Service Failure Research Articles

Pengaruh Kegagalan Fungsional, Kegagalan Sistem, Kegagalan Informasi dan Kegagalan Layanan Terhadap Penggunaan M-Banking Serta Kaitannya dengan Kepuasan Nasabah BCA Mobile di Indonesia

The rapid development of technology is the main driver of the emergence of digital-based products and services. One of the technology-based banking service products is mobile banking. In Indonesia, the mobile banking that has the most users is BCA Mobile. However, with the largest number of users, the quality of BCA Mobile itself still often complained about by its users. The purpose of this study was to determine how much influence functional failure, system failure, information failure, and service failure have on the use of BCA Mobile and its relationship with user satisfaction. The method used is quantitative method. The sampling technique uses SRS or simple random sampling with a sample size of 400 respondents who are BCA Mobile users in Indonesia. Data collection using a questionnaire distributed via Google Form. The data analysis technique used is SEM - PLS with SmartPLS 4.0 software. The results of this study indicate that functional failure, information failure, and service failure have no effect on the use of BCA Mobile, while system failure has a negative and significant effect on the use of BCA Mobile, and the use of BCA Mobil affects user satisfaction.

Why do Consumers Switch? The Role of Relational Uncertainty in Service Failures

Why do Consumers Switch? The Role of Relational Uncertainty in Service Failures

Integrated Control of Thermal Residual Stress and Mechanical Properties by Adjusting Pulse-Wave Direct Energy Deposition

Directed energy deposition with laser beam (DED-LB) components experience significant residual stress due to rapid heating and cooling cycles. Excessive residual tensile stress can lead to cracking in the deposited sample, resulting in service failure. This study utilized digital image correlation (DIC) and thermal imaging to observe the in situ temporal evolution of strain and temperature gradients across all layers of a deposited 316 L stainless steel thin wall during DED-LB. Both continuous-wave (CW) and pulsed-wave (PW) laser modes were employed. Additionally, the characteristics of thermal cracks and geometric dislocation density were examined. The results reveal that PW mode generates a lower temperature gradient, which in turn reduces thermal strain. In CW mode, the temperature–stress relationship curve of the additive manufacturing sample enters the “brittleness temperature zone”, leading to the formation of numerous hot cracks. In contrast, PW mode samples are almost free of cracks, as the metal avoids crack-sensitive regions during solidification, thereby minimizing hot crack formation. Overall, these factors collectively contribute to reduced residual stress and improved mechanical properties through the adjustment of pulsed-wave laser deposition.

Exploring the mechanism of sustained consumer trust in AI chatbots after service failures: a perspective based on attribution and CASA theories

In recent years, artificial intelligence (AI) technology has been widely employed in brand customer service. However, the inherent limitations of computer-generated natural language content occasionally lead to failures in human-computer interactions, potentially damaging a company’s brand image. Therefore, it is crucial to explore how to maintain consumer trust after AI chatbots fail to provide successful service. This study constructs a model to examine the impact of social interaction cues and anthropomorphic factors on users’ sustained trust by integrating the Computers As Social Actors (CASA) theory with attribution theory. An empirical analysis of 462 survey responses reveals that CASA factors (perceived anthropomorphic characteristics, perceived empathic abilities, and perceived interaction quality) can effectively enhance user trust in AI customer service following interaction failures. This process of sustaining trust is mediated through different attributions of failure. Furthermore, AI anxiety, as a cognitive characteristic of users, not only negatively impacts sustained trust but also significantly moderates the effect of internal attributions on sustained trust. These findings expand the research domain of human-computer interaction and provide insights for the practical development of AI chatbots in communication and customer service fields.

The Mediating Role of Customer Commitment in the Relationship Between Customer-Company Fit and Customer Voice: An Empirical Study on Restaurant Customers

Objective: This study discusses value congruence (person-organization fit) and voice behaviors, which are frequently discussed in the field of organizational behavior, in the context of marketing. The study aims to examine the effect of customer-company fit (CC fit) on customer voice behavior as well as the role that customer commitment plays in this effect. Design/methodology: Research data was obtained from 390 restaurant customers in a province in Turkey through the convenience sampling method using the survey technique. The SmartPLS program (PLS-SEM) was used to conduct the analyses. The research model was analysed using partial least squares path analysis (PLS-SEM) and bootstrapping (5,000 bootstrap samples). Findings: The findings indicate that CC fit directly affects both customer commitment and customer voice behavior. Furthermore, the findings revealed that customer commitment plays a mediating role CC fit’s effect on customer voice behavior. Theoretical and Practical implications: The present study is the first in the literature to discuss the various relationships between these variables (CC fit, customer commitment and customer voice) together. Another distinctive feature of the study is that the sample consists of restaurant customers. The high-contact nature of restaurant service makes service failures inevitable. For this reason, it is much more important to reveal customer voice behaviors in restaurant services than in other sectors. In this respect, the results of the study may provide guiding information to the sector.

Unraveling Magnet Structural Defects in Permanent Magnet Synchronous Machines—Harmonic Diagnosis and Performance Signatures

Rare-earth-based permanent magnets (PMs) have a vital role in numerous sustainable energy systems, such as electrical machines (EMs). However, their production can greatly harm the environment and their supply chain monopoly presents economic threats. Alternative materials are emerging, but the use of rare-earth PMs remains dominant due to their exceptional performance. Damage to magnet structure can cause loss of performance and efficiency, and propagation of cracks in PMs can result in breaking. In this context, prolonging the service life of PMs and ensuring that they remain damage-free and suitable for re-use is important both for sustainability reasons and cost management. This paper presents a new harmonic content diagnosis and motor performance analysis caused by various magnet structure defects or faults, such as cracked or broken magnets. The proposed method is used for modeling the successive physical failure of the magnet structure in the form of crack formation, crack growth, and magnet breakage. A surface-mounted permanent magnet synchronous motor (PMSM) is studied using simulation in Ansys Maxwell software (Version 2023), and different cracks and PM faults are modeled using the two-dimensional finite element method (FEM). The frequency domain simulation results demonstrate the influence of magnet cracks and their propagation on EM performance measures, such as stator current, distribution of magnetic flux density, back EMF, flux linkage, losses, and efficiency. The results show strong potential for application in health monitoring systems, which could be used to reduce the occurrence of in-service failures, thus reducing the usage of rare-earth magnet materials as well as cost.

Intentional or Designed? The Impact of Stance Attribution on Cognitive Processing of Generative AI Service Failures.

With the rapid expansion of the generative AI market, conducting in-depth research on cognitive conflicts in human-computer interaction is crucial for optimizing user experience and improving the quality of interactions with AI systems. However, existing studies insufficiently explore the role of user cognitive conflicts and the explanation of stance attribution in the design of human-computer interactions. This research, grounded in mental models theory and employing an improved version of the oddball paradigm, utilizes Event-Related Spectral Perturbations (ERSP) and functional connectivity analysis to reveal how task types and stance attribution explanations in generative AI influence users' unconscious cognitive processing mechanisms during service failures. The results indicate that under design stance explanations, the ERSP and Phase Locking Value (PLV) in the theta frequency band were significantly lower for emotional task failures than mechanical task failures. In the case of emotional task failures, the ERSP and PLV in the theta frequency band induced by intentional stance explanations were significantly higher than those induced by design stance explanations. This study found that stance attribution explanations profoundly affect users' mental models of AI, which determine their responses to service failure.

Does service innovation reduce customer defection intentions among disappointed customers? A latent growth modeling approach in the hotel sector

Despite the importance of service innovation research, it remains unclear how service innovation paradoxically induces customer defections during service failures. Using a three time-lag interval design, our findings show that customers’ negative responses to hotel betrayals outweigh their positive responses to service innovations. Thus, we demonstrate that service innovation does not always positively impact organizational performance. Similar outcomes arise when examining the insignificance of static online reviews and review change rates. We find that while customers experiencing hotel service failures are likelier to end their relationships with said hotels as their negative perceptions increase, their experiences are unaffected by the rate at which the hotel implements service innovations. Finally, online reviews directly and indirectly affect defection intentions in service failure contexts, especially when time is a factor.

Can service robots recover themselves? The effect of service recovery agents and robot service failure types on customer response

As service robots become more widely deployed, the risk of service failures has increased, making it crucial to understand how service enterprises can effectively recover from such failures. Drawing on role congruity theory, we explore the performance of different recovery agents across various robot failure contexts. Through three experiments, we demonstrate that customer forgiveness and satisfaction improve for process failures caused by robots when service recovery is handled by human staff or a warm robot. Conversely, recovery efforts by human staff or a competent robot boost customer forgiveness and satisfaction in response to outcome failures triggered by robots. In addition, perceived role congruity acts as a mediator in the above interaction effects. Moreover, proactive recovery moderates these effects. This study offers a nuanced understanding of service recovery by refining robot service failure and recovery agent typologies, thereby enriching research in robot service recovery.

EXPRESS: GENDER STEREOTYPING IN ROBOT SERVICE FAILURES

AI agents, such as service robots, could encompass gender cues. However, little is known regarding whether and how customers apply gender stereotyping to service failures in gendered service tasks performed by robots. Drawing on the gender stereotype theory, we investigate the joint effects of robot gender (feminine vs. masculine) and task type (female-dominated vs. male-dominated) on customer dissatisfaction following service failures. Study 1 reveals that feminine service robots are perceived as more communal but equally agentic as their masculine counterparts. Study 2 demonstrates that feminine (vs. masculine) service robots generate lower customer dissatisfaction when failing a female-dominated task. However, this discrepancy diminishes when failures occur in a male-dominated task. Perceived communion and tolerance serially mediate such robot gender effect. Our findings suggest that using feminine robots across all service categories may be a cost-effective strategy for hospitality organizations, eliminating the need to vary robot gender by task type.

Language Styles, Recovery Strategies and Users’ Willingness to Forgive in General Artificial Intelligence Service Recovery: A Mixed Study

As the prevalence of generative artificial intelligence (GenAI) in the service sector continues to grow, the impact of the language style and recovery strategies utilized during service failures remains insufficiently explored. This study, grounded in the theory of social presence and dual-process theory, employed a mixed-method approach combining questionnaire surveys and event-related potential (ERP) experiments to investigate the effect of different language styles (rational vs. humorous) and recovery strategies (gratitude vs. apology) on users’ willingness to forgive during the GenAI service recovery process. It further delves into the chained mediating role of perceived sincerity and social presence in this process. The findings revealed that a humorous language style was more effective in enhancing users’ willingness to forgive compared to a rational style, primarily through the enhancement of users’ perceived sincerity and sense of social presence; recovery strategies played a moderating role in this process, with the positive impact of perceived sincerity on social presence being significantly amplified when the GenAI service adopted an apology strategy. ERP results indicated that a rational language style significantly induced a larger N2 component (cognitive conflict) in apology scenarios, while a humorous style exhibited higher amplitude in the LPP component (positive emotional evaluation). This research unveils the intricate relationships between language style, recovery strategies, and users’ willingness to forgive in the GenAI service recovery process, providing important theoretical foundations and practical guidance for designing more effective GenAI service recovery strategies, and offering new insights into developing more efficacious GenAI service recovery tactics.

The strength of stance: The impact of brand activism on resistance to negative information, purchasing, and premium paying intents across different types of failures

This paper represents a pioneering exploration of how brand activism influences consumer responses to performance and value-related failures. Specifically, across two experiments, the research explores how brand activism influences consumer resistance to negative information, purchase intent, and willingness to pay more, in response to product, service, and value-related failures. Study 1 (N = 237) reveals that consumers who are more involved in activism and are exposed to a product failure from activist brands exhibit higher resistance to negative information compared to those who encounter product failures from non-activist brands. This suggests that activist branding may protect companies by cultivating a resilient, loyal consumer base in such adverse situations. Study 2 (N = 261) extends these findings by examining whether value-related (vs service-related) failures impact the buffering effect generated by a brand’s activist stance. Results indicate that activist brands particularly benefit from a protective buffer in the context of service failure scenarios. However, this buffer does not extend to value-related failures, leading to harsher consumer judgments and intentions. The research significantly contributes to brand activism literature by uniquely integrating the themes of brand activism and brand transgressions, exploring how brand activism affects consumer responses to both performance and value-related failures.

A service failure assessment model for smart product consumption experience based on customer perception

Customer perception is an important consideration factor in evaluating the quality of human-computer interaction services. Sustainable user experiences and marketing strategies can be created by analyzing customer perception. By understanding consumer satisfaction with product services in the customer perception area, appropriate product service failure prevention strategies can be formulated. A service failure evaluation model is proposed in this study, which considers the customer tolerance area to accurately evaluate consumers’ behavioral experiences from purchasing to using products. The concept of tolerance area is introduced, and a combination of the fuzzy Failure Mode and Effect Analysis (FMEA) method and the Technique for Order Preference by Similarity to an Ideal Solution (TOPSIS) method is used to construct a human-computer interaction service failure evaluation model. Potential service failure factors of smart speakers are accurately evaluated by this model, and these service failure factors are ranked within the tolerance area. The research identifies voice misinterpretation and signal connectivity issues as the primary risk factors impacting the quality of human-computer interaction for smart speakers. The application of this method not only enhances the evaluation of smart speaker human-computer interaction services quality but also aids in the precise identification and prioritization of critical failure modes. The proposed service failure prevention strategies can reduce consumer dissatisfaction and provide innovative references for smart product design and marketing. The findings bolster empirical evidence for service failure prevention strategies in smart products and pave the way for novel perspectives on enhancing the quality of human-computer interaction services.

Improving community understanding of cascading effects of critical infrastructure service failure: An experimental interactive learning process

Effective disaster preparedness relies on the community's understanding of hazards and risks, emphasizing the need to address cascading effects from critical infrastructure failures due to natural hazards. Understanding cascading effects in communities facing multiple hazards is crucial due to the increasing complexity of critical infrastructure (CI) services. Limited awareness can amplify vulnerabilities and socioeconomic losses, and thus capacity building at the community level is essential for reducing hazard risks. This study utilized an interactive learning process (ILP), a participatory approach to engage the community in sharing experiences and building coping capacity for cascading effects. ILP was facilitated through facilitator-learner interaction and participatory exercises, aided by visualisation tools that enriched the learning experience of participants. The use of visualisation tools in ILP simplified comprehension of intricate interdependencies, helping participants to brainstorm and think critically to explore potential solutions. We explore the effectiveness of three visualisation tools that support the ILPs in improving community understanding of the cascading effects. Through experimentation and comparison, insights are gained into how learners engage with different ILPs and their impact on overall learning outcomes. The study concluded by selecting the visualisation tool supporting ILP that was effective in fostering engagement, improving understanding, and promoting knowledge retention.

Customer’s social cognition in service recovery satisfaction with human vs robot agent

Purpose Service failures evoke negative customer emotions, which human agents respond to through emotional labor. In turn, customers empathize with the human agent, providing a satisfying service recovery experience. However, robot agents could replace human agents and replicate emotional labor strategies. This study addresses whether customers empathize with apologetic robot agents and how it would affect the service recovery experience. Design/methodology/approach Drawing on emotional labor, social cognition and justice theory, two online scenario-based experiments (N1 = 411; N2 = 253) were designed in which customers watched a video simulating an interaction with a human or a robot agent during a service recovery procedure. Findings Study 1 shows that robot agents handle emotionally driven service recovery interactions and prompt desirable postrecovery behaviors (e.g. brand loyalty). Study 2 identifies customers’ empathy and compassion as mediators, explaining the effect of normative empathic display on customers' perceptions of interactional justice and behavioral intentions. Practical implications Robot agents are reliable substitutes for human agents in handling service recovery procedures. Customers can empathize with robot agents, leading to satisfying service experiences. Originality/value This study demonstrates customers’ capacity to empathize with robot agents during a service recovery procedure. It is also the first application in service research of the EmpaToM experimental procedure from social neuroscience to explore the social cognition dynamic between customers and service agents at the service encounter.

Effects of customer inoculation on artificial intelligence service failure

Purpose This paper aims to explore the effectiveness of customer inoculation strategies in the context of AI service failures in the hospitality and tourism industries. Furthermore, it examines how these strategies can enhance customer complaint behavior and satisfaction with service recovery, thereby improving the overall service experience. Design/methodology/approach Four distinct studies were conducted: Study 1 investigated the influence of customer inoculation on complaint behavior post-AI service failure. Study 2 assessed the impact of service remedies on customer satisfaction. Study 3 explored the implications of initial purchase and usage intentions. Finally, Study 4 validated the findings using a large-scale online survey. Findings The results indicated that customer inoculation significantly increases customer complaint behavior and satisfaction with service remedies following AI service failures. They also showed that this relationship is mediated by psychological distance. Furthermore, customer inoculation positively affects initial purchase and usage intentions, demonstrating effectiveness at various customer engagement stages. Practical implications This study enriches the literature on AI hospitality service failure and recovery by introducing the novel concept of customer inoculation. Additionally, it significantly contributes to the inoculation theory literature, which covers diverse fields. Practically, this study proposes an efficient and low-cost strategy for marketers. Originality/value This study introduces the concept of customer inoculation in the context of AI service failures, a novel approach in the hospitality and tourism literature. It provides empirical evidence of the efficacy of the strategy, bridging a crucial gap in understanding customer behavior in the face of technological disruptions.

Comparing human and synthetic data in service research: using augmented language models to study service failures and recoveries

Comparing human and synthetic data in service research: using augmented language models to study service failures and recoveries

How to Talk to AI: The Role of Preset Prompt Language Styles in Shaping Conversational Experience

With the proliferation of AI conversational agents, the design of preset prompts—text suggestions that guide user interactions—has become crucial for enhancing user experience. This study investigates the impact of different preset prompt language styles (social-oriented vs. task-oriented) on user satisfaction. Utilizing two empirical studies, we examined how these language styles influence user perceptions of an AI agent’s warmth and competence, and how these perceptions mediate overall satisfaction. In the first study, participants interacted with an AI agent using either social-oriented or task-oriented prompts under conditions of service success or failure. The results indicated that social-oriented prompts significantly enhance user satisfaction by increasing perceptions of warmth, but not competence. However, this positive effect diminishes in the event of service failure. In the second study, we explored the moderating effect of task urgency. Findings revealed that the positive impact of social-oriented prompts on satisfaction is significant in low-urgency tasks but not in high-urgency scenarios. These insights underscore the importance of prompt language style in AI interactions and provide practical implications for designing more effective AI communication strategies, especially in customer service contexts.

The Influence Of Service Failure Severity On Brand Forgiveness Study On Telkomsel Users

The aims of this study are: (1) To determine the service failure severity from internet network issues according to Telkomsel users; (2) To determine the influence of service failure severity from internet network issues on brand forgiveness of Telkomsel users; and (3) To determine the influence of service failure severity from internet network issues on each brand forgiveness dimension of telkomsel users. The study used an online survey to collect data. The sample size for this study was 100 respondents. Data analysis was conducted using mean analysis and PLS-SEM. Research Findings: The study findings indicate that: (1) the service failure severity from internet network issues, according to Telkomsel users, is categorized as high; (2) the service failure severity from internet network issues has a significant negative influence on the brand forgiveness of Telkomsel users; (3) the service failure severity from internet network issues has a significant negative influence on each brand forgiveness dimension (cognitive, affective, behavioral) of Telkomsel users. This study simplifies the research conducted by Alnawas et al. (2023) by focusing only on two main variables, service failure severity and brand forgiveness, in order to provide a more focused and in-depth understanding of the relationship between severity of service failure and brand forgiveness

Timing and content of serious illness conversations for patients with advanced heart failure in a specialty-aligned palliative care service

BackgroundPatients with advanced heart failure (AHF) desire communication around values and goals prior to treatment decisions. ObjectivesTo evaluate the timing and content of the first serious illness communication (SI conversation) for patients with AHF after referral to a specialist palliative care (PC) team (HeartPal). MethodsIn this retrospective cohort study, we used electronic health records to identify patients referred to HeartPal and their first SI conversations at a tertiary care hospital between October 2018 and September 2021. We used natural language processing and predetermined codes to quantify prevalence of prior goals of care conversations by the cardiology team within six months preceding the HeartPal consultation and the prevalence of hopes, fears, and seven conversation content codes. Consecutive SI conversations and patient outcomes were followed until March 2022. ResultsOf 468 patients (mean age: 64 years, 72 % male, 66 % referred for goals of care conversation), 25.2 % had prior documented goals of care conversations preceding the HeartPal consultation. During the study period, 206 (44.0 %) patients died (median time from initial SI conversation to death: 65 days, IQR 206) and 43.2 % engaged in multiple SI conversations before death. SI conversation analysis (n = 324) revealed that patients hoped to “be at home” (74.1 %, n = 240), “be independent” (65.7 %, n = 213) and “live as long as possible” (53.4 %, n = 173). Conversation content included goals of care (83.0 %), strengths (83.0 %), decision-making (79.3 %), spirituality (71.0 %), coping (52.2 %), and prognostic communication (43.5 %). ConclusionSpecialist PC service provides documentation of goals and values and offers longitudinal follow-up for patients with AHF.