Scholars have questioned the effectiveness of several customer relationship management strategies. The author investigates the differential effects of customer relationship perceptions and relationship marketing instruments on customer retention and customer share development over time. Customer relationship perceptions are considered evaluations of relationship strength and a supplier’s offerings, and customer share development is the change in customer share between two periods. The results show that affective commitment and loyalty programs that provide economic incentives positively affect both customer retention and customer share development, whereas direct mailings influence customer share development. However, the effect of these variables is rather small. The results also indicate that firms can use the same strategies to affect both customer retention and customer share development.

/pdf/understanding-the-effect-of-customer-relationship-management-2bdnthpwpj.pdf

Understanding the Effect of Customer Relationship Management Efforts on Customer Retention and Customer Share Development

Empirical studies that use structural equation modeling (SEM) are widespread in information systems research. During the last few years, the component-based approach partial least squares (PLS) for testing structural models has become increasingly popular. At the same time, this approach's limitations have become a greater concern. Some researchers even suggest using alternative approaches that are considered superior to PLS. However, we believe that PLS is an adequate choice if the research problem meets certain characteristics and the technique is properly used. Thus, the intention of this article is to resolve potential uncertainties that researchers intending to use PLS might have. Consequently, we provide a nontechnical overview of PLS and outline the ongoing discourses on SEM in general and the PLS approach in particular. Furthermore, we present a basic framework for empirical research applying PLS as well as a detailed explanation of the different process steps. Finally, examples of information systems research using PLS are summarized to demonstrate its beneficial application and the appropriateness of the proposed framework. This article can serve as a helpful guide for inexperienced researchers applying PLS for the first time, but also as a reference guide for researchers with a better understanding of the field. Keywords: structural equation modeling, partial least squares, information systems research I. INTRODUCTION The information systems (IS) discipline examines socioeconomic systems that are characterized by the interplay between hardware and software on the one hand, as well as individuals, groups, and organizations on the other. For example, technology adoption, acceptance, and success, as well as the conditions under which these can be achieved are typical issues that are addressed by IS research. These research fields are similar in that their investigation requires the researcher to cope with constructs such as the beliefs, perceptions, motivation, attitude, or judgments of the individuals involved. These constructs are usually modeled as latent variables (LVs) that can be measured only through a set of indicators. Structural equation models describe the relationships between several of these LVs. A number of algorithms and software programs are available to estimate their relationships based on a dataset. Among these algorithms, the partial least squares (PLS) algorithm has become increasingly popular both in IS research and in other disciplines such as marketing (Albers 2010; Henseler et al. 2009) or strategic management (Hulland 1999). However, reminders of this approach's limitations have recently become more prominent. Consequently, researchers opt for a more careful application of PLS. Especially its statistical power at small sample sizes, the overall model fit, as well as the misspecification of measurement models have been the focus of recent discussions. To resolve the uncertainties that researchers intending to use PLS might have, we investigate current discourses on the PLS approach. In the following sections, we: * demonstrate the increasing popularity of PLS in the IS research community * outline the PLS approach by providing a nontechnical overview and reflecting on the ongoing discussion on structural equation modeling (SEM) in general and on PLS in particular * discuss differences between PLS and covariance-based approaches * present a basic framework for empirical research applying the PLS approach * provide examples of its beneficial application in IS research As a result, this paper helps SEM beginners and advanced researchers to make an informed decision about whether to use SEM or other alternative approaches to SEM. Even more, we present up-to-date recommendations on how to apply PLS appropriately. Section II demonstrates the increasing popularity of PLS for SEM in IS research by conducting a systematic review of literature that appeared in two prestigious IS journals during the last fifteen years. …

/pdf/structural-equation-modeling-in-information-systems-research-2jjhybpjex.pdf

Structural Equation Modeling in Information Systems Research Using Partial Least Squares

Along with the development of scientific disciplines, namely social sciences, hypothesized relationships become increasingly more complex. Besides the examination of direct effects, researchers are more and more interested in moderating effects. Moderating effects are evoked by variables whose variation influences the strength or the direction of a relationship between an exogenous and an endogenous variable. Investigators using partial least squares path modeling need appropriate means to test their models for such moderating effects. We illustrate the identification and quantification of moderating effects in complex causal structures by means of Partial Least Squares Path Modeling. We also show that group comparisons, i.e. comparisons of model estimates for different groups of observations, represent a special case of moderating effects by having the grouping variable as a categorical moderator variable. We provide profound answers to typical questions related to testing moderating effects within PLS path models:

1.

How can a moderating effect be drawn in a PLS path model, taking into account that the available software only permits direct effects?




2.

How does the type of measurement model of the independent and the moderator variables influence the detection of moderating effects?




3.

Before the model estimation, should the data be prepared in a particular manner? Should the indicators be centered (by having a mean of zero), standardized (by having a mean of zero and a standard deviation of one), or manipulated in any other way?




4.

How can the coefficients of moderating effects be estimated and interpreted?And, finally:




5.

How can the significance of moderating effects be determined?






Borrowing from the body of knowledge on modeling interaction effect within multiple regression, we develop a guideline on how to test moderating effects in PLS path models. In particular, we create a graphical representation of the necessary steps to take and decisions to make in the form of a flow chart. Starting with the analysis of the type of data available, via the measurement model specification, the flow chart leads the researcher through the decisions on how to prepare the data and how to model the moderating effect. The flow chart ends with the bootstrapping, as the preferred means to test significance, and the final interpretation of the model outcomes.

Testing Moderating Effects in PLS Path Models: An Illustration of Available Procedures

In this study, we highlight the need and develop a framework for customer engagement (CE) by reviewing the marketing literature and analyzing popular press articles. By understanding the evolution of customer management, we develop the theory of engagement, arguing that when a relationship is satisfying and has emotional connectedness, the partners become engaged in their concern for each other. As a result, the components of customer engagement include both the direct and the indirect contributions of CE. Based on the theoretical support, our proposed framework elaborates on the components of CE as well as the antecedents (satisfaction and emotion) and consequences (tangible and intangible outcomes) of CE. We also discuss how convenience, nature of the firm (B2B vs. B2C), type of industry (service vs. product), value of the brand (high vs. low), and level of involvement (high vs. low) moderate the link between satisfaction and direct contribution, and between emotions and indirect contribution of CE, respectively. Further, we show how customer engagement can be gained and how firm performance can be maximized by discussing relevant strategies.

Customer engagement: the construct, antecedents, and consequences

In this research, the authors propose that the relationship between satisfaction and repurchase behavior is moderated by customer, relational, and marketplace characteristics. They further hypothesize that the moderating effects emerge if repurchase is measured as objective behavior but not if it is measured as repurchase intentions. To test for systematic differences in effects, the authors estimate identical models using both longitudinal repurchase measures and survey measures as the dependent variable. The results suggest that the relationship between customer satisfaction and repurchase behavior is contingent on the moderating effects of convenience, competitive intensity, customer involvement, and household income. As the authors predicted, the results are significantly different for self-reported repurchase intentions and objective repurchase behavior. The conceptual framework and empirical findings reinforce the importance of moderating influences and offer new insights that enhance the understanding of what drives repurchase behavior.

/pdf/do-satisfied-customers-buy-more-examining-moderating-1w5dvv3t5j.pdf

Do Satisfied Customers Buy More? Examining Moderating Influences in a Retailing Context:

Previous research on the relationship between customer satisfaction and loyalty has
largely neglected the issue of moderator variables. The authors develop a set of
hypotheses related to the moderating effect of selected personal characteristics on the
satisfaction-loyalty link. These hypotheses are tested in a consumer durables context
using multiple group causal analysis. Empirical findings provide reasonable support for
the theoretical arguments. Specifically, variety seeking, age, and income are found to be
important moderators of the satisfaction-loyalty relationship.

/pdf/personal-characteristics-as-moderators-of-the-relationship-18vg84p2z7.pdf

Personal characteristics as moderators of the relationship between customer satisfaction and loyalty—an empirical analysis

Konzeptualisierung und Operationalisierung komplexer Konstrukte. Ein Leitfaden für die Marketingforschung

Die Erzielung einer zufriedenen Kundenbasis hat sich in den letzten beiden Jahrzehnten
sowohl in der Wissenschaft als auch in der Praxis zu einem Schwerpunktthema entwickelt.
Die wissenschaftlichen Publikationen, welche sich mit dem Konstrukt Kundenzufriedenheit,
dessen Determinanten und Mesmoglichkeiten auseinandersetzen, haben seit Anfang der 80er
Jahre stetig zugenommen (vgl. u.a. die richtungsweisenden Arbeiten von
Anderson/Fornell/Lehmann 1994, Churchill/Surprenant 1982, Day 1977, 1982 a,b, 1984,
Fornell 1992, LaBarbera/Mazursky 1983, Oliver 1977, 1980, 1981, 1997, Oliver/DeSarbo
1988, Woodruff/Cadotte/Jenkins 1983, 1987). Das zunehmende Interesse der
Marketingforscher an dem Phanomen der zufriedenen Kunden hat in der Unternehmenspraxis
ein Pendant: In der kontinuierlichen Messung bzw. Steigerung der Kundenzufriedenheit sehen
immer mehr Unternehmen eine zentrale Herausforderung der heutigen Zeit. Mit Einfuhrung
von Customer Care-Programmen aller Art, Masnahmen zur Qualitatssicherung durch Total
Quality Management und Mitarbeitervergutung auf Basis von Kundenzufriedenheitsurteilen
versuchen sie, dem Ziel Kundenzufriedenheit ein Stuck naher zu kommen (vgl.
beispielsweise die Praxisbeispiele aus Simon/Homburg 1997). Dokumentiert wird diese
Zielsetzung in Unternehmensleitsatzen wie beispielsweise “Customer Satisfaction is the
number-one priority for every employee” (Rank Xerox) oder “Zufriedene Kunden sind unser
grostes Unternehmensvermogen” (Hertz Autovermietung).
In jungster Zeit vermehrt sich jedoch die Kritik an der Sinnhaftigkeit des Ziels
Kundenzufriedenheit. Allen Bemuhungen, die Zufriedenheit von Kunden zu erhohen, liegt
implizit die Annahme zugrunde, das zufriedene Kunden ihrem Anbieter treu bleiben und
somit diesem zu einer Reihe von wirtschaftlichen Vorteilen (z.B. durch Wiederkauf,
Mehrkauf, Cross-Selling-Effekte, positive Mundpropaganda, geringere Preiselastizitat etc.)
verhelfen (vgl. u.a. Keaveney 1995, S. 71, Rust/Zahorik/Keiningham 1995, S. 59). Das diese
Annahme keineswegs immer zutreffen mus, zeigen Aussagen wie “Forty percent of
customers who claimed to be satisfied switched suppliers without looking back” (Stum/Thiry
1991, S. 35) oder “... selbst Zufriedenheit mit dem Produkt schutzt nicht vor Untreue, denn
gerade zufriedene Kunden gehoren zu dauerhaften Markenwechslern” (Gierl 1993, S. 90).
Kundenzufriedenheit scheint demnach keineswegs in jedem Fall zu Kundenbindung zu fuhren
- verliert damit das Ziel Kundenzufriedenheit seine Relevanz? Die Frage ist zu verneinen: Die
Zufriedenheit von Kunden stellt zwar keinen Garant fur deren Bindung dar, ist aber
unbestritten doch in den meisten Fallen eine zentrale Voraussetzung fur diese (vgl. u.a.
Burmannn 1991, S. 249, Jones/Sasser 1995, S. 89). Allerdings scheint es dringend
erforderlich, den Zusammenhang zwischen Kundenzufriedenheit und Kundenbindung naher
zu analysieren. Bloemer und Kasper (1995, S. 311) schreiben hierzu: “Also some other effects 
are found indicating that the relationship between consumer satisfaction and brand loyalty is
not simple and straightforward”.
Das vorliegende Arbeitspapier analysiert die Beziehung zwischen Kundenzufriedenheit und
Kundenbindung. Dazu beschaftigen wir uns in einem ersten Schritt sukzessiv mit den beiden
Konstrukten Kundenzufriedenheit und Kundenbindung. Im Anschlus werden die
verhaltenstheoretischen Grundlagen des Zusammenhangs zwischen Kundenzufriedenheit und
Kundenbindung dargestellt und eine Basishypothese bezuglich des Zusammenhangs
abgeleitet. Darauf aufbauend analysieren wir die Literatur im Hinblick auf Publikationen, die
diese Beziehung bereits theoretisch oder empirisch untersucht haben. Die Konzeptualisierung
zentraler moderierender Einflusgrosen des betreffenden Zusammenhangs ist Gegenstand des
folgenden Abschnitts. Abschliesend zeigen wir anhand eines empirischen Beispiels unter
Anwendung der moderierten Regressionsanalyse, das die Auswirkungen der
Kundenzufriedenheit auf die Kundenbindung tatsachlich von moderierenden Variablen
beeinflust werden.

Der Zusammenhang zwischen Kundenzufriedenheit und Kundenbindung

The paper presents empirical evidence for conditions under which the relationship between customer satisfaction and customer loyalty will be stronger or weaker. Drawing on relational exchange theory the authors suggest that the relationship between satisfaction and loyalty will be weaker in the case of a highly relational exchange. Empirical findings in an international business-to-business context support this reasoning. The paper provides implications for practice and suggests a set of issues for future research.

/pdf/relationship-characteristics-as-moderators-of-the-46ydoyoavu.pdf

Annette Giering

Papers

Personal characteristics as moderators of the relationship between customer satisfaction and loyalty—an empirical analysis

Konzeptualisierung und Operationalisierung komplexer Konstrukte. Ein Leitfaden für die Marketingforschung

Der Zusammenhang zwischen Kundenzufriedenheit und Kundenbindung

Relationship Characteristics as Moderators of the Satisfaction-Loyalty Link: Findings in a Business-to-Business Context

Kundenzufriedenheit: ein Garant für Kundenloyalität?