Service Quality and Customer Loyalty in a Post-Crisis Context. Prediction-Oriented Modeling to Enhance the Particular Importance of a Social and Sustainable Approach

Research into the influence of service quality on customer loyalty has typically focused on confirming isolated direct causal influences regarding particular dimensions of quality, usually undertaken in the context of positive, firm-customer relations. The present study extends analysis of these factors through a new lens. First, the study was undertaken in a market context following a crisis that has had far-reaching consequences for customers’ relational behaviors. We explore the case of the Spanish banking industry, a sector that accurately reflects these new relational conditions, including a rising demand for more socially responsible banking. Second, we propose a holistic model that combines the effects of four key factors associated with service quality (outcome, personnel, servicescape and social qualities). We also apply an innovative predictive methodological technique using partial least squares (PLS) and qualitative comparative analysis (QCA) that enables us not only to determine the direct causal effects among variables, but also to consider different scenarios in which to predict customer loyalty. The results highlight the role of outcome and social qualities. The novelty of the social qualities factor helps to underscore the importance of social, ethical and sustainable practices to customer loyalty, although personnel and servicescape qualities must also be present to improve the predictive capability of service quality on loyalty

The Need of an Optimal QoS Repository and Assessment Framework in Forming a Trusted Relationship in Cloud: A Systematic Review

© 2017 IEEE. Due to the cost-effectiveness and scalable features of the cloud the demand of its services is increasing every next day. Quality of Service (QOS) is one of the crucial factor in forming a viable Service Level Agreement (SLA) between a consumer and the provider that enable them to establish and maintain a trusted relationship with each other. SLA identifies and depicts the service requirements of the user and the level of service promised by provider. Availability of enormous service solutions is troublesome for cloud users in selecting the right service provider both in terms of price and the degree of promised services. On the other end a service provider need a centralized and reliable QoS repository and assessment framework that help them in offering an optimal amount of marginal resources to requested consumer. Although there are number of existing literatures that assist the interaction parties to achieve their desired goal in some way, however, there are still many gaps that need to be filled for establishing and maintaining a trusted relationship between them. In this paper we tried to identify all those gaps that is necessary for a trusted relationship between a service provider and service consumer. The aim of this research is to present an overview of the existing literature and compare them based on different criteria such as QoS integration, QoS repository, QoS filtering, trusted relationship and an SLA

The Fuzzy and Dynamic Nature of Trust

Trust is one of the most fuzzy, dynamic and complex concepts in both social and business relationships. The difficulty in measuring Trust and predicting Trustworthiness in service-oriented network environments leads to many questions. These include issues such as how to measure the willingness and capability of individuals in the Trust dynamic and how to assign a concrete level of Trust to an individual or Agent. In this paper, we analyze the fuzzy, dynamic and complex nature of Trust.The dynamic nature of Trust creates the biggest challenge in measuring Trust and predicting Trustworthiness. In order to develop a Trustworthiness Measure and Prediction Method, we first need to understand what we can actually measure in a Trust Relationship

How are hospitals using artificial intelligence in strategic decision making? —a scoping review

Artificial intelligence (AI) is a useful tool for clinical decision-making in hospitals, and for strategic decision-making in other industries. This scoping review provides a comprehensive review of the potential for AI to improve strategic decision-making in hospitals by exploring current applications of AI in this area. Peer-reviewed publications and conference presentations associated with AI for strategic decision-making were identified in Health Administration, Computer Science and Business and Management databases to answer the research question; how are hospitals using AI in strategic decision-making? The review found 19 published AI applications for hospital strategic decision-making. The applications used a variety of knowledge-based, probabilistic reasoning and data-driven AI, that generally followed the course of AI maturity. They focused on specific decisions, with none providing a comprehensive framework for strategic decision-making drawing on existing enterprise- or system-wide data. There was little evidence of evaluation of the AI applications, with no cost-benefit evaluation. The scoping review suggests the need for substantial improvement in the understanding of AI and its application among hospital decision-makers leading to greater organisational maturity. This would suggest that journals and researchers require evaluative and economic research and that training to improve understanding of AI be provided for board members, managers and clinicians

Risk-based framework for SLA violation abatement from the cloud service provider's perspective

© The British Computer Society 2018. The constant increase in the growth of the cloud market creates new challenges for cloud service providers. One such challenge is the need to avoid possible service level agreement (SLA) violations and their consequences through good SLA management. Researchers have proposed various frameworks and have made significant advances in managing SLAs from the perspective of both cloud users and providers. However, none of these approaches guides the service provider on the necessary steps to take for SLA violation abatement; that is, the prediction of possible SLA violations, the process to follow when the system identifies the threat of SLA violation, and the recommended action to take to avoid SLA violation. In this paper, we approach this process of SLA violation detection and abatement from a risk management perspective. We propose a Risk Management-based Framework for SLA violation abatement (RMF-SLA) following the formation of an SLA which comprises SLA monitoring, violation prediction and decision recommendation. Through experiments, we validate and demonstrate the suitability of the proposed framework for assisting cloud providers to minimize possible service violations and penalties