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

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

Küpper T..

Impact of Social CRM Technology Use on Performance - An Organizational Perspective.

 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

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

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

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.

ICEIS2014-DoctoralConsortium

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

REFERENCES

Alvarez, G., 2013. Hype Cycle for E-Commerce , 2013,

Gartner, Inc.

Askool, S. & Nakata, K., 2011. A conceptual model for

acceptance of social CRM systems based on a scoping

study. Ai & Society, 26(3), pp.205–220.

Bernet PR, 2013. Social Media wird Alltag: Integration

nimmt zu. Bernet ZHAW Studie Social Media Schweiz

2013 bernet.ch/studien, pp.1–26.

Vom Brocke, J. et al., 2009. Reconstructing the Giant: On

the Importance of Rigour in Documenting the

Literature Search Process. In 17th European

Conference on Information Systems. pp. 3226–3238.

Chen, J. et al., 2009. Measuring CRM effectiveness:

Construct development, validation and application of a

process-oriented model. Total Quality Management,

20(3), pp.283–299.

Faase, R., Helms, R. & Spruit, M., 2011. Web 2.0 in the

CRM domain: defining social CRM. International

Journal of Electronic Customer Relationship

Management, 5(1), pp.1–22.

Greenberg, P., 2010. The impact of CRM 2.0 on customer

insight. Journal of Business & Industrial Marketing,

25(6), pp.410–419.

Hair, J.F. et al., 2013. A Primer on Partial Least Squares

Strucutral Equation Modeling PLS-SEM, Thousand

Oaks: SAGE Publications, Inc.

Kim, H.-S. & Kim, Y.-G., 2009. A CRM performance

measurement framework: Its development process and

application. Industrial Marketing Management, 38(4),

pp.477–489.

Lehmkuhl, T. & Jung, R., 2013. Towards Social CRM –

Scoping the concept and guiding research. In BLED

2013 Proceedings. pp. 190–205.

Moore, G.C. & Benbasat, I., 1991. Development of an

Instrument to Measure the Perceptions of Adopting an

Information Technology Innovation. Information

Systems Research, 2(3), pp.192–222.

Paré, G., 2004. Investigating Information Systems with

Positivist Case Study Research. Communications of

the Association for Information Systems, 13(1),

pp.233–264. Available at: http://aisel.aisnet.org/cais/

vol13/iss1/18.

Perreault, W. D. & Leigh, L. E., 1989. Reliability of

nominal data based on qualitative judgments. Journal

of Marketing Research, 26(2), pp.135–148.

Raubenbush, S.W. & Bryk, A.S., 2002. Hierarchical

Linear Models: Applications and Data Analysis

Methods Second., Newbury Park: CA: Sage.

Reinartz, W., Krafft, M. & Hoyer, W.D., 2004. The

Customer Relationship Management Process: Its

Measurement and Impact on Performance. Journal of

Marketing Research, 41(8), pp.293–305.

Sarner, A. et al., 2011. Predicts 2012: Social CRM

Remains an Immature Area. Gartner Research Inc.,

(November 2011), pp.1–7.

Sarner, A. & Sussin, J., 2012. Predicts 2013: Social CRM

Gartner Research.

Sedera, D., Wang, W. & Tan, F., 2009. Towards a CRM

and SCM Benefits Measurement Model. In PACIS

2009 Proceedings. pp. 1–12.

Trainor, K.J., 2012. Relating Social Media Technologies

to Performance: A Capabilities-Based Perspective.

Journal of Personal Selling and Sales Management,

32(3), pp.317–331.

Walther, S. et al., 2013. Evaluating Operational Cloud

Enterprise Systems Success: An Organizational

Perspective. In 21st European Conference on

Information Systems (ECIS). pp. 1–12.

Wang, W., Sedera, D. & Tan, F., 2009. Measuring CRM

and SCM Benefits: A Preliminary Measurement

Model. In PACIS 2009 Proceedings. pp. 1–12.

Woodcock, N. et al., 2011. The evolving data architecture

of social customer relationship management. Journal

of Direct, Data and Digital Marketing Practice, 12(3),

pp.249–266.

Yin, R. K., 2009. Case Study Research. Design and

Methods 5th ed., Thousands Oaks: Sage Publications.

Zablah, A. R. et al., 2012. Performance Implications of

CRM Technology Use : A Multilevel Field Study of

Business Customers and Their Providers in the

Telecommunications Industry. Information Systems

Research, 23(2), pp.418–435.

APPENDIX

Figure 7: Possible two-level hierarchical linear regression.

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