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This paper aims to solve the web service selection problem using an efficient meta-heuristic algorithm. The problem of selecting a set of web services from a large-scale service environment (web service repository) while maintaining Quality-of-Service (QoS), is referred to as web service selection (WSS). With the explosive growth of internet services, managing and selecting the proper services (or say web service) has become a pertinent research issue.

In this paper, to address WSS problem, the authors propose a new modified fruit fly optimization approach, called orthogonal array-based learning in fruit fly optimizer (OL-FOA). In OL-FOA, they adopt a chaotic map to initialize the population; they add the adaptive DE/best/2mutation operator to improve the exploration capability of the fruit fly approach; and finally, to improve the efficiency of the search process (by reducing the search space), the authors use the orthogonal learning mechanism.

To test the efficiency of the proposed approach, a test suite of 2500 web services is chosen from the public repository. To establish the competitiveness of the proposed approach, it compared against four other meta-heuristic approaches (including classical as well as state-of-the-art), namely, fruit fly optimization (FOA), differential evolution (DE), modified artificial bee colony algorithm (mABC) and global-best ABC (GABC). The empirical results show that the proposed approach outperforms its counterparts in terms of response time, latency, availability and reliability.

In this paper, the authors have developed a population-based novel approach (OL-FOA) for the QoS aware web services selection (WSS). To justify the results, the authors compared against four other meta-heuristic approaches (including classical as well as state-of-the-art), namely, fruit fly optimization (FOA), differential evolution (DE), modified artificial bee colony algorithm (mABC) and global-best ABC (GABC) over the four QoS parameter response time, latency, availability and reliability. The authors found that the approach outperforms overall competitive approaches. To satisfy all objective simultaneously, the authors would like to extend this approach in the frame of multi-objective WSS optimization problem. Further, this is declared that this paper is not submitted to any other journal or under review.

Given the fierce competition in financial services, service failure management and trust restoration tactics are becoming strategic priorities. Studies investigating trust restoration have increased over the years due to the significance of trust in services and the frequency of trust violations. Drawing on the sense-making and defensive approaches of attribution theory, this study aims to explore the effectiveness of various trust recovery tactics (e.g. apology, explanation, and investigation) in financial services considering the prevalence of service failure severity.

Based on a scenario-based survey, this study gathered data from 402 consumers of different banks in Pakistan. The study analyzed the data using ordinary least square regressions and structural equation modeling.

The study indicated that explanation is more effective in repairing character-competence and commitment-based trust, while investigation remained highly effective for inducing congruence-based trust. Interestingly, an apology was more effective for communication-based trust repairing, while context-based trust recovery was unaffected against all recovery tactics. Despite the prevalence of severe service failure, recovery actions proved fully effective for character-competence and commitment-based trust while partially effective for congruence-based trust recovery. This study also found that severe service failure undermines the effectiveness of recovery actions in repairing communication and context-based trust.

The study extends the literature on trust recovery by integrating sense-making and defensive attribution theory. The sense-making approach contributes to the existing knowledge on trust recovery by elucidating how consumers and service providers develop a shared understanding to facilitate the recovery mechanism of multidimensional trust in financial services.

This study aims to ascertain the forms of social enterprises created for public services and the dimensions of community-based management of public facilities. It seeks to understand how community-based facilities management (CbFM) can apply to the management of public services created by social enterprises in developing communities.

The study examines research studies on CbFM through a scoping review of papers published between 1997 and 2022.

The scoping review revealed that there are five dimensions of CbFM for developing communities: CbFM, urban facilities management, sustainable CbFM, urban infrastructure management and management of community hubs. It also revealed that social enterprises have been used to manage services, and for social inclusion, and to increase the efficiency of tangible infrastructure in communities.

The scoping review included literature from 1997 to 2022 to understand the development trends in CbFM in developing communities. It is possible that literature from a broader timeframe could have produced more in-depth understanding of the subject investigated.

The paper articulates a framework of CbFM models for public services in developing communities and developed a database of the relevant studies, which can further guide future researchers, stakeholders and policymakers in this area.

The comprehensive review produced a framework for community management of public services. It also identified that there is a paucity of literature on social infrastructure. It highlighted the need for skillsets to support community-based enterprises. There are limited studies that touch on the development of performance indicators for developing communities.

This editorial aims to organise the 17 United Nations Sustainable Development Goals (SDGs) into seven ServCollab service research themes to provide a way forward for service research that improves human and planetary life.

A conceptual approach is used that draws on observations from the scholarly experience of the editors.

This editorial offers seven research themes for service researchers: services that enable the WELL-BEING of the human species; services that provide OPPORTUNITY for all humans; services that manage RESOURCES for all humans; ECONOMIC services for work and growth for all humans; services from INSTITUTIONS that offer fair and sustainable living for all humans; service ecosystems with the PLANET; and COLLABORATION services for sustainable development partnerships.

Service scholars are urged to pursue collaborative research that reduces suffering, improves well-being and enables well-becoming for the sustainability and prosperity of Planet Earth.

This editorial provides service scholars with a new framework synthesising the SDGs into research themes that help focus further service research.

This paper explores optimal service resource management strategy, a continuous challenge for health information service to enhance service performance, optimise service resource utilisation and deliver interactive health information service.

An adaptive optimal service resource management strategy was developed considering a value co-creation model in health information service with a focus on collaborative and interactive with users. The deep reinforcement learning algorithm was embedded in the Internet of Things (IoT)-based health information service system (I-HISS) to allocate service resources by controlling service provision and service adaptation based on user engagement behaviour. The simulation experiments were conducted to evaluate the significance of the proposed algorithm under different user reactions to the health information service.

The results indicate that the proposed service resource management strategy, considering user co-creation in the service delivery, process improved both the service provider’s business revenue and users' individual benefits.

The findings may facilitate the design and implementation of health information services that can achieve a high user service experience with low service operation costs.

This study is amongst the first to propose a service resource management model in I-HISS, considering the value co-creation of the user in the service-dominant logic. The novel artificial intelligence algorithm is developed using the deep reinforcement learning method to learn the adaptive service resource management strategy. The results emphasise user engagement in the health information service process.

By introducing the mass customization service mode into the cloud logistics environment, this paper studies the joint optimization of service provider selection and customer order decoupling point (CODP) positioning based on the mass customization service mode to provide customers with more diversified and personalized service content with lower total logistics service cost.

This paper addresses the general process of service composition optimization based on the mass customization mode in a cloud logistics service environment and constructs a joint decision model for service provider selection and CODP positioning. In the model, the two objective functions of minimum service cost and most satisfactory delivery time are considered, and the Pareto optimal solution of the model is obtained via the NSGA-II algorithm. Then, a numerical case is used to verify the superiority of the service composition scheme based on the mass customization mode over the general scheme and to verify the significant impact of the scale effect coefficient on the optimal CODP location.

(1) Under the cloud logistics mode, the implementation of the logistics service mode based on mass customization can not only reduce the total cost of logistics services by means of the scale effect of massive orders on the cloud platform but also make more efficient use of a large number of logistics service providers gathered on the cloud platform to provide customers with more customized and diversified service content. (2) The scale effect coefficient directly affects the total cost of logistics services and significantly affects the location of the CODP. Therefore, before implementing the mass customization logistics service mode, the most reasonable clustering of orders on the cloud logistics platform is very important for the follow-up service combination.

The originality of this paper includes two aspects. One is to introduce the mass customization mode in the cloud logistics service environment for the first time and summarize the operation process of implementing the mass customization mode in the cloud logistics environment. Second, in order to solve the joint decision optimization model of provider selection and CODP positioning, this paper designs a method for solving a mixed-integer nonlinear programming model using a multi-layer coding genetic algorithm.

Based on appraisal theory and social response theory, this study aims to explore the mechanism of AI failure types on consumer recovery expectation from the perspective of service failure assessment and validate the moderate role of anthropomorphism level.

Three scenario-based experiments were conducted to validate the research model. First, to test the effect of robot service failure types on customer recovery expectation; second, to further test the mediating role of perceived controllability, perceived stability and perceived severity; finally, to verify the moderating effect of anthropomorphic level.

Non-functional failures reduce consumer recovery expectation compared to functional failures; perceived controllability and perceived severity play a mediating role in the impact of service failure types on recovery expectation; the influence of service failure types on perceived controllability and perceived severity is moderated by the anthropomorphism level.

The findings enrich the influence mechanism and boundary conditions of service failure types, and have implications for online enterprise follow-up service recovery and improvement of anthropomorphic design.

Pricing is widely acknowledged as a market entry challenge for servitising companies. The purpose of this research is to ascertain the attributes that contribute to willingness to pay (WTP) for industrial services and suggest incorporating those attributes to a pricing model.

Three attributes (Quality of Service, Nearness of Service Provider and Brand Equity of Service Provider) were analyzed at three respective levels to ascertain their importance on WTP. Conventional conjoint analysis (CCA), using an orthogonal design, was the method used. The 346 respondents were decision-makers and top management professionals from various industries.

Brand Equity emerged as the most significant attribute contributing to WTP, having more than 45% importance – followed by the Quality and Nearness.

The scope of the study is limited to the industries and its Allies. However, the relative importance of the attributes may vary depending on the type of service.

The importance of attributes and their WTP preference helps future researchers create a pricing model involving these attributes. This helps service providers price their services rationally, thus succeeding in servitization.

Product life is extended because the manufacturers themselves are servicing it and also help recycle the product with their expertise. Servitization is also helpful for the Indian economy, as it is turning into a manufacturing economy.

This research investigates three attributes that contribute to WTP, in accordance with their level of contribution. It also provides a direction to establish an adequate pricing model for industrial services.

This paper is part of the Special Issue series Improving Life on Planet Earth – A Call to Action for Service Research to Achieve the Sustainable Development Goals (SDG). This paper aims to provide the groundwork for Service Research Theme 2 – services that provide OPPORTUNITY for all humans. Service Research Theme 2 comprises SDG4, quality education; SDG5, gender equality; and SDG10, reduced inequalities and seeks to mobilize ServCollab’s aspirations to reduce human suffering, improve human well-being and enable well-becoming.

A scoping review appraising existing service research related to SDG4, SDG5 and SDG10 was conducted, establishing interlinkages, identifying patterns within each SDG and then assembling a research agenda for service researchers.

The scoping review identifies 18 patterns in service research (six patterns per SDG) pertaining to Service Research Theme 2. Common patterns among the SDG4, SDG5 and SDG10 included underrepresentation, consumer-centricity, the absence of explicit SDG linkages, the predominance of one theoretical anchor and the preference for quantitative studies, particularly surveys. Overall, the scoping review found that service research related to Service Research Theme 2 is patchy in that it is overdeveloped in some topics, methodologies and methods yet underdeveloped or silent in others.

The high-level research problem of Service Research Theme 2 is as follows: How have services provided OPPORTUNITY for all humans? This paper analyzes patterns in service research and, from these patterns, assembles a research agenda that sparks and guides further research.