Jason Triche

Toward an integrated framework for innovation in service: A resource-based view and dynamic capabilities approach

Organizations today desire strategies that place them on the frontiers of service innovation. In this research, we provide a novel research framework that incorporates resources and capabilities and how they affect elements of service innovation. This research draws on the resource-based view of the firm to define firm’s internal resources and outside (i.e., relational) capabilities. Because firms compete in a dynamically changing market, we introduce dynamic service capabilities as three distinct processes that allow resources and relational capabilities to affect components of service innovation. Three propositions are introduced in addition to specific examples of promising future empirical work using service-oriented firms. This study aims to contribute to service innovation through a theoretical foundation in which we posit how firms develop their service innovation through a dynamic service capabilities framework. Contributions to theory and practice are discussed in addition to limitations to the study.

Investigating the role of business processes and knowledge management systems on performance: A multi-case study approach

In the ever-changing and competitive market place, organisations continuously need to improve their competitive advantage. One method to accomplish this is to form collaborative networks. Both knowledge management (KM) and KM systems play a pivotal role in the success of collaborative networks since information sharing and knowledge assets are so critical to the network. There has been a vast amount of research on KM systems but very little is known about how it affects individual and organisational performance. Drawing on the task–technology fit theory, in this study, we explore the fit or alignment between business process (task) and KM systems (technology) and its impact on KM systems utilisation based on multiple case studies. Subsequently, we investigate the impacts of both the task–technology fit and KM systems utilisation on individual and business performance. This paper contributes to the collaborative network/KM literature in several ways. First, it extends the task–technology fit theory to an important context of collaborative network/KM. Second, it replaces task with business process, which has the potential to help explain KM systems’ success on business performance. Third, the paper explores the positive impact of task–technology fit on KM system utilisation and business performance. Fourth and finally, the study provides insight into the future development of KM systems and how to better align them with managerial purposes.

Relationship between learning styles and effectiveness of online learning systems

Online learning has become a popular medium to disseminate knowledge for both institutions of learning and for companies. The economic benefits to deliver knowledge and training online are well documented; however, there are still issues as to its effectiveness. One way that online learning may be more effective is by taking into account a student’s learning style. Our research seeks to understand if online learning tools account for learning styles, will users find them useful and easier to use thus resulting in a successful online learning environment? We propose an extended Technology Acceptance Model (TAM) to include learning styles as an external variable. Our results show significance for six of the seven hypotheses. Educators and corporate training departments can use these findings to design a better online learning environment for their students and workforce.

A Comprehensive Framework to Enhance the Effectiveness of the Recruiting Experience for Data Science Graduates

The allure of the IS major depends on the successful placements of recent IS graduates in rewarding careers. The rise of the data science field provides an opportunity to rebrand and rebuild IS departments using the careerplacement successes of IS graduates as a springboard. This paper describes a framework that IS departments can use to coordinate between the employer demand side and the graduate supply side of the data science job market. We developed the framework based on empirical evidence gained over several years in successfully placing IS graduates into IS consulting firms across the US. The framework contains four different perspectives: the university IS department, the organizations hiring IS graduates, the IS graduates themselves, and career-development professionals at the university and college level. IS departments seeking to place their graduates in the data science field can use this framework.