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This paper aims to develop a conceptual framework for further understanding the risks embedded in co-creation services in high-contact service transactions. It delineates behavioral and economic perspectives focusing on agency costs, risk behavior, compensation structure and provides a context in which information is processed.

Following an extensive review of the literature, propositions are advanced that relate an agent’s risk behavior to information processing, compensation and the propensity to engage in co-creation. These propositions provide a complementary context for understanding risks in the co-creation process.

The propositions detail how a service agent’s information processing can be enhanced if the customer’s expected utility from transactions is maximized by managing the agent’s risk behavior and earnings potential. A compensation structure that balances fixed base and variable pay can motivate risk-taking and the agent’s propensity to engage in co-creation.

This paper extends the understanding of agency risks in the co-creation of hospitality services that integrates economic and behavioral perspectives with information processing. Theoretical implications include a broader context of the risks underlying co-creation. Practical implications relate to how earnings potential could be maximized by considering the agent’s risk behavior and the expected utility arising from such transactions.

Current networks are providing plenty of services that users can access and use. These services are more and more pervasive and deployed in different networks distributed across an environment. This raises the problem of managing such environments in order to grant access to services from anywhere and to adapt the environment’s networks to dynamic changes. Also, there is a need of an autonomous behavior to reduce human intervention and assure environment’s consistency. This autonomous and distributed behavior leads to the definition and integration of existing and new technologies to enable autonomous distributed management. This is fulfilled by providing paradigms that bring awareness about the surroundings and enabled tools to manage and adapt the environment’s resources. The main problem is to dynamically provide auto‐configuration of networks to deal with the frequent changes which results from users’ roaming, changing services constraints, and changing services themselves, and adding, upgrading or removing policies. The outcome of these issues is a dynamic system with complex management. Hence, this paper proposes the integration of different techniques to provide an autonomous, distributed, and secure management including auto‐configuration, adaptation, and auto‐protection of pervasive environments. Then, policies control the behavior of the environment, devices configuration and the enforcement of security mechanisms to protect sensitive data. Also, mobile agents are employed to distribute management tasks across the distributed environment. In order to provide auto‐protection capabilities, the autonomous behavior of the environment have to be secured. Actually, this security issue is addressed by defining an agent‐based Public Key Infrastructure (PKI) with X.509 certificates. The agent ensures then that the security functions are applied across all the distributed networks, where specific agents are responsible for conveying necessary information and certificates to local environments. Finally, the paper proposes a semantic‐based privacy management approach using ontologies to decide how privacy information is handled in the environment.

Increased competition from their traditionally, “non‐insurance” partners in the financial services industry has forced “insurance only” companies to enter areas once considered exclusively part of the financial services market. The melding of the insurance, banking, and brokerage industries into one composite financial service industry has created confusion in the minds of many customers who were once able to delineate neatly company product lines. In order to remain competitive in a deregulated industry that has more or less encroached on all financial service providers, insurance companies and their sales representatives have had to reevaluate their traditional methods of attracting and keeping customers. To be successful in this endeavor, agents must take a closer look at just how they treat their customers. Therefore, examines a model proposed as an instrument to assess some of the possible determinants of customer loyalty. The model is based on agents’ ability to deliver on factors related to service quality. The measurement scale used in the study is based on the importance/performance paradigm. The results, somewhat surprisingly, indicate that respondents believe their individual agent is performing at an above average level. Provides conclusions and recommendations.

In the vision of pervasive computing, numerous heterogeneous devices, various information services, and users performing daily activities are physically co‐located in a environment. How can we coordinate the services and devices to assist a particular user in receiving a particular service so as to maximize the user’s satisfaction? To solve this human‐centered coordination issue, we propose an agent‐based service coordination framework for pervasive computing. It is called location‐aware middle agent framework. The middle agent takes account of the user location in cognitive way (based on location‐ontology), and determines best‐matched services for the user. Based on this coordination framework, we have developed a multi‐agent architecture for pervasive computing, called CONSORTS (Coordination System of Real‐world Transaction Services). In this paper, we first outline some requirements of the human‐centered service coordination in pervasive computing. Secondly, we describe location‐aware middle agent framework to fill the requirements. Lastly, we outline CONSORTS, an prototype of location‐aware middle agent framework, and two applications of CONSORTS, location‐aware information assistance services in a museum and wireless‐LAN based location systems on FIPA agent Networks.

The purpose of this paper is to describe UWIRS (Ubiquitous Web Information Retrieval Solution), an agent‐based Web Information Retrieval (WIR) solution designed taking into account the unique features of the World Wide Web (WWW) and the limitations of existing WIR solutions for ubiquitous environments.

UWIRS can offer recommendation services by using the Multi‐Agent Vizier Recommendation Framework (Vizier). Vizier was designed under a generic approach and therefore can provide services to information retrieval applications so these may offer product recommendations that consider several adaptation/personalization dimensions (e.g. user dimension, context, among others).

Overall, the main challenge resides on: location, retrieval, integration and presentation of information from the WWW, quickly and accurately, to satisfy a user's singular information needs.

In UWIRS, agents cooperate in order to retrieve personalized information, considering user needs, goals, preferences and contextual features. UWIRS's agents are responsible for: interpreting user input and adding adaptation information by means of a query enrichment process; identifying and selecting the appropriate data sources taking into consideration the Profile Set (composed of User, Device and Information‐Provider Profiles); executing query routing and the information retrieval process; integrating and filtering the retrieved results; and lastly, coherent presentation of quality and relevant ubiquitous information (anytime, anywhere and anyhow) that satisfies the user's particular information needs and constraints associated to his/her access device.

This paper aims to explore the matching effect of hospitality brand image (cool vs non-cool) and service agents (Artificial intelligence [AI] vs human staff) on brand attitude, with a focus on assessing the role of feeling right as a mediator and service failure as a moderator.

This paper tested the hypotheses through three experiments and a Supplementary Material experiment, which collectively involved 835 participants.

The results indicated that the adoption of AI by cool brands can foster the right feeling and enhance consumers’ positive brand attitudes. In contrast, employing human staff did not lead to improved brand attitudes toward non-cool brands. Furthermore, the study found that service failure moderated the matching effect between service agents and cool brand images on brand attitude. The matching effect was observed under successful service conditions, but it disappeared when service failure occurred.

The findings offer practical guidance for hospitality companies in choosing service agents based on brand image. Cool brands can swiftly transition to AI, reinforcing their modern, cutting-edge image. Traditional brands may delay AI adoption or integrate it strategically with human staff.

To the best of the authors’ knowledge, this paper represents one of the first studies to address the issue of selecting the optimal service agent based on hospitality brand image. More importantly, it introduces the concept of a cool hospitality brand image as a boundary condition in the framework of AI research, providing novel insights into consumers’ ambivalent responses to AI observed in previous studies.

It is widely evident that trust and commitment are important pillars for strengthening the relationship between financial service firms and their customers. However, it has not been explored how the service quality, perceived cost and role of agents are important for financial service firms. To overcome this gap, this study aims to investigate the role of service quality, perceived cost and the role of agents as the commitment–trust factors in the financial insurance service (Takaful) in Malaysia, enhancing customer satisfaction.

The study follows a quantitative design in which primary data was collected using a survey instrument. The measurement instrument was adapted from the previous research, and data were collected from 264 customers of the Takaful financial service organizations in Malaysia. The data were analyzed using variance-based structural equational modeling in Smart-PLS software.

This research has revealed several useful insights that demonstrate a significant impact on service quality, perceived cost and the agents’ role in forging close relationships with their customers. Corporate image has a moderating role in relationships and has significantly impacted takaful insurance companies. The results imply that regardless of the corporate image of the financial service organizations, customers are concerned about the prices and the quality of the agents’ services.

In this study, only the predictors such as service quality, perceived costs and agents’ roles as trust–commitment factors were examined to determine customer satisfaction. Other investigations are highly recommended, such as value co-creation in takaful, takaful customer experience and takaful trust. This study offers insights to takaful insurance companies on how to keep up a positive corporate image, which will boost their trust–commitment factors and ultimately increase customer satisfaction.

By presenting commitment–trust factors and company image in an identifiable framework, the current study has expanded the discussion on takaful financial insurance services. The methodology is developed and rigorously tested to gauge customer satisfaction in takaful financial service organizations’ context.

The purpose of this paper is to explore influencing factors that affect the effectiveness of service recovery strategies using social network from operations management perspective. Specifically, the authors study the relationships between social media agent responses to customer complaints, customer emotion changes and customer satisfaction. Furthermore, the authors investigate the roles of recovery speed and failure severity in the service recovery process using social network platform.

The results are based on 347 mini cases drawn from the Twitter accounts of a sample of airlines. Grounded theory approach is used to conduct qualitative analysis using NVivo 9, a qualitative data analysis program. A conceptual framework was developed, then tested using χ² analysis.

Agent responses that do not require customers to take further initiatives for problem solving have positive effects on customer emotion alleviation and satisfaction. In contrast, responses that provide further directions poses negative effect on service recovery outcomes. There is a strong positive linkage between customer emotion change and customer satisfaction. Surprisingly, the direct effect of recovery speed on customer emotion and satisfaction is not supported by the data. Rather, it plays a moderating role in affecting the relationship between agent responses and customer satisfaction. The qualitative data further reveals the pivotal role of failure severity, one of key service failure attributes.

The authors study service businesses’ recovery strategies using social media. A conceptual framework is developed to link agent responses, customer emotion changes and customer satisfaction from the lens of service providers, using an operations-oriented approach. Finding on recovery speed and failure severity reveal that these variables play different roles when service recovery is operated on social media platform as compared to traditional channels. Additionally, relying on tweets as data sources has constrained us from assessing other long-term service recovery outcomes such as loyalty, repurchase intent and word of mouth. The drawback is resulted from the limited information conveyed through tweets, which tends to be short and brief. The study focusses on the airline industry, which limits the generalizability of the findings to other service industries.

The authors highlight the value and potential of service recovery strategies using social network and provide insights for recovery operations where agent responses should be focussing on real time problem solving. The findings support the benefits of empowering social network agents for service recovery operations. Improving recovery speed should be less of a priority as it serves as a qualifier when service recovery is operated via social network. Given the pivotal role of failure severity, it is critical for social network agents to stand in the shoes of the complaining customers, making imminent assessment of the actual failure severity and taking action accordingly in real time. In the meantime, effective communication through social network may help to lower perceived magnitude of failure by customers, which in turn enhance the effectiveness of other service recovery efforts.

This study is the first attempt to investigate the service recovery process using social media from an operations-oriented perspective. The results supports the potentials of employing service recovery strategies using social media.

A 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.

A 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.

The 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%.

Service 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.

The 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.

The purpose of this paper is to analyze perceptions of service cannibalization from sales agents when faced with increased online sales, and their consequences on the employee. The authors assess the effect of service cannibalization perceptions on insecurity, satisfaction, alienation, sales agents’ effort. The study also examines relationships between effects on sales agents’ service sabotage during service delivery.

Data were collected from 497 travel agency sales agents, and structural equation modeling was used to examine hypothesized relationships.

The results suggest that sales agents’ perceptions of service cannibalization influence employees, and have repercussions regarding service sabotage.

Mediators were not tested, and the model does not capture the phenomenon’s complexity. This study reinforces the importance of capturing sales agents’ perceptions from travel agency managers in reducing negative consequences on employees, which is particularly important given multichannel marketing when online marketing channels coexist with traditional sales forces.

This study offers insights to firms regarding perceived cannibalization and its consequences on sales agents’ motivation. Organizations should find ways to minimize insecurity, dissatisfaction, and alienation.

This study examines psychological influences of the addition of an internet channel on sales agents’ job-related outcomes, and its relationship with sales agents’ service sabotage during service delivery.