chapter 4 data analysis & interpretation 4.0.0.0...

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Data Analysis & Interpretation Chapter 4

Introduction 4.0.0.0

Data analysis forms the crux of any Research, particularly those which involve primary

data collection. Contrary to popular perception Data Analysis is much more than

number crunching of n

collection has to be processed and analysed in accordance with the outline laid down

to the computation of certain measures along with searching for patterns of

relationship that exist among data-

The present research being Mixed Methods Research involves analysis of both

Quantitative as well as Qualitative data. PHASE II PART A is quantitative descriptive

study. A quantitative study - standardised and scientific research containing an

objective analysis of the problem or phenomenon - is an inquiry into a research

problem, constituted of numerous variables which are quantifiable and analysed with

statistical procedures. Quantitative research has to do with numbers and data amenable

to quantification. One widely used quantitative technique the survey was deployed in

the research. It is usually based on a large number of cases, where a broad overview of

the existence of a phenomenon (in the case of this research, Competency Mapping) in

a population (Organisations operating in India) is required. Qualitative data on the

other hand is not easily and immediately quantifiable unless coded and categorised in

some way. The study collected quantitative data using closed-ended questions in the

two sets of questionnaires administered to HR and Non-HR Managers. PHASE II

PART B was a qualitative study. The qualitative data was collected using open-ended

questionnaires in the two sets of questionnaire as well as by way of conducting in-

depth interviews with Consultants, CEOs, HR Heads and Functional Heads.

In the current research, SPSS Version 19 and MS Excel are used for data analysis.

Qualitative Analysis was done using NVivo Software, proprietary qualitative software

produced by Google. The research Hypotheses framed and mentioned in the Research

Methodology chapter are verified using various tools available as a part of inferential

statistics viz. parametric tests like t-test, independent samples test and non-parametric

tests like Chi-Square. Measures of central tendency like mean, mode and median as

160

well as frequency tables were used for Descriptive Statistics to provide information of

a set of data or sample.

In the present research work fourteen hypotheses are put to test. The responses for the

questions pertaining to hypotheses were collected using five-point Likert-type scales.

Data was collected by administering two sets of questionnaires personally to the

respondents- HR and Non HR Managers. Data collected was organized, coded and

entered into software packages like SPSS version 19 and MS Excel. To test the

hypotheses formulated, software package SPSS (Statistical Package for Social

Sciences) was used; MS Excel was used for visual display of descriptive statistics

displayed in the form of graphs, charts and frequency distributions

This chapter begins with Descriptive statistics pertaining to the various variables

(independent as well as dependent) - describing the variables of interest in detail.

Thereafter, various hypotheses are tested using appropriate parametric and non-

parametric tools. Objective of the present study, hypotheses are followed by the

analysis of the data.

Objectives of the PHASE II PART A The Survey 4.1.0.0

To determine the usage or otherwise of Competency Mapping in

organizations operational in India

To map the awareness and usage of Competency mapping tools

To ascertain the extent of involvement of different levels of

management in implementation and Integration of Competency

mapping

Identification of Critical Success factors for implementation of

Competency Mapping in Organisations

To gain insights into the extent of applicability of Competency

Mapping to sub systems of HR like

Recruitment and Selection

Training & Development

Performance Management

Compensation & Reward

Succession planning

161

Career Planning

Building appropriate Culture

Change Enablement

Talent Management

To determine the effectiveness of Competency mapping in Sub-

systems of HR like

Recruitment and Selection

Training & Development

Performance Management

Compensation & Reward

Succession planning

Career Planning

Building appropriate Culture

Change Enablement

Talent Management

To gain insights into the roadblocks for usage of Competency mapping

in Organisations

To gain insights into benefits perception of Competency mapping

among HR and Non HR/Line Managers

To compare and contrast Competency Mapping with other HR

Interventions and practices.

Variables studied for PHASE II Part A 4.2.0.0

Independent variables: 4.2.1.0

Industry/Sector to which the organization belongs (Manufacturing v/s Service)

Employee / Staff Strength ( Size) of organization

Age or No of years of establishment of organization

Department / Functional area of employee (HR v/s Non HR)

Dependent variables: 4.2.2.0

Extent of usage of Competency Mapping

Application of CM in various Sub systems of HR

Effectiveness of CM in various Sub systems of HR

162

Impact of Competency Mapping in terms of measurable performance indicators

such as Employee productivity, attrition, bottom line

Perception of HR Managers and non HR managers on various aspects of

Competency Mapping

Awareness levels of non HR Managers for various tools of competency

mapping

Perceived roadblocks by HR and non HR Managers in terms of

Lack of dedicated staff

Inability of Non HR/Line Managers in handling Competency

Mapping recruitment

Lack of employee training in Competency Mapping usage

Difficulty in identifying competencies in detail

Difficulty in implementing competency based models

Competencies being in flux owing to volatile business environment

High attrition rate

Lack of time

Frequent transfers and Mobility

Multitasking expected from employees

Lack of dedicated resources

Lack of top Management Support

Perceived Benefits in terms of

Better productivity

Cost savings

Transparency in various HR processes

Providing a benchmark for measuring performance and identifying

employee potential

Identifying employee training and development needs

Designing appropriate training activities for employees

Assists monitoring of individual progress

Clarifies link between pay and performance

Creating meaningful grade structure

Improved alignment between individual and team behaviours

Perceived Critical Success factors in terms of

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Top management buy-in

Dedicated HR Resource

Availability of Competency Mapping tools

Training of HR Managers in Competency Mapping related skills

Adequate financial resources

Allocation of dedicated time for Competency mapping

Hypothesis of the PHASE II PART A- The Survey 4.3.0.0

H10 Competency Mapping is not used in Organizations in India.

H20 There are no significant differences in awareness levels of Competency mapping

tools/ practices between functions (department type)

H30 Indian Organizations do not use Competency Mapping for the purpose of

Recruitment and Selection Training & Development Performance Management Compensation & Reward Career Planning Building appropriate Culture Succession Planning Change Enablement Talent Management

H40 Competency Mapping Applications in HR Sub-systems are not dependent on

Sector or industry Department or Managerial function of the respondent Employee/ Staff Strength (Size) of the Organisation Age (Years of establishment) of the Organisation

H50 Competency based HRM is not effective across HR subsystems such as

Recruitment and Selection Training and Development Performance Management Compensation & Reward Career Planning Building Appropriate Culture Succession Planning Change Enablement

164

Talent Management

H60 do not vary significantly

from assumed/expected mean (µ=3, Neutral)

H70: Mean Roadblock opinions of managers in organizations without Competency

Mapping implementation does not differ from that of managers in organizations with

Competency Mapping implementation

H80 Roadblock perceptions for Competency Mapping implementation is not dependent

on

Industry or sector. Managerial functions/ Department (HR v/s Non HR) Size of the organization Age of the organization

H90 not vary significantly from the

assumed mean (µ=3, Neutral)

H100 Benefit Perceptions for Competency based HRM implementation are not

dependent on

Industry or sector. Managerial functions Size of the organization Age of the organization

H110 Managers

mean (µ=3, Neutral)

H120 CSFs Perceptions for Competency Mapping Implementation are not dependent

on

Industry or Sector. Department/ Managerial functions Employee Strength (Size) of the organization Age of the organization

H130 The observed mean opinion of Managers regarding the impact of Competency

Mapping on the following chosen Measurable Indicators is significantly higher as

compared to the expected mean opinion (H0: µ = 2.5).

Sales Revenue increase Profit increase Productivity increase

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Attrition reduction Cost reduction per recruit Improvement in the ratio of high performing hires to total hires Top talent retention

H140 The opinion of Managers on the desired outcomes pertaining to the seven

performance parameters does not vary with

Industry or sector

Department/ Managerial functions

Employee Strength (Size) of the organization

Age of the organization

Analysis of the Data PHASE II PART A- The Survey 4.4.0.0

Analysis of the Survey Study Descriptive Statistics of Respondent 4.4.1.0

4.4.1.1 Sector wise Break-

The sample size for the Survey to map the current status, usage and extent of

formalisation of Competency Mapping in organizations operational in India is 653.

There were two separate set of questionnaires one for HR Managers and the other for

Non HR Manager - which were administered to a total of 653 respondents. Both sets of

questionnaire were administered to managers belonging to industries across

manufacturing and service sectors. The following pie chart visually displays the sector

wise break-

166

Sector wise break- Figure: 4.1

The pie chart shows that 71.8% of the respondents belong to organization in the

service sector; 23.7% to organizations in the manufacturing sector and 4.5% were

indeterminable (owing to respondents agreeing to respond only on conditions of strict

confidentiality and anonymity). Thus the break-up approximates the real world where

also roughly 70% of the organizations (by count) in the formal organizational domain

in India belong to service sector.

4.4.1.2 Employee Strength-wise break-

In order to make sense of the large data pertaining to organizations with employee

strength ranging from below 100 to lakhs, the organizations were grouped in five

classes as follows

Employee strength below 100

Employee strength between 101 to 500



Employee strength above 5000

155

469

29

Sectoral Representation of Respondents' Organizations

Manufacturing

Service

Indermined

167

Employee Strength-wise break- organizations Figure: 4.2

The employee strength-wise break-up of organizations is displayed using a bar chart.

28 % of the respondents refused to share employee strength details as well as

however from the rest of the data the sample appears to represent a

4.4.1.3 Age-wise break- Organization

The age of the organizations ranged from below five years to 100 years. Hence the

organizations were grouped into following categories

Organizations with age up to 10 years

Organizations with age between 11 -20 years

Organizations with age between 21-30 years

Organizations with age between 31-40 years

Organizations with age above 40 years

0

20

40

60

80

100

120

140

160

180

200

<100 101 to 500 501 to 1000 1001 to 5000 >5000 Indetermined/ Witheld on

Request

88

119

52

107 101

186 Employee Strength

168

Age-wise break- organizations Figure: 4.3

Around 63% of the respondents shared their organization

respondents were either not aware or refused to share their emplo . A good

13.3 % of the organizations belonged to the category of organizations above 40 years;

18.07% of the organizations belonged to the category of organizations between 11 to

20 years; approximately 17% of the organizations belonged to the category of

organizations with age upto 10 years; 8.8% of the organizations belonged to

organizations between 21 to 30 years; 5.2% of the organizations belonged to the

category of organizations between 31 to 40 years.

0

50

100

150

200

250

111 118

58

34

87

245 Age of Organization

169

4.4.1.4 Industry-wise break-up of Organization

Industry-wise break- organizations Figure: 4.4

653 Manager Respondents for the Survey belong to diverse industry spanning the

sake of convenience and representation they are classified or grouped into 7

representative categories to give a fair idea of the spread of sample organizations co-

Organizations are grouped into Consulting,

IT, ITES, Telecom, BPO/ KPO; 10.4% of the respondents belonged to Mining,

Manufacturing and Engineering ; 15.9% of the Managerial Respondents belonged to

BFSI; 11.17% belonged to Shipping, Logistics, Infrastructure & Construction; 6.43%

of the Manager respondents came from Consumer Services industry; 14.24% of

respondent had their rightful place in Business-to-Business; Pharma & Consumer

Goods/ Durables accounted for 8.11% of the total respondents; a miniscule 1.07% of

the respondents fitted in the Government, NGO/NPO industry. 4.1% of the

respondents were averse to sharing their demographics or did not prefer to disclose

their co-ordinates as a part of Company philosophy. An exhaustive list of industries in

each of the above 8 categories is enclosed in Appendix H.

186

68

104

73

42

93

53 7 27

Industry-wise Break-up of Respondents' Organizations (N=653) Consulting, IT, ITES, Telecom, BPO / KPO

Mining, Manufacturing, Engineering

BFSI

Shipping, Logistics, Infrastructure &Construction

Consumer Services

Business-to-Business

Pharma, Consumer Goods / Durables

Government, NGO, NPO

Others / Prefer not to disclose

170

4.4.1.5 Turnover wise break-up of organization

Turnover-wise break- organizations Figure: 4.5

The turnover of the sample varied from below Rs. 100 crore to above Rs. 100,000. For

the sake of manageability and ease of understanding the responses were classified into

five categories. Of the total 653 Survey Respondents, only 228 revealed turnover of

their organization, the remaining either claimed ignorance or stated being bound to

Non Disclosure Agreement with their organization. Some respondents working with an

MNC Subsidiary had no clue about the turnover of their organization but reported

figures pertaining to the global level, others working in Conglomerate again had no

clue about the turnover of their subsidiary and stated figures pertaining to the group

turnover (Organisations within a Conglomerate revealed substantial variance in terms

of CM practices). A substantial number of Organisations not being listed, their

turnover data were not available in public domain.

Of the 228 Valid responses, 25% belonged to the category of turnover below Rs. 100

crore, 25.4% reported turnover above Rs.10, 000 crore, 18.85 % indicated turnover

from Rs. 101 crore to Rs. 500 crore, 17.% belonged to the category of organization

with turnover Rs. 501 crore to 2000crore, 13.5% fell in the category of organizations

with turnover between Rs. 2001 crore to Rs. 10,000 crore.

0

10

20

30

40

50

60

INR 100 Croreor less

INR 101 to500 Crore

INR 501 to2,000 Crore

INR 2,001 to10,000 Crore

Above INR10,000 Crore

57

43 39 31

58

Turnover-wise Spread of Respondents' Organizations (228 Valid Responses / Searches)

Count

171

4.4.1.6 Function-wise break-up of respondents

Function-wise Break-up of Survey Respondents Figure: 4.6

26% of the total respondents belonged to HR and the rest 74% belonged to Non-HR

departments. The rationale for this 1:3 ratio of HR and Non-HR managers in Survey

was to approximate the real world where a similar ratio is applicable. Further, the

extent of usage of Competency mapping could only be revealed by way of capturing

responses from Non-HR / Line Managers whose participation and involvement was

essential for its successful implementation in an Organization.

Analysis of the Survey Study- Descriptive Statistics capturing Current 4.4.2.0

state of affairs as regards Competency Mapping Usage & Awareness in

Organisations

This section captures existing Scenario of Competency Mapping on the following

aspects

Extent of Usage of Competency Mapping

Awareness of Competency Mapping tools

Usage of Competency Mapping tools

Approaches used in developing Competency frameworks

Degree of Involvement of different stakeholders viz. top management, middle

management, operating management, consultant etc. in development and

26%

74%

Function-wise Break-up of Survey Respondents

(Total Respodent Base = 653)

HR

Non-HR

172

implementation of Competency Mapping

Allocation of responsibility for Competency Mapping implementation and

usage in Organizations

Agency, Basis and Scope of outsourcing, if outsourced

Various particulars indicating usage of Competency Mapping

Role of Line Managers in Competency Mapping

The above data is represented by way of pie charts, bar charts and frequency

distribution tables. This gives a good idea of how the respondents have reacted to the

items and how good the items and measures are.

4.4.2.1 Competency Mapping Usage

Of the 653 respondents, a slight majority (55.27%) belongs to organizations following

Competency Mapping processes; 40.88% admitted to competency mapping not being

used at all and 5.35% did not respond. The same is depicted in the pie chart below

Competency Mapping Usage and Non Usage in Organisations Figure: 4.7

Of the 351 Competency implementers, 17% started using Competency Mapping less

than a year back and another 23.36% have been using Competency Mapping only for

the past 1-2 years. Together they constitute 40.7% of the organizations who are at the

introduction or initial stage of usage. 42% organizations have been using Competency

Mapping for more than five years and 16.5% have been following Competency

mapping for the past 3-5 years.

351 267

35

Use of Competency Mapping (N=653)

Competency Mapping Used

Competency Mapping Not Used

No response

173

Break-up of Years of Usage within Competency Mapping Users Figure: 4.8

This is in stark contrast to developed country like US where more than 80% of the

organizations are Competency driven (Shippman et.al). This has also to be viewed in

the context of Competency Mapping origins and history which started in India in

1970s when T.V Rao implemented it in L&T. Even after 35 years of its introduction in

India, the progress of Competency Mapping and its usage has been quite tardy. If one

organizations are using

competency in a formalised manner. This can be asserted because Competency

Mapping implementation and integration in HR sub-systems takes three to five years.

The data is displayed visually using pie chart above

4.4.2.2 Competency Mapping Tools Awareness

Responses were sought on awareness of numerous tools used at various stages of

Competency Mapping process -namely Competency Identification, Competency

Assessment, Competency Validation and Development of frameworks. The awareness

of tools indirectly and in a somewhat crude manner also revealed the use of such tools.

The bar chart below along with the frequency distribution table reveals that for all the

tools except one Job Role Clarification-the number of people who are not aware

outnumber those aware of the tools in case of Competency Mapping non-users or non-

implementers.

Respondents were least aware of Threshold scales, Repertory Grids and Forced Rank

order.

150

58

82

61

Break-up of Years of usage within CM Users (N=351)

>5 years

3-5 years

1-2 years

Less than a year

174

Responses on awareness of Competency mapping tools are depicted in the bar chart

with an appended frequency distribution table in the ensuing figure

Awareness of Competency Mapping tools amongst Respondents from Figure: 4.9Organisations without Competency Mapping Implementation

In Organisations where Competency Mapping was already there or implemented, Job

role clarification scored maximum awareness followed by Assessment Centres and

Development Centres. One crude inference could be that of the 351 organizations

using Competency Mapping 69% deployed ACs and DCs. Respondents from

Competency Mapping Implemented Organisations were least aware of Repertory grids

and threshold scales among the tools surveyed, similar to respondents from non-CM

Aware

020406080

100120140

Axis

Titl

e

Aware_IndustryBenchmark

Aware_MockCenters

Aware_Assess

mentCenters

Aware_DevelopmentCenters

Aware_ExpertP

anels

Aware_BARS

Aware_ThresholdScal

es

Aware_ReportoryGrid

s

Aware_ForcedRankOr

der

Aware_JobRoleClarifica

tion

Aware_ChecklistsForObservati

onsAware 37 24 38 35 33 12 6 7 8 69 37Not Aware 96 109 94 97 99 120 126 125 123 63 95

Awareness of CM Tools - Respondents from Organizations without CM Implementation

175

implemented organizations.

Awareness of Competency Mapping tools amongst Respondents from Figure: 4.10Organisations with Competency Mapping Implementation

The preceding bar chart and frequency distribution table (Fig. 4.10) depict the

awareness or otherwise of Competency Mapping Tools amongst respondents from

organizations where Competency Mapping was being used. Understandably, the

awareness levels are higher for organizations where Competency Mapping is being

followed.

Aware

050

100150200250300

Axis

Titl

e

Aware_IndustryBenchmark

Aware_MockCenters

Aware_Assess

mentCenters

Aware_DevelopmentCenters

Aware_ExpertP

anels

Aware_BARS

Aware_ThresholdScal

es

Aware_ReportoryGrid

s

Aware_ForcedRankOr

der

Aware_JobRoleClarifica

tion

Aware_ChecklistsForObservati

onsAware 225 92 240 220 171 88 69 44 92 268 206Not Aware 110 240 95 114 160 244 263 287 241 67 129

Awareness of CM Tools - Respondents from CM Implemented Organizations

176

4.4.2.3 Extent of usage of various HR Interventions in Organisations

Extent of Usage of HR Intervention by Organizations as Reported by HR Figure: 4.11Managers

The above figure depicts the extent of usage of a plethora of interventions namely, Job

Design, Job Enlargement, Laboratory Training, Career Planning, Stress Management,

Team Building, Process Consultation, Quality Circles, Role Negotiation, Role

JobDesign

JobEnlargement

LaboratoryTraining

CareerPlanning

Stress

Management

Team

Building

ProcessConsultatio

n

Quality

Circles

Role

Negotiation

Role

AnalysisTechnique

s

GridOrganizationalDevelopment

Third

Party

Interventio

n

Organizatio

nMirror

QualityOf

Work

Life

Very High 17.8 14.2 6.25 14.3 6.80 19.1 11.7 11.0 7.64 9.66 7.64 7.59 4.90 15.8High 28.7 30.6 17.3 23.2 23.1 38.3 28.2 20.0 29.1 22.7 28.4 22.0 18.1 31.0Medium 29.4 26.5 18.0 32.8 26.5 26.0 24.8 31.0 22.9 25.5 25.6 20.6 23.7 21.3Low 12.3 14.2 20.8 10.2 22.4 10.9 14.4 14.4 20.8 20.0 12.5 16.5 16.0 15.8None / Very Low 11.6 14.2 37.5 19.1 21.0 5.48 20.6 23.4 19.4 22.0 25.6 33.1 37.0 15.8

0.00%

20.00%

40.00%

60.00%

80.00%

100.00%

120.00%St

acke

d Co

unt o

f Rat

ings

Extent of Usage of HR Intervention by HR Function Respondents (in %)

177

Analysis Techniques, Grid Organizational Development, Third Party Intervention,

Organization Mirror and Quality of Work Life. The above bar chart in a way depicts

what measures are employed by Organizations for the purpose of Change and

Development. Of the 167 HR respondents, 122.reported the use of Competency

Mapping in their organizations and 65 (38.92%) reported Competency Mapping usage

of more than 3 years,

The base being the same for all HR Interventions, Team- Building was reported to be

used the most among the select HR Interventions what with 57.53% of the respondents

reporting high or very high usage; Quality of Work Life with 46.8% and Job design

with 46.5% Managers reporting High or very High usage stood at second third in terms

of extent of usage. Laboratory training followed by Organization Mirror and Third Part

Intervention were used the least. Job design and Job Enlargement were also found to

be used to a considerable extent in Organizations.

Weighted Average for Degree/Extent of Usage of HR Intervention Figure: 4.12

The above bar chart depicts the extent of Usage of HR Interventions in terms of

Weighted Average. The weighted average is highest for Team Building, Job Design

and Quality of work life indicating high usage of these interventions. The weighted

average is lowest for Laboratory Training, followed by Organization Mirror and third

Party Intervention indicating low usage of these interventions as reported by Key

0.000.501.001.502.002.503.003.504.00

Weighted Average for Extent of Usage of HR Intervention

178

informants namely, HR Managers.

4.4.2.4 Usage of Competency Mapping Tools

The question on Usage of Competency mapping tools was responded to only by those

using Competency Mapping. Job role Clarification found maximum usage amongst all

Competency Mapping tools followed by industry benchmarks and assessment centres.

Repertory grids, BARS and Threshold scales were used the least.

Usage of Competency Mapping tools amongst Respondents from Figure: 4.13Organisations with Competency Mapping Implementation

Used

050

100150200250300

Axis

Titl

e

Usage_IndustryBenchmark

Usage_MockCe

nters

Usage_Assess

mentCenters

Usage_DevelopmentCenters

Usage_ExpertP

anels

Usage_BARS

Usage_ThresholdScales

Usage_RepertoryGrids

Usage_ForcedRankOr

der

Usage_JobRoleClarifica

tion

Usage_ChecklistsForObservatio

nsUsed 190 70 178 173 130 53 55 35 76 210 158Not Used 117 232 126 129 172 246 246 264 225 94 143

Usage of CM Tools - Respondents from CM Implemented Organizations

179

4.4.2.5 CM Tools When Used

Competency Mapping Tool Usage pertaining to Competency Mapping Figure: 4.14Stages of Mapping of Job Competencies, Competency Assessment and

Competency Validation

IndustryBenchmark_WhenUsed

MockCenters_WhenUse

d

AssessmentCenters_

WhenUsed

DevelopmentCenters_WhenUsed

ExpertPanels_WhenUse

d

BARS_WhenUse

d

ThresholdScales_WhenUsed

RepertoryGrids_WhenUsed

ForcedRankOrder_

WhenUsed

JobRoleClarification_WhenUsed

ChecklistsForObservationa_WhenUse

d

BehaviouralEventInterview_WhenUsed

All Three 0 0 0 1 0 0 1 0 0 1 1 0Assesment & Validation 1 0 0 1 0 0 0 0 1 0 1 2Identification & Validation 1 0 0 0 0 0 0 0 0 1 0 0Identification & Assesment 2 0 0 2 0 0 0 0 0 0 0 2Validation only 4 1 3 4 8 0 3 2 0 3 4 1Assesment only 5 7 23 13 8 3 3 1 7 13 8 13Identification only 19 4 5 9 9 3 3 3 7 16 8 10

0

5

10

15

20

25

30

35

40Co

unt o

f Res

pons

es

Mapping Tool Usage to CM Stages

180

The Figure 4.14 depicts Competency Mapping Tools and their Usage in Various

Stages of Competency Mapping Process viz, Mapping of Job Role Competencies,

Assessment of Competencies and Competency Validation.

The preceding bar chart clearly depicts that Industry Benchmarks and Job Role

Clarification were used for Competency Identification; Assessment Centres were used

the most for the purpose of Competency Assessment followed by Development

Centres, Job Role Clarification and Behavioural Event Interviews/Behavioural

Description Interviews; Repertory Grids, Experts Panels and Threshold scales in that

order were used the most for Competency validation; Behavioural Event interviews

and Industry Benchmarks were used for both Competency Identification and

Assessment; Industry Benchmarks and Job Role Clarification were used both for

Identification and validation; BEI and Forced ranked order were used both for

Assessment and Validation. Of all the Competency Mapping tools Assessment Centers

were deployed the most, followed by Job Role Clarification and Industry Benchmark

in some stage of Competency mapping.

4.4.2.6 Approaches to develop CM Framework

Organizational Approaches to Development of Competency Mapping Framework

Organizational Approach to Development of Competency Mapping Figure: 4.15Framework

The above chart clearly depicts that 62.71 % of the HR Managers proclaimed that

37.29%

62.71

Development of Competency Mapping (%)

Is Periodic

Is Continuous

181

Development of Competency Mapping Framework was continuous affair in their

organization and 37.29% stated that development exercise is Periodic. In other words

roughly 40% of the organizations have not reached maturity as far as Competency

Mapping approach is concerned and their frameworks are not in tune with the dynamic

Environments. Periodic Development also indicates a fits and starts policy which does

not augur well for its integration in various HR sub-systems

4.4.2.7 Organizational Approach to Competency Assessment

Organizational Approach to Competency Assessment Figure: 4.16

The above pie-chart clearly depicts that a good 67.52% of the HR respondents stated

that Competency Assessment is continuous in their organization and 32.48% reported

that assessment is a one-time event. In other words in roughly one-third of the

organizations Competency Assessment has not reached a stage of maturation and

completely integrated in Sub-Systems of HR like Performance Management,

Succession Planning, Promotions etc.

32.48%

67.52%

Competency Assesment

One-time

Continuous

182

4.4.2.8 Organisational Approach to Competency Frameworks Alignment to

Business Goals

Organisational Approach to Competency Frameworks alignment to Figure: 4.17Business Goals

The above pie-chart portrays that 68.70% of the organizations align their Competency

Frameworks to Long-term Business Goals, thereby paving the way for

institutionalization of Competency based HRM in their organizations; 28.69% stated

that the frameworks are aligned to Short-term business goals indicating that

Competency Mapping has not been integrated completely with Organisational

processes; 2.61% claimed that the frameworks were aligned to both short term and

long term goals

28.69%

68.70%

2.61%

Competency Frameworks Alignment

Short-term Business Goals

Long-term Business Goals

Both Short and Long Term

183

4.4.2.9 Organisational Allocation of Responsibility for Competency Mapping

Exercise

Allocation of Responsibility for Competency mapping Exercise Figure: 4.18

The above pie-chart depicts the allocation of Responsibility for Competency mapping

Exercise is mostly shouldered by HR Managers (51.63%) followed by others23.77%

(i.e Consultants or Consultancy Firms); only 22.95% organizations had Line Managers

shouldering the responsibility; a miniscule 1.64% of the organizations had a sharing of

responsibility amongst all three parties namely HR managers, Line Managers and

Consulltants

51.63%

22.95%

23.77%

1.64%

Responsibility for CM Exercise

HR Managers

Line Managers

Others

All

184

4.4.2.10 Degree of Involvement of various stakeholders in Development and

Implementation of Competency Mapping

Degree of Involvement of various stakeholders in Figure: 4.19Development and Implementation of Competency Mapping

The respondents were asked to rate the degree of involvement of various stakeholders

viz, Board of Directors, CEOs, TOP management, Middle Management, Operating

Management and Consultants on a four point scale.

-

Nil

LowMedium

High

0

20

40

60

80

100

120

140

160

Coun

t of V

alid

Res

pons

es

Board OfDirectors

ChiefExecutive

TopManagement

MiddleManagement

OperatingManagement Consultant(s)

Nil 62 44 17 8 26 124Low 86 80 49 38 45 64Medium 75 96 106 141 87 47High 87 90 141 125 151 71

"Yes" Responses to Degree of Involvement in Development and Implementation of CM (310

Valid Responses from 351 Implementors)

185

Degree of Involvement in Development and Implementation of competency mapping

are indicated in the above bar chart and frequency distribution table. It is evident that

in Competency Mapping Implementing Organisations, involvement of Middle

Management was the highest followed by top management and operating management

(the two were almost on par).

4.4.2.11 Allocation of responsibility for Competency Mapping

implementation and usage in Organizations

Allocation of Internal Responsibility for Competency Mapping

Competency Mapping is implemented and used by various players in the organization

viz. HR Managers, Line or Non HR Managers, Dedicated task force or a combination

of two or even three of the afore-mentioned. In most organizations Competency

Mapping remains the sole responsibility of HR managers, followed by Line Managers

and dedicated task force.

Internal Allocation of Responsibility amongst various players in Figure: 4.20Development and Implementation of Competency Mapping

Somewhat low involvement of Line managers in the bar chart points to the fact that

Competency mapping has not been fully integrated with all key HR processes, because

Competency Mapping Implementation in certain areas viz. Performance appraisal,

020406080

100120

19

50 51 53 66

112

Who is internally responsible for Competency Mapping

186

Promotions, Succession Planning requires participation of Line Managers. The

responsibility for Competency Mapping is best equally distributed among HR and Line

managers to ensure its success, lest competency mapping becomes just another HR

fad.

External Involvement and Responsibility in Competency mapping Exercise

In some organizations Competency mapping is partly or fully outsourced.

External Allocation of Responsibility amongst various players in Figure: 4.21Development and Implementation of Competency Mapping

The above bar chart depicts Consultancy firms handled the maximum of outsourced

assignments of Competency mapping followed by individual consultants.

4.4.2.12 Overall responsibility of Competency Mapping

The overall responsibility of Competency Mapping is borne most by In-house players

HR managers, Line managers or task force, followed by responsibility borne jointly

by In-house and outsourced resources. Very rarely (<1%) is responsibility of the

competency mapping process outsourced completely.

0102030405060

ExternalInstitution

No outsourcedsupport

IndividualConsultant

Consultancy Firm

9 12

34

60

Who is externally responsible for Competency Mapping

187

Overall responsibility of Competency Mapping Figure: 4.22

4.4.2.13 Basis of Outsourcing

Further probing on the modalities of (joint or exclusive) outsourcing of the CM process

fetches up interesting data fully 46% of such outsourcing happens on a time-bound

basis whereas 38% is outsourced on project-basis.

Basis of Outsourcing Figure: 4.23

About 10% outsourcing is done on retainership whereas combinations of the above

three bring up the rear minisculely (See chart above).

3 (0.99%)

92 (30.36%)

208 (68.65%)

Who is responsible overall for Competency Mapping

Exclusively Outsourced

Joint

Only In-house

46.27%

38.81%

10.45% 2.99% 1.49%

Basis of Outsourcing

Time-bound

Project Bound

Retainer Basis only

Project and Retainer Basis

Time and Project-bound

188

4.4.2.14 Scope of Outsourcing

As far as the span of activities outsourced within the overall outsourcing done for CM

processes is concerned, a large chunk (57.5%) is solely for the operational activity of

individual competency assessment only, one of the objectives being to impart a

modicum of impartiality to the actual evaluation process.

Scope of Outsourcing Figure: 4.24

nd

outsourcing at exactly 10% each, other permutational combinations thus bringing up

the rear. (See figure above)

4.4.2.15 Line Mana

Coming around to the dynamics of in-house activities for CM, Line managers in

majority claimed not to have been part of the CM exercise either as assessor or subject

in 59% of the cases. Informal mapping with HR managers in some of those very

organizations elaborated further that line managers are sometimes part of the exercise

as subjects without their knowledge quite often by design.

57.5%

10.0%

10.0%

7.5%

7.5% 5.0% 2.5%

Scope of Outsourcing

Assessment of IndividualCompetencies only

Framework Development &Assessment of Competencies

Mapping & Assesment ofCompetencies

Mapping of Competencies

Development of Framework

Mapping Competencies &Framework development

189

Figure: 4.25

Line Managers, when being aware of being involved, whether as part of a task-force

or individually / departmentally, were most likely to be both subjects and assessors,

and then subjects and assessors respectively owing to the healthy sample skew of

middle-level managers for our data.

59% 25%

13% 3%

Line Managers' Participatory Role in CM

Neither Subject nor Assesor

Subject & Assesor

Subject only

Assesor only

190

Extent of application of CM in HR Subsystems by Organization's Figure: 4.26Employee Strength

Recruitment and SelectionTraining And Development

Performance ManagementCompensation and Reward

Career PlanningBuilding Appropriate Culture

Succession PlanningChange Enablement

Talent Management0%

10%

20%

30%

40%

50%

60%

Non

e / V

ery

Low

Low

Med

ium

High

Very

Hig

hN

one

/ Ver

y Lo

wLo

wM

ediu

mHi

ghVe

ry H

igh

Non

e / V

ery

Low

Low

Med

ium

High

Very

Hig

hN

one

/ Ver

y Lo

wLo

wM

ediu

mHi

ghVe

ry H

igh

Non

e / V

ery

Low

Low

Med

ium

High

Very

Hig

h

Nor

mal

ized

Ext

ent o

f app

licat

ion,

in %

None /Ver

yLow

LowMedium

HighVer

yHigh

None /Ver

yLow

LowMedium

HighVer

yHigh

None /Ver

yLow

LowMedium

HighVer

yHigh

None /Ver

yLow

LowMedium

HighVer

yHigh

None /Ver

yLow

LowMedium

HighVer

yHigh

Recruitment and Selection 21% 11% 29% 29% 11% 6% 8% 35% 33% 17% 0% 9% 22% 52% 17% 12% 12% 21% 26% 28% 9% 6% 12% 58% 15%

Training And Development 25% 21% 21% 29% 4% 15% 13% 25% 31% 17% 9% 4% 22% 39% 26% 9% 9% 18% 44% 21% 3% 5% 11% 58% 24%

Performance Management 21% 11% 21% 39% 7% 15% 13% 27% 35% 10% 4% 4% 13% 57% 22% 9% 11% 23% 32% 26% 2% 5% 17% 47% 30%

Compensation and Reward 32% 14% 25% 25% 4% 9% 28% 40% 15% 9% 17% 13% 22% 39% 9% 7% 28% 28% 19% 18% 6% 8% 29% 38% 20%

Career Planning 29% 18% 32% 14% 7% 15% 17% 40% 25% 4% 13% 30% 22% 26% 9% 7% 26% 26% 23% 18% 9% 17% 30% 39% 5%

Building Appropriate Culture 25% 18% 32% 18% 7% 8% 27% 25% 31% 8% 17% 13% 26% 35% 9% 9% 26% 26% 32% 7% 8% 20% 21% 42% 9%

Succession Planning 30% 19% 22% 26% 4% 13% 19% 23% 31% 15% 22% 9% 39% 26% 4% 9% 19% 32% 28% 12% 6% 14% 29% 47% 5%

Change Enablement 36% 14% 21% 25% 4% 21% 21% 35% 15% 8% 22% 22% 39% 13% 4% 23% 21% 19% 33% 4% 5% 21% 29% 38% 8%

Talent Management 25% 7% 25% 39% 4% 15% 15% 35% 27% 8% 9% 35% 22% 26% 9% 7% 16% 25% 37% 16% 3% 12% 14% 53% 18%

Extent of application of CM in HR Subsystems by Organization's Employee Strength

<100 | 101-500 | 501-1000 | 1001-5000 | >5000

191

4.4.2.16 Effectiveness of CM in various sub-systems of HR

Respondent HR mangers in organizations where competency mapping was

implemented were asked to rate the effectiveness of Competency mapping process in

various Sub-Systems of HR on a five point Likert-type scale with 1 being totally

ineffective and 5 being totally effective.

Effectiveness of CM usage in Sub-Systems of HR as reported by HR Figure: 4.27Managers

The above stacked bar chart portraying the reported effectiveness of Competency

Mapping in various HR processes clearly points to its high effectiveness in the area of

Recruitment

&Selecti

on

Training &

Development

PerformanceManagementSyste

m

Compensatio

n &Rewar

d

CareerPlanni

ng

Building

AppropriateCultur

e

Succession

Planning

Change

Enablement

TalentManagement

Totally Effective 20.66% 19.01% 19.01% 13.22% 20.00% 14.88% 17.36% 9.92% 13.22%Effective 42.98% 43.80% 47.11% 40.50% 27.50% 37.19% 36.36% 31.40% 42.15%Medium 23.97% 23.97% 22.31% 26.45% 32.50% 26.45% 27.27% 37.19% 30.58%Ineffective 7.44% 10.74% 8.26% 13.22% 15.00% 15.70% 13.22% 14.88% 12.40%Totally Ineffective 4.96% 2.48% 3.31% 6.61% 5.00% 5.79% 5.79% 6.61% 1.65%

0.00%

20.00%

40.00%

60.00%

80.00%

100.00%

120.00%

Stac

ked

Coun

t of

Ratin

gs

Effectiveness of CM Usage according to HR Respondents

192

Performance Management, followed by Recruitment & Selection and Training &

development. Competency Mapping usage was reported to be relatively less effective

in Change Management, followed by Career planning, Building appropriate culture

and Compensation & rewards. It can be interpreted that while Organisations in India

have still some way to go in the full deployment of Competency Driven processes, the

overall enthusiasm for CM as an HR intervention is high for the more obvious and

more easily integratable aspects of HRM. Competency Mapping in most organizations

is associated with performance management while there are on-going attempts to

successfully integrate it in other areas or Sub-systems of HR. Once Competency

mapping is deployed in Recruitment and Selection, Training & Development and

PMS, efforts should be made to use it in other system as well to derive maximum

benefits from the whole exercise.

Weighted Average of Effectiveness ratings of Competency mapping usage Figure: 4.28in HR Sub-systems by HR Managers

The weighted averages for effectiveness of CM usage is above the mean score of 3 for

all 9 HR functions according to HR Managers, indicating an over-all positive opinion

of the usefulness of CM in this very important segment of organizational users and

0.00

1.00

2.00

3.00

4.00

5.00

3.67

3.66

3.70

3.40

3.43

3.40

3.46

3.23

3.53

Effectiveness of CM Usage according to HR Respondents - Weighted Average

193

opinion builders. It is highest for Performance Management Systems, closely followed

by Recruitment and Selection, Training and Development and Talent Management in

quick succession. It is lowest for Change Enablement which is quite understandable

owing to the esoteric and late stage integration nature of this complex and lesser

practised (actively and separately) aspect of HRM.

4.4.2.17 Extent of CM Usage Indicators in Organizations:

Of the 351 respondents formally acknowledging CM presence in their organizations, a

them as indicated in the graph below.

194

Extent of CM Usage Indicators in Organizations Figure: 4.29

The highe

. For 3 of the 9 statements, the disagreeing respondents

outweigh agreeing respondents, and of these, the lowest agreement was for the

below, that response would have had a lower percentage still, were it not for some

0%10%20%30%40%50%60%70% 60% 68%

45% 52%

49%

37% 42%

69% 70%

Nor

mal

ized

"Ye

s" re

spon

ses,

in %

Organization

hasComp

Dictionary

ClarityOf

Purpose

ofComp

ModelInitiati

ve

Employees

Involved in

CompModelDevelopment

Adequate

Reward for

Supporting

CompMappi

ng

Time-frame

forModelImple

mentationToo

Short

Time-frame

forModelImple

mentationTooLong

Historyof

PoorlyImplemente

dChanges toHR

processes

Employees

Informed ofOwnLevel

OfCompetency

Employee

Rewarded ifCompLevel

MeetsRequir

edLevel

"Yes" percentage for CMImplemented Organizations'

Managers, Valid Responses=31360% 68% 45% 52% 49% 37% 42% 69% 70%

"Yes" Responses to Specifics of Competency Mapping Usage Statements

(313 valid responses from 351 Implementors)

195

respondents from smaller, younger organizations.

Analysing for possibilities of inter-sectoral or other differentiating variables to the

Extent of CM Usage Indicators in Organizations Figure: 4.30

Sectorally, no appreciable differences seem to exist when Extent of Usage Data is

viewed after application of the sectoral (Manufacturing v/s Services) cut, indicating

that there is fair inter-sectoral agreement to the extent of CM Usage among

respondents

0%10%20%30%40%50%60%70%80%

Nor

mal

ized

"Yes

" re

spon

ses,

in %

Organization

hasComp

Dictionary

ClarityOf

Purpose

ofComp

ModelInitiati

ve

Employees

Involved inCompModelDevelopment

Adequate

Reward for

Supporting

CompMappi

ng

Time-frame

forModelImplementa

tionToo

Short

Time-frame

forModelImplementa

tionTooLong

History of

PoorlyImplemente

dChanges toHR

processes

Employees

Informed ofOwnLevel

OfCompetency

Employee


MeetsRequir

edLevel

"Yes" percentage forManufacturing Sector Managers,

Valid Responses=7364% 75% 45% 47% 41% 41% 36% 70% 64%

"Yes" percentage for ServiceSector Managers, Valid

Responses=21660% 65% 46% 53% 50% 34% 43% 69% 71%

"Yes" Responses to Specifics of CM Usage for Manufacturing v/s Service Sector Managers

196

Functional Comparison of Extent of CM Usage Indicators in Figure: 4.31Organizations

Similarly, functionally too, no appreciable differences seem to exist when Extent of

Usage Data is viewed after application of the functional (HR v/s Line / Operations)

cut, indicating that there is fair inter-departmental agreement to the extent of CM

Usage among respondents

0%

10%

20%

30%

40%

50%

60%

70%

80%

Nor

mal

ized

"Ye

s" re

spon

ses,

in %

Organization

hasComp

Dictionary

ClarityOf

Purpose

ofComp

ModelInitiati

ve

Employees

Involved in


Adequate

Reward for

Supporting

CompMappi

ng

Time-frame

forModelImple

mentationToo

Short

Time-frame

forModelImple

mentationTooLong

Historyof

PoorlyImplemente

dChanges toHR

processes

Employees

Informed ofOwnLevel

OfCompetency

Employee


MeetsRequir

edLevel

"Yes" percentage for HRManagers, Valid Responses=113 61% 77% 47% 52% 44% 42% 48% 72% 77%

"Yes" percentage for Non-HRManagers, Valid Responses=200 59% 63% 44% 51% 51% 34% 40% 68% 66%

"Yes" Responses to Specifics of CM Usage for HR v/s Non-HR Managers

197

-wise Comparison of Extent of CM UsageFigure: 4.32Indicators

Age category wise, there is broad agreement on extent of usage for for

for supporting

0%20%40%60%80%

100%

Nor

mal

ized

"Ye

s" re

spon

ses,

in %

Organization

hasComp

Dictionary

ClarityOf

Purpose

ofComp

ModelInitiati

ve

Employees

Involved inCompModelDevelopment

Adequate

Reward for

Supporting

CompMappi

ng

Time-frame

forModelImplementa

tionToo

Short

Time-frame

forModelImplementa

tionTooLong

History of

PoorlyImplemente

dChanges toHR

processes

Employees

Informed ofOwnLevel

OfCompetency

Employee


MeetsRequir

edLevel

"Yes" percentage forOrganization Age upto 10 years,

Valid Responses=5045% 64% 40% 52% 48% 37% 39% 69% 75%

"Yes" percentage forOrganization Age=11-20 years,

Valid Responses=5769% 76% 61% 59% 45% 43% 48% 76% 74%

"Yes" percentage forOrganization Age = 21-30 years,

Valid Responses=2358% 67% 46% 42% 46% 35% 48% 61% 70%

"Yes" percentage forOrganization Age= 31-40 years,

Valid Responses=1457% 57% 29% 53% 50% 14% 50% 71% 86%

"Yes" percentage forOrganization Age > 40 years,

Valid Responses=4577% 75% 44% 48% 48% 35% 29% 75% 67%

"Yes" Responses to Specifics of CM Usage by Organization's Age

198

-

differences exist for one or the other age category for the remaining statements. Across

age categories, Organizations between 11-

responses for all statements combined at 61.24%.

The trend is somewhat different when Extent of Usage is viewed Employee-strength

category-wise.

199

Employee Strength Category-Figure: 4.33

While there is broad agreement on extent of usage for

-frame too short for

-

0%20%40%60%80%

100%

Nor

mal

ized

"Ye

s" re

spon

ses,

in %

Organization

hasComp

Dictionary

ClarityOf

Purpose

ofComp

ModelInitiati

ve

Employees

Involved in


Adequate

Reward for

Supporting

CompMappi

ng

Time-frame

forModelImplementa

tionToo

Short

Time-frame

forModelImplementa

tionTooLong

Historyof

PoorlyImplemente

dChanges toHR

processes

Employees

Informed ofOwnLevel

OfCompetency

Employee


MeetsRequir

edLevel

"Yes" percentage for EmployeeStrength upto 100 , Valid

Responses=2141% 41% 23% 32% 50% 41% 73% 55% 57%

"Yes" percentage for EmployeeStrength = 101-500, Valid

Responses=5142% 67% 54% 48% 53% 47% 43% 71% 57%


Responses=2163% 78% 39% 45% 48% 39% 52% 70% 70%


Responses=5163% 61% 31% 47% 45% 39% 35% 57% 66%

"Yes" percentage for EmployeeStrength above 5000, Valid

Responses=6276% 78% 55% 68% 44% 27% 34% 75% 82%

"Yes" Responses to Specifics of CM Usage by Organization's Employee Strength

200

from Organizations with 100 or less staff break away from consensus for

end. Across Strength categories,

4.4.2.18 Ranking of CM Stages for Severity of Challenge:

278 respondents ranked three CM

Ranking of CM Stages for Severity of Challenge Figure: 4.34

The weighted average of the data ranks Development of Framework highest on

severity of challenge, followed by Assessing Individual competencies. Mapping of

Competencies is deemed the easiest of the three.

1.80001.85001.90001.95002.00002.05002.10002.15002.20002.2500

Mapping JobCompetencies

DevelopingCompetencyFramework

AssessingEmployee

Competencies

Ranking of CM Stages for Severity of Challenge (278 valid responses)

Weighted Average Score

201

4.4.2.19 Overall Managerial Responses to Competency Mapping Roadblock

Statements: Roadblocks Yes No

Competency Mapping Roadblocks as reported by HR and Non HR Figure: 4.35Managers

The respondents were asked to identify roadblocks/challenges met in the usage of

Competency Mapping or reasons for Non-implementation. A total of 16 Roadblock

statements were given from which the respondents were expected to identify the ones

that closely described the roadblocks/barriers met by them. The above bar chart clearly

depicts the biggest roadblock in implementation of Competency Mapping as stated by

-

-

Validity of

0%5%

10%15%20%25%30%35%40%45%50% 47%

42%

27% 29%

21%

33%

22%

28%

37%

17%

28%

16%

28%

12%

25%

16%

"Yes" Responses to CM Roadblock Statements

"Yes" percentage, Base=457

202

The responses to Roadblocks were sought by way of two questions dichotomous

with Yes/ No responses for 16 items and five point Likert-type scale to capture

s. The Likert-type scale responses have been

analysed in the Hypothesis testing section.

4.4.2.20 Differences in Roadblocks met by Implementers and those

perceived by Non Implementers

The roadblock / challenges statements have been also analysed in terms of differences

in responses from Managers belonging to organization with Competency Mapping and

those where Competency mapping is not followed. (Figure 4.36 on succeeding page)

Unclear Description of Competencies.

The three biggest challenges or reasons for non-implementation as cited by Managers

from Non-

-availability of Competency Mapping

-rater

203

Comparison of Roadblocks reported by CM Implementers and Non Figure: 4.36Implementers

0% 10% 20% 30% 40% 50% 60%

Lack Of Adequate Time Available

Assessors' Lack Of Skills / Knowledge

Lack Of Budgetary Support

Business Environment Related

Organizational Resources Related

Non-Availability Of Comp. Mapping Tools

Complexity In Use of CM Tools

Complexity Of Implementation

Unclear Description Of Competencies

Non-Availability Of Assessors

Lack Of Assessor Training

Question Mark On Validity Of Exercises

Lack Of Organizational Commitment

Lack Of Inter-Rater Reliability

Non-Involvement Of Line Managers

Lack Of Data Security

40%

34%

26%

25%

18%

33%

16%

23%

31%

13%

17%

9%

25%

8%

19%

9%

54%

49%

28%

33%

22%

34%

29%

33%

43%

21%

38%

22%

30%

16%

29%

23%

"Yes" Responses to Roadblock Statements for Organizations with and without CM

Implementation

"Yes" percentage for Impementers, Base=223

"Yes" percentage for CM Non-Impementers, Base=212

204

Differences in Roadblocks reported by HR Managers and Non HR Figure: 4.37Managers

The above bar- of Adequate

Non Involvement of

Line M locks. Non HR-

0% 10% 20% 30% 40% 50% 60%

















55%

55%

35%

32%

22%

31%

29%

38%

38%

24%

40%

15%

32%

17%

41%

24%

45%

38%

25%

28%

20%

34%

20%

25%

37%

15%

25%

16%

27%

10%

21%

14%

"Yes" Responses to CM Roadblock Statements for HR v/s Non-HR Managers

"Yes" percentage for Non-HR Managers, Base=363

"Yes" percentage for HR Managers, Base=94

205

-involvem

The age of the organizations in sample varies from below five to more than 100 years.

O to 10 years (2) 11 to 20

years (3) 21 to 30 years (4) 31 to 40 years (5) above 40 years. (Figure 4.38 on

succeeding page)

For organization

For organizations in the age-group of 31-

Knowledge were the three prime roadblocks

Organisations in the age- of Skills/

three most prominent roadblocks

Non availability of

Young Organisations with age under 10 pointed

most prominent roadblocks. The following chart depicts the responses on Roadblocks

206

Organisation- age wise Reported Roadblocks Figure: 4.38

0% 10% 20% 30% 40% 50% 60%

















"Yes" Responses to CM Roadblock Statements by Organization's Age

"Yes" percentage for Organization Age > 40 years, Base=63

"Yes" percentage for Organization Age= 31-40 years, Base=26

"Yes" percentage for Organization Age = 21-30 years, Base=43

"Yes" percentage for Organization Age=11-20 years, Base=88

"Yes" percentage for Organization Age upto 10 years, Base=81

207

Figure: 4.39

The Employee strength of respondent organization varied from below 100 to above

100,000, hence the organizations were grouped in five categories of

0% 10% 20% 30% 40% 50% 60%

















"Yes" Responses to CM Roadblock Statements by Organization's Employee strength

"Yes" percentage for Organization Employee Strength > 5000, 63 Valid Responses

"Yes" percentage for Employee Strength between 1001-5000, 73 Valid Responses



"Yes" percentage for Employee Strength upto 100, 70 Valid Responses

208

1) Employee strength below 100

2) Employee strength between 101-500



5) Employee strength above 5000

-involvement of

(Figure 4.39 on previous page)

Organisations with employee strength between 1001-5000 the chief roadblocks were

For organizations with Employee strength between 501-1000

/

Organisations with employee strength 101-

/ Knowledge and Non availability of Competency mapping

Organisation having employee strength of up

adequate time available as the biggest roadblocks

209

Reported Roadblocks Sector wise Figure: 4.40

The above bar chart clearly depicts that there are very little or negligible differences in

acknowledgement of individual roadblocks between manufacturing and service

sectors.

4.4.2.21 Distribution of Roadblock Ratings

Managers responses on a five point Likert-type scale on 12 Roadblock statements are

depicted below

0%5%

10%15%20%25%30%35%40%45%50%

49% 47%

26% 29% 25%

38%

25% 28%

35%

14%

34%

15%

33%

12%

25%

46%

40%

28% 28%

20%

31%

21% 27%

38%

18%

25%

15%

26%

11%

24%

"Yes" Responses to CM Roadblock Statements by Sector

"Yes" percentage for Manufacturing Sector, 103 Valid Responses

"Yes" percentage for Service Sector, 339 Valid Responses

210

Distribution of Roadblock Ratings Figure: 4.41

The responses for all 12 Roadblock statements are skewed around the mid-point three

thereby revealing that majority of Managers do not disagree with the 12 Roadblock

statements.

onestwos

threesfours

fives050

100150200250

Coun

t

Lack OfDedica

tedStaff

LineMgrs

Inability to

handleC M-

BasedRecruitment

Lack ofEmploy

ees'Trainin

g onCM

Diffficulty in

Identifying

Competencies

Difficult to

ImplementCM-

basedModels

CompetenciesRequired InFlux

HighAttrition Rate

Paucityof

Time

Frequent

Transfers &

Mobility

Muti-taskingExpectation

Lack ofDedica

tedResour

ces

Lack ofTop

ManagementSuppor

t

ones 94 75 67 75 73 44 70 60 100 74 73 89twos 83 106 90 101 106 91 91 100 94 83 79 57threes 156 160 146 164 190 227 154 179 164 138 145 142fours 73 98 104 95 93 97 111 106 94 120 112 107fives 128 94 123 97 70 71 104 86 77 117 121 134

Distribution of Roadblock Ratings 529 valid responses

211

4.4.2.22 Distribution of Benefit ratings

HR and Non HR Managers responses on a five point Likert-type scale on 10 CM

Benefits statements are depicted below. The distribution of responses on a five point

Likert-type scale for each of the 10 Benefits statement is skewed towards 4 in most

cases and five in few cases, thereby revealing broad and strong agreement on benefits

accruing to organizations when following Competency Mapping.

212

Distribution of Benefits Ratings Figure: 4.42

onestwos

threesfours

fives

020406080

100120

Bett

er P

rodu

ctiv

ityCo

st S

avin

gsTr

ansp

aren

cy in

HR

proc

esse

sBe

nchm

arki

ng o

f Em

ploy

ees'

Perf

orm

ance

Iden

tific

atio

n of

T&

D N

eeds

Desig

ning

Tra

inin

g Ac

tiviti

esM

onito

ring

Indi

vidu

als'

Prog

ress

Clar

ifies

Pay

-Per

form

ance

Lin

kCr

eate

s Mea

ning

ful G

radi

ng S

truc

ture

Coun

t of R

atin

g

BetterProducti

vity

CostSavings

Transparency in

HRprocess

es

Benchmarking ofEmploye

es'Perform

ance

Identification of

T&DNeeds

Designing

TrainingActivitie

s

Monitoring

Individuals'

Progress

ClarifiesPay-

PerformanceLink

CreatesMeanin

gfulGradingStructur

e

Alignment

between

Individual-TeamBehavio

urones 17 10 15 8 12 8 11 11 15 12twos 17 31 24 18 17 18 26 32 24 33threes 52 70 71 60 70 72 66 85 82 85fours 77 84 97 101 79 95 93 89 89 77fives 116 86 73 94 103 88 85 63 71 74

Distribution of Benefit Ratings

213

4.4.2.23 Distribution of CSF Ratings

HR and Non HR Managers responses on a five point Likert-type scale on 6 CSF

statements are depicted below

Distribution of CSF Ratings Figure: 4.43

The distribution of CSF ratings in the above bar-chart and the appended table reveals

that majority of responses are garnered by threes and fours on the rating scale for six

statements. It can be stated that Manager Respondents either agree or strongly agree

with the CSFs, only a miniscule disagree or strongly disagree with the selected CSFs.

ones

twosthrees

foursfives

020406080

100120

Axis

Titl

e

TopManagement

Buy-In

Dedicated HRresource

Availability ofCM Tools

Training HRManagers

AdequateFinancial

Resources

DedicatedTime

Allocationones 6 5 3 2 6 5twos 29 21 22 16 21 15threes 81 75 87 67 86 85fours 81 98 104 106 103 101fives 89 90 72 98 71 82

Distribution of CSF Ratings

214

4.4.2.24 Measure of CM Implementation on Performance Indicators

Likert-type scale on 6

Measurable Indicators are depicted below

Impact Measurement of Competency mapping implementation on Figure: 4.44Performance Indicators

The above bar chart depicts the impact of competency mapping on seven performance

0

20

40

60

<10%

11-20%

21-30%

>30%

14 38

35

28

18 46

26

27

9 38

37

33

41

35

26

17

34

31

29

21

19 49

27

23

20 39

21 42

Axis

Titl

e

<10% 11-20% 21-30% >30%Sales Revenue Increased 14 38 35 28Profit Increased 18 46 26 27Productivity Increased 9 38 37 33Attrition Reduced 41 35 26 17Cost Per Recruit Reduced 34 31 29 21Ratio of High Performing Hires To

Total Hires 19 49 27 23

Top Talent Retention 20 39 21 42

Measure of CM Implementation on Performance Indicators

215

indicators like Sales Revenue, Profit, Productivity, attrition, Cost per recruit, ratio of

high performing hires to Total hires and top talent retention. For all measurable

indicators except one (Cost Per Recruit Reduced) the category with highest frequency

is 11-20% meaning that in organizations where competency mapping was

implemented the positive impact was in the range of 11-20% . As regards Cost per

Recruit reduced, the category with highest frequency was less than 10%.

Hypothesis Testing 4.5.0.0

Analysis of the Survey Study- Competency Mapping Usage 4.5.1.0

Objective:

To determine Competency mapping usage or otherwise in Organisations Operating in India

To determine the number of years for which Competency Mapping has been used in Organisations

Hypothesis:

H10 Competency Mapping is not used in Organizations in India.

H1a Competency Mapping is used in Organisations in India

4.5.1.1 Runs Test on CM Usage (Y/N)

NPar Tests Runs Test

Usage_CM_YN Remarks

Test Valuea

2

z value calculated > z value tabulated (1.96)

(p < 0.05)

Null Hypothesis rejected

Cases < Test Value 267

Cases >= Test Value 351

Total Cases 618

Number of Runs 175

Z 10.606

Asymp. Sig. (2-tailed) .000

a. Median

Table: 4.1 Runs Test on Competency Mapping Usage in Organisations (Test

Value: Median)

216

Runs Test 2

Usage_CM_YN Remarks

Test Valuea

2.00

z value calculated > z

value tabulated (1.96) (p

< 0.05)

Total Cases 618

Null Hypothesis

rejected

Number of Runs 175

Z 10.606

Asymp. Sig. (2-tailed) .000

a. User-specified.

Table: 4.2 Runs Test on Competency Mapping Usage in Organisations (Test

Value: User Specified)

The runs test for responses sought on Competency mapping usage in organization on

dichotomous scale

distributed.

The above two tables depict z value of 10.606 which is higher than the tabulated value

of 1.96. Hence the difference is significant and Null Hypothesis is rejected. It can be

deduced that Competency Mapping is used in Organisations.

4.5.1.2 Binomial Test on CM Usage (Y/N)

NPar Tests

The non-parametric test of binomial was run on responses to Competency mapping

ion of

values is binomially distributed. The p value associated with the this comparison is

.001 indicating that the number of organizations with competency mapping and those

without do differ significantly from the binomial assumption of equal probability of

either.

217

Binomial Test

Usage_CM_YN

Categ

ory N

Obser

ved

Prop.

Test

Prop.

Exact Sig. (2-

tailed)

Remarks

Group 1(Organisations

with No Competency

Mapping Usage)

Group 2 (Organisations

witht Competency

Mapping Usage)

Total

1 267 .43 .50 .001 P<0.05. Null Hypothesis is

rejected

2 351 .57

618 1.00

Table: 4.3 Binomial Test on Competency mapping Usage

4.5.1.3 One-Sample Chi-Square on CM Usage (Y/N)

NPar Tests

Chi-Square Test

Frequencies

Usage_CM_YN

Competency Mapping Usage in No. of Years Observed N Expected N Residual

1(Organisations with No Competency Mapping Usage) 267 309.0 -42.0

2 (Organisations with Competency Mapping Usage)

351 309.0 42.0

Total 618

Table: 4.4 Observed and Expected Frequencies for One Sample Chi-Square

on Competency Mapping Usage

218

Test Statistics

Usage_CM_YN Remarks

Chi-Square 11.417a

Chi-Square Calculated >Chi-Square Tabulated (3.841). Difference

Significant. Null Hypothesis rejected (p<0.05)

Df 1

Asymp. Sig. .001

a. 0 cells (0.0%) have expected

frequency is 309.0.

frequencies less than 5. The minimum expected cell

Table: 4.5 Chi-Square Table for Competency Mapping Usage Yes /No

It is clear from the above two tables that Chi- square calculated is more than Chi-

square tabulated. Thus the difference is significant at .05 level of significance. From

Frequencies table it is evident that Observed frequencies for organizations using

Competency Mapping are more than Expected Frequencies. Hence null hypothesis

stating that Competency Mapping is not used in organizations is rejected. It can be

inferred that Competency Mapping is used in Organisations and more organizations

using it outnumber those not using it by almost 31%.

4.5.1.4 One-Sample Chi-Square on CM Usage (Number of Years)

Usage_CompetencyMapping

Competency Mapping Usage in No. of Years Observed N Expected NResidual

1(Organisations not using Competency mapping) 267 123.6 143.4

2 (Organisations using less than a year) 61 123.6 -62.6

3 (Organisations using for 1-2 years) 82 123.6 -41.6

4 (Organisations using for 3-5 years) 58 123.6 -65.6

5 (Organisations using for more than 5 years) 150 123.6 26.4

Total 618

Table: 4.6 Observed and Expected frequencies for Competency Mapping

Usage (No of Years).

219

Test Statistics

Usage_Competency

Mapping Remarks

Chi-Square 252.534a Chi-Square Calculated >Chi-Square Tabulated (9.488) at .05 level of Significance Hence Null Hypothesis is rejected. (p<0.05)

Df 4

Asymp. Sig. .000

a. 0 cells (0.0%) have expected frequencies less than 5. The minimum expected cell frequency is 123.6.

Table: 4.7 One Sample Chi-Square on CM Usage (No of years)

The one sample Chi-Square value for CM Usage categories (1 to 5) has an asymptotic

significance below 3 decimal places and is hence indicating the sample responses

deviate significantly from expected frequency value (Meaning equal frequency for

each of the five groups (618/5 = 123.6)).The five groups being i) Organisations not

using Competency mapping; ii) Organisations using for less than a year; iii)

organizations using for 1 -2 years; iv) Organisations using for 3-5 years; and v)

Organisations using for more than five years. The difference between Observed

Frequency (267) and expected Frequency(123.6) is most glaring in case of

Organisations not using Competency. The sample is thus significantly deviating from

goodness of fit, indicating that number of years of CM implementation varies

significantly from equal distribution

Analysis of the Survey Study- Awareness of Competency mapping Tools 4.5.2.0

Objective:

To determine whether the HR and Line / Non HR managers are aware of Various Competency Mapping Tools

Hypothesis

H20 There are no significant differences in awareness levels of Competency mapping

tools/ practices between functions (department type)

H2a There are significant differences in awareness levels of Competency mapping

tools/practices between functions (department type).

220

4.5.2.1 Chi-Square test of independence between Department type and CM tool

awareness

Aware_IndustryBenchmar

k

Total

Pearson Chi-

Square Value df

Asymp.

Sig. (2-

sided)

Exact

Sig. (2-

sided)

Exact

Sig. (1-

sided)

Remarks

1 2

Department_type * Aware_IndustryBenc

hmark

HR 47 86 133 6.276a 1 .012 .014 .008 Chi-Square Calculated>Chi Square tabulated (3.841)at .05. Null Hypothesis rejected (p<0.05)

Non-HR 169 183 352

Total 216 269 485

Department_type * Aware_MockCenters

HR 98 34 132 .218a 1 .641 .636 .361 Chi-Square Cal<Chi-Squaretab at .05. Null Hypothesis accepted. (p>0.05)

Non-HR 267 83 350

Total 365 117 482

Department_type * Aware_Assessment

Centers

HR 30 104 134 26.844a 1 .000 .000 .000 Chi-Square Calculated>Chi- Squaretabula

ted (3.841)at .05. Null Hypothesis rejected. (p<0.05)

Non-HR 169 181 350

Total 199 285 484

Department_type * Aware_Development

Centers

HR 49 84 133 5.350a 1 .021 .024 .013 Chi-Square Calculated>Chi-Square tabulated (3.841)at .05. Null Hypothesis rejected. (p<0.05)

Non-HR 170 180 350

Total 219 264 483

Department_type * Aware_ExpertPanels

HR 62 69 131 6.695a 1 .010 .013 .007 Chi-Square Calculated>Chi Square tabulated (3.841)at .05. Null Hypothesis rejected(p<0.05)

Non-HR 211 138 349

Total 273 207 480

221

Department_type * Aware_BARS

HR 106 25 131 .795a 1 .373 .457 .223 Chi-Square Cal<Chi-Squaretab at .05. Null Hypothesis accepted. (p>0.05)

Non-HR 270 80 350

Total 376 105 481

Department_type * Aware_ThresholdSc

ales

HR 108 23 131 .418a 1 .518 .574 .303 Chi-Square Cal<Chi-Squaretab at .05. Null Hypothesis accepted (p>0.05)

Non-HR 297 53 350

Total 405 76 481

Department_type * Aware_ReportoryGri

ds

HR 112 18 130 1.948a 1 .163 .183 .111 Chi-Square Cal<Chi-Squaretab at .05. Null Hypothesis accepted (p>0.05)

Non-HR 317 33 350

Total 429 51 480

Department_type * Aware_ForcedRank

Order

HR 93 39 132 7.149a 1 .008 .009 .006 Chi-Square Calculated>Chi-Square tabulated (3.841)at .05. Null Hypothesis rejected(p<0.05)

Non-HR 285 64 349

Total 378 103 481

Department_type * Aware_JobRoleClarif

ication

HR 27 107 134 6.075a 1 .014 .013 .008 Chi-Square Calculated>Chi - Squaretabula

ted (3.841)at .05. Null Hypothesis rejected(p<0.05)

Non-HR 110 240 350

Total 137 347 484

Department_type * Aware_ChecklistsFo

rObservations

HR 49 85 134 10.658a 1 .001 .001 .001 Chi-Square Calculated>Chi-Square tabulated (3.841)at .05. Null Hypothesis rejected(p<0.05)

Non-HR 186 164 350

Total 235 249 484

Table: 4.8 Chi-Square test for independence between functions (HR and Non

HR) and CM tools Awareness

222

The preceding table clearly shows that there is a significant difference between

awareness levels of HR and Non HR Managers regarding seven Competency Mapping

tools viz. Industry Benchmark, Assessment Centres, Development Centres, expert

panels, Forced Rank order, Job Role Clarification, Checklists for Observations.

(N>=480). Null hypothesis is thus rejected for the seven afore-mentioned Competency

Mapping tools and accepted for 4 Tools.

Hence it can be concluded that there are differences in awareness levels of HR and

Non HR Managers wing to lack of usage and exposure to these tools. In other words

Awareness levels are decidedly lower among Non HR Managers owing to lack of

usage and exposure to these Competency mapping tools

Analysis of the Survey Study-Competency Mapping applications and 4.5.3.0

Effectiveness in Various Sub-systems of HR

Objectives:

To find out the extent of applicability of Competency Mapping to various Sub-

systems of HR

To determine if CM Applications in HR Sub-systems are dependent on

Sector or industry

Department or Managerial function of the respondent

Employee/ Staff Strength (Size) of the Organization

Age (Years of establishment) of the Organization

Hypothesis:

H30 Indian Organizations do not use Competency Mapping for the purpose of


H3a Indian Organizations use Competency Mapping for the purpose of

223


4.5.3.1 One-Sample t-test for CM Applications in sub-systems of HR.

One-Sample Test

Test Value = 3

Remarks

t cal Df t tab

Sig.

(2-

tailed

)

Mean

Differen

ce

95% Confidence

Interval of the

Difference

Lower Upper

ApplicationsComp

Mapping_Recruitm

entandSelection

8.169 334 1.960 .000 .522 .40 .65

t cal > t tab at .05 level of significance. Null Hypothesis is rejected

ApplicationsComp

Mapping_TrainingA

ndDevelopment

8.187 334 1.960 .000 .522 .40 .65


ApplicationsComp

Mapping_Performa

nceManagement

10.834 334 1.960 .000 .669 .55 .79


ApplicationsComp

Mapping_Compens

ationAndReward

2.774 332 1.960 .006 .174 .05 .30


ApplicationsComp

Mapping_CareerPl

anning

.807 333 1.960 .420 .051 -.07 .17

t cal < t tab at .05 level of significance. Null Hypothesis is accepted

224

ApplicationsComp

Mapping_BuildingA

ppropriateCulture

.612 333 1.960 .541 .039 -.09 .16


ApplicationsComp

Mapping_Successi

onPlanning

1.316 332 1.960 .189 .084 -.04 .21


ApplicationsComp

Mapping_ChangeE

nablement

-2.488 333 1.960 .013 -.159 -.28 -.03


ApplicationsComp

Mapping_TalentMa

nagement

4.427 333 1.960 .000 .281 .16 .41


Table: 4.9 One sample t-test for CM Applications in Sub-Systems of HR

As seen from the preceding table the observed mean scores of the survey sample for

the CM Applications in various Sub-systems of HR are above the expected mean

opinion (µ = 3) except for CM Applications in the area of Change Enablement where

mean=2.83).

The differences in observed and expected mean scores for these reasons are significant

at the .05 level according to the table except for Career Planning, Building Appropriate

Culture and Succession Planning

Hence, null hypothesis is rejected for all but four Applications Of Competency

Mapping and it can be concluded that organizations use Competency mapping in all

areas except Career Planning, Building Appropriate Culture, Succession Planning and

Change Enablement.

4.5.3.2 Competency Mapping Applications in HR Subsystems by Sector

225

H40 Competency Mapping Applications in HR Sub-systems are not dependent on


H4a Competency Mapping Applications in HR Sub-systems are dependent on


Chi-Square for Sector Type

Chi-Square Tests

Value df

Asymp. Sig. (2-sided)

Remarks

ApplicationsCompMapping_RecruitmentandSelection

* Industry_type

Pearson Chi-Square 2.094a 4 .719

Chi-Square calculated< Chi-Square tabulated (9.488). Null Hypothesis accepted. (p>.05)

Likelihood Ratio 2.097 4 .718

Linear-by-Linear Association

1.394 1 .238

N of Valid Cases 310

a. 0 cells (0.0%) have expected count less than 5. The minimum expected count is 6.46.

ApplicationsCompMapping_TrainingAndDevelopment

* Industry_type


Chi-Square calculated< Chi-Square tabulated

(9.488). Null Hypothesis accepted. (p>.05)



.314 1 .576



ApplicationsCompMapping_PerformanceManagement

* Industry_type

Pearson Chi-Square 7.000a 4 .136 Chi-Square

calculated< Chi-Square tabulated Likelihood

Ratio 6.812 4 .146

226


.001 1 .971 (9.488). Null Hypothesis accepted. (p>.05)



ApplicationsCompMapping_CompensationAndRewar

d * Industry_type






1.354 1 .245



ApplicationsCompMapping_CareerPlanning *

Industry_type






2.228 1 .136



ApplicationsCompMapping_BuildingAppropriateCultu

re * Industry_type






.027 1 .868



ApplicationsCompMapping_SuccessionPlanning *

Industry_type

Pearson Chi-Square 10.669a 4 .031 Chi-Square calculated

> Chi-Square tabulated (9.488). Null Hypothesis rejected. (p<0.05)


Linear-by-Linear 6.070 1 .014

227

Association



ApplicationsCompMapping_ChangeEnablement *

Industry_type


Chi-Square calculated< Chi-Square tabulated (9.488). Null Hypothesis accepted. (p>.05)



1.111 1 .292



ApplicationsCompMapping_TalentManagement *

Industry_type






1.288 1 .256



Table: 4.10 Chi-Square test of Independence for CM Applications in Sub-

Systems of HR by Sector

The Chi-Square test of Independence for CM Applications in Sub-systems of HR

shows that in all cases but one namely, succession planning, the application does not

vary with sector or is independent of sector. In other words, CM Applications are

independent of the sector to which the organization belongs.

228

Independent Samples Test

Levene's

Test for

Equality of

Variances t-test for Equality of Means

Remarks

F Sig. t cal df t tab

Sig.

(2-

taile

d)

Mean

Differ

ence

Std.

Error

Differ

ence

95%

Confidence

Interval of the

Difference

Lower Upper

Applications

CompMappi

ng_Recruit

mentandSel

ection

Equal

variances

assumed

.151 .698 1.181 308 1.968 .238 -.182 .154 -.484 .121


Equal

variances

not

assumed

1.184 130.42

6 .238 -.182 .153 -.485 .122

Applications

CompMappi

ng_Training

AndDevelop

ment

Equal

variances

assumed

.742 .390 .559 308 1.968 .576 -.087 .155 -.391 .218


Equal

variances

not

assumed

.534 120.50

7 .594 -.087 .162 -.408 .234

Applications

CompMappi

ng_Perform

anceManag

ement

Equal

variances

assumed

3.41

1 .066 .036 308 1.968 .971 -.005 .151 -.302 .292


Equal

variances

not

assumed

.034 116.80

6 .973 -.005 .161 -.325 .314

229

Applications

CompMappi

ng_Compen

sationAndR

eward

Equal

variances

assumed

1.02

1 .313 1.164 306 1.968 .245 -.177 .152 -.477 .122


Equal

variances

not

assumed

1.149 124.99

0 .253 -.177 .154 -.483 .128

Applications

CompMappi

ng_CareerP

lanning

Equal

variances

assumed

.449 .503 1.495 307 1.968 .136 -.227 .152 -.527 .072


Equal

variances

not

assumed

1.472 124.31

1 .143 -.227 .154 -.533 .078

Applications

CompMappi

ng_Building

Appropriate

Culture

Equal

variances

assumed

1.36

0 .244 .165 307 1.968 .869 .025 .154 -.278 .329


Equal

variances

not

assumed

.170 134.49

5 .865 .025 .149 -.270 .321

Applications

CompMappi

ng_Success

ionPlanning

Equal

variances

assumed

.037 .848 2.484 306 1.968 .014 -.381 .153 -.683 -.079


Equal

variances

not

assumed

2.500 129.05

7 .014 -.381 .152 -.682 -.079

Applications

CompMappi

ng_Change

Equal

variances

assumed

.895 .345 1.054 307 1.96

8 .293 -.162 .154 -.464 .140

t cal < t tab at .05 level of significanc

230

Enablement Equal

variances

not

assumed

.995 116.51

0 .322 -.162 .163 -.484 .160

e. Null Hypothesis is accepted

Applications

CompMappi

ng_TalentM

anagement

Equal

variances

assumed

.043 .836 1.135 307 1.96

8 .257 -.172 .152 -.471 .126


Equal

variances

not

assumed

1.114 123.70

8 .267 -.172 .155 -.479 .134

Table: 4.11 Independent Samples t-test for CM Applications by Sector type

The preceding tables show that there is no significant difference in means applications

of two groups of sector Manufacturing and Service-for all Application except one i.e.

Succession planning. The mean for Service Sector is significantly higher than

Manufacturing Sector in this case. Thus it can be inferred that CM Applications in all

Sub-systems of HR except one do not differ or vary with sector.

4.5.3.3 Chi-Square test of Independence for CM Applications in HR Subsystems

and Department

Chi-Square Tests

Value df

Asymp. Sig. (2-sided) Remarks

Department_type * ApplicationsCompMapping_

RecruitmentandSelection


Chi-Square calculated<Chi-Square tabulated (9.488). Null Hypothesis is accepted (p>0.05)



6.641 1 .010



231


TrainingAndDevelopment


Chi-Square calculated<Chi-Square tabulated (9.488).Null Hypothesis is accepted (p>0.05)



.128 1 .720




PerformanceManagement


Chi-Square calculated >Chi-Square tabulated (9.488).Null Hypothesis is rejected (p<0.05)



6.461 1 .011



Department_type * ApplicationsCompMapping_CompensationAndReward


Chi-Square calculated<Chi-Square tabulated. Null Hypothesis is accepted (9.488). (p>0.05)



4.483 1 .034




CareerPlanning


Chi-Square calculated >Chi-Square tabulated (9.488). Null Hypothesis is rejected (p<0.05



9.638 1 .002



Department_type * Pearson Chi-Square 5.973a 4 .201 Chi-Square

232

ApplicationsCompMapping_BuildingAppropriateCulture


calculated<Chi-Square tabulated (9.488). Null Hypothesis is accepted (p>0.05)


2.775 1 .096




SuccessionPlanning





2.291 1 .130




ChangeEnablement





2.070 1 .150




TalentManagement





2.370 1 .124



Table: 4.12 Chi-Square Test of Independence for CM Application and

Department

233

The preceding table clearly reveals that Competency Mapping Applications are not

dependent on the department to which the Manager respondent belonged for all

applications except two namely Performance Manageme and Career Planning .

This can probably be explained by non-involvement of Line managers in the afore-

mentioned areas.

4.5.3.4 Independent Samples t-test for CM Applications by Department

(functions)


Levene's Test

for Equality of


Remarks

F Sig. t df

Sig.

(2-

tailed

) t tab

Mea

n

Differ

ence

Std.

Error

Differ

ence

95%

Confidence

Interval of

the

Difference

Lowe

r

Upp

er

Applicatio

nsCompM

apping_R

ecruitment

andSelecti

on

Equal

variances

assumed

4.961 .027 2.599 333 .010 1.968 -.341 .131 -.599 -.083

t cal > t tab

at .05 level of significance. Null Hypothesis is rejected

Equal

variances

not

assumed

2.695 290.21

9 .007 -.341 .126 -.590 -.092

Applicatio

nsCompM

apping_Tr

ainingAnd

Developm

ent

Equal

variances

assumed

.150 .699 .358 333 .721 1.968 -.047 .132 -.307 .213

t cal < t tab

at .05 level of significance. Null Hypothesis is accepted

Equal

variances

not

assumed

.360 267.33

3 .719 -.047 .131 -.305 .211

234

Applicatio

nsCompM

apping_Pe

rformance

Managem

ent

Equal

variances

assumed

2.327 .128 2.563 333 .011 1.968 -.324 .127 -.573 -.075

t cal > t tab


Equal

variances

not

assumed

2.589 269.04

5 .010 -.324 .125 -.571 -.078

Applicatio

nsCompM

apping_C

ompensati

onAndRe

ward

Equal

variances

assumed

.780 .378 2.129 331 .034 1.968 -.275 .129 -.529 -.021

t cal > t tab


Equal

variances

not

assumed

2.122 256.09

7 .035 -.275 .130 -.530 -.020

Applicatio

nsCompM

apping_C

areerPlan

ning

Equal

variances

assumed

1.261 .262 3.146 332 .002 1.968 -.404 .129 -.657 -.152

t cal > t tab


Equal

variances

not

assumed

3.091 246.90

5 .002 -.404 .131 -.662 -.147

Applicatio

nsCompM

apping_Bu

ildingAppr

opriateCul

ture

Equal

variances

assumed

1.645 .200 1.670 332 .096 1.968 -.219 .131 -.477 .039

t cal < t tab


Equal

variances

not

assumed

1.640 246.33

8 .102 -.219 .134 -.482 .044

Applicatio

nsCompM

apping_Su

Equal

variances

assumed

.041 .840 1.517 331 .130 1.968 -.200 .132 -.460 .059

t cal < t tab

at .05 level of

235

ccessionPl

anning Equal

variances

not

assumed

-1.524 262.55

5 .129 -.200 .131 -.459 .058

significance. Null Hypothesis is accepted

Applicatio

nsCompM

apping_C

hangeEna

blement

Equal

variances

assumed

1.016 .314 1.441 332 .150 1.968 -.190 .132 -.449 .069

t cal < t tab


Equal

variances

not

assumed

1.443 262.19

2 .150 -.190 .131 -.448 .069

Applicatio

nsCompM

apping_Ta

lentManag

ement

Equal

variances

assumed

.159 .690 1.543 332 .124 1.968 -.202 .131 -.460 .056

t cal < t tab


Equal

variances

not

assumed

1.547 263.52

5 .123 -.202 .131 -.460 .055

Table: 4.13 Independent Samples t-test For CM Applications by Department

(functions)

The preceding table reveals that the means of the two groups of department (HR and

Non HR) differ significantly in case of Recruitment and Selection, Performance

management, Compensation & benefits and Career planning. Hence null hypothesis is

rejected in these cases (p<0.05). This can probably be explained by the non-

involvement of Line/ Non HR Managers in the areas of Recruitment and Selection,

PMS, Compensation and benefits and Career planning and hence the low application

ratings given by them. Null Hypothesis is accepted in all other cases owing to no or

negligible difference in means of two groups and significance value being p>0.05.

236

4.5.3.5 Chi-Square tests for CM Applications in HR Subsystems by

Organisational Age Category

Chi-Square Tests

Value df


Org_Age_Cat * ApplicationsCompMapping_RecruitmentandSelecti

on


Chi-Square tabulated (26.296) >Chi-Square calculated. Null Hypothesis is accepted (p>0.05)



7.055 1 .008


a. 13 cells (52.0%) have expected count less than 5. The minimum expected count is .96.

Org_Age_Cat * ApplicationsCompMapping_TrainingAndDevelopme

nt


Chi-Square tabulated (26.296) > Chi-Square calculated. Null Hypothesis is accepted (p>0.05)



4.011 1 .045



Org_Age_Cat * ApplicationsCompMapping_PerformanceManageme

nt


Chi-Square tabulated (26.296) > Chi-Square calculated.Null Hypothesis is accepted (p>0.05)



2.522 1 .112


237


Org_Age_Cat * ApplicationsCompMapping_CompensationAndRew

ard





8.552 1 .003



Org_Age_Cat * ApplicationsCompMappin

g_CareerPlanning





3.594 1 .058



Org_Age_Cat * ApplicationsCompMapping_BuildingAppropriateCul

ture





.051 1 .821



Org_Age_Cat * Pearson Chi-Square 17.794a 16 .336 Chi-Square tabulated

238

ApplicationsCompMapping_SuccessionPlanning


(26.296) > Chi-Square calculated. Null Hypothesis is accepted (p>0.05) Linear-by-

Linear Association

5.035 1 .025




g_ChangeEnablement


Chi-Square tabulated (26.296) < Chi-Square calculated .Null Hypothesis is rejected (p<0.05)



.904 1 .342




g_TalentManagement





1.537 1 .215



Table: 4.14 Chi-Square test of Independence between Organisational Age

Category and CM Applications in HR Sub-systems

The preceding table clearly reveals that there is no association between Organisational

Age Category and CM applications pertaining to all HR Sub-systems except one which

is Change Enablement

239

4.5.3.6 Chi-Square tests for CM Applications in HR Subsystems by

Organisational Staff Strength Category

Chi-Square of independence was run to find association between Organisational Staff

-systems

Chi-Square Tests

Value df


Org_Staff_Strength * ApplicationsCompMapping_RecruitmentandSelecti

on


Chi-Square tabulated (26.296) < Chi-Square calculated Null Hypothesis is rejected (p<0.05)



3.829 1 .050



Org_Staff_Strength * ApplicationsCompMapping_TrainingAndDevelopme

nt





24.088 1 .000



Org_Staff_Strength * ApplicationsCompMapping_PerformanceManageme

nt





19.144 1 .000

240



Org_Staff_Strength * ApplicationsCompMapping_CompensationAndRew

ard





16.873 1 .000



Org_Staff_Strength * ApplicationsCompMappin

g_CareerPlanning





6.247 1 .012



Org_Staff_Strength * ApplicationsCompMapping_BuildingAppropriateCul

ture


Chi-Square tabulated (26.296) > Chi-Square calculated Null Hypothesis is accepted (p>0.05)



3.984 1 .046



241


g_SuccessionPlanning


Chi-Square tabulated (26.296)> Chi-Square calculated Null Hypothesis is accepted (p>0.05)



5.385 1 .020




g_ChangeEnablement





8.880 1 .003




g_TalentManagement





15.534 1 .000



Table: 4.15 Chi-Square test of Independence between Organisational Staff

Strength and CM Applications in HR Sub-systems

The preceding tables clearly reveal that CM Applications in HR sub-systems vary with

Organisational Staff strength for Recruitment & Selection, Training & Development

Performance Management, Compensation & reviews, Career Planning, Change

242

Enablement and Talent Management. Hence Null Hypothesis is rejected for the afore-

mentioned HR Sub-systems.

Analysis of the Survey Study - Competency Mapping Application 4.5.4.0

Effectiveness in various Sub-systems of HR.

Objective :

To determine the effectiveness of CM Applications in Sub-systems of HR like

Recruitment & Selection, Training and Development, Performance Management,

Compensation & Reward, Career Planning, Building Appropriate Culture, Succession

Planning, Change Enablement and Talent Management

Hypothesis:

H50 Competency based HRM is not effective across HR subsystems such as

Recruitment and Selection Training and Development Performance Management Compensation & Reward Career Planning Building Appropriate Culture Succession Planning Change Enablement Talent Management

H5a Competency based HRM is effective across HR subsystems such as

Recruitment and Selection Training and Development Performance Management Compensation & Reward Career Planning Building Appropriate Culture Succession Planning Change Enablement Talent Management

243

4.5.4.1 One Sample t-test CM Application Effectiveness

One-Sample Test

Remarks

Test Value = 3

t cal df t tab

Sig. (2-

tailed)

Mean

Difference

95% Confidence

Interval of the

Difference

Lower Upper

UsageEffectiveness_Re

cruitmentSelection

7.054 120 1.980 .000 .669 .48 .86


UsageEfffectiveness_Tr

ainingAndDevelopment

7.362 120 1.980 .000 .661 .48 .84


UsageEffectiveness_Per

formanceMgmtSystem

7.884 120 1.980 .000 .702 .53 .88


UsageEffectiveness_Co

mpensationAndReward

4.107 120 1.980 .000 .405 .21 .60


244

UsageEffectiveness_Car

eerPlanning

4.156 119 1.980 .000 .425 .22 .63


UsageEffectiveness_Bui

ldingAppropriateCulture

3.970 120 1.980 .000 .397 .20 .59


UsageEffectiveness_Su

ccessionPlanning

4.614 120 1.980 .000 .463 .26 .66


UsageEffectiveness_Ch

angeEnablement

2.450 120 1.980 .016 .231 .04 .42


UsageEffectiveness_Tal

entManagement

6.245 120 1.980 .000 .529 .36 .70


Table: 4.16 One sample t-test for Competency Mapping usage/ application

Effectiveness in HR Sub-systems

The observed mean scores for CM usage effectiveness is above the expected mean

score of 3(µ=3) and the differences in observed and excepted mean scores for these

reasons are significant at the .05 level according to the above table.

Hence, null hypothesis is rejected and we conclude that HR managers (in

245

organizations where Competency Mapping is implemented) are of the opinion that the

Competency Mapping Usage is effective across HR Sub-segments viz. Recruitment &

Selection, Training and Development, Performance management System,

Compensation & Reward, Career planning, Building Appropriate culture, Succession

Planning, Change Enablement and Talent management.

4.5.4.2 One Sample Chi-Square test for CM usage effectiveness

Test Statistics

Chi-Square

df Asymp. Sig. Remarks

UsageEffectiveness_RecruitmentSelection 56.149a 4 .000

(p< 0.05) Null hypothesis rejected

UsageEfffectiveness_TrainingAndDevelopment 59.041a 4 .000


UsageEffectiveness_PerformanceMgmtSystem 70.033a 4 .000


UsageEffectiveness_CompensationAndReward 44.331a 4 .000


UsageEffectiveness_CareerPlanning 27.750b 4 .000


UsageEffectiveness_BuildingAppropriateCulture 35.322a 4 .000


UsageEffectiveness_SuccessionPlanning 34.826a 4 .000


UsageEffectiveness_ChangeEnablement 44.331a 4 .000


UsageEffectiveness_TalentManagement 63.091a 4 .000


a. 0 cells (0.0%) have expected frequencies less than 5. The minimum expected cell frequency is 24.2.

b. 0 cells (0.0%) have expected frequencies less than 5. The minimum expected cell frequency is 24.0.

Table: 4.17 One Sample Chi-Square for Competency Mapping Usage

effectiveness.

The One Sample Chi-Square for Competency Mapping Usage / Application

effectiveness clearly reveals that in Organisations where CM finds application , it is

highly effective in all the chosen 9 sub-systems of HR namely, Recruitment and

Selection, Training & Development, Performance Management Systems,

Compensation & Reward, Career planning, Building Appropriate Culture, Succession

Planning, Change Enablement and Talent Management. ( Chi-Square calculated is

above Chi-Square tabulated (9.488) for all the chosen 9 sub-systems of HR)

246

Analysis of the Survey Study Perceived Roadblocks and their 4.5.5.0

Association with independent variables like Sector/Industry, Employee/Staff

Strength, Age, Department/ Function of the Manager Respondent and Whether

CM Implemented

One of the key objectives of the study is to critically examine conditions prevailing in

the Indian organizational context for the implementation and continued use of

Competency Mapping. To this end, the survey incorporates questions on challenges

and roadblocks to effective implementation and meaningful usage of Competency

Mapping.

Objective:

To identify roadblocks for usage of Competency mapping in Organisations

To reveal the perceptions of Managers on various roadblocks faced or

apprehended during Competency mapping Implementation

To find if Roadblock perceptions are dependent on

o Industry or sector.

o Department / Managerial functions

o Employee / Staff Strength (size) of the organization

o Age of the organization

Hypothesis:

H60

from assumed/expected mean (µ=3, Neutral)

H6a ions about CM Roadblocks vary significantly from assumed

/expec ted mean (µ>3)

247

4.5.5.1 t-test for Roadblocks statements

One-Sample Test

Test Value = 3

t Df T tab

Sig.

(2-

tailed

)

Mean

Differe

nce

95%

Confidence

Interval of the

Difference

Remarks Lower Upper

Perception_Lac

kOfDedicatedSt

aff 1.799 533 1.96 .073 .109 -.01 .23

t cal < t tab at .05 level of significance. Null hypothesis is accepted ( p>0.05)

Perception_Lin

eMgrsInabilityT

oHandleCompe

tencyBasedRec

ruitment

1.011 532 1.96 .312 .056 -.05 .17

t cal < t tab at .05 level of significance Null hypothesis is accepted (p>0.05)

Perception_Lac

kOfEmployeesT

rainingOnCM 4.141 529 1.96 .000 .238 .12 .35

t cal > t tab at .05 level of significance. Null hypothesis is rejected (p<0.05)

Perception_Difff

icultyToIdentify

CompetenciesI

nDetail

1.279 531 1.96 .202 .071 -.04 .18


Perception_Diffi

cultToImplemen

tCompetencyBa

sedModels

.684 531 1.96 .494 -.036 -.14 .07


Perception_Co

mpetenciesReq

dInFlux 2.367 529 1.96 .018 .113 .02 .21

t cal > t tab at .05 level of significance. Null Hypothesis is rejected (p<0.05)

248

Perception_Hig

hAttritionRate 2.958 529 1.96 .003 .166 .06 .28


Perception_Pau

cityOfTime 2.070 530 1.96 .039 .109 .01 .21


Perception_Fre

quentTransfers

AndMobility 1.539 528 1.96 .124 -.087 -.20 .02

t cal < t tab at .05 level of significance. Null Hypothesis is accepted (p>0.05)

Perception_Mut

itasking 4.011 531 1.96 .000 .231 .12 .34

t cal > t tab at .05 level of significance. Null Hypothesis is rejected. (p<0.05)

Perception_Lac

kOfDedicatedR

esources 4.213 529 1.96 .000 .243 .13 .36


Perception_Lac

kOfTopMgmtSu

pport 4.382 528 1.96 .000 .265 .15 .38


Table: 4.18 One Sample t-test on Roadblock perception (Test Value = 3)

As can be seen from the two preceding tables, the observed mean Roadblock

perception of HR and Non HR Managers as regards various Roadblocks statement is

The differences in observed mean scores and expected mean scores are not significant

in case of on

249

only for the above five roadblocks statements.

Out of 12, 2 Roadblocks means are < 3 but insignificantly. Of the remaining 10, three

have means >3 but insignificantly, however for the remaining seven means are above

three and significantly so. Therefore these seven Roadblocks are actually faced by the

respondent sets (N>= 528)

Hypothesis:

H70: Mean Roadblock opinions of managers in organizations without Competency

Mapping implementation does not differ significantly from that of managers in

organizations with Competency Mapping implementation.

H7a: Mean Roadblock opinions of managers in organizations without Competency

Mapping implementation does not differ significantly from that of managers in

organizations with Competency Mapping implementati

4.5.5.2 Independent Samples t-test for Roadblock statements


Levene's Test

for Equality of


Remarks

F Sig. t df t tab

Sig.

(2-

tailed

)

Mea

n

Differ

ence

Std.

Error

Differ

ence

95%

Confidence

Interval of

the

Difference

Lowe

r

Upp

er

Percept

ion_La

ckOfDe

Equal

variances

assumed

3.512 .061 2.336 499 1.96

5 .020 -.290 .124 -.534 -.046

t calculated >t tabulated

at 0.05 level of

significance. Null

250

dicated

Staff Equal

variances

not assumed

2.326 474.

113 .020 -.290 .125 -.536 -.045

hypothesis

rejected

Percept

ion_Lin

eMgrsI

nability

ToHan

dleCom

petenc

yBased

Recruit

ment

Equal

variances

assumed

.104 .747 3.546 499 1.96

5 .000 -.402 .113 -.624 -.179

t calculated>t tabulated

at 0.05 level of

significance. Null

hypothesis

rejected

Equal

variances

not assumed 3.551 485.

688 .000 -.402 .113 -.624 -.179

Percept

ion_La

ckOfE

mploye

esTrain

ingOnC

M

Equal

variances

assumed

5.064 .025 4.432 499 1.96

5 .000 -.518 .117 -.748 -.288 t calculated >t tabulated

at 0.05 level of

significance. Null

hypothesis

rejected

Equal

variances

not assumed

4.417 476.

667 .000 -.518 .117 -.749 -.288

Percept

ion_Diff

ficultyT

oIdentif

yComp

etencie

sInDeta

il

Equal

variances

assumed

7.695 .006 2.448 499 1.96

5 .015 -.280 .114 -.505 -.055


at 0.05 level of

significance. Null

hypothesis

rejected

Equal

variances

not assumed 2.426

462.

805 .016 -.280 .115 -.507 -.053

Percept

ion_Diff

icultToI

mplem

entCo

mpeten

cyBase

dModel

s

Equal

variances

assumed

.048 .826 1.129 499 1.96

5 .260 -.122 .108 -.335 .091

t calculated< t tabulated

at 0.05 level of

significance. Null

hypothesis

accepted

Equal

variances

not assumed 1.123 472.

891 .262 -.122 .109 -.336 .092

251

Percept

ion_Co

mpeten

ciesRe

qdInFlu

x

Equal

variances

assumed

.013 .908 .180 499 1.96

5 .857 .018 .099 -.176 .212 t calculated< t tabulated

at 0.05 level of

significance. Null

hypothesis

accepted

Equal

variances

not assumed

.180 483.

645 .857 .018 .099 -.176 .212

Percept

ion_Hig

hAttritio

nRate

Equal

variances

assumed

1.798 .181 .029 499 1.96

5 .977 -.003 .116 -.231 .224 t calculated< t tabulated

at 0.05 level of

significance. Null

hypothesis

accepted

Equal

variances

not assumed

.029 491.

126 .977 -.003 .115 -.230 .223

Percept

ion_Pa

ucityOf

Time

Equal

variances

assumed

1.368 .243 3.316 499 1.96


at 0.05 level of

significance. Null

hypothesis

rejected

Equal

variances

not assumed

3.316 483.

202 .001 -.355 .107 -.565 -.145

Percept

ion_Fre

quentTr

ansfers

AndMo

bility

Equal

variances

assumed

.122 .727 .718 499 1.96

5 .473 -.084 .117 -.313 .145 t calculated< t tabulated

at 0.05 level of

significance. Null

hypothesis

accepted

Equal

variances

not assumed

.717 482.

580 .473 -.084 .117 -.313 .145

Percept

ion_Mu

titaskin

g

Equal

variances

assumed

4.878 .028 2.628 499 1.96


at 0.05 level of

significance. Null

hypothesis

rejected

Equal

variances

not assumed

2.612 470.

866 .009 -.310 .119 -.544 -.077

Percept

ion_La

ckOfDe

Equal

variances

assumed

3.893 .049 3.989 499 1.96

5 .000 -.472 .118 -.705 -.240


at 0.05 level of

significance. Null

252

dicated

Resour

ces

Equal

variances

not assumed

3.980 478.

443 .000 -.472 .119 -.705 -.239

hypothesis

rejected

Percept

ion_La

ckOfTo

pMgmt

Suppor

t

Equal

variances

assumed

2.466 .117 4.518 499 1.96


at 0.05 level of

significance. Null

hypothesis

rejected

Equal

variances

not assumed

4.507 478.

183 .000 -.551 .122 -.791 -.311

Table: 4.19 Independent Samples t-test for Roadblock Perception differences

between Implementers and Non Implementers

The analysis of above table clearly reveals that there are significant differences

between the perceptions/opinions of the managers in organizations where competency

mapping is adopted and in those where it has not been implemented except for four

statements namely, Difficulty to adopt Competency based models, Higher attrition

rate, competencies required in a flux, frequent transfers and mobility. The null

hypothesis is thus rejected for the remaining eight Roadblock statements.

One sample t-test for the two populations / samples should therefore independently be

re-conducted to test the validity of the original null hypothesis regarding CM

Roadblocks afresh, as mean scores for non-implementers are higher for these 8 cases.

Hypothesis:

H80 Roadblock perception for Competency Mapping implementation is not

dependent on

Industry or sector Managerial functions Size of the organization Age of the organization

H8a Roadblock perceptions for Competency Mapping implementation is dependent

on

Industry or sector

253

Managerial functions Size of the organization Age of the organization

4.5.5.3 Independent Sample t-test for Roadblock Perceptions by Sector Type


Independent Sample t-test for Roadblocks by Industry Type:

Levene's Test for Equality of


F Sig. t cal df t

tab

Sig. (2-

tailed)

Remarks

Perception_LackOfDedicatedStaff

Equal variances assumed

0.476 0.49 1.226 512 1.965 0.22

1 t cal < t tab at .05 level of significance Null hypothesis is accepted for this roadblock (p>0.05)

Equal variances not assumed

1.24 200.623

0.216

Perception_LineMgrsInabilityToHandleCompetencyBasedRecruitment


0.6 0.439 0.363 511

1.965 0.71

7 t cal < t tab at .05 level of significance Null hypothesis is accepted for this roadblock

(p>0.05)


0.356 193.934

0.722

Perception_LackOfEmployeesTrainingOnCM


1.895 0.169 0.523 509

1.965 0.60

1 t cal < t tab at .05 level of significance. Null hypothesis is accepted for this roadblock (p>0.05)


0.54 205.518

0.59

Perception_DiffficultyToIdentifyCompetenciesInDetail


0.219 0.64 0.125 510 1.965 0.9 t cal < t tab at .05 level of

significance. Null hypothesis is accepted for this roadblock (p>0.05)


0.123 187.978

0.902

254

Perception_DifficultToImplementCompetencyBasedModels


0.085 0.771 2.117 510

1.965 0.03

5 t cal > t tab at .05 level of significance. Null hypothesis is rejected for this roadblock (p<.05)


2.153 197.545

0.033

Perception_CompetenciesReqdInFlux


0.039 0.843 2.168 508

1.965 0.03

1 t cal > t tab at .05 level of significance Null hypothesis is rejected for this roadblock (p<.05)


2.129 187.707

0.035

Perception_HighAttritionRate


0.123 0.726 0.704 508

1.965 0.48

2 t cal < t tab at .05 level of significance Null hypothesis is accepted for this roadblock (p>0.05)


0.701 191.669

0.484

Perception_PaucityOfTime


0.465 0.495 0.166 509

1.965 0.86

8 t cal < t tab at .05 level of significance Null hypothesis is accepted for this roadblock (p>.05)


0.159 180.595

0.874

Perception_FrequentTransfersAndMobility


0.246 0.62 0.072 507 1.965 0.94

3 t cal < t tab at .05 level of significance .Null hypothesis is accepted for this roadblock (p>.05)


0.07 185.757

0.944

Perception_Mutitasking


0.051 0.822 0.056 510

1.965 0.95



0.056 193.859

0.956

255

Perception_LackOfDedicatedResources


0.728 0.394 1.065 508

1.965 0.28



1.098 202.207

0.274

Perception_LackOfTopMgmtSupport


0.041 0.84 0.666 507 1.965 0.50

6 t cal < t tab at .05 level of significance .Null hypothesis is accepted for this roadblock (p>.05)


0.662 191.376

0.508

Table: 4.20 Independent Samples t-test for Roadblock perceptions by

Industry/Sector type

The independent samples t-tests roadblocks industry type (i.e manufacturing v/s

service) clearly indicates that the two groups of service and manufacturing industry

come from the same population since no significant difference exists between the two

on ten of the twelve roadblock perceptions statement. The two roadblock perception

4.5.5.4 Chi-Square test of Independence for Roadblocks by Sector Type

(Manufacturing (1) or Service (2))

Chi-Square Tests

Industry_type * Perception_LackOfDedicatedSt

aff

Value df Asymp. Sig. (2-sided)

Remarks

Pearson Chi-Square 3.629a 4 0.459

Chi-Square calculated < Chi-Square tabulated (9.488). Null hypothesis is accepted for this roadblock (p>0.05)

Likelihood Ratio 3.662 4 0.454


1.501 1 0.221

256



Industry_type * Perception_LineMgrsInabilityToHandleCompetencyBasedRecru

itment

Pearson Chi-Square .594a 4 0.964

Chi-Square calculated < Chi-Square tabulated(9.488).Null hypothesis is accepted for this roadblock (p>0.05)



0.132 1 0.716



Industry_type * Perception_LackOfEmployeesT

rainingOnCM





0.274 1 0.601



Industry_type * Perception_DiffficultyToIdentify

CompetenciesInDetail





0.016 1 0.9

N of Valid 512

257

Cases


Industry_type * Perception_DifficultToImpleme

ntCompetencyBasedModels





4.45 1 0.035



Industry_type * Perception_CompetenciesReqd

InFlux





4.667 1 0.031



Industry_type * Perception_HighAttritionRate





0.495 1 0.482


258


Industry_type * Perception_PaucityOfTime


Chi-Square calculated > Chi-Square tabulated (9.488). Null hypothesis is rejected for this roadblock (p<0.05)



0.028 1 0.868



Industry_type * Perception_FrequentTransfers

AndMobility





0.005 1 0.943



Industry_type * Perception_Mutitasking





0.003 1 0.955


259


Industry_type * Perception_LackOfDedicatedRe

sources





1.134 1 0.287



Industry_type * Perception_LackOfTopMgmtSu

pport





0.444 1 0.505



Table: 4.21 Chi-Square test of Independence for Roadblocks by Sector type

The preceding tables clearly reveal that for Sector Type, Null Hypothesis is accepted

for 11 out of 12 cases.The Chi-Square Significance value is above 0.05 for 11 of 12

roadblocks, ex

differences between the two sectors of manufacturing and service as regards

Roadblocks Perceptions. It can be stated with 95% confidence that the 11 roadblocks

are Universal across the two sectors. In other words organizations in both sectors face

or apprehend the same roadblocks.

260

4.5.5.5 Independent Sample t-test for Roadblock Perceptions by Managerial

Function Type


Independent Sample t-test: Roadblocks by Department Type


Variances

t-test for Equality of Means

Remark

F Sig. t df t tab Sig. (2-

tailed)

Perception_LackOfDedicatedStaff


0.001 0.971 1.513 532 1.965 0.131 t cal < t tab at .05 level of significance Null hypothesis is accepted for this roadblock (p>.05)


1.54 241.227 0.125

Perception_LineMgrsInabilityToHandleCompetencyBasedRecruitment


0.188 0.665 0.716 531 1.965 0.474 t cal < t tab at .05 level of significance Null hypothesis is accepted for this roadblock (p>0.05)


0.714 235.314 0.476

Perception_LackOfEmployeesTrainingOnCM




0.05 234.255 0.96

Perception_DiffficultyToIdentifyCompetenciesInDetail




0.023 244.1 0.982

Perception_DifficultToImplementCompetencyBase


0.423 0.516 0.18 530 1.965 0.857 t cal < t tab at .05 level of significance .Null

261

dModels Equal variances not assumed

0.182 235.905 0.856

hypothesis is accepted for this roadblock (p>0.05)

Perception_CompetenciesReqdInFlux




0.825 241.567 0.41

Perception_HighAttritionRate


1.178 0.278 0.483 528 1.965 0.629 t cal < t tab at .05 level of significance .Null hypothesis is accepted for this roadblock (p>0.05)


0.472 220.683 0.637

Perception_PaucityOfTime


0.006 0.939 0.874 529 1.965 0.383 t cal < t tab at .05 level of significance .Null hypothesis is accepted for this roadblock (p>0.05)


0.866 225.808 0.387

Perception_FrequentTransfersAndMobility




0.963 235.171 0.337

Perception_Mutitasking




1.066 234.902 0.288

Perception_LackOfDedicatedResources


0.376 0.54 0.422 528 1.965 0.673 t cal < t tab at .05 level of significance Null

262


0.424 231.698 0.672

hypothesis is accepted for this roadblock (p>0.05)

Perception_LackOfTopMgmtSupport




0.959 225.764

0.338

Table: 4.22 Independent Samples t-test on Roadblock perceptions of

respondents across two departments (HR/Line)

The independent samples t-test analysis indicates that the means of two groups of

respondent managers from HR and Non HR on Roadblock perceptions did not differ

significantly. Thus it can be concluded that the roadblock perceptions do not vary with

department. Managers belonging to the both HR and Line departments face or

apprehend same roadblocks as regards Competency mapping.

4.5.5.6 Chi-Square test of Independence for Roadblocks on Function Type (HR

(1) or Non-HR (2))

Chi-Square Tests

Value df


Remarks

Department_type * Perception_LackOfDedic

atedStaff


Chi-Squarecalculated< Chi-Squaretabulated (9.488).Null hypothesis accepted. (p>0.05)



2.284 1 .131



Department_type * Pearson Chi-Square 1.382a 4 .847 Chi-Squarecalculated<

263

Perception_LineMgrsInabilityToHandleCompetenc

yBasedRecruitment


Chi-Squaretabulated (9.488)Null hypothesis accepted. (p>0.05)


.513 1 .474



Department_type * Perception_LackOfEmplo

yeesTrainingOnCM


Chi-Squarecalculated< Chi-Squaretabulated (9.488)Null hypothesis accepted. (p>0.05)



.003 1 .960



Department_type * Perception_DiffficultyToIdentifyCompetenciesInD

etail





.000 1 .982



Department_type * Perception_DifficultToImplementCompetencyBase

dModels





.032 1 .857



Department_type * Perception_Competencie

sReqdInFlux

Pearson Chi-Square 3.689a 4 .450 Chi-Squarecalculated<

Chi-Squaretabulated (9.488)Null hypothesis accepted. (p>0.05)


264


.642 1 .423



Department_type * Perception_HighAttrition

Rate


Chi-Squarecalculated< Chi-Squaretabulated (9.488) Null hypothesis accepted. (p>0.05)



.234 1 .629



Department_type * Perception_PaucityOfTim

e


Chi-Squarecalculated< Chi-Squaretabulated (9.488). Null hypothesis accepted. (p>0.05)



.764 1 .382



Department_type * Perception_FrequentTran

sfersAndMobility





.902 1 .342



Department_type * Perception_Multitasking




Linear-by-Linear 1.103 1 .294

265

Association



Department_type * Perception_LackOfDedic

atedResources


Chi-Square calculated< Chi-Square tabulated (9.488)Null hypothesis accepted. (p>0.05)



.178 1 .673



Department_type * Perception_LackOfTopM

gmtSupport


Chi-Square calculated< Chi-Square tabulated (9.488)Null hypothesis accepted. (p>0.05)



.939 1 .333



Table: 4.23 Chi-Square test of Independence between Roadblock Perception

and Department (Function type)

The Chi-Square Significance value is above 0.05 for all 12 roadblocks. Thus for

Function Type, Null Hypothesis is accepted for all 12 Roadblocks. The preceding table

clearly indicates that Null Hypothesis is accepted for all the roadblocks and thus it can

be stated that Roadblock perception of respondent managers are not dependent on

department or function to which they belong. In other words both HR and Non HR

Managers share similar perceptions on Roadblocks faced or apprehended.

266

4.5.5.7 Chi-Square Test for Roadblock Perceptions by Employee/Staff Strength

Value df


Remarks

Org_Staff_Strength * Perception_LackOfDe

dicatedStaff


Chi-Square calculated <Chi-Square tabulated (26.296). Null hypothesis is accepted for this roadblock (p>0.05)


Linear-by-Linear Association 4.735 1 0.03



Org_Staff_Strength * Perception_LineMgrsInabilityToHandleCompetencyBasedRecruit

ment



Chi-Square calculated <Chi-Square tabulated (26.296)Null hypothesis is accepted for this roadblock (p>0.05)




Org_Staff_Strength * Perception_LackOfEmployeesTrainingOn

CM


Chi-Square calculated >Chi-Square tabulated (26.296)Null hypothesis is rejected for this roadblock (p<0.05)





Org_Staff_Strength * Perception_Diffficulty

Pearson Chi-Square 13.224a 16 0.656 Chi-Square calculated

<Chi-Square tabulated (26.296)Null

267

ToIdentifyCompetenciesInDetail

Likelihood Ratio 13.833 16 0.611 hypothesis is accepted for this roadblock (p>0.05)




Org_Staff_Strength * Perception_DifficultToImplementCompete

ncyBasedModels







Org_Staff_Strength * Perception_Compete

nciesReqdInFlux







Org_Staff_Strength * Perception_HighAttrit

ionRate




Linear-by-Linear Association 0 1 0.991



268

Org_Staff_Strength * Perception_PaucityOf

Time







Org_Staff_Strength * Perception_FrequentTransfersAndMobility





2.371 1 0.124



Org_Staff_Strength * Perception_Mutitaski

ng







Org_Staff_Strength * Perception_LackOfDe


<Chi-Square tabulated (26.296)Null

269

dicatedResources Likelihood Ratio 18.588 16 0.291 hypothesis is accepted for this roadblock (p>0.05)




Org_Staff_Strength * Perception_LackOfTo

pMgmtSupport








and Employee / Staff Strength

The Chi-Square Significance value is above 0.05 for 11 of 12 roadblocks, except

out of 12 cases. This can be interpreted as all except one (Lack of Employee Training

on Competency Mapping) Roadblock perceptions being universal across organizations

irrespective of Employee Strength. The strength of organizations in the sample varies

from below 100 to above 100,000.

270

4.5.5.8 Chi-Square on Age of the Organization (<10 years (1), 11-20 years (2), 21-

30 years(3), 31-40 years (4) or >40 years (5))

Chi-Square Tests

Org_Age_Cat * Perception_LackOfDedicatedStaff

Value df Asymp. Sig. (2-sided) Remarks


Chi-Square calculated >Chi-Square tabulated (26.296)Null hypothesis is rejected for this roadblock (p<0.05)



2.045 1 0.153



Org_Age_Cat * Perception_LineMgrsInabilityToHandleCompetencyBasedRecruitm

ent





1.242 1 0.265



Org_Age_Cat * Perception_LackOfEmployeesTrai

ningOnCM


Chi-Square calculated > Chi-Square tabulated (26.296)Null hypothesis is rejected for this roadblock (p<0.05)



0.912 1 0.339



271

Org_Age_Cat * Perception_DiffficultyToIdentifyC

ompetenciesInDetail





0.259 1 0.611



Org_Age_Cat * Perception_DifficultToImplementC

ompetencyBasedModels





0.223 1 0.637



Org_Age_Cat * Perception_CompetenciesReqdIn

Flux





0.01 1 0.922



Org_Age_Cat * Perception_HighAttritionRate





0.01 1 0.92

N of Valid 343

272

Cases


Org_Age_Cat * Perception_PaucityOfTime





0.907 1 0.341



Org_Age_Cat * Perception_FrequentTransfersAn

dMobility





1.658 1 0.198



Org_Age_Cat * Perception_Mutitasking





0.838 1 0.36



Org_Age_Cat * Perception_LackOfDedicatedReso

urces


<Chi-Square tabulated (26.296).Null hypothesis is accepted for this


273


0.025 1 0.873

roadblock (p>0.05)



Org_Age_Cat * Perception_LackOfTopMgmtSupp

ort





0.578 1 0.447




and Organisation Age Category

The Chi-Square Significance value is above 0.05 for 10 of 12 roadblocks, except

preceding table clearly indicates that all but two afore-mentioned Roadblock

perceptions are independent of Organisational age category. The age of organizations

in the sample varies from below 10 years to above 40 years (with organization above

100 years also included in the sample)

Analysis of the Survey Study Perceived Benefits and their Association 4.5.6.0

with independent variables like Sector/Industry, Employee/Staff Strength, Age,

Department/ Function of the Manager Respondent

Objective:

To ascertain the perceptions of Managers on various Benefits that accrue to

Organisations owing to Competency mapping Usage

To find if Benefits perceptions are dependent on


274

o Department / Managerial function of the respondent

o Employee / Staff Strength (Size) of the organization


Hypothesis

H90


H9a

mean (µ>3, Neutral)

H100 Benefit Perceptions for Competency based HRM implementation are not

dependent on


H10a Benefit Perceptions for Competency based HRM implementation are dependent

on


4.5.6.1 One-Sample t-test (Test Value = 3) on Benefit Perceptions

One-Sample Test

Test Value = 3

Remarks

t df

Sig. (2-

tailed) Mean

Difference

95% Confidence Interval of the

Difference

Lower Upper

PerceptionBenefits_BetterProductivity 13.084 278 .000 .925 .79 1.06

t calculated >t tabulated at .05 level of significance. (p<0.05)

PerceptionBenefits_CostsSavings 10.947 280 .000 .730 .60 .86


PerceptionBenefits_TransparencyHRprocesses

10.151 279 .000 .675 .54 .81 t calculated >t tabulated at .05 level of significance. (p<0.05)

PerceptionBenefits_BenchmarkEmpPerformance

14.805 280 .000 .907 .79 1.03 t calculated >t tabulated at .05 level of significance. (p<0.05)

275


PerceptionBenefits_TrgAndDevelopmentNeeds

13.130 280 .000 .868 .74 1.00 t calculated >t tabulated at .05 level of significance. (p<0.05)

PerceptionBenefits_DesigningTrgActivities


PerceptionBenefits_MonitoringIndividualProgress


PerceptionBenefits_ClarifiesPayPerformanceLink


PerceptionBenefits_CreatesMeaningfulGradingStr


PerceptionBenefits_AlignmentIndTeamBehaviour


Table: 4.26 One-Sample t-test for Benefit Perceptions

The above table depicts that means of all perceptions are way above 3. In fact they are

in the range of 3.60 to 3.90. The differences between observed mean and assumed

means are significant for all benefit perceptions. In other words all Managerial

respondents strongly agree with all benefits perceptions.

276

4.5.6.2 Independent Sample t-test for Benefit Perceptions by Industry Type:


Independent Sample t-test for Benefit Perceptions by Industry Type:


Variances


F Sig. t cal* df t tab Sig. (2-tailed) Remarks

PerceptionBenefits_BetterProductivity


0.52 0.47 2.41 259 1.970 0.017 t cal > t tab at .05 level of significance Null hypothesis is rejected for this benefit (p<0.05)


2.56 115.86 0.012

PerceptionBenefits_CostsSavings


4.63 0.03 0.659 261 1.970 0.51 t cal < t tab at .05 level of significance Null hypothesis is accepted for this benefit (p>0.05)


0.612 95.173 0.542



1.93 0.17 0.92 260 1.970 0.359 t cal < t tab at .05 level of significance Null hypothesis is accepted for this benefit (p>0.05)


0.96 112.25 0.339



3.89 0.05 1.38 261 1.970 0.169 t cal < t tab at .05 level of significance Null hypothesis is accepted for this benefit(p>0.05)


1.49 122.42 0.139



1.06 0.3 1.53 261 1.970 0.128 t cal < t tab at .05 level of significance Null hypothesis is accepted for this benefit


1.57 111.92 0.119

277

(p>0.05)




(p>0.05)


1.72 111.74 0.089



9.68 0 1.65 261 1.970 0.101 t cal < t tab at .05 level of significance Null hypothesis is accepted for this benefit

(p>0.05)


1.87 135.85 0.063

PerceptionBenefits_ClarifiesPayPerformanceLink



(p>0.05)


1.283 100.25 0.202




(p>0.05)


0.64 111.98 0.522




(p>0.05)


1.49 104.51 0.138

*Ignoring signs

Table: 4.27 Independent Samples t-test for Benefits Perception by


The Independent Sample t-test on Benefit Perceptions for two groups of Managers

from Manufacturing and Service shows that for all except one Benefit Perception there

278

is no difference in the mean perception. It can be stated that with 95% Confidence that

benefits perception of Respondent managers from the two groups of manufacturing

stated that Perceived Benefits are alike across the two Sectors of manufacturing and

service.

4.5.6.3 Chi-Square test of Independence for Benefit Perceptions on Sector Type

(Manufacturing (1) or Service (2))

Chi-Square Tests

Chi-Square for Benefits perception on Sector type


Remarks

Industry_type * PerceptionBenefits_BetterPr

oductivity


Chi-Square calculated > Chi-Square tabulated (9.488). Null hypothesis is rejected for this benefit

(p<0.05)



5.708 1 0.017



Industry_type * PerceptionBenefits_CostsSa

vings


Chi-Square Calculated < Chi-Square Tabulated (9.488).Null hypothesis is accepted for this benefit

(p>0.05)



0.436 1 0.509



279

Industry_type * PerceptionBenefits_Transpar

encyHRprocesses


Chi-Square Calculated < Chi-Square Tabulated (9.488). Null hypothesis is accepted for this benefit

(p>0.05)



0.846 1 0.358



Industry_type * PerceptionBenefits_Benchm

arkEmpPerformance



(p>0.05)



1.891 1 0.169



Industry_type * PerceptionBenefits_TrgAndD

evelopmentNeeds



(p>0.05)



2.316 1 0.128



Industry_type * Pearson 4.425a 4 0.351 Chi-Square Calculated <

Chi-Square Tabulated

280


Chi-Square (9.488) Null hypothesis is accepted for this benefit

(p>0.05) Likelihood Ratio 4.507 4 0.342


2.762 1 0.096



Industry_type * PerceptionBenefits_Monitori

ngIndividualProgress


Chi-Square Calculated < Chi-Square Tabulated (9.488Null hypothesis is accepted for this benefit

(p>0.05)



2.695 1 0.101



Industry_type * PerceptionBenefits_Clarifies

PayPerformanceLink


Chi-Square Calculated < Chi-Square Tabulated (9.488). Null hypothesis is accepted for this benefit

(p>0.05)



1.778 1 0.182



Industry_type * PerceptionBenefits_Creates

Pearson Chi-Square 1.323a 4 0.857 Chi-Square Calculated <

Chi-Square Tabulated (9.488).Null hypothesis

281

MeaningfulGradingStr Likelihood Ratio 1.451 4 0.835

is accepted for this benefit

(p>0.05) Linear-by-Linear Association

0.389 1 0.533



Industry_type * PerceptionBenefits_Alignme

nt IndTeamBehaviour


Chi-Square Calculated < Chi-Square Tabulated

(9.488). Null hypothesis is accepted for this benefit

(p>0.05)



2.274 1 0.132



Table: 4.28 Chi-Square Test of independence between Benefit Perceptions and

Industry type

The Chi-Square Significance value is above 0.05 for nine out of ten Benefit

that for respondents from the Manufacturing Sector, the overwhelming response is for

ndents, it is less skewedly

282

Industry_type * PerceptionBenefits_BetterProductivity

Crosstab

Count


Total 1 2 3 4 5

Industry_type 1 1 5 9 11 37 63

2 15 11 40 59 73 198

Total 16 16 49 70 110 261

Table: 4.29 Crosstab Table for Sector \ Industry type and Perception Benefits

_Better Productivity

Thus for Sector Type, Null Hypothesis is accepted in all but one case. Hence it can be

stated that for 9 out of 10 Perception Benefits that they do not vary across sectors i.e

perception benefits are not dependent on Sector type.

4.5.6.4 Independent Sample t-test for Perceived Benefits by Department Type


Independent Sample t-test for Percieved Benefits by Department Type

Levene's Test for Equality of Variances



tailed) Remarks



0.22 0.64 0.15 277 1.969 0.883 t calculated < t tabulated (1.645). Null hypothesis is accepted for this benefit

(p>0.05)


0.15 238.76 0.883

PerceptionBenefits_CostsSa


0.01 0.94 0.28 279 1.969 0.781 t calculated < t tabulated (1.645). Null hypothesis is accepted for this

283

vings Equal variances not assumed

0.28 239.6 0.781

benefit

(p>0.05)




(p>0.05)


0.83 251.7 0.41




(p>0.05)


0.4 243.63 0.689



3.95 0.05 2.41 279 1.969 0.017 t calculated > t tabulated (1.645). Null hypothesis is rejected for this benefit

(p<0.05)


2.46 253.42 0.015




(p>0.05)


1.02 241.92 0.311




(p>0.05)


1.06 239.29 0.293

PerceptionBenefits_ClarifiesPayPerfor


0.94 0.33 0.63 278 1.969 0.527 t calculated < t tabulated (1.645). Null hypothesis is accepted for this

284

manceLink Equal

variances not assumed

0.62 222.34 0.535

benefit

(p>0.05)




(p>0.05)


0.34 222.28 0.734




(p>0.05)


0.27 251 0.789

Table: 4.30 Independent Samples t-test for Perceived Benefits by

Department/Function Type

The preceding table depicts that mean opinion of two groups of HR and Non HR

Managers does not differ as regards 9 out of 10 perceived benefits. The mean opinion

of HR and Non HR differs on only one item i.e. Training and development needs

4.5.6.5 Chi-Square for Benefit Perception on Department/Function Type (HR (1)

or Non-HR (2))

Chi-Square Tests

Department_type * PerceptionBenefits_BetterProduct

ivity


Remarks

Pearson Chi-Square 1.204a 4 0.877 Chi-Square

calculated<Chi-Square tabulated (9.488). Null hypothesis is accepted for this benefit

(p>0.05)



0.022 1 0.882

285



Department_type * PerceptionBenefits_CostsSavings


Chi-Square calculated < Chi-Square tabulated


(p>0.05)



0.078 1 0.78



Department_type * PerceptionBenefits_Transparency

HRprocesses


Chi-Square calculated < Chi-Square tabulated (9.488). Null hypothesis is accepted for this benefit

(p>0.05)



0.658 1 0.417



Department_type * PerceptionBenefits_BenchmarkE

mpPerformance



(p>0.05)



0.159 1 0.69

N of Valid 281

286

Cases


Department_type * PerceptionBenefits_TrgAndDevel

opmentNeeds



(p>0.05)



5.713 1 0.017



Department_type * PerceptionBenefits_DesigningTrg

Activities



(p>0.05)



1.02 1 0.313



Department_type * PerceptionBenefits_MonitoringInd

ividualProgress



(p>0.05)



1.115 1 0.291


287


Department_type * PerceptionBenefits_ClarifiesPayP

erformanceLink


Chi-Square calculated < Chi-Square tabulated


(p>0.05)



0.401 1 0.526



Department_type * PerceptionBenefits_CreatesMeani

ngfulGradingStr



(p>0.05)



0.121 1 0.728



Department_type * PerceptionBenefits_AlignmentInd

TeamBehaviour



(p>0.05)



0.07 1 0.792


288


Table: 4.31 Chi-Square test of Independence between Department type and

Perceived Benefits

The preceding table indicates that the Chi-Square Significance value is above 0.05 for

all ten Benefit Perceptions. Thus for Function Type, Null Hypothesis is accepted for all

10 cases. Hence, it can be stated that Perceived Benefits are independent of

Department or Function

4.5.6.6 Chi-Square test on Size of the Organization (<=100 (1), 101-500 (2), 501-

1000 (3), 1001-5000 (4) or >5000 (5)) and Perceived Benefits

Chi-Square Tests

Value df


Remarks

Org_Staff_Strength * PerceptionBenefits_BetterPr

oductivity


Chi-Square calculated < Chi-Square Tabulated (26.296). Null hypothesis is accepted for this benefit

(p>0.05)



4.761 1 0.029



Org_Staff_Strength * PerceptionBenefits_CostsSa

vings


calculated < Chi-Square Tabulated (26.296).Null hypothesis is accepted for


289


3.683 1 0.055

this benefit

(p>0.05)



Org_Staff_Strength * PerceptionBenefits_Transpar

encyHRprocesses


Chi-Square calculated < Chi-Square Tabulated (26.296).Null hypothesis is accepted for this benefit

(p>0.05)



0.338 1 0.561



Org_Staff_Strength * PerceptionBenefits_Benchm

arkEmpPerformance


Chi-Square calculated < Chi-Square Tabulated

(26.296).Null hypothesis is accepted for this benefit

(p>0.05)



0.161 1 0.688



Org_Staff_Strength * PerceptionBenefits_TrgAndD

evelopmentNeeds


calculated > Chi-Square Tabulated (26.296).Null hypothesis is rejected for this benefit


Linear-by-Linear

8.73 1 0.003

290

Association (p<0.05)



Org_Staff_Strength * PerceptionBenefits_Designin

gTrgActivities


Chi-Square calculated < Chi-Square Tabulated (26.296Null hypothesis is accepted for this benefit

(p>0.05)



4.53 1 0.033



Org_Staff_Strength * PerceptionBenefits_Monitori

ngIndividualProgress



(p>0.05)



4.262 1 0.039



Org_Staff_Strength * PerceptionBenefits_Clarifies

PayPerformanceLink


calculated < Chi-Square Tabulated (26.296)Null hypothesis is accepted for this benefit

(p>0.05)



0.614 1 0.433

291



Org_Staff_Strength * PerceptionBenefits_Creates

MeaningfulGradingStr



(p>0.05)



2.839 1 0.092



Org_Staff_Strength * PerceptionBenefits_Alignme

ntIndTeamBehaviour



(p>0.05)



1.806 1 0.179



Table: 4.32 Chi-Square test of independence between Perceived Benefits and

Employee Strength

The Chi-Square Significance value is above 0.05 for nine out of ten Benefit

significantly different owing to the fact that for respondents from organizations with

<=100 employees, the response is evenly distributed on the five points of the scale,

-

292

s

table below. This is most probably due to the training process not being fully fledged

in smaller organizations.

Org_Staff_Strength * PerceptionBenefits_TrgAndDevelopmentNeeds

Crosstab

Count


Total 1 2 3 4 5

Org_Staff_Strength <101 4 3 4 4 5 20

101-500 4 5 9 10 13 41

501-1000 0 0 6 6 9 21

1001-5000 0 2 14 12 19 47

>5000 0 4 16 18 22 60

Total 8 14 49 50 68 189

Table: 4.33 Crosstab between Employee Strength and Perceived benefit

Thus for Size i.e Staff/Employee Strength of the Organization, Null Hypothesis is

accepted in all but one case.

4.5.6.7 Chi-Square test on Age of the Organization (<10 years (1), 11-20 years (2),

21-30 years(3), 31-40 years (4) or >40 years (5))

Chi-Square Tests

Value df


Remarks

Org_Age_Cat * PerceptionBenefits_BetterP

roductivity


calculated < Chi-Square Tabulated (26.296) Null hypothesis is accepted for this benefit

(p>0.05)



2.753 1 0.097

293



Org_Age_Cat * PerceptionBenefits_CostsS

avings



(26.296) Null hypothesis is accepted for this benefit

(p>0.05)



0.739 1 0.39



Org_Age_Cat * PerceptionBenefits_Transp

arencyHRprocesses



(p>0.05)



0.041 1 0.839



Org_Age_Cat * PerceptionBenefits_Bench

markEmpPerformance



(p>0.05)



0.501 1 0.479



Org_Age_Cat * Pearson Chi-Square 12.116a 16 0.736 Chi-Square

calculated < Chi-

294



Square Tabulated (26.296).Null hypothesis is accepted for this benefit

(p>0.05)


1.869 1 0.172



Org_Age_Cat * PerceptionBenefits_Designi

ngTrgActivities



(p>0.05)



1.363 1 0.243



Org_Age_Cat * PerceptionBenefits_Monitor

ingIndividualProgress




(p>0.05)



2.317 1 0.128



Org_Age_Cat * PerceptionBenefits_Clarifie

sPayPerformanceLink



(p>0.05)



0.51 1 0.475


295


Org_Age_Cat * PerceptionBenefits_Creates

MeaningfulGradingStr



(p>0.05)



0.172 1 0.678



Org_Age_Cat * PerceptionBenefits_Alignm

entIndTeamBehaviour


Chi-Square calculated > Chi-Square Tabulated (26.296).Null hypothesis is rejected for this benefit

(p<0.05)



1.772 1 0.183



Table: 4.34 Chi-Square test of Independence between Perceived Benefits and

Organisational Age

The above table clearly shows that the Chi-Square Significance value is above 0.05 for

nine out of ten Benefit Pe

categories.

Thus for Age of the Organization, Null Hypothesis is accepted in all but one case. In

other words it can be stat

296

Analysis of the Survey Study CSFs and their Association with 4.5.7.0

independent variables like Sector/Industry, Employee/Staff Strength, Age,

Department/ Function of the Manager Respondent

Objective:

To ascertain the perceptions of Managers on various Critical Success Factors

for successful implementation of Competency mapping.

To find if CSFs perceptions are dependent on


o Department / Managerial function of the respondent

o Employee / Staff Strength (Size) of the organization


Hypothesis :

H110


H11a

mean (µ>3, Neutral)

H120 CSFs Perceptions for Competency Mapping Implementation are not dependent

on

Industry or sector Department/ Managerial functions Employee Strength (Size) of the organization Age of the organization

H12a CSFs Perceptions for Competency Mapping implementation are dependent on

Industry or sector Department/ Managerial functions Employee Strength (Size) of the organization Age of the organization

297

4.5.7.1 One-Sample t-test (Test Value = 3) for CSFs

One-Sample Test

Test Value = 3

Remarks

t df

Sig. (2-

tailed) Mean

Difference

95% Confidence

Interval of the Difference

Lower Upper

CSF_TopManagementBuyIN

12.093 285 .000 .762 .64 .89


CSF_DedicatedHRresource

14.532 288 .000 .855 .74 .97


CSF_AvailabilityCompMappingTools

13.674 287 .000 .764 .65 .87


CSF_TrgHRManagers

17.918 288 .000 .976 .87 1.08


CSF_AdequateFinancialResources

12.753 286 .000 .739 .62 .85


CSF_DedicatedTimeAllocation

14.745 287 .000 .833 .72 .94


CSF_OtherFactors

6.530 58 .000 .847 .59 1.11


Table: 4.35 One Sample t-test for Managerial Perceptions on CSFs

As seen from the preceding table the observed mean scores of the survey sample for

all the six CSFs are above the expected mean opinion (µ = 3).

The differences in observed and excepted mean scores are all significant at the .05

level according to above table.

298

Hence, null hypothesis is rejected and we state that managers are of the opinion that

the following CSFs are relevant for successful implementation of Competency

mapping in an Organisation:

Top management Buy-in Dedicated HR Resource Availability of Competency Mapping Tools Training of HR Managers Adequate Financial Resources Dedicated Time allocation

4.5.7.2 Independent Sample t-test for CSFs by Industry Type:


Independent Sample t-test for CSFs by Industry Type:

Levene's Test for Equality of Variances t-test for Equality of Means

F Sig. t tab df Sig. (2-

tailed) Remarks



1.643 0.201 0.437 1.970 266 0.662 t calculated < t tabulated at .05 level of significance.. Null hypothesis is accepted for this CSF

(p>0.05)


0.413 100.912 0.681



0.55 0.459 0.341 1.970 269 0.733 t calculated < t tabulated at .05 level of significance. Null hypothesis is accepted for this CSF

(p>0.05)


0.351 118.627 0.726




(p>0.05)


0.599 115.848 0.55

299

CSF_TrgHRManagers



(p>0.05)


0.512 106.447 0.61




(p>0.05)


0.936 119.573 0.351



0.062 0.803 0.388 1.970 268 0.698 t calculated < t tabulated at .05 level of significance. Null hypothesis is accepted for this CSF (p>0.05)


0.394 116.01 0.694

Table: 4.36 Independent Samples t-test for CSFs by Sector type

(Manufacturing v/s Service)

The preceding table indicates that mean perception of managers from the two groups

of Manufacturing and Service do not differ significantly. Independent Samples t-test

for CSFs by Sector type is not significant for all CSFs. Hence it can be stated with

95% confidence that CSFs are universally applicable across sectors.

300

4.5.7.3 Chi-Square test of Independence on Sector Type (Manufacturing (1) or

Service (2))

Chi-Square Tests

Value df


Remarks

Industry_type * CSF_TopManagementBuyIN


Chi-Square calculated < Chi-Square tabulated (9.488) at0.05 level of significance. Null hypothesis is accepted for this CSF

(p>0.05)



0.192 1 0.661



Industry_type * CSF_DedicatedHRresource


Chi-Square calculated < Chi-Square tabulated (9.488) at0.05 level of significance Null hypothesis is accepted for this CSF

(p>0.05)



0.117 1 0.733



Industry_type * CSF_AvailabilityCompMappi

ngTools


Chi-Square calculated < Chi-Square tabulated (9.488) at 0.05 level of significance .Null hypothesis is accepted for this CSF

(p>0.05)



0.349 1 0.555


301


Industry_type * CSF_TrgHRManagers


Chi-Square calculated < Chi-Square tabulated (9.488) at 0.05 level of significance Null hypothesis is accepted for this CSF

(p>0.05)



0.282 1 0.596



Industry_type * CSF_AdequateFinancialReso

urces



(p>0.05)



0.801 1 0.371



Industry_type * CSF_DedicatedTimeAllocatio

n



(p>0.05)



0.151 1 0.698



Table: 4.37 Chi-Square test of Independence between CSFs and


302

The preceding table clearly reveals that the Chi-Square Significance value is above

0.05 for all six CSFs, thus for Sector Type, Null Hypothesis is accepted. It can be

inferred that the CSFs for successful implementation of Competency Mapping do not

depend on Sector. In other words CSFs are universal.

4.5.7.4 Independent Sample t-test for CSFs by Department\Function Type


Independent Sample t-test for CSFs by Department Type


Variances



tailed) Remarks



0.011 0.915 0.098 284 1.969 0.922 t calculated < t tabulated at .05 level of significance. Null hypothesis is accepted for this CSF

(p>0.05)


0.098 237.001 0.922



1.593 0.208 1.882 287 1.969 0.061 t calculated < t tabulated at .05 level of significance Null hypothesis is accepted for this CSF

(p>0.05)


1.903 250.43 0.058




(p>0.05)


1.127 236.632 0.261

CSF_TrgHRManagers


3.556 0.06 1.928 287 1.969 0.055 t calculated < t tabulated at .05 level of

303


1.992 266.238 0.047

significance Null hypothesis is accepted for this CSF

(p>0.05)




(p>0.05)


0.472 253.49 0.637




(p>0.05)


1.751 241.259 0.081

Table: 4.38 Independent Samples t-test for CSFs by Department/Function

Type

The preceding table indicates that the mean perception of two groups namely HR and

Non HR managers do not differ significantly. Thus it can be stated that CSFs do not

depend on department. In other words all CSFs hold true for both department/function

types and do not significantly vary by department.

4.5.7.5 Chi-Square test on Function Type (HR (1) or Non-HR (2))

Chi-Square Tests

Chi-Square on Department/Function Type (HR (1) or Non-HR (2))

Value df


Remarks

Department_type * CSF_TopManagementBuyIN

Pearson Chi-Square .721a 4 0.949 Chi-Square

calculated < Chi-Square tabulated (9.488). Null hypothesis is accepted for this CSF


Linear-by-Linear

0.01 1 0.922

304

Association (p>0.05)



Department_type * CSF_DedicatedHRresource


Chi-Square calculated < Chi-Square tabulated (9.488). Null hypothesis is accepted for this CSF

(p>0.05)



3.512 1 0.061



Department_type * CSF_AvailabilityCompMappingT

ools



(p>0.05)



1.284 1 0.257



Department_type * CSF_TrgHRManagers



(p>0.05)



3.683 1 0.055



Department_type * Pearson 1.663a 4 0.798 Chi-Square

305



(p>0.05)



0.217 1 0.641



Department_type * CSF_DedicatedTimeAllocation



(p>0.05)



3.03 1 0.082



Table: 4.39 Chi-Square test of independence between CSFs and

department/function

The preceding table indicates that the Chi-Square Significance value is above 0.05 for

all six CSFs, thus for Function Type, Null Hypothesis is accepted. Thus it can be

concluded with 95% confidence that CSFs are independent of department/ function.

Opinion of Managers across departments does not vary i.e. CSFs are universal.

306

4.5.7.6 Chi-Square test on Size of the Organization (<=100 (1), 101-500 (2), 501-

1000 (3), 1001-5000 (4) or >5000 (5)) and CSFs

Value df


Remarks

Org_Staff_Strength * CSF_TopManagementBu

yIN


Chi-Square Calculated < Chi-Square tabulated (26.296).Null hypothesis is accepted for this CSF

(p>0.05)



2.322 1 0.128



Org_Staff_Strength * CSF_DedicatedHRresour

ce



(p>0.05)



9.322 1 0.002



Org_Staff_Strength * CSF_AvailabilityCompMa

ppingTools



(p>0.05)



0.52 1 0.471



307

Org_Staff_Strength * CSF_TrgHRManagers



(p>0.05)



0.101 1 0.75



Org_Staff_Strength * CSF_AdequateFinancialR

esources



(p>0.05)



0.061 1 0.805



Org_Staff_Strength * CSF_DedicatedTimeAlloc

ation



(p>0.05)



0.292 1 0.589



Table: 4.40 Chi-Square test of Independence between CSFs and Employee/

Staff strength

The preceding table on Chi-Square clearly indicates that the Chi-Square Significance

value is above 0.05 for all six CSFs, thus for Employee Strength, Null Hypothesis is

308

accepted. Thus it can be concluded with 95% confidence that CSFs are independent of

Staff/Employee strength or do not vary with changes in Organisational Staff strength.

Sampled Organisations with staff strength varying from below 100 to above a lakh

concur on six CSFs.

4.5.7.7 Chi-Square test on Age of the Organization (<10 years (1), 11-20 years (2),

21-30 years(3), 31-40 years (4) or >40 years (5)) and CSFs

Value df


Remarks

Org_Age_Cat * CSF_TopManagementBuyIN



(p>0.05)



3.074 1 0.08



Org_Age_Cat * CSF_DedicatedHRresource


Chi-Square Calculated < Chi-Square tabulated (26.296). Null hypothesis is accepted for this CSF

(p>0.05)



2.725 1 0.099



Org_Age_Cat * CSF_AvailabilityCompMappin

gTools



(p>0.05)



0.031 1 0.861


309


Org_Age_Cat * CSF_TrgHRManagers



(p>0.05)



0.553 1 0.457



Org_Age_Cat * CSF_AdequateFinancialResou

rces



(p>0.05)



0.52 1 0.471



Org_Age_Cat * CSF_DedicatedTimeAllocation



(p>0.05)



1.8 1 0.18



Table: 4.41 Chi-Square test of Independence between Organisational Age and

CSFs

The above two tables on crosstab and Chi-Square indicate that the Chi-Square

Significance value is above 0.05 for all six CSFs, thus for Age of the organization,

Null Hypothesis is accepted. Thus it can be stated with 95% confidence that for

310

Organisations with age ranging from below 10 years to above 40 years perceived CSFs

do not vary

In summary CSFs are not dependent on any criteria be it Sector, Function, Age or

Employee Strength of the organization, and therefore may be deemed to be universal.

Analysis of the Survey Study Measurable Performance Indicators and 4.5.8.0

their associations

Objective:

To determine whether Impact of Competency on selected Performance

indicators is measured by Organisations

To ascertain if competency mapping has positive impact on chosen Measurable

performance indicators

Hypothesis:

H130 The observed mean value of responses of Managers regarding the impact of

Competency Mapping on the following chosen Measurable Indicators is not

significantly higher as compared to the expected mean value (H0: µ = 2.5).

Sales Revenue increase

Profit increase

Productivity increase

Attrition reduction

Cost reduction per recruitment

Improvement in ratio of high performing hires to total hires

Top talent retention

H13a The observed mean value of responses of Managers regarding the impact of

Competency Mapping on the following chosen Measurable Indicators is significantly

higher as compared to the expected mean value (Ha: µ > 2.5).

Sales Revenue increase

Profit increase

Productivity increase

Attrition reduction

Cost reduction per recruitment

311

Improvement in ratio of high performing hires to total hires

Top talent retention

H140 The opinion of Managers on the desired outcomes pertaining to the seven

performance parameters does not vary with

Department/ Managerial functions

Industry or sector



H14a The opinion of Managers on the desired outcomes pertaining to the seven

performance parameters does vary with

Department/Managerial functions

Industry or sector



4.5.8.1 One-Sample t-test (Assumed Mean = 2.5) on Measurable Indicators

Responses were sought from Managers of organizations where Competency Mapping

was followed regarding the desired impact of Competency mapping on performance

indicators viz. Sales Revenue Increased, Profit increased, Productivity Increased,

attrition reduced, cost per recruit reduced, Increase in ratio of High performing Hires to

Total hires, and Top Talent reduction.

The response distribution for each Measurable Indicator was analysed using a One

sample t-test

312

Test Value = 2.5

t df t-tab

Sig.

(2-

tailed

)

Mea

n

Differ

ence

95%

Confidence

Interval of the

Difference Remarks

Lower Upper

MeasurableIndicat

ors_SalesRevenu

eIncreased 1.856 114 1.984 .066 .170 -.01 .35

t calculated< t tabulated. Null hypothesis is accepted for this Measurable

Indicator

(p>0.05)

MeasurableIndicat

ors_ProfitIncrease

d .319 116 1.984 .750 .030 -.16 .22

t calculated< t tabulated Null hypothesis is accepted for this Measurable

Indicator

(p>0.05)

MeasurableIndicat

ors_ProductivityIn

creased 3.492 116 1.984 .001 .303 .13 .48

t calculated >t tabulated. Null hypothesis is rejected for this Measurable

Indicator

(p<0.05)

MeasurableIndicat

ors_AttritionRedu

ced 3.511 118 1.984 .001 -.340 -.53 -.15

t calculated >t tabulated Null hypothesis is rejected for this Measurable

Indicator

(p<0.05)

MeasurableIndicat

ors_CostPerRecr

uitReduced 1.756 114 1.984 .082 -.178 -.38 .02

t calculated < t tabulated Null hypothesis is accepted for this Measurable

Indicator

(p>0.05)

MeasurableIndicat

ors_RatioHighPerf

ormingHiresToTot

alHires

.468 117 1.984 .641 -.042 -.22 .14


Indicator

(p>0.05)

MeasurableIndicat

ors_TopTalentRet

ention 1.953 121 1.984 .053 .197 .00 .40


Indicator

(p>0.05)

Table: 4.42 Table One Sample t-test for Measurable performance indicators

As seen from the preceding table, the observed mean scores of the survey sample for

313

four parameters sales revenue Increased, Profit Increased, Productivity increased, Top

talent retention are above the expected mean opinion (µ = 2.5). For two viz. Cost per

Recruit reduced and Ratio of High performing hires to total Hires improved it is

slightly lower, and noticeably lower for Attrition reduced.

The differences in observed and excepted mean scores are not significant at the .05

level according to table for five measurable indicators and are highly significant for

two namely Productivity increased and attrition reduced

Hence, null hypothesis is accepted for five indicators and we state that managers are

of the opinion that the CM usage has had / is expected to have a positive impact on the

five measurable indicators in the range 11 to 20% or less than 10%

4.5.8.2 Independent Sample t-test on Measurable Indicators for department type

Independent Sample

t-test for department

type

Levene's

Test for

Equality of


F Sig. t df t tab

Sig.

(2-

taile

d)

Mea

n

Differ

ence

Std.

Error

Differ

ence

95%

Confidence

Interval of

the

Difference

Remarks

Lower

Upp

er

Measur

ableIndi

cators_

SalesRe

venueIn

creased

Equal

variances

assumed

.057 .812 .744 113 1.98

2 .458 .141 .189 -.234 .516

t calculated < t tabulated at .05 level of significance. Null hypothesis is accepted for this Measurable Indicator

(p>0.05)

Equal

variances

not

assumed

.744 88.

499 .459 .141 .189 -.235 .517

Measur

ableIndi

cators_

Equal

variances

assumed

.059 .809 .947 115 1.98

2 .345 -.181 .191 -.560 .198

t calculated < t tabulated .05 level of significance Null

314

ProfitInc

reased Equal

variances

not

assumed

.942 96.

951 .348 -.181 .192 -.563 .200

hypothesis is accepted for this Measurable Indicator

(p>0.05)

Measur

ableIndi

cators_

Producti

vityIncre

ased

Equal

variances

assumed

1.044 .309 1.087 115 1.98

2 .279 .195 .179 -.160 .550

t calculated < t tabulated at .05 level of significance Null hypothesis is accepted for this Measurable Indicator

(p>0.05)

Equal

variances

not

assumed

1.113 97.

553 .268 .195 .175 -.153 .542

Measur

ableIndi

cators_

Attrition

Reduce

d

Equal

variances

assumed

1.680 .197 .117 117 1.98

2 .907 .023 .198 -.370 .416


(p>0.05)

Equal

variances

not

assumed

.114 93.

516 .909 .023 .203 -.379 .426

Measur

ableIndi

cators_

CostPer

RecruitR

educed

Equal

variances

assumed

.020 .887 .892 113 1.98

2 .374 .184 .207 -.225 .594


(p>0.05)

Equal

variances

not

assumed

.884 95.

806 .379 .184 .209 -.230 .598

Measur

ableIndi

cators_

RatioHig

hPerfor

mingHir

esToTot

alHires

Equal

variances

assumed

1.100 .297 1.526 116 1.98

2 .130 .280 .183 -.083 .643


(p>0.05)

Equal

variances

not

assumed

1.542

104

.90

5

.126 .280 .181 -.080 .639

315

Measur

ableIndi

cators_T

opTalent

Reducti

on

Equal

variances

assumed

.397 .530 1.074 120 1.98

2 .285 .221 .206 -.187 .629

t calculated < t tabulated at .05 level of significance .Null hypothesis is accepted for this Measurable Indicator

(p>0.05)

Equal

variances

not

assumed

1.062 96.

737 .291 .221 .208 -.192 .635

Table: 4.43 Independent Sample t-test for Measurable indicators by

department type

The preceding table clearly reveals that null hypothesis is accepted for all the seven

performance indicators and thus it can be stated with 95% confidence that opinion of

managers regarding desirable outcomes on measurable indicators does not differ

significantly with department. Both HR and Non HR managers have the same opinions

on chosen performance indicators

4.5.8.3 Chi-square test of Independence on Measurable Indicators for

Department Type:

Value df


Remarks

Department_type * MeasurableIndicators_Sal

esRevenueIncreased


Chi-Square tabulated < Chi-Square calculated (7.815) at .05 level of significance. Null hypothesis is accepted for this Measurable Indicator

(p>0.05)



.556 1 .456



Department_type * MeasurableIndicators_Prof

itIncreased

Pearson Chi-Square 8.876a 3 .031 Chi-Square tabulated > Chi-

Square calculated (7.815) at .05 level of significance .Null hypothesis is rejected for this


316


.898 1 .343

Measurable Indicator

(p<0.05)



Department_type * MeasurableIndicators_Pro

ductivityIncreased


Chi-Square tabulated < Chi-Square calculated (7.815) at .05 level of significance .Null hypothesis is accepted for this Measurable Indicator

(p>0.05)



1.180 1 .277



Department_type * MeasurableIndicators_Attr

itionReduced


Chi-Square tabulated < Chi-Square calculated (7.815) at .05 level of significance Null hypothesis is accepted for this Measurable Indicator

(p>0.05)



.014 1 .907



Department_type * MeasurableIndicators_Cos

tPerRecruitReduced



(p>0.05)



.797 1 .372



317

Department_type * MeasurableIndicators_RatioHighPerformingHiresToT

otalHires



(p>0.05)



2.302 1 .129



Department_type * MeasurableIndicators_Top

TalentReduction


Chi-Square tabulated < Chi-Square calculated (7.815) at .05 level of significance .Null hypothesis is accepted for this Measurable Indicator

(p>0.05)



1.152 1 .283



Table: 4.44 Chi-square Test of Independence for Measurable indicators by

department type

The preceding table clearly reveals that opinion of managers across departments does

not vary significantly for all measurable indicators except one namely Profit increased.

318

4.5.8.4 Independent Sample t-test for Sector type


Independent Sample t-

test for Sector type

Levene's

Test for

Equality of


F Sig. t df

Sig.

(2-

taile

d)

Mea

n

Diffe

renc

e

Std.

Erro

r

Diff

ere

nce

95%

Confidence

Interval of

the

Difference

Remarks

t tab Low

er

Upp

er

Measura

bleIndica

tors_Sale

sRevenu

eIncreas

ed

Equal

variances

assumed

2.300 .13

2

.30

6 110

1.98

2

.76

0 -.068

.22

2 -.508 .372

t calculated < t

tabulated

(1.660 at

.05) level of

significanc

e

Null hypothesis is accepted for this Measurable Indicator (p>0.05)

Equal

variances

not

assumed

.28

1

36.72

5

.78

0 -.068

.24

2 -.558 .422

Measura

bleIndica

tors_Prof

itIncreas

ed

Equal

variances

assumed

.021 .88

4

.46

7 108

1.98

2

.64

1 .108

.23

1 -.350 .566

t calculated <

t tabulated at

.05 level of

significanc

e

Null hypothesis is accepted for this Measurable Indicator (p>0.05

Equal

variances

not

assumed .46

4

34.26

0

.64

5 .108

.23

3 -.364 .580

Measura

bleIndica

tors_Pro

Equal

variances

assumed

.000 .98

9

.33

2 112

1.98

2

.74

0 .074

.22

3 -.368 .516

t calculated < t

tabulated

(1.660 at

319

ductivityI

ncreased Equal

variances

not

assumed .33

0

33.72

7

.74

3 .074

.22

4 -.382 .530

.05) level of

significanc

e


Measura

bleIndica

tors_Attri

tionRedu

ced

Equal

variances

assumed

.319 .57

4

1.0

73 110

1.98

2

.28

5 .261

.24

3 -.221 .744

t calculated < t

tabulated

(1.660 at

.05) level of

significanc

e


Equal

variances

not

assumed 1.0

69

36.34

1

.29

2 .261

.24

4 -.234 .757

Measura

bleIndica

tors_Cos

tPerRecr

uitReduc

ed

Equal

variances

assumed

.213 .64

6

.07

5 110

1.98

2

.94

0 .019

.25

2 -.480 .518

t calculated < t

tabulated

(1.660 at

.05) level of

significanc

e

Null hypothesis is accepted for this Measurable Indicator (p>0.05

Equal

variances

not

assumed .07

3

35.40

1

.94

2 .019

.25

8 -.504 .542

Measura

bleIndica

tors_Rati

oHighPer

formingH

iresToTot

alHires

Equal

variances

assumed

.156 .69

4

1.0

56 111

1.98

2

.29

3 .240

.22

7 -.211 .691

tcalculated < t

tabulated

(1.660 at

.05) level of

significanc

e


Equal

variances

not

assumed 1.0

55

34.11

4

.29

9 .240

.22

8 -.222 .702

320

Measura

bleIndica

tors_Top

TalentRe

duction

Equal

variances

assumed

.000 .99

1

1.1

09 116

1.98

2

.27

0 .276

.24

9 -.217 .768

tcalculated < t

tabulated

(1.660 at

.05) level of

significanc

e


Equal

variances

not

assumed 1.0

95

37.31

6

.28

1 .276

.25

2 -.234 .786

Table: 4.45 Independent Samples t-test for Measurable Indicators for Sectors

(Manufacturing and Service)

The preceding table clearly reveals that the means of two groups of manufacturing and

service do not differ significantly. In other word it can be stated with 95% confidence

that mean opinions of managers from manufacturing and service on Measurable

performance indicators do not differ significantly hence the null hypothesis is

accepted foe all seven cases

321

4.5.8.5 Chi-square test of Independence between Measurable indicators and

Sector Type

Value df


Remarks

Industry_type * MeasurableIndicators_SalesRev

enueIncreased


Chi-Square calculated< Chi-Square tabulated (7.815) at .05 level of significance.

Null hypothesis is accepted for this Measurable Indicator

(p>0.05)



.094 1 .759



Industry_type * MeasurableIndicators_ProfitIncr

eased

Pearson Chi-Square .835a 3 .841



(p>0.05)

Likelihood Ratio .858 3 .835


.220 1 .639



Industry_type * MeasurableIndicators_Productivi

tyIncreased

Pearson Chi-Square .127a 3 .988 Chi-Square calculated<

Chi-Square tabulated (7.815) at .05 level of significance.


(p>0.05)

Likelihood Ratio .128 3 .988


.111 1 .739


322


Industry_type * MeasurableIndicators_AttritionR

educed




(p>0.05)



1.151 1 .283



Industry_type * MeasurableIndicators_CostPerR

ecruitReduced




(p>0.05)



.006 1 .940



Industry_type * MeasurableIndicators_RatioHigh

PerformingHiresToTotalHires




(p>0.05)



1.113 1 .291



Industry_type * MeasurableIndicators_TopTalent

Reduction

Pearson Chi-Square 1.258a 3 .739 Chi-Square calculated<

Chi-Square tabulated (7.815) at .05 level of significance.

Null hypothesis is


323


1.227 1 .268

accepted for this Measurable Indicator

(p>0.05



Table: 4.46 Chi-Square test of independence between Measurable Indicators

and industry type

The preceding table clearly reveals that Null hypothesis is accepted for all the

measurable indicators. Hence it can be stated that Measurable performance indicators

pertaining to Competency mapping are independent of industry type.

4.5.8.6 Chi-Square test of Independence between Measurable Indicators and

Org Age category

Chi-Square test of independence between

Measurable Indicators and Org Age cat


Remarks

MeasurableIndicators_SalesRevenueIncreased * Org_Age_Cat


Chi-Square calculated< Chi-Square Tabulated (21.026) at .05 level of significance.


(p>0.05)



.216 1 .642

N of Valid Cases 80


MeasurableIndicators_ProfitIncreased * Org_Age_Cat

Pearson Chi-Square 16.405a 12 .173 Chi-Square

calculated< Chi-Square Tabulated (21.026) at .05 level of significance

Null hypothesis is accepted for this Measurable



4.974 1 .026

324

N of Valid Cases 78

Indicator

(p>0.05)


MeasurableIndicators_ProductivityIncreased * Org_Age_Cat


Chi-Square calculated< Chi-Square Tabulated (21.026) at .05 level of significance


(p>0.05)



.061 1 .804

N of Valid Cases 80


MeasurableIndicators_AttritionReduced * Org_Age_Cat


Chi-Square calculated< Chi-Square Tabulated (21.026) at .05 level of significance Null hypothesis is accepted for this Measurable Indicator

(p>0.05)



4.534 1 .033

N of Valid Cases 77


MeasurableIndicators_CostPerRecruitReduced * Org_Age_Cat



(p>0.05)



.375 1 .540

N of Valid Cases 80


325

MeasurableIndicators_RatioHighPerformingHiresToTotalHires *

Org_Age_Cat



(p>0.05)



.039 1 .844

N of Valid Cases 78


MeasurableIndicators_TopTalentReduction * Org_Age_Cat



(p>0.05)



1.666 1 .197

N of Valid Cases 83


Table: 4.47 Table Chi-square test of Independence between measurable

indicators and Organisational Age category

The preceding table clearly depicts that measurable indicators do not vary with

organization age and all null hypothesis are accepted. Measurable indicators are

independent of organization age.

326

4.5.8.7 Chi-Square Test of independence between Measurable Indicators and

Staff Strength

Chi-Square on Measurable Indicators and Org Staff Strength


Remarks

MeasurableIndicators_SalesRevenueIncreased * Org_Staff_Strength



(p>0.05)



.382 1 .536

N of Valid Cases 89


MeasurableIndicators_ProfitIncreased *

Org_Staff_Strength



(p>0.05)



.519 1 .471

N of Valid Cases 86


MeasurableIndicators_ProductivityIncreased * Org_Staff_Strength



(p>0.05)



.045 1 .832

N of Valid Cases 89


MeasurableIndicators_AttritionReduced *

Org_Staff_Strength

Pearson Chi-Square 7.291a 12 .838 Chi-Square calculated<

Chi-Square Tabulated (21.026) at .05 level of significance Null hypothesis is accepted


327


.167 1 .683

for this Measurable Indicator

(p>0.05)

N of Valid Cases 85


MeasurableIndicators_CostPerRecruitReduced * Org_Staff_Strength



(p>0.05)



2.945 1 .086

N of Valid Cases 87


MeasurableIndicators_RatioHighPerformingHiresToTotalHires * Org_Staff_Strength



(p>0.05)



1.643 1 .200

N of Valid Cases 87


MeasurableIndicators_TopTalentReduction *

Org_Staff_Strength



(p>0.05)



.279 1 .597

N of Valid Cases 91


Table: 4.48 Table Chi-Square test of independence between Measurable

Indicators and Organisational Staff strength

328

The preceding table clearly shows that for all the measurable indicators there is no

association between the desired outcomes and the age of organization. Null hypothesis

is therefore accepted for all the cases

Conclusion: 4.6.0.0

Quantitative data analysis using Descriptive statistics and Hypothesis testing helped

achieve the research objectives of capturing the current scenario as regards

Perceptions/opinions of HR and line managers on Roadblocks, CSFs and Benefits

pertaining the same. The association between independent and dependent variables

was brought forth using Statistical tools like Independent Samples Test, Chi-Square

Test of Association. SPSS Software and MS Excel were used to analyse data.

chapter 4 data analysis & interpretation 4.0.0.0...

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