Research Report: Customer Services in Social Media Channels

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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.

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Research Report: Customer Services in Social Media Channels

  1. 1. Next Corporate Communication Research Center for Digital Business Page 1 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
  2. 2. Next Corporate Communication Research Center for Digital Business Page 2 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
  3. 3. Next Corporate Communication Research Center for Digital Business Page 3 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]
  4. 4. Next Corporate Communication Research Center for Digital Business Page 4 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).
  5. 5. Next Corporate Communication Research Center for Digital Business Page 5 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
  6. 6. Next Corporate Communication Research Center for Digital Business Page 6 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.
  7. 7. Next Corporate Communication Research Center for Digital Business Page 7 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.
  8. 8. Next Corporate Communication Research Center for Digital Business Page 8 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).
  9. 9. Next Corporate Communication Research Center for Digital Business Page 9 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
  10. 10. Next Corporate Communication Research Center for Digital Business Page 10 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.
  11. 11. Next Corporate Communication Research Center for Digital Business Page 11 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.
  12. 12. Next Corporate Communication Research Center for Digital Business Page 12 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.
  13. 13. Next Corporate Communication Research Center for Digital Business Page 13 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 telecommunication provider. 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
  14. 14. Next Corporate Communication Research Center for Digital Business Page 14 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 telecommunication provider. I will gladly test further offerings by this provider against my personal needs. .80 .83 .92 .87 Appendix: Constructs, Items, and Loadings References Anderson, J. and Gerbing, D.W. (1988), “Structural Equation Modeling in Practice: A Review and Recommended Two-Step Approach,” Psychological Bulletin, Vol. 103(3), 411-25 Anderson, E.W. and Sullivan, M.W. (1993), “The Antecedents and Consequences of Customer Satisfaction for Firms, “ Marketing Science, Vol. 12 (2), 125-143 Bagozzi, R.P., Yi, Y., and Philips, L.W. (1992), “Assessing Construct Validity in Organizational Research, “ Administrative Science Quarterly, Vol. 36 (September), 421-58 Bies, R.J. and Shapiro, D.L. (1987), “Interactional Fairness Judgements: The Influence of Causal Accounts, “ Social Justice Research, Vol. 1, 199-218
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  18. 18. Next Corporate Communication Research Center for Digital Business Page 18 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: alexander.rossmann@reutlingen-university.de

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