Research Report: Customer Services in Social Media Channels

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RESEARCH REPORT
Customer Services in Social Media Channels:
An Empirical Analysis
Research Project: Shaping the Customer Service Experience
Sept. 18, 2013
By Alexander Rossmann

Customer Services in Social Media Channels:
An Empirical Analysis
Abstract
In recent years, marketing scholars have invested heavily in exploring the role of social media in
marketing theory and practice. One valuable strategy for using social media in marketing
communication is to provide customer services in applications like Facebook or Twitter. This paper
evaluates a) the concept of perceived service quality in different service channels and b) the impact
customer service strategies have on customer loyalty, word of mouth communication, and cross-sell
preferences. The framework presented here is tested cross-channel against data collected from the
customer service department of a large telecommunication provider. The results elucidate the
effectiveness of customer service strategies in different channels.
Keywords:
Customer Service, Social Media, Word of Mouth, Customer Satisfaction, Customer Loyalty

Introduction
In recent years, marketing scholars have invested heavily in exploring the role of social media in
marketing theory and practice (Kozinets, de Valck, Wojnicki, and Wilner 2010; Trusov, Bodapati, and
Bucklin 2010; van der Lans, van Bruggen, Eliashberg, and Wierenga 2010). Global usage of main
social media sites like Facebook, YouTube, and Twitter has grown to a scale that can only be
described as ubiquitous. Facebook´s S-1 filing with the SEC
1
reveals that Facebook has 845 million
users who are interconnected by 100 billion friendships. Every day, Facebook users generate 2.7
billion likes or comments and upload 250 million photos. You Tube´s most recently released statistics
indicate a similar magnitude of user engagement. More than 800 million unique users visit YouTube
each month, 4 billion videos are viewed each day, and “more video is uploaded to YouTube in one
month than the three major US networks created in 60 years.”
2
While Twitter officially has 100 million
active users generating over 200 million Tweets per day, third party estimates range as high as 500
million registered accounts (Hoffman and Novak 2012, 69).
3
Obviously, social media applications like Facebook or Twitter provide marketing executives with
a raft of new options – targeting the impact of direct user interaction, say, or the online integration of
users in corporate value creation processes (DeVries, Gensler, and Leeflang 2012). One valuable
strategy in this connection is to provide customer services in social media channels. Most firms are
regularly confronted with complaining customers. At this critical stage of a relationship, complaint
strategies are the acid test of a firm´s customer orientation (Homburg and Fürst 2005). Whereas a
poor recovery may result in amplifying a negative evaluation (Bitner, Booms, and Tetrault 1990), an
excellent recovery can increase the relationship quality beyond where it was before the failure (Smith
and Bolton 1998).
Thus, marketing and service executives might analyze customer online communication, identify
service issues at an early stage, create satisfying service experiences, and give customers a direct
and convenient way to share their sentiments by word of mouth. The ultimate goal of service
strategies in social media channels is to turn complainers into fans. Marketing executives look to such
strategies to positively influence outcomes like customer satisfaction, loyalty, willingness to pay, and
tendency to repurchase (McCollough, Berry, and Yadav 2000; Anderson and Sullivan 1993).
Moreover, positive word of mouth communication in social networks may attract other users, thus
opening up novel prospects (Maxham III and Netemeyer 2003). In short: offering customer services in
social media channels is an important pathway for marketing innovation in various industries.
1
http://www.sec.gov/Archives/edgar/data/1326801/000119312512034517/d287954ds1.htm [Accessed 12/11/14]
2
http://www.youtube.com/t/press_statistics [Accessed 12/11/17]
3
http://twopcharts.com/twitter500million.php [Accessed 12/11/19]

For all the valuable contributions made by social media marketing research, a lot of important
questions still remain unexplored (Hoffman and Novak 2012). One is clarifying the preconditions for
differentiating service strategies; another is exploring the potential outcomes of such strategies. Above
all, we need to achieve a better understanding of a particular construct, namely perceived service
quality in social media channels. Marketing executives need to evaluate the importance of different
aspects of corporate service provision for the customer (Homburg and Fürst 2005). Most scholars
believe service quality impacts positively on different types of customer satisfaction (McCollough,
Berry, and Yadav 2000; Anderson and Sullivan 1993; Worsfold, Worsfold, and Bradley 2007; McColl-
Kennedy, Daus, and Sparks 2003). Therefore, it is important to assess if this hypothesis also holds for
customer services in social media channels. In addition, we know very little about the specifics of how
a service engagement plays out in social networks. And there is final area where research is needed:
evaluating the effectiveness of delivering customer services through social media (as compared to
other channels).
Responding to these gaps in current social media marketing research, this paper addresses the
following four research questions: (1) How should customer service quality in social media channels
be conceptualized on multiple levels? 2) Which aspects of customer service quality are important in
enhancing customer satisfaction? 3) What outcomes are effected by customer service quality and
customer satisfaction? 4) How effective are customer services delivered through social media
channels (as compared to customer services delivered through other channels)?
Conceptual framework
The conceptual framework for evaluating the above research questions is set out in Fig. 1. In
line with our previous discussion, our framework includes constructs related to different aspects of
perceived service quality. We assume that the perception of the customer with respect to complaint
handling influences customer satisfaction. In turn, we expect these to influence a specific set of
relationship outcomes, namely loyalty, word of mouth, and cross-sell preferences (Davidow 2003).
Furthermore, we assume that the causal chain in our framework is generally applicable to different
service channels.
Service quality and organizational complaint handling have been conceptualized in a variety of
ways. Most studies on organizational complaint management combine the construct of service quality
with the perception of fairness (Gelbrich and Roschk 2010; Homburg and Fürst 2005). While early
papers on post-complaint behavior center on fairness in general (Blodgett, Hill, and Tax 1997;
Goodwin and Ross 1989), it is now agreed that customers perceive fairness in three dimensions:
Distributive justice refers to the perceived outcome of a decision or exchange. This embraces the
apparent subjective benefit customers receive to offset the problem resulting from a company´s
failure (Smith, Bolton, and Wagner 1999).

Fig. 1: Conceptual Framework
Procedural justice refers to how the customer perceives the means of decision making and
conflict resolution used by the organization (Lind and Tyler 1988). A procedure is considered fair
when it is easy to access, provides the customer with some control over its disposition, is flexible, and
is concluded in a convenient and timely manner (Tax, Brown, and Chandrashekaran 1998, 62).
Interactional justice refers to how customers perceive the way they are treated. Treatment is
perceived as fair when customers assume the information is exchanged and the outcomes are
communicated in a polite and respectful manner (Patterson, Cowley, and Prasongsukarn 2006).
The distinctness of the three justice dimensions has recently been called into question
(Gelbrich and Roschk 2012). Davidow (2003) and Liao (2007) report on high correlations between the
three justice dimensions. Obviously, customers are unable to clearly distinguish between, say, a
professional service process (procedural justice) and respectful treatment (interactional justice).
Thus, Liao (2007) models perceived justice as a higher order latent variable in a confirmatory
factor analysis (CFA) using this construct as a single predictor of customer satisfaction. DeWitt,
Nguyen, and Marshall (2008) also include the justice dimensions in one latent variable in their CFA,
arguing that customers use a compensatory model when forming an overall perception of justice.
Furthermore, several papers integrate different aspects of distributive, procedural, and interactional
justice within one single construct (Homburg and Fürst 2005, Smith, Bolton, and Wagner 1999).
Customer
Effort
Procedural
Quality
Quality of
Interaction
Quality of
Solutions
Customer
Satisfaction
Fairness
H1
Perceived
Service Quality
Customer
Satisfaction
Outcomes
Customer Loyalty
Cross-Sell
Preferences
Word of Mouth
H2
H3
H4
H5
H6
H7
H8

Thus, service quality and customer justice in an organizational complaint context have been
conceptualized in multiple ways. Now, the goal of the present research is to identify and measure
different aspects of perceived service quality. We are additionally interested in comparing the effect of
these aspects in different service channels. Hence, the various facets of perceived service quality are
modeled as separate constructs. Moreover, the constructs used in our framework are modeled as
consistently as possible to avoid high correlations and measurement problems. Therefore, in our
research model, we distinguish between different aspects of interactional, distributive, and procedural
justice.
Despite the prevailing heterogeneity in theoretical orientations, many scholars agree that an
important dimension of service quality is the amount of effort customers need to invest in order to
solve a current problem (Tax, Brown, and Chandrashekaran 1998; Homburg and Fürst 2005). Thus,
customer effort is an important facet of distributive justice. Theories of distributive justice focus on the
allocation of benefits and costs (Deutsch 1985). Social exchange theory emphasizes the role of
distributive or exchange considerations in shaping interpersonal relations. Gelbrich and Roschk
(2010) analyze multiple facets of distributive justice in exchange situations. Recent research by
Dixon, Freeman, and Toman (2010) illustrates that companies create loyal customers primarily by
helping them solve their problems quickly and easily.
Framing the service challenge in terms of making it easy for the customer can be highly
illuminating, especially for companies that have been struggling to give satisfaction. In particular, a
high quality of customer service is associated with a low need for the customer to invest in own
efforts. Thus, the conceptual model in Fig. 1 postulates a negative relationship between customer
effort and customer satisfaction.
H1: The amount of effort customers need to invest to solve a
current problem has a negative impact on customer satisfaction.
Different aspects of procedural justice have been proposed and tested by several researchers
as antecedents for customer satisfaction in a service context. Nevertheless, the most important facet
of process quality is the question of timeliness and the required length of time to solve a current
problem (Smith, Bolton, and Wagner 1999; Homburg and Fürst 2005; Tax, Brown, and
Chandrashekaran 1998). This research tack postulates that customers expect a fast reaction to their
service complaints, while customers accept, in turn, that firms need a specific period of time to
analyze and solve specific issues. This leads to critical deviations between customer expectations
and firm behavior – firms may gratify or disappoint customers with their specific reaction policy and
their ability or inability to solve problems sustainably. Therefore, shortening the time needed to react
to customer complaints and the length of time required to solve current problems will amplify
customer satisfaction with the supplier organization.

H2: The perceived level of procedural quality in terms of timeliness and the required
length of time to solve a current problem impacts positively on customer satisfaction.
Moving on now to the issue of interaction during service provision, some marketing scholars
argue that the perceived quality of communication will facilitate customer satisfaction in a service
context. The integration of interactional factors helps to explain why some customers might feel
unfairly treated even though they would characterize the service processes and outcomes as fair
(Bies and Shapiro 1987). This refers to the behavior exhibited by employees towards complainants,
which includes customer perceptions of employee politeness (Goodwin and Ross 1989), employee
empathy (Tax, Brown, and Chandrashekaran 1998), and employee effort (Smith, Bolton, and Wagner
1999). It is also important for customers to perceive a high level of individuality during the service
process. Additional studies in service quality (Parasuraman, Zeithaml, and Berry 1998; Blodgett, Hill,
and Tax 1997) support the central role of interaction quality in complaint handling. Adapting this
perspective, the above research makes the assumption that higher quality of interaction strongly
impacts on customer satisfaction (H3).
H3: The perceived interaction quality during the service process
has a positive impact on customer satisfaction.
Our research framework integrates the construct of fairness as an antecedent for customer
satisfaction in a service context. As already mentioned, justice theory is used in more recent studies,
providing evidence that customers who perceive the organizational response to a complaint as fair go
on to display higher levels of customer satisfaction than those who perceive the response as unfair
(Maxham III and Netemeyer 2002; Patterson, Cowley, and Prasongsukarn 2006; Smith, Bolton, and
Wagner 1999). Fairness is perceived when the ratio of an individual´s output to input is balanced by
the ratio of the other party. Thus, the construct of fairness plays an important role in complaint
management research. While early studies center on fairness in general (Blodgett, Granbois, and
Walters 1993; Goodwin and Ross 1989), it is now agreed that customers perceive fairness in multiple
ways.
Moreover, fairness is only one single facet of a customer´s view on total service quality. Thus,
we integrate fairness as s single construct in our research framework and postulate that enhancing
the customer perception thereof will amplify customer satisfaction with the supplier organization.
H4: The perceived degree of fairness with respect to organizational
responses to complaints has a positive impact on customer
satisfaction.

Finally, the outlined model incorporates the quality of the service solution itself as a positive
precondition for customer satisfaction. A large-scale study of contact center and self-service
interactions determined that what customers really want (but rarely get) is a satisfactory solution to
their service issue (Dixon, Freeman, and Toman 2010). Thus, we hypothesize that customers
appreciate getting a viable solution to their current problem. By lifting their capacity to identify and
analyze customer issues, firms can deliver the right solution. Therefore, improving the quality of
service solutions can amplify customer satisfaction with the supplier organization.
H5: The perceived viability of a delivered service solution in order to solve a
current problem impacts positively on customer satisfaction.
Most existing studies on organizational complaint handling assume that customers´ evaluation
of service quality impact on customer satisfaction (Gelbrich and Roschk 2010). Therefore, the effect
of service quality on behavioral intentions is mediated by customer satisfaction. Homburg and Fürst
(2005) distinguish between complaint satisfaction and the overall satisfaction of a customer.
Complaint satisfaction refers to the degree to which the complainant perceives the company´s
recovery strategy as meeting or exceeding his or her expectations (Gilly and Gelb 1982; McCollough,
Berry, and Yadav 2000).
Overall customer satisfaction after the complaint refers to the degree to which the complainant
perceives the company´s general performance as meeting or exceeding expectations (Anderson and
Sullivan 1993). This type of satisfaction is cumulative in kind, whereas complaint satisfaction reflects
a form of transaction-specific satisfaction (Bolton and Drew 1991; McCollough, Berry, and Yadav
2000). Adopting this perspective of two different satisfaction constructs, most marketing scholars
argue that the behavioral intentions of a customer are predominantly driven by overall satisfaction
with an organization’s performance (Gelbrich and Roschk 2010). Thus, our research framework
integrates overall customer satisfaction as a mediating construct and driver of behavioral intentions.
Finally, the conceptual framework in Fig. 1 postulates three indirect effects of service quality
mediated by customer satisfaction. Thus, fostering service quality might impact positively on
customer loyalty to the supplier firm, word of mouth communication, and customer preferences to
engage in cross-sell behavior. Loyalty refers to a customer´s intention to continue to do business with
an organization (Homburg and Fürst 2005). It is referred to as repurchase intention (Blodgett, Hill,
and Tax 1997). Word of mouth (WOM) communication comprises the likelihood of spreading
information about an organization and the valence of this information (Davidow 2003).

Researchers usually combine these aspects in one construct yielding the likelihood of positive
WOM and negative WOM. We only consider positive WOM because after service failure positive
WOM can be clearly identified as following a complaint and subsequent recovery efforts. Blodgett and
Anderson (2000) show that post-failure positive WOM tends to result from effective service strategies.
It does not occur when customers do not complain. This is because failure persistence and the lack of
service recovery efforts prevent customers from recommending the organization.
Post-failure negative WOM may arise prior to a complaint as well as subsequent to ineffective
recovery efforts after a complaint. Additionally, some researchers expect that customer satisfaction
fosters loyalty and WOM, while also having a positive impact on repurchase intensions (Blodgett, Hill,
and Tax 1997). Such repurchase intentions might include additional products and services and also
stimulate cross-sell preferences. Therefore, enhancing customer satisfaction has a positive effect on
three different outcomes.
H6: The degree of customer satisfaction impacts positively on the intention of
customers to continue doing business with an organization.
H7: The degree of customer satisfaction impacts positively on the likelihood of
customers spreading favorable information about an organization.
H8: The degree of customer satisfaction impacts positively on the preferences of
customers to purchase additional products or services.
Method
Our research tested the formulated hypotheses using data supplied by the customer service
department of a telecommunications provider in Germany. The customers of this provider already
receive services through different channels. Customer services are delivered through traditional
channels (hotline, email, letter) and through social media like Facebook and Twitter. We decided to
use two different samples for our research, one from a traditional channel (hotline: sample A) and one
from the social media (sample B). Thus it would be possible to interview customers immediately after
a service experience in different channels. In sample A, customers were invited by email to take part
in the service survey immediately after a hotline contact. In sample B, customers received a
comparable invitation by email, by direct message (Twitter), or by direct mail (Facebook). All
interviews were conducted online.
The questionnaire was based on the same procedures as were recommended by Churchill
(1979) and Gerbing and Anderson (1988). Initially, ten interviews were conducted with marketing and
service executives of the telecommunication provider. These explorative interviews, lasting
approximately ten hours, helped to develop relevant measurement scales. Based on these interviews
and an extensive review of past research papers, preliminary versions of the questionnaire were

developed. Whenever possible, existing scale items were adapted to the context. Multi-item, seven-
point, Likert-type scale items were used to measure the constructs in the proposed model.
Subsequently, the questionnaire was mailed to a sample of 54 customers to verify the aptness of the
terminology used and the clarity of the instructions provided. After suitably improving the
questionnaire, a pretest involving 186 customers was conducted. With a view to eliminating items with
low loadings or high cross loadings, the measures for each construct were scanned for evidence
of validity and reliability. Finally, we integrated 220 customers from sample A and 220 customers
from sample B into the main study sample.
Results
The unidimensionality and convergent validity of the constructs were examined by confirmatory
factor analysis (CFA) performed on both samples using LISREL. All items load on their respective
constructs, and each loading is large and significant at the 0.01 level, demonstrating satisfactory
convergent validity (Anderson and Gerbing 1988). To assess the discriminant validity of the
constructs, a model constraining the correlation between a pair of constructs to 1 was compared with
an unconstrained model. To indicate discriminant validity, the unconstrained model must fit
significantly better than the constrained model (Bagozzi, Yi, and Phillips 1991). The pairwise chi-
square difference tests indicate, in each case, that the chi-square difference statistic is significant at
the .01 level, supporting discriminant validity. In addition, all pairs of constructs pass Fornell and
Larcker´s (1981) test of discriminant validity. That is, the amount of variance extracted by each
construct is greater than the squared correlation between the two constructs.
After the measurement models were deemed acceptable, we estimated a structural path model
to test the hypotheses depicted in Fig. 1. In the first instance, we tested the model stand alone on
each single sample. The fit indexes for sample A (χ
2
(300) = 470.61, CFI= .984; NFI= .964; RMSEA =
.052) and sample B (χ
2
(300) = 508.39, CFI= .984; NFI= .967; RMSEA = .056) suggest that the model
acceptably fits the data (Byrne 1998). A chi-square difference test reveals that a model with direct
effects (direct paths from the antecedent variables to the three target variables) does not have
significantly better fit indexes than our full mediation model (Fig. 1), suggesting that our model
provides a parsimonious explanation of the data (Bagozzi and Yi 1988). Additionally, we applied an
established multigroup method to analyze the differences between both samples according to our
research model (Ping 1995; Stone and Hollenbeck 1989). Therefore, we used an extended LISREL
model with mean structures (Jöreskog and Sörbom 1996). The fit indexes for multi-sample analysis
(χ
2
(618) = 992.40, CFI= .984; NFI= .964; RMSEA = .053) again suggests that the model acceptably
fits the data. Table 1 summarizes the results. All 8 main effects were supported in sample B (Social
Media), 7 of 8 main effects were supported in sample B (Hotline). Additionally, data from multigroup
analysis provides insight into different effects in both samples.

Hypotheses Main Effects
Sample A
“Hotline”
Main Effects
Sample B
“Social Media”
Multigroup Analysis
Sample A
“Hotline”
Sample B
“Social Media”
H1 β = -.17 (+) β = -.31 (+) -.19 -.28
H2 β = .24 (+) β = .29 (+) .23 .29
H3 n.s. (-) β = .22 (+) n.s. .26
H4 β = .10 (+) β = .24 (+) .21 .17
H5 β = .47 (+) β = .18 (+) .46 .19
H6 β = .69 (+) β = .76 (+) .55 .94
H7 β = .39 (+) β = .81 (+) .24 .72
H8 β = .65 (+) β = .63 (+) .68 .59
Table 1: Hypotheses, main effects, multigroup analysis
Discussion
The outlined research model has several important implications for customer services and
social media marketing. In the first instance, service quality can be resolved into (1) customer effort,
(2) procedural quality, (3) quality of interaction, (4) fairness, and (5) quality of solutions. Generally,
this framework for customer service quality is applicable to multiple service channels. However, the
depicted aspects of service quality impact differently on customer satisfaction. Thus, procedural
quality (β=.29), the quality of interaction (β=.26), and the reduction of customer efforts (β=.-28) are
especially important in services delivered through social media, whereas these effects are weaker or
not significant in a traditional hotline setting. Additionally, the most important predictor of customer
satisfaction in the hotline channel is the quality of service solutions. Therefore, customers in this
channel are particularly interested in the elimination of current problems, whereas social media users
also focus on customer effort and the quality of interaction.
Moreover, our empirical research favors the mediation hypothesis depicted in Fig. 1. Thus, the
effect of customer service strategies on relevant objectives like loyalty, word of mouth, or cross-sell
preferences is mediated by customer satisfaction. However, customer satisfaction impacts differently
on relevant target constructs in multiple channels. The results show customer satisfaction as having a
strong effect on customer loyalty in both researched channels. Thus, the link between satisfaction
and loyalty seems quite independent of the channel choice of customers. However, multigroup
analysis shows significant differences with regard to the effect of customer satisfaction on word of
mouth communication.

Such effects are particularly relevant for customer services delivered through the social media
(β=.72), whereas the same effect is considerably weaker for traditional hotline services (β=.24).
Accordingly, our findings correspond with previous results in social media research, which indicate a
high potential for word of mouth communication.
Even more promising for executives is the question of how effectively customer services are
delivered via social media channels, as compared to customer services delivered via other channels.
Independent of channel preferences, business executives should continue to focus on providing a
high quality of customer services, since such services play an important role in the development of
customer satisfaction, loyalty, word of mouth, and cross-sell decisions. On the other hand, customer
services delivered through the social media offer novel options for word of mouth communication.
Therefore, firms need to enlarge their service strategies and channel portfolio if they are interested in
leveraging such communication effects. A combination of different service channels promises to
impact most strongly of all on word of mouth, loyalty, and cross-sell preferences of customers.
Therefore, firms need to establish a viable set of service channels if they wish (sustainably) to turn
complainers into fans.

Constructs and Items Loading Constructs and Items Loading
A B A B
Customer Effort
Use of the customer services
of this telecommunication
provider means a lot of
effort.
Use of the customer services of
this telecommunication provider
is quite time-consuming.
Use of the customer services
delivered by this provider is
inconvenient.
Procedural Quality
The service agents of this provider
respond rapidly to customer
issues.
I´m absolutely satisfied with the
length of time required to solve
a current problem.
Generally it is not necessary to
wait too long for service
response and
recovery.
Quality of Interaction
The service agents of this tele-
communication provider respond
politely to my complaints.
The service agents of this
provider respond with a high
degree of empathy.
I feel a high level of individuality
when interacting with the
customer service of this
provider.
.88
.80
.84
.86
.80
.82
.88
.90
.93
.79
.82
.82
.84
.87
.89
.84
.84
.87
Customer Satisfaction
Overall, I´m quite satisfied with the
performance of this
telecommunication provider.
It was a good decision to
become a customer of this
provider.
This telecommunication provider
lives up to my expectations.
Customer Loyalty
I´m a loyal customer of this
If nothing changes, I will remain a
customer of this provider.
I will remain a customer of this
provider, even if competitors
offer slightly lower prices.
Word of Mouth
I often talk positively about this
telecommunication provider to
my friends.
I forward information about this
telecommunication provider to
my friends.
I actively recommend to my friends
that they become a customer of
this telecommunication provider.
Cross-Sell Preferences
I prefer to purchase further
telecommunication products
and services from this
provider.
.89
.88
.87
.79
.82
.82
.86
.91
.85
.83
.89
.89
.89
.82
.84
.84
.81
.83
.85
.87

Fairness
The customer services of this
telecommunication provider
lead to fair results.
The efforts I have to undertake
and those this
telecommunication provider has
to undertake to solve the current
problem are shared equally.
Overall, the complaint handling
by this telecommunication
provider was fair.
Quality of Solutions
The customer services of this
provider lead to proper solutions
to current problems.
I receive viable solutions from the
customer services of this
provider.
The quality of the solutions
delivered by the customer
service of this provider is quite
high.
.84
.87
.86
.85
.85
.87
.85
.79
.80
.89
.90
.92
I´m interested in new products
and services from this
I will gladly test further offerings by
this provider against my personal
needs.
.80
.83
.92
.87
Appendix: Constructs, Items, and Loadings
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About us
Alexander Rossmann is Professor for Marketing and Sales
at Reutlingen University and Project Director at the Institute
of Marketing, University of St. Gallen. Prior to this, he was
for ten years Managing Director of a leading consultancy
firm. His expertise covers relevant issues of social media
research, digital business, and relationship marketing.
Alexander holds a doctoral degree from the University of
St.Gallen and a masters degree from the University of
Tubingen and the State University of New York. He was
born near Stuttgart and is married with three children.
Next Corporate Communication is a partnership between research institutions and business partners
in order to shape the digital transformation. We live in an era of disruptive change - a time when
technology and society are evolving faster than the ability of many organizations to adapt. But digital
business is part and parcel of today's modern corporation. Our mission is to conduct research that is
both academically rigorous - but also relevant to business.
Contact us for further information.
Research Center for Digital Business
Prof. Dr. Alexander Rossmann
Alteburgstrasse 150
72762 Reutlingen
Germany
Direct Contact
Prof. Dr. Alexander Rossmann
Phone: +49 172 711 20 60
Email: alexander.rossmann@reutlingen-university.de

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