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This article introduces the Responsible AI for Service Excellence (RAISE) framework. RAISE is a strategic framework for responsibly integrating AI into service industries. It emphasizes collaborative AI design and deployment that aligns with the evolving global standards and societal well-being while promoting business success and sustainable development.

This multidisciplinary conceptual article draws upon the United Nations' Sustainable Development Goals (SDGs) and AI ethics guidelines to lay out three principles for practicing RAISE: (1) Embrace AI to serve the greater good, (2) Design and deploy responsible AI and (3) Practice transformative collaboration with different service organizations to implement responsible AI.

By acknowledging the potential risks and challenges associated with AI usage, this article provides practical recommendations for service entities (i.e. service organizations, policymakers, AI developers, customers and researchers) to strengthen their commitment to responsible and sustainable service practices.

This is the first service research article to discuss and provide specific practices for leveraging responsible AI for service excellence.

The purpose of this paper is to demonstrate that customer engagement behavior may not always be a positive experience for customers. Specifically, the paper examines the effect of sources of help (employee vs customer) on customer satisfaction, and the underlying mechanism behind such an effect.

Three studies were conducted to test the hypotheses, and bootstrapping was used to analyze the proposed mediation and moderation models.

The results from the studies demonstrated the effect of sources of help (employee vs customer) on customer satisfaction. Specifically, compared to those who have received help from employees, customers who have received help from other customers showed lower satisfaction toward the firm. The relationship between sources of help and satisfaction was mediated by an affective factor, embarrassment, and a cognitive factor, altruistic motivation. In addition, the relationship between embarrassment and satisfaction was moderated by concern for face.

Firms should devote more resources toward minimizing customers’ embarrassment during service encounters and demonstrate altruistic motivation to provide voluntary help to lead customers to reciprocate helping.

The current research provides a new perspective on customer engagement behavior during service encounters. This research highlights the negative outcomes of receiving help from other customers.

Defects in concrete surfaces are inevitably recurring during construction, which needs to be checked and accepted during construction and completion. Traditional manual inspection of surface defects requires inspectors to judge, evaluate and make decisions, which requires sufficient experience and is time-consuming and labor-intensive, and the expertise cannot be effectively preserved and transferred. In addition, the evaluation standards of different inspectors are not identical, which may lead to cause discrepancies in inspection results. Although computer vision can achieve defect recognition, there is a gap between the low-level semantics acquired by computer vision and the high-level semantics that humans understand from images. Therefore, computer vision and ontology are combined to achieve intelligent evaluation and decision-making and to bridge the above gap.

Combining ontology and computer vision, this paper establishes an evaluation and decision-making framework for concrete surface quality. By establishing concrete surface quality ontology model and defect identification quantification model, ontology reasoning technology is used to realize concrete surface quality evaluation and decision-making.

Computer vision can identify and quantify defects, obtain low-level image semantics, and ontology can structurally express expert knowledge in the field of defects. This proposed framework can automatically identify and quantify defects, and infer the causes, responsibility, severity and repair methods of defects. Through case analysis of various scenarios, the proposed evaluation and decision-making framework is feasible.

This paper establishes an evaluation and decision-making framework for concrete surface quality, so as to improve the standardization and intelligence of surface defect inspection and potentially provide reusable knowledge for inspecting concrete surface quality. The research results in this paper can be used to detect the concrete surface quality, reduce the subjectivity of evaluation and improve the inspection efficiency. In addition, the proposed framework enriches the application scenarios of ontology and computer vision, and to a certain extent bridges the gap between the image features extracted by computer vision and the information that people obtain from images.