Development and Implementation of IT-Enabled Business Processes: A Knowledge Structure View

As competitive pressures mount, organizations must continue to evolve their business processes in order to survive. Increasingly, firms are developing new IT-enabled business processes in response to rising competition, greater customer expectations, and challenging economic conditions. The success rate of these projects remains low despite much industry experience and extensive academic study. Managerial and organizational cognition represents a potentially fruitful lens for studying the design and implementation of IT-enabled business processes. This view assumes that individuals are information workers who spend their days absorbing, processing, and disseminating information as they pursue their goals and objectives. Individuals develop cognitive representations, called knowledge structures, to represent their complex informational environment. Knowledge structures in turn help individuals to assimilate and process a bewildering flow of informational cues. Given the large degree of communication and information sharing required during the design and implementation of new business processes, it follows that knowledge structures likely play a large role in the success of these projects. This dissertation, organized as three essays, attempts to address this gap by investigating the influence of knowledge structures on the successful design and implementation of IT-enabled business processes. Essay 1 utilizes a case study method to observe the evolution of knowledge processes and the role of knowledge structures across three large-scale IT projects occurring over a ten-year period at a Fortune 100 company. Essay 2 investigates the knowledge building potential of business process models for both individual- and group-level knowledge. Essay 3 develops an individual-level model of business process appraisal by incorporating constructs from the job/role literatures into a popular IT appraisal mechanism. The resulting business process appraisal model is then tested as an early indicator of project success. Essay 2 and 3 hypotheses were tested using a field study in an organization which recently implemented a new purchasing and receiving process as part of a larger ERP project. Results suggest support for the proposed models. Important implications for research and practice are discussed

Influencing the Relationship between Job Clarity and Turnover Intention through User Training During Enterprise System Implementation

Enterprise system (ES) implementations introduce pervasive and disruptive change to organizations. End-users struggling to cope with such change often develop an internal self-preservation narrative that, if not managed, can lead to employee turnover. Turnover is a visibly-assertive response to ES implementations that has lasting negative effects on organizations. The job role literature suggests that an individual’s intention to leave an organization is greater when they lack clarity concerning their own work tasks and their role in achieving broader organizational goals. These clarity perceptions evolve during ES implementations as individuals become aware that their existing job context is no longer relevant to the post-implementation organization. It seems likely that the strength of relationship between job clarity and turnover intention will also evolve during this time. Accordingly, this study uses PLS-SEM multi-group analysis to examine changes in this relationship during an ES implementation at a Fortune 100 manufacturer and finds a significant increase in the influence of job clarity deficiencies on turnover intention following end-user training. These results suggest that ES implementation teams should focus their efforts on building job clarity of the post-implementation work context

Neonatal pain detection in videos using the iCOPEvid dataset and an ensemble of descriptors extracted from Gaussian of Local Descriptors

Abstract Diagnosing pain in neonates is difficult but critical. Although approximately thirty manual pain instruments have been developed for neonatal pain diagnosis, most are complex, multifactorial, and geared toward research. The goals of this work are twofold: 1) to develop a new video dataset for automatic neonatal pain detection called iCOPEvid (infant Classification Of Pain Expressions videos), and 2) to present a classification system that sets a challenging comparison performance on this dataset. The iCOPEvid dataset contains 234 videos of 49 neonates experiencing a set of noxious stimuli, a period of rest, and an acute pain stimulus. From these videos 20 s segments are extracted and grouped into two classes: pain (49) and nopain (185), with the nopain video segments handpicked to produce a highly challenging dataset. An ensemble of twelve global and local descriptors with a Bag-of-Features approach is utilized to improve the performance of some new descriptors based on Gaussian of Local Descriptors (GOLD). The basic classifier used in the ensembles is the Support Vector Machine, and decisions are combined by sum rule. These results are compared with standard methods, some deep learning approaches, and 185 human assessments. Our best machine learning methods are shown to outperform the human judges