Designing Mobile Brand Communities: Concept and Empirical Illustration

Brand communities are a commonly used marketing instrument to enhance customer attraction and retention. Well-known success stories of brand communities include brands such as Jeep®, Apple Macintosh®, or Harley Davidson®. Despite the great importance for companies to effectively manage the social facet of their products, research in this field has been rare. In literature, indications for success factors and constituting elements of communities have been found. However, seldom have viable business cases for the community operator been provided.

We propose the mobile channel to be a particularly promising media for establishing brand communities. Due to the significant and still increasing worldwide penetration of mobile devices and current always-on location sensitive mobile services, innovative community concepts bear the potential for substantial value creation, which tends to result in positive effects on customer loyalty and brand equity for the community operator.

In this article, we review selected literature to develop propositions on how concepts for mobile brand communities can be developed. Building on the 4 constituting elements of a community, which are member entities, shared interest, common space of interaction, and relation, combined with the specific characteristics of the mobile channel, which are location awareness, ubiquity, identification, and immediacy, we developed a procedure on how to design mobile brand communities according to perceived consumer value. We use the case of a mobile content provider to illustrate the suggested process. Starting from the theoretical mobile community model, we apply means–end chains to do justice to the specific brand values. In concluding, we propose a 4-step model of key mobile brand community design tasks.     

Getting a feel for price affect: A conceptual framework and empirical investigation of consumers' emotional responses to price information

Behavioral pricing research is cognitively biased. Therefore, the research agenda for this paper is to examine consumers' emotional responses to price information, or price affect. A conceptual framework of price affect based on appraisal theory is proposed. Moreover, a psychometric measure of price affect capturing positive and negative emotions is derived. A field experiment involving N = 1533 consumers reveals that a price increase leads to changes in price affect. Also, negative price affect is related to passive consumer behavior, whereas positive price affect is associated with proactive consumer behavior. Yet, a price increase reduces the importance of price affect in predicting consumer behavior. In addition, both price cognitions and price affect mediate the effect of a price increase on consumer behavior. Consistent with appraisal theory, a price increase exerts its causal influence on price affect through changes in price cognitions. Similarly, price affect mediates the effect of price cognitions on consumer behavior. Finally, price affect improves the prediction of consumer behavior beyond price cognitions. Results suggest that price affect is a stand alone, previously overlooked predictor of consumer behavior. Implications are discussed. © 2008 Wiley Periodicals, Inc.     

Charts and demand: Empirical generalizations on social influence

Social influence on consumer behavior has long been a subject of academic research in various scientific fields. According to research by Salganik, Dodds, and Watts (2006), music demand is a function of social influence between consumers. Market concentration tends to increase when information on demand becomes publicly available. In addition, stochastic agglomeration caused by social influence decreases the predictability of market success. These heavily cited findings challenge traditional market research and provide important insights on the impact of social media and sales charts. We test the stability of their results by replicating the study on music demand in a slightly different setting. We further investigate the generalizability of findings by probing other product categories and different phases of purchase decisions, i.e., interest, consideration, and actual demand. Across all categories and across all dependent variables, we are able to replicate the direction of the effects. We do, however, consistently obtain smaller effect sizes than reported in the original paper.     

Comparing automated text classification methods

Online social media drive the growth of unstructured text data. Many marketing applications require structuring this data at scales non-accessible to human coding, e.g., to detect communication shifts in sentiment or other researcher-defined content categories. Several methods have been proposed to automatically classify unstructured text. This paper compares the performance of ten such approaches (five lexicon-based, five machine learning algorithms) across 41 social media datasets covering major social media platforms, various sample sizes, and languages. So far, marketing research relies predominantly on support vector machines (SVM) and Linguistic Inquiry and Word Count (LIWC). Across all tasks we study, either random forest (RF) or naive Bayes (NB) performs best in terms of correctly uncovering human intuition. In particular, RF exhibits consistently high performance for three-class sentiment, NB for small samples sizes. SVM never outperform the remaining methods. All lexicon-based approaches, LIWC in particular, perform poorly compared with machine learning. In some applications, accuracies only slightly exceed chance. Since additional considerations of text classification choice are also in favor of NB and RF, our results suggest that marketing research can benefit from considering these alternatives.     

Consumer decision making and variety of offerings: The effect of attribute alignability

In recent years, many companies have considerably increased their number of offering varieties. The underlying rationale for such product strategies is substantiated by the belief that assortment proliferation would better satisfy customers' diverse preferences. However, empirical evidence exists suggesting that if there are too many varieties to choose from, customers sometimes either refrain from making a purchase at all, or else resort to simple selection heuristics. This article approaches the issue of assortment variety from a decision‐theoretical perspective, by positing circumstances under which expanding the number of varieties will positively or negatively affect consumer behavior. Herein, the concept of attribute alignability provides explanatory potential. Two experimental studies are presented which analyze the effect of the number of product varieties on customers' decision‐making behavior by means of manipulating the choice settings in a virtual car configurator. It can be shown that whether the product attributes in question are alignable or nonalignable is the decisive factor in explaining customer decision making under variety. Furthermore, “pseudo‐alignability”is achieved easily via the relabeling of product options. These findings yield concrete managerial insights for the customer‐oriented design of product lines consisting of a basic product and several varieties derived from it. ©2009 Wiley Periodicals, Inc.     

Consideration of ethical attributes along the consumer decision-making journey

Journal of the Academy of Marketing Science - Information about ethical strengths and weaknesses of individual products (e.g., cause-related marketing, corporate social responsibility records) is...     

The effect of default options on choice—Evidence from online product configurators

Many firms use product configurators to enable customers to specify their desired products online. In such systems, defaults are pre-specified for levels of product features by the manufacturer or dealer. For example, when configuring a racing bike online, a default is predefined (e.g., the Shimano Ultegra model) for all required features (e.g., the gearshift levers). Such defaults, which may even adapt to previous choices, ensure that a functional and fully defined product emerges at the end of the configuration process. However, when designing sales systems, companies often fail to realize that these defaults also affect customer decision-making. We demonstrate the effect by a study that makes use of a fully simulated racing bike configurator. We find the following results: Moving the default of one feature (e.g. wheels) from the lowest to the highest level results in an increase in sales. In addition, the feature level defined as the default also acts as a reference point by increasing the sales of levels near to it. In order to maximize sales, the default should be set at the level of a feature that is between the medium and the highest price level. To conclude we discuss how manufacturers and dealers subtly yet powerfully influence the decision-making process with their sales systems.     

Antecedents and temporal dynamics of strategic divergence in multinational corporations: Evidence from Europe

Coordinating consistent strategy implementation has been identified a key challenge for multi-national corporations. Based on intraorganizational evolutionary models of strategy formation, this paper thus empirically investigates the antecedents and temporal dynamics of strategic divergence. Strategic divergence is the deviation of a firm's resource allocation decisions with its articulated concept of corporate strategy. Large-scale empirical analysis of 11,406 resource allocation decisions of twenty-five publicly listed, multi-national and multi-business European firms indicates that decision type, operational and divisional manager involvement in decision making and structural context changes exert a significant influence on strategic divergence. Importantly, results further suggest that firms’ levels of strategic divergence tend to increase over time and that the antecedents of strategic divergence have a differential impact as time passes.