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"Customer service"
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Blame the Bot
Chatbots have become common in digital customer service contexts across many industries. While many companies choose to humanize their customer service chatbots (e.g., giving them names and avatars), little is known about how anthropomorphism influences customer responses to chatbots in service settings. Across five studies, including an analysis of a large real-world data set from an international telecommunications company and four experiments, the authors find that when customers enter a chatbot-led service interaction in an angry emotional state, chatbot anthropomorphism has a negative effect on customer satisfaction, overall firm evaluation, and subsequent purchase intentions. However, this is not the case for customers in nonangry emotional states. The authors uncover the underlying mechanism driving this negative effect (expectancy violations caused by inflated pre-encounter expectations of chatbot efficacy) and offer practical implications for managers. These findings suggest that it is important to both carefully design chatbots and consider the emotional context in which they are used, particularly in customer service interactions that involve resolving problems or handling complaints.
Journal Article
Developing a service quality scale for artificial intelligence service agents
Purpose
Service providers and consumers alike are increasingly adopting artificial intelligence service agents (AISA) for service. Yet, no service quality scale exists that can fully capture the key factors influencing AISA service quality. This study aims to address this shortcoming by developing a scale for measuring AISA service quality (AISAQUAL).
Design/methodology/approach
Based on extant service quality research and established scale development techniques, the study constructs, refines and validates a multidimensional AISAQUAL scale through a series of pilot and validation studies.
Findings
AISAQUAL contains 26 items across six dimensions: efficiency, security, availability, enjoyment, contact and anthropomorphism. The new scale demonstrates good psychometric properties and can be used to evaluate service quality across AISA, providing a means of examining the relationships between AISA service quality and satisfaction, perceived value as well as loyalty.
Research limitations/implications
Future research should validate AISAQUAL with other AISA types, as they diffuse throughout the service sector. Moderating factors related to services, the customer and the AISA can be investigated to uncover the boundary conditions under which AISAQUAL is likely to influence service outcomes. Longitudinal studies can be carried out to assess how ongoing use of AISA can change service outcomes.
Practical implications
Service managers can use AISAQUAL to effectively monitor, diagnose and improve services provided by AISA while enhancing their understanding of how AISA can deliver better service quality and customer loyalty outcomes.
Originality/value
Anthropomorphism is identified as a new service quality dimension. AISAQUAL facilitates theory development by providing a reliable scale to improve the current understanding of consumers’ perspectives concerning AISA services.
Journal Article
AI voice bots: a services marketing research agenda
Purpose
This paper aims to document how AI has changed the way consumers make decisions and propose how that change impacts services marketing, service research and service management.
Design/methodology/approach
A review of the literature, documentation of sales and customer service experiences support the evolution of bot-driven consumer decision-making, proposing the bot-driven service platform as a key component of the service experience.
Findings
Today the focus is on convenience, the less time and effort, the better. The authors propose that AI has taken convenience to a new level for consumers. By using bots as their service of choice, consumers outsource their decisions to algorithms, hence give little attention to traditional consumer decision-making models and brand emphasis. At the moment, this is especially true for low involvement types of decisions, but high involvement decisions are on the cusp of delegating to AI. Therefore, management needs to change how they view consumers’ decision-making-processes and how services are being managed.
Research limitations/implications
In an AI-convenience driven service economy, the emphasis needs to be on search ranking or warehouse stock, rather than the traditional drivers of brand values such as service quality. Customer experience management will shift from interaction with products and services toward interactions with new service platforms such as AI, bots. Hence, service marketing, as the authors know it might be in decline and be replaced by an efficient complex attribute computer decision-making model.
Originality/value
The change in consumer behavior leads to a change in the service marketing approach needed in the world of AI. The bot, the new service platform is now in charge of search and choice for many purchase situations.
Journal Article
This is service design methods : a companion to this is service design doing
\"In this book, you'll find 54 hands-on descriptions that help you do the key methods used in service design. These methods include instructions, guidelines, and tips and tricks for activities, within research, ideation, prototyping, and facilitation. This is the print version of the method companion to the book \"This is service design doing\" (#TiSDD). It includes the same content that you can find free on the book website, tisdd.com, but nicely revisualized and presented in a professional bound format ...\"--Back cover.
BOOK
Customer engagement in service
We develop a framework to facilitate customer engagement in service (CES) based on the service-dominant (S-D) logic. A novel feature of this framework is its applicability and relevance for firms operating both in developed and emerging markets. First, we conduct a qualitative study involving service managers from multinational companies (MNCs) across the developed and emerging markets to understand the practitioner viewpoints. By integrating the insights from the interviews and the relevant academic literature, this framework explores how interaction orientation and omnichannel model can be used to create positive service experience. We also identify the factors that moderate the service experience, and categorize them as follows: offering-related, value-related, enabler-related, and market-related. Further, we also propose that perceived variation in service experience moderates the influence of service experience on satisfaction and emotional attachment, which ultimately impacts customer engagement (CE). From these factors, we advance research propositions that discuss the creation of positive service experience. One of the study’s key contributions is that MNCs can focus their attention on the moderators to ensure consistency in positive service experience, in an effort to enhance CE.
Journal Article
Human-Computer Interaction in Customer Service: The Experience with AI Chatbots—A Systematic Literature Review
Artificial intelligence (AI) conversational agents (CA) or chatbots represent one of the technologies that can provide automated customer service for companies, a trend encountered in recent years. Chatbot use is beneficial for companies when associated with positive customer experience. The purpose of this paper is to analyze the overall customer experience with customer service chatbots in order to identify the main influencing factors for customer experience with customer service chatbots and to identify the resulting dimensions of customer experience (such as perceptions/attitudes and feelings and also responses and behaviors). The analysis uses the systematic literature review (SLR) method and includes a sample of 40 publications that present empirical studies. The results illustrate that the main influencing factors of customer experience with chatbots are grouped in three categories: chatbot-related, customer-related, and context-related factors, where the chatbot-related factors are further categorized in: functional features of chatbots, system features of chatbots and anthropomorphic features of chatbots. The multitude of factors of customer experience result in either positive or negative perceptions/attitudes and feelings of customers. At the same time, customers respond by manifesting their intentions and/or their behaviors towards either the technology itself (chatbot usage continuation and acceptance of chatbot recommendations) or towards the company (buying and recommending products). According to empirical studies, the most influential factors when using chatbots for customer service are response relevance and problem resolution, which usually result in positive customer satisfaction, increased probability for chatbots usage continuation, product purchases, and product recommendations.
Journal Article