Sophisticated extrapolation techniques have had a negligible payoff for accuracy in forecasting. As a result, major changes are proposed for the allocation of the funds for- future research on extrapolation. Meanwhile, simple methods and the combination of forecasts are recommended.

Forecasting by Extrapolation: Conclusions from 25 Years of Research

Neonatal Facial and Cry Responses to Invasive and Non-invasive Procedures

The article reviews the book “Organizational Entry: Recruitment, Selection, and Socialization of Newcomers,” John P. Wanous.

Organizational Entry: Recruitment, Selection, and Socialization of Newcomers

This paper gives an overview of the cutting-edge approaches that perform facial cue analysis in the healthcare area. The document is not limited to global face analysis but it also concentrates on methods related to local cues (e.g., the eyes). A research taxonomy is introduced by dividing the face in its main features: eyes, mouth, muscles, skin, and shape. For each facial feature, the computer vision-based tasks aiming at analyzing it and the related healthcare goals that could be pursued are detailed.

https://www.mdpi.com/2078-2489/11/3/128/pdf

Analysis of Facial Information for Healthcare Applications: A Survey on Computer Vision-Based Approaches

Current knowledge management (KM) technologies and strategies advocate two different approaches: knowledge codification and knowledge sharing networks. However, the extant literature has paid limited attention to the interaction between them. This research draws upon the literature on formal modeling of networks to examine the interaction between knowledge codification and knowledge sharing networks. The analysis suggests that an increase in codification may damage existing network-sharing ties. Anticipating that, individuals may hoard their knowledge to protect their network ties, even when there are nontrivial rewards for codification. We find that despite the aforementioned tension between the codification and the network approach, a firm may still benefit from combining the two approaches. Specifically, when the future sharing potential between knowledge workers is high, a combination of the two approaches may outperform a codification-only or a network-only approach as the codification reward causes fewer network ties to break down and the benefit from increased codification can offset the loss of some network ties. However, when the future sharing potential is low an increase in codification reward can quickly break down the whole network, thus firms may be better off by pursuing a codification-only or a network-only strategy.

The Interaction Between Knowledge Codification and Knowledge Sharing Networks

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

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 individualand 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. This dissertation is approved for recommendation to the Graduate Council. Dissertation Directors: _______________________________________ Dr. Fred D. Davis _______________________________________ Dr. Pankaj Setia Dissertation Committee: _______________________________________ Dr. Paul Cronan _______________________________________ Dr. Viswanath Venkatesh _______________________________________ Dr. Rene Riedl (ex officio) DISSERTATION DUPLICATION RELEASE I hereby authorize the University of Arkansas Libraries to duplicate this dissertation when needed for research and/or scholarship. Agreed __________________________________________ Rick Brattin Refused __________________________________________ Rick Brattin ACKNOWLEDGEMENTS I would like to express my appreciation to those who have assisted me in the completion this research, and to those who have helped me greatly during my time in this program. First, thank you to the members of my dissertation committee: Co-chairs Dr. Fred Davis and Dr. Pankaj Setia, and committee members Dr. Paul Cronan, Dr. Viswanath Venkatesh, and Dr. Rene Riedl. Without their guidance and support, this dissertation would not have been possible. I would especially like to thank Dr. Fred Davis for his support of my acceptance to the program as a non-traditional doctoral student, and for his help and encouragement along the way. I would also like to thank the other members of the faculty and staff of the Department of Information Systems, who made this experience valuable and enjoyable. I also thank the many other doctoral students who provided support, feedback, and fun over the past few years. Finally, to my wife Lucretia, and my kids Taylor, Michael, and Jarrett, who sacrificed much while I pursued my dream. Far too often they had to adjust so that I could study. Without their continued love, support, and encouragement, this would not have been possible.

/pdf/development-and-implementation-of-it-enabled-business-1pmk9ovasg.pdf

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

This chapter provides an introduction to deep learners and deep learner descriptors for medical applications. A basic outline of the deep learning (DL) process and five methods for exploiting DL with the Convolutional Neural Network (CNN), is presented, as well as a summary of the chapters in this book.

An Introduction to Deep Learners and Deep Learner Descriptors for Medical Applications

Like many other service organizations, drop-in peer tutoring centers often struggle to determine the required number of qualified tutors necessary to meet learner expectations. Service work is largely a response to probabilistic calls for staff action and therefore difficult to forecast with precision. Moreover, forecasting models under long planning horizons often lack the complexity or specificity necessary to accurately predict flexible labor demand due to sparse availability of influential model inputs. This study builds upon the flexible demand literature by exploring the use of neural networks for labor demand forecasting for a drop-in peer tutoring center of a large university. Specifically, this study employs a neural network solution that includes a genetic algorithm to search for optimal solutions using evolutional processes. The proposed forecasting model outperforms traditional smoothing and extrapolation forecasting methods.

A Neural Network Solution for Forecasting Labor Demand of Drop-In Peer Tutoring Centers with Long Planning Horizons.

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 individualâ€™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.

Rick Brattin

Papers

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

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

An Introduction to Deep Learners and Deep Learner Descriptors for Medical Applications

A Neural Network Solution for Forecasting Labor Demand of Drop-In Peer Tutoring Centers with Long Planning Horizons.

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