Faculty Usage of Social Media and Mobile Devices: Analysis of Advantages and Concerns

This study seeks to understand the perceptions of professors using social media (also called Web 2.0 tools) in the classroom, what kinds of mobile devices are used to access the social media used, and what drives individuals to use them. In addition, it seeks to identify the advantages and concerns faculty has with the use of social media for classroom instruction. Two-Way Multivariate Analysis of Variance (MANOVA) procedure was used to ascertain whether differences existed between two dependent variables and (a) gender, (b) different academic ranks, and (c) gender *rank to determine if there are any interaction effects between genders regarding the magnitude of their perceptions of advantages and concerns about social media use for classroom instruction as they migrate through the ranks. Professors, regardless of sex or rank, held statistically the same views of the advantages as well as the concerns related to social media usage in the classroom

Testing the Accuracy of Employee-Reported Data: An Inexpensive Alternative Approach to Traditional Methods

Although Information Technology (IT) solutions improve the collection and validation of operational data, Operations Managers must often rely on self-reported data from workers to make decisions. The problem with this data is that they are subject to intentional manipulation, thus reducing their suitability for decision-making. A method of identifying manipulated data, digital analysis, addresses this problem at low cost. In this paper, we demonstrate how one uses this method in real-world companies to validate self-reported data from line workers. The results of our study suggest that digital analysis estimates the accuracy of employee reported data in operations management, within limited contexts. These findings lead to improved operating performance by providing a tool for practitioners to exclude inaccurate information

Testing the accuracy of employee-reported data: An inexpensive alternative approach to traditional methods

Although Information Technology (IT) solutions improve the collection and validation of operational data, Operations Managers must often rely on self-reported data from workers to make decisions. The problem with this data is that they are subject to intentional manipulation, thus reducing their suitability for decision-making. A method of identifying manipulated data, digital analysis, addresses this problem at low cost. In this paper, we demonstrate how one uses this method in real-world companies to validate self-reported data from line workers. The results of our study suggest that digital analysis estimates the accuracy of employee reported data in operations management, within limited contexts. These findings lead to improved operating performance by providing a tool for practitioners to exclude inaccurate information.

RUPERT: A Modelling Tool for Supporting Business Process Improvement Initiatives

Business process improvement (BPI) will be a high priority topic for CEOs in the near future. Currently available BPI approaches, however, lack means for adequately codifying, documenting and processing knowledge created in a BPI project. Therefore we developed RUPERT (Regensburg University Process Excellence and Reengineering Toolkit), which is a tool for managing knowledge in a BPI project, covering all stages of the knowledge lifecycle. In this paper, we describe the design and implementation of RUPERT