Telehealth, the use of information and communications technologies (ICTs) to support care delivery at a distance, is increasingly used in health systems worldwide. A service system is defined as a configuration of people, technologies, and other resources that interact with other service systems to create mutual value. Adopting a service system perspective thus allows understanding a telehealth service as an ICT-enabled service system. Adequately configuring resources, both tangible (e.g., hardware) and intangible (e.g., knowledge), is key to co-creating value through service systems. However, existing service system engineering methods and tools are not yet able to comprehensively capture the nature, role, and status of resources within service systems. In particular, while conceptual modelling is recognized as an excellent tool of understanding, designing, and monitoring for service engineering, existing conceptual modelling notations have limited abilities to express configurations of resources. In order to address this gap, the following research objectives are proposed: 1) Develop a conceptual framework of resource configurations as the basis for further developing a metamodel of resource configurations; 2) Develop a metamodel of resource configurations in ICT-enabled service systems that can formally express the constructs, relationships, and constraints within the domain of resource configurations; 3) Demonstrate and evaluate the metamodel by conducting a multiple-case study in the field of telehealth. This study will focus on telehealth as a representative instance of ICT-enabled service systems. The research design is guided by the Design Science Research Methodology (DSRM). DSRM provides a well-structured process for developing and evaluating information systems artifacts, such as the proposed metamodel, that can solve practical problems while contributing to a knowledge base. A multiple-case study of telehealth services at a Canadian hospital will support the evaluation and refinement of the metamodel. The results of this research project include both conceptual and practical contributions. The metamodel of resource configurations derived from the reviewed literature and conceptual framework will provide a formal understanding of resource configurations in ICT-enabled service systems. The metamodel may also be adopted as a tool for professionals to capture and analyze resource configurations in the domain of ICT-enabled services such as telehealth.

Western enterprises have problems that people in essential position get older and population of baby boom will retire, and enterprises in Taiwan have the gap of talents in next generation. ¡§The War of Talent¡¨ is the most servious war that enterprises will face in next decade, and Asian and Pacific companies particularly focus on successor issue. Systematic succession management originates from the Western, which is different from the Eastern enterprises. Therefore, the main purpose of this study is to explore the relationships among organizational cultures, succession planning cognition, organizational performance. Besides, the moderating effect of developmental human resource configuration on organization culture and succession planning cognition. This study adopted confirmatory factor analysis (CFA) and multiple regression analysis to anylyze data based on 181 returned questionnaires of HR managers in various industries The findings are as follows: 1. There were significant differences based on different organizational characteristics in organizational culture, succession planning cognition, and organizational performance. 2. The different organizational culture significantly affected succession planning cognition, and organizational performance, respectively. However, there is no siganificant correlation between succession planning cognition and organizational performance. 3. There was not mediating effect of succession planning cognition on organizational culture and organizational performance. 4. There was the moderating effect of developmental human resource configuration between organizational culture and succession planning cognition. The results could be the references to the relative enterprises to help them understand how to communicate succession planning program with organizational members and improve organizational performance.

During the process of the financial reform, financial institutions improved their management physique positively and redured their exceed lending effectively. Besides crediting control, financial institutions still have to rely on the professional debt collector to have the debt resolutions, and processing the collection procedure to maintain the management achievements and risk control. The present study aimed on the point of view of Human Capital, and investigated the relationshiop among Developmental Human Resource Configuration, Professional Commitment and Perceived Orangizational Support of the bank debt collector. In addition, the relationship between the Professional Commitment and Perceived Organizational Support will depend on the individual Emotional Intelligence. Therefore, this study also investigated bank debt collector¡¦s Emotional Intelligence as the moderator to the relationship between Professional Commitment and Percived Organizational Support. Based on the data from 94 supervisors and 454 employees in bank debt collection center, and adopted Hierarchical Linear Models to process the cross level analysis and found the results that Developmental Huaman Resource Configuation could raise debt collectors¡¦ Normative Professional Commitment to affect their Perceived Organizational Support, and their Emontonal Intelligence could moderate the relationship between their Porfessioanl Commitment and Perceived Oganizational Support. The results could be the references to the relative institutions to help them understand the development of the bank debt collectors to archive working performance. The findings are as follows: 1.Devlopemental Huaman Resource Configuation positively and significantly affected Normative Professional Commitment. 2.Bank debt collectors¡¦Professional Commitment positively and significantly affected Perceived Organizational Support. 3.Bank debt collectors¡¦Normative Professional Commitment as an indirect mediator positively and significantly affected to the relationship between Developmental Human Resource Configuation and Perceived Organizational Support. 4.Bank debt collectors¡¦ Emotional Intelligence as the moderator positively and significantly affected to the relationship between Affective and Continuance Professional Commitment and Perceived Organizational Support.

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Les organisations adoptent de plus en plus les Systèmes (PAIS) pour gérer leurs processus métiers basés sur les services en utilisant les modèles de processus appelés «modèles de processus métiers». Motivés par l’adaptation aux exigences commerciales et par la réduction des coûts de maintenance, les organisations externalisent leurs processus dans le Cloud Computing. Selon l'Institut NIST, Cloud Computing est un modèle qui permet aux fournisseurs de partager leurs ressources et aux utilisateurs d’y accéder de manière pratique et à la demande. Dans un tel environnement multi-tenant, l'utilisation de modèles de processus configurables permet aux fournisseurs de processus Cloud de fournir un processus personnalisable qui peut être configuré par différents tenants en fonction de leurs besoins.Un processus métier peut être spécifié par plusieurs perspectives tel que la perspective de flux de contrôle, la perspective des ressources, etc. Plusieurs approches ont été proposées au niveau des premières perspectives, notamment le flux de contrôle. Cependant, la perspective ressource, qui est d'une importance égale, était négligée et pas explicitement définie. D’un côté, la gestion de la perspective ressource spécifiquement l’allocation des ressources Cloud est un thème d’actualité qui implique plusieurs recherches. La modélisation et la configuration des ressources sont une tâche sensible nécessitant un travail intensif. Malgré l’existence de différentes approches, elles traitent principalement les ressources humaines plutôt que des ressources Cloud. D’un autre côté, malgré le fait que le concept des modèles de processus configurables est très complémentaire au Cloud, la manière dont comment les ressources sont configurées et intégrées est à peine manipulée. Les approches proposées travaillant sur l’extension de la configuration de ressources, ne couvrent pas les propriétés Cloud notamment l’élasticité et le partage.Pour répondre à ces lacunes, nous proposons une approche pour supporter la modélisation et la configuration de l’allocation des ressources Cloud dans les modèles de processus configurables. Nous visons à (1) définir une description unifiée et formelle pour la perspective ressource, (2) assurer une allocation de ressource correcte, sans conflits et optimisée, (3) Aider les fournisseurs de processus à concevoir leur allocation de ressources configurable de manière fine afin d'éviter des résultats complexes et importants, et (4) Optimiser la sélection des ressources Cloud par rapport aux exigences liées aux propriétés Cloud (élasticité et partage) et propriétés QoS.Pour ce faire, nous proposons d'abord un cadre sémantique pour une description de ressources sémantiquement enrichies dans les processus métiers visant à formaliser les ressources Cloud consommées à l'aide d'une base de connaissances partagée. Ensuite, nous nous basons sur les processus métiers sociales pour fournir des stratégies afin d'assurer une allocation de ressources contrôlée sans conflits en termes de ressources. Par la suite, nous proposons une nouvelle approche qui étend les modèles de processus configurables pour permettre une allocation de ressources Cloud configurable. Notre objectif est de déplacer l'allocation de ressources Cloud du côté des tenants vers le côté du fournisseur de processus Cloud pour une gestion centralisée des ressources. Après, nous proposons des approches génétiques qui visent à choisir une configuration optimale des ressources d'une manière efficace sur le plan énergétique en améliorant les propriétés QoS.Afin de montrer l'efficacité de nos propositions, nous avons développé concrètement (1) une série de preuves de concepts, en tant que partie de validation, pour aider à concevoir des modèles de processus et remplir une base de connaissances de modèles de processus hétérogènes avec des ressources Cloud et (2) ont effectué des expériences sur des modèles de processus réels à partir de grands ensembles de données / Organizations are recently more and more adopting Process-Aware Information Systems (PAIS) for managing their service-based processes using process models referred to as business process models. Motivated by adapting to the rapid changing business requirements and reducing maintenance costs, organizations are outsourcing their processes in an important infrastructure which is Cloud Computing. According to the NIST Institute, Cloud Computing is a model that enables providers sharing their computing resources (e.g., networks, applications, and storage) and users accessing them in convenient and on-demand way with a minimal management effort. In such a multi-tenant environment, using configurable process models allows a Cloud process provider to deliver a customizable process that can be configured by different tenants according to their needs.A business process could be specified from various perspectives such as the control-flow perspective, the organizational perspective, the resource perspective, etc. Several approaches have been correctly proposed at the level of the first perspectives, in particular the control-flow, i.e., the temporal ordering of the process activities. Nevertheless, the resource perspective, which is of equal importance, has been neglected and poorly operated. The management of the resource perspective especially the Cloud resource allocation in business processes is a current interesting topic that increasingly involves many researches in both academics and industry. The design and configuration of resources are undoubtedly sensitive and labor-intensive task. On the one hand, the resource perspective in process models is not explicitly defined. Although many proposals exist in the literature, they all targeted human resources rather than Cloud resources. On the other hand, despite of the fact that the concept of configurable process models is highly complementary to Cloud Computing, the way in how resources can be configured and integrated is hardly handled. The few proposals, which have been suggested on extending configuration to resources, do not cover required Cloud properties such as elasticity or multi-tenancy.To address these limitations, we propose an approach for supporting the design and configuration of Cloud resource Allocation in configurable business process models. We target to (1) define a unified and formal description for the resource perspective, (2) ensure a correct, free-of-conflict and optimized use of Cloud resource consumption, (3) assist process providers to design their configurable resource allocation in a fine-grained way to avoid complex and large results, and (4) optimize the selection of Cloud resources with respect to the requirements related to Cloud properties (elasticity and shareability) and QoS properties.To do so, we first suggest a semantic framework for a semantically-enriched resource description in business processes aiming at formalizing the consumed Cloud resources using a shared knowledge base. Then, we build upon social business processes to provide strategies in order to ensure a controlled resource allocation without conflicts in terms of resources. Next, we propose a novel approach that extends configurable process models to permit a configurable Cloud resource allocation. Our purpose is to shift the Cloud resource allocation from the tenant side to the Cloud process provider side for a centralized resource management. Afterwards, we propose genetic-based approaches that aim at selecting optimal resource configuration in an energy efficient manner and to improve non-functional properties.In order to show the effectiveness of our proposals, we concretely developed (i) a set of proof of concepts, as a validation part, to assist the design of process models and populate a knowledge base of heterogeneous process models with Cloud resources, and (ii) performed experiments on real process models from large datasets