Engineering Research Journal
ISSN: 1110-1180
Vol 3, Issue 6, pp. 121-135
www.mnf-eng.com
Measuring Customer Relationship Management
(CRM) Maturity in Iran Insurance industries
Case Study: Tehran Insurance Enterprises
Pantea Parsi1, Faramarz Fathnezhad2, Alireza Rashidi Komijan3
1. Department of Management and Economics, Science and Research Branch, Islamic Azad University,
Tehran, Iran
2. Department of Science, Urmia Branch, Islamic Azad University, Urmia, Iran
3. Department of Industrial Engineering, Firoozkooh Branch, Islamic Azad University, Firoozkooh, Iran
Abstract: The risk of launching customer relationship management (CRM) project must be lowered through
proper planning. Thus, it is necessary to determine the current and desired status in organization such that
the proper investment takes place for achieving the desired status. Maturity model can be useful for
determining this gap and the way to move to an upper place. Therefore, this applied survey was conducted.
The participants were chosen from insurance industry experts including researchers, scholars, deputies, and
technical directors of insurance companies in Tehran. According to studies, critical success factors (CSFs)
contain three key aspects of each CRM strategy including human, technology, and process factor of customer
relationship management (CRM). The research studied critical success factors of CRM through a
questionnaire in terms of human, technology, and process attitudes. Then, the collected data were verified by
factor analysis and the proposed model tested by using LISREL software. Once model was fitted, significance
of estimating various factors demonstrated. Next, insurance industry experts were surveyed about the
appropriate level of each CRM factors considering software capability maturity model. Finally, a maturity
model of insurance industry of Tehran City introduced.
Key terms: Customer relationship management (CRM), critical success factors (CSFs), maturity model
Introduction
According to systemic view, organizations and in particular insurance companies may not last without
dynamic and creative interaction with the external environment. As in contrast to traditional attitudes
toward organizations management merely relied merely on internal resources, today, customers play a major
role in organizations’ stability. Indeed, organizations’ external environments, nowadays, are highly variable
and changing such that the present age referred as the information technology age in which half of human
knowledge will be totally abolished, every 5 years, replaced by new knowledge and information. In such
situations, if organizations fail to rapidly adapt with these changes, it (will) probably lead to collapse.
Therefore, organizations require adapting with changes and providing some foundations for progress and
development (Heavy and Miscle, 2008; Lin, Chen, and Chan Chio, 2009). There are various approaches by
which organizations adapt to external environment.
However, many theorists believed that most new emerging management notions including demand,
strategic management, comprehensive quality management, customer relationship management, etc all
originate from organizations’ unstable external environments (Applied research center, 2001). As long as
organizations had static, unchanging external environments, organizations, and managements could achieve
goals by merely concentrating on internal environment; while, following by external environment evolution,
organizations were no more able to independently perform their tasks. Thus, interacting with the
environment turned into a requirement for organizations such that enables organizations to provide the
critical and required resources (Heavy and Miscle, 2008). Customer relationship management is a strategy
that helps organizations (Lin et al, 2009). While Ted, in his studies, showed CRM-based organizations can
benefit twice in comparison to organizations lacking such potential. Customer relationship management
Eng Res J, Vol 3, Issue 6, 121-135, 2015
refers to all processes and technologies in organizations for identifying, encouraging, developing, maintaining,
and service delivering to customers. CRM indicates strategies function based which capable and beneficial
customers can be identified and maintained (as cited by Lin et al, 2009). Customer-oriented approach is a key
factor of organizations’ success guiding organizations concentration on customers and their attitudes. CRM
focuses on collecting information, needs, ideas, and customers’ requests, and creating an integrated system for
better service and higher customer satisfaction. Today’s organizations require service delivery beyond
customer requirements such that they can attract their satisfaction and consent; thus, they require
innovative communication systems and CRM. This management system enables organization to form a
connected chain of customers taking the advantages of integrated information. Considering customerorientation of insurance companies, it satisfies management attitudes in term of trust and customers’ longterm loyalty.
Insurance companies must have close relation with buyers, especially non-obligatory insurance buyers
in order to offer proper service and insurance consistent with their needs. Customer relationship management
is a commercial strategy for establishing a bilateral value identifying all customer’s aspects, creating
customer’s knowledge, forming customer relations, and making attitudes about organization’s service or
products. Therefore, studying such valued notion in customer-based insurance enterprises is a necessity.
Moreover, establishing a customer relationship management system (CRM) in insurance enterprises is now
necessary since the world has entered into trade organization and increased competition among insurance
companies requiring satisfaction and customers’ loyalty. All components of assessing maturity level of CRM
classified into three following groups by reviewing and analyzing the offered models: human factor,
technology factor, and process factor. In other words, implementing these three factors in insurance firms will
lead to hopeful signs about the efficacy of CRM establishment.
Research background
The philosophy and history of customer relationship management (CRM)
Mass marketing and production methods changed competition area by increasing productions during
mid-20th century. The purchase process allowing the customer and seller to spend some time on knowing each
other has totally changed. The customers were no more unique and the sellers lost customers’ individual
needs. During 1950-1960, organizations were mainly concerned about how to produce customers’ fast growing
requirements and to attract newly entered customers through using marketing techniques. For decades,
management experts recommended organizations to customer orientation, while, the organizations remained
production-oriented. However, current producers compete in a completely different environment and
transactional marketing based products, price, place, and promotions cannot be merely effective.
Nowadays, most firms try in long-term to communicate and reconnect with current and new customers to
enhance customers’ loyalty. Some effectively compete and win this game by implementing relation marketing
principles through using strategic, technology-based, and functional “customer relationship management”
programs. Anyway, several various factors enable companies to reorganize around customers. Wide changing
of business processes, progress of service delivery, as well as inexpensive software strategies for “mass
customization” are some effective factors in this area.
Customer relationship management and relationship marketing
Customer relationship management originates from the philosophy of relationship marketing; thus,
this section explains and distinguishes the relation between these two notions in order to remove any
ambiguities. Table 1 compares traditional (conventional) marketing and customer relationship management
that is effective in explaining what CRM includes and not.
Table 1. Traditional marketing in comparison with CRM
Traditional marketing
Customer relationship management (CRM)
 Concentrate on transaction
 Concentrate on customer
 Short-term concentration
 Concentrate on life period
 Unit transaction
 Multi transactions
 One-way and at once communication
 Connected and two-way communication
 Mass segmentation
 Individual segmentation
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As above table, there are major philosophical differences seen between customer relationship
management and traditional marketing. CRM has a long-term, permanent view toward customer
relationship; whereas, traditional marketing is more short-term, not relying on permanent relationships with
customer. On the other hand, as earlier discussed, customer relationship management largely associated with
marketing issues; therefore, it formed based on one of marketing paradigms. Relationship marketing
previously discussed, often introduced as the philosophical foundation of customer relationship management.
Customer relationship management (CRM), on the contrary to relationship marketing, exclusively
does not focus on creating and maintain close, collaborative interactional communication. In fact, customer
relationship management associates with maintaining and developing a portfolio of relationships with the
most profitable customers containing a transactional to relational scope. It means that customer relationship
management is a strategy-based relation providing an ideal combination of customer relationships; while,
relational marketing only focuses responsibilities required to create and maintain relational transactions.
Finally, CRM embraces a set of activities, for instance, sub processes associated with potential and
actual customers’ assessment and prioritization and marketing tactics that are absent in relational
marketing. Indeed, relational marketing efforts are sub process of wider strategic-oriented customer
relationship management processes.
Therefore, though the similarity of these two phenomena, considerable differences seen in the process
expected outputs (such as close and collaborative transactional communication instead of a set of relations
with profitable customers). Further, the wider nature of CRM distinguished these two phenomena. More
preciously, if relational marketing viewed as a constructive philosophy focuses on customer retention,
customer relationship management can be regarded as the philosophy organizational implementation.
Significance of customer relationship management
Customer concentration is the critical, key factor for surviving of Small-Medium Size Enterprises
(SMEs). As stated, acquiring new customer takes five times of keeping an existing customer. In this regard,
Landmark report on the significance and necessity of customer relationship management contains followings:
Firms lose their customers within five years.
5% customer decrease, increases profitability up to 25%.
Attracting a new customer costs 6 to 7 times than keeping the existing one.
The more knowledge about our customers and the more relation such that they know we care for their
business, the higher opportunities. A study on why customers stop purchasing a particular brand,
demonstrates that over 68% of dropouts expressed personnel indifference as the main reason. Therefore,
customer retention in SMEs is highly important according to limited sources. In addition, dissatisfied
customer may harm organization’s market, as they harm competition and convince other customers to avoid
exchange with the organization. No surprise, customer relationship management is a critical issue in business
world. CRM provides required communication and decision-making for permanent and continuous, high
quality, and low cost services to all stakeholders (Andrade, 2003).
Customer relationship management (CRM) gives more sale time to sale force and increases sale
efficiency. It also increases response time and the quality of customer service, and allows marketing unit to
better understand customers’ trends and issues. It enables organizations have a common customer attitude;
and allows customers to select how to interact with the company, and permits the organization to treat
customers as an individual.
Customer success factors (CSFs)
Customer success factors (CSFs) are features, conditions, or variables that if correctly supports and
manages, can significantly influence the success of a company competing in a particular industry. CSFs apply
for identifying and prioritizing technical systems and business requirements. As Samers and Nelson claimed
these factors are constant samples improving the process and if prioritizes based on the significance at each
implementation stage, they will be more effective.
As mentioned in CRM definitions, customer relationship management is a set of factors leading to
successful customer relationship management due to effective and coordinated interaction. This section tries
to describe the effective factors in the final maturity of CRM, in details, in order to get familiarize with the
final model and the significance of each factor clears.
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Human factor
While business technology and processes are critical for the success of customer relationship
management, these organizational individuals are the building blocks of customer relationships. Human
resources play an inevitable, valuable role in any organizations. They manage business, communicate
information throughout the organization, and communicate with customers. Some believe that organizational
individuals are the most important asset of any organization (Berry, 1986). All staff organization must clearly
recognize their roles and meet the required abilities and defined orientations in recruitment programs (Brown
and Gulycz, 2002). As organizations make any efforts to reorient on customers, organizational individuals
must also adjust with cultural norms, organizational structures, and changed reward and assessment
method. Fail to deliver change and its effect on staff can lead to project failure. Moreover, changing success
assessment and staff reward system is the prerequisite of any cultural change (Chen and Popovich, 2003).
Organizations need motivated, empowered, and smart systems and people to achieve high performance in
business and relation with valuable customers. Employees must be sensitive to market and have the ability to
understand customers; in addition, they must be able to attract customers’ attention and trust. Telephson and
Thomas expressed that individuals’ expertise and power in organization enhances personal commitment in
service enterprises; consequently, communication expands as increased personal commitment. In order to
maintain valuable employees, they are firstly identified; then, rewarded based on customer-based behavior.
Management must ensure that occupational assessment, service compensation programs, and reward systems
adjust based on customer orientation such that facilitate and encourage this orientation. However, how
personnel assessed, determines how they behave.
Technology factor
Technology infrastructure is a key requirement for CRM. Findings demonstrate that the progressing
role of technology provides a major advantage for customers and enterprises (Bitner et al, 2000). Technology
is an enabler; however, organizations with CRM software packages can never claim having CRM. It means
that organizations require applying an integrated method not islands of technology in organization.
Information technology (IT) historically introduced as enabling redesigning business processes to achieve
fundamental progresses in organizational performance. IT helps in redesigning business processes through
facilitating changing in working operations and creating innovative relation methods with customers,
providers, and internal stakeholders.
Technology used for process mechanization and is a critical component of CRM. However, customer
relationship management (CRM) is a new phenomenon in organizations; it includes not only structural and
technological discussions, but also embraces process challenges and potentials. If an incorrect process
mechanized as part of customer relationship management, the only event is a wrong process mechanized.
Indeed, information technology tools contribute in facilitating various customer processes in relationship
management such as dividing customers based on value or predicting their behaviors. Supporting such
processes requires establishing effective communication throughout organization through infrastructure or
architecturing information technology (Zikmund et al, 2003). Selecting technology and functional program in
major CRM projects is the organizations second priority comparing process changing (Chang, 2002). Most
management deceived by software programs forgets the need of changing internal structures and systems
prior to CRM investment.
Process factor
Business process is a set of tasks and activities leading to an expected business yield. In addition,
process is a group of activities turning organizational data (like human resource) into expected yields. The
process element of CRM is the most sensitive dimension as mechanized improper business processes of CRM
only speed up implementing wrong processes. While, most firms have business processes to deal with
customers (such as processes directly face with customers during purchase, payment, and using services),
often it is necessary to update or even change these processes. Process implementation and management is
difficult in an environment with high number of sale and marketing employees. Clearly, main and basic
processes apply in all CRM areas; despite technological aspects, the philosophical basis of customer
relationship management (CRM) is relational marketing, efficacy, the value of life period, customer
satisfaction, and retention through business management.
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Moreover, customer relationship management is a combination of business and technology processes
looking for identifying organization customers in order to figure out “who are they”, “what do they do”, and
“what do they prefer”. CRM is a continuous activity requiring reengineering business main processes, the
processes initiates from customer opinions and includes customer feedback.
Such processes must continuously review in terms of customer and organization acceptability.
Optimizing customer relationships requires a thorough understanding of all clients including profitable or
non-profitable; and then, organizing business processes for personally encountering with customers based on
their needs and values.
Monitoring relationships management processes looks like complaints management and significant
activity service management in meeting organization objectives and improving relationships. Processes must
be made, written, and recognized to realize improvement.
Research methodology
The present research methodology is an applied one since its expected results can be applied in
insurance and similar firms. It is a descriptive survey gathering filed data.
Research variables
Independent variable
Independent variables include critical success factors of customer relationship management (human
factor, technology factor, process factor).
Dependent variable
Dependent variable or output response or criterion is the observed or measured variable to determine
the effect of independent variable. The research dependent variable is maturity model of customer
relationship management.
In order to determine critical success factors, the factors’ priority and weights, as well as assigning
factors to maturity levels of CRM, the participants were the experts including researchers, scholars, deputies,
and technical managements of insurance enterprises. Research tried to select the participants with the
maximum expertise. The participants selected by random sampling method. The experts were identified from
insurance enterprises of Tehran, Iran and the questionnaires were distributed. Sample volume of insurance
industry experts in Tehran city (infinite participants) determined through using Cochran formula.
Where
: is sampling confidence level, 95% here; so the value is 1.96.
P: the participants with the certain features.
q: the participants lacking the certain features (q=1-p).
d: the negligible error that is usually 5-7%; however, here is 6%.
Substituting given values in Cochran formula, main sample volume equals 267. To verify research results,
310 questionnaires distributed among individuals.
Sample volume
300
Table 2. Determining sample volume
Relative error (d)
Confidence level
(
)
0.06
0.95
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Z
1.96
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Data gathering instrumentations
Theoretical data and primary factors were gathered through library and internet sources including
books, articles, and case studies.
CRM maturity model determined through questionnaire such that the experts attitudes were
questioned.
Technical questions
It includes 17 questions. This part of the questionnaire designed to be as understanding as possible.
The questions were in Likert scale. CSFs of customer relationship management were classified and ranked
through distributing a questionnaire measuring experts’ opinions and attitudes about factors’ ranking and
rating.
The questionnaire firstly explained the maturity model integration levels; next, the respondees asked
to express their suggested level from 1 to 5, indicating the levels of one to five final models, for each factor
(human factor, technology factor, process factor). This questionnaire also distributed among the individuals
selected for the first questionnaire.
Questions of the first questionnaire included 3 general factors of customer relationship management
(human factor, technology and process factors); alpha coefficient of each section separately measured based on
research hypotheses. Sample volume was 30. Alpha coefficients of each section were as follows:
Table 3. First questionnaire reliability
Human factor questions reliability
Cronbach's alpha
Number of questions
0.791
4
Technology factor questions reliability
Cronbach's alpha
Number of questions
0.768
6
Process factor questions reliability
Cronbach's alpha
Number of questions
0.789
7
Alpha coefficient in the second questionnaire provided to determine factors’ maturity level and critical
factors of CRM is as follows:
Table 4. Reliability of factors’ maturity level questions
Cronbach alpha
Number of questions
0.81
3
Customer relationship management (CRM) maturity level
The research developed maturity model levels inspired from Capability Maturity Model Integration
(CMMI); the only difference is that this model founded on critical success factors (CSFs) rather than KPA.
Critical success factors used software process improvement (SPI) maturity level for developing maturity level
(Niazi et al, 2005). This model directs organizations for evaluating and improving CRM implementation
capabilities.
Classifying critical success factors leads to prioritizing based on maturity model. Model levels includes
the literature extracted CSFs confirmed by experts. Insurance firms must consider all these factors to achieve
maturity levels.
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Figure 1. CRM maturity level final factors and levels in insurance industry
Data analysis
Research hypotheses were tested by using structural equation modeling technique and through
LISREL software version 8.8 and SPSS software version 18. To do this, data normality were initially tested.
Then, confirmatory factor analysis was conducted for the first questionnaire. Finally, research main and
alternative hypotheses’ model was performed.
Research hypotheses
CRM human factor has a significant relation with CRM maturity levels in insurance industry.
CRM technology factor has a significant relation with CRM maturity levels in insurance industry.
CRM process factor has a significant relation with CRM maturity levels in insurance industry.
Nowadays, insurance industry is ready to accept main factors of implementing CRM.
Prior to research statistical testing, data normality test is done to make sure of data normality. In
testing data normality, the null hypothesis of data normality tested at 5%. Thus, significance level of ≥0.05,
maintains the null hypothesis. In other words, data have normal distribution. Data normality, at significance
level of 5%, was tested by Kolmogorov-Smirnov. Statistical hypotheses of data normality test are as follows:
H0: data have normal
distribution.
H1: Data have abnormal
distribution.
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As structural model-based studies rely on data normality; so, data normality was firstly tested.
Confirmatory factor analysis and structural equations modeling do not require normality of all data; rather,
normality of constructs will suffice (Clein, 2010).
N
Mean
Standard deviation
Kolmogorov-Smirnov
Significance
Table 5. Data normality test
Human factor
Technology factor
300
300
3.948
3.930
0.811
0.743
2.172
2.267
0.597
0.892
Process factor
300
3.976
0.750
1.780
0.354
According to Table 5, significance level is larger than 0.05 in all items. Thus, there is no reason to
reject the hypothesis meaning that data distribution of all dimensions is normal. Therefore, parametric and
confirmatory factor analysis can be used.
Confirmatory factor analysis
Observed factor of all items was larger than 0.3 demonstrating that the correlation between hidden
variables (dimensions of all main constructs) with observable variables is acceptable. Identifying variables’
correlation followed by significance testing. T-value statistics determine significance of the relation between
variables. Since significance is analyzed at 0.05; hence, if t-value statistics test is bigger than the critical
value of 1.96, the correlation is significant. According to measurement results of all used scales at confidence
level 5%, t-value statistics is larger than 1.96 demonstrating that all observed correlations are significant.
Table 6. Standard factor loading of confirmatory factor analysis of CSFs scale
Factors (Indices)
Standard factor loading
T-value statistics
Employees training
0.59
Human factor
Recruiting empowered employees
0.68
9.65
Employees satisfaction
0.61
8.90
Proper reward system
0.78
10.64
Applying CRM analytical measures
0.73
Information management
0.65
11.23
Applying CRM operational program
0.74
12.87
Technology factor Technology integration
0.68
11.80
Applying collaborative tools
0.74
12.89
Information technology infrastructure
0.79
12.90
Process integration
0.73
Client development
0.78
13.56
Client problems management
0.76
13.14
Acquainting with client
0.68
11.62
Process factor
Focusing on client
0.64
10.97
Client welcoming
0.59
10.11
Client restoration
0.71
12.17
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Figure 2. Standard factor loading of CSFs scale confirmatory factor analysis
Figure 3. Significance of t-value of confirmatory factor analysis of CSFs scale
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Table 7. Structural model Goodness of fit indexes
Fitting indexes
SRMR RMSEA GFI AGFI NFI NNFI
Accepted values <0.05
<0.1
>0.9 >0.9
>0.9 >0.9
Calculated
0.028
0.031
0.92 0.98
0.96 0.97
values
IFI
0-1
0.98
Studying research hypotheses
All factors and dimensions were studied by using one-sample t-test. The null hypothesis is that there
is no significant relation between understudied variable and CRM maturity level; and alternative hypothesis
is test claim. Since data were collected in Likert scale, the mean number 3 was determined i.e. the middle
point in Likert scale. Thus, research hypotheses statistically define as follows:
H0 : µ ≤ 3
H1 : µ > 3
Regarding confidence level of 95%, if significance level (p-value) is less than error value of 5%, null
hypothesis rejects, and alternative hypothesis maintains. Obviously, t-test statistics value will be larger than
t-critical value (t0.05) i.e. ≥1.96. In addition, confidence distance upper and lower boundaries will be also
positive. One-sample t-test results are presented in the following section.
Studying first hypothesis
Four questions were raised to study human factor status. Table 8 represents one-sample t-test results
based on mean attitude. Respondees’ mean attitude in human factor was 3.915 that is bigger than Likert
scale average point. Significance value was 0, which is smaller than error value of 0.05; thus, the observed
mean is significant. T-value was 22.722 that is larger than the critical value of 1.96. Furthermore, confidence
distance at both upper and lower boundaries was a value larger than zero (positive) maintaining the
alternative hypothesis. Table 9 shows one-sample t-test results of human factor, too.
According to the statistical findings at confidence level 95%, it is stated that CRM human factor and its
indices have a significant relation with CRM maturity level in insurance industry.
Research hypothesis
Human factor
Table 8. One-sample T-test results for human factor
Mean
t-value
Significance level Confidence distance 95%
Lower boundary Upper boundary
3.915
22.722
0.000
0.836
0.994
Table 9. One-sample t-test results for human factor indices
Mean
t-value Significance
Confidence distance 95%
level
Lower
Upper
boundary
boundary
Employees training
3.950
20.628 0.000
0.859
1.041
Recruiting
empowered 4.003
20.123 0.000
0.905
1.101
employees
Employees satisfaction
4.163
21.165 0.000
1.055
1.272
Proper reward system
3.543
9.266
0.000
0.428
0.659
Research hypotheses
Result
Maintain
Maintain
Maintain
Maintain
Studying second hypothesis
Six questions were raised for investigating technology status. Table 10 shows one-sample t-test
results for technology factor. Respondees’ mean response is 3.815 that is larger than Likert scale midpoint.
Significance level equaled 0.000, less than error value 0.05; thus, the observed mean is significant. In
addition, t-value statistics is 18.802, which is larger than the critical value of 1.96. Both upper and lower
boundaries of confidence distance were older than zero (positive) indicating that the alternative hypothesis
will be maintained. Moreover, Table 11 also shows one-sample t-test results of technology factor indices.
Based on these statistical findings at confidence level of 95%, it can be stated that CRM technology
factor and its indices significantly correlates with CRM maturity level in insurance industry.
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Research hypothesis
Technology factor
Table 10. One-sample t-test results for technology factor
Mean
t-value
Significance level Confidence distance 95%
Lower boundary
Upper boundary
3.815
18.802
0.000
0.730
0.900
Table 11. One-sample t-test results for technology factor indices
Mean
t-value Significance Confidence distance 95%
level
Lower boundary
Upper boundary
CRM
3.673
12.183 0.000
0.565
0.782
Using CRM analytical
tools
Information
3.703
12.273 0.000
0.591
0.816
management
Applying
CRM 4.047
20.013 0.000
0.944
1.150
operational program
Integrated technology
3.627
10.691 0.000
0.511
0.742
Applying collaborative 3.870
15.096 0.000
0.757
0.983
tools
Information technology 3.970
18.274 0.000
0.866
1.074
infrastructures
Research hypotheses
Result
Maintain
Maintain
Maintain
Maintain
Maintain
Maintain
Studying the third hypothesis
Process factor studied through seven questions. Table 12 represents one-sample t-test results in term
of mean attitudes of 3.932, which is bigger than Likert scale midpoint. Significance level is 0.000 smaller than
error level of 0.05; thus, the observed mean is significant. The t-value statistics of 22.051 is larger than the
critical value of 1.96. Both upper and lower boundaries of confidence level are bigger than zero (positive value)
that maintain the alternative hypothesis (test claim). Table 13 provides one-sample t-test results of process
factor indices, too.
According to statistical findings at 95%, it can be concluded that CRM process factors and its indices
significantly relate with CRM maturity level in insurance industry.
Research hypothesis
Process factor
Table 12. One-sample t-test results for process factor
Mean
t-value
Significance level Confidence distance 95%
Lower boundary
Upper boundary
3.932
22.051
0.000
0.848
1.015
Table 13. One-sample t-test results for process factor indices
Mean t-value Significance
Confidence distance 95%
level
Lower
Upper
boundary
boundary
Process integration
3.840 14.973 0.000
0.730
0.950
Customer development
4.113 19.120 0.000
0.999
1.228
Customers
problem 3.867 14.969 0.000
0.753
0.981
management
Acquinting
with 3.923 16.730 0.000
0.815
1.032
customer
Focusing on customer
3.840 15.593 0.000
0.734
0.946
Welcoming to customer
4.090 19.846 0.000
0.982
1.198
Customer restoration
3.850 14.791 0.000
0.737
0.963
Research hypotheses
212
Results
Maintain
Maintain
Maintain
Maintain
Maintain
Maintain
Maintain
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In general, 17 indices were used for measuring information technology maturity that analysis results
shows significance of these indices by using one-sample t-test in the above table. Regarding table results, it
concluded that all critical factors’ indices to measure CRM maturity level are considerable and have
significant relation with CRM maturity level in insurance industry.
Studying the fourth hypothesis
Considering all the studied factors’ indices (human, technology and process) were effective in
implementing CRM; thus, the fourth hypothesis that insurance industry is now ready to accept main indices
of implementing CRM will maintain.
Criteria
Human factor indices
Technology factor indices
Process factor indices
Table 14. Summarizing hypotheses testing
Result
It can be applied for implementing CRM.
It can be applied for implementing CRM.
It can be applied for implementing CRM.
Assigning factors to CMMI maturity model levels
Once the experts confirmed three factors of human, technology, and process by analyzing the first
questionnaire data, the second questionnaire distributed to determine the factors of CRM levels; in addition,
the experts’ opinion about each factor proper level attained according to each level properties in term of
CMMI model. Hypotheses were tested by Chi-square method. The results of testing the hypotheses are as
follows:
Chi-square null and alternative hypotheses are as follows:
H0: There is no significant difference in the factor recommended levels.
H1: There is a significant difference in the factor recommended levels.
If test statistic value is larger than Chi-square value, it concluded that there is a significant
difference. In SPSS software, it is when sig value is less than 0.05. Now, regarding hypotheses, the results of
each factor leveling separately offered.
Human factor level
Human factor hypotheses, considering Chi-square, are as follows:
H0: There is no significant difference among human factor recommended levels.
H1: There is significant difference among human factor recommended levels.
Observed frequency and the expected frequency of such factor are as follows:
Table 15. The expected and observed frequencies of human factor
Maturity model levels
Observed frequency
Expected frequency
Difference
Level 1
23
60
-37
Level 2
70
60
10
Level 3
81
60
21
Level 4
113
60
53
Level 5
13
60
-47
----------Total
300
Table 16. Human factor t-statistics
115.467a
Degree of freedom
4
Significance number (sig) 0.00
As sig is less than 0.05, H0 of the significant difference among the recommended levels rejects; thus, it
concluded that there is a significant difference seen among recommended levels. According to this conclusion
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and table of observed frequencies, it can be concluded that frequency of level 4 is more; thus, human factor
places at the fourth level of maturity level.
Technology factor level
According to Chi-square results, technology factor hypotheses are as following:
H0: There is no significant level among technology factor recommended levels.
H1: There is a significant level among technology factor recommended levels.
Observed and expected frequencies of this factor are:
Table 17. Technology factor expected and observed frequencies
Maturity model levels
Observed frequency
Expected frequency
Difference
Level 1
56
60
-4
Level 2
115
60
55
Level 3
96
60
36
Level 4
24
60
-36
Level 5
9
60
-51
--------Total
300
Table 18. Technology factor t-statistics
137.233a
Degree of freedom
4
Significance number (sig.) 0.00
Regarding sig value is less than 0.05, H0 stating there is no significant difference in the recommended
levels will be rejected; thus, it concluded that there is a significant difference among the recommended levels.
According to this conclusion and observed frequency table, technology factor assigned to maturity level 2 as
the frequency of level 2 is higher.
Process factor level
Process factor hypotheses, according to Chi-square method, are as follows:
H0: There is no significant difference in process factor recommended levels.
H1: There is a significant difference in process factor recommended levels.
The expected and observed frequencies of the process factor are as follows:
Table 19. Process factor expected and observed frequencies
Maturity model levels
Observed frequency
Expected frequency
Difference
Level 1
23
60
-37
Level 2
70
60
10
Level 3
118
60
58
Level 4
76
60
16
Level 5
13
60
-47
--------Total
300
Table 20. Process factor t- statistics
121.633a
Degree of freedom
4
Significance number (sig.) 0.00
As sig value is less than 0.05, H0 indicating there is no significant difference in the recommended
levels will be rejected. Therefore, it can be concluded that there is significant differences in recommended
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levels. Regarding this conclusion and the frequency table, as frequency of level 3 is higher, comparing all
other levels, level 3 assigns to process factor.
Discussion and conclusion
According to conducted studies and following analyzing experts’ opinions, research findings are as
follows:
Research findings related with CRM maturity effective indices
In order to determine the effective factors and indices on CRM maturity, three factors of human,
technology, and process including 17 indices extracted following CRM literature review. These factors and
indices offered to the experts in the form of the first questionnaire; the results of which are as follows:
Factor analysis findings
The results of factor analysis, once the first questionnaire filled out, are as follows:
All CRM critical factors and indices were confirmed by using confirmatory factor analysis. Next,
confirmatory factor analysis determined the significance and fitting level. The results demonstrated that all
factors were approved.
Statistical hypotheses test results
As stated in the first chapter, the research contained 4 statistical hypotheses. Thus, the four
hypotheses were measured by one-sample t-test when factor analysis tested factors. One-sample t-test results
are as follows:
First hypothesis testing
CRM human factor and its indices significantly relates with CRM maturity level in insurance
industry.
According to one-sample t-test, the significance level of this test is zero, smaller than 0.05. Further, t
statistics is 22.722 larger than the critical value 1.96. Thus, first hypothesis accepted. Findings of the present
study are consistent with results of previous studies. Lewis (2007) studied 13 CRM successful factors and 55
indices for CRM implementation. CSFs cover 3 critical dimensions of each CRM including human factor,
technology factor, and process factor. In fact, research findings reveal human factor as an important factor in
CRM maturity level of insurance industry. Therefore, insurance enterprises must particularly concern human
factor in insurance industry.
Second hypothesis testing
CRM technology and its indices significantly relates with CRM maturity level in insurance industry.
According to one-sample t-test, the significance level of this test is zero, smaller than 0.05. Moreover, t
statistics is 18.802, which is larger than the critical value of 1.96; thus, research second hypothesis maintains.
Findings of the present study are consistent with results of previous studies. Sohrabi et al (2009), in a study
named measuring CRM maturity model (Case study: Information technology organizations), studied some
dimensions. 8 factors identified as CRM maturity main factors. The eight factors embraced 37 more basic
indices. Indeed, research results demonstrate technology as an important factor in CRM maturity. Iran
insurance authorities must improve insurance enterprises’ technology as much as possible. Regarding earlier
studies and the present research findings, it can be concluded that technology factor increases CRM maturity.
Third hypothesis testing
CRM process factor and its indices significantly relates with CRM maturity level in insurance
industry.
According to one-sample t-test, the significance level of this test is zero, which is smaller than 0.05.
Moreover, t statistics is 22.051, which is larger than the critical value of 1.96; thus, research second
hypothesis maintains. The present findings approve earlier research results. Lin et al (2009) studied this
issue in a study naming CRM and innovation capacity (case study: Taipei). The results indicate that CRM has
a positive, significant impact in enhancing innovation capacity. In fact, research findings reveal that CRM
processes are one of important factors in CRM maturity such that Iran insurance authorities must improve
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CRM processes in insurance enterprises as much as possible. Considering the present findings and previous
studies, it concluded that CRM processes lead to increased maturity.
Fourth hypothesis testing
Insurance industry is now ready to accept CRM implementation main indices.
Since all indices were effective on CRM implementation; thus, the fourth hypothesis maintains. In
fact, results of the three hypotheses show that insurance enterprises have the capability to accept CRM;
however, each critical factor indices’ strength and weakness requires a particular attention.
It is worth notifying that most CRM maturity model studies focus on processes and only a few of them
covered technology and human factors. Therefore, the developed model is more comprehensive than the
similar models concerning several factors.
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