Customer Emotions Research Articles

The effect of service climate on customer emotion and customer satisfaction

The purpose of this study is to empirically verify the impact of the effect service climate on customer emotion and customer satisfaction. For this research, a survey was conducted for consultants currently engaged in consulting. Using SmartPLS 3.3.2, technical were conducted. The importance and suitability of the path model were verified through structural model analysis, and the mediating effect of customer emotion was verified through the analysis of specific indirect effects. According to the results of the study, the first company's service climate affects all positive customer emotion: pleasantness, pleasure and happiness. Second, the corporate service climate has a positive effect on customer satisfaction. Third, positive customer sentiment affects customer satisfaction. Fourth, when the service climate of the company affects customer satisfaction, the positive customer emotion, pleasant, enjoyment, and happiness, played a mediating role, and the independent variables were all partially mediated. Among them, happiness appeared to be the most influential factor, showing that it is important to satisfy the customer's happy feelings when the service climate affects customer satisfaction. In order to satisfy customers through research results, establishing and utilizing service climate is important, and since customer's happiness affects service climate and satisfaction, continuous measures to satisfy customer's happiness should be developed. This study is aimed at a small number of restaurant companies, generalizing the results is limited. Nevertheless, it is meaningful to study which of the positive emotions affects a lot by subdividing the customer's feelings when the service climate affects customer satisfaction

Do Customers Regulate their Emotions? Development and Validation of a Model of Customer Emotional Labor

Adopting an interdisciplinary perspective, this article integrates service literature on value cocreation with the psychological literature on emotional labor. Highlighting the co-production of services by both customers and employees, this research applies emotional labor theory to customers’ emotion regulation and expression. We explore the argument that customers perceive emotional display rules in service establishments and engage in goaldirected emotion regulation (i.e., customer emotional labor; CEL), using qualitative (Study 1) and quantitative (Study 2) methodologies. Descriptive findings from Study 1 provide evidence for the existence of CEL. Study 2 assesses the psychometric soundness of a newly developed customer display rules scale, and quantitatively tests a conceptual framework by examining antecedents and outcomes of customer emotion regulation. Findings of each study, the implications of this work, and avenues for future service management research are addressed.

Research on the influence of compensation methods and customer sentiment on service recovery effect

With the development of Internet technology, online retailing has become a part of people's life, but there is still a lack of research on the service recovery of online retail. This study adopts the scenario experiment method to conduct an empirical study. The service recovery scenario is designed as 2 × 2, the subjects are required to choose a most similar scene and fill in the questionnaire according to their own service recovery experience of online shopping service. This study discusses the influence mechanism of compensation methods and customer sentiment on service recovery effect in online retail service recovery situation. The results show as follows: compared with utilitarian compensation, spiritual compensation has a greater positive impact on customer positive emotions, and spiritual compensation is more effective in reducing negative emotions; positive and negative emotions of customers have positive and negative effects on customer satisfaction after service recovery, and customer satisfaction after service recovery is the preparation and prelude to the formation of customer loyalty. The research results can provide a reference for online retailers to formulate feasible and effective service recovery strategies, improve the effect of service recovery, and promote the healthy development of online retail business model.

Do Customer Emotions Affect Agent Speed? An Empirical Study of Emotional Load in Online Customer Contact Centers

Problem definition: Research in operations management has focused mainly on system-level load, ignoring the fact that service agents and customers express a variety of emotions that may impact service processes and outcomes. We introduce the concept of emotional load—the emotional demands that customer behaviors impose on service agents—to analyze how customer emotions affect service worker’s behavior. Academic/practical relevance: Most theories in organizational behavior literature predict that emotions expressed by customers reduce agent’s cognitive abilities and therefore, should reduce the agent’s speed (e.g., by increasing the service time required to serve an angry customer). We aim to shed light on the magnitude of that phenomenon while addressing important econometric challenges. We also investigate an important mechanism that drives this relation, namely agent effort. We discuss practical opportunities that arise from measuring emotional load and how it can be used to enhance productivity. Methodology: We measure the emotional load of agents using sentiment analysis tools that quantify positive/negative customer emotion expressions in an online chat-type contact center and link it to agent behavior: response time (RT) and the length and number of messages required to complete a service request. Identifying a causal effect of customer emotion on agent behavior using observational data is challenging because there are confounding factors associated with the complexity of service requests, which are related to both customer emotions and agent behavior. Our identification strategy uses panel data and exploits the variation across messages within a focal request using fixed effects to control for unobserved factors associated with case complexity. Instrumental variables are also used to address issues of measurement error and other endogeneity problems; the instruments are based on exogenous shocks to agent performance indicators that have been studied in the service operations literature. Results: Analyses show that emotional load created by negative customer emotions increases agent RT, the length of the agent messages (a measure of effort), and the required number of messages needed to complete a service request. Emotional load and agent RT reciprocally affect each other, with long agent RTs and a high number of messages producing more negative customer emotion. Managerial implications: We suggest that the emotional content in customer communications should be an important factor to consider when assigning workload to agents in a service system. Our study provides a rigorous methodology to measure the emotional content from customer text messages and objectively evaluate its associated workload. We discuss how this can be used to improve staffing decisions and dynamic workload routing through real-time monitoring of emotional load.

After-service gifts: evaluating how presence, context and value impact customer satisfaction and customer delight

ABSTRACT Many firms aim to create positive emotional experiences, and even delight, for their customers with the end desire of creating loyal customers. However, there is a lack of research evaluating tactics that firms can employ to create these emotions. In response, the current research evaluates the impact of an after-service gift on customer emotions and ultimately recommendation intentions. Across two studies, results show the effectiveness of the after-service gift in creating customer delight, but not customer satisfaction. Both context (degree of luxury) and value of the after-service gift moderate the relationships between the gift and the customer reaction.

Pengaruh Store Atmosphere Dan Faktor Sosial Terhadap Emosi Positif Konsumen Produk Zara Store Di Pakuwon Mall Surabaya

The following research analyzes the effect of store atmosphere and social factors on positive consumer emotions. This study uses quantitative analysis techniques with multiple linear regression analysis models. Data collection was carried out using a questionnaire media that was distributed to respondents taken at random as many as one hundred customers who visited the ZARA STORE counter at Pakuwon Mall Surabaya. Based on the results of data calculations, it shows that: first, all independent variables (X) have a partial and simultaneous influence on the dependent variable (Y). Second, the t-test results of the store atmosphere variable (X1) show that the store atmosphere has a partial effect on positive consumer emotions (Y) and the social factor variable (X2) partially influences Consumer Positive Emotions (Y). In addition, the simultaneous influence analysis of the two independent variables (X) on the dependent variable (Y) and get a result that is Fcalculation is greater than Ftable so the variables X proved to have a simultaneous effect on the Y variable. The results of the analysis This has supported several opinions derived from several previous studies which explained that the store environment is a character or element that must be considered by business people so as to form an image that will be attached to a brand and affect the emotions of customers or consumers who play a role in forming positive influences and significant impact on consumer impulsive buying behavior

Competitiveness Management Based on Factors of Customer Perception of Goods and Services

The authors of the article showed the necessity in today’s conditions to include esthetic (sensual) component in communications with customers. The current standing of the subject in academic literature is depicted, practical use of scientists’ theoretical achievements is shown and concrete examples from world practice are investigated. To use effectively customer emotions and perception in marketing management in general and in shaping goods and services offer it is needed together with marketing research, which is usually done in order to find out the attitude of potential customers to basic customer characteristics being offered to test product or service on expected emotional perception. As a result of the subject analysis the authors pointed out to objective preconditions for further and deeper research in this direction. Topicality of this problem researching follows from the necessity to strengthen the emotional element in marketing. Adding the emotional component to customer value can provide a serious advantage for promoting new goods and services and renewing the image of existing ones and brands as a whole.

Customer Experience and Satisfaction in Private Insurance Web Areas.

Digital transformation has allowed to offer additional services—which complement the main product—both in terms of use, emotional, and relationship terms. Focusing on a traditionally rational insurance customer offering a value that explores the customer’s emotions, from co-creating with the user, allows brand differentiation. Given this idea, this document has three purposes. First is identify the true role of expectations and the perceived quality of the customer’s digital experience. Second is to identify the relationship between customer experience and satisfaction gained in private insurance web areas. Third is to identify the most valued digital attributes by the user. A sample of 4,178 customers registered was analyzed using the partial least-squares technique. The model is highly predictive to customer experience and evidence the important relationship between the WOW effect and satisfaction, as well as the weak but double role that expectations play on insurance digital self-service. The model demonstrates that expectations are only relevant before web consumption, because during the process it is the perceived digital quality, in particular the usefulness, information, and technology, that offers a true customer experience. This article offers high academic value because it more accurately defines the determinants of the digital insurance customer experience and its effect on customer satisfaction in digital services. While expectations influence attitude before service, this research reveals that perceived digital quality is what delivers a true customer experience. Strategically, the implications are immediate in the field of business as it shows the importance of co-creation in digital design, not only because of the significant savings in implementation costs but also because it guarantees a greater experience, essential in the loyalty of its customers. This ensures the sustainable growth of the company.

The Impact of Empirical Marketing on Customer emotions of Foreign Sports Brands selected in Tasikmalaya

Introduction & Objective: Nowadays, brands are seeking to retain and attract customers and inevitably offer products to them and use methods that increase their satisfaction. On the other hand, the topic of customer emotions is considered the interested topics of researchers because it can be the factors that may have an impact on sales. According to the newness of the empirical marketing and the lack of sufficient research in this field in Indonesia especially in the field of sports marketing and in order to fill the existing gap, the purpose of the present study is to evaluate empirical marketing on customer emotions and satisfaction of foreign sports brands selected In Tasikmalaya. Methodology: The research population is customers of sport brands in Tasikmalaya and the statistical sample was the customers of four selected brands, Nike, Adidas, Reebok and Puma. Sample numbers 349 were calculated using software and sample size was obtained. Random sampling and research tool were standard questionnaire of Tsaur 2007 and data processing has been performed through PLS software. SPSS22 was used in the descriptive statistic section. Discussion & Conclusion: The results showed that empirical marketing structure affected emotions variable as well as emotions structure affected satisfaction variable at 99% confidence level. So, emotions are one of the cases that accompanied with customer satisfaction in empirical marketing and managers need to take a deeper look at the emotions.

Using AI to predict service agent stress from emotion patterns in service interactions

PurposeA vast body of literature has documented the negative consequences of stress on employee performance and well-being. These deleterious effects are particularly pronounced for service agents who need to constantly endure and manage customer emotions. The purpose of this paper is to introduce and describe a deep learning model to predict in real-time service agent stress from emotion patterns in voice-to-voice service interactions.Design/methodology/approachA deep learning model was developed to identify emotion patterns in call center interactions based on 363 recorded service interactions, subdivided in 27,889 manually expert-labeled three-second audio snippets. In a second step, the deep learning model was deployed in a call center for a period of one month to be further trained by the data collected from 40 service agents in another 4,672 service interactions.FindingsThe deep learning emotion classifier reached a balanced accuracy of 68% in predicting discrete emotions in service interactions. Integrating this model in a binary classification model, it was able to predict service agent stress with a balanced accuracy of 80%.Practical implicationsService managers can benefit from employing the deep learning model to continuously and unobtrusively monitor the stress level of their service agents with numerous practical applications, including real-time early warning systems for service agents, customized training and automatically linking stress to customer-related outcomes.Originality/valueThe present study is the first to document an artificial intelligence (AI)-based model that is able to identify emotions in natural (i.e. nonstaged) interactions. It is further a pioneer in developing a smart emotion-based stress measure for service agents. Finally, the study contributes to the literature on the role of emotions in service interactions and employee stress.

Managing service recovery experience: Effects of the forgiveness for older consumers

The research studies empirically the role that fairness plays in shaping emotional and behavioral forgiveness among older U.S. consumers (55 years of age and older). A theoretical model is proposed and tested that proposes, consistent with theory on emotions, that perceptions of justice affect behavioral forgiveness through emotional forgiveness. An alternative theory questions whether the effects of justice are totally mediated by emotion. Comparative results support the latter model. The cognitive perceptions of justice display a stronger effect compared to the mediated effect through emotional forgiveness. Additionally, an examination of moderation by psychological age suggests that relatively old and young (psychologically) consumers react in the same manner. While clearly, service managers need to be sensitive to the emotions of senior customers, the results here suggest a stronger role for cognitive perceptions of justice. Thus, service providers need to provide senior consumers with clearly understandable explanations for any service failure and explain how the recovery efforts will compensate for the failure.

Fitness Centers Ambience-Customer Behavioral Intentions Relationship: The Mediating Role of Customer Emotional States

This research paper aims to investigate how the ambient conditions in fitness centers affect customer behavioral intentions through examining the mediating role of customer emotional states while in the fitness center.  To this end, a conceptual model of relevant research variables was developed based on the extant literature. A questionnaire survey was administered to a convenient sample of 420 fitness center members approached in 10 randomly selected fitness centers in Jordan. The model was validated and tested using structural equation modeling with AMOS 21.0. The findings of analysis demonstrated that fitness center ambience was significant in predicting customers’ pleasure, thereby, their behavioral intentions towards the fitness center. Mediation analysis verified the mediating role of customer pleasure but not arousal on the relationship between fitness center ambient conditions and customer behavioral intentions.  Research findings show the significance of ambient conditions as an influential marketing tool. Also, such findings are deemed insightful to the operators of fitness centers on how the fitness center ambience can be deployed to enrich customer affective experience and to encourage positive behavioral intentions in turn. Discussion, conclusions, and research agenda for future research were also identified.

A neurophysiological exploration of the dynamic nature of emotions during the customer experience

This paper explores the benefits of measuring emotions and their dynamic nature during the customer experience with neurophysiological measures. In this study, emotions are measured during a service interaction (with self-service technology or a human employee) going through a series of touchpoints, including a service failure. We show that creating a loyalty card with the help of a service employee or self-service technology did not impact customers’ perceived service satisfaction and their behavioral intentions. This paper demonstrates that neurophysiological measures such as Galvanic Skin Response might be better equipped to unveil the dynamic nature of emotions (e.g., arousal) during the customer experience and that valence measured by neurophysiological tools (using electroencephalography) better reconciles with the effect found for satisfaction and behavioral intentions. Our findings have implications for both researchers and practitioners who want to understand and bolster customer experiences, thereby taking customer emotions and its appropriate measurement tools into consideration.

Customer Acceptance of Use of Artificial Intelligence in Hospitality Services: An Indian Hospitality Sector Perspective

This research endeavour tested and validated the artificially intelligent device use acceptance (AIDUA) three-stage AI acceptance framework in the context of the Indian hospitality sector. For this purpose, data on the constructs that captured primary appraisal (i.e., social influence, hedonic motivation and anthropomorphism), secondary appraisal (i.e., performance and effort expectancy), emotion, willingness to use AI devices and objection to use AI devices were captured from 210 guests/customers from 14 luxury hotels spread across the union territory of New Delhi and the state of Chandigarh in India. Findings that emerge from this study validate the fact that customers do indeed go through three stages of decision-making process before they demonstrate their proclivity to use AI devices or exhibit objection to use AI devices. In particular, the study found that both performance and effort expectancy influenced customer emotion which, in its turn, exercised its effect on the construct of willingness to use AI devices and objection to use AI devices among hotel customers. Accordingly, drawing from the findings of this study, implications for practitioners, decision-makers, and academic researchers are discussed in the article.

Half human, half machine – augmenting service employees with AI for interpersonal emotion regulation

PurposeWith the advent of increasingly sophisticated AI, the nature of work in the service frontline is changing. The next frontier is to go beyond replacing routine tasks and augmenting service employees with AI. The purpose of this paper is to investigate whether service employees augmented with AI-based emotion recognition software are more effective in interpersonal emotion regulation (IER) and whether and how IER impacts their own affective well-being.Design/methodology/approachFor the underlying study, an AI-based emotion recognition software was developed in order to assist service employees in managing customer emotions. A field study based on 2,459 call center service interactions assessed the effectiveness of the AI in augmenting service employees for IER and the immediate downstream consequences for well-being relevant outcomes.FindingsAugmenting service employees with AI significantly improved their IER activities. Employees in the AI (vs control) condition were significantly more effective in regulating customer emotions. IER goal attainment, in turn, mediated the effect on employee affective well-being. Perceived stress related to exposure to the AI augmentation acted as a competing mediator.Practical implicationsService firms can benefit from state-of-the-art AI technology by focusing on its capacity to augment rather than merely replacing employees. Furthermore, signaling IER goal attainment with the help of technology may provide uplifting consequences for service employee affective well-being.Originality/valueThe present study is among the first to empirically test the introduction of an AI-fueled technology to augment service employees in handling customer emotions. This paper further complements the literature by investigating IER in a real-life setting and by uncovering goal attainment as a new mechanism underlying the effect of IER on the well-being of the sender.

Analysis of Machine Learning Approaches for Sentiment Analysis of Twitter Data

For the past few years, sentiment analysis has been growing rapidly and with the abundance of computation power and plethora of machine learning algorithms, sentiment analysis has found numerous applications and acceptance as research area in machine learning. This paper covers analysis of sentiment analysis dealing with different aspects of its applications such as customer reviews, product reviews, film reviews, emotion detection, market research or many more such areas. To conduct sentiment analysis, data is extracted from various social media platforms like Twitter, Facebook etc. The data available on these social media platforms is primarily unstructured, therefore to analyze this data it must be pre-processed, feature vector identified and further implementation of models to trained and tested on different algorithms. There are several algorithms such as SVM, Naïve Bayes, K-means, KNN, decision tree, random forest and other algorithms, which are used to evaluate and hybrid to improve the efficiency and accuracy of the model.

Cognitive and emotional factors contributing to green customer citizenship behaviours: a moderated mediation model

Purpose This study aims to provide novel insight into cognitive and emotional factors contributing to green customer citizenship behaviours, as mediated by green attitudes in general and moderated by culture. Design/methodology/approach The investigation was guided by the “value attitude behaviour hierarchy” and green customer emotions, which served as a framework for understanding the mediating effect of attitude on the relationships between cognitive and emotional factors (green consumption values and emotional affinity towards nature) and customer citizenship advocacy and feedback behaviours. Data was obtained from respondents in the USA and South Korea. Multi-group confirmatory factor analysis, structural equation modelling, bootstrapping and Wald tests were performed to conclude on the model and verify the moderating effect of culture on the indirect effects established. Findings In both countries, green consumption values and emotional affinity towards nature positively influence green attitudes and stimulate feedback behaviour. Green attitudes only predict advocacy in the USA. Culture moderates the majority of the indirect effects examined. Research limitations/implications The model presents a novel approach to stimulate green advocacy and feedback behaviours and may aid firms in closing the “green gap” and co-create value with customers. Firms could profit from customers advocating the benefits of green purchasing to other customers and providing feedback on interventions required that will convince reluctant customers to make a purchase. Originality/value This study offers a multicultural perspective on the connection between a novel set of cognitive and emotional factors and green customer citizenship advocacy and feedback behaviours that may directly and indirectly influence green purchasing, value co-creation and closing of the “green gap”.

Effect of Entrepreneurial Passion and Positive Emotions on Customer Responses: Mixed Methods Approach

Purpose: In the highly competitive franchise market, the perceived entrepreneurial passion and emotions of customers are very important, because it...

Geospatial and Social Media Analytics for Emotion Analysis of Theme Park Visitors using Text Mining and GIS

Scrutinizing the emotions of customers and social media analytics are gaining popularity in the recent days. However, analysis of the emotions of visitors in theme parks are done on a lesser scale. In this paper, based on social media messages, the emotions of the visitors of a theme park is analyzed using geospatial as well as social media analytics convergence and visualization of cohesive places where expressions are gathered. Based on the Russell’s Circumplex Model of Affect, the words and emotions are analyzed in around 50,000 tweets collected of which 20,400 tweets contained one or more such words. Analysis of exploratory spatial data based on GIS and analysis of text mining represents various emotion in each quadrant based on the tweets. The visitor emotions are associated to various topics and emotions of considerable spatial variations. Based on the significant clustering of emotions in each quadrant, the areas of riding attraction in the theme park are identified and displayed using this research approach. Based on the analysis and implications of this research work, it is possible to develop ways in which the pleasant emotions of the visitors can be evoked by practitioners.

Post-service recovery emotion and customer trust: The role of satisfaction as mediation

Service failure is inevitable, leading to the provision of a recovery method by companies to overcome customer mistrust, that creates certain emotions. This research, therefore, aims to examine the indirect effect of customer emotion on trust and service recovery mediated by satisfaction. Data were obtained from 104 participants that read the vignette on service failure and recovery. This study used the data analysis of Process Hayes to measure the role of mediation. The result showed that customer satisfaction moderated the indirect effect between emotion and trust after service failure. In addition, cognitive appraisal theory supports the interactional and procedural process used to provide positive judgment. The implication of this study is useful for companies to minimize the possibility of service failure and provide recovery in order to maintain customer trust.