Research Report: Customer Services in Social Media Channels

Next Corporate Communication Research Center for Digital Business

RESEARCH REPORT

Customer Services in Social Media Channels:

An Empirical Analysis

Next Corporate Communication

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 SEC1 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

SatisfactionOutcomes

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 (χ2(300) = 470.61, CFI= .984; NFI= .964; RMSEA =

.052) and sample B (χ2(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

(χ2(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.

Next Corporate Communication

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: [email protected]

mailto:[email protected]

Research Report: Customer Services in Social Media Channels

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