic Magazine to all regular donors in order to

facilitate a lifelong learning process on the ever-evolving developments in the Justice

system.

And in addition to the help we provide for our young clients and their families, we also

facilitate Community Engagement through the Restorative Justice process,

thereby balancing the interests of local businesses, schools, clergy, social assistance

organizations, elected officials, law enforcement entities, and all interested

stakeholders. Through these efforts, relationships are rebuilt & strengthened, local

businesses and communities are enhanced & protected from victimization, young

careers are developed, and our precious young people are kept out of the prison

pipeline.

Additionally, we develop Transformative “Void Resistance” (TVR) initiatives to elevate

concerns of our successes resulting in economic hardship for those employed by the

penal system.

TVR is an innovative-comprehensive process that works in conjunction with our

Transformative Justice initiatives to transition the original use and purpose of current

systems into positive social impact operations, which systematically retrains current

staff, renovates facilities, creates new employment opportunities, increases salaries and

is data proven to enhance employee’s mental wellbeing and overall quality of life – an

exponential Transformative Social Impact benefit for ALL community stakeholders.

This is a massive undertaking, and we need all the help and financial support you can

give! We plan to help 75 young persons per quarter-year (aggregating to a total of 250

per year) in each jurisdiction we serve) at an average cost of under $2,500 per client,

per year. *

Thank you in advance for your support!

* FYI:

1 In addition to supporting our world-class programming and support services, all regular donors receive our Quarterly e-Newsletter

(The e-Advocate), as well as The e-Advocate Quarterly Magazine.

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1. The national average cost to taxpayers for minimum-security youth incarceration,

is around $43,000.00 per child, per year.

2. The average annual cost to taxpayers for maximum-security youth incarceration

is well over $148,000.00 per child, per year.

- (US News and World Report, December 9, 2014);

3. In every jurisdiction in the nation, the Plea Bargain rate is above 99%.

The Judicial system engages in a tri-partite balancing task in every single one of these

matters, seeking to balance Rehabilitative Justice with Community Protection and

Judicial Economy, and, although the practitioners work very hard to achieve positive

outcomes, the scales are nowhere near balanced where people of color are involved.

We must reverse this trend, which is right now working very much against the best

interests of our young.

Our young people do not belong behind bars.

- Jack Johnson

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The Advocacy Foundation, Inc.

Helping Individuals, Organizations & Communities

Achieve Their Full Potential

…a compendium of works on

Nonprofit Organizational

Assessment

“Turning the Improbable Into the Exceptional”

Atlanta

Philadelphia

______

John C Johnson III

Founder & CEO

(878) 222-0450

Voice | Data | SMS


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Biblical Authority

______

Philippians 3:13-14 (NIV)

13

Brothers and sisters, I do not consider myself yet to have taken hold of it. But one

thing I do: Forgetting what is behind and straining toward what is ahead, 14 I press

on toward the goal to win the prize for which God has called me heavenward in Christ

Jesus.

Deuteronomy 8:18

18

But remember the Lord your God, for it is he who gives you the ability to produce

wealth, and so confirms his covenant, which he swore to your ancestors, as it is today.

1 Peter 4:10

10

Each of you should use whatever gift you have received to serve others, as

faithful stewards of God’s grace in its various forms.

2 Corinthians 13:5-6

5

Examine yourselves to see whether you are in the faith; test yourselves. Do you not

realize that Christ Jesus is in you—unless, of course, you fail the test? 6 And I trust that

you will discover that we have not failed the test.

Proverbs 2:6-8

6

For the Lord gives wisdom;

from his mouth come knowledge and understanding.

7

He holds success in store for the upright,

he is a shield to those whose walk is blameless,

8

for he guards the course of the just

and protects the way of his faithful ones.

Ephesians 6:12

12

For our struggle is not against flesh and blood, but against the rulers, against the

authorities, against the powers of this dark world and against the spiritual forces of evil

in the heavenly realms.

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Table of Contents

…a compilation of works on


Assessment

Biblical Authority

I. Introduction: Organizational Analysis………………………………….. 19

II. Needs Assessments……………………………………………………… 31

III. Organizational Culture………………………………………………… ... 53

IV. Organizational Effectiveness…………………………………………..... 89

V. Charitable Purposes…………………………………………………….... 91

VI. Social Impact Assessments……….…………………………………….. 97

VII. Social Influence………………………………...................................... 101

VIII. The Judge-Advisor System…………………………………………….. 111

IX. Predictive Analytics…………………………………………………….. 121

X. References……………………………………………………............... 143

Attachments

A. A Guide to Using OCA Tools

B. Nonprofit Organizational Assessment Tool

C. NpA Self-Assessment Tool

Copyright © 2003 – 2018 The Advocacy Foundation, Inc. All Rights Reserved.

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This work is not meant to be a piece of original academic

analysis, but rather draws very heavily on the work of

scholars in a diverse range of fields. All material drawn upon

is referenced appropriately.

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I. Introduction

Organizational Analysis

Organizational Analysis or more commonly Industrial analysis is the process of

reviewing the development, work environment, personnel, and operation of a business

or another type of association. This review is often performed in response to crisis, but

may also be carried out as part of a demonstration project, in the process of taking a

program to scale, or in the course of regular operations. Conducting a periodic detailed

organizational analysis can be a useful way for management to identify problems or

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inefficiencies that have arisen in the organization but have yet to be addressed, and

develop strategies for resolving them.

Organizational analysis focuses on the structure and design of the organization and

how the organization's systems, capacity and functionality influence outputs. Additional

internal and external factors are also accounted for in assessing how to improve

efficiency. Undertaking an organizational analysis is helpful in assessing an

organization's current well-being and capacity, and deciding on a course of action to

improve the organization's long-term sustainability. A restructuring of an Organization

may become necessary when either external or internal forces have created a problem

or opportunity for improvement in efficiency and effectiveness.

When performing an organizational analysis, many details emerge about the functions

and capacity of the organization. All of these details can make pinpointing what is

efficient and inefficient difficult. Using theoretical organizational models can help sort out

the information, and make it easier to draw connections. After working through these

theoretical models, the organizations present situation is more adequately addressed,

and the trajectory of the organization can be more fully determined.

Strategic Triangle Model

Organizational Analysis Models

This model relies on three key calculations to determine the efficiency and effectiveness

of an organization. First, is the value, or mission, that guides the organization. Second,

is operational capacity, the knowledge and capability to carry out the mission. Third, is

legitimacy and support, or the environment, that authorize the value of the organization,

and offer support, (specifically financial support). Using this model, a strategy for an

organization is considered good if these three components are in alignment.

SWOT Model

A SWOT analysis (alternatively SWOT matrix) is a structured planning method used to

evaluate the strengths, weaknesses, opportunities and threats involved in a project or in

a business venture. A SWOT analysis can be carried out for a product, place, industry

or person. It involves specifying the objective of the business venture or project and

identifying the internal and external factors that are favorable and unfavorable to

achieve that objective. The degree to which the internal environment of the entity

matches with the external environment is expressed by the concept of strategic fit.

Strengths: characteristics of the business or project that give it an advantage

over others.

Weaknesses: characteristics that place the business or project at a disadvantage

relative to others

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Opportunities: elements that the project could exploit to its advantage

Threats: elements in the environment that could cause trouble for the business or

project

First, the decision makers should consider whether the objective is attainable, given the

SWOTs. If the objective is not attainable a different objective must be selected and the

process repeated. Users of SWOT analysis need to ask and answer questions that

generate meaningful information for each category (strengths, weaknesses,

opportunities, and threats) to make the analysis useful and find their competitive

advantage.

The McKinsey 7S Model

Visual Representation of McKinsey 7S Framework

The McKinsey 7S Framework emphasizes balancing seven key

aspects of an organization, operating unit, or

project. Three of the seven elements—strategy,

structure, and systems—are considered "hard"

elements, easily identified, described, and analyzed. The

remaining four elements—shared value, staff, skill, and

style—are fluid, difficult to describe, and dependent

upon the actors within the organization at any given

time. The 7S organizational analysis framework is

based on the premise that all seven elements are

interdependent, and must be mutually reinforcing in order to be

successful. Changes in a single element can result in misalignment and

dysfunction throughout the organization, disrupting organizational harmony.

Rational Model

The rational model stems from the Frederick W. Taylor's (1911) Structural Perspective.

Taylor was the father of time-and-motion studies and founded an approach he called

"scientific management." It was Taylor's stance that organisations should be as

mechanistic and efficient as possible. These Scientific Management principles served a

valuable purpose for the Ford Motor Company, where the first American, massproduced

automobiles were being created. The rational model views organizations as a

mechanism that is made up of various parts that can be modified in order to create an

output in the shortest amount of time and without deviation.

Natural System Model

The natural system model is in many ways the opposite of the rational model in that it

focuses on the activities that may negatively impact the organization and therefore aims

at maintaining an equilibrium in order to meet its goals. The Natural System model

views organizations as an organic organism which is holistically interconnected. The

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parts of the organization are not seen as independent units but rather as a whole that

can orchestrate together to prepare for inevitable change.

Sociotechnical Model

The sociotechnical model, also known as Sociotechnical Systems (STS), is an

approach to complex organizational work design that recognizes the interaction

between people and technology in workplaces. The term also refers to the interaction

between society's complex infrastructures and human behavior. This model identifies

the environment as a key factor that interacts with the organization.

Cognitive Model

Behavior, cognitive, and other personal factors as well as environmental events,

operate as interacting determinants that influence each other bi-directionally. Personal

goals of the managers and staff are seen as assisting in the effort toward organizational

objective attainment. Decision making processes are focused on and specialization is

deemed as important to the flow of information.

Meta Models

Attempts have also been made to put elements of the above models into a kind of metamodel.

Based on a theorized blindness of a single perspective, Lee Bolman and

Terrence Deal have designed a model that splits analysis into four distinct paradigms.

These 'frames' are to be used as a pluralistic model, and therefore allow analysts to

change thinking by re-framing understanding and points of reference.

1. Structural Frame Here organizations are to be understood by role definitions

and clear hierarchy. Problems come from overlapping responsibilities and

unclear instructions. The assumptions are similar to the rational model shown

above and Taylorism.

2. Human Resource Frame According to this frame organizations exist to serve

society, they are places for growth and development. Problems come from when

people are not motivated or trained sufficiently. This is Similar to the

Sociotechnical model, or the work of Daniel Pink.

3. Political Frame this frame posits that organizations are cutthroat jungles, where

only the strongest survive. Problems come from poor power coalitions or overly

centralized power.

4. Symbolic Frame This frame supposes that organizations are deeply symbolic

and successful business is about the representation genuine meaning. Problems

occur when actors fail to play their parts.

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Bolman and Deal lay out these frames in their book Reframing Organizations: Artistry,

Choice and Leadership the authors also provide many examples of how best to apply

their four frames analysis.

Organizational Network Analysis

Organizational network analysis (ONA) is a method for studying communication within a

formal organization to make invisible patterns of information flow and collaboration in

strategically important groups visible. The method is applied by first mapping the

relationships among people, tasks, groups, knowledge and resources of organizational

systems. Then, analyzing the collected data with a social network analysis software in

order to find organic clusters, opinion leaders, peripheral and bridging actors, indirect

relations that are otherwise invisible.

Organizational Strategy

Organizational Strategies and Structure

"An organization can be said to have a strategy when the leaders and the organization

as a whole have committed themselves to a particular vision of how the organization will

operate to create value and sustain itself in the immediate future"

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Evaluating or crafting an organizational strategy requires analysis of the relationship

between mission, value and resources. Strategy allows managers to focus on an

organization's long-term plan and ensure that mission objectives are met.

Organizational strategy explores the relationship between unit and the environment. It

involves action—matching skills and resources with opportunities and threats.

According to Michael Porter, a professor from Harvard Business School and leading

expert in organizational strategy, the basics of a competitive model have Five Forces:

Threat of new entrants

Threat of substitute products or services

Bargaining power of customers

Bargaining power of suppliers

Intensity of competitive rivalry

Private and Public Strategy

Strategy can vary between public and private sectors. In the private sector the mission

is to make money for stockholders, however in the public sector its mission is full-filling

a social purpose or need. Measuring success is much harder in the public sector as it’s

based on when a social need or issue has been full-filled. There is often no direct link

between meeting mission and being sustainable. Sometimes a social value does not

align with financial performance or organizational survival.

Organizational Structure

How an organization is structured depends on the coordinating mechanism used to

produce the product or service. Think in terms of labor division for specific tasks and

how authority is to be distributed among employees. Henry Mintzberg outlines five ways

to consider labor division:

Simple Structure: Direct Supervision with little specialization

Machine Bureaucracy: Standardization of work with horizontal and vertical

specialization

Professional Bureaucracy: Standardization of skills with horizontal specialization

Divisional Form: Standardization of outputs with some horizontal and vertical

specialization (mainly between divisions)

Adhocracy: Mutual adjustments with much horizontal specialization

Performance Management

Performance management can be defined as 'an ongoing and continuous process of

communicating and clarifying job responsibilities, priorities, and performance

expectations in order to ensure understanding between supervisor and employee.'

An important aspect of performance management involves designing specific

measurable indicators as a means of gauging progress. Outcome indicators are not to

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be confused with actual outcomes, although both are pertinent to measuring progress.

Outcome indicators are assigned a specific numerical measurement "that indicates

progress toward achieving an outcome," but are not the outcomes themselves.

Example of Indicators vs. Outcomes

Performance indicators are typically quantified, with measurable descriptors like ratio,

incidence, proportion, or percentage, to demonstrate progress. When establishing

progress between reporting periods, an indicator may also express measurement by

using words such as the change in, or the difference to describe values for particular

reporting periods. By contrast, an outcome might measure the 'number of cases

correctly resolved during the time period or the percentage by which the number

increased this reporting period as compared to the previous period.' Thus, if

a transportation system outcome indicator measures the percentage of roads in good

condition, the transportation system outcome would be that roads be in acceptable and

durable condition.

Performance measurement systems are often criticized for putting emphasis on

indicators, at the expense of important outcome characteristics, which can lead to a

misallocation of program funding resources and strategic endeavor. It is vital that

indicators include a comprehensive set of outcomes that anticipate undesired ones.

Examples of putting more emphasis on indicators than outcomes, include a law

enforcement agencyfocusing solely on the number of police arrests, or a tax

agency focusing solely on amount of dollars collected. Such laser focus creates

a perverse incentive that might 'tempt staff to harass individual citizens [and]

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[businesses] to increase these values,' giving way to unintended consequences. Lastly,

comprehensiveness is contingent upon measurement of resources available, as well as

any existing data problems.

Challenging Outcomes to Measure

Prevention Programs- (How does one measure the number of incidences

prevented?)

Basic Research and long range planning activities- (Outcomes may take years to

surface)

Programs with anonymous customers- (Ex: Hotlines)

Programs in which major outcomes apply to a very small number of events

Control Systems in the Workplace

Companies encourage independence and innovation among employees in order to

remain competitive, but in an effort to avoid unnecessary risk and control failures,

companies must also put in place mechanisms to monitor employee progress. Included

here are four major types of control levers or systems that enable managers to reconcile

employee autonomy with effective control.

Diagnostic Control Systems- Building and supporting clear targets

Belief Systems- Communicating company core values and mission

Boundary Systems- Specify and enforce rules of the game

Interactive Control Systems- Open organizational dialogue to encourage learning

Contracting-Out and Collaboration

When organizations (usually in the public sector) do not have the internal capacity to

complete their mission contracting-out occurs. An analysis of the capacities, the

contract or agreement, and the relationship between collaborating stakeholders is

conducted. Analysis of contracting-out and/or collaborations can ensure goals are met

successfully prior to the beginning of a partnership, and correct inefficiencies throughout

the time frame of the collaboration.

The analysis should examine collaboration in three categories: capacity, the agreement,

and the relationship. When analyzing the capacities of the collaborating organizations,

examine the contractor’s capacity to deliver and meet contract service requirements.

Explore the history of work and past successes as well as the financial standing of the

contractor. The organization that is contracting out should have the ability (now and in

the future) for monitoring, knowing when the contractor has fulfilled the contract, and

for capacity building.

An analysis of the agreement, or contract, should look for several indicators of future

success. The contract should be compatible with the mission statements of the

collaborating organizations. Adequate funding for completion of the contract is

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necessary. Outcome definitions and measures must be clear. A realistic time line should

be present with a plan in place for handling potential set-backs. An agreed upon system

for feedback throughout the collaboration should be built into the agreement.

The relationship between collaborating organizations is important to consider. Alignment

of collaborating organizations' cultures is a significant and often overlooked element of

contracting-out. Alignment of the values, mission, communication style, and outcome

measurements increase the likelihood of a successful collaboration.

Analysis of Multiple Organizations

Cooperation and Coalitions

Organizational analysis can analyze a single organization and its internal functioning as

well as a coalition of actors in collaboration for a certain goal. Such collaboration can be

analyzed for inter-actor cooperation, information sharing and capacity. A good example

is "Organizational analysis of maternal mortality reduction program in Madagascar" by

Harimanana, Barennes and Reinharz. This study used the Gamson’s Coalition Theory

and Hining & Greenwood’s archetypes to assess the misalignment of the process by

which several agencies including the Madagascar health Ministry provide prenatal

services and information to women in Madagascar. Their results show several

problems. Incongruity among actors disperses the services and therefore makes it

difficult for women to access support. Cultural inconsistencies and failure to recognize

social context, diminishes the cooperation and effectiveness of the actors. Also, the

Madagascar health ministry needs basic materials and funding to provide adequate

services to women. Additionally, Cumbersome directives created inefficiencies. The

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Analysis of the information indicated that the Madagascar Ministry of Health is a poor

leader of this effort, the programs did not translate well on the local level and the

different actors did not cooperate well. The study also identified capacity limits in health

care but due to the misalignment and uncooperative actors, the NGO’s did not properly

address the lack of capacity. This study demonstrates a complex organizational

analysis. The multiple aspects of the misalignment hampered information flows. In

addition, inter-actor misunderstanding increased the inefficiencies of the program. This

analysis could help the functioning of the program in the future.

Organizational Analysis Examples

Reflective Practitioner Model: Washington D.C. School System

In the early 2000s, the Washington D.C. public schools (DCPS) faced an organizational

crisis. D.C. mayor Adrian Fenty sought advice to determine what was the best way to

effectively improve the Washington D.C. public schools. Fenty employed Michelle

Rhee as the DCPS superintendent. Rhee initiated her job by analyzing all the factors

that affected the DCPS. After evaluating all the factors Rhee decided to restructure the

DCPS. Rhee set defined metrics in order to hold teachers accountable and measure

whether they were reaching goals. Rhee wanted to eliminate tenure for teachers in

order to increase teacher accountability. Rhee wanted to increase the DCPS efficiency,

and believed that restructuring the teachers would achieve this.The process and results

were controversial but illustrate an organizational approach to overcoming a policy

crisis.

Strategic Triangle Model OK: Casa de Esperanza

In 1982, a group of women formed a shelter in St. Paul, Minnesota to address the

needs of Latina women in the community that were victims of domestic violence. Casa

de Esperanza immediately reached capacity, but the majority of occupants were

Caucasian and African-American women. The Board of Directors was surprised to

realize that very few women from the Latina community were utilizing the shelter. Casa

de Esperanza continued to serve women from all backgrounds, and received

government stipends for their work. The organization strove to be multicultural, while

also maintaining the same mission of empowering Latinas. Many of the staff members

identified with the mission of helping all women, while the Board of Directors maintained

their stance on specifically helping Latinas.

The theoretical model of the, "strategic triangle," can be applied in order to better

understand the organizational challenges of Casa de Esperanza. The mission and

capacity of the organization are misaligned due to a few key factors. The mission of the

organization is vague and overly broad, which led the staff and Board to develop

opposing views of the mission. Most importantly, they could not agree on who their

target demographic was. The organization’s capacity is rooted in helping women from

all backgrounds with a variety of services, while the mission seems to indicate they

serve Latina women predominantly. The environment suggests that there is a need

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amongst a broader population than just Latina women. In addition, the government is

the main source of funding for the organization and they are failing at financial

sustainability. In order to bring these three components into alignment, the organization

would need to make a clear and specific mission statement, tailor their capacity to reach

that mission, and look for alternate forms of funding. Other recommendations could be

made using the strategic triangle model. The model is a useful tool to examine the

organizations in a crisis situation.

Organizational Structure and Operations Model: New York City Transit Authority

In the 1990s, the New York City Transit Authority (NYCTA) was having problems with

sustainability. There was a steep decline in ridership coupled with an increase in riders

who avoided paying the fare. There was an increase in crime in the subways, as well as

more homeless and panhandlers congregating in the stations. When Alan Kiepper

became the head of the NYCTA, he decided to restructure the organization, and place

more of a focus on stations. Kiepper believed that New Yorkers would regain trust in the

Transit Authority if they saw crime decline and repercussions for fare avoidance.

Therefore, Kiepper used the organizational structure model to improve the

organization's efficiency. Stations were given station managers who were responsible

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for overseeing all problems within the station. The previous division of labor was broken

down, and employees began to work across departments in order to improve the

stations. This is an example of focusing on an organization's structure while performing

an organizational analysis.

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II. Needs Assessments

A Needs Assessment is a systematic process for determining and addressing needs,

or "gaps" between current conditions and desired conditions or "wants". The

discrepancy between the current condition and wanted condition must be measured to

appropriately identify the need. The need can be a desire to improve current

performance or to correct a deficiency.

A needs assessment is a part of planning

processes, often used for improvement in

individuals, education/training, organizations, or

communities. It can refine and improve a

product such as a training or service a client

receives. It can be an effective tool to

clarify problems and identify

appropriate interventions or

solutions. By clearly identifying the

problem, finite resources can be directed

towards developing and implementing a

feasible and applicable solution. Gathering

appropriate and sufficient data informs the

process of developing an effective product

that will

address the groups needs and wants. Needs

assessments

are only effective when they are ends-focused

and provide concrete evidence that can be used to determine which of the possible

means-to-the-ends are most effective and efficient for achieving the desired results.

Needs assessments can help improve the quality of policy or program decisions—thus

leading to improvements in performance and the accomplishment of desired results.

Improving results—that is, moving from current to desired performance—is typically a

worthwhile and valuable effort. The results of a needs assessment will guide

subsequent decisions—including the design, implementation, and evaluation of projects

and programs that will lead to achieving desired results.

Defining 'need' is an essential starting place for needs assessments. Though the word

need is used casually in many context without a definition, in order to assess them a

need is often defined as a gap in results where its satisfaction, or partial satisfaction, is

necessary for the achievement of another specific socially-permissible result. Each

need therefore consist of two related gaps in results, leading to the assessment (size,

direction, characteristics, etc.) of each gap as well as the relationship among the gaps.

This distinguishes needs assessments from surveys of people 'wants" or favorite

solutions.

There are three perspectives on need in a needs assessment; perceived need,

expressed need and relative need.

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1. Perceived needs are defined by what people think about their needs, each

standard changes with each respondent.

2. Expressed needs are defined by the number of people who have sought help

and focuses on circumstances where feelings are translated into action. A major

weakness of expressed needs assumes that all people with needs seek help.

3. Relative needs are concerned with equity and must consider differences in

population and social pathology.

History

Considered the "father of needs assessment", Roger Kaufman first developed a model

for determining needs defined as a gap in results. This particular emphasis in results

focuses on the outcomes (or ends) that result from an organization's products,

processes, or inputs (the means to the ends). Kaufman argues that an actual need can

only be identified independent of premature selection of a solution (wherein processes

are defined as means to an end, not an end unto themselves). To conduct a quality

needs assessment according to Kaufman, first determine the current results, articulate

the desired results, and the distance between results is the actual need. Once a need is

identified, then a solution can be selected that is targeted to closing the gap. Kaufman's

model in particular identifies gaps in needs at the societal level, what Kaufman calls

"Mega" planning, along with gaps at the Macro (or organizational) and Micro level (the

level of individuals and small groups). Organizational elements vary among the three

different levels: they are outcomes at the Mega level, outputs at the Macro level, and

products at the Micro level. A Mega level needs assessment should be conducted if the

primary beneficiary of the desired results is society itself (as with the results of a clean

environment or continuing profit). If the desired results are not directly societal, but are

delivered to society (such as automobiles or college graduates), then a Macro level

assessment should be performed. If the desired results are building blocks for larger

results (such as a single sale or a passed inspection), then a Micro level needs

assessment is appropriate.

Kaufman articulated 13 indicators for societal well-being, which there will be no losses

of life nor elimination or reduction of levels of well-being, survival, self-sufficiency, and

quality of life from any source, including (but not limited to):

1. War and/or riot and/or terrorism

2. Shelter

3. Unintended human-caused changes to the environment, including permanent

destruction of the environment and/or rendering it non-renewable

4. Murder, rape, or crimes of violence, robbery, or destruction of property

5. Substance abuse

6. Disease

7. Pollution

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8. Starvation and/or malnutrition

9. Child abuse

10. Partner/spouse/elder abuse

11. Destructive behavior, including child, partner, spouse, self, elder, and others

12. Discrimination based on irrelevant variables including color, race, creed, sex,

religion, national origin, age, and location

13. Poverty

Applications

Depending on the scope of the project a needs assessment can be a costly and laborintensive

project. A general twelve step process might entail the following:

1. Confirm the issue and audiences

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2. Establish the planning team

3. Establish the goals and objectives

4. Characterize the audience

5. Conduct information and literature search

6. Select data collection methods

7. Determine the sampling scheme

8. Design and pilot the collection instrument

9. Gather and report data

10. Analyze data

11. Manage data

12. Synthesize data and create report

Over the past four decades, there has been a proliferation of models for needs

assessment with dozens of models to choose from. Needs assessments have been

widely used in educational planning, as well as in business fields through the process of

"market research," which determines customer needs and wants for products and

services. Other terms used somewhat interchangeably to describe this process include

needs analysis, market analysis, front end analysis, and discrepancy analysis.

Extensive vs. Intensive

The broad difference between extensive and intensive needs assessment is that

extensive research uses a large number of cases to determine the characteristics of a

population, while intensive research examines one or a few cases in depth to

understand cause and effect. A variety of data collection and decision making tools and

processes can be used for each, including the examples below (also see Watkins, West

Meiers, Visser, 2011).

The use of population-based indicators is common in extensive needs assessments and

has several strengths. These strengths include that such data are available for broad

geographical areas, available on a large number of individuals or cases, allow

description of entire populations, allow trend analysis over time, are relatively easy to

access, inexpensive to use, and perceived as unbiased. Another method commonly

used in extensive needs assessments is the survey. The strengths of the survey

method are: they allow for direct feedback to the public as well as stakeholders, can

foster public awareness about a problem or concern, can be customized to address

specific issues, can be targeted to specific population groups or geographic areas, and

can provide very timely results. An additional potential data source for extensive needs

assessments are service and program databases. The strengths of this source of data

are: they often contain data collected over many years, are readily accessible by

existing program staff, provide the most current data, and they are relatively

inexpensive to operate and maintain.

One type of extensive needs assessment is SWOT analysis. SWOT stands for

strengths, weaknesses, opportunities, and threats. The basic process involves

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gathering information about an organization’s activities and outcomes within a set time

period. The figure below lists a simplified version of the SWOT process.

A Model SWOT Analysis

1. Recruit research group of 10-20 stakeholders or core group members for one to three meetings

lasting approximately two hours each.

2. Generate a list of successes and failures of the group or organization over the past year. Allow for

some limited discussion of each, without dwelling on any.

3. Generate a list of the group’s or organization’s strengths and weaknesses, and the external

environment’s opportunities and threats, based on the understanding of successes and failures.

4. Brainstorm ideas for maximizing strengths and minimizing weaknesses while taking advantage of

the environment’s opportunities and neutralizing its threats.

Once the group has identified needs, they then generally turn to intensive needs

assessment in order to rank the identified needs so they can choose what they will

address. An important thing to note is that while the ambitious may want to dive right

into their list of needs, generally money and time constraints do not allow for all needs

to be addressed and that is where an intensive needs assessment is useful.

As mentioned earlier, intensive needs assessment requires the ranking of priorities.

While there are many methods to rank needs, it is important to develop ranking criteria.

Feasibility is often used as criteria, but it is often useful for a group to identify their own

set of criteria. This part of the research is not so much concerned with developing a

detailed plan for solving the needs situation, but rather for examining the depth of the

need and potentially required resources. Force field analysis, developed by Kurt Lewin,

is one method for facilitating determining needs feasibility. An example taken from

Stoecker1 states that if, "for example, feasibility is defined as degree of staff expertise

and time, or funds to buy expertise and time, the force field analysis can look for data

indicating available staff expertise and time and/or available external funds and

expertise". The illustration below displays a model force field analysis.

A Model Force Field Analysis

1. Recruit research group of 10-20 stakeholders or core group members for one or more meetings

lasting approximately two hours each.

2. Review the list of needs developed through a SWOT analysis or other procedure. Allow for some

limited discussions of each without dwelling on any.

3. Develop criteria for rating the feasibility of meetings needs.

4. Using the feasibility criteria, collect information on facilitating and impending forces inside the

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group or organization and outside it. This can be done through separate data collection or in a

meeting if the stakeholders are well informed.

5. Apply the data to determine the feasibility of meeting each need.

As mentioned previously, the use of population-based indicators does have several

strengths; however, it also has several weaknesses. These include that such data

reveal problems more readily than they do solutions, may not include specific variables

of interest, are difficult to alter in terms of type of data collected, not always available in

a timely manner, and any individual data point may be of questionable validity.

Population-based indicators data are thus not generally useful for intensive needs

assessments. Service and program databases are also not useful data sources for

intensive needs assessments, because they do not provide data on unmet needs that

are not directly addressed by the given service or program, address demand for only

that program or service, only provide data for those who seek and participate in the

program or service, and some data elements may be of uncertain quality. The use of

surveys, however, can be appropriate for intensive as well as extensive needs

assessments. Regardless of the method used, intensive needs assessments typically

allow deeper analysis and greater flexibility in terms of type of data collected. While

often not as convenient as extensive needs assessments, they can be quite useful for

determining needs in a small setting. One method of data collection for intensive needs

assessments is a structured group. Some strengths of this method are: 1) it allows

account of many different perspectives, as they involve diverse sets of people, including

the target audience, key informants, stakeholders, and the general community, in direct

conversation; 2) it can foster acceptance of and cooperation with the entire needs

assessment process within the community and various target populations; 3) it accounts

for opinions, perceptions, and desires in a manner that no other method does; 4) it

generates new ideas about an existing problem as well as potential solutions; 5) it can

be conducted relatively quickly and provide immediate feedback; and 6) it is relatively

inexpensive. However, because intensive needs assessments typically require much

more coordination and planning in the data collection phase and it is often inappropriate

to generalize from them, extensive needs assessments seem to be much more

common.

Examples

The "Santa Clara County Trends & Needs Assessment Report" is an extensive

community needs assessment conducted by United Way Silicon Valley, a non-profit

organization that claims to be a leading expert on human needs in Silicon Valley. The

report’s purpose is to define and measure the most pressing needs in Santa Clara

County.

An example of an intensive needs assessment is a project conducted by the

Environmental Law Institute, titled Building Capacity to Participate in Environmental

Protection Agency Activities: A Needs Assessment and Analysis. In that study, in-depth

interviews with open-ended questions were conducted with experts on citizen

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participation in environmental issues and community capacity building. The purpose of

the interviews was to identify: 1) areas most in need of an investment in capacity

building; 2) capacity building tools and techniques that are perceived to be effective by

communities and citizens; 3) effective mechanisms for delivering capacity building tools;

and 4) approaches that could be taken to implement capacity building efforts. After the

interviews were conducted, the next step was to analyze each need and approach that

had been identified by the interviewees and accordingly identify possible constraints

and barriers to implementation, design issues, and potential efficacy for each approach

in addressing perceived capacity building needs. Another phase of this needs

assessment, occurring concurrently with the others and informing the construction and

analysis of the various approaches examined, was a literature review on public

participation relevant to capacity building.

Needs Chain Model

A needs chain model is a framework that allows organizations to simultaneously

consider the individuals' needs within an organization and the organization's needs, in

order to prioritize resources and identify areas of improvement for the organization.

Once the organization has completed the model, it gives them a better picture of the

organization's priorities. One of the benefits of this model is that it can be used to help

decision makers quickly come to solutions for priorities that may change over time.

A needs chain model is composed of aligned horizontal and vertical processes, in which

there are four different kinds of needs that describe and identify the ultimate

performance goal, solutions, and what might affect these solutions. These needs

include:

Performance need: A state of existence or level of performance required for

satisfactory functioning.

Instrumental need: An intervention, product, or substance that is required to

obtain a satisfactory level of functioning in a particular context.

Conscious need: Need that are known to those who have them.

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Unconscious need: Need that is unknown to those who have them.

Also, it has four vertical factors that consider:

Organizational need: Needs that usually pertain to behavior or tangible

outcomes, such as market share or sales target.

Individual needs: Needs that usually pertain to the individual’s attitudes about the

organization or themselves, such as job satisfaction.

Causes

Level of objectivity for all needs: The objectivity level requires all needs to have a

certain level of objectivity and to be based on deep investigation or further

analysis.

The needs chain model provides tools that assist organizations in prioritizing resources

and identifying areas that require improvement. Figure 1 identifies four main types of

need that must be considered, for example, for determining the organization’s goals and

the instrument needs with full understanding of the unconscious needs while a different

factor determines the objectivity level.

Figure 1: Needs Chain Model

Instrument

needs

Unconscious needs

Conscious

needs

Performance

needs

Organization

level

Training/workshop

Work consistency/tasks

clarity/management

transparency

Learning

Market

share/sales

revenue

Individual

level

Real applications

Job

satisfaction/Recognition/Job

security/Motivation

Knowledge or

skills (English

speaking

skills)

Sales

target/performing

task effectively

Objectivity

level

Must be high

objective

Must be high objective

Must be high

objective

Must be high

objective

Data about each of these levels comes from different data collection methods:

Organizational level: Goals of the organization

Individual level: Surveys or interviews

The most difficult data to collect in this model are the unconscious needs. In order to

gather this information about the individual, careful methods must be used to allow for

trust from the individual while discussing sensitive topics about their thoughts on the

organization.

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Training

Training needs assessment is a systematic inquiry of training needs within an

organization for the purposes of identifying priorities and making decisions, and

allocating finite resources in a manner consistent with identified program goals and

objectives.

Though beginning with training as the desired solution, it has been argued, diminishes

the value of the needs assessment, the popularity of the term "training needs

assessment" has made it part of the training and adult learning lexicon.

There are three levels of a training needs assessment:

Organizational Assessment evaluates the level of organizational performance. An

assessment of this type will determine the skills, knowledge, and ability needs of an

agency. It also identifies what is required to alleviate the problems and weaknesses of

the agency as well as to enhance strengths and competencies. Organizational

assessment takes into consideration factors such as changing demographics, political

trends, technology, and the economy.

Occupational Assessment examines the skills, knowledge, and abilities required for

affected occupational groups. Occupational assessment identifies how and which

occupational discrepancies or gaps exist, as well as examining new ways to do work

that could fix those discrepancies or gaps.

Individual Assessment analyzes how well an individual employee is doing a job and

determines the individual's capacity to do new or different work. Individual assessment

provides information on which employees need training and what kind.

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The benefits of training needs assessments are:

Training needs are put in context of organizational needs (business drivers)

Validation and/or augmentation of sponsor's ideas about the need for training

Assurance that training design will respond to need

Identification of non-training issues influencing performance

Assurance of survival of training function

Establishment of a foundation for post-training evaluation

Conducting a needs analysis

Conducting a needs analysis is usually done to gauge what training is needed for new

employees or to identify and find solutions to:

1. Problems with performance

2. New system, task or technology

3. An organizational need to benefit from an opportunity

Organizational Training Needs

There are many tools to gather information about employee performance, which work

best in different circumstances.

Observation: First hand observation and analysis in a setting in which the

observer is not interfering with normal productivity. Used to gather first hand data

about an employee's strengths and weaknesses.

Interviews: Using a series of predetermined questions to gauge opinions and

perceptions. This tool allows the employee to comment on their performance,

and allows the interviewer to ask in depth questions about performance.

Questionnaires: Allows for a big picture of the environment by asking

respondents identical questions. Allows for more respondents than individual

interviews, and takes less time. The data collected can be analyzed in a more

quantitative way than with interviews.

Job Descriptions: Study of all responsibilities of a certain job to define an

employees expectations and responsibilities, allowing for more thorough training

and supervision.

The Difficulty Analysis: identification of an employee's duties that cause them the

most difficulty, and allowing for more training in those areas.

Problem Solving Conference: A conference setting that allows employees and

other staff to identify a plan for a new task or technology and mold the training to

it.

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Appraisal Reviews: Within a performance review, questioning the employee

about their duties and training. Allows supervisor to uncover reasons for poor

performance.

Analysis of Organizational Policy: reviewing the organization's policy on training,

and the amount and type of training offered to employees.

When using any of these methods, these three things should be kept in mind:

1. These tools should be used in combination, never rely on just one

2. They may be used to identify training needs in different groups or types of

employees

3. They should be applied to individual employees because of variation in training

between employees.

Community

A community needs assessment is a combination of information gathering, community

engagement and focused action with the goal of community improvement. A community

needs assessment identifies the strengths and weaknesses (needs) within a

community. A community needs assessment is also unique and specific to the needs

within a community and is usually an extension of a community's strategic planning

process. The community needs assessment places great emphasis on the abilities of

the people in the community, and on the agencies and organizations within that

community that provides services to the children and families. Community leaders, local

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government, advocacy groups or a combination of these then address these identified

needs through policy change or development.

A community needs assessment can be broadly categorized into three types based on

their respective starting points: First, needs assessments which aim to discover

weaknesses within the community and create a solution (Community Needs

Assessment I). Second, needs assessments which are structured around and seek to

address an already known problem or potential problem facing the community

(Community Needs Assessment II). Third, needs assessments of an organization which

serves the community (domestic violence centers, community health clinics etc.)

(Community Needs Assessment III).

Community needs assessments are generally executed in four steps: planning and

organizing, data collection, coding and summarizing the needs assessment results, and

sharing the results with the community to facilitate action planning. During the planning

and organizing phase stakeholders are identified, local organizations and/or local

government begin to collaborate. Depending on the type of needs assessment being

conducted one can tailor their approach.

Types and Strategies for Planning and Organizing

Community needs assessment I – This type of needs assessment seeks to evaluate

the strengths and weaknesses within a community and create or improve services

based on the identified weaknesses. Organizing this type of needs assessment is

primarily structured around how to best obtain information, opinions, and input from the

community and then what to do with that information. This process may be broken into

targeted questions which can direct the project overall. The following are sample

questions taken from “A Community Needs Assessment Guide” from The Center for

Urban Research & Learning:

Define goals for the needs assessment.

What is the specific purpose of the needs assessment?

How will the data from the community be used; to set a new agenda, support a

new program or support new changes in service delivery or policies?

What is the timeline for the needs assessment?

If applicable, identify the target population. How will a sample from the population

be chosen? Are there any special considerations which need to be considered in

the most effective way to approach/obtain information and cooperation from said

population?

Community Needs Assessment II – This type of needs assessment is constructed

around a known problem or potential problem facing the community for example,

disaster preparedness, how to address an increase in violent crime etc. This type of

community needs assessment centers less around the direct involvement of the

community but rather the governing entities, stakeholders, businesses, advocacy

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groups and organizations which will be potentially affected or can contribute to the

community need. Potential organization questions could include:

Identifying relevant stakeholders. This includes stakeholders affected by the

problem or stakeholders of the program/or solution being addressed. The

program staff, the funders, and the consumers of the program.

Learn more about the community and its residents.

Review already existing material regarding the community problem or potential

problem.

Sharing expectations, goals, and approach regarding the needs assessment with

the other partners.

Discuss and identify potential users of the agenda/solution likely to be generated

by the needs assessment process.

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Community Needs Assessment III – This final type of needs assessment is based

within an organization which either serves the community at large, is currently

addressing a need within the community, or is dedicated to an under-served population

within the community. This type of needs assessment centers around improving the

efficiency or effectiveness of such organizations. Potential organization questions could

include:

Learn about the organizational culture and its philosophy by interviewing staff,

including the executive director.

Review existing materials regarding the community need and the organization.

Tour the community and learn more about the target population or problem the

organization serves.

Conduct a literature review to see what the recent research has to offer, review

relevant archival information and what previous needs assessments by the

organization have found.

Where is the program in terms of the implementation and development of service

delivery?

What current resources do the organization and its programs offer?

Identify and learn about the program that would most benefit from a needs

assessment.

Implementing a Community Needs Assessment – The exact methodology to

implementing a community needs assessment is partially determined by the type of

assessment that is being performed (discussed above). However, general guidelines

can be proposed.

1. Use of focus groups

2. Creating a needs assessment survey

3. Collecting and analyzing data

4. Community public forums

5. Producing a final report and planning action committees

Selecting members of a focus group first requires choosing a target community,

population, or demographic which will structure the community needs assessment. This

information guides the selection process for a focus group. The principle of the focus

group is to select members who are diverse yet share a degree of commonality. This

may sound paradoxical yet it isn’t necessarily. Generally speaking the commonality

between focus group members is a vested interest and stake in their community. Thus,

focus group members might include: "local politicians, business owners, block club

leaders and community activists. Another focus group would consist of adult resident of

the community; and a third consisting of youth residents of the community".

Focus groups solicit input from community members on broad, open-ended questions,

such as:

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What do you like about your community?

What concerns you within your community?

How would you improve your community?

What changes do you foresee/fear/want to see in your community within the next

10 years?

Questions such as these can help target potential strengths, weaknesses, opportunities

and needs for change or growth.

With the targeted objectives discovered in the focus group, the community needs

assessment survey can be created and dispersed.

Leaders of the community needs

assessment can then summarize the data

through computer analysis programs such

as Access or SPSS. The results are then

brought to the community through a public

forum.

Public forums are the place where the

information collected through the survey,

the identified strengths, weaknesses, and

concerns of the community are presented

for open public discussion.

Finally, the results of the focus groups, survey, and public forum present a direction

which the final report can detail. Action groups are formed and solutions and guidelines

are enacted to ensure the changes desire are realized.

Local Governments

Local city governments have a department dedicated to the sole purpose of funding

nonprofit organizations that see about the current needs of the children and families

who reside in that city. The purpose of these departments is to ensure that nonprofit

organizations that receive funding from the Children, Families Department will provide

families with children with the necessary services that are essential to children growing

up healthy, have access to a quality education, and thrive in safe homes and

neighborhoods.

An example is the Department of Children, Youth and Their Families in San Francisco,

California. This specific city department conducts a needs assessment every three

years to develop a strategic plan to guide the department during their funding cycle

when they send out a request for proposal (RFP) for organizations to apply for grants,

which will enable these community organizations to continue to provide services to the

children and families in their community.

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Conduction

According to Sharma, Lanum and Saurez-Balcazar (2000) "the goals of a 'needs

assessment' is to identify the assets of a community and determine potential concerns

that it faces" (p. 1). A needs assessment therefore becomes crucial in the initial stages

of an intervention. A needs analysis is focused on identifying the possible barriers to

successful program intervention in a community and possibly finding solutions to these

challenges. Service providers in Monitoring and Evaluation (M&E) work are also

concerned with assessment and provision of services to different stakeholders. Such

services may include an assessment closely related to a needs assessment that

focuses on whether current services are effective or not, and if not, identifying the gaps

in implementation; or an assessment of whether potential services are likely to be

effective once they have been implemented (Rossi, Lipsey & Freeman, 2004). These

assessments highlight the close relationship between needs assessment, monitoring,

and evaluation; while each applies similar tools, each also has independent objectives

and requires unique skills.

In community development work, practitioners are concerned with identifying barriers

that stand in the ways of positive community progress. In many cases, an organization

or community is faced by challenges with regards to some social issue, provision or

access to services and it is the job of the practitioner, in consultation with stakeholders,

to decide about how best to go about finding helpful interventions and implementing

solutions to this.

A community level needs assessment is beneficial and crucial to any planned

intervention on behalf of communities facing difficulties with regard to some community

issue. A community level needs assessment will assist the practitioner to determine the

nature and scope of a problem at which an intervention might be aimed, with the aim of

finding out what possible interventions might be successful in alleviating the problem

(Rossi, Lipsey & Freeman, 2004).

A community needs assessment will also uncover which members of the community are

most likely to benefit from a planned intervention and who might not be. Community

level needs assessment will also give direction to planners in terms of where resources

need to be allocated for the intervention so that they are not wasted. Community level

needs assessments should include the community at all stages of planning, and should

consider all people that might be affected by the planned intervention, including

children, the elderly and the mentally ill.

Tools

There are a number of components in a community level needs assessment, all of

which are aimed at gathering data that will answer what the practitioner needs to know

and inform the decisions that he or she makes. According to the National Consumer

Supporter Technical Assistance Center (www.ncstac.org.) the following are crucial

components of a community level needs assessment.

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Assessment

Community Demographics

Community demographics assist the practitioner to get a feel of the field that they are

working in. Demographics include things like age ranges, the number of people living in

a certain area within the community, the number or percentage of people within a

certain socio economic status and gender characteristics. Demographic information

about certain population groups can be found online at such official websites as

www.statssa.gov in South Africa.

Consumer Leadership

Consumer leadership assessment is an assessment of the frequency with which

community members use or are likely to use an existing or planned service. This

assessment is meant to give an indication of the need for the existing or proposed

intervention or service.

Consumer leadership assessment is meant to give an indication of the different types of

leadership activities and roles that are related to transformation in relation to some

health or social issue that is being addressed. This may give an indication as to the

degree of the need for an intervention or not.

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Service Gaps

An assessment of service gaps is meant to give an indication of the types of services

that are needed the most at the particular point of time in which the assessment is being

conducted (www.ncstac.org). A scale measuring the availability, accessibility, provider

choice and cultural responsiveness of services, rated on a scale from 0-no

availability/non-existent, to 3-outstanding and responsive is provided by the National

Consumer Supporter Technical Assistance Center. The scale also assesses the

availability of other services in the community such as support groups, education and

employment services that may be of interest to the practitioner.

Methodology and Data Collection

The following are the actual tools that can be involved in the process of gathering data

to be used in the community needs assessment.

Community/Social Survey

Surveys can be used especially in relation to the gathering of community demographics

where a large number of people may be involved, and also in which multiple variables

such socio-economic status, education levels and employment are being measured in

relation to the planned intervention. Large scale surveys involving many people can

reveal useful information, while smaller surveys may be less generalizable and used

only in the context within which they are conducted. Survey design will vary depending

on context, such as internet and phone surveys for well resourced communities or face

to face surveys for less resourced communities.

Community Mapping

Often, a practitioner may be wanting to implement a service that they think will be of

benefit to the community. The problem facing the practitioner will be where and how to

place the service at a particular point in the community, and whether that service is

likely to be used. Community mapping is where the practitioner gets people in the

community to draw a map of the community of the places that they visit the most and

how often they go there. This will give an indication of where to locate a service so that

it is conveniently placed and accessible to community participants whom it is intended

to service. The problem may arise where there are differences between the places that

people visit.

Seasonal Calendar

A seasonal calendar allows the practitioner to look at seasonal trends that may affect

the use or implementation of a service in a community. Seasonal trends may reveal

decreases in the supply of labor, periods of hunger that may affect for example school

children’s performance at school and so on. Seasonal calendars may reveal important

reasons for the gaps between service utilization and intervention outcomes. This will

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allow the practitioner to plan for other things that may not have been considered as part

of the intervention but which will greatly improve the quality of the intervention and make

life better for the community members. To use the seasonal calendar as a data

collection tool, the practitioner gets community members to write a list of the things that

they have to do throughout the year. These things are related to work, cultural activities,

certain times of the year in which participants are unavailable at all and so on, and to

plot how they share them with other members of the community.

Focus Group Sessions

Focus groups are sessions in which community members can answer questions about

different aspects of the intervention in the form of planned discussions. This is a good

opportunity to actually find out about the needs and concerns of the community. It is

also a good opportunity for addressing service gaps and what needs to be done about

them.

Bayview Hunters Point

Examples

This is a good example as needs are identified in several different ways, such as

research, survey analysis, and current gaps in service provision. All of this information

can be used as analysis towards future policy implementation or as a focal point for

discussion.

The author examined significant statistics that showed a need within the community of

Bayview Hunters Point in order to "identify gaps in service delivery system to create a

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road map for improving neighborhood conditions by rationalizing the allocation of city

dollars to social service programs" (Burke, 7). For example, in 2003, 174 children were

removed from family homes in the Bayview; this is more than 18% of all children

removed from their family in San Francisco. Such numbers could signify a need within

the foster care system or family resources. The author also looked at the broad-based

survey, Project Connect, which gathered data from 10,330 households specifically

about their needs for services and current service utilization practices in the summer of

2004. The analysis from 1,551 Bayview households showed that their priorities, in

order, are 1) childcare services, 2) health services, 3) tutoring/educational services, 4)

immigrant services, 5) food bank/meal services. According to the Child Care Planning

and Advisory Council, in 2002 the unmet need for subsidized care in Bayview Hunters

Point included 2,379 slots for children 0-13. Such needs were gathered from identifying

how many slots exist, and whether families can pay for those slots.

Environmental

Gupta et al. developed a model focused at the community level they term community

needs analysis. Their model involves identifying material

problems/deficits/weaknesses and advantages/opportunities/strengths, and evaluating

possible solutions that take those qualities into consideration. (Note this is different from

Kaufman's Mega model that focuses on identifying societal-level needs).

Community needs assessment involves assessing the needs that people have in order

to live in:

1. an ecologically sustainable environment

2. a community that maintains and develops viable social capital

3. a way that meets their own economic and financial requirements

4. a manner that permits political participation in decisions that affect themselves

Community needs assessment as a technique thus forms a part of an Ecologically

Sustainable Community Economic Development (ESCED). It forms a first step in

any project that aims to secure:

1. Ecological enhancement: minimizing ecological impact or ameliorating any

ecological damage

2. Social vitality: building a community that meets all the social and human needs of

its members

3. Economic resilience: "shock-proofing" local "green" business enterprises as

much as possible

4. Political participation in ways that ensure the participation of people in political

decisions that affect them

Community needs assessment has especial usefulness in action-learning projects, and

in ensuring that organizations meet green objectives of: [50][51]

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social justice

participatory democracy

non-violent resolution of conflict

ecologically sustainable development

Mental Health Promotion Program for Rural Communities In Ireland

A cross-sectional study of the mental health beliefs and perceptions was conducted

which employed a combination of interviewer-administered questionnaires that explored

the levels of awareness, current practices, attitudes and stigma concerning depression

and suicide among a randomly selected quota sample of community members in

Ireland. Community needs assessments can be used for a variety of reasons.

Communities are the experts in their own experience. In order to define and create

solutions for communities, needs assessments should be conducted.

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III. Organizational Culture

Organizational Culture encompasses values and behaviors that "contribute to the

unique social and psychological environment of a business. The organizational culture

influences the way people interact, the context within which knowledge is created, the

resistance they will have towards certain changes, and ultimately the way they share (or

the way they do not share) knowledge. Organizational culture represents the collective

values, beliefs and principles of organizational members and is a product of factors

such as history, product, market, technology, strategy, type of employees, management

style, and national culture; culture includes the organization's vision, values, norms,

systems, symbols, language, assumptions, environment, location, beliefs and habits.

Ravasi and Schultz (2006) characterise organizational culture as a set of shared

assumptions that guide behaviors. It is also the pattern of such collective behaviors and

assumptions that are taught to new organizational members as a way of perceiving and,

even thinking and feeling. Thus organizational culture affects the way people and

groups interact with each other, with clients, and with stakeholders. In addition,

organizational culture may affect how much employees identify with an organization.

Schein (1992), Deal and Kennedy (2000), and Kotter (1992) advanced the idea that

organizations often have very differing cultures as well as subcultures. Although

a company may have its "own unique culture", in larger organizations there are

sometimes co-existing or conflicting subcultures because each subculture is linked to a

different management team. Flamholtz and Randle (2011) suggest that one can view

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organizational culture as "corporate personality" They define it as it consisting of the

values, beliefs, and norms which influence the behavior of people as members of an

organization.

Origins

The term of culture in the organizational context was first introduced by Dr. Elliott

Jaques in his book The Changing Culture of a Factory, in 1951. This is the published

report of "a case study of developments in the social life of one industrial community

between April, 1948 and November 1950". The "case" is a publicly held British company

engaged principally in the manufacture, sale, and servicing of metal bearings. The study

is concerned with the description, analysis, and development of the corporate group

behaviours.

According to Dr. Elliott Jaques "the culture of the factory is its customary and traditional

way of thinking and doing of things, which is shared to a greater or lesser degree by all

its members, and which new members must learn, and at least partially accept, in order

to be accepted into service in the firm..." In simple terms, to the extent that people can

share common wishes, desires and aspirations, they can commit themselves to work

together. It is a matter of being able to care about the same things, and it applies to

nations as well as to associations and organizations within nations.

Elaborating on the work in The Changing Culture of a Factory Dr. Elliott Jaques in his

concept of requisite organization established the list of valued entitlements or

organizational values that can gain from people their full commitment. Together they

make an organizational culture or credo:

Fair and just treatment for everyone, including fair pay based upon equitable pay

differentials for level of work and merit recognition related to personal

effectiveness appraisal.

Leadership interaction between managers and subordinates, including shared

context, personal effectiveness appraisal, feedback and recognition, and

coaching.

Clear articulation of accountability and authority to engender trust and confidence

in all working relationships.

Articulation of long-term organizational vision through direct communication from

the top.

Opportunity for everyone individually or through representatives to participate in

policy development.

Work for everyone at a level consistent with their level of potential capability,

values and interests.

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Opportunity for everyone to progress as his or her potential capability matures,

within the opportunities available

The role of managerial leadership at every organizational level is to make these

organizational values operationally real.

Usage

Organizational culture refers to culture in any type of organization including that of

schools, universities, not-for-profit groups, government agencies, or business entities. In

business, terms such as corporate culture and company culture are often used to

refer to a similar concept. The term corporate culture became widely known in the

business world in the late 1980s and early 1990s. Corporate culture was already used

by managers, sociologists, and organizational theorists by the beginning of the 80s. The

related idea of organizational climateemerged in the 1960s and 70s, and the terms are

now somewhat overlapping.

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If organizational culture is seen as something that characterizes an organization, it can

be manipulated and altered depending on leadership and members. Culture as root

metaphor sees the organization as its culture, created through communication and

symbols, or competing metaphors. Culture is basic, with personal experience producing

a variety of perspectives.

The organizational communication perspective on culture views culture in three different

ways:

Traditionalism: views culture through objective things such as stories, rituals, and

symbols

Interpretivism: views culture through a network of shared meanings (organization

members sharing subjective meanings)

Critical-interpretivism: views culture through a network of shared meanings as

well as the power struggles created by a similar network of competing meanings.

Business executive Bernard L. Rosauer (2013) defines organizational culture as

an emergence – an extremely complex incalculable state that results from the

combination of a few ingredients. In "Three Bell Curves: Business Culture

Decoded" Rosauer outlines the three manageable ingredients which (he claims) guide

business culture:

1. employee (focus on engagement)

2. the work (focus on eliminating waste increasing value) waste

3. the customer (focus on likelihood of referral)

Rosauer writes that the Three Bell Curves methodology aims to bring leadership, their

employees, the work and the customer together for focus without distraction, leading to

an improvement in culture and brand. He states: "If a methodology isn't memorable, it

won't get used. The Three Bell Curves Methodology is simple (to remember) but

execution requires strong leadership and diligence. Culture can be guided by managing

the ingredients."

Reliance of the research and findings of Sirota Survey Intelligence, which has been

gathering employee data worldwide since 1972, the Lean Enterprise

Institute, Cambridge, MA, and Fred Reichheld/Bain/Satmetrix research relating to

NetPromoterScore.

Typology of Cultural Types

Many factors can contribute to the type of culture which is observed in large

organizations and large institutions. The list ranges from depictions of relative strength

to political and national issues.

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Strong and Weak Types of Organizational Culture

Flamholtz and Randle state that: "A strong culture is that people clearly understand and

can articulate. A weak culture is one that employees have difficulty defining,

understanding, or explaining." Strong culture is said to exist where staff respond to

stimulus because of their alignment to organizational values. In such environments,

strong cultures help firms operate like well-oiled machines, engaging in outstanding

execution with only minor adjustments to existing procedures as needed.

Conversely, there

is weak culture where

there is little

alignment with

organizational values,

and control must be

exercised through

extensive procedures

and bureaucracy.

Research shows that

organizations that

foster strong cultures

have clear values that

give employees a

reason to embrace

the culture. A "strong"

culture may be

especially beneficial

to firms operating in

the service sector

since members of

these organizations

are responsible for

delivering the service

and for evaluations

important constituents

make about firms. Organizations may derive the following benefits from developing

strong and productive cultures:

Better aligning the company towards achieving its vision, mission, and goals

High employee motivation and loyalty

Increased team cohesiveness among the company's various departments and

divisions

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Promoting consistency and encouraging coordination and control within the

company

Shaping employee behavior at work, enabling the organization to be more

efficient

Irving Janis defined groupthink as "a mode of thinking that people engage in when they

are deeply involved in a cohesive in-group, when the members' strivings for unanimity

override their motivation to realistically appraise alternative courses of action." This is a

state in which even if group members have different ideas, they do not challenge

organizational thinking. As a result, innovative thinking is stifled. Groupthink can lead to

lack of creativity and decisions made without critical evaluation. Groupthink can occur,

for example, when group members rely heavily on a central charismatic figure in the

organization or where there is an "evangelical" belief in the organization's values.

Groupthink can also occur in groups characterized by a friendly climate conducive to

conflict avoidance.

Healthy

Culture is the organization's immune system. – Michael Watkins

What Is Organizational Culture? And Why Should We Care? – Harvard Business

Review

Organizations should strive for what is considered a "healthy" organizational culture in

order to increase productivity, growth, efficiency and reduce counterproductive behavior

and turnover of employees. A variety of characteristics describe a healthy culture,

including:

Acceptance and appreciation for diversity

Regard for fair treatment of each employee as well as respect for each

employee's contribution to the company

Employee pride and enthusiasm for the organization and the work performed

Equal opportunity for each employee to realize their full potential within the

company

Strong communication with all employees regarding policies and company issues

Strong company leaders with a strong sense of direction and purpose

Ability to compete in industry innovation and customer service, as well as price

Lower than average turnover rates (perpetuated by a healthy culture)

Investment in learning, training, and employee knowledge

Additionally, performance oriented cultures have been shown to possess statistically

better financial growth. Such cultures possess high employee involvement, strong

internal communications and an acceptance and encouragement of a healthy level of

risk-taking in order to achieve innovation. Additionally, organizational cultures that

explicitly emphasize factors related to the demands placed on them by industry

technology and growth will be better performers in their industries.

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According to Kotter and Heskett (1992), organizations with adaptive cultures perform

much better than organizations with unadaptive cultures. An adaptive culture translates

into organizational success; it is characterized by managers paying close attention to all

of their constituencies, especially customers, initiating change when needed, and taking

risks. An unadaptive culture can significantly reduce a firm's effectiveness, disabling the

firm from pursuing all its competitive/operational options.

Healthy companies are able to deal with employees' concerns about the well-being of

the organization internally, before the employees would even feel they needed to raise

the issues externally. It is for this reason that whistleblowing, particularly when it results

in serious damage to a company's reputation, is considered to be often a sign of a

chronically dysfunctional corporate culture. Another relevant concept is the notion of

"cultural functionality".

Specifically, some organizations have "functional" cultures while others have

"dysfunctional" cultures. A "functional" culture is a positive culture that contributes to an

organization's performance and success. A "dysfunctional" culture is one that hampers

or negatively affects an organization's performance and success.

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Management Types of Communication

There are many different types of communication that contribute in creating an

organizational culture:

Metaphors such as comparing an organization to a machine or a family reveal

employees' shared meanings of experiences at the organization.

Stories can provide examples for employees of how to or not to act in certain

situations.

Rites and ceremonies combine stories, metaphors, and symbols into one.

Several different kinds of rites affect organizational culture:

Rites of passage: employees move into new roles

Rites of degradation: employees have power taken away from them

Rites of enhancement: public recognition for an employee's

accomplishments

Rites of renewal: improve existing social structures

Rites of conflict reduction: resolve arguments between certain members or

groups

Rites of integration: reawaken feelings of membership in the organization

Reflexive comments are explanations, justifications, and criticisms of our own

actions. This includes:

Plans: comments about anticipated actions

Commentaries: comments about action in the present

Accounts: comments about an action or event that has already occurred

Such comments reveal interpretive meanings held by the speaker as well as the social

rules they follow.

Fantasy Themes are common creative interpretations of events that reflect

beliefs, values, and goals of the organization. They lead to rhetorical visions, or

views of the organization and its environment held by organization members. [30]

Bullying Culture Type

Bullying is seen to be prevalent in organizations where employees and managers feel

that they have the support, or at least implicitly the blessing, of senior managers to carry

on their abusive and bullying behaviour. Furthermore, new managers will quickly come

to view this form of behaviour as acceptable and normal if they see others get away with

it and are even rewarded for it.

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When bullying happens at the highest levels, the effects may be far reaching. That

people may be bullied irrespective of their organisational status or rank, including senior

managers, indicates the possibility of a negative ripple effect, where bullying may be

cascaded downwards as the targeted supervisors might offload their own aggression on

their subordinates. In such situations, a bullying scenario in the boardroom may actually

threaten the productivity of the entire organisation.

Culture of Fear Type

Ashforth discussed potentially destructive sides of leadership and identified what he

referred to as petty tyrants, i.e. leaders who exercise a tyrannical style of management,

resulting in a climate of fear in the workplace. Partial or intermittent

negative reinforcement can create an effective climate of fear and doubt. When

employees get the sense that bullies "get away with it", a climate of fear may be the

result. Several studies have confirmed a relationship between bullying, on the one hand,

and an autocratic leadership and an authoritarian way of settling conflicts or dealing with

disagreements, on the other. An authoritarian style of leadership may create a climate

of fear, where there is little or no room for dialogue and where complaining may be

considered futile.

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In a study of public-sector union members, approximately one in five workers reported

having considered leaving the workplace as a result of witnessing bullying taking place.

Rayner explained these figures by pointing to the presence of a climate of fear in which

employees considered reporting to be unsafe, where bullies had "got away with it"

previously despite management knowing of the presence of bullying.

Tribal Type of Culture

David Logan and coauthors have proposed in their book Tribal Leadership that

organizational cultures change in stages, based on an analysis of human groups and

tribal cultures. They identify five basic stages:

1. Life sucks (a subsystem severed from other functional systems like tribes, gangs

and prison—2 percent of population);

2. My life sucks (I am stuck in the Dumb Motor Vehicle line and can't believe I have

to spend my time in this lost triangle of ineffectiveness—25 percent of

population);

3. I'm great (and you're not, I am detached from you and will dominate you

regardless of your intent—48 percent of population);

4. We are great, but other groups suck (citing Zappo's and an attitude of unification

around more than individual competence—22 percent of population) and

5. Life is great (citing Desmond Tutu's hearing on truth and values as the basis of

reconciliation—3 percent of population).

This model of organizational culture provides a map and context for leading an

organization through the five stages.

Personal Culture

Organizational culture is taught to the person as culture is taught by his/her parents thus

changing and modeling his/her personal culture. Indeed, employees and people

applying for a job are advised to match their "personality to a company's culture" and fit

to it. Some researchers even suggested and have made case studies research on

personality changing.

National Culture Type

Corporate culture is used to control, coordinate, and integrate company

subsidiaries. However differences in national cultures exist contributing to differences in

the views on management. Differences between national cultures are deep rooted

values of the respective cultures, and these cultural values can shape how people

expect companies to be run, and how relationships between leaders and followers

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should be, resulting in differences between the employer and the employee regarding

expectations. (Geert Hofstede, 1991) Perhaps equally foundational; observing the vast

differences in national copyright (and taxation, etc.) laws suggests deep rooted

differences in cultural attitudes and assumptions about property rights and sometimes

about the desired root function, place, or purpose of corporations relative to the

population.

Multiplicity

Xibao Zhang (2009) carried out an empirical study of culture emergence in the Sino-

Western international cross-cultural management (SW-ICCM) context in China. Field

data were collected by interviewing Western expatriates and Chinese professionals

working in this context, supplemented by non-participant observation and documentary

data. The data were then analyzed objectively to formulate theme-based substantive

theories and a formal theory.

The major finding of this study is that the human cognition contains three components,

or three broad types of "cultural rules of behavior", namely, Values, Expectations, and

Ad Hoc Rules, each of which has a mutually conditioning relationship with behavior. The

three cognitive components are different in terms of the scope and duration of their

mutual shaping of behavior. Values are universal and enduring rules of behavior;

Expectations, on the other hand, are context-specific behavioral rules; while Ad Hoc

Rules are improvised rules of behavior that the human mind devises contingent upon a

particular occasion. Furthermore, they need not be consistent, and frequently are not,

among themselves. Metaphorically, they can be compared to a multi-carriage train,

which allows for the relative lateral movements by individual carriages so as to

accommodate bumps and turns in the tracks. In fact, they provide a "shock-absorber

mechanism", so to speak, which enables individuals in SW-ICCM contexts to cope with

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conflicts in cultural practices and values, and to accommodate and adapt themselves to

cultural contexts where people from different national cultural backgrounds work

together over extended time. It also provides a powerful framework which explains how

interactions by individuals in SW-ICCM contexts give rise to emerging hybrid cultural

practices characterized by both stability and change.

One major theoretical contribution of this "multi-carriage train" perspective is its

allowance for the existence of inconsistencies among the three cognitive components in

their mutual conditioning of behavior. This internal inconsistency view is in stark contrast

to the traditional internal consistency assumption explicitly or tacitly held by many

culture scholars. The other major theoretical contribution, which follows logically from

the first one, is to view culture as an overarching entity which is made of a multiplicity of

Values, Expectations, and Ad Hoc Rules. This notion of one (multiplicity) culture to an

organization leads to the classification of culture along its path of emergence into

nascent, adolescent, and mature types, each of which is distinct in terms of the pattern

of the three cognitive components and behavior.

Effects

Research suggests that numerous outcomes have been associated either directly or

indirectly with organizational culture. A healthy and robust organizational culture may

provide various benefits, including the following:

Competitive edge derived from innovation and customer service

Consistent, efficient employee performance

Team cohesiveness

High employee morale

Strong company alignment towards goal achievement

Although little empirical research exists to support the link between organizational

culture and organizational performance, there is little doubt among experts that this

relationship exists. Organizational culture can be a factor in the survival or failure of an

organization – although this is difficult to prove given that the necessary longitudinal

analyses are hardly feasible. The sustained superior performance of firms

like IBM, Hewlett-Packard, Procter & Gamble, and McDonald's may be, at least partly, a

reflection of their organizational cultures.

A 2003 Harvard Business School study reported that culture has a significant effect on

an organization's long-term economic performance. The study examined the

management practices at 160 organizations over ten years and found that culture can

enhance performance or prove detrimental to performance. Organizations with strong

performance-oriented cultures witnessed far better financial growth. Additionally, a 2002

Corporate Leadership Council study found that cultural traits such as risk taking, internal

communications, and flexibility are some of the most important drivers of performance,

and may affect individual performance. Furthermore, innovativeness, productivity

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through people, and the other cultural factors cited by Peters and Waterman (1982) also

have positive economic consequences.

Denison, Haaland, and Goelzer (2004) found that culture contributes to the success of

the organization, but not all dimensions contribute the same. It was found that the

effects of these dimensions differ by global regions, which suggests that organizational

culture is affected by national culture. Additionally, Clarke (2006) found that a safety

climate is related to an organization's safety record.

Organizational culture is reflected in the way people perform tasks, set objectives, and

administer the necessary resources to achieve objectives. Culture affects the way

individuals make decisions, feel, and act in response to the opportunities and threats

affecting the organization.

Adkins and Caldwell (2004) found that job satisfaction was positively associated with

the degree to which employees fit into both the overall culture and subculture in which

they worked. A perceived mismatch of the organization's culture and what employees

felt the culture should be is related to a number of negative consequences including

lower job satisfaction, higher job strain, general stress, and turnover intent.

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It has been proposed that organizational culture may affect the level of employee

creativity, the strength of employee motivation, and the reporting of unethical behavior,

but more research is needed to support these conclusions.

Organizational culture also affects recruitment and retention. Individuals tend to be

attracted to and remain engaged in organizations that they perceive to be compatible.

Additionally, high turnover may be a mediating factor in the relationship between culture

and organizational performance. Deteriorating company performance and an unhealthy

work environment are signs of an overdue cultural assessment.

Change

When an organization does not possess a healthy culture or requires some kind of

organizational culture change, the change process can be daunting. Organizational

culture can hinder new change efforts, especially where employees know their

expectations and the roles that they are supposed to play in the organization. This is

corroborated by Mar (2016:1) who argues that 70% of all change efforts fail because of

the culture of an organization's employees. One major reason why such change is

difficult is that organizational cultures, and the organizational structures in which they

are embedded, often reflect the "imprint" of earlier periods in a persistent way and

exhibit remarkable levels of inertia. Culture change may be necessary to reduce

employee turnover, influence employee behavior, make improvements to the company,

refocus the company objectives and/or rescale the organization, provide better

customer service, and/or achieve specific company goals and results. Culture change is

affected by a number of elements, including the external environment and industry

competitors, change in industry standards, technology changes, the size and nature of

the workforce, and the organization's history and management.

There are a number of methodologies specifically dedicated to organizational culture

change such as Peter Senge's Fifth Discipline. There are also a variety of psychological

approaches that have been developed into a system for specific outcomes such as

the Fifth Discipline's "learning organization" or Directive Communication's "corporate

culture evolution." Ideas and strategies, on the other hand, seem to vary according to

particular influences that affect culture.

Burman and Evans (2008) argue that it is 'leadership' that affects culture rather than

'management', and describe the difference. When one wants to change an aspect of the

culture of an organization one has to keep in consideration that this is a long term

project. Corporate culture is something that is very hard to change and employees need

time to get used to the new way of organizing. For companies with a very strong and

specific culture it will be even harder to change.

Prior to a cultural change initiative, a needs assessment is needed to identify and

understand the current organizational culture. This can be done through employee

surveys, interviews, focus groups, observation, customer surveys where appropriate,

and other internal research, to further identify areas that require change. The company

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must then assess and clearly identify the new, desired culture, and then design a

change process.

Cummings & Worley (2004, p. 491 – 492) give the following six guidelines for cultural

change, these changes are in line with the eight distinct stages mentioned by Kotter

(1995, p. 2):

1. Formulate a clear strategic vision (stage 1, 2, and 3). In order to make a cultural

change effective a clear vision of the firm's new strategy, shared values and

behaviors is needed. This vision provides the intention and direction for the

culture change (Cummings & Worley, 2004, p. 490).

2. Display top-management commitment (stage 4). It is very important to keep in

mind that culture change must be managed from the top of the organization, as

willingness to change of the senior management is an important indicator

(Cummings & Worley, 2004, page 490). The top of the organization should be

very much in favor of the change in order to actually implement the change in the

rest of the organization. De Caluwé & Vermaak (2004, p 9) provide a framework

with five different ways of thinking about change.

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3. Model culture change at the highest level (stage 5). In order to show that the

management team is in favor of the change, the change has to be notable at first

at this level. The behavior of the management needs to symbolize the kinds of

values and behaviors that should be realized in the rest of the company. It is

important that the management shows the strengths of the current culture as

well; it must be made clear that the current organizational culture does not need

radical changes, but just a few adjustments. (See for more: Deal & Kennedy,

1982; [4] Sathe, 1983; Schall; 1983; Weick, 1985; DiTomaso, 1987). This process

may also include creating committees, employee task forces, value managers, or

similar. Change agents are key in the process and key communicators of the

new values. They should possess courage, flexibility, excellent interpersonal

skills, knowledge of the company, and patience. As McCune (May 1999) puts it,

these individuals should be catalysts, not dictators.

4. The fourth step is to modify the organization to support organizational change.

This includes identifying what current systems, policies, procedures and rules

need to be changed in order to align with the new values and desired culture.

This may include a change to accountability systems, compensation, benefits

and reward structures, and recruitment and retention programs to better align

with the new values and to send a clear message to employees that the old

system and culture are in the past.

5. Select and socialize newcomers and terminate deviants (stage 7 & 8 of Kotter,

1995, p. 2). A way to implement a culture is to connect it to organizational

membership, people can be selected and terminated in terms of their fit with the

new culture (Cummings & Worley, 2004, p. 491). Encouraging employee

motivation and loyalty to the company is key and will also result in a healthy

culture. The company and change managers should be able to articulate the

connections between the desired behavior and how it will affect and improve the

company's success, to further encourage buy-in in the change process. Training

should be provided to all employees to understand the new processes,

expectations and systems.

6. Develop ethical and legal sensitivity. Changes in culture can lead to tensions

between organizational and individual interests, which can result in ethical and

legal problems for practitioners. This is particularly relevant for changes in

employee integrity, control, equitable treatment and job security (Cummings &

Worley, 2004, p. 491). It is also beneficial, as part of the change process, to

include an evaluation process, conducted periodically to monitor the change

progress and identify areas that need further development. This step will also

identify obstacles of change and resistant employees, and acknowledge and

reward employee improvement, which will encourage continued change and

evolvement. It may also be helpful and necessary to incorporate new change

managers to refresh the process. Outside consultants may also be useful in

facilitating the change process and providing employee training. Change of

culture in organizations is very important and inevitable. Cultural innovation [42] is

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bound to be more difficult than cultural maintenance because it entails

introducing something new and substantially different from what prevails in

existing cultures. People often resist changes, hence it is the duty of

management to convince people that likely gain will outweigh the losses. Besides

institutionalization, deification is another process that tends to occur in strongly

developed organizational cultures. The organization itself may come to be

regarded as precious in itself, as a source of pride, and in some sense unique.

The organization's members begin to feel a strong bond with it that transcends

material returns, and they begin to identify with it. The organization turns into a

sort of clan.

Mergers and Cultural Leadership

One of the biggest obstacles in the way of the merging of two organizations is

organizational culture. Each organization has its own unique culture and most often,

when brought together, these cultures clash. When mergers fail employees point to

issues such as identity, communication problems, human resources problems, ego

clashes, and inter-group conflicts, which all fall under the category of "cultural

differences".

One way to combat such difficulties is through cultural leadership. Organizational

leaders must also be cultural leaders and help facilitate the change from the two old

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cultures into the one new culture. This is done through cultural innovation followed by

cultural maintenance.

Cultural innovation includes:

Creating a new culture: recognizing past cultural differences and setting

realistic expectations for change

Changing the culture: weakening and replacing the old cultures

Cultural maintenance includes:

Integrating the new culture: reconciling the differences between the old

cultures and the new one

Embodying the new culture: Establishing, affirming, and keeping the new

culture

Corporate Subcultures

Corporate culture is the total sum of the values, customs, traditions, and meanings that

make a company unique. Corporate culture is often called "the character of an

organization", since it embodies the vision of the company's founders. The values of a

corporate culture influence the ethical standards within a corporation, as well as

managerial behavior.

Senior management may try to determine a corporate culture. They may wish to impose

corporate values and standards of behavior that specifically reflect the objectives of the

organization. In addition, there will also be an extant internal culture within the

workforce. Work-groups within the organization have their own behavioral quirks and

interactions which, to an extent, affect the whole system. Roger Harrison's four-culture

typology, and adapted by Charles Handy, suggests that unlike organizational culture,

corporate culture can be 'imported'. For example, computer technicians will have

expertise, language and behaviors gained independently of the organization, but their

presence can influence the culture of the organization as a whole.

Legal Aspects

Corporate culture can legally be found to be a cause of injuries and a reason for fining

companies in the US, e.g., when the US Department of Labor Mine Safety and Health

Administration levied a fine of more than 10.8 million US dollars on Performance Coal

Co. following the Upper Big Branch Mine disaster in April 2010. This was the largest fine

in the history of this U.S. government agency. [44]

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Critical Views

Criticism of the usage of the term by managers began already in its emergence in the

early 80s. Most of the criticism comes from the writers in critical management

studies who for example express skepticism about the functionalist and unitarist views

about culture that are put forward by mainstream management writers. They stress the

ways in which these cultural assumptions can stifle dissent management and reproduce

propaganda and ideology. They suggest that organizations do not have a single culture

and cultural engineering may not reflect the interests of all stakeholders within an

organization.

capitalist organizations.

Parker (2000) has suggested

that many of the assumptions

of those putting forward

theories of organizational

culture are not new. They

reflect a long-standing tension

between cultural and

structural (or informal and

formal) versions of what

organizations are. Further, it is

reasonable to suggest that

complex organizations might

have many cultures, and that

such sub-cultures might

overlap and contradict each

other. The neat typologies of

cultural forms found in

textbooks rarely acknowledge

such complexities, or the

various

economic

contradictions that exist in

Among the strongest and most widely recognized writers on corporate culture, with a

long list of articles on leadership, culture, gender and their intersection, is Linda

Smircich. As a part of the critical management studies, she criticizes theories that

attempt to categorize or 'pigeonhole' organizational culture.

She uses the metaphor of a plant root to represent culture, saying that it drives

organizations rather than vice versa. Organizations are the product of organizational

culture; we are unaware of how it shapes behavior and interaction (also implicit in

Schein's (2002) underlying assumptions), and so how can we categorize it and define

what it is?

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Research and Models

Several methods have been used to classify organizational culture. While there is no

single "type" of organizational culture and organizational cultures vary widely from one

organization to the next, commonalities do exist and some researchers have developed

models to describe different indicators of organizational cultures. Some are described

below:

Hofstede

Hofstede (1980) looked for differences between over 160 000 IBM employees in 50

different countries and three regions of the world, in an attempt to find aspects of culture

that might influence business behavior. He suggested things about cultural differences

existing in regions and nations, and the importance of international awareness and

multiculturalism for their own cultural introspection. Cultural differences reflect

differences in thinking and social action, and even in "mental programs", a term

Hofstede uses for predictable behavior. Hofstede relates culture to ethnic and regional

groups, but also organizations, professional, family, social and subcultural groups,

national political systems and legislation, etc.

Hofstede suggests the need for changing "mental programs" with changing behavior

first, which will lead to value change. Though certain groups like Jews and Gypsies

have maintained their identity through centuries, their values show adaptation to the

dominant cultural environment.

Hofstede demonstrated that there are national and regional cultural groupings that

affect the behavior of organizations and identified four dimensions of culture (later five)

in his study of national cultures:

Power distance (Mauk Mulder, 1977) – Different societies find different solutions

regarding social inequality. Although invisible, inside organizations power

inequality of the "boss-subordinate relationships" is functional and according to

Hofstede reflects the way inequality is addressed in the society. "According to

Mulder's Power Distance Reduction theory subordinates will try to reduce the

power distance between themselves and their bosses and bosses will try to

maintain or enlarge it", but there is also a degree to which a society expects there

to be differences in the levels of power. A high score suggests that there is an

expectation that some individuals wield larger amounts of power than others. A

low score reflects the view that all people should have equal rights.

Uncertainty avoidance is the way of coping with uncertainty about the future.

Society copes with it with technology, law and religion (though different societies

have different ways of addressing it), and according to Hofstede organizations

deal with it with technology, law and rituals, or in two ways – rational and nonrational,

with rituals being the non-rational. Hofstede listed some of the rituals as

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the memos and reports, some parts of the accounting system, a large part of the

planning and control systems, and the nomination of experts.

Individualism vs. collectivism – disharmony of interests on personal and

collective goals (Parsons and Shils, 1951). Hofstede raises the idea that society's

expectations of Individualism/Collectivism will be reflected by the employee

inside the organization. Collectivist societies will have more emotional

dependence on members in their organizations; when in equilibrium an

organization is expected to show responsibility to members. Extreme

individualism is seen in the US. In fact, collectivism in the US is seen as "bad".

Other cultures and societies than the US will therefore seek to resolve social and

organizational problems in ways different from American ways. Hofstede says

that a capitalist market economy fosters individualism and competition, and

depends on it, but individualism is also related to the development of the middle

class. Some people and cultures might have both high individualism and high

collectivism. For example, someone who highly values duty to his or her group

does not necessarily give a low priority to personal freedom and self-sufficiency.

Masculinity vs. femininity – reflects whether a certain society is predominantly

male or female in terms of cultural values, gender roles and power relations.

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Long- Versus Short-Term Orientation which he describes as "The long-term

orientation dimension can be interpreted as dealing with society's search for

virtue. Societies with a short-term orientation generally have a strong concern

with establishing the absolute Truth. They are normative in their thinking. They

exhibit great respect for traditions, a relatively small propensity to save for the

future, and a focus on achieving quick results. In societies with a long-term

orientation, people believe that truth depends very much on situation, context

and time. They show an ability to adapt traditions to changed conditions, a strong

propensity to save and invest, thriftiness, and perseverance in achieving results."

These dimensions refer to the effect of national cultures on management, and can be

used to adapt policies to local needs. In a follow up study, another model [46] is

suggested for organizational culture.

O'Reilly, Chatman, and Caldwell

Two common models and their associated measurement tools have been developed by

O'Reilly et al. and Denison.

O'Reilly, Chatman & Caldwell (1991) developed a model based on the belief that

cultures can be distinguished by values that are reinforced within organizations. Their

Organizational Cultural Profile (OCP) is a self reporting tool which makes distinctions

according eight categories – Innovation, Supportiveness, Stability, Respect for People,

Outcome Orientation, Attention to Detail, Team Orientation, and Aggressiveness. The

model is also suited to measure how organizational culture affects organizational

performance, as it measures most efficient persons suited to an organization and as

such organizations can be termed as having good organizational culture. Employee

values are measured against organizational values to predict employee intentions to

stay, and turnover. This is done through an instrument like Organizational Culture

Profile (OCP) to measure employee commitment.

Daniel Denison

Daniel Denison's model (1990) asserts that organizational culture can be described by

four general dimensions – Mission, Adaptability, Involvement and Consistency. Each of

these general dimensions is further described by the following three sub-dimensions:

Mission – Strategic Direction and Intent, Goals and Objectives and Vision

Adaptability – Creating Change, Customer Focus and Organizational Learning

Involvement – Empowerment, Team Orientation and Capability Development

Consistency – Core Values, Agreement, Coordination/Integration

Denison's model also allows cultures to be described broadly as externally or internally

focused as well as flexible versus stable. The model has been typically used to

diagnose cultural problems in organizations.

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Deal and Kennedy

Deal and Kennedy (1982) defined organizational culture as the way things get done

around here.

Deal and Kennedy created a model of culture that is based on 4 different types of

organizations. They each focus on how quickly the organization receives feedback, the

way members are rewarded, and the level of risks taken:

1. Work-hard, play-hard culture: This has rapid feedback/reward and low risk

resulting in: Stress coming from quantity of work rather than uncertainty. Highspeed

action leading to high-speed recreation. Examples: Restaurants, software

companies.

2. Tough-guy macho culture: This has rapid feedback/reward and high risk,

resulting in the following: Stress coming from high risk and potential loss/gain of

reward. Focus on the present rather than the longer-term future. Examples:

police, surgeons, sports.

3. Process culture: This has slow feedback/reward and low risk, resulting in the

following: Low stress, plodding work, comfort and security. Stress that comes

from internal politics and stupidity of the system. Development of bureaucracies

and other ways of maintaining the status quo. Focus on security of the past and

of the future. Examples: banks, insurance companies.

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4. Bet-the-company culture: This has slow feedback/reward and high risk,

resulting in the following: Stress coming from high risk and delay before knowing

if actions have paid off. The long view is taken, but then much work is put into

making sure things happen as planned. Examples: aircraft manufacturers, oil

companies.

Edgar Schein

According to Schein (1992), culture is the most difficult organizational attribute to

change, outlasting organizational products, services, founders and leadership and all

other physical attributes of the organization. His organizational model illuminates culture

from the standpoint of the observer, described at three levels: artifacts, espoused

values and basic underlying assumptions.

At the first and most cursory level of Schein's model is organizational attributes that can

be seen, felt and heard by the uninitiated observer – collectively known as artifacts.

Included are the facilities, offices, furnishings, visible awards and recognition, the way

that its members dress, how each person visibly interacts with each other and with

organizational outsiders, and even company slogans, mission statements and other

operational creeds.

Artifacts comprise the physical components of the organization that relay cultural

meaning. Daniel Denison (1990) describes artifacts as the tangible aspects of culture

shared by members of an organization. Verbal, behavioral and physical artifacts are the

surface manifestations of organizational culture.

Rituals, the collective interpersonal behavior and values as demonstrated by that

behavior, constitute the fabric of an organization's culture. The contents of myths,

stories, and sagas reveal the history of an organization and influence how people

understand what their organization values and believes. Language, stories, and myths

are examples of verbal artifacts and are represented in rituals and ceremonies.

Technology and art exhibited by members of an organization are examples of physical

artifacts.

The next level deals with the professed culture of an organization's members –

the values. Shared values are individuals' preferences regarding certain aspects of the

organization's culture (e.g. loyalty, customer service). At this level, local and personal

values are widely expressed within the organization. Basic beliefs and assumptions

include individuals' impressions about the trustworthiness and supportiveness of an

organization, and are often deeply ingrained within the organization's culture.

Organizational behavior at this level usually can be studied by interviewing the

organization's membership and using questionnaires to gather attitudes about

organizational membership.

At the third and deepest level, the organization's tacit assumptions are found. These are

the elements of culture that are unseen and not cognitively identified in everyday

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interactions between organizational members. Additionally, these are the elements of

culture which are often taboo to discuss inside the organization. Many of these

'unspoken rules' exist without the conscious knowledge of the membership. Those with

sufficient experience to understand this deepest level of organizational culture usually

become acclimatized to its attributes over time, thus reinforcing the invisibility of their

existence. Surveys and casual interviews with organizational members cannot draw out

these attributes—rather much more in-depth means is required to first identify then

understand organizational culture at this level. Notably, culture at this level is the

underlying and driving element often missed by organizational behaviorists.

Using Schein's model, understanding paradoxical organizational behaviors becomes

more apparent. For instance, an organization can profess highly aesthetic and moral

standards at the second level of Schein's model while simultaneously displaying

curiously opposing behavior at the third and deepest level of culture. Superficially,

organizational rewards can imply one organizational norm but at the deepest level imply

something completely different. This insight offers an understanding of the difficulty that

organizational newcomers have in assimilating organizational culture and why it takes

time to become acclimatized. It also explains why organizational change agents usually

fail to achieve their goals: underlying tacit cultural norms are generally not understood

before would-be change agents begin their actions. Merely understanding culture at the

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deepest level may be insufficient to institute cultural change because the dynamics of

interpersonal relationships (often under threatening conditions) are added to the

dynamics of organizational culture while attempts are made to institute desired change.

According to Schein (1992), the two main reasons why cultures develop in

organizations is due to external adaptation and internal integration. External adaptation

reflects an evolutionary approach to organizational culture and suggests that cultures

develop and persist because they help an organization to survive and flourish. If the

culture is valuable, then it holds the potential for generating sustained competitive

advantages. Additionally, internal integration is an important function since social

structures are required for organizations to exist. Organizational practices are learned

through socialization at the workplace. Work environments reinforce culture on a daily

basis by encouraging employees to exercise cultural values. Organizational culture is

shaped by multiple factors, including the following:

External environment

Industry

Size and nature of the organization's workforce

Technologies the organization uses

The organization's history and ownership

Gerry Johnson

Gerry Johnson (1988) described a cultural web, identifying a number of elements that

can be used to describe or influence organizational culture:

The Paradigm: What the organization is about, what it does, its mission, its

values.

Control Systems: The processes in place to monitor what is going on. Role

cultures would have vast rule-books. There would be more reliance on

individualism in a power culture.

Organizational Structures: Reporting lines, hierarchies, and the way that work

flows through the business.

Power Structures: Who makes the decisions, how widely spread is power, and

on what is power based?

Symbols: These include organizational logos and designs, but also extend to

symbols of power such as parking spaces and executive washrooms.

Rituals and Routines: Management meetings, board reports and so on may

become more habitual than necessary.

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Stories and Myths: build up about people and events, and convey a message

about what is valued within the organization.

These elements may overlap. Power structures may depend on control systems, which

may exploit the very rituals that generate stories which may not be true.

Stanley G. Harris

Schemata (plural of schema) are knowledge structures a person forms from past

experiences, allowing the person to respond to similar events more efficiently in the

future by guiding the processing of information. A person's schemata are created

through interaction with others, and thus inherently involve communication.

Stanley G. Harris (1994) argues that five categories of in-organization schemata are

necessary for organizational culture:

1. Self-In-Organization Schemata: a person's concept of oneself within the

context of the organization, including her/his personality, roles, and behavior.

2. Person-In-Organization Schemata: a person's memories, impressions, and

expectations of other individuals within the organization.

3. Organization Schemata: a subset of person schemata, a person's generalized

perspective on others as a whole in the organization.

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4. Object/Concept-In-Organization Schemata: knowledge an individual has of

organization aspects other than of other persons.

5. Event-In-Organization Schemata: a person's knowledge of social events within

an organization.

All of these categories together represent a person's knowledge of an organization.

Organizational culture is created when the schematas (schematic structures) of differing

individuals across and within an organization come to resemble each other (when any

one person's schemata come to resemble another person's schemata because of

mutual organizational involvement), primarily done through organizational

communication, as individuals directly or indirectly share knowledge and meanings.

Charles Handy

Charles Handy (1976), popularized Roger Harrison (1972) with linking organizational

structure to organizational culture. The described four types of culture are:

1. Power culture: concentrates power among a small group or a central figure and

its control is radiating from its center like a web. Power cultures need only a few

rules and little bureaucracy but swift in decisions can ensue.

2. Role culture: authorities are delegated as such within a highly defined structure.

These organizations form hierarchical bureaucracies, where power derives from

the personal position and rarely from an expert power. Control is made by

procedures (which are highly valued), strict roles descriptions and authority

definitions. These organizations have consistent systems and are very

predictable. This culture is often represented by a "Roman Building" having

pillars. These pillars represent the functional departments.

3. Task culture: teams are formed to solve particular problems. Power is derived

from the team with the expertise to execute against a task. This culture uses a

small team approach, where people are highly skilled and specialized in their

own area of expertise. Additionally, these cultures often feature the multiple

reporting lines seen in a matrix structure.

4. Person culture: formed where all individuals believe themselves superior to the

organization. It can become difficult for such organizations to continue to operate,

since the concept of an organization suggests that a group of like-minded

individuals pursue organizational goals. However some professional partnerships

operate well as person cultures, because each partner brings a particular

expertise and clientele to the firm.

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Kim Cameron and Robert Quinn

Kim Cameron and Robert Quinn (1999) conducted research on organizational

effectiveness and success. Based on the Competing Values Framework, they

developed the Organizational Culture Assessment Instrument that distinguishes four

culture types.

Competing values produce polarities like flexibility vs. stability and internal vs. external

focus – these two polarities were found to be most important in defining organizational

success. The polarities construct a quadrant with four types of culture:

Clan culture (internal focus and flexible) – A friendly workplace where leaders act

like father figures.

Adhocracy culture (external focus and flexible) – A dynamic workplace with

leaders that stimulate innovation.

Market culture (external focus and controlled) – A competitive workplace with

leaders like hard drivers

Hierarchy culture (internal focus and controlled) – A structured and formalized

workplace where leaders act like coordinators.

Cameron and Quinn designated six characteristics of organizational culture that can be

assessed with the Organizational Culture Assessment Instrument (OCAI).

Clan cultures are most strongly associated with positive employee attitudes and product

and service quality. Market cultures are most strongly related with innovation and

financial effectiveness criteria. The primary belief in market cultures that clear goals and

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contingent rewards motivate employees to aggressively perform and meet stakeholders'

expectations; a core belief in clan cultures is that the organization's trust in and

commitment to employees facilitates open communication and employee involvement.

These differing results suggest that it is important for executive leaders to consider the

match between strategic initiatives and organizational culture when determining how to

embed a culture that produces competitive advantage. By assessing the current

organizational culture as well as the preferred situation, the gap and direction to change

can be made visible as a first step to changing organizational culture.

Robert A. Cooke

Robert A. Cooke defines culture as the behaviors that members believe are required to

fit in and meet expectations within their organization. The Organizational Culture

Inventory measures twelve behavioral norms that are grouped into three general types

of cultures:

Constructive cultures, in which members are encouraged to interact with people

and approach tasks in ways that help them meet their higher-order satisfaction

needs.

Passive/defensive cultures, in which members believe they must interact with

people in ways that will not threaten their own security.

Aggressive/defensive cultures, in which members are expected to approach

tasks in forceful ways to protect their status and security.

Constructive Cultures

In constructive cultures, people are encouraged to be in communication with their coworkers,

and work as teams, rather than only as individuals. In positions where people

do a complex job, rather than something simple like a mechanical task, this culture is

efficient.

1. Achievement: completing a task successfully, typically by effort, courage, or skill

(pursue a standard of excellence) (explore alternatives before acting) – Based on

the need to attain high-quality results on challenging projects, the belief that

outcomes are linked to one's effort rather than chance and the tendency to

personally set challenging yet realistic goals. People high in this style think ahead

and plan, explore alternatives before acting and learn from their mistakes.

2. Self-Actualizing: realization or fulfillment of one's talents and potentialities –

considered as a drive or need present in everyone (think in unique and

independent ways) (do even simple tasks well) – Based on needs for personal

growth, self-fulfillment and the realization of one's potential. People with this style

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demonstrate a strong desire to learn and experience things, creative yet realistic

thinking and a balanced concern for people and tasks.

3. Humanistic-Encouraging: help others to grow and develop (resolve conflicts

constructively) – Reflects an interest in the growth and development of people, a

high positive regard for them and sensitivity to their needs. People high in this

style devote energy to coaching and counselling others, are thoughtful and

considerate and provide people with support and encouragement.

4. Affiliative: treat people as more valuable than things (cooperate with others) –

Reflects an interest in developing and sustaining pleasant relationships. People

high in this style share their thoughts and feelings, are friendly and cooperative

and make others feel a part of things.

Organizations with constructive cultures encourage members to work to their full

potential, resulting in high levels of motivation, satisfaction, teamwork, service quality,

and sales growth. Constructive norms are evident in environments where quality is

valued over quantity, creativity is valued over conformity, cooperation is believed to lead

to better results than competition, and effectiveness is judged at the system level rather

than the component level. These types of cultural norms are consistent with (and

supportive of) the objectives behind empowerment, total quality management,

transformational leadership, continuous improvement, re-engineering, and learning

organizations.

Passive/defensive

Cultures

Norms that reflect

expectations for members

to interact with people in

ways that will not threaten

their own security are in

the Passive/Defensive

Cluster.

The

four

Passive/Defensive

cultural norms are:

Approval

Conventional

Dependent

Avoidance

In organizations with Passive/Defensive cultures, members feel pressured to think and

behave in ways that are inconsistent with the way they believe they should in order to

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be effective. People are expected to please others (particularly superiors) and avoid

interpersonal conflict.

Rules, procedures, and orders are more important than personal beliefs, ideas, and

judgment. Passive/Defensive cultures experience a lot of unresolved conflict and

turnover, and organizational members report lower levels of motivation and satisfaction.

Aggressive/defensive Cultures

This style is characterized with more emphasis on task than people. Because of the

very nature of this style, people tend to focus on their own individual needs at the

expense of the success of the group. The aggressive/defensive style is very stressful,

and people using this style tend to make decisions based on status as opposed to

expertise.

1. Oppositional – This cultural norm is based on the idea that a need for security

that takes the form of being very critical and cynical at times. People who use this

style are more likely to question others work; however, asking those tough

question often leads to a better product. Nonetheless, those who use this style

may be overly-critical toward others, using irrelevant or trivial flaws to put others

down.

2. Power – This cultural norm is based on the idea that there is a need for prestige

and influence. Those who use this style often equate their own self-worth with

controlling others. Those who use this style have a tendency to dictate others

opposing to guiding others' actions.

3. Competitive – This cultural norm is based on the idea of a need to protect one's

status. Those who use this style protect their own status by comparing

themselves to other individuals and outperforming them. Those who use this

style are seekers of appraisal and recognition from others.

4. Perfectionistic – This cultural norm is based on the need to attain flawless

results. Those who often use this style equate their self-worth with the attainment

of extremely high standards. Those who often use this style are always focused

on details and place excessive demands on themselves and others.

Organizations with aggressive/defensive cultures encourage or require members to

appear competent, controlled, and superior. Members who seek assistance, admit

shortcomings, or concede their position are viewed as incompetent or weak. These

organizations emphasize finding errors, weeding out "mistakes" and encouraging

members to compete against each other rather than competitors. The short-term gains

associated with these strategies are often at the expense of long-term growth.

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Adam Grant

Adam Grant, author of the book Give and Take, distinguishes organizational cultures

into giver, taker and matcher cultures according to their norms of reciprocity. In a giver

culture, employees operate by "helping others, sharing knowledge, offering mentoring,

and making connections without expecting anything in return", whereas in a taker

culture "the norm is to get as much as possible from others while contributing less in

return" and winners are those who take the most and are able to build their power at the

expense of others. The majority of organizations are mid-way, with a matcher culture, in

which the norm is to match giving with taking, and favours are mostly traded in closed

loops.

In a study by Harvard researchers on units of the US intelligence system, a giver culture

turned out to be the strongest predictor of group effectiveness.

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As Grant points out, Robert H. Frank argues that "many organizations are

essentially winner-take-all markets, dominated by zero-sum competitions for rewards

and promotions". In particular, when leaders implement forced ranking systems to

reward individual performance, the organisational culture tends to change, with a giver

culture giving way to a taker or matcher culture.

Also awarding the highest-performing individual within each team encourages a taker

culture.

Stephen McGuire

Stephen McGuire (2003) defined and validated a model of organizational culture that

predicts revenue from new sources. An Entrepreneurial Organizational Culture (EOC) is

a system of shared values, beliefs and norms of members of an organization, including

valuing creativity and tolerance of creative people, believing that innovating and seizing

market opportunities are appropriate behaviors to deal with problems of survival and

prosperity, environmental uncertainty, and competitors' threats, and expecting

organizational members to behave accordingly.

Elements

People and empowerment focused

Value creation through innovation and change

Attention to the basics

Hands-on management

Doing the right thing

Freedom to grow and to fail

Commitment and personal responsibility

Emphasis on the future

Eric Flamholtz

Eric Flamholtz (2001; 2011) has identified and validated a model of organizational

culture components that drive financial results (Flamholtz and Randle, 2011). The

model consist of five identified dimensions of corporate culture: 1) treatment of

customers, 2) treatment of people, 3) performance standards and accountability, 4)

innovation and change, and 5) process orientation. These five dimensions have been

confirmed by factor analysis (Flamholtz and Narasimhan-Kannan, 2005) in addition,

Flamholtz has published empirical research that show the impact of organizational

culture on financial performance (Flamholtz, 2001).

Flamholtz has also proposed that organizational (corporate) culture is not just an asset

in the economic sense; but is also an "asset" in the conventional accounting sense

(Flamholtz 2005). Flamholtz and Randle have also examined the evolution of

organizational culture at different stages of organizational growth (Flamholtz and

Randle, 2014).

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Ethical Frameworks and Evaluations of Corporate Culture

Four organizational cultures can be classified as apathetic, caring, exacting, and

integrative.

An apathetic culture shows minimal concern for either people or performance.

A caring culture exhibits high concern for people but minimal concern for

performance issues.

An exacting culture shows little concern for people but a high concern for

performance.

An integrative culture combines a high concern for people and performance.

A cultural audit is an assessment of an organization's values.

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IV. Organizational Effectiveness

Organizational Effectiveness is the concept of how effective an organization is in

achieving the outcomes the organization intends to produce. Organizational

Effectiveness groups in organizations directly concern themselves with several key

areas. They are talent management, leadership development, organization

design and structure, design of measurements and scorecards, implementation of

change and transformation, deploying smart processes and smart technology to

manage the firms' human capital and the formulation of the broader Human Resources

agenda. If an organization has practices and programs in the areas above, the OE

group does many or all of the following roles

Examines

alignment

between

the areas

and

improves

them

Improves

trade-offs

between

reliability,

speed and

quality in

the above

areas

Strategizes

for higher

adoption

rates in

these areas

Facilitates/initiates/catalyses capability building : structure, process and people

Rapid advances in social sciences and technology aided by clever experimentation and

observation is bringing several truths to the light of society. There are several disciplines

of social sciences that help the OE Practitioner be successful.

Four of them are outlined below

Decision Making - Ways in which real people make decisions, enabling them real

time to make good decisions, improving quality of decisions by leveraging

adjacent disciplines ( for example- Behavioral economics) and replicating

relevant experiments, creating new ones and implementing their results to make

organizations effective

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Change & Learning – Ways in which real people learn, change, adopt and align,

get “affected” by dynamics in the environment and leveraging this knowledge to

create effective organizations that are pioneers of change and learning

Group Effectiveness – Ways in which real people work well together, especially

in bringing new ideas and innovation, working of people to people protocols,

impact of digitization and virtualization in organizations on these protocols

Self-Organizing & Adaptive Systems– Ways in which self-organizing systems

and highly networked systems work, learnings from them and the tangible ways

by which they can be put to play to make organizations more effective

The broader idea of organizational effectiveness is applied for non-profit

organizations towards making funding decisions. Foundations and other sources

of grants and other types of funds are interested in organizational effectiveness of those

people who seek funds from the foundations. Foundations always have more requests

for funds or funding proposals and treat funding as an investment using the same care

as a venture capitalist would in picking a company in which to invest.

According to Richard et al. (2009) organizational effectiveness captures organizational

performance plus the myriad internal performance outcomes normally associated with

more efficient or effective operations and other external measures that relate to

considerations that are broader than those simply associated with economic valuation

(either by shareholders, managers, or customers), such as corporate social

responsibility.

However, scholars of nonprofit organizational effectiveness acknowledge that the

concept has multiple dimensions and multiple definitions. For example, while most

nonprofit leaders define organizational effectiveness as 'outcome accountability,' or the

extent to which an organization achieves specified levels of progress toward its own

goals, a minority of nonprofit leaders define effectiveness as 'overhead minimization,' or

the minimization of fundraising and administrative costs. Hence, Organizational

effectiveness is typically evaluated within nonprofit organizations using logic models.

Logic models are a management tool widely used in the nonprofit sector in program

evaluation. Logic models are created for specific programs to link specific, measurable

inputs to specific, measurable impacts. Typically, logic models specify how program

inputs, such as money and staff time, produce activities and outputs, such as services

delivered, which in turn lead to impacts, such as improved beneficiary health.

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V. Charitable Purposes

Internal Revenue Service

02 April 2018

The exempt purposes set forth in Internal Revenue Code section 501(c)(3)

are charitable, religious, educational, scientific, literary, testing for public safety,

fostering national or international amateur sports competition, and the prevention of

cruelty to children or animals. The term charitable is used in its generally accepted

legal sense and includes relief of the poor, the distressed, or the underprivileged;

advancement of religion; advancement of education or science; erection or

maintenance of public buildings, monuments, or works; lessening the burdens of

government; lessening neighborhood tensions; eliminating prejudice and discrimination;

defending human and civil rights secured by law; and combating community

deterioration and juvenile delinquency.

________

Charities Must Operate Exclusively for

Charitable Purposes

Upholding the IRS’s denial of a tax exemption, the Tax Court found the organization at

issue, created to provide gaming activities in a “sober” environment, was not operated

exclusively for charitable purposes.

By Stephen D. Kirkland, CPA/CFF

December 10, 2015

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The U.S. Tax Court recently issued an opinion focusing on the requirements for an

organization to qualify for a tax exemption under Sec. 501(c)(3), emphasizing that an

organization must strictly comply with those requirements.

In GameHearts, T.C. Memo. 2015-218, the Tax Court held in a declaratory judgment

proceeding that the IRS was justified in denying the nonprofit organization GameHearts

a tax exemption. The organization claimed in its bylaws that it would promote “adult

sobriety and the general welfare of the citizens of the State of Montana.”

In its Form 1023, Application for Recognition of Exemption Under Section 501(c)(3),

GameHearts said it was committed to “providing alternative forms of entertainment”

including “free and low cost tabletop gaming activities in a supervised non-alcoholic,

sober environment …” GameHearts also stated that it was working toward the

betterment of the region by attracting participants to its activities “during evening hours,

as opposed to frequenting bars and casinos in the area, as well as to inspire decision

making and problem solving abilities by teaching and promoting educational and

strategic games and activities ...” On its Form 1023, GameHearts also said it depended

on donations from the gaming community and was largely a “mobile tutorial program.”

To encourage adult sobriety, GameHearts offered tutorials on how to play card games

and miniature games and offered “organized play.” Many of the games were similar to

those offered in nearby for-profit casinos. GameHearts claimed that another purpose for

its programs was to teach participants how to develop relationships with retailers and

game manufacturers and teach important life skills and work ethics.

Its stated purpose for providing free services was to appeal to the poor, distressed

citizens in the community. However, GameHearts also claimed that it would help “boost

the overall market shares of the industry by introducing new and motivated players into

the environment.”

The IRS concluded that GameHearts was not organized or operated exclusively for

exempt purposes because (1) GameHearts failed to establish that it benefited a

charitable class; (2) GameHearts’ nonexempt activities were more substantial than its

exempt activities; and (3) GameHearts did not meet the exempt purpose requirements

of Regs. Sec. 1.501(c)(3)-1(d) since it did not limit its activities to addicts with low

incomes.

The requirements for Exempt Status

In its opinion, the court included two important reminders. First, the organization bears

the burden of proving that it meets the requirements under Sec. 501(c)(3). Second, a

statute creating an exemption “must be strictly construed.”

The exemption under Sec. 501(c)(3) is available to the following organizations:

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Corporations … organized and operated exclusively for religious, charitable, scientific,

testing for public safety, literary, or educational purposes, or to foster national or

international amateur sports competition …, or for the prevention of cruelty to children or

animals, no part of the net earnings of which inures to the benefit of any private

shareholder or individual …

Although an organization must be both organized and operated exclusively for taxexempt

purposes, the court focused primarily on whether GameHearts was operated for

charitable purposes.

Regs. Sec. 501(c)(3)-1(d)(2), the term includes, but is not limited to:

The court

explained that

the term

“charitable” is

used in Sec.

501(c)(3) in its

generally

accepted legal

sense.

According to

[r]elief of the poor and distressed or of the underprivileged; … lessening of the burdens

of Government; and promotion of social welfare by organizations designed to

accomplish any of the above purposes, or (i) to lessen neighborhood tensions; … or (iv)

to combat community deterioration and juvenile delinquency.

The Tax Court had previously held that the term “charitable” could include “any

benevolent or philanthropic objective not prohibited by law or public policy which tends

to advance the well-doing and well-being of man” (Hutchinson Baseball Enters., Inc., 73

T.C. 144, 152 (1979), aff’d, 696 F.2d 757 (10th Cir. 1982) (quoting Peters, 21 T.C. 55,

59 (1953))).

Operating Exclusively for Charitable Purposes

According to Regs. Sec. 501(c)(3)-1(c)(1), an organization will be regarded as operated

exclusively for one or more exempt purposes only if it engages primarily in activities that

accomplish one or more of the exempt purposes specified in Sec. 501(c)(3).

The IRS’s primary issue with GameHearts was that it was not operated exclusively for

charitable purposes because of the way it promoted sobriety and the general welfare of

the people of Montana. A single, substantial nonexempt purpose will disqualify an

organization despite the importance of its exempt purpose. Also, if an organization

serves private rather than public interests, it will not qualify.

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GameHearts claimed that it was in fact operating for charitable purposes because it

provided relief for the poor, distressed, or underprivileged and promoted general welfare

by encouraging community-minded sobriety. The IRS argued that more than an

insubstantial part of GameHearts’ activities furthered “nonexempt social and

recreational interests” because GameHearts offered gaming to anyone who was over

18 years old and sober.

The IRS’s positions were that (1) gaming is recreational, and (2) GameHearts did not

limit its services to a charitable class. GameHearts countered, and the court agreed,

that the specific type or nature of recreation is not relevant.

GameHearts also argued that it served a charitable class because its programs did not

compete with the for-profit gaming industry.

The court focused on how an organization that offers a recreational activity to achieve a

charitable purpose can qualify as a charitable organization. A single activity can have

more than one purpose. The purpose of the activity, not the nature of it, is

determinative. Therefore, the Court focused on whether GameHearts’ primary purpose

for engaging in its sole activity (gaming) was an exempt purpose or whether there was

another substantial nonexempt purpose (recreation).

Prior Case Law

In its analysis, the court considered earlier cases on this point.

B.S.W. Group, Inc., 70 T.C. 352 (1978), involved an organization that provided

consulting services to not-for-profit, limited-resource organizations that were engaged in

various rural-related activities. The court did not find that B.S.W. Group was not exempt

because its activities might be a trade or business, but it did conclude that its activities

were commercial rather than charitable. The organization failed to show that it did not

compete with commercial consulting businesses. Also, B.S.W. Group charged fees for

its services, which produced a net profit, and also did not get any public support besides

the fees. B.S.W. Group also failed to limit its clientele to other organizations that were

exempt under Sec. 501(c)(3). As a result, B.S.W. Group did not qualify for exemption

because the primary purpose for its sole activity (consulting) was commercial rather

than educational, scientific, or charitable.

In Schoger Foundation, 76 T.C. 380 (1981), a religious retreat facility in Colorado was

not operated primarily for an exempt religious purpose. Although wholesome family

recreation and contemplating nature might provide a religious or uplifting experience,

Schoger Foundation failed to show how its religious retreat experience differed from

experiences available at any other quiet inn or lodge in Colorado.

Hutchinson Baseball Enters., Inc., 73 T.C. 144 (1979), involved an organization that

primarily promoted baseball in the surrounding community by maintaining a baseball

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field for the public, providing coaches and instruction for children, and sponsoring a

baseball camp. As a result, the organization was operating for an exempt purpose.

However, in Wayne Baseball, Inc., T.C. Memo. 1999-304, the organization’s nonexempt

social and recreational activities were substantial in comparison to the organization’s

promotion of baseball in the community. The sole activity sponsored by the organization

was the operation of an adult amateur baseball team, and the primary beneficiaries

were the individual team participants. Allowing spectators to watch the baseball games

without charge was incidental to the purpose of providing enjoyment, recreation, and

social interaction for the team participants.

In Peters, 21 T.C. 55 (1953), a foundation qualified for exempt status since it was

organized to promote social welfare by furnishing public swimming facilities to all

residents of a school district, especially those who did not have access to private

facilities.

In Columbia Park & Recreation Ass’n, 88 T.C. 1 (1987), aff’d, 838 F.2d 465 (4th Cir.

1988), the association, which was organized to provide recreational facilities for

residents of a planned community, did not qualify for exemption because it benefited

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only residents of the planned community, limited access to them, and obtained funding

only from those residents rather than by voluntary contributions from the public. The key

difference between Petersand Columbia Park & Recreation was not whether the

activities themselves were charitable, but whether the organizations were organized for

charitable purposes.

Conclusion

The GameHearts court recognized that recreational therapy could be a way to achieve

a charitable purpose. Nonetheless, GameHearts did not show that it was “operated

exclusively” for one or more exempt purposes. The organization was denied tax-exempt

status because there was a single, substantial nonexempt purpose, despite the

importance of the exempt purpose. Although promoting sober recreation may benefit

the community, the form of recreation GameHearts offered was also offered by for-profit

entities. Even though GameHearts did not profit from the recreation that it offered and it

could not offer recreational gaming that competed with for-profit casinos, tax-exempt

status was denied. Recreation was found to be a significant purpose, in addition to the

therapy provided, because of the inherently commercial nature of the recreation and the

ties to the for-profit gaming industry.

As a final note, the court mentioned that the IRS had encouraged GameHearts to apply

for exemption as a social welfare organization under Sec. 501(c)(4) instead of trying to

qualify as a charity under Sec. 501(c)(3), but GameHearts had declined.

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VI. Social Impact

Assessments

Social Impact Assessment (SIA) is a methodology to review the social effects of

infrastructure projects and other development interventions. Although SIA is usually

applied to planned interventions, the same techniques can be used to evaluate the

social impact of unplanned events, for example disasters, demographic

change and epidemics.

Definition

The origins of SIA largely derive from the environmental impact assessment (EIA)

model, which first emerged in the 1970s in the U.S, as a way to assess the impacts on

society of certain development schemes and projects before they go ahead - for

example, new roads, industrial facilities, mines, dams, ports, airports, and other

infrastructure projects. In the United States under the National Environmental Policy

Act, social impact assessments are federally mandated and performed in conjunction

with environmental impact assessments.

SIA has been incorporated into the formal planning and approval processes in several

countries, in order to categorize and assess how major developments may affect

populations, groups, and settlements. Though the social impact assessment has long

been considered subordinate to the environmental impact assessment, new models,

such as the Environmental Social Impact Assessment (ESIA), take a more integrated

approach where equal weight is given to both the social and environmental impact

assessments.

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Contents

Definitions for "social impact assessment" vary by different sectors and applications.

According to the International Association for Impact Assessment, "Social impact

assessment includes the processes of analyzing, monitoring and managing the

intended and unintended social consequences, both positive and negative, of planned

interventions (policies, programs, plans, projects) and any social change processes

invoked by those interventions. Its primary purpose is to bring about a more sustainable

and equitable biophysical and human environment."

SIAs originate from the 1970s and were originally used in Anglo-Saxon environments

with indigenous peoples, like the United States, Canada and Australia. Use of SIA's, in

general in combination with Environmental Impact Assessments (ESIA) has since then

developed, and are legally required in many other countries, ranging from development

countries like Sierra Leone and Chad, emerging markets like Chili and Philippines but

also other OECD countries like Greenland and South Africa.

Increased pressure on available land and increasingly educated and competent local

communities can lead to high costs for public acceptance of complex projects with

adverse risks and effects. SIAs are increasingly seen as an effective instrument to bring

these costs down by determining views of affected communities on risks, effects and

mitigation measures based on a sound socio-economic baseline study.

The IFC Performance Standards are generally seen as the benchmark for ESIAs and

insert this in an overarching Envrironmental and Social Risk Management System,

which is based on proven risk management techniques. The IFC Performance Standard

is used by multinational companies and commercial investors. Commercial banks have

united themselves under the Equator Principles with over 90 members in 37 countries.

SIA overlaps with monitoring and evaluation (M&E). Evaluation is particularly important

in the areas of:

1. public policy,

2. health and education initiatives, and

3. international development projects more generally, whether conducted by

governments, international donors, or NGOs.

In all these sectors, there is a case for conducting SIA and evaluations at different

stages.

The Hydropower Sustainability Assessment Protocol is a sector specific method for

checking the quality of environmental and social assessments and management plans.

Non-experts and local people should participate in the design and implementation of

proposed developments or programmes. For example, in the field of research projects,

it can be identified what people using social media platforms say and share about the

social impact of science. This can be achieved in the process of doing an SIA, through

adopting a participatory and democratic research process. Some SIAs go further than

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this, to adopt an advocacy role. For example, several SIAs carried out in Queensland,

Australia, have been conducted by consultants working for local Aboriginal communities

who oppose new mining projects on ancestral land. A rigorous SIA report, showing real

consequences of the projects and suggesting ways to mitigate these impacts, gives

credibility and provides evidence to take these campaigns to the planning officers or to

the courts.

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VII. Social Influence

Social Influence occurs when a person's emotions, opinions or behaviors are affected

by others intentionally or unintentionally.

Social influence takes many forms and can be seen in conformity, socialization, peer

pressure, obedience, leadership, persuasion, sales, and marketing. In 1958, Harvard

psychologist Herbert Kelman identified three broad varieties of social influence.

1. Compliance is when people appear to agree with others but actually keep their

dissenting opinions private.

2. Identification is when people are influenced by someone who is liked and

respected, such as a famous celebrity.

3. Internalization is when people accept a belief or behavior and agree both

publicly and privately.

Morton Deutsch and Harold Gerard described two psychological needs that lead

humans to conform to the expectations of others. These include our need to be right

(informational social influence) and our need to be liked (normative social

influence). [3] Informational influence (or social proof) is an influence to accept

information from another as evidence about reality. Informational influence comes into

play when people are uncertain, either because stimuli are intrinsically ambiguous or

because there is social disagreement. Normative influence is an influence to conform to

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the positive expectations of others. In terms of Kelman's typology, normative influence

leads to public compliance, whereas informational influence leads to private

acceptance.

Types

Social influence is a broad term that relates to many different phenomena. Listed below

are some major types of social influence that are being researched in the field of social

psychology. For more information, follow the main article links provided.

Kelman's Varieties

There are three processes of attitude change as defined by Harvard

psychologist Herbert Kelman in a 1958 paper published in the Journal of Conflict

Resolution. The purpose of defining these processes was to help determine the effects

of social influence: for example, to separate public conformity (behavior) from private

acceptance (personal belief).

Compliance

Compliance is the act of responding favorably to an explicit or implicit request offered by

others. Technically, compliance is a change in behaviorbut not necessarily

in attitude; one can comply due to mere obedience or by otherwise opting to withhold

private thoughts due to social pressures. [4] According to Kelman's 1958 paper, the

satisfaction derived from compliance is due to the social effect of the accepting

influence (i.e., people comply for an expected reward or punishment-aversion).

Identification

Identification is the changing of attitudes or behaviors due to the influence of someone

who is admired. Advertisements that rely upon celebrityendorsements to market their

products are taking advantage of this phenomenon. According to Kelman, the desired

relationship that the identifier relates to the behavior or attitude change. [2]

Internalization

Internalization is the process of acceptance of a set of norms established by people or

groups that are influential to the individual. The individual accepts the influence because

the content of the influence accepted is intrinsically rewarding. It is congruent with the

individual's value system, and according to Kelman the "reward" of internalization is "the

content of the new behavior".

Conformity

Conformity is a type of social influence involving a change in behavior, belief, or thinking

to align with those of others or with normative standards. It is the most common and

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pervasive form of social influence. Social psychology research in conformity tends to

distinguish between two varieties: informational conformity (also called social proof, or

"internalization" in Kelman's terms ) and normative conformity ("compliance" in Kelman's

terms).

In the case of peer pressure, a person is convinced to do something that they might not

want to do (such as taking illegal drugs) but which they perceive as "necessary" to keep

a positive relationship with other people (such as their friends). Conformity from peer

pressure generally results from identification with the group members or from

compliance of some members to appease others.

Conformity can be in appearance, or may be more complete in nature; impacting an

individual both publicly and privately.

Compliance (also referred to as acquiescence) demonstrates a public conformity to a

group majority or norm, while the individual continues to privately disagree or dissent,

holding on to their original beliefs or to an alternative set of beliefs differing from the

majority. Compliance appears as conformity, but there is a division between the public

and the private self.

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Conversion includes the private acceptance that is absent in compliance. The

individual's original behaviour, beliefs, or thinking changes to align with that of others

(the influencers), both publicly and privately. The individual has accepted the behavior,

belief, or thinking, and has internalized it, making it his own. Conversion may also refer

to individual members of a group changing from their initial (and varied) opinions to

adopt the opinions of others, which may differ from their original opinions. The resulting

group position may be a hybrid of various aspects of individual initial opinions, or it may

be an alternative independent of the initial positions reached through consensus.

What appears to be conformity may in fact be congruence. Congruence occurs when an

individual's behavior, belief, or thinking is already aligned with that of the others, and no

change occurs.

In situations where conformity (including compliance, conversion, and congruence) is

absent, there are non-conformity processes such as independence and anti-conformity.

Independence, also referred to as dissent, involves an individual (either through their

actions or lack of action, or through the public expression of their beliefs or thinking)

being aligned with their personal standards but inconsistent with those of other

members of the group (either all of the group or a majority).

Anti-conformity, also referred to as counter-conformity, may appear as independence,

but it lacks alignment with personal standards and is for the purpose of challenging the

group. Actions as well as stated opinions and beliefs are often diametrically opposed to

that of the group norm or majority. The underlying reasons for this type of behavior may

be rebelliousness/obstinacy or it may be to ensure that all alternatives and view points

are given due consideration.

Minority influence

Minority influence takes place when a majority is influenced to accept the beliefs or

behaviors of a minority. Minority influence can be affected by the sizes of majority and

minority groups, the level of consistency of the minority group, and situational factors

(such as the affluence or social importance of the minority). [6] Minority influence most

often operates through informational social influence (as opposed to normative social

influence) because the majority may be indifferent to the liking of the minority. [7]

Self-fulfilling Prophecy

A self-fulfilling prophecy is a prediction that directly or indirectly causes itself to become

true due to positive feedback between belief and behavior. A prophecy declared as truth

(when it is actually false) may sufficiently influence people, either through fear or logical

confusion, so that their reactions ultimately fulfill the once-false prophecy. This term is

credited to sociologist Robert K. Merton from an article he published in 1948.

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Reactance

Reactance is the adoption of a view contrary to the view that a person is being

pressured to accept, perhaps due to a perceived threat to behavioral freedoms. This

phenomenon has also been called anticonformity. While the results are the opposite of

what the influencer intended, the reactive behavior is a result of social pressure. [9] It is

notable that anti-conformity does not necessarily mean independence. In many studies,

reactance manifests itself in a deliberate rejection of an influence, even if the influence

is clearly correct.

Obedience

Obedience is a form of social influence that derives from an authority figure.

The Milgram experiment, Zimbardo's Stanford prison experiment, and the Hofling

hospital experiment are three particularly well-known experiments on obedience, and

they all conclude that humans are surprisingly obedient in the presence of perceived

legitimate authority figures.

Persuasion

Persuasion is the process of guiding oneself or another toward the adoption of an

attitude by rational or symbolic means. Robert Cialdini defined six "weapons of

influence": reciprocity, commitment, social proof, authority, liking, and scarcity.

These "weapons of influence" attempt to bring about conformity by directed means.

Persuasion can occur through appeals to reason or appeals to emotion.

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Psychological Manipulation

Psychological manipulation is a type of social influence that aims to change the

behavior or perception of others through abusive, deceptive, or underhanded tactics. By

advancing the interests of the manipulator, often at another's expense, such methods

could be considered exploitative, abusive, devious, and deceptive.

Social influence is not necessarily negative. For example, doctors can try

to persuade patients to change unhealthy habits. Social influence is generally perceived

to be harmless when it respects the right of the influenced to accept or reject it, and is

not unduly coercive. Depending on the context and motivations, social influence may

constitute underhanded manipulation.

Abusive Power and Control

Controlling abusers use tactics to exert power and control over their victims. The goal of

the abuser is to control and intimidate the victim or to influence them to feel that they do

not have an equal voice in the relationship.

Propaganda

Propaganda is information that is not objective and is used primarily to influence an

audience and further an agenda, often by presenting facts selectively to encourage a

particular synthesis or perception, or using loaded language to produce an emotional

rather than a rational response to the information that is presented.

Hard Power

Hard power is the use of military and economic means to influence the behavior or

interests of other political bodies. This form of political power is often aggressive

(coercion), and is most effective when imposed by one political body upon another of

lesser military and/or economic power. Hard power contrasts with soft power, which

comes from diplomacy, culture and history.

Antecedents

Many factors can affect the impact of social influence.

Social Impact Theory

Social impact theory was developed by Bibb Latané in 1981. This theory asserts that

there are three factors which increase a person's likelihood to respond to social

influence:

Strength: The importance of the influencing group to the individual

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Immediacy: Physical (and temporal) proximity of the influencing group to the

individual at the time of the influence attempt

Number: The number of people in the group

Cialdini's "Weapons of Influence"

Robert Cialdini defines six "weapons of influence" that can contribute to an individual's

propensity to be influenced by a persuader:

Reciprocity: People tend to return a favor.

Commitment and consistency: People do not like to be self-contradictory. Once

they commit to an idea or behavior, they are averse to changing their minds

without good reason.

Social proof: People will be more open to things that they see others doing. For

example, seeing others compost their organic waste after finishing a meal may

influence the subject to do so as well.

Authority: People will tend to obey authority figures.

Liking: People are more easily swayed by people they like.

Scarcity: A perceived limitation of resources will generate demand.

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Unanimity

Social Influence is strongest when the group perpetrating it is consistent and committed.

Even a single instance of dissent can greatly wane the strength of an influence. For

example, in Milgram's first set of obedience experiments, 65% of participants complied

with fake authority figures to administer "maximum shocks" to a confederate. In

iterations of the Milgram experiment where three people administered shocks (two of

whom were confederates), once one confederate disobeyed, only ten percent of

subjects administered the maximum shocks.

Status

Those perceived as experts may exert social influence as a result of their perceived

expertise. This involves credibility, a tool of social influence from which one draws upon

the notion of trust. People believe an individual to be credible for a variety of reasons,

such as perceived experience, attractiveness, knowledge, etc. Additionally, pressure to

maintain one's reputation and not be viewed as fringe may increase the tendency to

agree with the group. This phenomenon is known as groupthink. Appeals to authority

may especially affect norms of obedience. The compliance of normal humans to

authority in the famous Milgram experiment demonstrate the power of perceived

authority.

Those with access to the media may use this access in an attempt to influence the

public. For example, a politician may use speeches to persuade the public to support

issues that he or she does not have the power to impose on the public. This is often

referred to as using the "bully pulpit." Likewise, celebrities don't usually possess any

political power, but they are familiar to many of the world's citizens and, therefore,

possess social status.

Power is one of the biggest reasons an individual feels the need to follow through with

the suggestions of another. A person who possesses more authority (or is perceived as

being more powerful) than others in a group is an icon or is most "popular" within a

group. This person has the most influence over others. For example, in a child's school

life, people who seem to control the perceptions of the students at school are most

powerful in having a social influence over other children.

Culture

Culture appears to play a role in the willingness of an individual to conform to the

standards of a group. Stanley Milgram found that conformity was higher in Norway than

in France. This has been attributed to Norway's longstanding tradition of social

responsibility, compared to France's cultural focus on individualism. Japan likewise has

a collectivist culture and thus a higher propensity to conformity. However, a 1970 Aschstyle

study found that when alienated, Japanese students were more susceptible

to anticonformity (giving answers that were incorrect even when the group had

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collaborated on correct answers) one third of the time, significantly higher than has

been seen in Asch studies in the past.

While gender does not significantly affect a person's likelihood to conform, under certain

conditions gender roles do affect such a likelihood. Studies from the 1950s and 1960s

concluded that women were more likely to conform than men. But a 1971 study found

that experimenter bias was involved; all of the researchers were male, while all of the

research participants were female. Studies thereafter found that the likelihood to

conform almost equal between the genders. Furthermore, men conformed more often

when faced with traditionally feminine topics, and women conformed more often when

presented with masculine topics. In other words, ignorance about a subject can lead a

person to defer to "social proof".

Emotions

Emotion and

disposition may affect

an individual's

likelihood of

conformity or

anticonformity. In

2009, a study

concluded that fear

increases the chance

of agreeing with a

group, while romance

or lust increases the

chance of going

against the group.

Social Structure

Social Networks

A social network is a social structure made up of nodes (representing individuals or

organizations) which are connected (through ties, also called edges, connections,

or links) by one or more types of interdependency (such as friendship, common

interests or beliefs, sexual relations, or kinship). Social network analysis uses the lens

of network theory to examine social relationships. Social network analysis as a field has

become more prominent since the mid-20th century in determining the channels and

effects of social influence. For example, Christakis and Fowler found that social

networks transmit states and behaviors such as obesity, smoking, drinking and

happiness.

Identifying the extent of social influence, based on large-scale observational data with a

latent social network structure, is pertinent to a variety of collective social phenomena

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including crime, civil unrest, and voting behavior in elections. For example,

methodologies for disentangling social influence by peers from external influences—

with latent social network structures and large-scale observational data—were applied

to US presidential elections, stock markets, and civil unrest.

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VIII. The Judge-Advisor

System

A Judge–Advisor System (JAS) is a type of advice structure often studied

in advice taking research, a subset of decision-making in the social sciences. The two

roles in a JAS are the judge and advisor roles. The judge is the decision maker who

evaluates information concerning a particular decision and makes the final judgment on

the decision outcome. The advisor is an individual who provides advice, information, or

suggestions to the judge. A key component of the dynamics in a JAS is the

differentiation between the two roles in that while the advisor provides input to the

decision, actual decision-making power resides solely with the judge. This one person

decision power differentiates the JAS and related models such as Hollenbeck's

Hierarchical Decision-Making Team model from more widely studied models where the

final decision is mutually decided upon by the team as a whole.

While JASs can be most easily thought of as between superiors and subordinates (such

as in student–advisor or worker–manager relationships), differential social or power

standings are not necessary. All that is required is that only one individual (the judge)

has the final say in the decision outcome; all other input given to the judge may be

taken under consideration but need not be acted on. Therefore, even a situation where

a friend receives advice from a peer can be considered a JAS.

Though examples of JASs are prevalent in real-world settings, they are studied most

frequently in laboratory experiments in which judge/advisor roles are randomly

assigned and situations/variables are manipulated at a between-subjects level. Such

manipulations allow for a systematic study of the factors that affect how a judge reacts

and responds to advisor advice.

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Advice Utilization

Advice utilization is the degree to which judges take advisor advice into consideration in

their final decision outcome, and advice discounting is any effect that lowers the degree

of advice utilization. Both of these terms are frequently used interchangeably in JAS

literature, as they are related in opposition to one another (i.e., advice discounting is a

lack of advice utilization). The amount of utilization is one of the most considered

outcomes of a JAS decision process and depends on all the types of inputs described

below. In addition to these inputs, there are theories for other sources of advice

discounting in decision-making literature; three of the most dominant theories are

differential information, anchoring, and egocentric bias. [4] The differential information

theory proposes that advice discounting stems from the fact that, unlike with people's

own opinions, they are not aware of advisors' internal reasons for their opinions and so

are less apt to fully accept them. The second theory, anchoring, suggests that people

use their own opinion as to the starting point for their choice, and only use advisor input

to a certain extent that will adjust their initial position up or down. The third

theory, egocentric bias, proposes advice discounting happens due to judges believing

they are superior to others, so weigh their own opinion stronger than inputs from any

other source.

In JAS literature, one of the most robust advice discounting classification is egocentric

advice discounting, which draws conceptually from the basic theories of anchoring and

egocentric bias. Simply put, egocentric advice discounting is the tendency of individuals

to prefer advice and opinions that closely align to their own opinions formed prior to

hearing any input. Therefore, judges tend to overly weigh advice from advisors that is

similar to their own viewpoint regardless of what sort of expertise an advisor appears to

have. Conversely, if the advice given is very dissimilar to the judge's initial opinions, that

advice will be discounted much more than should be justified given the advisor's level of

expertise.

Judge Decision-Making Style

Antecedents to Advice Utilization

Decision-making style refers to differences in the ways individuals approach decision

tasks and respond to situations. In a JAS, judges' differing styles can affect the way they

accept and respond to advisor advice. Five styles identified by Scott and Bruce (1995)

are rational, intuitive, dependent, spontaneous and avoidant.

Rational: relying on logical evaluations and exhaustive searches for all relevant

information

Intuitive: relying on intuition, hunches, and other intangibles

Dependent: relying on others for advice and direction

Spontaneous: relying on a strong urge to make decisions as soon as possible

Avoidant: relying on strategies for putting off the decision-making process as long

as possible

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These styles are not mutually exclusive within an individual, but there are discernible

pattern differences across people. While not explicitly examined in many JAS studies,

an understanding of these decision-making style differences can inform future

understanding of advice-taking dynamics.

Decision-Making Competence

Judges may differ in their susceptibility to a number of different errors in decisionmaking;

these characteristic differences are considered their specific decision-making

competencies. While decision-making competency has been broken down into

categories in several different ways, one of the most widely accepted frameworks is the

Adult Decision-Making Competence scale (A-DMC), developed by Bruine de Bruin et

al. The A-DMC consists of 7 categories of decision-making competencies that include

dimensions such as Resistance to Framing and Recognizing Social Norms (see Bruine

de Bruin et al., 2007, for a full description). Weaknesses in these different areas make

judges more susceptible to particular errors in judgment and may influence the way

advisor input is received and acted upon.

Trust and Confidence

The level of trust a judge has with an advisor is directly related to the degree to which

advice is taken into account. When judges trust their advisors, they are more likely to

accept the advice given to them, all other factors being equal. [12] Note that the trust

relationship in a JAS is frequently unbalanced due to the greater importance of trust for

the judge than the advisor. This results because the judge must place a certain amount

of trust in the advisors in order to utilize their advice in the decision, which only the

judge is ultimately responsible for. Advisors, on the other hand, typically need not trust a

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judge in order to simply relay a suggestion or information. Given this dynamic,

considerations of trust levels need only be made from the judge's and not the advisor's

perspective. Key influencers of JAS trust levels include perceived advisor confidence

and subject expertise.

Type of Advice

The type of advice given by an advisor can influence the way it is received by the judge.

In a JAS, the concept of advice needs to be broader than the typical definition of a

recommendation for a particular outcome in a decision situation. While this sort of input

is certainly one kind of advice, other types of advice also exist. Dalal and Bonaccio

(2010) suggest that there are 4 different kinds of advice:

Recommendation for: advice in favor of a particular alternative

Recommendation against: advice against one or more alternatives

Information: neutral advice giving more information about the alternatives without

suggesting a particular one

Decision Support: no specific outcome advice; instead, input or support to guide

the judge's decision-making process

Judges react to these four types of advice with differential preferences. While specifics

of the particular type of decision task and the judge's individual differences can affect

the degree of preference between types, initial research shows Information-type advice

to be most preferred. This sort of advice has been little recognized in much of the past

advice-taking literature and is expected to receive more attention in the future.

Task Type

The difficulty of the decision task influences the extent judges rely on advisor inputs.

When a difficult task is given to a judge, there is a tendency to over-rely on the advice

received from advisors; conversely, judges tend to rely less than they should on advisor

information when the task seems relatively easy. For example, if judges need to make a

decision about which stocks will be best performers based on complex financial data

they are given, they will be likely to defer to the advice of their advisors regardless of

their supposed expertise since the judge's own grasp of the situation is so low.

However, if the decision task seems more straightforward or simple to the judges, they

will be far likelier to weigh their own opinions more heavily than their advisors' inputs

regardless of the states of expertise the advisors have.

While the most often used decision tasks in JAS literature are ones that involve picking

the "right" or "best" option, an entirely different kind of decision to consider is one

involving a choice based on taste or preference. These situations come up frequently in

life and are part of almost every consumer decision about the kind of music to buy,

clothes to wear, or restaurants to visit. Though less explored in JAS literature, Yaniv et

al. (2011) provided evidence that in these situations of preference, similarity of the

advisor to the judge is the strongest predictor of how much the judge will accept the

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advice. This similarity can be either in regard to a previous history of the advisor having

similar preferences in a given category (e.g., they rate the same kinds of songs highly)

or relating to basic demographic characteristics (e.g., they are around the same age).

Solicitation of Advice

Advice from an advisor can be either solicited (the judge seeks out input) or unsolicited

(input is given automatically without being requested). The degree of advice utilization

has been shown to be influenced by which of these two situations is true for the

decision situation at hand. As may be expected by conventional wisdom, advice

utilization is typically higher for solicited versus unsolicited advice. When people seek

out advice, it is implied that they are open to considering opinions other than their own

and prone to higher advice utilization. Conversely, unsolicited advice can be seen as

intrusive or as a type of criticism from the advisor about the judge's competency.

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Other Factors Contributing to Advice Utilization

Financial

Several characteristics of the decision task or judge–advisor relationship have been

shown to decrease the effect of egocentric discounting. First, if there is a financial

incentive for making the correct or best decision, judges tend to rely more heavily on

their advisors. Similarly, when judges must pay for advice, that input is taken much

more into account than when it is freely given. The effect of paying for advice is thought

to have foundations in the economic theory of sunk costs.

Advisor Characteristics

In situations where the judges perceive the advisors to have expert knowledge, the

advice received is taken into account more, regardless of similarity to the judge's own

opinions. This finding is intuitive: the less someone knows about a situation in

relationship to their advisor, the more likely they are to take that person's advice into

account. Advisor characteristics commonly associated with superior knowledge such as

being older, more educated or more experienced also have been shown to decrease

egocentric discounting in decision-making situations.

Task Difficulty

Beyond advisor characteristics and financial factors, the nature of the decision task itself

can influence the degree to which advice is accepted. In relatively easy tasks, judges

tend to consider advisor input to a lesser degree than they should, based on the known

expertise of the advisor. Similarly, when presented with a very difficult task, judges tend

to over-rely on the advisor inputs. This dynamic is important to keep in mind when trying

to identify real-world situations where people are vulnerable to being extremely

influenced by people posing as "experts".

Accuracy of Judge's Final Decision

Consequences of Advice Utilization

Decision-making outcomes in a JAS (or other advice-giving structures) have been

widely shown to be more accurate than those from situations with isolated decision

makers. This result should be expected given that advice situations often allow judges

access to knowledge above and beyond what they could have as an individual. When

judges have access to multiple advisors with different information sources, their

decision accuracy improves even more. A potential reason for this is due to the

averaging across advisors that the judge does when integrating the different pieces of

advice. Like in forecasting, the individual variations between advice become less

pronounced, and judges are left with more definitive advice that has the strength of

consensus behind it.

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Judge's Confidence in Final Decision

There are several key aspects of the JAS system that influence the degree to which the

judge has confidence in his decision being accurate or correct. The amount of

agreement between advisors has been shown to affect judges' confidence in their

decision, such that higher disagreement between advisors is associated with low

confidence. Another factor that has demonstrated influence over judge confidence is the

amount of effort the judge must put forth to understand and react to the advice proffered

by the advisors. As effort to process and comprehend advice increases, so does a

judge's overall confidence in their final decision. Lastly, it was found that judges could

actually become overconfident in their decisions when having to rely almost completely

on advisor recommendations (due to not possessing nearly enough task-specific

information themselves).

Applications

Examples of judge–advisor systems can be found in many real-world situations. A

recent example of an important JAS situation was that of the controversy around the

federal loan guarantees to the now-bankrupt Solyndra. In this situation, as in many

other situations that reach the presidential office, there are many sources of diverse

advice that the president and other decision-makers receive. For example, both the

director of the National Economic Council and the Treasury secretary advised the

president that they believed the selection guidelines were not thorough enough and

might allow for funding of unnecessary, risky companies. However, the Energy

Secretary, under pressure from Congress, advised the president to actually speed up

loans and decrease scrutiny on the selection process. As demonstrated by several

studies, advisors with differing viewpoints and differing degrees of unique information

can interact with decision-makers in complex and sometimes detrimental ways. The

decision-makers are then in the difficult position of aggregating all this advice and

making the most informed policy decision. As with the Solyndra controversy, these

decisions can sometimes fall under great scrutiny and not produce the most effective

solution.

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JAS situations can also arise in more common settings that typical individuals can

experience. A very common JAS arises when individuals receive advice from doctors

and other medical professionals. For example, an individual with diabetes might receive

specific advice about better controlling their blood sugar after a situation that required

that they go to the hospital. That individual then may seek second opinions about that

advice before coming to a decision regarding whether or not they will change their

behavior to fit more with those recommendations. As is found in the JAS research,

people often do not fully utilize doctors' recommendations, often to their own detriment.

In each of these situations, being able to figure out the way to make the advice system

most efficient and productive has clear benefits. Understanding the most effective ways

to give advice has great potential in training programs for advisors, mentors, and in

management training as a whole. An example of such application is seen in the work by

Wilkins et al. (1999) on the development of the Raven and CoRaven decision-making

aids used by the military to filter and represent massive amounts of battlefield data for

strategic planning. Using principles derived from JAS research, the authors were able to

analyze and better understand the aids, with the result being a more effective system

that makes battlefield decision-making less of a risky process. In this situation, the

researchers treated the intelligent software as an advisor, and the commanding officer

as the judge. Under this assumption, the researchers then applied past and current JAS

research findings to critically evaluate the software with the hopes of improving its

functionality. This utilization of JAS research is an example of one of the most promising

and direct applications of the paradigm – collaborative technology, which can facilitate

decision-making processes that are too complex for human cognition alone.

Judge advisor systems research can also be applied to business, finance, education,

and many other fields in which hierarchical group-decision making is common.

Applications of such research could be used to make time-sensitive decisions in highimpact

situations such as emergency rooms more efficient and accurate, potentially

saving the lives of patients in need. The JAS framework could be effectively applied in

public affairs to increase the speed at which new policies are created and enacted.

Other direct and indirect applications are possible for virtually every situation in which

hierarchical group decision-making exists.

Future Research Directions

JAS research is still a developing field with growth needed in a couple key areas. One

area of interest is a deeper understanding about the motives of decision-makers in JAS

situations beyond decision accuracy and autonomy. In the real world, decision-makers

frequently have many motives beyond making the most accurate and informed decision,

often due to social influences. Some additional motives that have already been cited

include attempting to diffuse responsibility for a decision, minimizing the amount of effort

on behalf of the decision-maker, and maintaining good rapport with the advisor(s).

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As mentioned previously, a less-explored subject matter that has surfaced in the JAS

literature pertains to decisions regarding matters of taste. Due to the relative newness of

this work, there is ample opportunity for further research. New questions raised by this

research include the effect of normative influences on taste preferences and hotcold

empathy gaps (i.e. individuals' preference for a certain food in a "not hungry"

versus "hungry" state).

Finally, one major topic that has been cited as needing further study is extending the

context of decision-making beyond what has already been observed to see how these

contexts affect the JAS. This research area is related to concerns about the

generalizability of many JAS studies to real-world decision-making situations; in other

words, that the stimuli in controlled lab settings are impoverished compared to the

stimuli individuals experience in their own lives. Thus, there has been a call for research

that replicates previous findings in a more "rich" situational context.

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IX. Predictive Analytics

Predictive Analytics' encompasses a variety of statistical techniques from data

mining, predictive modelling, and machine learning, that analyze current and historical

facts to make predictions about future or otherwise unknown events.

In business, predictive models exploit patterns found in historical and transactional data

to identify risks and opportunities. Models capture relationships among many factors to

allow assessment of risk or potential associated with a particular set of conditions,

guiding decision-makingfor candidate transactions.

The defining functional effect of these technical approaches is that predictive analytics

provides a predictive score (probability) for each individual (customer, employee,

healthcare patient, product SKU, vehicle, component, machine, or other organizational

unit) in order to determine, inform, or influence organizational processes that pertain

across large numbers of individuals, such as in marketing, credit risk assessment, fraud

detection, manufacturing, healthcare, and government operations including law

enforcement.

Predictive analytics is used in actuarial science, marketing, financial

services, insurance, telecommunications, retail, travel, mobility, healthcare, child

protection, pharmaceuticals, capacity planning, social networking and other fields.

One of the best-known applications is credit scoring, which is used throughout financial

services. Scoring models process a customer's credit history, loan application, customer

data, etc., in order to rank-order individuals by their likelihood of making future credit

payments on time.

Definition

Predictive analytics is an area of statistics that deals with extracting information from

data and using it to predict trends and behavior patterns. The enhancement of

predictive web analytics calculates statistical probabilities of future events online.

Predictive analytics statistical techniques include data modeling, machine learning, AI,

deep learning algorithms and data mining. Often the unknown event of interest is in the

future, but predictive analytics can be applied to any type of unknown whether it be in

the past, present or future. For example, identifying suspects after a crime has been

committed, or credit card fraud as it occurs. The core of predictive analytics relies on

capturing relationships between explanatory variables and the predicted variables from

past occurrences, and exploiting them to predict the unknown outcome. It is important to

note, however, that the accuracy and usability of results will depend greatly on the level

of data analysis and the quality of assumptions.

Predictive analytics is often defined as predicting at a more detailed level of granularity,

i.e., generating predictive scores (probabilities) for each individual organizational

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element. This distinguishes it from forecasting. For example, "Predictive analytics—

Technology that learns from experience (data) to predict the future behavior of

individuals in order to drive better decisions." In future industrial systems, the value of

predictive analytics will be to predict and prevent potential issues to achieve near-zero

break-down and further be integrated into prescriptive analytics for decision

optimization. Furthermore, the converted data can be used for closed-loop product life

cycle improvementwhich is the vision of the Industrial Internet Consortium.

Predictive Analytics Process

1. Define Project : Define the project outcomes,

deliverable, scope of the effort, business

objectives, identify the data sets that are

going to be used.

2. Data Collection : Data mining for

predictive analytics prepares data from

multiple sources for analysis. This

provides a complete view of customer

interactions.

3. Data Analysis : Data Analysis is the

process of inspecting, cleaning and

modelling data with the objective of

discovering useful information, arriving at

conclusion

4. Statistics : Statistical Analysis enables

to validate the assumptions, hypothesis

and test them using standard

statistical models.

5. Modelling : Predictive modelling

provides the ability to automatically

create accurate predictive models about future. There are also options to choose

the best solution with multi-modal evaluation.

6. Deployment : Predictive model deployment provides the option to deploy the

analytical results into everyday decision making process to get results, reports

and output by automating the decisions based on the modelling.

7. Model Monitoring : Models are managed and monitored to review the model

performance to ensure that it is providing the results expected.

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Types

Generally, the term predictive analytics is used to mean predictive modeling, "scoring"

data with predictive models, and forecasting. However, people are increasingly using

the term to refer to related analytical disciplines, such as descriptive modeling and

decision modeling or optimization. These disciplines also involve rigorous data analysis,

and are widely used in business for segmentation and decision making, but have

different purposes and the statistical techniques underlying them vary.

Predictive Models

Predictive modelling uses predictive models to analyze the relationship between the

specific performance of a unit in a sample and one or more known attributes or features

of the unit. The objective of the model is to assess the likelihood that a similar unit in a

different sample will exhibit the specific performance. This category encompasses

models in many areas, such as marketing, where they seek out subtle data patterns to

answer questions about customer performance, or fraud detection models. Predictive

models often perform calculations during live transactions, for example, to evaluate the

risk or opportunity of a given customer or transaction, in order to guide a decision. With

advancements in computing speed, individual agent modeling systems have become

capable of simulating human behaviour or reactions to given stimuli or scenarios.

The available sample units with known attributes and known performances is referred to

as the "training sample". The units in other samples, with known attributes but unknown

performances, are referred to as "out of [training] sample" units. The out of sample units

do not necessarily bear a chronological relation to the training sample units. For

example, the training sample may consist of literary attributes of writings by Victorian

authors, with known attribution, and the out-of sample unit may be newly found writing

with unknown authorship; a predictive model may aid in attributing a work to a known

author. Another example is given by analysis of blood splatter in simulated crime

scenes in which the out of sample unit is the actual blood splatter pattern from a crime

scene. The out of sample unit may be from the same time as the training units, from a

previous time, or from a future time.

Descriptive Models

Descriptive models quantify relationships in data in a way that is often used to classify

customers or prospects into groups. Unlike predictive models that focus on predicting a

single customer behavior (such as credit risk), descriptive models identify many different

relationships between customers or products.

Descriptive models do not rank-order customers by their likelihood of taking a particular

action the way predictive models do. Instead, descriptive models can be used, for

example, to categorize customers by their product preferences and life stage.

Descriptive modeling tools can be utilized to develop further models that can simulate

large number of individualized agents and make predictions.

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Decision Models

Decision models describe the relationship between all the elements of a decision—the

known data (including results of predictive models), the decision, and the forecast

results of the decision—in order to predict the results of decisions involving many

variables. These models can be used in optimization, maximizing certain outcomes

while minimizing others. Decision models are generally used to develop decision logic

or a set of business rules that will produce the desired action for every customer or

circumstance.

Applications

Although predictive analytics can be put to use in many applications, we outline a few

examples where predictive analytics has shown positive impact in recent years.

Analytical Customer Relationship Management (CRM)

Analytical customer relationship management (CRM) is a frequent commercial

application of predictive analysis. Methods of predictive analysis are applied to

customer data to pursue CRM objectives, which involve constructing a holistic view of

the customer no matter where their information resides in the company or the

department involved. CRM uses predictive analysis in applications for marketing

campaigns, sales, and customer services to name a few. These tools are required in

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order for a company to posture and focus their efforts effectively across the breadth of

their customer base. They must analyze and understand the products in demand or

have the potential for high demand, predict customers' buying habits in order to promote

relevant products at multiple touch points, and proactively identify and mitigate issues

that have the potential to lose customers or reduce their ability to gain new ones.

Analytical customer relationship management can be applied throughout the customers'

lifecycle (acquisition, relationship growth, retention, and win-back). Several of the

application areas described below (direct marketing, cross-sell, customer retention) are

part of customer relationship management.

Child Protection

Over the last 5 years, some child welfare agencies have started using predictive

analytics to flag high risk cases. The approach has been called "innovative" by the

Commission to Eliminate Child Abuse and Neglect Fatalities (CECANF), and

in Hillsborough County, Florida, where the lead child welfare agency uses a predictive

modeling tool, there have been no abuse-related child deaths in the target population as

of this writing.

Clinical Decision Support Systems

Experts use predictive analysis in health care primarily to determine which patients are

at risk of developing certain conditions, like diabetes, asthma, heart disease, and other

lifetime illnesses. Additionally, sophisticated clinical decision support

systems incorporate predictive analytics to support medical decision making at the point

of care. A working definition has been proposed by Jerome A. Osheroff and

colleagues: Clinical decision support (CDS) provides clinicians, staff, patients, or other

individuals with knowledge and person-specific information, intelligently filtered or

presented at appropriate times, to enhance health and health care. It encompasses a

variety of tools and interventions such as computerized alerts and reminders, clinical

guidelines, order sets, patient data reports and dashboards, documentation templates,

diagnostic support, and clinical workflow tools.

A 2016 study of neurodegenerative disorders provides a powerful example of a CDS

platform to diagnose, track, predict and monitor the progression of Parkinson's

disease. Using large and multi-source imaging, genetics, clinical and demographic data,

these investigators developed a decision support system that can predict the state of

the disease with high accuracy, consistency and precision. They employed classical

model-based and machine learning model-free methods to discriminate between

different patient and control groups.

Similar approaches may be used for predictive diagnosis and disease progression

forecasting in many neurodegenerative disorders

like Alzheimer’s, Huntington’s, amyotrophic lateral sclerosis, and for other clinical and

biomedical applications where Big Data is available.

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Collection Analytics

Many portfolios have a set of delinquent customers who do not make their payments on

time. The financial institution has to undertake collection activities on these customers

to recover the amounts due. A lot of collection resources are wasted on customers who

are difficult or impossible to recover. Predictive analytics can help optimize the

allocation of collection resources by identifying the most effective collection agencies,

contact strategies, legal actions and other strategies to each customer, thus significantly

increasing recovery at the same time reducing collection costs.

Cross-Sell

Often corporate organizations collect and maintain abundant data (e.g.

customer records, sale transactions) as exploiting hidden relationships in the data can

provide a competitive advantage. For an organization that offers multiple products,

predictive analytics can help analyze customers' spending, usage and other behavior,

leading to efficient cross sales, or selling additional products to current customers. This

directly leads to higher profitability per customer and stronger customer relationships.

Customer Retention

With the number of competing services available, businesses need to focus efforts on

maintaining continuous customer satisfaction, rewarding consumer loyalty and

minimizing customer attrition. In addition, small increases in customer retention have

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been shown to increase profits disproportionately. One study concluded that a 5%

increase in customer retention rates will increase profits by 25% to 95%. Businesses

tend to respond to customer attrition on a reactive basis, acting only after the customer

has initiated the process to terminate service. At this stage, the chance of changing the

customer's decision is almost zero. Proper application of predictive analytics can lead to

a more proactive retention strategy. By a frequent examination of a customer's past

service usage, service performance, spending and other behavior patterns, predictive

models can determine the likelihood of a customer terminating service sometime

soon. An intervention with lucrative offers can increase the chance of retaining the

customer. Silent attrition, the behavior of a customer to slowly but steadily reduce

usage, is another problem that many companies face. Predictive analytics can also

predict this behavior, so that the company can take proper actions to increase customer

activity.

Direct Marketing

When marketing consumer products and services, there is the challenge of keeping up

with competing products and consumer behavior. Apart from identifying prospects,

predictive analytics can also help to identify the most effective combination of product

versions, marketing material, communication channels and timing that should be used

to target a given consumer. The goal of predictive analytics is typically to lower the cost

per order or cost per action.

Fraud Detection

Fraud is a big problem for many businesses and can be of various types: inaccurate

credit applications, fraudulent transactions (both offline and online), identity thefts and

false insurance claims. Some examples of likely victims are credit card issuers,

insurance companies, retail merchants, manufacturers, business-to-business suppliers

and even services providers. A predictive model can help weed out the "bads" and

reduce a business's exposure to fraud.

Predictive modeling can also be used to identify high-risk fraud candidates in business

or the public sector. Mark Nigrini developed a risk-scoring method to identify audit

targets. He describes the use of this approach to detect fraud in the franchisee sales

reports of an international fast-food chain. Each location is scored using 10 predictors.

The 10 scores are then weighted to give one final overall risk score for each location.

The same scoring approach was also used to identify high-risk check kiting accounts,

potentially fraudulent travel agents, and questionable vendors. A reasonably complex

model was used to identify fraudulent monthly reports submitted by divisional

controllers.

The Internal Revenue Service (IRS) of the United States also uses predictive analytics

to mine tax returns and identify tax fraud.

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Recent advancements in technology have also introduced predictive behavior analysis

for web fraud detection. This type of solution utilizes heuristics in order to study normal

web user behavior and detect anomalies indicating fraud attempts.

Portfolio, Product or Economy-Level Prediction

Often the focus of analysis is not the consumer but the product, portfolio, firm, industry

or even the economy. For example, a retailer might be interested in predicting storelevel

demand for inventory management purposes. Or the Federal Reserve Board might

be interested in predicting the unemployment rate for the next year. These types of

problems can be addressed by predictive analytics using time series techniques (see

below). They can also be addressed via machine learning approaches which transform

the original time series into a feature vector space, where the learning algorithm finds

patterns that have predictive power.

Project Risk Management

When employing risk management techniques, the results are always to predict and

benefit from a future scenario. The capital asset pricing model (CAP-M) "predicts" the

best portfolio to maximize return. Probabilistic risk assessment (PRA) when combined

with mini-Delphi techniquesand statistical approaches yields accurate forecasts. These

are examples of approaches that can extend from project to market, and from near to

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long term. Underwriting (see below) and other business approaches identify risk

management as a predictive method.

Underwriting

Many businesses have to account for risk exposure due to their different services and

determine the cost needed to cover the risk. For example, auto insurance providers

need to accurately determine the amount of premium to charge to cover each

automobile and driver. A financial company needs to assess a borrower's potential and

ability to pay before granting a loan. For a health insurance provider, predictive analytics

can analyze a few years of past medical claims data, as well as lab, pharmacy and

other records where available, to predict how expensive an enrollee is likely to be in the

future. Predictive analytics can help underwrite these quantities by predicting the

chances of illness, default, bankruptcy, etc. Predictive analytics can streamline the

process of customer acquisition by predicting the future risk behavior of a customer

using application level data. Predictive analytics in the form of credit scores have

reduced the amount of time it takes for loan approvals, especially in the mortgage

market where lending decisions are now made in a matter of hours rather than days or

even weeks. Proper predictive analytics can lead to proper pricing decisions, which can

help mitigate future risk of default.

Technology and Big Data Influences

Big data is a collection of data sets that are so large and complex that they become

awkward to work with using traditional database management tools. The volume, variety

and velocity of big data have introduced challenges across the board for capture,

storage, search, sharing, analysis, and visualization. Examples of big data sources

include web logs, RFID, sensor data, social networks, Internet search indexing, call

detail records, military surveillance, and complex data in astronomic, biogeochemical,

genomics, and atmospheric sciences. Big Data is the core of most predictive analytic

services offered by IT organizations. Thanks to technological advances in computer

hardware—faster CPUs, cheaper memory, and MPP architectures—and new

technologies such as Hadoop, MapReduce, and in-database and text analyticsfor

processing big data, it is now feasible to collect, analyze, and mine massive amounts of

structured and unstructured data for new insights.

It is also possible to run predictive algorithms on streaming data. Today, exploring big

data and using predictive analytics is within reach of more organizations than ever

before and new methods that are capable for handling such datasets are proposed.

Analytical Techniques

The approaches and techniques used to conduct predictive analytics can broadly be

grouped into regression techniques and machine learning techniques.

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Regression Techniques

Regression models are the mainstay of predictive analytics. The focus lies on

establishing a mathematical equation as a model to represent the interactions between

the different variables in consideration. Depending on the situation, there are a wide

variety of models that can be applied while performing predictive analytics. Some of

them are briefly discussed below.

Linear Regression Model

The linear regression model analyzes the relationship between the response or

dependent variable and a set of independent or predictor variables. This relationship is

expressed as an equation that predicts the response variable as a linear function of the

parameters. These parameters are adjusted so that a measure of fit is optimized. Much

of the effort in model fitting is focused on minimizing the size of the residual, as well as

ensuring that it is randomly distributed with respect to the model predictions.

The goal of regression is to select the parameters of the model so as to minimize the

sum of the squared residuals. This is referred to as ordinary least squares (OLS)

estimation and results in best linear unbiased estimates (BLUE) of the parameters if and

only if the Gauss-Markov assumptions are satisfied.

Once the model has been estimated we would be interested to know if the predictor

variables belong in the model—i.e. is the estimate of each variable's contribution

reliable? To do this we can check the statistical significance of the model's coefficients

which can be measured using the t-statistic. This amounts to testing whether the

coefficient is significantly different from zero. How well the model predicts the

dependent variable based on the value of the independent variables can be assessed

by using the R² statistic. It measures predictive power of the model i.e. the proportion of

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the total variation in the dependent variable that is "explained" (accounted for) by

variation in the independent variables.

Discrete Choice Models

Multiple regression (above) is generally used when the response variable is continuous

and has an unbounded range. Often the response variable may not be continuous but

rather discrete. While mathematically it is feasible to apply multiple regression to

discrete ordered dependent variables, some of the assumptions behind the theory of

multiple linear regression no longer hold, and there are other techniques such as

discrete choice models which are better suited for this type of analysis. If the dependent

variable is discrete, some of those superior methods are logistic regression, multinomial

logit and probit models. Logistic regression and probit models are used when the

dependent variable is binary.

Logistic regression

In a classification setting, assigning outcome probabilities to observations can be

achieved through the use of a logistic model, which is basically a method which

transforms information about the binary dependent variable into an unbounded

continuous variable and estimates a regular multivariate model (See Allison's Logistic

Regression for more information on the theory of logistic regression).

The Wald and likelihood-ratio test are used to test the statistical significance of each

coefficient b in the model (analogous to the t tests used in OLS regression; see above).

A test assessing the goodness-of-fit of a classification model is the "percentage

correctly predicted".

Multinomial Logistic Regression

An extension of the binary logit model to cases where the dependent variable has more

than 2 categories is the multinomial logit model. In such cases collapsing the data into

two categories might not make good sense or may lead to loss in the richness of the

data. The multinomial logit model is the appropriate technique in these cases, especially

when the dependent variable categories are not ordered (for examples colors like red,

blue, green). Some authors have extended multinomial regression to include feature

selection/importance methods such as random multinomial logit.

Probit Regression

Probit models offer an alternative to logistic regression for modeling categorical

dependent variables. Even though the outcomes tend to be similar, the underlying

distributions are different. Probit models are popular in social sciences like economics.

A good way to understand the key difference between probit and logit models is to

assume that the dependent variable is driven by a latent variable z, which is a sum of a

linear combination of explanatory variables and a random noise term.

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We do not observe z but instead observe y which takes the value 0 (when z < 0) or 1

(otherwise). In the logit model we assume that the random noise term follows a logistic

distribution with mean zero. In the probit model we assume that it follows a normal

distribution with mean zero. Note that in social sciences (e.g. economics), probit is often

used to model situations where the observed variable y is continuous but takes values

between 0 and 1.

Logit Versus Probit

The probit model has been around longer than the logit model. They behave similarly,

except that the logistic distribution tends to be slightly flatter tailed. One of the reasons

the logit model was formulated was that the probit model was computationally difficult

due to the requirement of numerically calculating integrals. Modern computing however

has made this computation fairly simple. The coefficients obtained from the logit and

probit model are fairly close. However, the odds ratio is easier to interpret in the logit

model.

Practical reasons for choosing the probit model over the logistic model would be:

There is a strong belief that the underlying distribution is normal

The actual event is not a binary outcome (e.g., bankruptcy status) but a

proportion (e.g., proportion of population at different debt levels).

Time Series Models

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Time series models are used for predicting or forecasting the future behavior of

variables. These models account for the fact that data points taken over time may have

an internal structure (such as autocorrelation, trend or seasonal variation) that should

be accounted for. As a result, standard regression techniques cannot be applied to time

series data and methodology has been developed to decompose the trend, seasonal

and cyclical component of the series. Modeling the dynamic path of a variable can

improve forecasts since the predictable component of the series can be projected into

the future.

Time series models estimate difference equations containing stochastic components.

Two commonly used forms of these models are autoregressive models (AR)

and moving-average (MA) models. The Box–Jenkins methodology (1976) developed by

George Box and G.M. Jenkins combines the AR and MA models to produce

the ARMA (autoregressive moving average) model, which is the cornerstone of

stationary time series analysis. ARIMA (autoregressive integrated moving average

models), on the other hand, are used to describe non-stationary time series. Box and

Jenkins suggest differencing a non-stationary time series to obtain a stationary series to

which an ARMA model can be applied. Non-stationary time series have a pronounced

trend and do not have a constant long-run mean or variance.

Box and Jenkins proposed a three-stage methodology involving model identification,

estimation and validation. The identification stage involves identifying if the series is

stationary or not and the presence of seasonality by examining plots of the series,

autocorrelation and partial autocorrelation functions. In the estimation stage, models are

estimated using non-linear time series or maximum likelihood estimation procedures.

Finally the validation stage involves diagnostic checking such as plotting the residuals to

detect outliers and evidence of model fit.

In recent years time series models have become more sophisticated and attempt to

model conditional heteroskedasticity with models such as ARCH (autoregressive

conditional heteroskedasticity) and GARCH (generalized autoregressive conditional

heteroskedasticity) models frequently used for financial time series. In addition time

series models are also used to understand inter-relationships among economic

variables represented by systems of equations using VAR (vector autoregression) and

structural VAR models.

Survival or Duration Analysis

Survival analysis is another name for time-to-event analysis. These techniques were

primarily developed in the medical and biological sciences, but they are also widely

used in the social sciences like economics, as well as in engineering (reliability and

failure time analysis).

Censoring and non-normality, which are characteristic of survival data, generate

difficulty when trying to analyze the data using conventional statistical models such as

multiple linear regression. The normal distribution, being a symmetric distribution, takes

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positive as well as negative values, but duration by its very nature cannot be negative

and therefore normality cannot be assumed when dealing with duration/survival data.

Hence the normality assumption of regression models is violated.

The assumption is that if the data were not censored it would be representative of the

population of interest. In survival analysis, censored observations arise whenever the

dependent variable of interest represents the time to a terminal event, and the duration

of the study is limited in time.

An important concept in survival analysis is the hazard rate, defined as the probability

that the event will occur at time t conditional on surviving until time t. Another concept

related to the hazard rate is the survival function which can be defined as the probability

of surviving t[he] time.

Most models try to model the hazard rate by choosing the underlying distribution

depending on the shape of the hazard function. A distribution whose hazard function

slopes upward is said to have positive duration dependence, a decreasing hazard

shows negative duration dependence whereas constant hazard is a process with no

memory usually characterized by the exponential distribution.

Some of the distributional choices in survival models are: F, gamma, Weibull, log

normal, inverse normal, exponential etc. All these distributions are for a non-negative

random variable.

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Duration models can be parametric, non-parametric or semi-parametric. Some of the

models commonly used are Kaplan-Meier and Cox proportional hazard model (non

parametric).

Classification and Regression Trees (CART)

Globally-optimal classification tree analysis (GO-CTA) (also called hierarchical optimal

discriminant analysis) is a generalization of optimal discriminant analysis that may be

used to identify the statistical model that has maximum accuracy for predicting the value

of a categorical dependent variable for a dataset consisting of categorical and

continuous variables. The output of HODA is a non-orthogonal tree that combines

categorical variables and cut points for continuous variables that yields maximum

predictive accuracy, an assessment of the exact Type I error rate, and an evaluation of

potential cross-generalizability of the statistical model. Hierarchical optimal discriminant

analysis may be thought of as a generalization of Fisher's linear discriminant analysis.

Optimal discriminant analysis is an alternative to ANOVA (analysis of variance) and

regression analysis, which attempt to express one dependent variable as a linear

combination of other features or measurements. However, ANOVA and regression

analysis give a dependent variable that is a numerical variable, while hierarchical

optimal discriminant analysis gives a dependent variable that is a class variable.

Classification and regression trees (CART) are a non-parametric decision tree

learning technique that produces either classification or regression trees, depending on

whether the dependent variable is categorical or numeric, respectively.

Decision trees are formed by a collection of rules based on variables in the modeling

data set:

Rules based on variables' values are selected to get the best split to differentiate

observations based on the dependent variable

Once a rule is selected and splits a node into two, the same process is applied to

each "child" node (i.e. it is a recursive procedure)

Splitting stops when CART detects no further gain can be made, or some pre-set

stopping rules are met. (Alternatively, the data are split as much as possible and

then the tree is later pruned.)

Each branch of the tree ends in a terminal node. Each observation falls into one and

exactly one terminal node, and each terminal node is uniquely defined by a set of rules.

A very popular method for predictive analytics is Leo Breiman's random forests.

Multivariate Adaptive Regression Splines

Multivariate adaptive regression splines (MARS) is a non-parametric technique that

builds flexible models by fitting piecewise linear regressions.

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An important concept associated with regression splines is that of a knot. Knot is where

one local regression model gives way to another and thus is the point of intersection

between two splines.

In multivariate and adaptive regression splines, basis functions are the tool used for

generalizing the search for knots. Basis functions are a set of functions used to

represent the information contained in one or more variables. Multivariate and Adaptive

Regression Splines model almost always creates the basis functions in pairs.

Multivariate and adaptive regression spline approach deliberately overfits the model and

then prunes to get to the optimal model. The algorithm is computationally very intensive

and in practice we are required to specify an upper limit on the number of basis

functions.

Machine Learning Techniques

Machine learning, a branch of artificial intelligence, was originally employed to develop

techniques to enable computers to learn. Today, since it includes a number of advanced

statistical methods for regression and classification, it finds application in a wide variety

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of fields including medical diagnostics, credit card fraud detection, face and speech

recognition and analysis of the stock market. In certain applications it is sufficient to

directly predict the dependent variable without focusing on the underlying relationships

between variables. In other cases, the underlying relationships can be very complex

and the mathematical form of the dependencies unknown. For such cases, machine

learning techniques emulate human cognition and learn from training examples to

predict future events.

A brief discussion of some of these methods used commonly for predictive analytics is

provided below. A detailed study of machine learning can be found in Mitchell (1997).

Neural Networks

Neural networks are nonlinear sophisticated modeling techniques that are able

to model complex functions. They can be applied to problems

of prediction, classification or control in a wide spectrum of fields such

as finance, cognitive psychology/neuroscience, medicine, engineering, and physics.

Neural networks are used when the exact nature of the relationship between inputs and

output is not known. A key feature of neural networks is that they learn the relationship

between inputs and output through training. There are three types of training used by

different neural networks: supervised and unsupervised training and reinforcement

learning, with supervised being the most common one.

Some examples of neural network training techniques are backpropagation, quick

propagation, conjugate gradient descent, projection operator, Delta-Bar-Delta etc. Some

unsupervised network architectures are multilayer perceptrons, Kohonen

networks, Hopfield networks, etc.

Multilayer Perceptron (MLP)

The multilayer perceptron (MLP) consists of an input and an output layer with one or

more hidden layers of nonlinearly-activating nodes or sigmoid nodes. This is determined

by the weight vector and it is necessary to adjust the weights of the network. The back

propagation employs gradient fall to minimize the squared error between the network

output values and desired values for those outputs. The weights adjusted by an iterative

process of repetitive present of attributes. Small changes in the weight to get the

desired values are done by the process called training the net and is done by the

training set (learning rule).

Radial Basis Functions

A radial basis function (RBF) is a function which has built into it a distance criterion with

respect to a center. Such functions can be used very efficiently for interpolation and for

smoothing of data. Radial basis functions have been applied in the area of neural

networks where they are used as a replacement for the sigmoidal transfer function.

Such networks have 3 layers, the input layer, the hidden layer with the RBF non-

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linearity and a linear output layer. The most popular choice for the non-linearity is the

Gaussian. RBF networks have the advantage of not being locked into local minima as

do the feed-forward networks such as the multilayer perceptron.

Support Vector Machines

Support vector machines (SVM) are used to detect and exploit complex patterns in data

by clustering, classifying and ranking the data. They are learning machines that are

used to perform binary classifications and regression estimations. They commonly use

kernel based methods to apply linear classification techniques to non-linear

classification problems. There are a number of types of SVM such as linear, polynomial,

sigmoid etc.

Naïve Bayes

Naïve Bayes based on Bayes conditional probability rule is used for performing

classification tasks. Naïve Bayes assumes the predictors are statistically independent

which makes it an effective classification tool that is easy to interpret. It is best

employed when faced with the "curse of dimensionality" problem, i.e. when the number

of predictors is very high.

k-Nearest Neighbors

The nearest neighbor algorithm (KNN) belongs to the class of pattern recognition

statistical methods. The method does not impose a priori any assumptions about the

distribution from which the modeling sample is drawn. It involves a training set with both

positive and negative values. A new sample is classified by calculating the distance to

the nearest neighboring training case. The sign of that point will determine the

classification of the sample. In the k-nearest neighbor classifier, the k nearest points are

considered and the sign of the majority is used to classify the sample. The performance

of the k-NN algorithm is influenced by three main factors: (1) the distance measure used

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to locate the nearest neighbors, (2) the decision rule used to derive a classification from

the k-nearest neighbors, and (3) the number of neighbors used to classify the new

sample. It can be proved that, unlike other methods, this method is universally

asymptotically convergent, i.e. as the size of the training set increases, if the

observations are independent and identically distributed (i.i.d.), regardless of the

distribution from which the sample is drawn, the predicted class will converge to the

class assignment that minimizes misclassification error. See Devroy et al.

Geospatial Predictive Modeling

Conceptually, geospatial predictive modeling is rooted in the principle that the

occurrences of events being modeled are limited in distribution. Occurrences of events

are neither uniform nor random in distribution—there are spatial environment factors

(infrastructure, socio-cultural, topographic, etc.) that constrain and influence where the

locations of events occur. Geospatial predictive modeling attempts to describe those

constraints and influences by spatially correlating occurrences of historical geospatial

locations with environmental factors that represent those constraints and influences.

Geospatial predictive modeling is a process for analyzing events through a geographic

filter in order to make statements of likelihood for event occurrence or emergence.

Tools

Historically, using predictive analytics tools—as well as understanding the results they

delivered—required advanced skills. However, modern predictive analytics tools are no

longer restricted to IT specialists. As more organizations adopt predictive analytics into

decision-making processes and integrate it into their operations, they are creating a shift

in the market toward business users as the primary consumers of the information.

Business users want tools they can use on their own. Vendors are responding by

creating new software that removes the mathematical complexity, provides user-friendly

graphic interfaces and/or builds in short cuts that can, for example, recognize the kind of

data available and suggest an appropriate predictive model. Predictive analytics tools

have become sophisticated enough to adequately present and dissect data

problems, so that any data-savvy information worker can utilize them to analyze data

and retrieve meaningful, useful results. For example, modern tools present findings

using simple charts, graphs, and scores that indicate the likelihood of possible

outcomes.

There are numerous tools available in the marketplace that help with the execution of

predictive analytics. These range from those that need very little user sophistication to

those that are designed for the expert practitioner. The difference between these tools is

often in the level of customization and heavy data lifting allowed.

Some open-source software predictive analytic tools include:

Apache Mahout

GNU Octave

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KNIME

OpenNN

Orange

R

scikit-learn (Python)

Weka

Commercial predictive analytic tools include:

Alpine Data Labs

Alteryx

Angoss Knowledge STUDIO

Actuate Corporation BIRT Analytics

IBM SPSS Statistics and IBM SPSS Modeler

KXEN Inc. Modeler

Mathematica

MATLAB

Minitab

LabVIEW [37]

Neural Designer

Oracle Advanced Analytics

Pervasive

Predixion Software

Presenso

RapidMiner

RCASE

Revolution Analytics

SAP HANA and SAP BusinessObjects Predictive Analytics

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SAS and its Enterprise Miner

Sidetrade

Stata

Statgraphics

Statistica

Tibco Software

Beside these software packages, specific tools have also been developed for industrial

applications. For example, Watchdog Agent Toolbox has been developed and optimized

for predictive analysis in prognostics and health management applications and is

available for MATLAB and LabVIEW.

The most popular commercial predictive analytics software packages according to the

Rexer Analytics Survey for 2013 are IBM SPSS Modeler, SAS Enterprise Miner, and

Dell Statistica.

PMML

The Predictive Model Markup Language (PMML) was proposed for standard language

for expressing predictive models. Such an XML-based language provides a way for the

different tools to define predictive models and to share them. PMML 4.0 was released in

June, 2009.

Criticism

There are plenty of skeptics when it comes to computers' and algorithms' abilities to

predict the future, including Gary King, a professor from Harvard University and the

director of the Institute for Quantitative Social Science. People are influenced by their

environment in innumerable ways. Predicting perfectly what people will do next requires

that all the influential variables be known and measured accurately. "People's

environments change even more quickly than they themselves do. Everything from the

weather to their relationship with their mother can change the way people think and act.

All of those variables are unpredictable. How they will impact a person is even less

predictable. If put in the exact same situation tomorrow, they may make a completely

different decision. This means that a statistical prediction is only valid in sterile

laboratory conditions, which suddenly isn't as useful as it seemed before."

In a study of 1072 papers published in Information Systems Research and MIS

Quarterly between 1990 and 2006, only 52 empirical papers attempted predictive

claims, of which only 7 carried out proper predictive modeling or testing.

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X. References

1. https://en.wikipedia.org/wiki/Organizational_analysis

2. https://en.wikipedia.org/wiki/Needs_assessment

3. https://en.wikipedia.org/wiki/Organizational_culture

4. https://en.wikipedia.org/wiki/Organizational_effectiveness

5. https://www.irs.gov/charities-non-profits/charitable-purposes

6. https://www.thetaxadviser.com/newsletters/2015/dec/charities-must-operate-exclusivelyfor-charitable-purposes.html

7. https://en.wikipedia.org/wiki/Social_impact_assessment

8. https://en.wikipedia.org/wiki/Social_influence

9. https://en.wikipedia.org/wiki/Judge%E2%80%93advisor_system

10. https://en.wikipedia.org/wiki/Predictive_analytics

11. https://hewlett.org/wp-content/uploads/2017/11/A-Guide-to-Using-OCA-Tools.pdf

12. https://coco-net.org/wp-content/uploads/2012/08/Nonprofit-Organizational-Assessment-

Tool.pdf

13. https://coco-net.org/wp-content/uploads/2012/08/Nonprofit-Organizational-Assessment-

Tool.pdf

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Notes

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Attachment A

A Guide to Using OCA Tools

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Attachment B


Assessment Tool

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Attachment C

NpA Self-Assessment Tool

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The e-Advocate Quarterly

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Issue Title Quarterly

Vol. I 2015 The Fundamentals

I

The ComeUnity ReEngineering

Project Initiative

Q-1 2015

II The Adolescent Law Group Q-2 2015

III

Landmark Cases in US

Juvenile Justice (PA)

Q-3 2015

IV The First Amendment Project Q-4 2015

Vol. II 2016 Strategic Development

V The Fourth Amendment Project Q-1 2016

VI


Juvenile Justice (NJ)

Q-2 2016

VII Youth Court Q-3 2016

VIII

The Economic Consequences of Legal

Decision-Making

Q-4 2016

Vol. III 2017 Sustainability

IX The Sixth Amendment Project Q-1 2017

X

The Theological Foundations of

US Law & Government

Q-2 2017

XI The Eighth Amendment Project Q-3 2017

XII

The EB-5 Investor

Immigration Project*

Q-4 2017

Vol. IV 2018 Collaboration

XIII Strategic Planning Q-1 2018

XIV

The Juvenile Justice

Legislative Reform Initiative

Q-2 2018

XV The Advocacy Foundation Coalition Q-3 2018

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XVI

for Drug-Free Communities


Juvenile Justice (GA)

Q-4 2018

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Vol. V 2019 Organizational Development

XVII The Board of Directors Q-1 2019

XVIII The Inner Circle Q-2 2019

XIX Staff & Management Q-3 2019

XX Succession Planning Q-4 2019

XXI The Budget* Bonus #1

XXII Data-Driven Resource Allocation* Bonus #2

Vol. VI 2020 Missions

XXIII Critical Thinking Q-1 2020

XXIV

The Advocacy Foundation

Endowments Initiative Project

Q-2 2020

XXV International Labor Relations Q-3 2020

XXVI Immigration Q-4 2020

Vol. VII 2021 Community Engagement

XXVII

The 21 st Century Charter Schools

Initiative

Q-1 2021

XXVIII The All-Sports Ministry @ ... Q-2 2021

XXIX Lobbying for Nonprofits Q-3 2021

XXX

XXXI

Advocacy Foundation Missions -

Domestic

Advocacy Foundation Missions -

International

Q-4 2021

Bonus

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Vol. VIII

2022 ComeUnity ReEngineering

XXXII

The Creative & Fine Arts Ministry

@ The Foundation

Q-1 2022

XXXIII The Advisory Council & Committees Q-2 2022

XXXIV

The Theological Origins

of Contemporary Judicial Process

Q-3 2022

XXXV The Second Chance Ministry @ ... Q-4 2022

Vol. IX 2023 Legal Reformation

XXXVI The Fifth Amendment Project Q-1 2023

XXXVII The Judicial Re-Engineering Initiative Q-2 2023

XXXVIII

The Inner-Cities Strategic

Revitalization Initiative

Q-3 2023

XXXVIX Habeas Corpus Q-4 2023

Vol. X 2024 ComeUnity Development

XXXVX

The Inner-City Strategic

Revitalization Plan

Q-1 2024

XXXVXI The Mentoring Initiative Q-2 2024

XXXVXII The Violence Prevention Framework Q-3 2024

XXXVXIII The Fatherhood Initiative Q-4 2024

Vol. XI 2025 Public Interest

XXXVXIV Public Interest Law Q-1 2025

L (50) Spiritual Resource Development Q-2 2025

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LI

Nonprofit Confidentiality

In The Age of Big Data

Q-3 2025

LII Interpreting The Facts Q-4 2025

Vol. XII 2026 Poverty In America

LIII

American Poverty

In The New Millennium

Q-1 2026

LIV Outcome-Based Thinking Q-2 2026

LV Transformational Social Leadership Q-3 2026

LVI The Cycle of Poverty Q-4 2026

Vol. XIII 2027 Raising Awareness

LVII ReEngineering Juvenile Justice Q-1 2027

LVIII Corporations Q-2 2027

LVIX The Prison Industrial Complex Q-3 2027

LX Restoration of Rights Q-4 2027

Vol. XIV 2028 Culturally Relevant Programming

LXI Community Culture Q-1 2028

LXII Corporate Culture Q-2 2028

LXIII Strategic Cultural Planning Q-3 2028

LXIV

The Cross-Sector/ Coordinated

Service Approach to Delinquency

Prevention

Q-4 2028

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Vol. XV 2029 Inner-Cities Revitalization

LXIV

LXV

LXVI

Part I – Strategic Housing

Revitalization

(The Twenty Percent Profit Margin)

Part II – Jobs Training, Educational

Redevelopment

and Economic Empowerment

Part III - Financial Literacy

and Sustainability

Q-1 2029

Q-2 2029

Q-3 2029

LXVII Part IV – Solutions for Homelessness Q-4 2029

LXVIII

The Strategic Home Mortgage

Initiative

Bonus

Vol. XVI 2030 Sustainability

LXVIII Social Program Sustainability Q-1 2030

LXIX

The Advocacy Foundation

Endowments Initiative

Q-2 2030

LXX Capital Gains Q-3 2030

LXXI Sustainability Investments Q-4 2030

Vol. XVII 2031 The Justice Series

LXXII Distributive Justice Q-1 2031

LXXIII Retributive Justice Q-2 2031

LXXIV Procedural Justice Q-3 2031

LXXV (75) Restorative Justice Q-4 2031

LXXVI Unjust Legal Reasoning Bonus

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Vol. XVIII 2032 Public Policy

LXXVII Public Interest Law Q-1 2032

LXXVIII Reforming Public Policy Q-2 2032

LXXVIX ... Q-3 2032

LXXVX ... Q-4 2032

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The e-Advocate Monthly Review

2018

Transformational Problem Solving January 2018

The Advocacy Foundation February 2018

Opioid Initiative

Native-American Youth March 2018

In the Juvenile Justice System

Barriers to Reducing Confinement April 2018

Latino and Hispanic Youth May 2018


Social Entrepreneurship June 2018

The Economic Consequences of

Homelessness in America S.Ed – June 2018

African-American Youth July 2018


Gang Deconstruction August 2018

Social Impact Investing September 2018

Opportunity Youth: October 2018

Disenfranchised Young People

The Economic Impact of Social November 2018

of Social Programs Development

Gun Control December 2018

2019

The U.S. Stock Market January 2019

Prison-Based Gerrymandering February 2019

Literacy-Based Prison Construction March 2019

Children of Incarcerated Parents April 2019

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African-American Youth in The May 2019

Juvenile Justice System

Racial Profiling June 2019

Mass Collaboration July 2019

Concentrated Poverty August 2019

De-Industrialization September 2019

Overcoming Dyslexia October 2019

Overcoming Attention Deficit November 2019

The Gift of Adversity December 2019

2020

The Gift of Hypersensitivity January 2020

The Gift of Introspection February 2020

The Gift of Introversion March 2020

The Gift of Spirituality April 2020

The Gift of Transformation May 2020

Property Acquisition for

Organizational Sustainability June 2020

Investing for Organizational

Sustainability July 2020

Biblical Law & Justice TLFA August 2020

Gentrification AF September 2020

Environmental Racism NpA October 2020

Law for The Poor AF November 2020

…

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2021

Biblically Responsible Investing TLFA – January 2021

International Criminal Procedure LMI – February 2021

Spiritual Rights TLFA – March 2021

The Theology of Missions TLFA – April 2021

Legal Evangelism, Intelligence,

Reconnaissance & Missions LMI – May 2021

The Law of War LMI – June 2021

Generational Progression AF – July 2021

Predatory Lending AF – August 2021

The Community Assessment Process NpA – September 2021

Accountability NpA – October 2021

Nonprofit Transparency NpA – November 2021

Redefining Unemployment AF – December 2021

2022

21 st Century Slavery AF – January 2022

Acquiesce to Righteousness TLFA – February 2022

ComeUnity Capacity-Building NpA – March 2022

Nonprofit Organizational Assessment NpA – April 2022

Debt Reduction AF – May 2022

Case Law, Statutory Law,

Municipal Ordinances and Policy ALG – June 2022

Organizational Dysfunction NpA – July 2022


Assessment NpA – August 2022

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…

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The e-Advocate Quarterly

Special Editions

Crowdfunding Winter-Spring 2017

Social Media for Nonprofits October 2017

Mass Media for Nonprofits November 2017

The Opioid Crisis in America: January 2018

Issues in Pain Management

The Opioid Crisis in America: February 2018

The Drug Culture in the U.S.

The Opioid Crisis in America: March 2018

Drug Abuse Among Veterans

The Opioid Crisis in America: April 2018

Drug Abuse Among America’s

Teens

The Opioid Crisis in America: May 2018

Alcoholism

The Economic Consequences of June 2018

Homelessness in The US

The Economic Consequences of July 2018

Opioid Addiction in America

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The e-Advocate Journal

of Theological Jurisprudence

Vol. I - 2017

The Theological Origins of Contemporary Judicial Process

Scriptural Application to The Model Criminal Code

Scriptural Application for Tort Reform

Scriptural Application to Juvenile Justice Reformation

Vol. II - 2018

Scriptural Application for The Canons of Ethics

Scriptural Application to Contracts Reform

& The Uniform Commercial Code

Scriptural Application to The Law of Property

Scriptural Application to The Law of Evidence

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Legal Missions International

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Vol. I 2015

I

II

God’s Will and The 21 st Century

Democratic Process

The Community

Engagement Strategy

Q-1 2015

Q-2 2015

III Foreign Policy Q-3 2015

IV

Public Interest Law

in The New Millennium

Q-4 2015

Vol. II 2016

V Ethiopia Q-1 2016

VI Zimbabwe Q-2 2016

VII Jamaica Q-3 2016

VIII Brazil Q-4 2016

Vol. III 2017

IX India Q-1 2017

X Suriname Q-2 2017

XI The Caribbean Q-3 2017

XII United States/ Estados Unidos Q-4 2017

Vol. IV 2018

XIII Cuba Q-1 2018

XIV Guinea Q-2 2018

XV Indonesia Q-3 2018

XVI Sri Lanka Q-4 2018

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Vol. V 2019

XVII Russia Q-1 2019

XVIII Australia Q-2 2019

XIV South Korea Q-3 2019

XV Puerto Rico Q-4 2019


Vol. VI 2020

XVI Trinidad & Tobago Q-1 2020

XVII Egypt Q-2 2020

XVIII Sierra Leone Q-3 2020

XIX South Africa Q-4 2020

XX Israel Bonus

Vol. VII 2021

XXI Haiti Q-1 2021

XXII Peru Q-2 2021

XXIII Costa Rica Q-3 2021

XXIV China Q-4 2021

XXV Japan Bonus

Vol VIII 2022

XXVI Chile Q-1 2022

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The e-Advocate Juvenile Justice Report

______

Vol. I – Juvenile Delinquency in The US

Vol. II. – The Prison Industrial Complex

Vol. III – Restorative/ Transformative Justice

Vol. IV – The Sixth Amendment Right to The Effective Assistance of Counsel

Vol. V – The Theological Foundations of Juvenile Justice

Vol. VI – Collaborating to Eradicate Juvenile Delinquency

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The e-Advocate Newsletter

Genesis of The Problem

Family Structure

Societal Influences

Evidence-Based Programming

Strengthening Assets v. Eliminating Deficits

2012 - Juvenile Delinquency in The US

Introduction/Ideology/Key Values

Philosophy/Application & Practice

Expungement & Pardons

Pardons & Clemency

Examples/Best Practices

2013 - Restorative Justice in The US

2014 - The Prison Industrial Complex

25% of the World's Inmates Are In the US

The Economics of Prison Enterprise

The Federal Bureau of Prisons

The After-Effects of Incarceration/Individual/Societal

The Fourth Amendment Project

The Sixth Amendment Project

The Eighth Amendment Project

The Adolescent Law Group

2015 - US Constitutional Issues In The New Millennium

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2018 - The Theological Law Firm Academy

The Theological Foundations of US Law & Government

The Economic Consequences of Legal Decision-Making

The Juvenile Justice Legislative Reform Initiative

The EB-5 International Investors Initiative

2017 - Organizational Development

The Board of Directors

The Inner Circle

Staff & Management

Succession Planning

Bonus #1 The Budget

Bonus #2 Data-Driven Resource Allocation

2018 - Sustainability

The Data-Driven Resource Allocation Process

The Quality Assurance Initiative

The Advocacy Foundation Endowments Initiative

The Community Engagement Strategy

2019 - Collaboration

Critical Thinking for Transformative Justice

International Labor Relations

Immigration

God's Will & The 21st Century Democratic Process

The Community Engagement Strategy

The 21st Century Charter Schools Initiative

2020 - Community Engagement

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Extras

The Nonprofit Advisors Group Newsletters

The 501(c)(3) Acquisition Process

The Board of Directors

The Gladiator Mentality

Strategic Planning

Fundraising

501(c)(3) Reinstatements

The Collaborative US/ International Newsletters

How You Think Is Everything

The Reciprocal Nature of Business Relationships

Accelerate Your Professional Development

The Competitive Nature of Grant Writing

Assessing The Risks

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About The Author

John C (Jack) Johnson III

Founder & CEO

Jack was educated at Temple University, in Philadelphia, Pennsylvania and Rutgers

Law School, in Camden, New Jersey. In 1999, he moved to Atlanta, Georgia to pursue

greater opportunities to provide Advocacy and Preventive Programmatic services for atrisk/

at-promise young persons, their families, and Justice Professionals embedded in the

Juvenile Justice process in order to help facilitate its transcendence into the 21 st Century.

There, along with a small group of community and faith-based professionals, “The Advocacy Foundation, Inc." was conceived

and developed over roughly a thirteen year period, originally chartered as a Juvenile Delinquency Prevention and Educational

Support Services organization consisting of Mentoring, Tutoring, Counseling, Character Development, Community Change

Management, Practitioner Re-Education & Training, and a host of related components.

The Foundation’s Overarching Mission is “To help Individuals, Organizations, & Communities Achieve Their Full Potential”, by

implementing a wide array of evidence-based proactive multi-disciplinary "Restorative & Transformative Justice" programs &

projects currently throughout the northeast, southeast, and western international-waters regions, providing prevention and support

services to at-risk/ at-promise youth, to young adults, to their families, and to Social Service, Justice and Mental

Health professionals” everywhere. The Foundation has since relocated its headquarters to Philadelphia, Pennsylvania, and been

expanded to include a three-tier mission.

In addition to his work with the Foundation, Jack also served as an Adjunct Professor of Law & Business at National-Louis

University of Atlanta (where he taught Political Science, Business & Legal Ethics, Labor & Employment Relations, and Critical

Thinking courses to undergraduate and graduate level students). Jack has also served as Board President for a host of wellestablished

and up & coming nonprofit organizations throughout the region, including “Visions Unlimited Community

Development Systems, Inc.”, a multi-million dollar, award-winning, Violence Prevention and Gang Intervention Social Service

organization in Atlanta, as well as Vice-Chair of the Georgia/ Metropolitan Atlanta Violence Prevention Partnership, a state-wide

300 organizational member, violence prevention group led by the Morehouse School of Medicine, Emory University and The

Original, Atlanta-Based, Martin Luther King Center.

Attorney Johnson’s prior accomplishments include a wide-array of Professional Legal practice areas, including Private Firm,

Corporate and Government postings, just about all of which yielded significant professional awards & accolades, the history and

chronology of which are available for review online. Throughout his career, Jack has served a wide variety of for-profit

corporations, law firms, and nonprofit organizations as Board Chairman, Secretary, Associate, and General Counsel since 1990.


Clayton County Youth Services Partnership, Inc. – Chair; Georgia Violence Prevention Partnership, Inc – Vice Chair; Fayette

County NAACP - Legal Redress Committee Chairman; Clayton County Fatherhood Initiative Partnership – Principal

Investigator; Morehouse School of Medicine School of Community Health Feasibility Study - Steering Committee; Atlanta

Violence Prevention Capacity Building Project – Project Partner; Clayton County Minister’s Conference, President 2006-2007;

Liberty In Life Ministries, Inc. – Board Secretary; Young Adults Talk, Inc. – Board of Directors; ROYAL, Inc - Board of

Directors; Temple University Alumni Association; Rutgers Law School Alumni Association; Sertoma International; Our

Common Welfare Board of Directors – President)2003-2005; River’s Edge Elementary School PTA (Co-President); Summerhill

Community Ministries; Outstanding Young Men of America; Employee of the Year; Academic All-American - Basketball;

Church Trustee.

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www.TheAdvocacyFoundation.org

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Nonprofit Organizational Assessment

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