Process-based Service Research Articles

Online student engagement and place attachment to campus in the new service marketplace: an exploratory study

PurposeThe pandemic has accelerated the use of virtual learning spaces and led to rethinking post-pandemic course delivery. However, it remains unclear whether students’ online engagement in e-servicescapes can influence attachment to a place, i.e. a physical servicescape. This study conducted an exploratory study to inform place attachment and actor engagement literature in an online service context.Design/methodology/approachQuantitative survey design was used and 98 usable responses were collected from undergraduate and postgraduate students at a major New Zealand university during the COVID-19 pandemic in 2020. The questionnaire consisted of 23 items relating to three dimensions of online student engagement and 19 items referring to six dimensions of campus attachment.FindingsResults of the exploratory study indicate that classmate community in online lectures, referring to student–student interactions, can positively influence five of the dimensions of campus attachment, including place identity, place dependence, affective attachment, social bonding and place memory, even though students are physically not on campus. However, it cannot influence place expectation. Moreover, instructor community (student–instructor interaction) and learning engagement (student–content interaction) in online lectures have insignificant impact on campus attachment.Research limitations/implicationsThis study emphasises the social dimension when interacting in e-servicescapes. Person-based interactions are more influential than content-based interactions for student engagement. Educational service providers should integrate the e-servicescape and the physical servicescape by encouraging more student–student interactions to contribute to service ecosystem well-being at the micro, meso and macro levels.Originality/valueThis study indicates that customer-to-customer interaction serves to integrate customer engagement across the digital and physical realms for process-based services like education.

Application of Service Modular Design Based on a Fuzzy Design Structure Matrix: A Case Study from the Mining Industry

The development of customized service is an important way to transform and upgrade China’s mining industry. However, in practice, there remain problems, such as the slow market response speed of service providers and the contradiction between the large-scale development of service providers and the personalized service needs of service demanders. This paper uses the theory and method of service modular design to solve these problems and explores the process-based service modular design method. Service modular design depends largely on the determination of the relationship between service activities and the reasonable division of modules. However, previous research has rarely made use of modular design methods and modeling tools in the mining service context. At the same time, evaluations of the relationship between service activities relying on knowledge and those relying on experience have been inconclusive. Therefore, this paper proposes a service modularization design method based on the fuzzy relation analysis of a design structure matrix (DSM) that solves the optimal module partition scheme. Triangular fuzzy number and fuzzy evidence theory are used to evaluate and fuse the multidimensional and heterogeneous relationship between service activities, and the quantitative processing of the comprehensive relationship between service activities is carried out. On this basis, the service module structure is divided, followed by the construction of the mathematical programming model with the maximum sum of the average cohesion degree in the module and the average coupling degree between modules as the driving goal. The genetic algorithm is used to solve the problem, and the optimal module division result is obtained. Finally, taking the service modular design of SHD coal production enterprises in China as an example, the feasibility of the proposed method is verified.

Method towards reconstructing collaborative business processes with cloud services using evolutionary deep Q-learning

Service-oriented architecture (SOA) is a significant framework that enables intelligent information systems to offer business process-based services, namely Business Process as a Service (BPaaS). Cloud service-based business model reconstruction within and across enterprises has become an important issue to obtain competitive advantages. Finding appropriate service component is a critical phase in enterprise collaboration, to improve the quality and correlations among collaborative service-providers. Existed methods for business processes reconstruction have not systematically and fully considered the quality correlations in finer-grain, i.e. task-level or activity-level, and temporal performance in a process model optimization. Moreover, the existed approaches might fail to work in an uncertain cloud environment where the quality parameters are unknown in advance. Q-learning has proven its worth in an uncertain cloud environment. However, advances in Q-learning are challenges to leverage in collaborative business process reconstruction. The reconstruction algorithms based on Q-learning suffered from the two core drawbacks: lack of effective exploration and extremely slow convergence property. A hybrid Evolutionary Deep Q-Learning-based BPaaS reconstruction algorithm, named as EDQL-BPR, is proposed by leveraging Particle Swarm Optimization to improve Deep Q-Learning algorithm for systematically optimizing collaborative business processes reconstruction. In order to verify the effectiveness of the proposed algorithm, an annotated transition system has been developed supporting for all possible behaviors state convention from heterogeneous initial states to the target with several collaborative information at activity level, and encoding annotated BP representation matrix automatically. Then, an autonomous three-layer framework has been built to facilitate business process discovery and service reconstruction. In this framework, our optimal collaborative services composition is treated as a multi-objective constraint optimization problem via Evolutionary Deep Q-learning with updated strategy of Q-value, and each annotated component launch a service discovery by service agent. Extensive evaluation results show that EDQL-BPR can outperform several representative business process reconstructions in terms of model optimal convergence, QoS optimality, effectiveness and efficiency under heterogeneous service selection workloads in an uncertain Cloud environment.

Dynamic QoS-Aware Cloud Service Selection Using Best-Worst Method and Timeslot Weighted Satisfaction Scores

Abstract The number of cloud services has dramatically increased over the past few years. Consequently, finding a service with the most suitable quality of service (QoS) criteria matching the user’s requirements is becoming a challenging task. Although various decision-making methods have been proposed to help users to find their required cloud services, some uncertainties such as dynamic QoS variations hamper the users from employing such methods. Additionally, the current approaches use either static or average QoS values for cloud service selection and do not consider dynamic QoS variations. In this paper, we overcome this drawback by developing a broker-based approach for cloud service selection. In this approach, we use recently monitored QoS values to find a timeslot weighted satisfaction score that represents how well a service satisfies the user’s QoS requirements. The timeslot weighted satisfaction score is then used in Best-Worst Method, which is a multi-criteria decision-making method, to rank the available cloud services. The proposed approach is validated using Amazon’s Elastic Compute Cloud (EC2) cloud services performance data. The results show that the proposed approach leads to the selection of more suitable cloud services and is also efficient in terms of performance compared to the existing analytic hierarchy process-based cloud service selection approaches.

Study on the feasibility of modelling notations for integrated Product-Service Systems Engineering

Developing products and services concurrently bears great potential as well as challenges. Systems Engineering (SE) is an interdisciplinary approach, which aims to enable the realization of successful systems. Model-based systems engineering (MBSE) is a SE approach focusing on the utilization of models for information exchange instead of documents. Methods and tools for MBSE are advanced and effective. Nevertheless, the field predominantly focuses on technical systems rather than on services and business models. In the context of the increasing importance of the Service Economy and rising innovative business models, an extension of the MBSE methods and tools to a service- and business model-centric view constitutes a research gap. Sufficient methods for requirements definition in PSS design have been developed. In concept design, the outline or rough concept of the product is defined. This step is currently not supported with useful methods. In order to address this research gap, the authors have proposed Integrated Product Service Architectures (IPSSAs) in previous research, which depict physical product architectures and service architectures in one integrated model. In previous studies, Systems Modelling Language (SysML) was identified as suitable modelling notations for IPSSAs, but several drawbacks of the notation were noted. Within this paper, a study is presented, which identifies suitable combinations of modelling notations and diagrams for IPSSA modelling. The combination of several diagrams from SysML, Business Process Modelling Notation (BPMN) and Data Flow Architecture is used on the practical example of ELSA (Engineering Living Systems of Autonomy), a service robot for guest and plant care. In order to address the lack of process-based service modelling in SysML, BPMN is added to common SysML modelling. In order address the data flow dimension, a third modelling notation, the Data Flow Architecture is proposed.

A context-based service matching approach towards functional reliability for industrial systems

ABSTRACTAs service-oriented computing is commonly used in industrial systems to deal with the continually changing business environment, matching processes with existing services becomes one of the most important phases in constructing, integrating and optimizing information systems. The precision of semantic service matching becomes a critical factor in ensuring the correctness of functionalities in system processes. However, the dependability of semantic service matching cannot be warranted in complex contexts. To improve semantic service matching for industrial systems, a context-based semantic service matching approach named ‘Process-Based service MatchMaker’(PBMM) is proposed in this paper. It selects the suitable services for the process from candidate services through taking the dependencies of related services process into consideration.

Understanding the performance of software-as-a-service based on service-dominant logic

How to improve performance of software-as-a-service remains a problem. Drawing from the service-dominant logic, the relational view, and the social exchange theory, we develop a model to delineate the relationships between performance, relationship-specific services, and internal services. The premise of this model is that the service provider's internal services play a key role in motivating the client to collaborate with the vendor. This helps the provider offer relationship-specific services, characterized as knowledge-based and process-based services and the provider's performance relies on these services. Results of empirical testing using responses from 102 firms support the proposed hypotheses. Our findings provide a number of useful implications for research and managers.

Managing Temporality: A Service Learning Program in Brazil

In this paper, we argue that temporality is a key factor in students’ learning process in management-oriented service-learning programs. We propose a process-based service learning model that sugge...

Process-Based Service Quality Assessment of Automobile Maintenance Service

Process-Based Service Quality Assessment of Automobile Maintenance Service