Understanding Goal-Directed Emotions in Agile Software Development Teams

Agile software development is people oriented and emphasizes on teamwork. The emotional experiences of the team members may creep up and significantly influence their behaviors. Our study examines the role of emotion in agile software development using a multi-site case study of two agile project teams. We develop a framework that explains how the project and individual goals trigger emotions and how the emotions influence behaviors and the project outcome. Our research highlights that agile project goals are interconnected with the individual goal and the misalignment between them bring about negative emotion when team members appraise the goal achievement situations

Damage detection and identification of parameter matrices using residual force vector

Beginning with incomplete mode shape measurement data, this study presents analytical equations to predict the actual stiffness and mass matrices. The measured modal data, including the measurement, manufacturing and modeling errors, should be updated for subsequent analysis. In this study, the incomplete mode shape data are expanded to a full set of degrees-of-freedom (DOFs) based on the generalized inverse method and the concept of residual force vector. The corrected parameter matrices are straightforwardly derived using the estimated mode shape data and the pseudo inverse method. The validity of the proposed method is evaluated based on the number of measured modes in an application, and its limitations are investigated

Formalizing Theory Development in IS Design Science Research: Learning from Qualitative Research

The parallels between design science research and various types of qualitative research as well as the synergies between the two research paradigms have been pointed out in many recent design science research in IS (DSRIS) papers. Commonly, for example, a qualitative research method, action research, has been used or proposed for validation of a DSRIS artifact. Building on insights into the similarities of the two methodologies, we have surveyed the qualitative research literature in search of techniques from that area that could be applicable to theory construction and refinement in DSRIS. We have found four techniques widely used in theory construction in qualitative research that are immediately applicable to DSRIS, thus leveraging the work in an older discipline for the benefit of DSRIS. In addition we explicate the similarity between qualitative research and DSRIS in a more detailed manner than has been done previously

Isolated fallopian tubal torsion with underdiagnosed entity: a case report

Isolated fallopian tubal torsion refers to twisting only the tube without involving the ipsilateral ovary. This is a rare type of adenexal torsion and is a gynecological emergency. Since this disease mostly occurs in women of childbearing age, accurate diagnosis and appropriate treatment are more important issues. It is known that ovarian cyst torsion occurs well in sizes greater than 5 cm, but the epidemiology of isolated fallopian tubal torsion is unknown. Even clinically, in patients complaining of abdominal discomfort, a small andexal cyst is difficult to consider as a cause of pain. We presented an isolated fallopian tubal torsion case that can be twisted despite its small size

Why Do Family Members Reject AI in Health Care? Competing Effects of Emotions

Artificial intelligence (AI) enables continuous monitoring of patients’ health, thus improving the quality of their health care. However, prior studies suggest that individuals resist such innovative technology. In contrast to prior studies that investigate individuals’ decisions for themselves, we focus on family members’ rejection of AI monitoring, as family members play a significant role in health care decisions. Our research investigates competing effects of emotions toward the rejection of AI monitoring for health care. Based on two scenario-based experiments, our study reveals that emotions play a decisive role in family members’ decision making on behalf of their parents. We find that anxiety about health care monitoring and anxiety about health outcomes reduce the rejection of AI monitoring, whereas surveillance anxiety and delegation anxiety increase rejection. We also find that for individual-level risks, perceived controllability moderates the relationship between surveillance anxiety and the rejection of AI monitoring. We contribute to the theory of Information System rejection by identifying the competing roles of emotions in AI monitoring decision making. We extend the literature on decision making for others by suggesting the influential role of anxiety. We also contribute to healthcare research in Information System by identifying the important role of controllability, a design factor, in AI monitoring rejection

Why do Family Members Reject AI in Health Care? Competing Effects of Emotions

Artificial intelligence (AI) enables continuous monitoring of patients’ health, thus improving the quality of their health care. However, prior studies suggest that individuals resist such innovative technology. In contrast to prior studies that investigate individuals’ decisions for themselves, we focus on family members’ rejection of AI monitoring, as family members play a significant role in health care decisions. Our research investigates competing effects of emotions toward the rejection of AI monitoring for health care. Based on two scenario-based experiments, our study reveals that emotions play a decisive role in family members’ decision making on behalf of their parents. We find that anxiety about health care monitoring and anxiety about health outcomes reduce the rejection of AI monitoring, whereas surveillance anxiety and delegation anxiety increase rejection. We also find that for individual-level risks, perceived controllability moderates the relationship between surveillance anxiety and the rejection of AI monitoring. We contribute to the theory of Information System rejection by identifying the competing roles of emotions in AI monitoring decision making. We extend the literature on decision making for others by suggesting the influential role of anxiety. We also contribute to healthcare research in Information System by identifying the important role of controllability, a design factor, in AI monitoring rejection

Dwell Time Optimization of Alert-Confirm Detection for Active Phased Array Radars

Alert–confirm detection is a highly efficient method to improve phased array radar search performance. It comprises sequential detection in two steps: alert detection, in which a target is detected at a low detection threshold, and confirm detection, which is triggered by alert detection with a longer dwell time to minimize false alarms. This paper provides a design method for applying the alert–confirm detection to multifunctional radars. We find optimum dwell times and false alarm probabilities for each alert detection and confirm detection under the dual constraints of total false alarm probability and maximum allowable dwell time per position. These optimum values are expressed as a function of the mean new target appearance rate. The proposed alert–confirm detection increases the maximum detection range even with a shorter frame time than that of uniform scanning