PurposeAs the workforce becomes increasingly diversified, it becomes increasingly important for managers to understand the conflict resolution attitudes brought to information systems (IS) by both men and women. This research was designed to investigate assumptions that may exist regarding the relationship between gender and conflict resolution. Specifically, the intent of this study was to compare the conflict resolution strategies of males and females majoring in IS in order to determine if gender‐based differences exist.Design/methodology/approachThe Thomas‐Kilmann Conflict Mode Instrument was utilized to assess the conflict resolution styles of 163 traditional‐age (18‐22) students enrolled in undergraduate IS courses at a large Midwestern university. Both ANOVA and t‐test analyses were utilized to investigate the relationship between gender and conflict resolution style.FindingsResults of this study indicate that, when compared with their male counterparts, women are more likely to utilize a collaborative conflict resolution style and men are more likely to avoid conflict. As collaboration is generally considered more productive and avoidance more disruptive in the conflict resolution process, the study suggests that women may possess more effective conflict resolution attributes than their male counterparts.Originality/valueThe results of this paper lend support to the theory that an individual's gender may be related to the development of conflict resolution styles. These findings also support the premise that female students in IS are highly adapted with regard to their ability to work collaboratively (and thereby successfully) in situations where conflict is likely to occur.
The study examines the correlates of burnout in systems (IS) professionals. While there has been little previous research in the area of burnout among IS professionals, anecdotal evidence shows that burnout causes a negative impact on the peiformance of IS employees. These negative impacts can take the form of cynicism, dissatisfaction, and turnover (McGee, 1996). In this study we empirically examine the correlations of burnout with several work attributes that are considered to be either antecedents or consequences of burnout. Two role stressors are examined in this study -role ambiguity and role conflict. These variables are theorized to be antecedents of burnout. In addition, two dimensions of organizational commitment-affective and continuance commitment-are examined as possible consequences of burnout. The emotional exhaustion subscale of the Maslach Burnout Inventory is used to measure burnout in 312 IS professionals. Both role stressors were found to co "elate positively with burnout. In addition, affective commitment was found to be negatively correlated and continuance commitment positively correlated with burnout.Job stress has been noted to be a key factor that can affect the performance and tenure of IS professionals. While little previous research has examined burnout in order to identify its antecedents and consequences among IS professionals, anecdotal evidence does suggest that burnout can have a significant impact on both the performance and commitment of IS employees.For example, McGee (1996) notes that burnout is the root cause of turnover among help-desk employees. In addition, she noted that burned-out help-desk analysts tended to take out their frustrations on the users they were trying to assist and that "burnout manifests itself in shortness with the customer; talking down to them" (p. 116).Other practical consequences of stress and burnout have also been identified in the psychology literature. Kahill (1988) grouped these consequences into five categories: physical, emotional, interpersonal, attitudinal, and behavioral. These
In 1979 Haralick famously introduced a method for analyzing the texture of an image: a set of statistics extracted from the co-occurrence matrix. In this paper we investigate novel sets of texture descriptors extracted from the co-occurrence matrix; in addition, we compare and combine different strategies for extending these descriptors. The following approaches are compared: the standard approach proposed by Haralick, two methods that consider the co-occurrence matrix as a three-dimensional shape, a gray-level run-length set of features and the direct use of the co-occurrence matrix projected onto a lower dimensional subspace by principal component analysis. Texture descriptors are extracted from the co-occurrence matrix evaluated at multiple scales. Moreover, the descriptors are extracted not only from the entire co-occurrence matrix but also from subwindows. The resulting texture descriptors are used to train a support vector machine and ensembles. Results show that our novel extraction methods improve the performance of standard methods. We validate our approach across six medical datasets representing different image classification problems using the Wilcoxon signed rank test. The source code used for the approaches tested in this paper will be available at: http://www.dei.unipd.it/wdyn/?IDsezione=3314&IDgruppo_pass=124&preview=.
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.
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