Customer Frustration Research Articles

Chatbot Interactions and Customer Loyalty: Analyzing The Role of Personalization and Responsiveness

This study aims to analyze the role of chatbots in enhancing customer loyalty by assessing how aspects such as personalization and responsiveness affect customer experience. The study sampled 250 respondents who use customer service in e-commerce companies in the city of Surakarta. The research employed multiple linear regression techniques. The results indicate that personalization and responsiveness in chatbot interactions are factors that play a positive and significant role in increasing customer loyalty. Quick and accurate chatbot interactions that align with customer needs can provide assurance and reduce uncertainty and customer frustration. This study is limited to examining the aspects of personalization and responsiveness in chatbot interactions concerning customer loyalty, and the scope of the research is restricted to customers in the city of Surakarta. Future research opportunities could explore other aspects beyond personalization and responsiveness and expand the research scope beyond just Surakarta.

Frustration and Anger Mitigation After Service Failure: The Effect of Other Consumer's Explanation and Employee's Explanation on Frustration and Anger After the Service Failure.

Based on the attribution and appraisal theories of emotion, this study investigates whether a consumer's frustration and anger after a service failure reduces in different ways after hearing explanations from different sources (other customer vs employee vs none) under different blame attribution circumstances (situational vs service provider), and its subsequent influence on complaining intention. In Study 1, valid data from 239 participants (46.9% female, Mage=35.6 years) were used to test the interaction effect of the explanation source and blame attribution on frustration and anger. In Study 2, using valid answers from 253 students at Korea University (57.9% female, Mage=20.9 years), Study 1 was replicated and, in addition, tested the moderated mediating impact on complaining intention. The overall theoretical model was tested with ANOVA and Hayes process model 8. When blame attribution was situational, the employee's explanation did not mitigate either frustration or anger, whereas the other customer's explanation mitigated frustration but not anger. In contrast, when blame attribution was towards the service provider, the employee's explanation mitigated both frustration and anger, whereas the other customer's explanation mitigated only frustration. In addition, the mitigation of frustration and anger by other customers subsequently led to a decrease in complaining intention, which was stronger and only significant when blame attribution was situational. However, only anger acted as a mediator between the employee's explanation and complaining intention, which did not vary according to blame attribution. The results of the study advance the current knowledge on informational support as a service recovery process by suggesting the crucial role of other consumers in mitigating the target customer's frustration, especially under situational service failure, which successively leads to a decrease in complaining intention, whereas the employee's explanation decreases complaining intention only through the mitigation of anger.

FROST: Fallback Voice Apps Recommendation for Unhandled Voice Commands in Intelligent Personal Assistants.

Intelligent personal assistants (IPAs) such as Amazon Alexa, Google Assistant and Apple Siri extend their built-in capabilities by supporting voice apps developed by third-party developers. Sometimes the smart assistant is not able to successfully respond to user voice commands (aka utterances). There are many reasons including automatic speech recognition (ASR) error, natural language understanding (NLU) error, routing utterances to an irrelevant voice app, or simply that the user is asking for a capability that is not supported yet. The failure to handle a voice command leads to customer frustration. In this article, we introduce a fallback skill recommendation system (FROST) to suggest a voice app to a customer for an unhandled voice command. There are several practical issues when developing a skill recommender system for IPAs, i.e., partial observation, hard and noisy utterances. To solve the partial observation problem, we propose collaborative data relabeling (CDR) method. To mitigate hard and noisy utterance issues, we propose a rephrase-based relabeling technique. We evaluate the proposed system in both offline and online settings. The offline evaluation results show that the FROST system outperforms the baseline rule-based system. The online A/B testing results show a significant gain of customer experience metrics.

Robust assortment optimization under sequential product unavailability

Assortment planning is a central piece in the revenue management strategy of every retail company. In this paper, we study a robust assortment optimization problem for substitutable products under a sequential ranking-based choice model and a cardinality constraint. Our choice model captures the increasing customer frustration of finding multiple products unavailable as a factor affecting purchasing decisions. To model the highly uncertain order in which a customer explores the products to buy, we present a bi-level optimization approach to maximize the expected revenue assuming that the customer visits a sequence of unavailable products that minimizes the likelihood of staying in the store. We show that the resulting problem is NP-hard and develop exact and greedy solution approaches that can solve different instances efficiently in terms of both solution time and optimality gap. We develop a model extension that includes multiple customer categories and also show a special case of the problem that can be solved in polynomial time. We perform a computational study to demonstrate the performance of our methods and illustrate the sensitivity of the optimal assortment to variations in the input parameters.

The psychology of frustration: Appraisals, outcomes, and service recovery

AbstractThis study fills a gap in the consumer emotions literature regarding customer frustration in a customer service setting. Most research on customer emotions has examined anger, happiness or affect in general, largely ignoring the particularities of frustration. Consistent with appraisal theory, we use five experiments to explore the different appraisal dimensions that define frustration and its relation to customer loyalty and satisfaction. Contrary to common belief, we show that frustration is not simply the result of goal‐blocking, but rather of a more complex combination of appraisals which differentiate it from anger and lead to distinct effects on satisfaction and loyalty. We also examine how the effects of frustration on loyalty and satisfaction are mitigated by service recovery in a further experiment and an event reconstructive method. Our results test appraisal theory, inform theory on customer emotions and have important implications for our understanding of customer satisfaction and loyalty following frustrating customer service encounters.

How customer service representatives lose control of the call

The contact centre industry has been growing rapidly in the Philippines over the last two decades and now boasts over one million customer service representatives (CSRs). Outsourcing work to this destination, where English may not be the first language, can lead to communication difficulties. Problems of locally recruited CSRs ‘losing control of the call’, leading to customer frustration and poor feedback, have previously been attributed to poor grammar and incomprehensible accents. However, more recent research has suggested that such communicative problems actually stem from a more general inability to build relationships and appropriately select, explain and describe information about the product or service and, if needed, instruct the client on what to do. This paper therefore examines ‘losing control of a call’ in terms of the overall exchange. Specifically, two calls were examined to analyse how information was organised, packaged and developed to the satisfaction (or not) of the client. We argue that discrete grammatical inaccuracies and regional accents do not result in losing control as much as the way overall meaning is managed by the CSR. The implications of these initial findings could be of importance to the recruitment, training, coaching and appraisal of CSRs in an industry where the nature of communication breakdown remains poorly understood.

Data science and GIS-based system analysis of transit passenger complaints to improve operations and planning

Transit user complaints support system resilience by serving as a data source for service improvements. This study shows how geographic information system (GIS)-based analysis, econometric models, and latent class analysis can improve system-wide understanding of passenger complaints. The analyzed dataset consists of 718 passenger complaints concerning the operation of municipal lines in Jerusalem as the study region. The analytical methods consists of GIS-based analysis and statistical modeling: mapping, recursive bivariate probit estimation, negative binomial model estimation, and latent class analysis. The GIS-based analysis showed that the spatial distribution of complaints changes over time as a function of service disruption type and geographical area. The recursive bivariate probit model results indicated that the most acute sources of frustration are service problem recurrence and monetary loss, with the former caused by overcrowding, delays and line cancellations. The negative binomial model results shows that the number of complaints increases with an increase in the passenger boarding to bus arrivals ratio. Latent class analysis reveals that, in terms of both prevalence and customer frustration, overcrowding delays and line cancellations are the most acute problems in the study region. The proposed interface between transit complaints and GIS databases can readily be implemented by transport operators and authorities.

Service gap in hotel website load performance

Purpose The purpose of this paper is to investigate how well hotel website load time performance compared against customer expectation benchmarks. In a competitive market, service interactions are important. As customers move to mobile devices, the time to load a website is a critical part of the service delivery. Long load times can lead to poor service experiences, customer frustration and lost business. Hotel website load times on both mobile and desktop devices were examined and compared to service expectations. Design/methodology/approach The study used an online service to assess and compare website load performance using both desktop and mobile devices for 259 international hotel company and sub-brand websites. Findings The time to load hotel websites was significantly slower on mobile devices compared to desktops. Load times on both platforms exceeded 3 s, which is considered best practice. Long load times represent a service gap and can cause dissatisfaction resulting in a potential customer abandoning the website for a competitor’s site, thus affecting sales. Research limitations/implications While the population for the study was robust in size and contained most of the major hotel companies worldwide, it was not exhaustive. Data also represent a snapshot and will change over time. Load times vary based on test location, access device and network traffic. Additionally, web page load times and customer expectations will change as technology evolves. Originality/value Increased use of mobile devices for hotel reservations increases the importance of mobile service delivery. This is the first known study to measure hotel website load times for mobile devices, and to examine both mobile and desktop performance against best practice. The results of this study highlight a service gap, which can lead to loss of business. Given the consistency of the results, the authors suspect that this is an issue that has not been recognized within the industry. This study is valuable because it exposes an issue of website design not generally addressed in the hospitality industry, even though tools are available to monitor site performance.

Modeling and Computational Characterization of Twitter Customer Service Conversations

Given the increasing popularity of customer service dialogue on Twitter, analysis of conversation data is essential to understanding trends in customer and agent behavior for the purpose of automating customer service interactions. In this work, we develop a novel taxonomy of fine-grained “dialogue acts” frequently observed in customer service, showcasing acts that are more suited to the domain than the more generic existing taxonomies. Using a sequential SVM-HMM model, we model conversation flow, predicting the dialogue act of a given turn in real time, and showcase this using our “PredDial” portal. We characterize differences between customer and agent behavior in Twitter customer service conversations and investigate the effect of testing our system on different customer service industries. Finally, we use a data-driven approach to predict important conversation outcomes: customer satisfaction, customer frustration, and overall problem resolution. We show that the type and location of certain dialogue acts in a conversation have a significant effect on the probability of desirable and undesirable outcomes and present actionable rules based on our findings. We explore the correlations between different dialogue acts and the outcome of the conversations in detail using an actionable-rule discovery task by leveraging a state-of-the-art sequential rule mining algorithm while modeling a set of conversations as a set of sequences. The patterns and rules we derive can be used as guidelines for outcome-driven automated customer service platforms.

Lean Six Sigma Approach to Improve Overall Equipment Effectiveness Performance: A Case Study in the Indian Small Manufacturing Firm

Due to increased competition in the global market, manufacturers are focusing on product quality, cost of the product and product delivery time. Every manufacturing firm exercises their new concepts and innovative ideas for continuous improvement in their business process in order to sustain in their market. Lean six sigma approaches is the combination of lean methods and six sigma process tools and it is a well consolidated process for improving their processes. Many small sized manufacturing firms are yet unacquainted about the lean and six sigma process tools. Thus, a company should initiate a new quality system to continually improve and increase both their quality and performance efficiency. The case study was conducted in a Small and medium-sized pump manufacturing company and one of these machines had a poor OEE with a large variety. This led to on-time delivery problems which is a root cause for customer frustration. The poor OEE led to high costs in terms of rework and re-inspections of the products. Following a typical root because analysis is made; the problems are removed and improved the Critical to Quality characteristics (CTQs) which have increased the OEE and reducing its variability. Tools such as Single Minute Exchange of Die (SMED), 5s and six sigma process tools were implemented to remove the root causes. The aim of this paper is to illustrate the use lean Six Sigma approach in the small and medium sized manufacturing firm to improve Overall Equipment Effectiveness (OEE).

Optimizing Cash Management Model Using Computational Intelligence

In today’s technical era, the financial organizations have great challenges to optimize the cash management process. Maintaining minimum cash leads to customer frustration. At the same time, upholding excess cash is a loss to the organization. Hence, soft computing based cash management solutions are required to maintain optimal cash balance. An Artificial Neural Network (ANN) is one such technique which plays a vital role in the fields of cognitive science and engineering. In this study, a novel ANN-based cash Forecasting Model (ANNCFM) has been proposed to identify the cash requirement on daily, weekly and monthly basis. The six cash requirement parameters: Reference Year (RY), Month of the Year (MOY), Working Day of the Month (WDOM), Working Day of the Week (WDOW), Salary Day Effect (SDE) and Holiday Effect (HDE) were fed as input to ANNCFM. Trials were carried out for the selection of ANNCFM network parameters. It was found that number of hidden neurons, learning rate and the momentum when set to 10, 0.3 and 0.95, respectively yielded better results. Mean Absolute Percentage Error (MAPE) and Mean Squared Error (MSE) were used to evaluate the performance of the proposed model. MSE that was less than 0.01 proves the capability of the proposed ANNCFM in estimating the cash requirement.

Investigating the impact of surprise rewards on consumer responses

Adopting an experimental approach, this research compared surprise rewards with membership discount rewards in terms of their impact on customer responses of delight, frustration and satisfaction. In addition, this research examined the circumstances under which surprise rewards should be offered in order to yield maximum benefits for hospitality firms. In particular, the study examined how the customer's cumulative satisfaction (high vs. low) influences the effectiveness of surprise rewards (vs. membership discount rewards) in increasing customer delight and satisfaction and decreasing customer frustration. Consistent with the theoretical predictions, results show that surprise rewards are more effective than membership discount rewards for enhancing customer delight and satisfaction and attenuating customer frustration, particularly when the customer's cumulative satisfaction is low. These findings have important implications for the hospitality industry. Hospitality managers and marketers could use this information to design effective loyalty reward programs.

EMOTION DETECTION IN EMAIL CUSTOMER CARE

Prompt and knowledgeable responses to customers’ email are critical in maximizing customer satisfaction. Such messages often contain complaints about unfair treatment due to negligence, incompetence, rigid protocols, unfriendly systems, and unresponsive personnel. In this paper, we refer to these email messages as emotional email. They provide valuable feedback to improve contact center efficiency and the quality of the overall customer care experience, which in turn results in increased customer retention. We describe a method that uses salient features to identify emotional email in the customer care domain. Salient features in customer care related email are expressions of customer frustration, dissatisfaction with the business, and threats to either leave, take legal action, and/or report to authorities. Compared to a baseline system using word unigram features, our proposed approach significantly improves emotional email detection performance.

Building bank brands: How leadership behavior influences employee commitment

Front line employees are critical to service brand success, as their performance brings brand promises to life. Banking employees, like others, must remain committed to their employers, to live the brand, particularly during periods of economic uncertainty and customer frustration. Employees' commitment influences their brand adoption and brand-supporting behavior during service encounters. Effective leadership fosters employee commitment and brand supporting behaviors. This study examines the nature of employee commitment in banking, distinguishing between affective, continuance and normative commitment. The study explores bank leaders, examining whether initiating structure leader behavior or considerate leader behavior is most effective in encouraging employee commitment. Data from a sample of 438 employees in a leading Irish bank reveals the optimal leadership style for employee commitment.

Self-scan checkouts and retail loss: Understanding the risk and minimising the threat

Using retailer case studies and a survey of employees, data are presented assessing the extent to which the introduction of self-scan checkouts in the retail environment affects the rate of shrinkage. It is argued that although available data currently suggest that they have little effect upon rates of shrinkage, the unique nature of the self-scan environment requires a more nuanced approach to the way in which crime prevention theory is used to understand the potential risks associated with this technology. It is concluded that retailers should think about creating ‘zones of control’ for self-scan areas and that offending behaviour generated by customer frustration with this technology offers further evidence of the way in which theories of neutralisation, situational prevention and cognitive dissonance can inform the increasingly complex interplay between consumers and retail spaces.

Frequent (flier) frustration and the dark side of word‐of‐web: exploring online dysfunctional behavior in online feedback forums

PurposeFollowing the perspective of frustration theory customer frustration incidents lead to frustration behavior such as protest (negative word‐of‐mouth). On the internet customers can express their emotions verbally and non‐verbally in numerous web‐based review platforms. The purpose of this study is to investigate online dysfunctional customer behavior, in particular negative “word‐of‐web” (WOW) in online feedback forums, among customers who participate in frequent‐flier programs in the airline industry.Design/methodology/approachThe study employs a variation of the critical incident technique (CIT) referred to as the critical internet feedback technique (CIFT). Qualitative data of customer reviews of 13 different frequent‐flier programs posted on the internet were collected and analyzed with regard to frustration incidents, verbal and non‐verbal emotional effects and types of dysfunctional word‐of‐web customer behavior. The sample includes 141 negative customer reviews based on non‐recommendations and low program ratings.FindingsProblems with loyalty programs evoke negative emotions that are expressed in a spectrum of verbal and non‐verbal negative electronic word‐of‐mouth. Online dysfunctional behavior can vary widely from low ratings and non‐recommendations to voicing switching intentions to even stronger forms such as manipulation of others and revenge intentions.Research limitations/implicationsResults have to be viewed carefully due to methodological challenges with regard to the measurement of emotions, in particular the accuracy of self‐report techniques and the quality of online data. Generalization of the results is limited because the study utilizes data from only one industry. Further research is needed with regard to the exact differentiation of frustration from related constructs. In addition, large‐scale quantitative studies are necessary to specify and test the relationships between frustration incidents and subsequent dysfunctional customer behavior expressed in negative word‐of‐web.Practical implicationsThe study yields important implications for the monitoring of the perceived quality of loyalty programs. Management can obtain valuable information about program‐related and/or relationship‐related frustration incidents that lead to online dysfunctional customer behavior. A proactive response strategy should be developed to deal with severe cases, such as sabotage plans.Originality/valueThis study contributes to knowledge regarding the limited research of online dysfunctional customer behavior as well as frustration incidents of loyalty programs. Also, the article presents a theoretical “customer frustration‐defection” framework that describes different levels of online dysfunctional behavior in relation to the level of frustration sensation that customers have experienced. The framework extends the existing perspective of the “customer satisfaction‐loyalty” framework developed by Heskett et al.

Customer frustration in loyalty programs

PurposeThis article aims to examine the negative effects of loyalty programs from the perspective of frustration theory. It seeks to develop a model of customer frustration on the basis of frustration theory and an exploratory qualitative study.Design/methodology/approachFirst, frustration is defined as a special form of dissatisfaction and a general model of frustration in business relationships is developed by evaluating the literature on frustration theory. Second, an explorative and qualitative focus group study among participants of a loyalty program for frequent travelers is conducted. A multi‐level iterative content analysis of the participants' statements reveals the existence of different categories of frustration incidents. Third, the findings of the study are used to develop a system of propositions that generate a specific model of customer frustration in loyalty programs.FindingsSeven categories of frustration incidents that were triggered by the loyalty program and lead to frustration sensation and subsequent frustration behavior, like protest or avoidance, could be identified. With four categories of incidents – inaccessibility, worthlessness, qualification barrier and redemption costs – customers' frustration sensation and behavior are directed on the program itself (program‐related frustration incidents). For the other three – discrimination, economization and defocusing – frustration sensation and behavior also affect the perception of the relationship with the firm (relationship‐related incidents).Research limitations/implicationsThe exact differentiation of frustration from related constructs should be the topic of further research. The findings of the empirical study are of limited generalizability because the object of investigation was a single company's loyalty program in a special industry sector. Hence, the introduced propositions should be further specified and tested in a large‐scale quantitative study in different sectors and with a number of companies and programs. Further work is necessary to allow deeper insights into the relationships between the elements in the customer frustration model.Practical implicationsSeveral implications for planning and implication arise from the results of the study. Management has to make sure that program‐related and relationship‐related negative effects are avoided. That calls for offering only those benefits that represent genuine additional value to customers and for ensuring that the benefits can be claimed at any time and without any additional effort by the customer. Furthermore, the perceived quality of the program should be monitored to obtain prompt information about possible customer frustration and indications of protest (i.e. customer complaints) should be viewed with particular attention.Originality/valueThis paper provides new insights into the so far highly neglected negative side effects of loyalty programs. Also, innovative is the first‐time application of the frustration construct to the analysis of customer behavior in the context of loyalty programs. The contribution is of high value for all who research in the field of customer relationship management and customer loyalty.

The Customer as Ally: The Role of the Customer in the Finance Sector Union’s Campaigning

In recent years the Finance Sector Union has pursued a range of strategies which rely on the incorporation of customers into its campaigns. These strategies extend from the employment of a discourse of shared worker and customer concerns in its communications with union members and the public through to the development of alliances with consumer and community groups. While easy to advance in an industry where customer disillusionment and frustration is widespread, these appeals for solidarity and the formation of alliances are not unproblematic. In the light of lessons drawn from US literature on community campaigning and the experiences of other Australian unions, this article explores the limitations and potential of union strategies that seek to incorporate the customer and evaluates the alliances formed between the Finance Sector Union and community groups.

Technology Readiness Index (Tri)

The role of technology in customer-company interactions and the number of technology-based products and services have been growing rapidly. Although these developments have benefited customers, there is also evidence of increasing customer frustration in dealing with technology-based systems. Drawing on insights from the extant literature and extensive qualitative research on customer reactions to technology, this article first proposes the construct of technology readiness of people and discusses its conceptualization. It then describes a program of research that was undertaken to operationalize the construct, develop and refine a multiple-item scale to measure it, and assess the scale’s psychometric properties. The article concludes with a discussion of potential practical applications of the scale and an agenda for additional research aimed at deepening our understanding of technology’s role in marketing to and serving customers.

Development of a critical task methodology for furniture evaluation

In customer service operations at post offices, furniture is installed behind the clerk's counter to provide temporary storage of special mail to be collected or brought in by the customer. Such furniture allows the clerk to perform almost all required functions in full sight of the customer, preventing customer frustration at not understanding what the clerk is doing and enhancing security by not leaving the customer unobserved. It also means that customers can observe any errors and difficulties encountered by the clerk, suggesting that an ergonomic analysis of the storage furniture is required before deployment takes place. This paper shows how a suitable methodology based on critical tasks was derived, and how the results led to specific design recommendations.