Effectiveness of Corporate Social Media Activities to Increase Relational Outcomes

This study applies social media analytics to investigate the impact of different corporate social media activities on user word of mouth and attitudinal loyalty. We conduct a multilevel analysis of approximately 5 million tweets regarding the main Twitter accounts of 28 large global companies. We empirically identify different social media activities in terms of social media management strategies (using social media management tools or the web-frontend client), account types (broadcasting or receiving information), and communicative approaches (conversational or disseminative). We find positive effects of social media management tools, broadcasting accounts, and conversational communication on public perception

Exploring Voice of Customers to Chatbot for Customer Service with Sentiment Analysis

Abstract. Chatbots have been widely employed across a wide variety of companies and industries, from small- and medium-sized businesses to large corporations, and from e-commerce to financial institutions. Although chatbots have proven to be far more efficient and quicker than human agents, they do not always provide customers with a satisfactory experience because they lack a personal touch. Customer issues are often left unresolved and many are unsatisfied with chatbot services. This is unfavorable for firms that use chatbots for customer services as this jeopardizes their relationship with valued consumers. Thus, customer input is essential to streamline the product innovation process. This study uses a hybrid method involving lexicon-based TextBlob and logistic regression techniques to identify the sentiments of consumers toward chatbots for customer services based on user-generated content on Twitter. The results show that although people generally have positive encounters with chatbots, the gap between positive and negative sentiments is relatively small. This research provides insights that businesses can use to improve chatbot technology based on the voice of the customer to provide users with higher quality customer services in the future, especially since unsatisfied customers could be a threat to a business’s performance. Keywords:  chatbot, customer services, sentiment analysis, social media mining, voice of customers

Measuring Service Encounter Satisfaction with Customer Service Chatbots using Sentiment Analysis

Chatbots are software-based systems designed to interact with humans using text-based natural language and have attracted considerable interest in online service encounters. In this context, service providers face the challenge of measuring chatbot service encounter satisfaction (CSES), as most approaches are limited to post-interaction surveys that are rarely answered and often biased. Asa result, service providers cannot react quickly to service failures and dissatisfied customers. To address this challenge, we investigate the application of automated sentiment analysis methods as a proxy to measure CSES. Therefore, we first compare different sentiment analysis methods. Second, we investigate the relationship between objectively computed sentiment scores of dialogs and subjectively measured CSES values. Third, we evaluate whether this relationship also exists for utterance sequences throughout the dialog. The paper contributes by proposing and applying an automatic and objective approach to use sentiment scores as a proxy to measure CSES

Contributions to chatbots and digital analytics in industry

Diese kumulative Dissertation umfasst zehn wissenschaftliche Artikel, die zur Forschung digitaler Analytik, Messung von Technologieakzeptanz und Chatbots beitragen. Ziel der Artikel ist es, die Entwicklung, Implementierung und Verwaltung von Technologien zu vereinfachen und zu unterstützen. Modelle werden entwickelt, welche die wichtigsten Schritte beschreiben und unter anderem relevante damit zusammenhängende Fragen auflisten, die zu beteiligenden Interessengruppen benennen und geeignete Tools vorstellen, welche berücksichtigt werden sollten. Es werden Chatbot Taxonomien entwickelt und vorgestellt, welche die Bandbreite der derzeit bestehenden Gestaltungsmöglichkeiten aufzeigen, während identifizierte Archetypen zu beobachtende Kombinationen aufzeigen. Die Identifizierung der häufigsten Gründe für Misserfolge und die Entwicklung kritischer Erfolgsfaktoren tragen ebenfalls zu dem Ziel bei, den Entwicklungs- und Managementprozess zu erleichtern. Da die Endnutzer über die Akzeptanz und Nutzung und damit über den Erfolg einer Technologie entscheiden, werden Ansätze genutzt, wie die Nutzerakzeptanz von Technologien gemessen werden kann und wie Nutzer frühzeitig in den Entwicklungsprozess eingebunden werden können

Marketing Intelligence: Boom or Bust of Service Marketing?

Marketing intelligence fosters two major developments within digital service marketing. On the one hand, a boom of services seems to have evolved, accelerated by the opportunities of marketing intelligence. It has contributed to the optimization of customer experiences, e.g., supported by mobile, personalized, and customized marketing services. On the other hand, (digital) self-services are likely to pervert the term “service”. Lifecycle marketing, including annoying marketing communication in real-time, automated price adjustment and programmatic advertising based on artificial intelligence, affects the vision of fully standardized marketing automation. Additionally, there are incentives to pollute the digital information in order to manufacture opinions. Fake news is one popular example. This leads to the (open) question if marketing intelligence means service boom or bust of marketing. This contribution aims to elaborate the boom-and-bust aspects of marketing intelligence and suggests a trade-off. The method applied in this paper will be a descriptive and conceptual literature review, through which the paradigmatic thoughts will be juxtaposed from the perspective of service

How Voice Can Change Customer Satisfaction: A Comparative Analysis between E-Commerce and Voice Commerce

Voice commerce is a newly evolving e-commerce channel where consumers communicate with dedicated systems on smart speakers or other devices using their voice, in order to find products. This paper comparatively investigates factors for customers’ satisfaction in voice commerce and ecommerce. Being the first study to scientifically analyze customer satisfaction factors in voice commerce and compare them with e-commerce, we conducted a survey with 178 consumers and used structural equation modeling for statistical hypotheses testing. The results show, that consumers have higher expectations in convenience for voice commerce than they have for ecommerce. Transaction process efficiency significantly influences satisfaction in voice commerce, but not in e-commerce. This research provides implications for future research on voice commerce strategy and system design

Interaction Mining: Making Business Sense of Customers Conversations through Semantic and Pragmatic Analysis

Via the Web a wealth of information for business research is ready at our fingertips. Analyzing this – unstructured - information, however, can be very difficult. Analytics has become the business buzzword distinguishing traditional competitors from ‘analytics competitors’ who have dramatically boosted their revenues. The latter competitors distinguish themselves through “expert use of statistics and modeling to improve a wide variety of functions” (Davenport, 2006, p. 105). However, not all information lends itself to statistics and models. Actually, most information on the Web is made for, and by, people communicating through ‘rich’ language. This richness of our language is typically missed or not adequately accounted for in (statistical) analytics (e.g. Text-mining) - and so is its real meaning - because it is hidden in semantics rather than form (e.g. syntax). In our efforts of turning unstructured data into structured data, important information – and our ability to distinguish ourselves from competitors - gets lost