Kim E. van Oorschot, H. Akkermans, L. V. Van Wassenhove, Yan Wang
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The authors find that monitoring the performance of the service after release, with fast mitigation when service incidents are discovered, appears to be superior. This involves organizational change when traditional methods, like quality assurance, become less important.Research limitations/implicationsThe performance of digital services needs to be monitored by combining automated data collection about the status of the service with data interpretation using human expertise. Investing in human expertise is equally important as investing in automated processes.Originality/valueThe authors draw on unique empirical data collected from a digital service provider's struggle with performance measurement of its service over a period of nine years. The authors use simulations to show the impact of complexity on staff allocation.","PeriodicalId":14234,"journal":{"name":"International Journal of Operations & Production Management","volume":" ","pages":""},"PeriodicalIF":7.1000,"publicationDate":"2022-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"3","resultStr":"{\"title\":\"Organizing for permanent beta: performance measurement before vs performance monitoring after release of digital services\",\"authors\":\"Kim E. van Oorschot, H. Akkermans, L. V. Van Wassenhove, Yan Wang\",\"doi\":\"10.1108/ijopm-03-2021-0211\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"PurposeDue to the complexity of digital services, companies are increasingly forced to offer their services “in permanent beta”, requiring continuous fine-tuning and updating. Complexity makes it extremely difficult to predict when and where the next service disruption will occur. 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Organizing for permanent beta: performance measurement before vs performance monitoring after release of digital services
PurposeDue to the complexity of digital services, companies are increasingly forced to offer their services “in permanent beta”, requiring continuous fine-tuning and updating. Complexity makes it extremely difficult to predict when and where the next service disruption will occur. The authors examine what this means for performance measurement in digital service supply chains.Design/methodology/approachThe authors use a mixed-method research design that combines a longitudinal case study of a European digital TV service provider and a system dynamics simulation analysis of that service provider's digital service supply chain.FindingsWith increased levels of complexity, traditional performance measurement methods, focused on detection of software bugs before release, become fragile or futile. The authors find that monitoring the performance of the service after release, with fast mitigation when service incidents are discovered, appears to be superior. This involves organizational change when traditional methods, like quality assurance, become less important.Research limitations/implicationsThe performance of digital services needs to be monitored by combining automated data collection about the status of the service with data interpretation using human expertise. Investing in human expertise is equally important as investing in automated processes.Originality/valueThe authors draw on unique empirical data collected from a digital service provider's struggle with performance measurement of its service over a period of nine years. The authors use simulations to show the impact of complexity on staff allocation.
期刊介绍:
The mission of the International Journal of Operations & Production Management (IJOPM) is to publish cutting-edge, innovative research with the potential to significantly advance the field of Operations and Supply Chain Management, both in theory and practice. Drawing on experiences from manufacturing and service sectors, in both private and public contexts, the journal has earned widespread respect in this complex and increasingly vital area of business management.
Methodologically, IJOPM encompasses a broad spectrum of empirically-based inquiry using suitable research frameworks, as long as they offer generic insights of substantial value to operations and supply chain management. While the journal does not categorically exclude specific empirical methodologies, it does not accept purely mathematical modeling pieces. Regardless of the chosen mode of inquiry or methods employed, the key criteria are appropriateness of methodology, clarity in the study's execution, and rigor in the application of methods. It's important to note that any contribution should explicitly contribute to theory. The journal actively encourages the use of mixed methods where appropriate and valuable for generating research insights.