The Multilevel Nature of Customer Experience Research: An Integrative Review and Research Agenda

Over the last three decades, customer experience (CE) has developed from a burgeoning concept to a widely recognized phenomenon in terms of both research and practice. To account for the complexity of consumption decisions, the CE literature encompasses both the rational information processing approach to consumer decision‐making and the experiential approach, which includes emotions, feelings and sub‐consciousness. The authors classify and examine CE research on two levels. Studies on static CE analyze experiences during touchpoints at one point in time, while studies on dynamic CE assess how experiences evolve over time. Furthermore, both static and dynamic CE research take place from two distinct theoretical perspectives: the organization and the consumer. As both theoretical perspectives essentially deal with the same phenomenon – the organizational perspective with the creation of CEs and the consumer perspective with the perception of customer experiences – there is potential for a productive symbiosis between them. The authors propose that connecting insights from both perspectives can contribute to a better understanding of what constitutes a CE for consumers and how firms can effectively manage it. First, the authors discuss the development of CE and argue that it has evolved into a broad and fragmented ‘umbrella construct’. Second, after distinguishing and defining static and dynamic CE, they systematically evaluate the state of knowledge in both the organizational and consumer perspectives. Finally, they develop an agenda for future research that integrates the consumer perspective into organizational CE research.     

Robustness of a multiple ranking procedure: a monte carlo experiment illustrating design and analysis techniques

This case study demonstrates statistical design and analysis techniques applicable to any Monte Carlo or simulation experiment, namely a 2^7?3 experimental design, antithetic variates, sample size determination, analysis of variance, regression analysis, and simultaneous inference. The example is a Monte Carlo investigation of the robustness of Bechhofer and Blumenthal’s multiple ranking procedure (MRP). The investigation shows that their procedure works often, but not always. Factors that make it break down, are identified.     

Review of random and group-screening designs

Experiments with real systems, but especially with simulated systems, may involve hundreds of factors. However, only a few factors are really important. Detecting these Important factors requires special designs such as random and group-screening de-signs. Random designs are simple, but they yield biased esti-mators. Group-screening is based on aggregation. The assumptions of group-screening are discussed in detail, and seem not very restrictive. References to applications of both design types are given.     

A scenario for sequential experimentation

This tutorial emphasizes the role of differecnt types of experimental design in a multi–stage investigation. In the initial phase group–screeningo can reveal the really important factors among hundreds of factors. Resooulution III designs are useful immediately after the screening phase, to investigate firstorder effects, provided higher–order effects are unimportants, i.e., validation is necessary. Resulotion IV designs may explain why a first–order model is not valid, i.e., they may yield unbiased estimators of sums of interactions. Resolution V designs yield unbiased estimators of the individual two–factor interactions. They can be easily extended to central composite designs to estimate pure quadratic effects of quantitative factors. Smaller steps are also possible, e.g. one run at a time, for model discrimination and calibration.     

Evaluation of management information systems

The economic evaluation of Management Information Systems may be based on the following theories and techniques: Control Theory, System Dynamics, (discrete-event) simulation, and gaming. Applications of these approaches are summarized. Advantages and disadvantages of the various approaches are presented.     

Small-sample behavior of weighted least squares in experimental design applications

In experimental design applications unbiased estimators s_i ² of the variances σ_i ² are possible. These estimators may be used in Weighted Least Squares (WLS) when estimating the parameters β. The resulting small-sample behavior is investigated in a Monte Carlo experiment. This experiment shows that an asymptotically valid covariance formula can be used if s_i ² is based on, say, at least 5 observations. The WLS estimator based on estimators s_i ² gives more accurate estimators of β, provided the σ_i ² differ by a factor, say, 10.