How main street drives Wall Street: customer (dis)satisfaction, short sellers, and abnormal returns

Although previous studies have established a direct link between customer-based metrics and stock returns, research is unclear on the mediated nature of their association. The authors examine the association of customer satisfaction and abnormal stock returns, as mediated by the trading behavior of short sellers. Using quarterly data from 273 firms over 2007–2017, the authors find that short interest—a measure of short seller activity—mediates the impact of customer satisfaction and dissatisfaction on abnormal stock returns. Customer dissatisfaction has a more pronounced effect on short selling compared with customer satisfaction. In addition, customer satisfaction and dissatisfaction are more relevant for firms with low capital intensity and firms that face lower competitive intensity. The results show that a one-unit increase in customer satisfaction is associated with a .56 percentage point increase in abnormal returns, while a one-unit increase in customer dissatisfaction is associated with a 1.34 percentage point decrease in abnormal returns

Is Investing in Social Media Really Worth It? How Brand Actions and User Actions on Social Media Influence Brand Value

Although previous studies have documented a positive link between traditional media and brand performance, how social media is related to brand value has not yet been comprehensively explored. We propose a conceptual model to address this research gap, collecting a unique data set that captures information on user and brand actions on three social media platforms (Facebook, Twitter, and YouTube), word-of-mouth, and brand value for 87 brands in 17 industries. We empirically test our model with partial least squares path modeling (PLS-PM). First, we test the direct effects and find that user actions on YouTube and brand actions on Facebook have a positive influence on brand value. Second, we enrich our model by including word-of-mouth as a mediator, finding that the effect of social media goes above and beyond pure word-of-mouth spread. We test for alternative models, by first accounting for sample heterogeneity and second by including brand strength as a control variable, finding that the main model results’ are indeed robust. Our study demonstrates that making use of social media positively relates to brand value, as well as validates a set of objective metrics to measure social media actions, thus advancing knowledge on social media marketing for both academics and practitioners

Is Investing in Social Media Really Worth It? How Brand Actions and User Actions on Social Media Influence Brand Value

Although previous studies have documented a positive link between traditional media and brand performance, how social media is related to brand value has not yet been comprehensively explored. We propose a conceptual model to address this research gap, collecting a unique data set that captures information on user and brand actions on three social media platforms (Facebook, Twitter, and YouTube), word-of-mouth, and brand value for 87 brands in 17 industries. We empirically test our model with partial least squares path modeling (PLS-PM). First, we test the direct effects and find that user actions on YouTube and brand actions on Facebook have a positive influence on brand value. Second, we enrich our model by including word-of-mouth as a mediator, finding that the effect of social media goes above and beyond pure word-of-mouth spread. We test for alternative models, by first accounting for sample heterogeneity and second by including brand strength as a control variable, finding that the main model results’ are indeed robust. Our study demonstrates that making use of social media positively relates to brand value, as well as validates a set of objective metrics to measure social media actions, thus advancing knowledge on social media marketing for both academics and practitioners

social media s impact on the consumer mindset when to use which sentiment extraction tool

Abstract User-generated content provides many opportunities for managers and researchers, but insights are hindered by a lack of consensus on how to extract brand-relevant valence and volume. Marketing studies use different sentiment extraction tools (SETs) based on social media volume, top-down language dictionaries and bottom-up machine learning approaches. This paper compares the explanatory and forecasting power of these methods over several years for daily customer mindset metrics obtained from survey data. For 48 brands in diverse industries, vector autoregressive models show that volume metrics explain the most for brand awareness and purchase intent, while bottom-up SETs excel at explaining brand impression, satisfaction and recommendation. Systematic differences yield contingent advice: the most nuanced version of bottom-up SETs (SVM with Neutral) performs best for the search goods for all consumer mind-set metrics but Purchase Intent for which Volume metrics work best. For experienced goods, Volume outperforms SVM with neutral. As processing time and costs increase when moving from volume to top-down to bottom-up sentiment extraction tools, these conditional findings can help managers decide when more detailed analytics are worth the investment

Mesurer la valeur des médias sociaux

Cette thèse se donne donc pour objectif d’estimer la valeur des médias sociaux pour les entreprises. Elle développe une série d’analyses théoriques portant sur les effets que peuvent avoir ces médias sur leurs performances. Elle suggère également aux gestionnaires et aux praticiens différentes façons d’exploiter leur présence sur les médias sociaux pour tirer un profit maximal des avantages que cette présence suscite. Des méthodes quantitatives comme la modélisation par équations structurelles, l’analyse des séries temporelles et les panels dynamiques sont mises en pratiques pour aborder le sujet de façon empirique et exhaustive. Afin d’atteindre ces objectifs, cette thèse soulève un certain nombre de questions de recherche : Les médias sociaux sont-ils créateurs de valeur pour les entreprises ? Quels effets ont les médias sociaux sur les marques, les consommateurs, les investisseurs ? Quels indicateurs sont essentiels pour mesurer la performance des médias sociaux ? Nous nous proposons de répondre à ces questions dans cette thèse qui s’organise en quatre articles distincts.L’article 1 développe plusieurs analyses théoriques dont l’objectif est de comprendre comment les indicateurs de médias sociaux affectent la valeur des marques.L’article 2 aborde la pertinence des médias sociaux sous un angle différent. Nous y analysons les trajectoires de l’impact des médias sociaux sur les consommateurs puis sur les investisseurs, ainsi que les explications de cet impact.L’article 3 se focalise plus particulièrement sur la relation entre les médias sociaux et le comportement du consommateur. Les effets des médias sociaux owned et earned sur les étapes séquentielles qui composent le parcours d’achat du consommateur y sont analysés, avec pour objectif de donner un meilleur aperçu de l’influence des médias sociaux sur la décision d’acheter et sur la satisfaction du consommateur.Enfin, l’article 4 se place dans le prolongement de l’article 1 pour étudier l’effet des médias sociaux et traditionnels sur la satisfaction des consommateurs et la valeur de marque.This dissertation focuses on assessing the value of social media. There are several theoretical and practical gaps in the stream of social media marketing literature, particularly in terms of the impact of social media on performance. This dissertation proposes a set of theoretical conceptualizations of how social media can impact performance, makes suggestions for managers and practitioners on how to leverage the social media presences in pursuit of the benefits of social media marketing, and makes recommendations for researchers on how to further contribute to this research domain. Quantitative methods such as structural equation modeling, time-series analysis and dynamic panel methods are applied to address the issue empirically and comprehensively

An empirical investigation of the antecedents of partnering capability

Abstract In this paper, we propose a new approach to evaluating firms’ Partnering Capability. While previous research treats Partnering Capability as an exogenous factor, we take into account its antecedents and thus conceive it as endogenous. Our motivations are driven by the fact that firms ex-ante evaluate their partners by assessing their Partnering Capability. We focus on departmental integration, customer service, and economic and operational performance as key antecedents of Partnering Capability. Our empirical findings show that Partnering Capability is directly induced by operational performance and departmental integration. In addition, customer service along with departmental integration generates a chain of indirect effects due to economic and operational performance. Finally, we investigate the importance-performance matrix analysis (IMPA) that further identifies the managerial levers to enhance Partnering Capability

The Impact of Brand Actions on Facebook on the Consumer Mind-Set

Part 2: Digital Marketing and Customer Relationship ManagementInternational audienceDespite all the surrounding hype, it is still not clear exactly how social media affects consumer behavior. In an effort to contribute to the current debate on the effectiveness of social media marketing this study aims to theorize and empirically demonstrate how brand’s social media efforts influence a wide array of consumer mind-set metrics that underlie the consumer purchase decision-making process. Specifically, we relate key dimensions of a brand’s social media actions (intensity, valence and richness) to well established consumer mind-set metrics ranging from awareness through attitude to satisfaction. We hypothesize that brand actions’ intensity (more brand posts) with neutral valence and richer content will have a strong impact on the consumer mind-set. Using a unique data set that captures both social media and consumer mind-set metrics for multiple brands, we propose empirically testing our model with panel vector auto regression

Multiple time series analysis for organizational research

While multiple time-series analysis (MTSA) is a well-established method in economics, marketing, and finance, few studies have applied MTSA in organizational research. With the growing availability of data sources that contain detailed time-series data and the increasing importance of longitudinal designs, we argue that MTSA blends well with organizational research. We exemplify the possible applications of MTSA to the topics of social media, innovation, ambidexterity, and top management teams. We illustrate the state-of-the-art MTSA technique – Vector Autoregressive (VAR) model – by explaining the key methodological steps needed to estimate and interpret the results and providing a software tutorial in R and STATA. In line with the rising popularity of social media data, we employ a dataset that combines public social media data from Facebook with corporate reputation data from a private data source. We conclude with a discussion on the applicability, limitations, and extensions of MTSA for academics and practitioners