PEERCEPTION OOFF AAGGEENNTTSS OONN...

Perception of Agents on the Marketing Strategies and Policies of the LIC

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PPEERRCCEEPPTTIIOONN OOFF AAGGEENNTTSS OONN TTHHEE MMAARRKKEETTIINNGG SSTTRRAATTEEGGIIEESS AANNDD PPOOLLIICCIIEESS OOFF TTHHEE LLIICC

4.1 Sample profile and business features 4.2 Agents’ Perception on Life Insurance Agency and Training

Programmes 4.3 Problems in Marketing Life Insurance 4.4 Agents’ experience on their Duties, Tasks and Activities (DTA),

Skills and Abilities (SA) and Knowledge in Marketing Life Insurance Products/Services (KIMLIPS)

4.5 Agents’ Perception on the Promotional Strategies of LIC India 4.6 Perceptions of Agents on Marketing Mix Strategies, Resources,

Activities and Programmes and Performance of the LIC 4.7 Approaches and practices of agents in marketing life insurance

and handling customer objections 4.8 Conclusion

The major portion of the business of the LIC in its life assurance

business is brought by its agency force. Therefore, the perception of agents

on the marketing strategies and policies followed by LIC has much

importance from the practical point of view. The quality of agency force

decides to a great extent the business size of LIC. The perception of agents

on the agency profession and the training imparted by LIC reflect their

attitude towards the profession. The problems faced by agents in the course

of marketing life insurance are multifaceted. The organisation should be able

to incorporate measures to overcome such difficulties in the policy

Co

nt

en

ts

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frameworks for marketing. Apart from the knowledge on products and services

of the LIC, the agent force should have adequate knowledge of competitors,

products and services. It is ultimately their experience and commitment

towards the profession that decide success in their career. The promotional

strategies of LIC are different from those its competitors. As LIC uses its

agency force effectively through imparting short/long-term training course in

marketing products and services, the organisation doesn’t depend on mass

advertising throughout the entire life of product but rather on introduction and

closure. In the successful implementation of strategy, the cooperation from the

entire organisational workforce is a prerequisite. The perception of the

agents on marketing strategies and resources and capabilities will reflect how

far the organisation is able to make their strategies understandable to the

practitioners. The perception of agents on the facilitating factors that make

the marketing process easy and hassle free will be an eye opener to the

organisation as to the effectiveness of its multifaceted programmes. The

major areas of objection, the vital factors that influence agents in marketing

life insurance products and service, the method of prospecting and criteria

followed by agents in recommending policies facilitate modifications in the

prevailing marketing policies and strategies. This analysis will be of

immense utility to the organisation in the formulation, implementation and

evaluation of policies, strategies and programmes as to marketing products.

The study is based on a sample survey among 310 Agents of LIC selected

at random from 5 divisions of Life Insurance Corporation of India in

Kerala.

The analysis is presented in seven parts.

1) Demographic Profile of sample agents and their business features


213

2) Perception of LIC agents on Life Insurance Agency and Training

Programmes

3) Problems in Marketing Life insurance

4) Agents’ experience of their duties, tasks, activities, skills, abilities

and knowledge in marketing life insurance products and services

5) Agents’ Perceptions on the promotional strategies of LICI

6) Agents’ Perceptions on Marketing mix strategies, resources,

activities, programmes and performance of the LIC

7) Approaches and practices of agents in marketing life insurance and

handling customer objections

4.1 Sample Profile and Business Features

The profile of sample explaining the demographic and occupational

features of agents of LIC is presented in the Table given below.

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Table 4.1 Sample Profile Categories Frequency Per cent Cumulative per cent Area (Place of Residence)

Rural 217 70 70 Urban 93 30 100

Gender Male 186 60 60 Female 124 40 100

Marital status Married 279 90 90 Unmarried 31 10 100

Education SSLC 64 20.6 20.6 PDC/+2 112 36.1 56.8 Degree 98 31.6 88.4 Post graduate 24 7.7 96.1 Others 12 4.0 100

Nature of Agency Agent under DO 295 95.2 95.2 Direct Agent 15 4.8 100

Nature of Membership NCM 111 35.8 35.8 BMC 82 26.5 62.3 DMC 48 15.5 77.7 ZMC 25 8.1 85.8 CMC 28 9.0 94.8 DAC 14 4.5 99.4 MDTRC 2 0.6 100

Monthly Income(self) ≤ 5000 30 9.7 9.7 5001-10000 94 30.3 40 10001-15000 62 20 60 15001-20000 38 12.3 72.3 20001-25000 26 8.4 80.6 > 25000 60 19.3 100

Age (Years) ≤ 25 13 4.2 4.2 26-35 59 19.0 23.2 36-45 124 40.0 63.2 46-55 84 27.1 90.3 ≥ 56 30 9.7 100

Working Experience (Years)

≤ 5 94 30.3 30.3 6-10 86 27.7 58.1 11-15 66 21.3 79.4 16-20 39 12.6 91.9 > 20 25 8.1 100

Average number of policies sold p.a

≤ 50 136 44 43.9 51-100 116 37.4 81.3 101-150 42 13.5 94.8 151-200 6 1.9 96.8 ≥ 201 10 3.2 100

Average sum assured of policies sold p.a (Lakhs)

< 50 126 40.6 40.6 51-100 100 32.4 72.9 101-150 64 20.6 93.5 151-200 10 3.2 96.8 ≥ 201 10 3.2 100

Source: Primary Data


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Table 4.1 exhibits the profile of the sample respondents (Agents) selected

for study. As per the Table, while 70 per cent of the respondents belong to rural

areas, 30 per cent belong to urban areas. Gender-wise classification shows that out

of the 310 selected agents, males constitute 60 per cent and females 40 per cent.

A majority of the respondents are married (90 per cent). Education-wise

classification reveals that 36.1 have PDC/+2 qualification, 31.6 per cent are

graduates, 20.6 per cent are SSLC qualified, and post-graduates and higher

qualified hands are very low coming to 7.7 per cent and 4 per cent respectively.

The largest majority of the sample (95.2 per cent) belong to Agents working

under Development Officers. It is also observed that Agents not belonging to any

of the clubs come to 35.8 per cent, while it is 26.5 per cent belong to BMC,

15.5 per cent to DMC, and membership in other clubs makes for less than

10 per cent individually. The monthly income status of respondents reveals that

90.3 per cent is having an income above `10000. Many of the respondents

(30.3 per cent) belong to a monthly income range of 5001 to 10000,

followed by 20 per cent and 19.3 per cent belonging to 10001 to 15000 and

above 25000 categories. A majority of the policyholders (86.1 per cent)

range between age of 26 to 55. The classification of the sample based on their

working experience shows that the majority of the selected agents (79.3 per cent)

have experience of less than or equal to 15 years, while those having experience

above 20 years come to 8.1 per cent. While going through the status of the

average number of policies sold (annually) and the average sum assured, it is

clear that 81.4 per cent and 73 per cent of respondents belong to number of

policies sold up to 100 and having sum assured up to 100 lakh.

4.1.1 Business in Rural and Urban Areas

The operational area of a life insurance agent has no boundary. Even

some agents might have business more in rural than in urban areas or vice

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versa, depending upon their concentration of business and place of residence. The

following Table shows the percentage of business (in terms of policies) sold by

agents in rural and urban area on an average over last 5 years of business.

Table 4.2 Percentage of Policies Sold in Urban and Rural Areas

Group percentage

Per cent Frequency

Percentage of Policies sold in Area Urban Rural

102(32.9)

2.3 7 100 0 1.3 4 90 10 11 34 80 20 4.2 13 75 25 3.5 11 70 30 1.3 4 65 35 5.5 17 60 40 0.6 2 55 45 3.2 10 50 50

208(67.1)

9.7 30 40 60 0.6 2 35 65 6.5 20 30 70 4.8 15 25 75 11 34 20 80 5.8 18 15 85 1 3 12 88

11.3 35 10 90 0.6 2 8 92 8.1 25 5 95 0.6 2 4 96 2.6 8 1 99 4.5 14 0 100


The Table shows that there were seven (7 ) agents who sold whole of

their polices (100 per cent) in urban area and no policies (0 per cent) in rural

areas and fourteen (14) agents sold the whole of their policies (100 per cent)

in rural areas and none of the policies (0 per cent ) in urban areas. Considering


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50 per cent of policies sold as basis for evaluation, the number and percentage

of agents to sell more than or less than the basis are shown as grouped

percentage and it reveals that while 67.1 per cent of the agents sold more than

50 per cent of policies in rural areas, 32.9 per cent of them sold less than

50 per cent of polices in the same areas. It clearly shows the prominence of

life insurance sales of LIC in rural areas than in urban areas.

4.1.2 Customer Base of LIC Agents Based on Occupational Status

The customer base of an LIC agent, the segment (s) which he concentrates

to accelerate business, has much importance in his business. Here agents are

required to rank the type of segment which they mostly depend on, for their

business. There might be certain characteristic features which each segment

possesses, and which attract the agents to choose a particular segment. For

salaried employees, the prominent feature is regular and stable earning and

least possibility of default on payment of premium as salary-saving scheme

option is available to such segment. It is very important from the marketing

point of view to see if these responses show any difference among the 5

divisions of the LIC in Kerala. Since data provides ranks given by respondents,

a nonparametric test is used.

Here Kruskal-Wallis test is used, which is a non parametric test equivalent

to ANOVA and an extension of Mann Whitney test, as there are more than

two groups.

The hypotheses can be stated thus:

H0: There is no difference in the median responses for client features among

the 5 divisions.

H1: There is difference in the median responses for client features among

the 5 divisions.

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The client features comprise Daily Wage Earners (DWE), Salaried

Employees (SE), Agriculturists/Farmers (AF), Business/Self-Employed people

(BSE), NRI/Foreign Employed (NRI), Professionals (PNL) and Pensioners

(PNR).

Table 4.3 Descriptive Statistics DWE SE AF BSE NRI PNL PNR

Mean 3.72 2.59 3.35 2.93 4.08 5.15 6.66 Standard Deviation 1.998 1.546 1.885 1.545 1.613 1.488 1.168


Table 4.4 Occupation-wise Mean Rank on Customer Base of LIC Agents Clients Category EKM KTYM KKD TSR TVPM N 52 61 84 59 54 Daily Wage Earners 154.01 146.02 165.48 156.11 151.46 Salaried Employees 146.48 147.78 177.82 153.08 140.83 Agriculturists/Farmers 193.4 144.7 116.58 161.43 185.26 Business/Self employed 126.21 176.57 162.79 160.68 142.9 NRI/Foreign Employed 175.88 155.58 131.1 160.37 168.41 Professionals 120.46 160.3 191.45 145.88 138.42 Pensioners 163.88 161.86 146.68 143.45 167.12


Table 4.5 Kruskal Wallis Test DWE SE AF BSE NRI PNL PNR

Chi-Square 1.891 8.153 33.135 11.317 10.650 27.493 4.578 Df 4 4 4 4 4 4 4 Asymp. Sig. 0.756 0.086 0.000* 0.023* 0.031* 0.000* 0.333

Source: Primary Data *Significant at 5 per cent level of significance

Table 4.3 of descriptive statistics shows that Salaried employees and

Business and Self employed people are more in the customer base of selected

agents. The lower the mean, the higher will be their proportion. The mean rank

Table shows that salaried employees are more in Thiruvananthapuram Division,

Agriculturists/farmers in Kottayam and Kozhikode Divisions, Professionals in

Ernakulum Division and Pensioners in Thrissur Division. The Kruskal Wallis


219

test is found to be significant at 5 per cent level of significance with regard to

all the hypotheses except Daily Wage Earners, Salaried Employees and

Pensioners, whose cases are rejected, as the p values are 0.756, 0.086 and 0.333

(p>0.05). It means that there is significant difference in the customer base of

agents with regard to Agriculturists/farmers, Business and Self-Employed,

NRI/Foreign Employed, and Professionals in the 5 Divisions of the LIC.

4.1.3 Perception of Agents on the Preference of Policyholders in Different Categories of Occupation on LIC Policies

Different segments of policyholders may have preference of a certain

type of policies based on their future needs, earning and saving pattern,

personal attitude, mode of return/benefits available from investment etc. Since

Daily Wage Earners are found to have no stable and regular income, they

won’t prefer policies with high sum assured and high premium. As the salaried

category has guaranteed a regular earning capacity, they would be in a position

to meet high premium needs. While taking into account the case of NRIs, they

earn large income for a shorter period of service. The income of agriculturists is

seasonal in nature, while the business/self employed group expects quick returns

on investment. Pensioners prefer short-term commitments with assured return

regularly. From the marketing point of view, it is very important to identify the

preferences prevailing among various customer segments towards certain

policies and the reasons behind such preference.

A cross-section of the population consisting of Daily Wage Earners

(DWE), Salaried Employees (SE), Agriculturists/Farmers (AF), Business

/Self-Employed (BSE), Non-resident Indians/foreign Employed (NRI/FE),

Professional (PNL) and Pensioner (PNR) are requested to rank different types of

Life insurance policies, such as Whole Life Policy (WLP), Endowment Policy

(EP), Money Back Policy (MBP), Medical/Health Policy (MHP), Term Policy

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(TP), Pension Plans (PP), Unit Linked Policies (ULIPS), Children Plans (CP),

Jeevan Anand (JA), Jeevan Tarang (JT), Micro Insurance Plans (MIP), Other

Plans (OP). If there is coherence among the ranks we say that there is high

concordance with respect to the rank. Kendall’s Coefficient of Concordance is

a test used for testing concordance. The most preferred type of policy among

each segment of policyholders can be identified by the analysis.

Hypotheses:-

H0:- There is no coherence among the rankings given by different occupational groups on preference towards LIC policies.

H1:- There is coherence among the rankings given by different occupational groups on preference towards LIC policies.

Table 4.6 LIC Policy-wise Mean Ranks among Different Occupational Groups

Policy Type Client Group DWE SE AF BSE NRI PNL PNR

WLP 10.01 7.96 7.43 7.48 8.57 8.25 8.48 EP 2.21 2.84 2.78 3.70 4.13 3.54 2.87 MBP 2.34 2.75 2.98 3.37 3.78 3.28 2.90 MHP 5.62 5.47 5.84 4.84 5.11 4.40 3.21 TP 8.16 7.40 7.65 7.02 7.39 6.55 6.72 PP 5.67 7.74 6.46 6.41 6.63 7.32 7.17 ULIPS 9.61 9.41 9.98 7.81 8.73 8.71 7.34 CP 4.41 4.33 4.56 5.96 4.98 6.45 6.20 JA 4.99 3.60 4.84 4.24 3.36 3.91 5.60 JT 6.73 4.67 5.88 5.18 3.92 4.69 6.45 MIP 8.60 10.91 8.86 10.55 10.84 10.39 10.47 OP 9.66 10.92 10.74 11.46 10.57 10.52 10.59 Test Statistics N 258 253 255 257 253 257 251 Kendall's Wa .581 .679 .499 .501 .555 .515 .530 Chi-Square 1649.168 1889.821 1400.535 1415.669 1544.495 1455.094 1463.594 Df 11 11 11 11 11 11 11 Asymp. Sig. 0.000* 0.000* 0.000* 0.000* 0.000* 0.000* 0.000* A. Kendall's Coefficient Of Concordance Source: Primary Data *Significant at 5 per cent level of significance


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With a view to identifying the most preferred schemes by different classes

of people, respondents from different occupational groups in the sample were

asked to mark their preferences to schemes in the order of preference. The

policy receiving the minimum score is the most preferred. Thus, the above

Table provides the most preferred schemes by the classes considered.

Further, it was probed whether the opinions expressed by the different

classes of respondents are coherent, i.e., they don’t vary very much. A test for

concordance is done and from the results, the views expressed by different

classes, DWE, SE, AF, BSE, NRI, PNL and PNR are found coherent and the

coefficient of concordance is significant as its p values in all cases are 0.000

(p <0.05). SE records the maximum concordance (KW value 0.679) and AF

(KW Value 0.499), the least.

4.1.4 Motive behind Taking up the Job of LIC Agency

The basic motive behind taking up the job of life insurance agency

involves the attitude and passion towards the profession. As the agency

force contributes over 90 percentage business to LIC, it is of great

importance to identify their basic motives behind choosing the profession

and nurturing their talents to the benefit of the organisation. Reading the

data along with the work experience of the agent work force will be

interesting. The analysis with Kruskal Wallis Test is done over 5 divisions

of LIC, i.e., Ernakulam (EKM), Kottayam (KTYM), Kozhikode (KKD),

Thrissur (TSR) and Thiruvananthapuram (TVPM), as to four (4) major motives,

i.e., Means of Livelihood (ML), Supplement to Personal/Family Income

(SPFI), Social Service (SLS), Success Stories of LIC Agents (SSA) and

Others.

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The hypotheses can be stated thus:

H0: There is no difference in the median responses for agencyship motives

among the 5 divisions.

H1: There is difference in the median responses for agencyship motives

among the 5 divisions.

Table 4.7 Descriptive Statistics

ML SPFI SLS SSA Others Mean 1.7 2.09 3.21 3.19 4.81 SD 1.041 0.943 0.944 0.989 0.555

Source: Primary Data Table 4.8 Mean Ranks on Motives behind Agency Ship

Motive behind agency ship EKM KTYM KKD TSR TVPM N 52 61 84 59 54 Means of Livelihood (ML) 153.62 167.36 139.6 155.31 168.87 Supplement Personal /Family Income (SPFI)

169.18 160.92 135.17 158.39 164.68

Social Service (SLS) 130.65 148.19 188.45 159.48 132.08 Success Stories of LIC Agents (SSA)

156.13 133.79 181.33 144.1 151.7

Others 151.23 157.07 144.5 166.45 162.98 Source: Primary Data

Table 4.9 Kruskal Wallis Test

ML SPFI SLS SSA Others Chi-Square 6.434 7.235 21.814 12.928 8.203 Df 4 4 4 4 4 Asymp. Sig. 0.169 0.124 0.000* .012* .084 Source: Primary Data *Significant at 5 per cent level of significance

The Table of descriptive statistics shows that means of livelihood is the

prime motive behind agencyship. The lower rank score indicates higher


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preference. The hypotheses for means of livelihood, supplement to personal/

family income and others are not rejected as their p values are 0.169, 0.124 and

0.084 respectively (p in all cases >0.05), while the hypotheses for social service

and success stories of LIC agents are rejected as their p values are 0.000 and

0.012 (p<0.05). It means that there is significant difference among divisions as to

the basic motive behind agencyship among selected agents with regard to “social

service” and “success stories of LIC Agents”.

4.2 Agents’ Perception on Life Insurance Agency and Training Programmes

The section presents the perceptions of agents on various aspects of the

insurance profession they are pursuing and the level of satisfaction on different

aspects of training given by the LIC. The perceptions of agents on the

profession figure out their attitude and approach towards the job and their

higher authorities, commitment to the job and ultimate satisfaction. The

effectiveness of training programmes plays a vital role in motivating them.

The evaluation of their perceptions may highlight the area to be concentrated

in having effective sales force. The right attitude of agent with effective

training may create miracles in marketing products and services.

4.2.1 Agents’ Perception on Life Insurance Agency as a Profession

While considering life insurance agency as a profession, there appear

many dimensions on which the agency staff are to be functional partners.

Depending upon the functional nature of people associated with the agency, the

level in the society may vary. The views expressed by the general public may

depend upon this feature. The respondents have recorded their views on the

possible job status/status of themselves (JST) expressed by the public. Further,

there are dimensions related to work consisting of nature of work leading to

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different levels of function, say, work load (WL), interaction with higher

authorities-say, interaction with Branch Managers/Development Officers

(IBMDO), and sales promotion campaigns (SPC). In order to make them

efficient, there are certain training programmes (TPS) arranged for the agents. The

respondents have expressed their views on all these variables using 5- point Likert

scale. Similarly, they have recorded their job satisfaction (JS) in the profession.

With a view to verifying whether there is any difference in these expressed

opinions among the agents in the 5 divisions, analysis of variance –the one way

ANOVA is done and the results are presented below along with the hypotheses.


Division JST WL IBMDO SPC TPS JS

EKM N Mean 15.5192 12.7308 15.9231 12.4038 14.7115 16.4423 52 Std. Deviation 2.63082 2.99094 2.82042 3.87177 2.85833 2.28733

KTYM 61 Mean 16.2623 12.8197 15.541 15.7213 15.6885 16.459 Std. Deviation 3.3561 4.19725 3.6082 3.68841 3.8839 3.38907

KKD 84 Mean 15.9167 13.1548 16.369 15.2381 14.9762 15.881 Std. Deviation 2.12392 2.31545 2.78888 2.11489 2.25472 2.15879

TSR 59 Mean 16.9322 11.5424 14.5763 15.4237 16.1525 16.5763 Std. Deviation 2.95871 4.47723 3.38457 3.41499 3.50769 3.22278

TVPM 54 Mean 16.1296 11.4444 16.2593 13.9259 15.0741 16.1111 Std. Deviation 2.78843 2.52285 2.67895 3.79101 3.26128 2.61106

Total 310 Mean 16.1484 12.4129 15.771 14.6645 15.3129 16.2613 Std. Deviation 2.77831 3.42045 3.12042 3.51378 3.16874 2.74589


The hypotheses can be stated as follows:

H0: There is no variation in the mean scores obtained for the variables

related to the perception of Agents about Life insurance agency as a

Profession across the 5 Divisions of the LIC in Kerala.

H1: There is variation in the mean scores obtained for the variables related to

the perception of Agents about Life insurance agency as a Profession

across the 5 Divisions of the LIC in Kerala.

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Table 4.11 ANOVA Table

Sum of Squares Df Mean

Square F Sig.

JST

* D

ivisi

on Between Groups (Combined) 62.152 4 15.538 2.040 0.089

Within Groups 2323.022 305 7.616

Total 2385.174 309

WL

*Div

isio

n

Between Groups (Combined) 156.936 4 39.234 3.460 0.009*

Within Groups 3458.213 305 11.338

Total 3615.148 309

IBM

DO

*D

ivisi

on


Within Groups 2877.177 305 9.433

Total 3008.739 309

SPC

*Div

isio

n


Within Groups 3390.130 305 11.115

Total 3815.110 309

TPS

*Div

ision

Between Groups (Combined) 81.610 4 20.403 2.060 0.086

Within Groups 3021.038 305 9.905

Total 3102.648 309

JS

* D

ivisi

on Between Groups (Combined) 23.311 4 5.828 .771 0.545

Within Groups 2306.524 305 7.562

Total 2329.835 309 Source: Primary Data *Significant at 5 per cent level of significance

The attitude and perception of agents towards the profession have much

relevance in marketing insurance products successfully. The perception of

agents on their Job Status, Work Load, Experience with BM/DO, Sales

Promotion & Training Programmes, and Job Satisfaction are analysed with

One way ANOVA to test the statistical significance of difference in the means

scores of the above listed elements.


227

The result of the One way ANOVA for the variables “Work Load,

Interaction With BM/DO, Sales Promotion Campaigns” across the 5 divisions

of LIC in Kerala gives F values of 3.460, 3.487 and 9.559 respectively,

which are significant at 5 per cent level (p<0.05). Hence, the null hypothesis

is rejected. This implies that there is difference in the mean scores obtained for

work load, interaction with BM/DO, and sales promotion campaigns across the 5

divisions of LIC in Kerala. In the case of job status, training programmes and job

satisfaction across the 5 divisions of LIC in Kerala, the corresponding F value of

2.040, 2.0660 and 0.771 are found to be not significant at 5 per cent level

(p>0.05). Hence, the null hypothesis is not rejected. This implies that there is no

difference in the mean scores obtained for job status, training programmes and

job satisfaction across the 5 divisions of LIC in Kerala. To conclude, while the

perceptions of agents differ significantly as to work load, interaction with

BM/DO, and sales promotion campaigns across the 5 divisions, their

perception is found to be similar in the case of other variables like job status,

training programmes and job satisfaction. The selected agents belonging to

TVPM Division perceive heavy workload (lowest mean score 11.4444), it is

the reverse of the case for KKD Division. With regard to interaction with

Branch Managers and Development Officers, the selected agents belonging to

KKD Division (highest mean score 16.369) perceive better relation while it is

not so in the case of TSR Division. The SPCs are found to be effective as per

the perception of selected agents in KTYM Division (highest mean score

15.7213), while it is ineffective in EKM Division.

4.2.2 Level of Satisfaction on Training among LIC Agents

Training plays a vital role in making the human resource efficient and

capable to enhance the productivity of the organisation. The training organised

by the institution may vary as to the nature of job, stage of service, category

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(level) of personnel, stage of product in market, etc. The perception on the

training given in equipping LIC Agents to meet the competitive market

environment will be much helpful in designing improved training techniques

at various levels of service/product marketing, reducing cost by eliminating

unnecessary programmes, identifying and taking proper remedial measures at

appropriate stages etc. The perception of agents as to satisfaction on the

various elements of training given to them is analysed with One way ANOVA

to test the statistical significance of difference in the means scores.

The hypotheses in this regard can be stated as follows:

H0: There is no variation in the mean scores obtained for the variable “Level

of Satisfaction on Training” among the 5 Divisions of LIC in Kerala.

H1: There is variation in the mean scores obtained for the variable “Level of

Satisfaction on Training” among the 5 Divisions of LIC in Kerala.

Table 4.12 Training-Level of Satisfaction Division Mean N Std. Deviation EKM 31.5385 52 6.98026 KTYM 33.4918 61 7.23561 KKD 35.6190 84 5.14115 TSR 35.2881 59 6.01163 TVPM 31.9444 54 5.87099 Total 33.8129 310 6.38798




Square F Sig.

Trai

ning

* Div

ision

Between Groups (Combined) 866.235 4 216.559 5.625 0.000* Within Groups 11742.914 305 38.501



229

The result of One-way ANOVA for the variable level of satisfaction on

training across the 5 divisions of LIC in Kerala gives an F value of 5.625,

which is found to be significant at 5 per cent level (p<0.05). Hence the null

hypothesis is rejected. This implies that there is difference in the mean scores

obtained for the level of satisfaction on training among LIC agents of the 5

divisions of LIC in Kerala. The mean score obtained for Kozhikode is found to

be high and that of Ernakulam to be low, which signifies high level of

satisfaction among LIC agents in Kozhikode division and low level of

satisfaction among agents in Ernakulam division.

4.3 Problems in Marketing Life Insurance

LIC agents face multiple problems while marketing insurance products.

It may relate to premium, customer perception/attitude/interest/knowledge/

approach/ financial status, management practices of LIC, services and

facilities, attitude of officials and unhealthy and unethical marketing practices

among agents. The respondents were asked to assign a score of one (1) if the

problem felt was very high in the course of marketing life insurance products

and services, and score five (5) if the problem was very little. The intensity of

these problems across the 5 divisions of LIC in Kerala, across rural and urban

areas is analysed with MANOVA model. To explain the possible variations in

the mean scores of these eight factors across the five Divisions and two areas

under study, a MANOVA is proposed to be used. The problems are looked

into from eight dimensions. Here, the eight variables are taken together, assuming

that the variables are more meaningful if taken together than when considered

separately. The eight variables are the major problems related to marketing life

insurance products and services, such as Problems related to Premium (PRPR),

Problems related to Customer Perception/Attitude/Interest/Knowledge (PRCPAIK),

Problems related to Customer Approach (PRCA), Problems related to Customer

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Financial Status (PRCFS), Problems related to Management Practices of LIC

(PRMPLIC), Problems related to Services and Facilities (PRSF), Problems

related to Attitude of Officials (PRAO) and unhealthy, unethical marketing

practices among agents (UHEMPAA). MANOVA is used here to test the

following hypotheses:

H0: There is no significant variation in the mean scores for the set of

variables describing problems of marketing insurance among the 5

Divisions of LIC and between different Areas under study.

H1: There is significant variation in the mean scores for the set of variables

describing problems of marketing insurance among the 5 Divisions of

LIC and between different Areas under study.

The output of MANOVA is presented in Estimated Marginal Means Table,

Multivariate Tests Table and Table of between- subjects effects of variables.

Table 4.14 Multivariate Test for Analyzing Variance in Problems of Marketing Life Insurance Products among Divisions and Areas

Effect Value F Hypothesis df

Error df Sig.

Intercept Pillai's Trace 0.978 3417.104a 8 607 0.000* Wilks' Lambda 0.022 3417.104a 8 607 0.000* Hotelling's Trace 45.036 3417.104a 8 607 0.000* Roy's Largest Root 45.036 3417.104a 8 607 0.000*

Area Pillai's Trace 0.223 21.734a 8 607 0.000* Wilks' Lambda 0.777 21.734a 8 607 0.000* Hotelling's Trace 0.286 21.734a 8 607 0.000* Roy's Largest Root 0.286 21.734a 8 607 0.000*

Division Pillai's Trace 0.308 6.356 32 2440 0.000* Wilks' Lambda 0.718 6.59 32 2240 0.000* Hotelling's Trace 0.359 6.789 32 2422 0.000* Roy's Largest Root 0.22 16.799b 8 610 0.000*



231

Table 4.15 Estimated Marginal Means Grand Mean

Dependent Variable

Mean

Std. Error

95% Confidence Interval Lower Bound Upper Bound

PRPR 8.752 0.091 8.573 8.93 PRCPAIK 21.798 0.18 21.445 22.152 PRCA 16.131 0.132 15.872 16.39 PRCFS 6.323 0.075 6.175 6.471 PRMPLIC 17.435 0.147 17.146 17.723 PRSF 13.782 0.127 13.533 14.032 PRAO 18.312 0.174 17.971 18.653 UHEMPAA 12.296 0.137 12.027 12.564


Table 4.16 Test of Between- Subject Effects

Source Dependent Variable

Type I Sum of Squares Df Mean

Square F Sig.

Area PRPR 264.556 1 264.556 53.387 0.000* PRCPAIK 1138.065 1 1138.065 58.42 0.000* PRCA 456.49 1 456.49 43.541 0.000* PRCFS 525.873 1 525.873 153.59 0.000* PRMPLIC 1.265 1 1.265 0.097 0.755 PRSF 56.402 1 56.402 5.816 0.016* PRAO 21.703 1 21.703 1.193 0.275 UHEMPAA 3.563 1 3.563 0.316 0.574

Division PRPR 134.483 4 33.621 6.785 0.000* PRCPAIK 288.881 4 72.22 3.707 0.005* PRCA 198.66 4 49.665 4.737 0.001* PRCFS 40.027 4 10.007 2.923 0.021* PRMPLIC 691.677 4 172.919 13.285 0.000* PRSF 246.148 4 61.537 6.345 0.000* PRAO 520.961 4 130.24 7.159 0.000* UHEMPAA 601.261 4 150.315 13.333 0.000*

Source: Primary data *Significant at 5 per cent level of significance

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232

Table 4.17 Area-wise Estimated Marginal Means

Dependent Variable Area Mean Std.

Error 95% Confidence Interval

Lower Bound Upper Bound PRPR Rural 8.098 0.127 7.848 8.348

Urban 9.405 0.127 9.155 9.655 PRCPAIK Rural 20.444 0.253 19.948 20.939

Urban 23.153 0.253 22.657 23.649 PRCA Rural 15.273 0.185 14.91 15.637

Urban 16.989 0.185 16.626 17.353 PRCFS Rural 5.402 0.106 5.195 5.61

Urban 7.244 0.106 7.036 7.452 PRMPLIC Rural 17.39 0.206 16.984 17.795

Urban 17.48 0.206 17.075 17.885 PRSF Rural 13.481 0.178 13.131 13.831

Urban 14.084 0.178 13.734 14.434 PRAO Rural 18.125 0.244 17.646 18.604

Urban 18.499 0.244 18.02 18.978 UHEMPAA Rural 12.371 0.192 11.994 12.749

Urban 12.22 0.192 11.843 12.597 Source: Primary Data


233

Table 4.18 Division wise Estimated Marginal Means Dependent Variable Division Mean Std.

Error 95% Confidence Interval

Lower Bound Upper Bound PRPR EKM 8.663 0.218 8.235 9.092

KTYM 8.967 0.202 8.571 9.363 KKD 7.958 0.172 7.621 8.296 TSR 9.076 0.205 8.674 9.479 TVPM 9.093 0.214 8.672 9.513

PRCPAIK EKM 21.894 0.433 21.044 22.744 KTYM 21.107 0.4 20.322 21.891 KKD 21.095 0.341 20.427 21.764 TSR 21.924 0.406 21.126 22.722 TVPM 22.972 0.425 22.138 23.806

PRCA EKM 16.106 0.318 15.482 16.729 KTYM 15.967 0.293 15.392 16.543 KKD 15.244 0.25 14.753 15.735 TSR 16.729 0.298 16.143 17.314 TVPM 16.611 0.312 15.999 17.223

PRCFS EKM 6.644 0.181 6.288 7.001 KTYM 6.016 0.168 5.687 6.345 KKD 6.107 0.143 5.827 6.387 TSR 6.229 0.17 5.894 6.563 TVPM 6.62 0.178 6.271 6.97

PRMPLIC EKM 17.317 0.354 16.623 18.012 KTYM 15.943 0.327 15.301 16.584 KKD 18.72 0.278 18.174 19.267 TSR 16.805 0.332 16.153 17.457 TVPM 18.389 0.347 17.707 19.071

PRSF EKM 12.817 0.305 12.218 13.417 KTYM 13.369 0.282 12.815 13.923 KKD 14.565 0.24 14.094 15.037 TSR 14.271 0.287 13.708 14.834 TVPM 13.889 0.3 13.3 14.477

PRAO EKM 17.163 0.418 16.342 17.985 KTYM 17.598 0.386 16.84 18.357 KKD 19.548 0.329 18.901 20.194 TSR 19.093 0.393 18.322 19.864 TVPM 18.157 0.41 17.351 18.963

UHEMPAA EKM 11.808 0.329 11.161 12.454 KTYM 11.025 0.304 10.428 11.622 KKD 13.702 0.259 13.194 14.211 TSR 12.008 0.309 11.401 12.615 TVPM 12.935 0.323 12.301 13.57


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234

The estimated marginal means and MANOVA Tables 4.14, 4.15, 4.16,

4.17 and 4.18 indicate that the mean scores of the eight variables of marketing

problems taken together vary over the five LIC divisions, and that the

problems of marketing as to PRPR, PRCPAIK and PRCA in the Kozhikode

division are higher than in the other 4 divisions, as the mean values are very low

(7.958, 21.095 and 15.244) while the problems of marketing as to PRCFS,

PRMPLIC and UHEMPAA in the Kottayam division are higher than in the

other 4 Divisions, as the mean values are very low (6.016, 15.943 and 11.025)

and the problems of marketing as to PRSF, and PRAO in the Ernakulam

Division are higher than in the other 4 Divisions as the mean values are very

low (12.817 and 17.163). The statistical significance of the variation of the

means confirms this. Moreover, the MANOVA characterized by powerful

Pillai’s Trace test is significant at 5 per cent level of significance (value of

F 6.356 with p=0.000<0.05). But, when the eight variables for the five

Divisions are taken independently, variation can also be found statistically

significant in the test of between-subjects effects (p<0.05). Similarly, in the

area-wise problems of marketing life insurance products, all the problems of

marketing except as to UHEMPAA are found higher in rural areas than in

urban areas as the mean values are low in this respect (8.098, 20.444, 15.273,

5.402, 17.390, 13.481 and 18.125) while as to UHEMPAA the problems of

marketing are higher in urban areas and in rural areas its mean value is low.

Besides, Pillai’s trace test is also significant at 5 per cent level of significance,

which indicates the mean score variation is statistically significant at 5 per cent

level of significance (F value 21.734 with p=0.000<0.05). In addition to this,

the test of between-subjects effects as to area shows that mean scores of 5

(PRPR, PRCPAIK, PRCA, PRCFS and PRSF) out of the 8 variables have

independent statistical significance among agents of rural and urban areas


235

(p values are less than 0.05) while that of three variables (PRMLIC, PRAO

and UHEMPAA) have no independent statistical significance (as the p values

are higher than 0.05).

4.4 Agents’ experience on their Duties, Tasks and Activities (DTA), Skills and Abilities (SA) and Knowledge in Marketing Life Insurance Products/Services (KIMLIPS)

The experience of agents on their duties, tasks and activities plays an

important role in deciding their proficiency in marketing the service products

of the LIC. The skills and abilities possessed by them and the knowledge on

products, services, procedures and systems expose their sustainability and

proficiency in the field. The three aspects enable agents to introspect on their

abilities and the skills they should possess to sustain and excel in the field. The

experience of agents on their duties, tasks and activities, skills and abilities

and their knowledge on products/services are analysed with One way ANOVA

to test the statistical significance of difference in the means scores of the

above-listed elements.

H0: There is no significant variation in the mean scores obtained for the

variables “DTA, SA and KIMLIPS” among the 5 Divisions of LIC in

Kerala considered.

H1: There is significant variation in the mean scores obtained for the variable

“DTA, SA and KIMLIPS” among the 5 Divisions of LIC in Kerala

considered.

Chapter 4

236


Division N Duties, Tasks,

Activities Skills And Abilities

Knowledge of Marketing Life

Insurance Products/Services

Mean SD Mean SD Mean SD EKM 52 39.2692 6.32491 52.6346 7.15111 40.7692 5.49015

KTYM 61 41.8852 8.26458 53.4426 7.26527 40.4262 5.36488

KKD 84 41.9405 4.44607 52.7857 5.58628 40.1786 4.88953

TSR 59 42.7458 8.95505 54.4237 5.47787 40.8983 5.34562

TVPM 54 40.3148 6.38342 52.2222 7.28054 41.1111 4.89384

Total 310 41.3516 6.97538 53.1032 6.50782 40.6258 5.15538



Sum of Squares

Df Mean Square

F Sig.

DTA

*

Div

isio

n Between Groups (Combined)

444.710 4 111.177 2.324 0.057

Within Groups 14589.964 305 47.836 Total 15034.674 309

SA

* D

ivisi

on Between Groups

(Combined) 171.707 4 42.927 1.014 0.400


KIM

LIPS

*

Div

isio

n Between Groups (Combined)

37.400 4 9.350 .349 0.845




237

The result of One-way ANOVA for the variable “duties, tasks and

activities (DTA), skills and abilities (SA), knowledge in marketing Life Insurance

products and services (KIMLIPS)” across the 5 divisions of LIC in Kerala give

F values 2.324, 1.014 and 0.349 which is found to be not significant at 5 per cent

level (p>0.05). Hence the null hypothesis is not rejected. This implies that there

is no significant difference in the mean scores obtained for “duties, tasks and

activities-skills and abilities-knowledge on products and services” among the 5

divisions of LIC in Kerala. To conclude, it is inferred that the selected agents

across the five divisions do not differ in respect of their experience on Duties,

Tasks, and Activities (DTA), Skills and Abilities (SA) and Knowledge on

Products and Service (KIMLIPS).

4.5 Agents’ Perception on the Promotional Strategies of LIC India

The LIC of India promotes its products through many media. The

effectiveness of the promotional effort may vary from the rural to the urban

area, because the demographic fundamentals of rural and urban areas are

different. The analysis intends to identify the most effective media chosen by

agents in Rural and Urban areas, the major reasons behind not reaching the

message of advertisement to customers effectively, and the effectiveness of the

promotional programmes of the LIC.

4.5.1 Media Effectiveness in Creating Awareness and Effecting Sales in Rural and Urban Area The choice of promotional media plays a vital role while formulating the

promotional strategy of an institution. The coverage or exposure, impact or

utility, frequency, continuity, media cost and availability vary with the media.

Depending upon area, the effectiveness may differ as to the type of media used

for propagation of product or service. The factors to be borne in mind while

Chapter 4

238

choosing the media are demographic profile of the area (target market), the

preference of the customers as to the media, nature of message/content to be

conveyed, stage of product life cycle, cost and benefit of promotional effort,

reach of the media (Internet is not very common among illiterate people),

etc. The decision is of great importance as it involves huge financial

commitment and is irreversible. The analysis attempts to identify the most

effective media used by the LIC in its promotional efforts in rural and urban

areas. As the life insurance agents are having close contact with policyholders,

they will have a clear image on customers’ evaluation of the usefulness of

the media in creating awareness and persuading them to enquire about or

purchase the products and services of the LIC. It will be helpful in identifying

the suitable media mix for rural and urban areas. To test variations in the mean

scores of effectiveness of media in both rural and urban areas, t-Test: Two-

Sample Assuming Equal Variances, is used and the output is presented

below.

The media used by the LIC, identified for analysis are TV Advertisements

(TVA), Radio Advertisements including FM Radio (RA), Advertisements in

Magazines/Journals (AMJ), Film Advertisements (FA), Posters, Banners, Sign

Boards, Hoardings (PBSH), Pamphlets, Leaflets, Brochures (PLB), Internet

and Web Advertising (IWA), Direct Mail, Email (DMEM), Word of Mouth

(WM), Sponsorships of Plays/Events (SPE), News Paper Advertisements

(NPA), Social Camps (free eye test, health check-up, etc) (SC), Calendars,

Diaries (CD), Wall Paintings (WP), Paper Inserts, Bound Inserts in Books

(PIBI), Brand Ambassadors, Celebrity Endorsements ( BACE), Tran-Slides and

Electric Display (TSED), Information Kiosks (IK), Customer Contact

Programmes (CCP) and Taxi Cab Advertising (TCA).


239

The hypotheses can be stated as:

H0: There is no variation in the mean scores obtained for the identified

promotional media between rural and urban areas.

H0: There is variation in the mean scores obtained for the identified

promotional media between rural and urban areas.

Table 4.21 T Test

Media Mean Variance Df t Stat P(T<=t) two-tail

Rural Urban Rural Urban TVA 5.906452 6.012903 1.987984 1.760351 618 -0.96809 0.33 RA 5.103226 4.948387 2.157595 2.152667 618 1.313133 0.18 AMJ 4.935484 5.722581 1.704562 1.735087 618 -7.47226 0.00* FA 4.796774 4.909677 1.942384 1.603466 618 -1.05567 0.29 PBSH 5.316129 5.622581 1.43372 1.65321 618 -3.071 0.00* PLB 5.283871 5.351613 1.970936 1.976292 618 -0.60033 0.54 IWA 3.680645 5.896774 2.179236 1.956947 618 -19.1856 0.00* DMEM 3.348387 5.687097 3.056227 3.303069 618 -16.3287 0.00* WM 6.358065 4.53871 0.897265 3.317267 618 15.60356 0.00* SPE 4.487097 4.535484 1.499833 1.602297 618 -0.4837 0.62 NPA 5.722581 5.822581 1.599165 1.440912 618 -1.00981 0.31 SC 4.687097 4.470968 1.937373 2.042844 618 1.907395 0.05 CD 5.541935 5.512903 2.009563 2.075885 618 0.252896 0.80 WP 4.483871 4.777419 2.367053 2.341883 618 -2.38177 0.00* PIBI 4.012903 4.512903 2.569412 2.619574 618 -3.86464 0.00* BACE 3.845161 3.964516 2.636142 2.921067 618 -0.89144 0.37 TSED 4.012903 5.387097 1.954526 2.762293 618 -11.1405 0.00* IK 3.803226 5.377419 1.679601 2.617611 618 -13.3704 0.00* CCP 5.154839 4.454839 2.014782 2.55297 618 5.766703 0.00* TCA 4.206452 4.480645 1.950767 2.587003 618 -2.2663 0.02*

Source: Primary Data *Significant at 5 per cent level of significance ** based on 310 observations in each media

Chapter 4

240

The result of the t test for the variables Advertisements in Magazines/

Journals, Posters/ Banners/Sign-Boards/Hoardings, Internet/Web Advertising,

Direct mail/Email, Word of Mouth, Wall Paintings, Paper Inserts and Bound

Inserts in books, Trans-slides and Electric Display, Information Kiosks,

Customer Contact Programme, Taxi Cab Advertising between rural and urban

areas with Df 618 is calculated to be significant at 5 per cent level (p< 0.05).

Hence the null hypothesis is rejected. This implies that there is significant

difference in the mean scores obtained for the said media. While Advertisements

in Magazines/Journals, Posters/Banners/Sign Boards/Hoardings,, Internet/Web

Advertising, Direct mail/Email, Wall Paintings, Paper Inserts and Bound

Inserts in Books, Trans-Slides/Electric Display, Information Kiosks, and Taxi

Cab Advertising are more effective in urban areas (as its mean values are

higher than rural area), Customer Contact Programme and Word of Mouth are

more effective in rural areas. In the case of media other than those specified

above, with Df 618, the variation is calculated to be not significant at 5 per cent

level (p>0.05). Hence the null hypothesis is not rejected. This implies that

there is no difference in the mean scores obtained for the media listed above,

which highlights their similarity in effectiveness in rural and urban areas.

4.5.2 Major Reasons for Not Reaching the Message of Advertisements

The promotional tool used for propagation or creating awareness or

persuading prospects to get attracted to product or service offered by institution

should be effective in all directions. The major factors identified here for

evaluation are selection of media, timing of advertisement, effort of staff in the

organisation, theme and intensity of use of media, language and layout of

advertisement etc. The analysis will be helpful to the LIC in evaluating the

reasons for ineffective reach of the messages of its advertisements so that cost

can be reduced to a great extent and effectiveness can be enhanced. The kruskal


241

Wallis test has been used to identify the major reasons behind not reaching the

message of advertisements to the prospective customers. The output of the test

is given below.

The major reasons identified for evaluation are Wrong Selection of

Advertisement Media (WSAM), Inappropriate Timing of Advertisement (ITA),

Deficiencies in the Working of Publicity Staff (DWPS), Less Emphasis to

Regional Languages (LERL), Inadequate Usage of Mass Media (IUMM),

Improper Theme and Message of Advertisement (ITMA), Unattractive Layout

of Advertisement (ULA) and Improper Language of Advertisement (ILA).

Table 4.22 Descriptive Statistics on Reasons for Not Reaching Message of Advertisements

Reasons

WSA

M

ITA

DW

PS

LE

RL

IUM

M

ITM

A

UL

A

ILA

Others

N 310 310 310 310 310 310 310 310 310 Mean 4.25 3.95 3.7 4.86 3.84 5.11 5.1 5.87 7.34 Std. Deviation 2.417 2.139 2.168 2.392 2.235 2.123 2.349 2.507 2.828


Table 4.23 Mean Ranks of Reasons for Not Reaching Message of Advertisements

Reasons EKM KTYM KKD TSR TVPM N 52 61 84 59 54 WSAM 176.66 154.34 136.74 132.63 190.59 ITA 154.77 155.56 163.96 141.88 157.85 DWPS 133.86 160.2 169.15 151.82 153.82 LERL 172.36 140.16 164.3 154.51 143.99 IUMM 183.44 137.46 168.92 123.51 163.06 ITMA 178.62 127.54 171.11 151.26 145.18 ULA 165.92 155.77 149.15 149.51 161.58 ILA 134.62 175.08 167.79 155.47 134.41


Chapter 4

242


H0: There is no difference in the median responses for the reasons behind not

reaching the message of advertisement among the 5 Divisions of the LIC.

H1: There is difference in the median responses for the reasons behind not

reaching the message of advertisement among the 5 Divisions of the

LIC.


WSA

M

ITA

DW

PS

LE

RL

IUM

M

ITM

A

UL

A

ILA

Chi-Square 19.092 2.199 5.435 5.41 17.783 13.131 1.664 10.77 Df 4 4 4 4 4 4 4 4 Asymp. Sig. 0.001* 0.699 0.245 0.248 0.001* 0.011* 0.797 0.029*


Table 4.22 of descriptive statistics shows that factors such as Deficiencies

in the working of publicity staff and Inadequate usage of mass media have

lower mean scores 3.7 and 3.84 respectively. The mean rank table shows that

all factors except Inappropriate timing of advertisement, Deficiencies in the

working of publicity staff, Less emphasis to regional languages and

Unattractive layout of advertisement are identified reasons in all the 5

Divisions. The hypotheses related to inappropriate timing of advertisement,

deficiencies in the working of publicity staff, less emphasis to regional

languages and unattractive layout of advertisement are not rejected as their p

values are seen to be 0 .699, 0.245, 0.248, 0.797 (p>0.05), and for other

reasons, the hypotheses are rejected as their p values are 0.001, 0.001, 0.011

and 0.029 (p<0.05). It can be inferred that while WSAM and IUMM in Thrissur


243

Division, ITMA in Kottayam and ILA in Thiruvananthapuram Division are

found to be the major reasons in not reaching /conveying the message of

advertisements effectively to customers.

4.5.3 Effectiveness of Promotional Programmes (PPS) in Achieving Promotional Objectives

The promotional programmes of any organisation should be able to

achieve its objectives set forth in relation to it. The promotional objectives

considered for evaluation relate to enhancing brand awareness, preference and

image, product knowledge, customer loyalty, voluntary promotion, etc. As the

implementation of Promotional Programmes is costly, its ineffectiveness can

lead to waste of money and effort. In formulating and redrafting the

programmes, it will be of immense help to identify whether such programmes

are capable of realising the objectives laid down. The agents’ perceptions on

the effectiveness of promotional programmes in realsing promotional objectives

are analysed with One way ANOVA and the output is presented in the

following Tables.

The following hypotheses can be set:

H0: There is no significant variation in the mean scores obtained for the

variable “effectiveness of promotional programmes of the LIC in

realising promotional objectives” among the 5 divisions of the LIC in

Kerala.

H1: There is significant variation in the mean scores obtained for the

variable “effectiveness of promotional programmes of the LIC in

realising promotional objectives” among the 5 divisions of the LIC in

Kerala.

Chapter 4

244


Division EKM KTYM KKD TSR TVPM Total Mean 49.2692 52.1967 57.0238 55.1864 49.6111 53.1323 N 52 61 84 59 54 310 Std. Deviation 7.12112 8.48689 7.55697 6.65783 9.41379 8.42549 Source: Primary Data

Table 4.26 One Way ANOVA


Square F Sig.

Promotional Objectives * Division

Between Groups (Combined)

3019.972 4 754.993 12.174 0.000*



The result of One-way ANOVA for the variable “effectiveness of

promotional programmes of the LIC in achieving promotional objectives”

across the 5 divisions of the LIC in Kerala gives an F value 12.174 which

is calculated to be significant at 5 per cent level (p<0.05). Hence the null

hypothesis is rejected. This implies that there is significant difference in the

effectiveness of promotional programmes in achieving promotional objectives

among the 5 divisions of the LIC in Kerala. The mean score too highlights that

the promotional programmes are most effective in Kozhikode division,

followed by Thrissur, Kottayam, Thiruvananthapuram and Ernakulam in

achieving promotional objectives in the order given.

4.6 Perceptions of Agents on Marketing Mix Strategies, Resources, Activities and Programmes and Performance of the LIC

The organisational performance of the LIC is influenced by many factors

in the competitive market place. The major factors are classified into Strategies


245

employed and Resources possessed and Activities and programmes pursued

by the LIC, for analysis. The presentation intends to identify the major

factors influencing the organisational performance. The Marketing Mix

Strategies comprise strategies related to Product (PDTS), Price (PRSS),

Promotion (PLSS), Distribution (DCSS), People (PESS), Physical Evidence

(PEFS) and Process (PSSS). The Resources, Activities and Programmes

include Marketing Resources and Capabilities (MRCS), Marketing Resource

Commitment activities (MRCA), Marketing Consensus activities (MCAS),

Cross-Functional Integration Activities (CFIA), Marketing Experience

Elements (MEES), and Marketing Communication Quality Activities

(MCQA). The Organisational performance variables identified are Market

Share (MS), Return on Investment (ROI), Sales Growth (SG), Customer

Retention (CR) and Corporate Image (CI).

The section covers:

1) Impact of Marketing Mix Strategies, Resources/Activities/

Programmes of Performance

2) Canonical Correlation of Marketing Mix Strategies and Resources

/Activities / Programmes, with Performance

4.6.1 Impact of Marketing Mix Strategies (MMS) and Resources, Activities and Programmes (RAP) on Market Share (MS)

The strategies effectively employed with optimum utilization of its

resources and integration of organisational activities in tune with it can

contribute to enhanced sale of products and thereby increase market share.

The following Tables reveal how far the performance indicator- Market

Share, is influenced by its strategies and resources based on Stepwise

Multiple Linear Regression Model.

Chapter 4

246

Table 4.27 Model Summary Model R R Square Adjusted R Square Std. Error of the Estimate

1 0.408 .166 .163 3.32224

2 0.429 .184 .179 3.29230


Table 4.28 ANOVA Model Sum of Squares Df Mean Square F Sig.

1 Regression 677.613 1 677.613 61.393 0.000 *

Residual 3399.487 308 11.037 Total 4077.100 309

2 Regression 749.446 2 374.723 34.571 0.000 * Residual 3327.654 307 10.839 Total 4077.100 309

Source: Primary Data * Significant at 5 per cent level of significance

Table 4.29 Coefficients

Model Unstandardized

Coefficients Standardized Coefficients t Sig.

B Std. Error Beta 1 (Constant) 17.661 .793 22.257 0.000*

Physical Evidence Strategies

.247 .032 .408 7.835 0.000*

2 (Constant) 16.089 .995 16.162 0.000*


.170 .043 .279 3.897 0.000*

Marketing Resources and Capabilities

.039 .015 .185 2.574 0.011*

Dependent Variable: Market Share Source: Primary Data *Significant at 5 per cent level of significance


247

Here the second model is statistically more valid for drawing inference

as the independent variables are found to be significant. From the above Table,

it may be observed that the regression is satisfactory with 18.4 per cent

explanation of the variation in market share and that this explanation is

statistically valid, as the Beta coefficients and their t values are statistically

significant (p < 0.05). Out of the 7 seven strategies (product-price-promotion-

place-people-physical evidence-process) and the 6 elements of marketing

resources, activities and programmes (MRCS, MRCA, MCAS, CFIA, MEES,

MCQA), physical evidence strategies and MRCS are found to be the elements

most influencing the market share of the LIC.

4.6.2 Impact of Marketing Mix Strategies and Resources, Activities and Programmes on Return on Investment (ROI)

Whatever may be the form of investment, an investor first of all looks at

its expected return before committing any investment. Here, the dependence of

Performance Indicator-Return on Investment on strategies and resources of

LIC are analysed with Step-wise Multiple Linear Regression. The return is

influenced not only by the organisational factors as Operational expenses,

promotional expenses, fixed commitments etc but also by the effectiveness of

the strategies implemented along with proper deployment of resources.

Table 4.30 Model Summary

Model R R Square Adjusted R Square

Std. Error of the Estimate

1 .523 0.274 0.272 3.90302 2 .571 0.327 0.322 3.76539 3 .596 0.355 0.349 3.69107 4 .618 0.382 0.374 3.61762 5 .629 0.395 0.385 3.5859 6 .640 0.409 0.398 3.54987 7 .648 0.421 0.407 3.52159


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248

Table 4.31 ANOVA

Model Sum of Squares Df Mean

Square F Sig.

1 Regression 1771.034 1 1771.034 116.259 0.000 *

Residual 4691.934 308 15.234 Total 6462.968 309






7 Regression 2717.689 7 388.241 31.306 0.000 * Residual 3745.279 302 12.402

Total 6462.968 309 Source: Primary Data * Significant at 5 per cent level of significance


249


Model Unstandardized Coefficients

Standardized Coefficients

B Std. E

Beta T Sig 1 (Constant) 8.959 1.171 7.652 0.000*

Marketing resources and capabilities 0.139 0.013 0.523 10.782 0.000* 2 (Constant) 11.307 1.227 9.213 0.000*

Marketing resources and capabilities 0.19 0.016 0.715 11.709 0.000* Promotional strategies -0.267 0.055 -0.299 -4.891 0.000*

3 (Constant) 11.106 1.204 9.222 0.000* Marketing resources and capabilities 0.146 0.02 0.548 7.285 0.000* Promotional strategies -0.338 0.057 -0.379 -5.948 0.000* Marketing consensus activities 0.167 0.046 0.283 3.673 0.000*

4 (Constant) 11.585 1.188 9.756 0.000* Marketing resources and capabilities 0.15 0.02 0.564 7.632 0.000* Promotional strategies -0.339 0.056 -0.38 -6.076 0.000* Marketing consensus activities 0.299 0.057 0.507 5.228 0.000* Marketing experience elements -0.175 0.048 -0.288 -3.681 0.000*

5 (Constant) 11.411 1.179 9.677 0.000* Marketing resources and capabilities 0.129 0.021 0.485 6.09 0.000* Promotional strategies -0.344 0.055 -0.385 -6.215 0.000* Marketing consensus activities 0.285 0.057 0.483 4.992 0.000* Marketing experience elements -0.185 0.047 -0.305 -3.911 0.000* Physical evidence strategies 0.123 0.049 0.162 2.534 0.012*

6 (Constant) 11.952 1.185 10.09 0.000* Marketing resources and capabilities 0.153 0.023 0.577 6.712 0.000* Promotional strategies -0.321 0.055 -0.36 -5.801 0.000* Marketing consensus activities 0.318 0.058 0.539 5.503 0.000* Marketing experience elements -0.189 0.047 -0.312 -4.04 0.000* Physical evidence strategies 0.179 0.053 0.235 3.413 0.001* Process strategies -0.158 0.059 -0.243 -2.684 0.008*

7 (Constant) 10.825 1.264 8.566 0.000* Marketing resources and capabilities 0.16 0.023 0.602 7.004 0.000* Promotional strategies -0.361 0.057 -0.404 -6.295 0.000* Marketing consensus activities 0.285 0.059 0.483 4.828 0.000* Marketing experience elements -0.181 0.047 -0.298 -3.885 0.000* Physical evidence strategies 0.176 0.052 0.231 3.386 0.001* Process strategies -0.187 0.06 -0.288 -3.142 0.002* Product strategies 0.131 0.054 0.148 2.426 0.016*


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Here the seventh model is statistically more valid for drawing inference

as the 7 independent variables are found to be significant. From the above

Table, it may be observed that the regression is fairly good with 42.1 per cent

explanation of the variation in return on investment and that this explanation is

statistically valid, as the Beta Coefficients and their t values are statistically

significant (p < 0.05). Out of the 7 seven marketing mix strategies (product-

price-promotion-place-people-physical evidence-process) and the 6 elements

of marketing resources, activities and programmes (MRCS, MRCA, MCAS,

CFIA, MEES and MCQA), marketing resources and capabilities, promotional

strategies, marketing consensus activities, marketing experience elements,

physical evidence strategies, process strategies and product strategies are

influencing the performance indicator return on investment of the LIC.

4.6.3 Impact of Marketing Mix Strategies and Resources, Activities and Programmes on Sales Growth (SG)

The Sales Growth, i.e., increase in number of polices marketed is

influenced by its strategies in the market place as to variety in product mix

offered, rate of premium charged, promotional efforts, approach and efficiency

of personnel in rendering service, the nearness to the point of service, and

amenities offered at service premises, etc. The dependence of performance

indicator-sales growth on strategies and resources, activities and programmes

of the LIC is analysed with Step-wise Multiple Linear Regression.

Table 4.33 Model Summary Model R R Square Adjusted R Square Std. Error of the Estimate

1 .443 0.196 0.193 3.4389 2 .467 0.218 0.213 3.39675 3 .481 0.231 0.223 3.37432 4 .494 0.244 0.234 3.35132 5 .507 0.257 0.245 3.32791 6 .519 0.27 0.255 3.30414



251

Table 4.34 ANOVA

Model Sum of Squares Df Mean Square F Sig.

1 Regression 888.014 1 888.014 75.09 0.000* Residual 3642.425 308 11.826 Total 4530.439 309

2 Regression 988.299 2 494.149 42.828 0.000*

Residual 3542.14 307 11.538 Total 4530.439 309

3 Regression 1046.315 3 348.772 30.632 0.000*

Residual 3484.124 306 11.386 Total 4530.439 309






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252


Model

Unstandardized Coefficients

Standardized Coefficients T Sig.

B Std. Error Beta

1 (Constant) 15.472 0.853 18.13 0.000* Marketing Experience Elements 0.225 0.026 0.443 8.665 0.000*

2 (Constant) 13.652 1.045 13.067 0.000* Marketing Experience Elements 0.163 0.033 0.32 4.883 0.000* Process Strategies 0.105 0.036 0.193 2.948 0.003*

3 (Constant) 14.011 1.05 13.344 0.000* Marketing Experience Elements 0.2 0.037 0.393 5.406 0.000* Process Strategies 0.151 0.041 0.277 3.697 0.000* People Strategies -0.058 0.026 -0.182 -2.257 0.025*

4 (Constant) 13.29 1.089 12.198 0.000* Marketing Experience Elements 0.171 0.039 0.337 4.418 0.000* Process Strategies 0.12 0.043 0.221 2.823 0.005* People Strategies -0.077 0.027 -0.242 -2.871 0.004* Distribution Channel Strategies 0.177 0.077 0.191 2.283 0.023*

5 (Constant) 13.234 1.082 12.229 0.000* Marketing Experience Elements 0.113 0.046 0.221 2.437 0.015* Process Strategies 0.08 0.046 0.147 1.745 0.082 People Strategies -0.098 0.028 -0.309 -3.489 0.001* Distribution Channel Strategies 0.182 0.077 0.197 2.373 0.018* Marketing Consensus Activities 0.124 0.054 0.251 2.304 0.022*

6 (Constant) 13.293 1.075 12.369 0.000* Marketing Experience Elements 0.107 0.046 0.211 2.332 0.02* Process Strategies 0.026 0.051 0.049 0.519 0.604 People Strategies -0.119 0.029 -0.375 -4.055 0.000* Distribution Channel Strategies 0.185 0.076 0.2 2.424 0.016* Marketing Consensus Activities 0.132 0.054 0.267 2.464 0.014* Physical Evidence Strategies 0.118 0.051 0.184 2.321 0.021*

7 (Constant) 13.433 1.039 12.93 0.000* Marketing Experience Elements 0.104 0.046 0.205 2.291 0.023* People Strategies -0.119 0.029 -0.374 -4.052 0.000* Distribution Channel Strategies 0.196 0.073 0.211 2.673 0.008* Marketing Consensus Activities 0.142 0.05 0.288 2.861 0.005* Physical Evidence Strategies 0.13 0.045 0.203 2.869 0.004*

Dependent variable: sales growth Source: Primary data *Significant at 5 per cent level of significance


253

Here, the sixth model is statistically more valid for inference as six

independent variables are found to be significant. From the above Table, it may be

observed that the regression is satisfactory with 27 per cent explanation of the

variation in sales growth and that this explanation is statistically valid, as the

Beta coefficients and their associated t values are statistically significant

(p < 0.05). Out of the 7 seven marketing mix strategies (product-price-promotion-


resources, activities and programmes (MRCS, MRCA, MCAS, CFIA, MEES and

MCQA), marketing experience elements, people strategies, distribution channel

strategies, marketing consensus activities and physical evidence strategies are

found to be dictating the performance indicator, i.e., sales growth of the LIC.

4.6.4 Impact of Marketing Mix Strategies and Resources, Activities and Programmes on Customer Retention (CR) Customer retention is a better indicator of organisational performance.

Retaining customers is more tedious than creation of new customers. As the

cost of retaining customer is lower than that of creation of new customers, the

firm should design its strategies and employ its resources in that direction. The

analysis presents the dependence of customer retention on the strategies and

resources of the concern, using Step-wise Multiple Linear Regression. It will

be interesting to identify those factors having great influence over the

customer retention ability of the LIC.


Model R R Square Adjusted R Square Std. Error of the Estimate 1 0.463 .215 .212 3.50435 2 0.498 .248 .243 3.43461 3 0.514 .264 .257 3.40305



255

Table 4.37 ANOVA

Model Sum of Squares Df Mean Square F Sig. 1 Regression 1033.698 1 1033.698 84.174 0.000*

Residual 3782.379 308 12.28 Total 4816.077 309

2 Regression 1194.534 2 597.267 50.631 0.000*

Residual 3621.543 307 11.797 Total 4816.077 309




Model Unstandardized

Coefficients Standardized Coefficients T Sig.

B Std. Error Beta 1 (Constant) 14.477 0.904 16.012 0.000*

Cross Functional Integration Activities

0.308 0.034 0.463 9.175 0.000*

2 (Constant) 13.213 0.95 13.911 0.000* Cross Functional Integration Activities

0.215 0.041 0.324 5.193 0.000*


0.152 0.041 0.23 3.692 0.000*

3 (Constant) 11.818 1.084 10.901 0.000* Cross Functional Integration Activities

0.176 0.044 0.264 4.019 0.000*


0.123 0.042 0.187 2.92 0.004*

Promotional Strategies 0.121 0.047 0.157 2.592 0.01* Dependent Variable: Customer Retention Source: Primary Data *Significant at 5 per cent level of significance

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From the above Table (based on the third model), it may be observed

that the regression is satisfactory with 26.4 per cent explanation of the

variation in customer retention and that this explanation is statistically valid, as

the Beta coefficients and their associated t values are statistically significant

(p < 0.05). Out of the 7 seven marketing mix strategies (product-price-promotion-


resources, activities and programmes (MRCS, MRCA, MCAS, CFIA, MEES

and MCQA), cross-functional integration activities, physical evidence

strategies and promotional strategies are dictating the performance indicator

customer retention of the LIC.

4.6.5 Impact of Marketing Mix Strategies and Resources, Activities and Programmes on Corporate Image (CI) A better image of an organisation is formed in the minds of customers

due to the influence of many factors deriving out of its services, resources and

activities, etc. In addition to the product features and benefits, the service

environment, the variety and quality of service rendered, easiness in handling

transactions, supportive and friendly approach of service and marketing

personnel, provision of simple and efficient systems for managing grievances

etc are capable of creating a better image of the LIC. The presentation intends

to identify the major elements having great impact on the customer image of

the organisation from the marketing point of view. The Regression analysis

provides the cause of interdependence between customer image and the

strategies and resources of the LIC.


257


Model R R Square Adjusted R Square Std. Error of the Estimate

1 0.563 .317 .315 3.46637

2 0.601 .361 .357 3.35730

3 0.611 .373 .367 3.33187

4 0.629 .396 .388 3.27620

5 0.638 .407 .397 3.25106 Source: Primary Data

Table 4.40 ANOVA

Model Sum of Squares Df Mean

Square F Sig.


2 Regression 1958.952 2 979.476 86.899 0.000*

Residual 3460.342 307 11.271 Total 5419.294 309

3 Regression 2022.275 3 674.092 60.722 0.000* Residual 3397.018 306 11.101

Total 5419.294 309

4 Regression 2145.572 4 536.393 49.974 0.000* Residual 3273.722 305 10.734

Total 5419.294 309 5 Regression 2206.189 5 441.238 41.747 0.000*

Residual 3213.105 304 10.569

Total 5419.294 309


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Model

Unstandardized Coefficients

Standardized Coefficients T Sig.

B Std. Error Beta

1 (Constant) 12.329 0.838 14.706 0.000* Marketing Resource Commitment Activities

0.471 0.039 0.563 11.959 0.000*


0.273 0.057 0.326 4.745 0.000*

Process Strategies 0.189 0.041 0.317 4.619 0.000* 3 (Constant) 10.128 1.028 9.856 0.000*

Marketing Resource Commitment Activities

0.335 0.063 0.4 5.341 0.000*

Process Strategies 0.229 0.044 0.385 5.214 0.000* People Strategies -0.059 0.025 -0.171 -2.388 0.018*


0.22 0.07 0.263 3.132 0.002*

Process Strategies 0.212 0.043 0.356 4.87 0.000* People Strategies -0.089 0.026 -0.255 -3.418 0.001* Cross Functional Integration Activities

0.194 0.057 0.275 3.389 0.001*

5 (Constant) 9.797 1.014 9.662 0.000*

Marketing Resource Commitment Activities

0.295 0.076 0.352 3.857 0.000*

Process Strategies 0.232 0.044 0.39 5.279 0.000* People Strategies -0.076 0.026 -0.219 -2.89 0.004* Cross Functional Integration Activities

0.294 0.07 0.417 4.17 0.000*

Marketing Consensus Activities

-0.16 0.067 -0.296 -2.395 0.017*

Dependent Variable: Customer Image Source: Primary Data *Significant at 5 per cent level of significance


259

From the above Table (based on the fifth model), it may be observed

that the regression is fairly good with 40.7 per cent explanation of the

variation in customer image and that this explanation is statistically valid, as

the Beta coefficients and their associated t values are statistically significant

(p < 0.05). Out of the 7 seven strategies (product-price-promotion-place-

people-physical evidence-process) and the 6 elements of marketing resources,

activities and programmes (MRCS-MRCA-MCAS-CFIA-MEES-MCQA),

marketing resource commitment activities, process strategies, people strategies,

cross-functional integration activities, marketing consensus activities are found

dictating the performance indicator customer image of the LIC.

4.6.6 Canonical Correlation Analysis

4.6.6.1 Canonical Correlation Analysis on Marketing Strategies with Performance

Considering success factors such as MS (Market Share), ROI (Return

On Investment), SG (Sales Growth), CR (Customer Retention) and CI

(Corporate Image) as well as strategies such as PDTS (Product Strategies),

PRSS (Pricing Strategies), PLSS (Promotional Strategies), DCSS

(Distribution Channel Strategies), PESS (People Service Strategies), PEFS

(Physical Evidence Strategies) and PSSS (Process Strategies), a natural

question will arise relating to the dependence structure between the 2 sets of

variables. One way to do this is to provide the usual correlation coefficients

and it will not be useful in delineating the more important dimensions of

such relationship. To examine the dimensions of the dependence of 2 sets of

variables, canonical correlation is an appropriate tool that can be employed.

The dimension reduction with these variables has resulted in 5 dimensions as

shown in Table 4.42.

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260

Table 4.42 Dimension Reduction Analysis

Roots Wilks L. F Hypoth. DF Error DF Sig. 1 .41638 8.30960 35.00 1256.00 .000* 2 .65589 5.59167 24.00 1044.30 .000* 3 .87879 2.64702 15.00 828.57 .001* 4 .94481 2.16671 8.00 602.00 .028* 5 .99268 0.744201 3.00 302.00 .528

Source : Survey data * significant at 5 per cent level of significance

Among these 5 dimensions, 4 dimensions are statistically significant

(p<0.05). In order to identify the dimensions resulting in canonical correlation

of 0.60429, 0.50362, 0.26435 and 0.21961, canonical correlation coefficients

are used.

Table 4.43 Standardized Canonical Coefficients- Dimensions

Variable 1 2 3 4 PDTS -.08783 -.67381 .60614 -.33506 PRSS -.13565 .08837 -.06022 1.28303 PLSS .10765 .62950 -.66299 -.25920 DCSS .05902 .98944 .54868 -1.04755 PESS -.02373 -.72352 -1.45519 .05997 PEFS -.44011 -.30077 .36305 -.58085 PSSS -.56483 .13488 .47693 .70476

Covariate 1 2 3 4 MS -.01387 -.08081 .25137 -.20264 ROI -.08137 -.22314 -.09110 .01482 SG .04553 .22913 .11686 -.12805 CR -.04618 .04621 -.27873 -.15752 CI -.16974 .06435 .05089 .32060

Source : Survey data


261

Tests of dimensionality for the canonical correlation analysis, as shown

in Table 4.42, indicate that the first four canonical dimensions are statistically

significant at .05 level. Dimension 1 has a canonical correlation of 0.60429

between the sets of variables, while dimension 2 has a canonical correlation of

0.50362, dimension 3 has a canonical correlation of 0.26435 and dimension 4

has a much lower canonical correlation of 0.21961.

Table 4.43 presents the standardized canonical coefficients for the four

dimensions across both sets of variables. For the variables, the first canonical

dimension is most strongly influenced by PLSS and PSSS (.10765, -.56483)

and for the second dimension by DCSS and PESS (.98944, -.72352), while the

third dimension is dominated by PDTS and PESS (.60614, -1.45519); PRSS

and DCSS (1.28303, -1.04755) influence the fourth dimension. For the

covariates, the first dimension comprises SG and CI (.04553 and -.16974).

The second dimension includes SG and ROI (.22913 and -.22314). The

third dimension consists of MS and CR (.25137, -.27873), and CI and MS

(.32060, -.20264) dominate the fourth dimension.

The first dimension relates to the promotional strategies (PLSS) and not

to process strategies (PSSS) on the one side, and sales growth (SG) and not

corporate image (CI) on the covariate. In simple terms, promotional (PLSS)

and not process strategies (PSSS) are important for sales growth (SG). Also, it

is to be noted that Process strategies (PSSS) are to be in such a way as no to

affect corporate image (CI).

It is Distribution channel strategies (DCSS) and not the people strategies

(PESS) on the one side and sales growth (SG) and not return on investment

(ROI) on the covariates that are related to the second dimension. It is

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Distribution channel strategies (DCSS) and not People strategies (PESS) that

play an important role in sales growth (SG).

With regard to the third dimension, it is product (PDTS) and not people

strategies (PESS) on the one side and market share (MS) and not customer

retention (CR) on the covariates that are related. This makes it clear that while

product strategies (PDTS) have a great influence on the market share (MS),

people strategies (PESS) are to be in tune with ensuring better customer

retention (CR).

The pricing strategies (PRSS) and not the distribution channel strategies

(DCSS) on the one side and corporate image (CI) and not the market share

(MS) on covariates are related to the fourth dimension. This highlights the

prominent role of pricing strategies (PRSS) in enhancing corporate image (CI).

At the same time, it is to be ensured that distribution channel strategies

(DCSS) support growth of market share (MS).

4.6.6.2 Canonical Correlation Analysis on Resources, Activities and programmes with Performance

While considering performance indicators such as MS (Market Share),

ROI (Return On Investment), SG (Sales Growth), CR (Customer Retention)

and CI (Corporate Image) along with Resources, Capabilities and Activities

such as Marketing Resources And Capabilities (MRCS) , Marketing Resource

Commitment Activities (MRCA), Marketing Consensus Activities (MCAS),

Cross Functional Integration Activities (CFIA), Marketing Experience Elements

(MEES) and Marketing Communication Quality Activities (MCQA), it will be

worthwhile to identify the relation through the dependence structure between the

2 sets of variables. To examine the dimensions of the dependence of 2 sets of

variables, canonical correlation is more appropriate tool than usual correlation


263

coefficients, as it won’t be useful in delineating the more important dimensions of

such relationship. The dimension reduction with these variables has resulted in

5 dimensions as shown in Table 4.44.

Table 4.44 Dimension Reduction Analysis

Roots Wilks L. F Hypoth. DF Error DF Sig.

1 .35741 11.71343 30.00 1198.00 .000* 2 .72929 4.97262 20.00 995.94 .000* 3 .89690 2.78714 12.00 796.66 .001* 4 .96199 1.96948 6.00 604.00 .068 5 .99282 1.09543 2.00 303.00 .336

Source: Survey Data *Significant at 5 per cent level of significance

Among these 5 dimensions, 3 dimensions are statistically significant

(p<0.05). In order to identify the dimensions resulting in canonical correlation

of 0.71409, 0.43229, 0.26011, and 0.17622, canonical correlation coefficients

are used.

Table 4.45 Standardized Canonical Coefficients- Dimensions

Variable 1 2 3 MRCS .65441 .62374 .17032 MRCA .53856 -.09524 .60401 MCAS -.40445 -.44171 -2.62971 CFIA .97315 .77773 1.42582 MEES -.81352 -.92183 -.18115 MCQA -.05715 -.58763 .62102 Covariate 1 2 3

MS -.03872 .05192 .17997 ROI .14724 .13461 -.20407 SG -.14282 -.33542 -.17700 CR .08288 .08064 .13047 CI .16650 -.07064 .09878


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Tests of dimensionality for the canonical correlation analysis, as shown

in Table 4.44, indicate that the first four canonical dimensions are statistically

significant at .05 level. Dimension 1 has a canonical correlation of 0.71409

between the sets of variables, while, for dimension 2, the canonical correlation

is 0.43229 and dimension 3 has a canonical correlation of 0.26011.

Table 4.45 presents the standardized canonical coefficients for the four

dimensions across both sets of variables. For the variables, the first and second

canonical dimensions are most strongly influenced by CFIA and MEES

(.97315, -.81352) and (.7773, -.92183) and the third dimension is dominated

by CFIA and MCAS (1.42582 and -2.62971). At the same time, MCQA and

MEES (1.22337, -2.01954) influence the fourth dimension. For the covariates,

the first dimension comprises CI and SG (.16650, -.14282). The second

dimension includes ROI and SG (.13461, -.33542). The third dimension

consists of MS and ROI (.17997, -.20407), and CI and MS (.15146, -.33428)

dominate the fourth dimension.

The CFIA (Cross- functional integration activities) and not MEES

(Marketing experience elements) on the one side and CI (corporate image) and

not SG (Sales growth) on the covariate are related to the first dimension. It

pinpoints that CFIA (Cross-functional integration activities) influence to a

great extent in deciding the CI (Corporate Image); at the same time MEES

(marketing experience elements) are to be tuned in such a way that it supports

SG (sales growth).

It is CFIA (Cross-functional integration activities) and not MEES

(Marketing experience elements) on the one side and return on investment

(ROI) and not sales growth (SG) on the covariates are related to the second

dimension. The relationship makes it clear that CFIA (Cross-functional


265

integration activities) play an important role in deciding the level of return on

investment (ROI). The MEES (Marketing experience elements) are to be

properly oriented towards enhancing sales growth (SG).

As to the third dimension, CFIA (Cross- functional integration activities)

and not MCAS (Marketing Consensus Activities) on the one side and Market

Share (MS) and not Return on Investment (ROI) on the covariates are related.

The relationship makes it clear that CFIA (Cross-functional integration

activities) play an important role in deciding the level of market Share (MS).

The MCAS (Marketing Consensus Activities) are to be tuned in such a way

that they contribute to enhanced Return on Investment (ROI).

4.7 Approaches and practices of agents in marketing life insurance and handling customer objections The major issues involved in marketing life insurance products and

services like objections in sale, method of handling objections, methods of

prospecting potential customers, services rendered before and after issue of

policy, criteria for recommending a policy to customer, and the major

influences in making the marketing career easier are discussed here.

4.7.1 Form of Objection Raised by Customers during Marketing Policies

It is usual that customers raise questions while committing an

investment. Objection is the best way to identify the actual state of the

potential investor and thus it serves as opportunity. They provide valuable

hints in the formulation and successful implementation of strategies. The

Table outlines the major possible objections faced by agents in their course of

marketing life insurance products and services.


267

Table 4.46 Objections Raised by Customers

Objection- Degree

Very High High Medium Low Very

Low Total

Product 26(8.4) 65(21) 102(32.9) 55(17.7) 82(20) 310(100) Person 8(2.6) 39(12.6) 98(31.6) 79(25.5) 86(27.7) 310(100) Place 18(5.8) 31(10) 49(15.8) 97(31.3) 115(37.1) 310(100) Process 38(12.3) 42(13.5) 116(37.4) 79(25.5) 35(11.3) 310(100) Price 33(10.6) 51(16.5) 127(41) 65(21) 34(11) 310(100) Promotion 13(4.2) 44(14.2) 55(17.7) 109(35.2) 89(28.7) 310(100) Physical Evidence 18(5.8) 55(17.7) 81(26.1) 77(24.8) 79(25.5) 310(100) Return/Liquidity 56(18.1) 77(24.8) 97(31.3) 54(17.4) 26(8.4) 310(100) Source: Primary Data Note: Figures in parenthesis represent percentage to total in respective rows

The analysis reveals that most of objections from the part of customers

arise from the aspect of “return/liquidity”. This implies that the policyholders

are not satisfied with the return aspect of life insurance products, as its long-

term investment period takes away the scope of easy liquidity in the form of

maturity closure. Objections as to product stand second to return/liquidity. As

far as promotion and place are concerned the level of objection is low.

4.7.2 Method of Handling Objections Raised by Customers

The approach of agents in handling objections raised by customers has a

significant impact on the customer base of agents and the level of satisfaction

of the policyholders. Even the LIC imparts training at periodical intervals as to

marketing insurance products and handling clients; how far it has been utilized

by agents in the field is important from the perspective of marketing. The

analysis exhibits a few common approaches followed by agents in handling

objections raised by customers as Avoid customer raising objections (ACRO),

Postpone the answer to the objection (PAO), Not answer and excuse objection

(DAEO) , Disagree with not being agreeable (DANBA) and Substantiate by

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268

bringing to the right point (SBRP) during the process of marketing and

servicing policies.


H0: There is no difference in the median responses for method of handling

customer objection among the 5 divisions

H1: There is difference in the median responses for method of handling

customer objection among the 5 divisions

Table 4.47 Methods of handling Objections Raised by Customers

N Mean Std. Deviation Avoid customers raising objections 310 3.69 1.468 Postpone the answer to the objection raised 310 3.33 1.003 Not answer and excuse objections 310 3.40 .982 Disagree with not being agreeable 310 2.95 1.257 Substantiate by bringing to the right point 310 1.52 1.120


Table 4.47 Ranks on Method of Handling Objection

Method Of Handling Objection Division

EKM KTYM KKD TSR TVPM N 52 61 84 59 54 Avoid customers raising objections

132.92 171.83 136.05 170.87 172.26

Postpone the answer to the objection raised

179.34 185.84 107.88 152.83 175.28

Not answer and excuse objections

151.56 127.95 180.54 167.27 138.6

Disagree with not being agreeable

144.06 117.13 208.46 144.98 138.97

Substantiate by bringing to the right point

159.22 158.64 153.1 160.34 146.82



269


ACRO PAO DAEO DANBA SBRP Chi- 14.99 42.732 17.239 50.067 1.67 Df 4 4 4 4 4 Asymp. 0.005* 0.000* 0.002* 0.000* 0.796


The hypotheses except for “substantiate by bringing to the right point”

are rejected as the p values are 0.005, 0.000, 0.002 and 0.000 (p<0.05) and

the stated hypothesis is not rejected as its p value is 0.796 (p>0.05). It

signifies that there is significant difference among the 5 divisions as to

handling objections with regard to all methods except “substantiate by

bringing to the right point”. To conclude, selected respondents have similar

attitudes towards the method “substantiate by bringing to the right point” in

all the 5 divisions.

4.7.3 Method of Catching a Prospect

LIC agents depend on various means or sources to expand their

customer base. The analysis depicts the most depended means among LIC

agents in locating their prospective clients, that exposes their nature and

approach of capturing clients. The identified means subject to analysis are

Regular Visit to the Organisation (RVO), Contact through Social Clubs

(CSC), Offering Personal Help (OPH), Contact through Friends/Relatives

(CFR), Undertaking Socially Responsible Commitments (USRC), Offer to Pay

Initial Premium (OPIP) and Old Customers’ Recommendations (OCR). The

output of Kruskal Wallis test is presented below.

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270


H0: There is no difference in the median responses for methods of catching

prospects among the 5 divisions.

H1: There is difference in the median responses for methods of catching

prospects among the 5 divisions.


Methods of catching prospects N Mean Std. Deviation Regular visit to the organisation 310 3.26 1.835 Contact through social clubs 310 4.45 1.628 Through offering personal help 310 4.37 1.903 Contact through friends and relatives 310 2.13 1.506 Undertaking socially responsible commitments

310 4.72 1.861

Offer to pay initial premium 310 6.26 1.924 Old customers’ recommendations 310 3.16 1.744


Table 4.50 Ranks on Methods of Catching Prospects

Methods of catching prospects EKM KTYM KKD TSR TVPM N 52 61 84 59 54 Regular visit to the organisation 136.4 150.95 174.74 142.02 163.83 Contact through social clubs 163.58 161.56 132.07 162.47 169.71 Through offering personal help 138.64 150.84 168.21 160.86 151.37 Contact through friends/relatives 158.33 146.34 168.3 146.13 153.45 Undertake social responsible commitments/political activities

158.16 166.56 141.15 169.78 147.17

Offer to pay initial premium 193.48 150.84 134.11 138.69 175.83 Old customers recommendations 143.92 132.53 194.73 137.09 151.69



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Table 4.51 Test statistics- Kruskal Wallis test

RVO CSC OPH CFR USR OPIP OCR Chi-Square 8.509 8.434 4.125 3.607 5.248 20.508 24.59 Df 4 4 4 4 4 4 4 Asymp. Sig. 0.075 0.077 0.389 0.462 0.263 0.000* 0.000*


The Table of descriptive statistics highlights that the most depended means

among selected agents is “contacts through friends and relatives” having the lower

mean. All the hypotheses except for “offer to pay initial premium” and “old

customers’ recommendations” are not rejected as their p values are 0.075, 0.077,

0.389, 0.462, and 0 .263 (p>0.05) while the hypotheses for the means stated

above are rejected as their p values are 0.000, 0.000, (p<0.05). It means that

there is significant difference among selected agents in the 5 Divisions as to

the methods of prospecting with regard to “offer to pay initial premium” and

“old customers’ recommendations”.

4.7.4 Services Rendered by Agents

Agents are serving as the dominant medium for distributing products and

services in the case of the LIC of India. The services of agents begin even

before the sale of policy and continue upto the settlement of the claim. During

the period they render multiple services like ensuring dispatch of policy

document, payment of premium (through premium collection points of

authorized agents), transfer, assignment and revival of policy in need etc. In

the technologically developed arena, they should be well versed in handling

systems for rendering quick and efficient services. The following Table

illustrates the major types of services rendered by agents. The criteria used for

evaluation are never (N), rarely (R), occasionally (O), frequently (F) and

always (A).

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Table 4.52 Services Rendered by Agents

Services rendered by agents N R O F A Accurate assessment of insurance needs 2(1) 11(4) 21(7) 96(31) 180(58)

Proper advice on selection of particular policy/period 0(0) 7(2) 25(8) 100(32) 178(57)

Filling up the insurance forms/applications 10(3) 7(2) 29(9) 74(24) 190(61)

Completing medical examination formalities 9(3) 19(6) 18(6) 89(29) 175(56)

Submission of application& receipt of documents in time 4(1) 11(4) 20(6) 64(21) 211(68)

Reminding premium due dates/collection of premium 11(4) 4(1) 24(8) 98(32) 173(56)

Filling nomination forms/ effecting alterations in policy 2(1) 12(4) 48(15) 110(35) 138(45)

Policy Transfer ,Assignment , issue of duplicate on loss 10(3) 38(12) 73(24) 99(32) 90(29)

Surrender of policy 24(8) 67(22) 79(25) 71(23) 69(22) Availing loan on policy, loan closure & settlement 9(3) 50(16) 65(21) 87(28) 99(32)

Revival of policy lapsed on default in premium payment 2(1) 37(12) 76(25) 105(34) 90(29)

Filling up claim forms. ensuring claim receipt in time 4(1) 36(12) 54(17) 117(38) 99(32)

Information regarding bonus, policy status 4(1) 52(17) 57(18) 127(41) 70(23)

Grievance settlement, clarification of doubts 8(3) 35(11) 44(14) 124(40) 99(32)

Providing latest information/guidelines on policy 2(1) 18(6) 58(19) 96(31) 136(44)

Maintenance of customer records 4(1) 9(3) 33(11) 94(30) 170(55)


The Table presents that most of the services are rendered by agents

except services on surrender of policy as the decision to surrender policy is

not an option chosen by policyholders frequently due to the cost involved in it.


273

It is resorted to by policyholders in case of complete inability to continue the

policy due to financial constraints or in exceptional cases of non- preference

towards the policy.

4.7.5 Criteria Used by Agents While Recommending a Policy The criteria depended on by an agent in recommending a policy has

great impact on his customer base. A knowledgeable agent makes the

customer understand the basic idea behind having life insurance policy and

tries to interlink the basic needs of the customer with the policy marketed. The

analysis exhibits the means most depended upon by agents in marketing

policies. Here Kruskal-Wallis test is used, which is the non-parametric

equivalent of ANOVA and an extension of Mann-Whitney U test, as there are

more than two groups in the category.


H0: There is no difference in the median responses for agents’ criteria on

recommendation of policy among the 5 divisions of the LIC.

H1: There is difference in the median responses for agents’ criteria on

recommendation of policy among the 5 divisions of the LIC.


N Mean Std. Deviation

Needs and Desires of Customer 310 1.51 0.88 Wealth and Income of Customer 310 2.76 1.224 Occupational Status of Customer 310 2.97 0.978 Age/Education/Marriage Needs of Customer 310 2.93 1.255 Rates of Agent’s Commission 310 4.76 1.106


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Table 4.54 Agent’s Criteria in Recommending Policy

EKM KTYM KKD TSR TVPM N 52 61 84 59 54 Needs and desires of customer 152.71 133.6 191.8 124.08 160.8 Wealth and income of customer 151.15 132.04 192.13 139.84 146.31 Occupational status of customer 191.74 165.97 114.38 154.14 174.23 Age/education/marriage requirements of customer

148.56 163.34 137.82 189.23 143.98

Rates of commission to agents 161.63 143 160.9 154.03 156.92 Source: Primary Data


NDC WIC OSC AEMRC RCA Chi-Square 36.964 22.385 32.45 14.114 2.2 Df 4 4 4 4 4 Asymp. Sig. 0.000* 0.000* 0.000* 0.007* 0.7

Source: Primary Data *significant at 5 per cent level of significance

The descriptive statistics Table shows that the needs and desires of the

customers are given prime importance while recommending a policy. The

mean rank Table depicts that the agent’s commission is not given as the prime

choice among any of the divisions. All the hypotheses except that related to

rate of agents’ commission are rejected as their p values are 0.000, 0.000,

0.000 and 0.007 (p<0.05) and the hypothesis related to rate of agent’s

commission is not rejected as its p value is 0.699 (p>0.05). It shows that

there is significant difference as to all variables except rates of agent’s

commission among the 5 divisions as the criteria to recommend a policy.


275

4.7.6 Level of Influence of Factors in Marketing Insurance Products and Services

Apart from the knowledge and skill of agents, there are multiple factors

that influence the productivity of agents in their marketing profession. The

organisational factors enlisted below are also powerful in making the

profession much stress-free and easy. The perceptions of agents on these

factors as to how they influence the marketing job are analysed in the Table.

The factors are Company Image (CYI), Product/Service Quality (PSQ), LIC

Promotional Measures (LICPM), Efficient Office Staff (EOS), Training

Availed from LIC (TLIC), Availability of Efficient Grievance Settlement

Mechanism (AEGSM), Speedy Claim/Dues Settlement (SCS), Higher Return

on Investment from LIC (HROI) and Cooperative Higher Authorities (CHA).


Division EKM KTYM KKD TSR TVPM Total

N 52 61 84 59 54 310

Mean SD Mean SD Mean SD Mean SD Mean SD Mean SD

CYI 4.69 0.729 4.84 0.416 4.87 0.339 4.76 0.795 4.78 0.538 4.8 0.569

PSQ 4.71 0.536 4.38 0.84 4.69 0.514 4.32 0.918 4.37 0.76 4.51 0.736

LICPM 3.77 0.675 3.57 1.31 4.42 0.824 3.66 1.24 3.74 0.828 3.88 1.053

EOS 3.52 0.852 3.16 1.381 3.36 1.168 3.54 1.304 3.31 0.886 3.37 1.153

TLIC 4.21 0.572 3.89 1.034 4.37 0.617 3.98 1.091 4.06 0.834 4.12 0.86

AEGSM 3.92 0.589 3.9 1.179 4.14 0.823 3.69 1.29 3.72 0.811 3.9 0.982

SCS 4.08 0.86 4.23 1.116 4.54 0.884 3.98 1.383 4.13 0.754 4.22 1.036

HROI 3.12 1.278 3.25 1.434 4 1.075 2.9 1.296 3.39 1.188 3.39 1.304

CHA 3.58 0.936 3.43 1.533 4.11 0.892 3.46 1.394 3.59 0.922 3.67 1.183


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276

The hypotheses can be stated as: H0: There is no significant variation in the mean scores obtained for the

variables related to factors influencing insurance products and services among the 5 Divisions of the LIC in Kerala.

H1: There is significant variation in the mean scores obtained for the variables related to factors influencing insurance products and services among the 5 Divisions of the LIC in Kerala.

Table 4.57 ANOVA


Square F Sig.

CYI* Division

Between Groups (Combined) 1.188 4 .297 .915 0.455 Within Groups 99.008 305 .325 Total 100.197 309

PSQ* Division

Between Groups (Combined) 9.060 4 2.265 4.360 0.002* Within Groups 158.427 305 .519 Total 167.487 309

LICPM* Division

Between Groups (Combined) 34.428 4 8.607 8.519 0.000* Within Groups 308.156 305 1.010 Total 342.584 309

EOS* Division

Between Groups (Combined) 5.674 4 1.419 1.069 0.372 Within Groups 404.919 305 1.328 Total 410.594 309

TLIC* Division


AEGSM* Division


SCS* Division

Between Groups (Combined) 13.194 4 3.299 3.159 0.014* Within Groups 318.448 305 1.044 Total 331.642 309

HROI* Division

Between Groups (Combined) 50.706 4 12.677 8.142 0.000* Within Groups 474.842 305 1.557 Total 525.548 309 Between Groups (Combined) 23.112 4 5.778 4.305 0.002* Within Groups 409.327 305 1.342 Total 432.439 309 Within Groups 369.320 305 1.211



277

The result of One-way ANOVA for the variables “product/service

quality, LIC-promotional measures, training availed from LIC, availability of

efficient grievance settlement mechanism, speedy claim/due settlement, higher

return on investment from LIC , cooperative higher authorities ” across the 5

divisions of the LIC in Kerala gives the F values of 14.360, 8.519, 3.612,

2.422, 3.159, 8.142 and 4.305 which are found to be significant at 5 per cent

level (p<0.05). Hence the null hypothesis is rejected. This implies that there is

significant difference in the mean scores obtained for the above variables among

the 5 divisions of the LIC in Kerala. While the One-way ANOVA for the

variables “company image” and “efficient office staff” across the 5 divisions of

the LIC in Kerala gives the F values of 0.905 and 1.069 which are calculated

to be not significant at 5 per cent level (p>0.05). Hence the null hypothesis is not

rejected. This implies that there is no significant difference in the mean scores

obtained for “company image” and “efficient office staff” among the 5 divisions

of the LIC in Kerala. In all the 5 divisions, high mean value pertains to the factor,

“Company image”. It shows that irrespective of Division, the same level of

influence of company image is seen in making the career easier in marketing life

insurance in all divisions. To conclude, the selected agents have varying

perceptions on the factors influencing the insurance marketing career easier

except for two factors, i.e., “company image” and “efficient office staff”.

4.8 Conclusion

This chapter examines the perceptions of LIC agents on their profession,

resources, activities, programmes and marketing strategies of LIC and

marketing approaches and practices. Considering the demographic profile of

agents, the majority is males and also married. As per the opinion of agents on

the preference of policyholders at the time of purchasing policy, salaried

employees are having higher coherence that indicates similarity in their

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278

attitude towards selection of particular policy/policies. As per the MANOVA

model, the customer financial status and premium are found to be the major

problems faced by agents in marketing life insurance among 5 divisions. The

agents in Kozhikode division feel that the promotional programmes of LIC are

found to be more effective in achieving promotional objectives compared to

other 4 divisions. The step wise multiple linear regression on the marketing

mix strategies, resources, activities and programmes with the performance

parameters of LIC provides that market share of LIC is greatly affected by its

physical evidence strategies and marketing resources and capabilities. At the

same time the marketing resources and capabilities, promotional strategies,

marketing consensus activities, marketing experience elements, physical

evidence strategies, process and product strategies dictate the return on

investment of LIC. Further it is found that the sales growth of LIC is

influenced by marketing experience elements, people and distribution channel

strategies, marketing consensus activities and physical evidence strategies.

The cross functional integration activities, physical evidence strategies and

promotional strategies reflect the customer retention level of LIC. The agents

of LIC feel that the image of the organisation is affected by marketing

resource commitment activities, process and people strategies, cross functional

integration activities and marketing consensus activities. The canonical

correlation model also establishes that the promotional strategies and

distribution channel strategies play an important role in determining the sales

growth of the LIC and that the product strategies and pricing strategies are

closely related to the market share and corporate image of the LIC. And the

evaluation of the resources, activities and programmes of the LIC with its

performance variables, the corporate image, return on investment and market

share are closely interlinked with its cross-functional integration activities.


279

References

[1]. Field, Andy (2010), Discovering Statistics Using SPSS Software, Sage publications.

[2]. Hinton, Perry R. Hinton, Charlotte Brownlow, Isabella McMurray and Bob Cozens (2004), SPSS Explained, First Edition, Routledge, Taylor and Francis Group Publications.

[3]. Gaur Ajay S., Sanjaya S. Gaur (2010), Statistical Methods for Practice and Research, Response Books, Sage Publications.

[4]. Everitt, Brian S (2010), Applied Multivariate Data Analysis, Wiley Publications.

[5]. Somekh, Bridget (2010), Theory and Methods of Social Research, 2nd Edition, Sage Publications

[6]. Silverman, David (2010), Qualitative Research, 3rd Edition, Sage Publications

[7]. Argyrous, George (2009), Statistics for Research- with a Guide to SPSS, Sage Publications.

[8]. Guthrie, Gerard (2010), Basic Research Methods- An Entry to Social Science Research, Sage Publications.

[9]. Hair, Joseph F. (2010), Multivariate Data Analysis, Pearson Publications.

[10]. Bernard Russell, H. (2011), Social Research Methods- Qualitative and Quantitative Approaches, Sage Publications.

[11]. Cunningham, James B, James O. Aldrich (2012), Using SPSS – An Interactive Hands on Approach, Sage Publications

[12]. Coakes Sheridan J, Lyndall steed and Peta Dzidic (2007), SPSS for windows – analysis without anguish, Wiley Publications.

[13]. George, Darren, Paul Mallery (2012], SPSS for windows- step by step, 10th Edition, Pearson Education

[14]. Julie Pallant (2007), SPSS Survival Manual, Mc Graw Hill Education

Chapter 4

280

[15]. Ho Robert (2006), Handbook of Univariate and Multivariate data analysis and interpretation with SPSS, Taylor and Francis Group

[16]. Carver, Robert H, Jane Gradwohl Nash (2012), Doing Data Analysis with SPSS, Cengage Learning

[17]. Pandya, Kiran. Smruti Bulsari and Sanjay Sinha (2012), SPSS in Simple Steps, Dreamtech Press

[18]. Goddard Wayne and Stuart Melville (2006), Research Methodology – An introduction, JUTA

[19]. Elliot Alan C. and Wayne A. Woodward (2010), Statistical Analysis- Quick Reference Guidebook with SPSS Examples, Sage publications

[20]. Mayers, Andrews (2013), Introduction to Statistics and SPSS in Psychology, Pearson Education.

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