Impact of Social CRM Technology Use on Performance
An Organizational Perspective
Torben Küpper
Institute of Information Management, University of St. Gallen, St. Gallen, Switzerland
1 STAGE OF THE RESEARCH
The research project “Social CRM” started at 3
rd
of
January 2013 and focuses on Social CRM
Performance and Social CRM Technology Use. The
research proposal solves a practical problem of our
corporate partners and is constantly being developed
in close cooperation.
2 OUTLINE OF OBJECTIVES
This Thesis in a Nutshell: The research
conceptualizes constructs of Social CRM
Technology Use and develops Social CRM
Performance constructs in order to test their
interactive impact empirically.
Generally, the new paradigm Social Customer
Relationship Management (Social CRM) (Askool
and Nakata, 2011) is ”[…] a philosophy and a
business strategy, supported by a technology
platform, business rules, processes and social
characteristics, designed to engage the customer in a
collaborative conversation in order to provide
mutually beneficial value in a trusted and transparent
business environment” (Greenberg, 2010). Another
definition describes Social CRM as ”[…] creating a
two-way interaction between the customer and the
firm. It is a CRM strategy that uses Web 2.0 services
to encourage active customer engagement and
involvement” (Faase et al., 2011). Therefore, Social
CRM deals with the integration of Web 2.0 and
Social Media into CRM (Lehmkuhl and Jung, 2013)
and enables collaboration in order to provide
mutually beneficial value.
The company’s implementation of Social CRM
is facing numerous challenges namely to measure
the use of Social Media and CRM Technology - (a)
Social CRM Technology Use - and (b) Social CRM
Performance constructs. The motivation for these
challenges confirms a practical and scientific
perspective: The (a) use of Social CRM Technology
on an organizational persepctive focuses on tools
(i.e. vendor solutions from, e.g., Lithium, Jive, etc.)
with required features (e.g., real time data
monitoring, analysis of individual data etc.) on a
more structured approach (Alvarez 2013; Sarner and
Sussin, 2012). Therefore, existing vendor solutions
have to extend their CRM-tools to embrace the
Social Media dimension (Alvarez, 2013). “SCRM
does not replace existing CRM efforts. Instead, it
adds more value by augmenting traditional systems”
(Woodcock et al., 2011). There is a rising
importance to develop and measure (b) Social CRM
Performance constructs (Bernet PR, 2013) (e.g., new
product performance (Trainor, 2012)) in order to
monitor their return on investment (Sarner et al.,
2011). To explain the impact of Social CRM
Technology Use on the Social CRM Performance,
this thesis (c) tests the interactive impact which
confirms the scientific perspective: “While social
CRM technologies may yield new outcomes not
currently examined within the traditional CRM
literature, they are expected to positively contribute
to the performance outcomes” (Trainor, 2012).
Particularly, to test the interactive impact enables the
company to determine strengths and weaknesses of
their Social CRM Technology Use. The
corresponding improvements are expected to have
an impact on performance.
Figure 1: Overview of this thesis (details are excluded).
To conclude, this thesis conceptualizes constructs of
(a) Social CRM Technology Use, (b) develop Social
CRM Performance constructs, and (c) test their
3
Küpper T..
Impact of Social CRM Technology Use on Performance - An Organizational Perspective.
Copyright
c
2014 SCITEPRESS (Science and Technology Publications, Lda.)
interactive impact empirically (see Figure 1).
The constructs of (a) Social CRM Technology
Use will be derived from literature as well as
adapted and redefind from Zablah et al., (2012) to
cover the new Social CRM approach. The (b) Social
CRM Performance constructs derives from the
“CRM performance measurement framwork” of
Kim & Kim (2009) and the individual (i.e.
customers) and organizational (i.e. companies)
perspective from Zablah et al. (2012). To test the
interactive impact a prerequesite step is to evaluate a
new measurement model, according to Moore and
Benbasat (1991), for (a) and (b). By the
quantification of all identified constructs it is
possible to (c) test the empirical impact (e.g.,
estimation of influence coefficients) of (a) Social
CRM Technology Use on (b) Social CRM
Performance or more specifically, on the customer’s
and company’s Social CRM Performance
.
3 RESEARCH PROBLEM
The prerequistie step to test the empirical impact
(i.e. the measurement model for Social CRM) is
sparsely addressed in extant literature. Authors focus
on CRM measurement models (e.g., Chen et al.,
2009; Reinartz et al., 2004; Sedera and Wang, 2009;
Sedera et al., 2009) or illustrate single Social CRM
performance artifacts without proving their
applicability (i.e. empirical impact). However, only
the model by Zablah et al. (2012), which focuses on
a customer’s and company’s perspective, has merely
been evaluated the performance implications of
technology use in the context of CRM. Therefore,
this thesis will answer the general research question
(RQ):
RQ: Does the Use of Social CRM Technology has
an Impact on Social CRM Performance?
The general research question can be decomposed in
five specific research querstions (RQ1 to RQ5). The
corresponding methodolgy and expected outcomes
are described and explained in the subsequent
sections.
RQ1: Which constructs were measured for Social
CRM Technology Use and Social CRM
Performance?
RQ2: What are the constructs for Social CRM
Technology Use?
RQ3: What are constructs for the Social CRM
Performance and how are they interrelated?
RQ4: Does the instruments of Social CRM
Performance and Social CRM Technology Use
measure the corresponding constructs?
RQ5: Which impact does Social CRM Technology
Use have on the customer’s and company’s Social
CRM performance?
4 STATE OF THE ART
The current literature analysis is also part of research
question 1 (see above) and reveals the identification
of a research gap. Their findings are documented on
the 16
th
International Conference on Enterprise
Information Systems (ICEIS 2014). A short
summary of the article are as follows: the literature
analysis sheds light on a number of articles relevant
for the Social CRM Performance, the Social CRM
Technology Use and their interactive impact in order
to identify state of the art measurement approaches
for Social CRM. The major finding (see Table 1)
reveals the lack of extant literature except for four
articles, which conceptualize single performance
approaches for Social CRM. Nevertheless, an
empirical approach is still missing. No article was
found that either conceptualizes or empirically
measures Social CRM Technology Use. Thus, no
article tests the impact empirically of Social CRM
Technology Use on Social CRM Performance. To
conclude, the literature review shed lights a research
gap for the overall research project.
Table 1: Result of the literature review.
Objectives Measurement Hits
Social CRM
Social CRM Performance
Conceptual 4
Empirical 0
Social CRM Technology
Use
Conceptual 0
Empirical 0
Impact of Social CRM
Technology Use on Social
CRM Performance
Conceptual 0
Empirical 0
5 METHODOLOGY
RQ1 is answered by conducting a literature review
according to vom Brocke et al. (2009). Leading
journals in the disciplines Information Systems and
Marketing are dissected for Social CRM
measurement. The representative coverage reveals a
number of relevant articles by analyzing the titles,
abstracts and keywords. A content analysis focuses
on categorizing the different concepts within a
framework (e.g., two dimensions named:
“performance” and “technology management”) in
ICEIS2014-DoctoralConsortium
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order to identify a research gap.
RQ2 follows a two-step approach according to
Wang et al. (2009): firstly, a literature analysis shed
light functional features for an organizational Social
CRM technology. The contribution of the literature
review will be validated, in a first round, with
exsting vendor solutions (market study). Secondly,
all vendors are re-analyzed exploratory in order to
identifiy new functional features. The contribution
of the literature review is a validated classification
through a sorting procedure of all identified features
into different categories of Social CRM Technology,
wich are derived from literature.
RQ3 follows a another two-step approach: first,
a literature review shed lights on articles relevant to
the research question. The contribution of the
literature review is a preliminary conceptual model.
Susequently, multiple semi-structured interviews
(Yin 2009) are carried out, followed by the adopted
four-step approach by Paré (2004) in order to
explore new Social CRM constructs. Different
industries (e.g., insurances, sports companies, banks,
etc.) are analyzed to gain a holistic view on the
different needs. For each company, a key social
media, communication or marketing managers is
interviewed.
RQ4 addresses the measurement of all
constructs in RQ2 and RQ3. The measurement is
based upon the approach according to Moore and
Benbasat (1991), illustrated by Walther et al. (2013)
(see Figure 2). Two surveys (one for the customers
and one for the companies) evaluate two
independent measurement scales. The first step, item
creation, generates items for each customer and
company scale. The second step, scale development,
validates the item generation conducting Q-sorting
and calculates an inter-rater reliability (Perreault &
Leigh 1989). The final step, instrument testing,
includes a pre-test with independent practical
experts and a field test with cooperate companies, as
well as scientists in this research field. The resulting
measurement is evaluated by a confirmatory factor
analysis with the first step of a structural equation
model (Hair et al. 2013).
RQ5 tests the impact of Social CRM
Technology Use on the Social CRM Performance.
More specifically, the contribution of RQ5 is to
estimate influence coefficients of Social CRM
Technology Use (e.g., usage of Social CRM analysis
technology) on the customer’s and company’s
performance (the two perspectives of the Social
CRM Performance). Firstly, hypotheses of the
influence coefficients are derived from conceptual
Social CRM and underpinned CRM literature.
Secondly, a regression model follows, which
estimates the influence coefficients. Particularly, a
two-level hierarchical linear regression (or two-level
nested model) will be applied with the statistical
software HLM 6.06. The two-level approach is
deemed appropriate to fit the two perspectives of the
Social CRM Performance. The customer’s data
defines the first level regression and the company’s
data the second level regression (Raubenbush &
Bryk 2002). Particularly, the Social CRM
Performance constructs are the dependent variables
and the constructs of Social CRM Technology Use
are the independents.
Figure 2: The three steps of a measurement model.
6 EXPECTED OUTCOME
RQ1 (published) is answered in section four, and is
not discussed repeatedly.
Figure 3: Constructs of Social CRM Technology Use.
RQ2 (submitted) discloses the constructs for
Social CRM Technology Use. A previous literature
review focuses on CRM technology use constructs
(e.g., CRM prioritization tools (Zablah et al. 2012))
and Social Media technology use (e.g., Social Media
analytical tools). The result of the literature review is
a preliminary conceptualization (i.e. different
constructs) of Social CRM Technology Use. The
market study completes the conceptual approach and
ImpactofSocialCRMTechnologyUseonPerformance-AnOrganizationalPerspective
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Figure 5: Possible result for the measurement model.
Figure 4: Result of Social CRM Performance.
the sorting procedure classifies the identified
features in six derived categories (see Figure 3):
monitoring & capturing, analysis, exploitation, IS
integration, communication and management.
RQ3 (in progress) sheds light the Social CRM
Performance. A previous literature review focuses
on Social CRM and the underpinning CRM
performance constructs in order to develop a
preliminary model. Explorative case studies extend
and complete the model. The preliminary findings
are shown in Figure 4. The constructs of the
customer’s perspective of the Social CRM
Performance (e.g., customer advantages) can be
deemed as a mediator of Social CRM Technology
Use on the company’s perspective of the Social
CRM Performance, which was empirical proven by
Zablah et al., (2012) in a CRM context.
RQ4 addresses the measurement model. Some of
the Social CRM Technology Use constructs as well
as Social CRM Performance constructs related items
are derived from extant literature. At first, a possible
result of the new developed measurement scale
could be an extension or re-specification (i.e. last
step of figure 2) of the Social CRM Performance
constructs, as well as the Social CRM Technology
Use constructs. Due to insignificant items, as a
second possible finding, a second order construct
(see Figure 5) could fit the items with higher
loadings.
RQ5 sheds light on the influence coefficients of
Social CRM Technology Use on the customer’s and
company’s Social CRM performance. Regarding the
expected findings in RQ3 (customer’s Social CRM
Performance constructs as mediator) the level-1
dependent variables are customer’s Social CRM
Performance constructs. According to Becker et al.
(2009) possible finding are moderators in the CRM
context (see Figure 6). The equation in the appendix
describes a possible two-level hierarchical linear
regression model to estimate the influence
coefficients. Due to the preliminary and expected
findings, Kendall’s Tau coefficient determines the
impact of the customer’s Social CRM performance
constructs on company’s Social CRM performance
constructs (Zablah et al., 2012). The overall result
will be positive significant influence coefficients for
the previously derived hypotheses.
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Figure 6: Detailed model overview of this thesis.
7 PRACTICAL IMPLICATION
Social CRM Technology Use: The different
conceptual categorizations will help companies to
define requirements for their Social CRM
Technology (e.g., Social CRM software needs an
analysis tool to process unstructured data). The
measurement of the Social CRM Technology Use
constructs reveals best practices of competitors and
therefore discloses improvements for the company.
Social CRM Performance: The customer’s
Social CRM performance constructs help the
company to acquire knowledge, which answers the
following question: how successful are the Social
CRM efforts for our customers’ performance. A
good practical implication is given by a high degree
of customers’ Social CRM performance, which
indicates (indirectly or directly) a long-term
relationship with the company. For the company’s
Social CRM Performance constructs, this thesis
enables companies to compare their Social CRM
Performance with competitors and to monitor their
performance over time.
The significant impact coefficients support the
company and the customer by enhancing the Social
CRM performance. The value of the influence
coefficients prioritizes the usage of company’s
Social CRM Technology resources (e.g., analysis
tools) in order to increase customers’ or company’s
Social CRM performance. The significance forces
the company to improve single Social CRM
Technology Use constructs and therefore influences
management decisions (e.g., distribution of the
Social CRM Technology resources).
8 CONTRIBUTION TO SCIENCE
This thesis will deliver three major contributions to
the scientific community:
Extension of CRM Technology Use: The empirical
investigation for CRM Technology Use will be
redefined with the new Social CRM constructs.
Current CRM Technology Use constructs (e.g.,
“CRM interaction support tools” and “CRM
priorization tools” (Zablah et al., 2012)) will be re-
specified within the Social CRM context (e.g.,
monitoring & capturing, analysis, etc.) and therefore
complete the research of Social CRM Technology
Use.
Adoption of the CRM Performance Measurement
Framework: With regard to the motivation in the
introduction the performance dimension is
investigated and will be adapted for the Social CRM
context. Particularly, the investigation of the
customer’s and company’s perspective according to
Zablah et al. (2012) will show new contributions to
the scientific community.
Performance Implications for Social CRM
Technology Use: The impact of Social CRM
Technology Use on Social CRM Performance is
tested with a statistical model (here: two-level
hierarchical linear regression) and adds first
theoretical and empirical insights, on two
perspectives, into the new paradigm Social CRM.
Particularly, the results help to understand the
underlying relationships (i.e. when X increased, then
it will have (no) significant impact on Y).
ImpactofSocialCRMTechnologyUseonPerformance-AnOrganizationalPerspective
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APPENDIX
Figure 7: Possible two-level hierarchical linear regression.
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