Journal of Global Information Management
Volume 27 • Issue 1 • January-March 2019
Measuring CRM Effectiveness in
Indian Stock Broking Services
Shiv Ratan Agrawal, Department of Management Studies, Sri Sri University, Cuttack, India
Divya Mittal, Department of Management Studies, Sri Sri University, Cuttack, India
ABSTRACT
The article tried to develop a multi-item scale for analyzing CRM effectiveness (CRME) from
the customer perspective in the Indian stock broking context. The results revealed that customer
satisfaction could be improved through to build customer trust and customer involvement substantially
by focusing on the CRM system which further influences customer retention and ultimately, customer
loyalty within stockbroking services. The findings of the article will help stockbrokers and their
managers for a tactical decision making of CRM system implementation and practices for customer
perspective. Despite the huge investment in CRM systems by the stockbrokers, critics have remained
unconvinced about the effectiveness of CRM for meeting desired business outcomes. The reason
being that broking firms often perceive CRM systems as a specific technology solution rather than
integrating customer needs with the firm’s strategy, people and business process which generates
a parallel need to develop a scale to measures CRM effectiveness in Indian stock broking services
from the customer perspective.
KEywoRdS
CRM, Customer Involvement, Customer Loyalty, Customer Retention, Customer Satisfaction, Trust
1. INTRodUCTIoN
In the past decade, the global share broking environment has undergone a remarkable transformation.
The changing regulatory framework, structural and technological factors have produced a level of
competition in the share broking industry across the world. The Indian stock broking sector is no
exception to this changing landscape and facing unprecedented challenges. Creating long-lasting
relationships with high-value customers is usually viewed as the key to profitability in an increasingly
dynamic business environment (Lee et al., 2010). CRM system is one of the primary strategic
initiatives in today’s business world. Indian broking firms have also invested heavily in CRM systems
to develop and nurture a long-term mutually benefiting relationship with the customers. Despite the
huge investment in CRM systems by the stockbrokers, critics have remained unconvinced about the
effectiveness of CRM for meeting desired business outcomes. The reason being that broking companies
often perceive CRM systems as a specific technology solution rather than integrating customer needs
with the firm’s strategy, people and business process (Sharma and Goyal, 2011). Understanding the
customer perspective is crucial for a firm since an effective CRM requires the business process and
technology-focused towards the customer (Padmavathy et al., 2012). Therefore, it is imperative to
measure CRM effectiveness in the Indian stock broking services. Additionally, there is a lack of
agreement on the impact of CRM systems on the main customer responses in stockbroking services.
Consequently, there is a parallel need to develop a scale that systematically and psychometrically
measures CRME, serving as a measurement foundation for customers’ perspective. Thus, the present
study tries to develop a multi-item scale for analyzing CRME from customers’ perspective in the
Indian stock broking context.
DOI: 10.4018/JGIM.2019010108
This article, originally published under IGI Global’s copyright on September 14, 2018 will proceed with publication as an Open Access
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2. THEoRETICAL BACKGRoUNd oF THE STUdy
2.1. Concept of Customer Relationship Management
Customer relationship management (CRM) refers to a set of relational practices that firms adopt to
enhance customer relationships (Padmavathy et al., 2012). CRM is viewed as a systematic process to
manage the customer relationship initiation, maintenance, and termination across all customer contact
points (Kevork and Vrechopoulos, 2009). In addition, it is viewed as the strategic use of information,
processes, technology, and people to manage the customer’s relationship with a firm (Lin et al., 2010).
Some firms view CRM primarily as an investment in technology and software, whereas others treat
CRM more expansively and are aggressive in developing productive relationships with customers
(Smith and Chang, 2010). Moreover, the implementation of customer-related strategies is a critical
factor in successful CRM programs. The previous studies showed that there is a great relationship
between the technology perspective of CRM and customer-related strategies (Hillebrand et al., 2011).
2.2. Components of CRM
In the relationship marketing literature, researchers proposed some CRM mechanisms for the successful
implementation and evaluation of CRM strategies. The present study focuses on the three most popular
CRM dimensions are included customer-centric, knowledge sharing and information processing:
2.2.1. Customer-Centric
It emphasizes the delivery of superior service and adding value to customers through customized
offerings (Wang, 2013). It can enable the firm to be truly customer-centric is to adopt a cross-functional
approach that delivers value to its customers (Oztaysi et al., 2011). Webster (2002) studied that crossfunctional processes and capabilities represent a key means of linking the firm to its customers. These
processes should be guided and driven by key performance objectives based on customer needs (Payne
and Frow, 2006). But the challenge for customer-centric firms is to design and manage customercentric IT applications that are flexible, easy to maintain and quickly integrated into existing systems
for better services (Lin et al., 2010).
2.2.2. Knowledge Sharing
It addresses the creation, transfer, and application of knowledge to serve customers (Wang, 2013).
Knowledge creation and knowledge utilization are the two most important behavioral dimensions
of knowledge sharing that often describe the knowledge-based view of the firm (Chow, 2011).
Such functions are strongly reflected in essential CRM activities that include capturing customer
information about their needs and preferences both directly and indirectly (Sin et al., 2005). These
events correspond to knowledge learning and generation, knowledge dissemination and sharing,
and knowledge responsiveness respectively (Vega-Vazquez et al., 2013). It is built on acquiring and
analyzing information obtained from the customer and transforming that information into useful
knowledge that can be exploited in ways that enhance business performance (Yi and Gong, 2013).
2.2.3. Information Processing
It refers to the sharing and exchange of essential and exclusive information through interactive
activities between firms and its customers (Parvatiyar and Sheth, 2001). Information technology and
systems play an important role in the information processing (Kincaid, 2003). The study represents
an enabling and facilitating role in providing an infrastructural basis, which supports the CRM effort
inside the firm, by managing the data required to understand customers (Saarijarvi et al., 2013).
The supportive role of IT includes maintaining a database as well as an accompanying software
and hardware capability that can well enable the organization to serve its customers in an effective
manner (Akroush et al., 2011).
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Although CRM has become an essential business strategy for the Indian stock broking firms,
limited research has been conducted to evaluate its effectiveness. Thus, the present study tries to
propose an instrument for measuring the effectiveness of CRM in Indian stock broking services.
2.3. CRM and Key Customer Responses
The rapid growth of IT and the Internet have given an opportunity to build better relationships with
customers that were not previously possible in the offline world (Lin et al., 2010). The stockbrokers
have a greater ability today to establish, nurture and sustain long-term customer relationships than
ever before (Kushwaha and Agrawal, 2014). Stockbrokers can evolve new approaches, and innovative
products to keep pace with growing expectations. On the other hand, customer preferences are
kept on changing at rapid speed, and their demands are turned insatiable (Yap et al., 2012). It has
become a challenging and tough job for brokers to build customer trust and involvement in firm’s
services (Kushwaha and Agrawal, 2014). To achieve this challenging task, brokers are turning toward
technology-based CRM system in stockbroking services (Kushwaha and Agrawal, 2015). CRM helps
them to use technology and human resources to gain insight into the behavior of customers and the
value of those customers. CRM has emerged in a couple of years as the convergence of some key
customer responses. Pan (2005) examined the CRM from a customer perspective and suggested that it
helps to the establishment of customer trust and involvement in firm’s CRM systems. Several studies
have identified that trust and customer involvement as central constructs for successful CRM (Parish
and Holloway, 2010). Thus, the study states that:
H1: CRM system has a positive and significant effect on trust.
H2: CRM system has a positive and significant effect on customer involvement.
2.3.1. Trust
The past studies suggest that trust has resulted in various contexts. There is a common notion that trust
is an essential component of commitment and involvement (Ballester and Aleman, 2001). Morgan
and Hunt’s (1994) defined trust as a customer’s confidence in a firm’s reliability and integrity. Trust
exists when there is sufficient confidence in customers that the seller will stand by their word and
fulfill the promises (Palmatier et al., 2006). It is one of the most important and accepted customer
outcomes in firm’s CRM context too (Yap et al., 2012). Trust was found to have a significant effect
on the development of long-term business relationships (Sivaraks et al., 2011). Askool and Nakata
(2011) stated that trust is one of the essential variables which play key roles in build and maintain
business relationships. In fact, if customers likely to be more trust in the service provider, they tend
to view that firm is honest and truthful with them (Odekerken-Schroder et al., 2003). Further, if
customers trust on their service provider, they feel that their firm is trustworthy to serve better than
others even after a service failure because of their higher levels of trust (Hedrick et al., 2007). Wong
and Sohal (2002) mentioned that trust influences overall relationship stability and quality. It is also
important in the development of customer satisfaction (Chenet et al., 2010; Gronroons, 1990). Kassim
and Abdulla (2006) studied that customer trust is a good outcome indicator of CRME, which also has
a significant impact on customer involvement. Even some researchers suggested that trust is more
significant than customer satisfaction in ensuring customer loyalty (Caceras and Paparoidamis, 2007).
The study seeks to build if a firm’s CRM system is associated with trust than how it impacts on
customer involvement and customer satisfaction in stockbroking services. Thus, the study states that:
H3: Trust has a positive and significant effect on customer involvement.
H4: Trust has a positive and significant effect on customer satisfaction.
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2.3.2. Customer Involvement
Customer involvement is related to customer participation in service development process (Sin et
al., 2005). It is a well-known fact that productions and consumptions are simultaneous in services
(Howcroft et al., 2007). It involves personal interaction between a firm and its customer. Customer
involvement through personal connections and enjoyable interactions are crucial for customer
satisfaction and long-term service relationships for the firm (Prenshaw et al., 2006). It is indispensable
for satisfactory service encounters (Fatima and Razzaque, 2013). Wulf et al. (2001) studied and
confirmed the necessity of customer involvement for a sound service provider-customer relationship.
Therefore, firms need to be keen on the point of service encounter (Baker et al., 2009). It may be
triggered at any time when customers interact with the firm’s employees (e.g., face-to-face interactions,
e-mail, SMS, phone/mobile calls, online pop-ups, etc.). When customers think themselves as a part
of the firm they more likely to involve with other activities of the firm (e.g., providing suggestion/
feedback for quality improvement, follow firm’s investment advises, cooperate and support the staff,
share personal information, etc.). High customer willingness to involve the firm has a positive impact
on their attitude to maintain long-lasting relationships (Varki and Wong, 2003). This involvement
may, in turn, have more overall satisfaction than those customers who are less involved with their
firms (Prenshaw et al., 2006). In the service marketing literature, the mediating role of customer
involvement has been investigated in prior studies which successfully generate the customer satisfaction
(Fatima and Razzaque, 2013). But satisfying customers in the context of stockbroking services are
quite different. It becomes more challenging and complex for share brokers due to the presence of
the risk element inherent in the stockbroking services (Howcroft et al., 2007). In such case, customer
involvement can also be an effective tool for developing customer satisfaction. Hence, it is important
as well as interesting to examine the impact of customer involvement on customer satisfaction in
stockbroking services. Thus, the study states that:
H5: Customer involvement has a positive and significant effect on customer satisfaction.
2.3.3. Customer Satisfaction
Customer satisfaction is described as the feeling of a customer towards a product/service after it has
been consumed (Oliver, 1980). It is an individual’s feeling of pleasure or disappointment resulting
from comparing a product’s/service’s perceived performance about his expectations (Brady and
Robertson, 2001). It is one of the key outcome variables that affect other customer responses such
as customer retention and loyalty (Oztaysi et al., 2011). It serves to link repurchase intention, crossbuying behavior, brand loyalty and word-of-mouth intention of a satisfied customer (Gupta and Dev,
2012). Many researchers have widely proved that there is a strong association between customer
satisfaction and behavioral outcomes such as customer retention and customer loyalty (Fathollahzadeh
et al., 2011). Also, it helps firms to maintain a long-lasting relationship with their clients that result
in better financial performance (Chi and Gursoy, 2009). It can be assumed that customer satisfaction
is a strong predictor of other customer behavioral outcomes such as customer retention and loyalty
(Guenzi and Pelloni, 2004). Due to the complex nature of services involved in stock broking services,
it is crucial to satisfying and retaining the existing customers (Kushwaha and Agrawal, 2015). Thus,
the present study tries to measure the same which would enable the stockbrokers to enhance their
performance through customer lifetime value. Thus,
H6: Customer satisfaction has a positive and significant effect on customer retention.
H7: Customer satisfaction has a positive and significant effect on customer loyalty.
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2.3.4. Customer Retention
Customer intention to remain with the firm in future is defined as customer retention (Parish and
Holloway, 2010). It is viewed as a customer’s likelihood of continuing to use the products/services of
the firm (Tohidinia and Haghighi, 2011). It refers to a customer engaging in a long-term relationship
with the firm that includes repeat patronage (Trasorras et al., 2009). Similarly, Price and Arnould
(1999) defined customer retention as the degree to which a customer exhibits repeat purchasing with
price tolerance to a service firm. In this regard, it has frequently been operationalized as a customer’s
price tolerance behavior to a service provider and possesses a positive attitudinal disposition (Edward
and Sahadev, 2011). Moreover, customer retention was frequently defined as observed behavior (Gupta
and Dev, 2012). It is determined as an important determinant of market share and profitability of a
firm (Gruca and Rego, 2005). It is also a major issue in the context of stockbroking services where
deregulation has given more options customers to opt their brokers (Kushwaha and Agrawal, 2015).
To avoid higher costs and improving profitability in the competitive world, it is crucial to retaining
the customer rather than recruiting new ones (Reichheld and Sasser, 1990). It was evident that the cost
of customer retention is substantially less than the relative cost of customer acquisition (Yap et al.,
2012). It not only shows customer satisfaction but also indicates about customer loyalty that enables
firms to maximize their market share through customer lifetime value (Lin and Wu, 2011). Thus,
H8: Customer retention has a positive and significant effect on customer loyalty.
2.3.5. Customer Loyalty
Ehigie (2006) defined customer loyalty as a feeling of commitment to a product, service or brand of the
firm. It includes a customer’s intention to recommend the service provider to others (Bendall-Lyon and
Powers, 2003). It shows a customer’s willingness to recommend the firm’s products/services to their
friends and relatives and intentions to continue patronizing (Ranaweera and Menon, 2013). Several
previous studies have claimed that customer satisfaction is a leading factor in determining customer
loyalty (Yap et al., 2012). When a customer is not satisfied, he/she is less likely to continue with the
existing firm as well as tends to negative word-of-mouth (Vera and Trujillo, 2013). Further, their
study suggested that this customer defection has a stronger negative impact on the firm’s financial
performance. Customer loyalty has been recognized as the key success factor in a firm’s success in this
competitive environment (Tohidinia and Haghighi, 2011). It may help firms to reduce their costs and
increase profitability, as we know that the cost of acquiring a new customer is five times more than
the cost of retaining an existing one (Yap et al., 2012). It is also an important aspect for stock brokers.
Based on the discussed literature, the current study presents the following conceptual model of
the research (Figure 1):
3. METHodoLoGy
3.1. Measurement Instrument
The measurement instrument was developed with multi-item measures for each construct based on an
extensive review of the past literature. A preliminary version of the survey instrument was developed
in both languages, English and Hindi. A draft of the questionnaire was examined by an academic
experienced in questionnaire design. The questionnaire was subsequently piloted with 30 different
active share traders/investors to assess the terminology, clarity of instructions and response format.
Minor amendments were made based feedback from the experts and pilot survey. The final set of 38
observable items was selected for six latent variables. The details of all constructs presented in Table
4 in Appendix A. The questionnaire consisted of two sections. In the first section, 38 questions were
related to stockbroking services towards customers. The last section contained questions regarding
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Figure 1. Conceptual Framework for the Study
demographic characteristics of the participants such as gender, age, education, profession and city
name with the state. Participants were also asked about one of the stockbroking firm name that is
operated by them frequently and duration of demat and trading account operation in years. The survey
instrument consisted of close-ended questions that the respondent has to answer in a set format. All
the items were put on a five-point Likert scale where a value of 1 expresses strongly disagree, and
value of 5 expresses strongly agree. Participants were asked to indicate their level of agreement with
each statement.
3.2. Sample Size and data Collection
Testing the suggested research hypotheses through structural equation modeling (SEM) needs to
set a prior sample size based on the latent and observed variables in the study (Westland, 2010). It
was obtained online through Daniel Soper’s apriori sample size calculator for SEM. The minimum
sample size recommended was 256 to detect the effects of the study model based on six latent and
38 observed variables with a probability level of 0.05.
The current study adopted descriptive research design based on survey method for developing
a multi-item scale for analyzing CRM effectiveness from customers’ perspective in the Indian stock
broking context. A cross-sectional research design with non-probability judgemental and snowball
sampling methods was conducted in the study. To avoid possible biases, the sample was collected
from different demographical characteristics and geographical locations of India. A survey of this kind
is a rather difficult exercise; especially participants were reluctant to disclose their data to outsiders.
Even, some of them did not mention their names and their stockbroker names in the questionnaire.
The study has tried to overcome this problem by informing the respondents that the present research
is purely for academic purpose and wholly focused on promoting the ordinary investors’ interest and
strengthening their protection. For generating valid results, questionnaires were personally distributed
to the customers of different share broker offices of five main cities of Madhya Pradesh (MP), India,
namely, Bhopal, Gwalior, Indore, Jabalpur, and Ujjain. They were contacted outside of the brokers’
offices during the stock market hours. They were assured complete anonymity of responses and were
insisted on returning the filled questionnaires within 5 to 10 minutes. Also, they were asked about
the references of other active share traders/investors for the same task. Additionally, the researcher
also had some working experience in stockbroking industry which was used as judgemental sampling
for data collection. The online generated link of the survey instrument (in the English version) was
prepared and sent to selected share traders/investors through their e-mail and requested to fill it and
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forward it to other traders/investors to whom they know. The online participants were contacted thrice
within a month through email for filling the questionnaire. A total of 583 questionnaires was received,
out of which 550 were found to be completely and accurately filled, the rest 33 were discarded due
to incomplete information. The study obtained a final sample size of 550 responses, for a response
rate of 94.3 percent. Moreover, out of 550 valid responses, 350 were collected from an online survey,
and the rest 200 were from the personal survey. The detailed profile of the respondents is presented
in Table 1.
4. dATA ANALySIS ANd RESULTS
4.1. Scale Validity and Reliability
The measurement scales were refined and validated by exploratory factor analysis (EFA) and
confirmatory factor analysis (CFA) in the study. Before inputting the data to factor analysis, it was
confirmed that the assumptions of normality, linearity, and homoscedasticity were not violated using
the Kaiser-Meyer-Olkin (KMO) index accompanied by Bartlett’s test was applied to examine if data
are inter-correlated. The KMO test has to have a value higher than 0.5, and Bartlett’s test has to be
significant at the level of P < 0.05 (Hair et al., 1998). The study found that KMO index is 0.927 with
Bartlett’s test (Chi-square = 473.08, the degree of freedom = 203; p = 0.000), indicating that the
sample size was adequate for applying factor analysis.
4.1.1. Factor Analysis
An exploratory factor analysis (EFA) has been conducted using principal components analysis with
varimax rotation on the all 38 items of six measurement scales in the study, under the restriction that
the eigenvalues of each generated factor were more than one. As shown in Table 2, of the 38 original
items, one item was excluded due to low factor loading (< 0.50).
4.1.2. Confirmatory Factor Analysis
After identifying 37 apparent factors, a confirmatory factor analysis was conducted to assess the
construct validity of each latent construct of study model (Hair et al., 1998). Construct validity is
examined through convergent validity and discriminant validity (Bagozzi and Edwards, 1998). As
shown in Table 2, factor loadings range from 0.513 to 0.904 and AVE ranges from 0.539 to 0.694,
both exceed the recommended threshold criterion of 0.50 (Hair et al., 2006). Additionally, it was found
that composite reliability (CR) and internal reliability (Cronbach’s alpha) of all the latent variables
are greater than the acceptable limit of 0.70 (Carmines and Zeller, 1988). It can be concluded that
data is reliable enough to be processed. Furthermore, discriminant validity has been assessed using
the Fornell and Larcker, (1981) criterion. Table 3 shows the values of the square root of the AVE are
all greater than the inter-construct correlations. Thus, measurement model reflects good construct
validity and reliability.
4.2. Assessment of Model Fit
It is a comprehensive statistical tool for examining relations between observed and latent constructs
(Bollen, 1989). As SmartPLS software does not provide a traditional assessment of overall model fit
(Chin, 1998), the study tested the theoretical model using the structural equation modeling (SEM)
approach with analysis of moment structures (AMOS) 22.0 software (Byrne, 2001). Model estimation
results in a good fit between the model and data: chi-square ( x 2 ) = 1486.6; p = 0.000; degree of
freedom (df) = 658; ( x 2 /df) = 2.26; goodness-of-fit index (GFI) = 0.901; adjusted goodness-of-fit
index (AGFI) = 0.763; comparative fit index (CFI) = 0.912; root mean square error of approximation
(RMSEA) = 0.069. All the values fulfill the acceptable limit (Byrne, 2010), which indicates an
excellent global model fit with the data collected.
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Table 1. Profile of the Respondents
Demographic Characteristics
Gender
Age
Education
Occupation
Duration of Account Operation
Data
Frequency
(n = 550)
Percentage
Male
484
88
Female
66
12
Less than 20 Years
0
0
20 – 30 Years
60
11
30 – 40 Years
131
23.8
40 – 50 Years
201
36.5
50 Years & Above
158
28.7
Undergraduate
38
7
Graduate
150
27.2
Postgraduate
329
59.8
Doctorate
33
6
Service
238
43.2
Professional
60
11
Businessman
101
18.3
Self-employed
46
8.3
Students
30
5.5
Housewife
16
3
Pensioner
51
9.2
Agriculturist
8
1.5
Less than 1 Year
23
4.2
1 – 3 Years
114
20.7
3 – 5 Years
95
17.3
5 – 7 Years
114
20.7
7 – 9 Years
84
15.3
9 Years & Above
120
21.8
4.3. Testing of Hypotheses
The paper used SmartPLS 2.0 to test the associated hypotheses, which provides more information
including t-statistics for concluding from the data (Chin, 2001). Standardized path coefficients (β),
t-statistics, and associated significance levels for all relationships in the study model have presented
in Figure 2. The study used two-tailed t-test with a significance level of 5%, the path coefficient will
be significant if the t-value is larger than 1.96. The results indicated that all the paths are significant
except third (H3), which implies that trust has not a positive and significant effect on customer
involvement (β = -0.013; t = 0.085; p > 0.05). However, it was found that CRM has a significant
effect on trust (β = 0.695; t = 10.771; p < 0.05) and customer involvement (β = 0.553; t = 4.024;
p < 0.05), thus supporting H1 and H2. Also, H4 which implies that trust had a significant effect on
customer satisfaction (β = 0.707; t = 10.637; p < 0.05), and H5, which implies customer involvement
had a significant effect on customer satisfaction (β = 0.196; t = 2.345; p < 0.05). In addition, it was
found that customer satisfaction has a significant effect on customer retention (β = 0.763; t = 14.824;
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Table 2. Measurement Model Summary
Construct
Items
Factor Loading
CRM:
(Customer Centric) CC
(Knowledge Sharing) KS
(Information Processing) IP
CC-1
0.623
CC-2
0.725
CC-3
0.606
CC-4
0.664
KS-1
0.758
KS-2
0.623
KS-3
0.680
KS-4
0.660
KS-5
0.681
IP-1
0.615
IP-2
0.513
IP-3
0.699
IP-4
0.729
T-1
0.848
T-2
0.864
T-3
0.814
T-4
0.782
T-5
0.841
CI-1
0.636
CI-2
0.477
(Eliminated)
CI-3
0.780
CI-4
0.711
CI-5
0.799
CS-1
0.639
CS-2
0.739
CS-3
0.775
CS-4
0.710
CS-5
0.802
CS-6
0.775
CS-7
0.857
CR-1
0.870
CR-2
0.642
CR-3
0.904
CR-4
0.725
CL-1
0.841
CL-2
0.794
CL-3
0.878
CL-4
0.818
Trust (T)
Customer Involvement (CI)
Customer Satisfaction (CS)
Customer Retention (CR)
Customer Loyalty (CL)
AVE
CR
Cronbach’s α
0.539
0.910
0.893
0.689
0.917
0.887
0.577
0.816
0.727
0.576
0.904
0.876
0.628
0.869
0.802
0.694
0.901
0.853
Note: AVE = average variance extracted, CR = composite reliability
p < 0.05) and customer loyalty (β = 0.289; t = 2.673; p < 0.05), thus supporting H6 and H7. At last,
customer retention had also a significant effect on customer loyalty (β = 0.580; t = 5.614; p < 0.05).
Thus, H1, H2, H4, H5, H6, H7, and H8 were accepted, while H3 was rejected.
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Table 3. Discriminant Validity of Latent Constructs
DIMENSIONS
CRM
CRM
0.734*
T
CI
CS
CR
T
0.695
0.830*
CI
0.544
0.372
0.760*
CS
0.650
0.580
0.459
0.759*
CR
0.570
0.664
0.307
0.663
0.792*
CL
0.580
0.624
0.361
0.531
0.601
CL
0.833*
Note: *Square roots of AVE shown on diagonal.
The study model has a high predictive power regarding customer loyalty, customer satisfaction,
and customer retention; it explains 67.6%, 64.1% and 58.2% of the construct’s variance respectively
(Figure 2). Additionally, the model explained 48.4% of the construct’s variance in trust and 29.6% in
customer involvement. The amount of variance explained by the study model is good enough, which
adds support to the theoretical soundness (Awwad, 2012).
5. dISCUSSIoN ANd CoNCLUSIoN
Several studies have recognized the importance of CRM system into the financial sector and found
its significant influence on the key customer response variables (Padmavathy et al., 2012; Akroush et
al., 2011). Among of these, it has been observed that many of them are limited to banking, insurance,
mutual funds and loans in the existing literature. Specifically, research attention is relatively little of
Figure 2. Structural Model
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CRM effectiveness in stockbroking services. Thus, the present study tried to develop a multi-item
scale for analyzing the CRM effectiveness in Indian stock broking services and its relationship with
key customer responses. In addition, the study identified the key new issues of interest within this
domain from a customer perspective that requires managerial and further research attention. It was
found that CRM is the strongest predictor of trust and followed by customer involvement. It means
that adoption of CRM system in stockbroking services is capable enough to build customer trust and
then customer involvement. Surprisingly, the study revealed that customer’s trust does not predict
customer involvement in the context of stockbroking services. It may be possible that customers trust
on their broking firms but proactively do not involve in its services. Also, employees do not take the
enough initiatives to involve customers in services. As we are aware that service is a process that
requires employee-customer involvement (Bennett and Durkin, 2002). Without it, service cannot be
accomplished. On the other side, the study indicated that trust and customer involvement influence
customer satisfaction significantly. It can be said that proper practicing of CRM system in stockbroking
firms positively improves customer trust and involvement which results in high customer satisfaction.
Although there is no direct link between trust and customer involvement in stock broking services, both
predict greater customer satisfaction. Further, the research disclosed that this customer satisfaction
significantly influences customer retention and loyalty that is the main motive of every firm to get
a loyal customer. In addition, the study indicated that there is a significant relationship between
customer retention to customer loyalty. It is interesting to emphasize that if a customer is satisfied
with a stockbroking services it leads to making him a long-lasting relationship with that firm and
become a loyal customer for its products and services. Therefore, it seems reasonable to conclude
that customer satisfaction can be improved through to build customer trust and customer involvement
substantially by focusing on the CRM system which further influences customer retention and
ultimately, customer loyalty within stockbroking services. Interestingly, it was found that customer
involvement is not a result of trust in CRM system, but customer trust and involvement both are
predicting customer satisfaction strongly in stockbroking services. Previous research studies have
well established the fact that trust has a positive impact on customer commitment and involvement
(Pesamaa and Hair, 2007) but the present study is unique which states that merely trust on firm and
its CRM system is not enough to engage customers in the service process. There is a parallel need to
focus on customer trust and involvement to enhance customer satisfaction in stockbroking services.
These findings were also supported by the predictive power of the study model.
The results of the study revealed that the CRM effectiveness multi-item scale conceptualized
and developed were both reliable and valid. It has thirty-seven precise constructs covering six latent
dimensions namely, CRM system, trust, customer involvement, customer satisfaction, customer
retention, and loyalty. The study provides insights into the CRM system in today’s changed scenario
of stockbroking industry and using it as a weapon for competitive differentiation. The critical
dimensions of customer-centric for CRM system identified in the study show that stockbroking
customers preferred customized investment products/services from brokers and followed by their
concern on maximum return on customer’s portfolio investment. Customers appreciate the broking
firm if it makes an effort to find out their specific investment need and then provide value-added
investment products and services accordingly. In the case of knowledge sharing scale for CRM system,
customers prefer those brokers who generate and disseminate proper investment advice for its clients.
Customers expect willing to help attitude from broker’s employees. They feel that firm’s employees
should be knowledgeable enough to understand and solve their problems with the prompt response.
After that, the study indicated that they want accuracy in all trading transactions done by the staff
members of the broker with updated investment information and market calls. Among the information
processing components of CRM, the study suggested that customers prefer the two-way interactive
communication from the broker. Additionally, they prefer a broking firm who continuously manages
and shares real-time information with clients such as, stock market information, trade confirmation,
account statement, etc. It can be achieved if a firm has the right technological infrastructure (software
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and hardware) to share information on all customers’ point such as telephone, mobile, SMS, email,
face-to-face communication, etc. as the study disclosed. Moreover, customer’s trust can be built if he
feels that his firm is honest and truthful to provide reliable services to him. If it is so, he feels that his
stockbroker is trustworthy to serve better than others. Also, the study disclosed that firms should keep
its promises towards customers in any situation and maintain consistent service standards to enhance
customers’ trust on firms’ services. The study indicated that most of the customers are involved with
their broking firm and its employees. They normally cooperate and support in-service process and
actively share their information with the firm. It has come to the light that customers usually follow
the firm’s investment advice but do not give regular feedback to improve services. Further, most
of the participants mentioned that they are overall satisfied with their existing broking firm. Also,
feel safe and secure during the transactions. Interestingly, many respondents equally admitted that
they prefer those stockbroking firms which attends customer complaints promptly and meets their
unique specifications for investment need which do not offer by other brokers. It is interesting to
emphasize that firm’s technology-based investment products and services fit well into their lifestyle.
It means that customers expect quick responses to their investment-related problems. In this regard,
the study findings suggested that the firms’ staff should respond efficiently to customer’s concerns
about investment products and services. However, the study indicated that broker’s technology-based
investment products and services are easy to use for customers. In the context of key variables of
customer retention, it was found that customers select the existing broker to repurchase the similar
investment products/services and intend to maintain long-lasting relationships. Even they prefer
cross-buying of other investment products from the existing firm such as mutual funds, insurance,
etc. Surprisingly, the study found that they will not mind or switch to another firm if the existing firm
charges higher prices for its investment products and services. It is important to note that if a customer
is satisfied, he does not think to switch to another firm even the existing firm charges higher prices
for its services. They continue to have repurchase intention with the same firm if they are satisfied.
At last, the key variables of customer loyalty construct indicated that most of the participants consider
existing share broking firm as their first choice. They also say positive words about the firm and its
products and services to other people. The study found that they are committed towards the firm
and its investment products/services and often recommend to others. The findings provided insight
into the dimensions that contribute to the acceptance of CRM system in stockbroking services from
customers’ perspective.
The study provides some key recommendations based on the findings and informal discussion with
participants during the survey. Most of the respondents revealed that they do not give regular feedback
to improve firm’s products and services. Stockbrokers should promote to install the feedback boxes
in their offices. Additionally, they must encourage their employees to talk to their customers in this
regard. The study data revealed that trust does not drive the involvement of customers in stockbroking
services. It may be possible that firm’s staff and services are not capable of building enough trust
to involve customers in the service process. The majority of participants had also complained that
stockbrokers initially take a personal interest in customer care during the time of customer acquisition
but later started to ignore them. It can be one of the reasons for customer dissatisfaction that can
affect customer retention and loyalty. As consumer behavior is changing rapidly, there is a need to
cultivate customers’ trust with innovative ways (Ganguli and Roy, 2011). A most important issue has
come to light during the discussion with participants that although they get the investment calls and
advice by their firm but do not follow the same. They invest based on their market knowledge and
expertise and suffer losses. It is important to conduct investment awareness programs for customers
by stockbrokers frequently. Additionally, the demographic classification of the study data revealed that
most of the elder people belonged to service, and business class category and admitted that they do
not get the time for offline investment or trading but want to do the through online. Simultaneously,
they revealed their lack of knowledge and confidence on online process. Here, the brokers’ role is
important to educate them with a proper demonstration of the online trading platform of their firms
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in drawing more business. Moreover, the study revealed that most of the respondents were male.
Typically, Indian women believe on savings. Mostly, they do not prefer investment due to risk factor
associated with the stock market (Sahi, 2013). Additionally, investment decisions are still dominated
by males in the Indian society (Paluri and Mehra, 2016). Of course, the situation is changing, but
it will take more time to aware and motivate Indian women for investment in stock market. As we
are aware that risk is the inevitable part of the investment, identified CRM dimensions would help
managers to generate more trust in customers. It can be an opportunity for stockbrokers to motivate
and educate Indian females to participate in investment and trading activities. It will improve the
market capitalization and ultimately financial performance of the stockbroking firms.
6. MANAGERIAL IMPLICATIoN
The phenomenon of declining customer satisfaction, retention and customer loyalty becomes the major
concern of the stockbrokers that determine financial performance of them (Kushwaha and Agrawal,
2015). The focus on key customer variables is important in a financial industry where deregulation
has given more options to customers (Levesque and McDougall, 1996). The stockbroking industry
is also not an exception and passing through a phase of customer market. In stockbroking sector,
customer satisfaction is quite a complex issue and also responsible for customer retention and loyalty.
Increasing expectations of customers from share broking services have led firms and their managers to
become customer-centric, which has been resulted in the introduction of CRM practices to improve the
quality of services (Kushwaha and Agrawal, 2014). The present study suggested several implications
for stock brokers and contributed to the existing literature based on empirical investigations. The
study indicated that the proper implementation of CRM system could build more customer trust and
involvement in the service process which triggers enough satisfaction for the customers. As previous
studies have suggested that customer satisfaction is a key construct to affect customer retention and
loyalty (Luo and Homburg, 2007), the findings of the present study also follow the same trend. The
study shows that customer satisfaction strongly drives customer retention and customer loyalty.
Moreover, it was found that customer retention is also the significant predictor of customer loyalty
in the context of stockbroking services.
The expectation of getting good returns on investments attracts many investors toward the
stock market. But they forget the fact that risk is associated with return and the stock market is not
an exception. Mostly, it has been observed that stockbrokers do not properly guide their customers
about the current market scenario or customers do not follow the investment advice which they get
from their brokers. Ultimately, customers suffer losses and start avoiding investment in future which
impact on financial performance of stockbrokers. The findings of the present study will be useful
for the stockbroking firms to formulate appropriate business strategies for building customer loyalty.
7. LIMITATIoNS ANd FUTURE RESEARCH dIRECTIoN
All the constructs in the study were measured at one point in time. It may be worthwhile to study
customer variables over time in order to be able to take into account the dynamics of consumer
behavioral patterns. The present study only emphasized on the customer’s side than on the firm’s
side. Future research should also recruit stockbroking firm’s employees and managers to generate
results more applicable. The findings of the study can serve as a guide towards further research in
same as well as in different fields. To confirm its applicability in other financial services like banking,
insurance, mutual funds, loans, etc., the same study should be replicated. As stockbroking firms have
always tried to keep its format standard in it’s all national operations, the scope of the study may not
be limited to a geographical location and will guide the stock brokers and managers to understand
customer behavior to attract and retain them. Although there were more male participants in the
study, it would not affect to generalize the results. Majority of Indian women are known for savings
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(Paluri and Mehra, 2016). Especially, in the case of the stock market, they keep distance due to the
risk factor (Sahi, 2013). Future studies can be conducted to know that how CRM system can help
to reduce the risk factor in the stock market. The present study would play an essential role in this
regard. However, irrespective of limitations, the current research has contributed towards the existing
literature on CRM effectiveness from a customer perspective in the stockbroking services and opens
a channel of research on multiple unexamined matters concerning the CRM effectiveness and its
impact on key customer variables.
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APPENdIX A
Table 4. List of scale items
Latent variables
Observable variables
Citation
CRM Dimensions
I. Customer Centric (CC)
II. Knowledge Sharing (KS)
III. Information Processing
(IP)
Trust (T)
Customer Involvement (CI)
Customer Satisfaction (CS)
Customer Retention (CR)
Customer Loyalty (CL)
Specific investment needs (CC-1)
Yap et al. (2012)
Customized investment products/services (CC-2)
Battor and Battour (2013)
Value-added investment products/services (CC-3)
Zeynep and Toker (2012)
Maximum return on portfolio investment (CC-4)
Lin et al. (2010)
Generate and disseminate investment advice (KS-1)
Zeynep and Toker (2012)
Updated investment information & calls (KS-2)
Zeynep and Toker (2012)
Employee knowledge (KS-3)
Gupta and Dev (2012)
Accurate trading transactions (KS-4)
Yap et al. (2012)
Willing to help (KS-5)
Akroush et al. (2011)
Technological infrastructure (IP-1)
Zeynep and Toker (2012)
Share information across all contact points (IP-2)
Battor and Battour (2013)
Manage and share real-time information (IP-3)
Battor and Battour (2013)
Two-way communication (IP-4)
Lin et al. (2010)
Reliable services (T-1)
Chenet et al. (2010)
Honest and truthful (T-2)
Yap et al. (2012)
Keep promises (T-3)
Tohidinia and Haghighi (2011)
Consistent service standards (T-4)
Padmavathy et al. (2012)
Trustworthy (T-5)
Tohidinia and Haghighi (2011)
Follow investment advice (CI-1)
Parish and Holloway (2010)
Regular feedback (CI-2)
Gupta and Dev (2012)
Cooperate and support (CI-3)
Yang (2012)
Sharing personal information (CI-4)
Hau and Ngo (2012)
Involved (CI-5)
Fatima and Razzaque (2013)
Easy to use (CS-1)
Vella and Caruana (2012)
Fit well (CS-2)
Vella et al. (2013)
Customer complaints (CS-3)
Smith and Chang (2010)
Efficiently responses (CS-4)
Akroush et al. (2011)
Safe and secure (CS-5)
Vera and Trujillo (2013)
Meet unique specifications (CS-6)
Yap et al. (2012)
Overall satisfied (CS-7)
Gupta and Dev (2012)
Long-lasting relationships (CR-1)
Hillebrand et al. (2011)
Higher prices (CR-2)
Coenen et al. (2013)
Repurchase (CR-3)
Smith and Chang (2010)
Cross-buying (CR-4)
Chan and Wang (2012)
Say positive words (CL-1)
Delcourt et al. (2013)
Recommend products/services (CL-2)
Oztaysi et al. (2011)
First choice (CL-3)
Delcourt et al. (2013)
Committed (CL-4)
Parish and Holloway (2010)
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Volume 27 • Issue 1 • January-March 2019
Shiv Ratan Agrawal is an Assistant Professor in the Department of Management Studies, Sri Sri University, Cuttack,
Odisha, India. He has been awarded Ph.D. in Management by National Institute of Technology (MANIT), Bhopal
(MP), India. His doctoral dissertation focused on CRM. He has spent three years in industry and five and half
years in education sector. He has worked in one of the premier B-Schools like Indian Institute of Management
(IIM), Bangalore, India. He has published research papers in Journal of Retailing and Consumer Services (Elsevier
Publication), International Journal of Bank Marketing (Emerald Group Publishing Limited), Journal of Research
in Interactive Marketing (Emerald Group Publishing Limited), International Journal of Environment and Waste
Management (Inderscience Enterprises Ltd.), and International Journal of Customer Relationship Marketing and
Management (IGI Global).
Divya Mittal is an Assistant Professor in the Department of Management Studies, Sri Sri University, Cuttack,
Odisha, India. Her doctoral dissertation focused on mutual funds. She has a nine years teaching experience.
Her research interest includes financial services, waste management, retailing, and services marketing. She has
published, among research papers. She has published research papers in Journal of Retailing and Consumer
Services (Elsevier Publication), International Journal of Bank Marketing (Emerald Group Publishing Limited), and
International Journal of Environment and Waste Management (Inderscience Enterprises Ltd.).
164