Technology support and demand for cloud infrastructure services: the role of service providers

Cloud computing;Technology support;Service co-production

Retana Solano, German F. ; Wu, D. J. Forman, Chris Business Narasimhan, Sridhar Niculescu, Marius F. Slaughter, Sandra Chellappa, Ramnath K. ; Wu, D. J.

University:Georgia Institute of Technology

Department:Business

关键词: Cloud computing;    Technology support;    Service co-production;   

Others  :  https://smartech.gatech.edu/bitstream/1853/50236/1/RETANASOLANO-DISSERTATION-2013.pdf

美国|英语

来源: SMARTech Repository

【 摘 要 】

Service providers have long recognized that their customers play a vital role in the service delivery process since they are not only recipients but also producers, or co-producers, of the service delivered. Moreover, in the particular context of self-service technology (SST) offerings, it is widely recognized that customers’ knowledge, skills and abilities in co-producing the service are key determinants of the services’ adoption and usage. However, despite the importance of customers’ capabilities, prior research has not yet paid much attention to the mechanisms by which service providers can influence them and, in turn, how the providers’ efforts affect customers’ use of the service.This dissertation addresses research questions associated with the role of a provider’s technology support and education in influencing customer use of an SST, namely public cloud computing infrastructure services. The unique datasets used to answer these research questions were collected from one of the major global providers in the cloud infrastructure services industry. This research context offers an excellent opportunity to study the role of technology support since, when adapting the standardized and commoditized components of the cloud service to their individual needs, customers may face important co-production costs that can be mitigated by the provider’s assistance. Specifically, customers must configure their computing servers and deploy their software applications on their own, relying on their own capabilities. Moreover, the cloud’s offering of on-demand computing servers through a fully pay-per-use model allows us to directly observe variation in the actual use customers make of the service.The first study of this dissertation examines how varying levels of technology support, which differ in the level of participation and assistance of the provider in customers’ service co-production process, influence the use that customers make of the service. The study matches and compares 20,179 firms that used the service between March 2009 and August 2012, and who over time accessed one of the two levels of support available: full and basic. Using fixed effects panel data models and a difference-in-difference identification strategy, we find that customers who have access to full support or accessed it in the past use (i.e., consume) more of the service than customers who have only accessed basic support. Moreover, the provider’s involvement in the co-production process is complementary with firm size in the sense that larger firms use more of the service than smaller ones if they upgrade from basic to full support. Finally, the provider’s co-participation through full support also has a positive influence on the effectiveness with which buyers make use of the service. Firms that access full support are more likely to deploy computing architectures that leverage on the cloud’s advanced features.The second study examines the value of early proactive education, which is defined as any provider-initiated effort to increase its customers’ service co-production related knowledge and skills immediately after service adoption. The study analyzes the outcome of a field experiment executed by the provider between October and November 2011, during which 366 randomly-selected customers out of 2,673 customers that adopted during the field experiment period received early proactive education treatment. The treatment consisted in a short phone call followed up by a support ticket through which the provider offered initial guidance on how to use the basic features of the service. We use survival analysis (i.e., hazard models) to compare the treatment’s effect on customer retention, and find that it reduces by half the number of customers who leave the service offering during the first week. We also use count data models to examine the treatment’s effect on customers’ demand for technology support, and find that the treated customers ask about 19.55% fewer questions during the first week of their lifetimes than the controls.