Modeling Fuzzy Data in Qualitative Marketing Research

In marketing, qualitative data are used in theory development to investigate marketing phenomena in more depth. After qualitative data are collected, the judgment-based classification of items into categories is routinely used to summarize and communicate the information contained in the data. In this article, the authors provide marketing researchers with a method that (1) provides useful substantive information about the proportion and degree to which items belong to several categories and (2) measures the classification accuracy of the judges. The model is called the fuzzy latent class model (FLCM), because it extends Dillon and Mulani\u27s (1984) latent class model by freeing it from the restrictive assumption that all items are crisp for a given categorization. Instead, FLCM allows for items to be either crisp or fuzzy. Crisp items belong exclusively to one category, whereas fuzzy items belong—in varying degree—to multiple categories. This relaxation in the assumption about the nature of qualitative data makes FLCM more widely applicable: Qualitative data in marketing research are often fuzzy, because they involve open-ended descriptions of complex phenomena. The authors also propose a moment-based measure of overall data fuzziness that is bounded by 0 (completely crisp) and 1 (completely fuzzy)

Marketing Strategy and Wall Street: Nailing Down Marketing's Impact

marketing¿finance interface, firm financial value, brand value, efficient markets, brand equit

Marketing Actions and the Value of Customer Assets: A Framework for Customer Asset Management

This article develops a framework for assessing how marketing actions affect customers’lifetime value to the firm. The framework is organized around four critical actions that firms must take to effectively manage the asset value of the customer base: database creation, market segmentation, forecasting customer purchase behavior, and resource allocation. In this framework, customer lifetime value is treated as a dynamic construct, that is, it influences the eventual allocation of marketing resources but is also influenced by that allocation. By viewing customers as assets and systematically managing these assets, a firm can identify the most appropriate marketing actions to acquire, maintain, and enhance customer assets and thereby maximize financial returns. The article discusses in detail how to assess customer lifetime value and manage customers as assets. Then, it identifies key research challenges in studying customer asset management and the managerial challenges associated with implementing effective customer asset management practices.Yeshttps://us.sagepub.com/en-us/nam/manuscript-submission-guideline

Getting Smart: Learning From Technology-Empowered Frontline Interactions

Smart technologies are rapidly transforming frontline employee-customer interactions. However, little academic research has tackled urgent, relevant questions regarding such technology-empowered frontline interactions. The current study conceptualizes (1) smart technology use in frontline employee-customer interactions, (2) smart technology–mediated learning mechanisms that elevate service effectiveness and efficiency performance to empower frontline interactions, and (3) stakeholder interaction goals as antecedents of smart technology–mediated learning. We propose that emerging smart technologies, which can substitute for or complement frontline employees’ (FLEs) efforts to deliver customized service over time, may help resolve the long-standing tension between service efficiency and effectiveness because they can learn or enable learning from and across customers, FLEs, and interactions. Drawing from pragmatic and deliberate learning theories, the authors conceptualize stakeholder learning mechanisms that mediate the effects of frontline interaction goals on FLEs’ and customers’ effectiveness and efficiency outcomes. This study concludes with implications for research and practice

Linking Brand Equity to Customer Equity

equity and brand equity are two of the most important topics to academic researchers and practition-ers. As part of the 2005 Thought Leaders Conference held at the University of Connecticut, the authors were asked to review what was known and not known about the relationship between brand equity and customer equity. During their discussions, it became clear that whereas two distinct research streams have emerged and there are distinct differences, the concepts are also highly related. It also became clear that whereas the focus of both brand equity and customer equity research has been on the end consumer, there is a need for research to understand the intermediary’s perspective (e.g., the value of the brand to the retailer and the value of a customer to a retailer) and the consumer’s perspective (e.g., the value of the brand versus the value of the retailer). This article represents general conclusions from the authors ’ discussion and suggests a modeling approach that could be used to investigate linkages between brand equity and customer equity as well as a modeling approach to determine the value of the manufacturer to a retailer

Machines vs. Ensembles: Effective MAPK Signaling through Heterogeneous Sets of Protein Complexes

A grant from the One-University Open Access Fund at the University of Kansas was used to defray the author’s publication fees in this Open Access journal. The Open Access Fund, administered by librarians from the KU, KU Law, and KUMC libraries, is made possible by contributions from the offices of KU Provost, KU Vice Chancellor for Research & Graduate Studies, and KUMC Vice Chancellor for Research. For more information about the Open Access Fund, please see http://library.kumc.edu/authors-fund.xml.Despite the importance of intracellular signaling networks, there is currently no consensus regarding the fundamental nature of the protein complexes such networks employ. One prominent view involves stable signaling machines with well-defined quaternary structures. The combinatorial complexity of signaling networks has led to an opposing perspective, namely that signaling proceeds via heterogeneous pleiomorphic ensembles of transient complexes. Since many hypotheses regarding network function rely on how we conceptualize signaling complexes, resolving this issue is a central problem in systems biology. Unfortunately, direct experimental characterization of these complexes has proven technologically difficult, while combinatorial complexity has prevented traditional modeling methods from approaching this question. Here we employ rule-based modeling, a technique that overcomes these limitations, to construct a model of the yeast pheromone signaling network. We found that this model exhibits significant ensemble character while generating reliable responses that match experimental observations. To contrast the ensemble behavior, we constructed a model that employs hierarchical assembly pathways to produce scaffold-based signaling machines. We found that this machine model could not replicate the experimentally observed combinatorial inhibition that arises when the scaffold is overexpressed. This finding provides evidence against the hierarchical assembly of machines in the pheromone signaling network and suggests that machines and ensembles may serve distinct purposes in vivo. In some cases, e.g. core enzymatic activities like protein synthesis and degradation, machines assembled via hierarchical energy landscapes may provide functional stability for the cell. In other cases, such as signaling, ensembles may represent a form of weak linkage, facilitating variation and plasticity in network evolution. The capacity of ensembles to signal effectively will ultimately shape how we conceptualize the function, evolution and engineering of signaling networks

Return on interactivity: The impact of online agents on newcomer adjustment

As service offerings grow in both range and complexity, how service providers and their customers interact is becoming increasingly important. In response to the challenge of optimizing these interactions, companies have introduced sophisticated online "socialization agents," whose purpose is to help new customers more effectively adjust to and function within the service environment. The objective of these online agents, or virtual employees, is to help customers evaluate new or unfamiliar service offerings, as well as help companies achieve greater levels of service delivery and financial performance. To investigate this, the authors analyze the process by which online agents help both new and current customers adjust to and function within new, unfamiliar, or complex service contexts. They examine the impact of an online agent on account performance in the banking industry. They find that both interaction style and content of the online agent significantly influence the newcomer adjustment process over time, which in turn influences firm-level performance