Defining failure in the nonprofit organization: The case of four human service agencies

Private nonprofit human service organizations provide a spectrum of services that aim to resolve societal problems. Their failure may leave needed and desired services unprovided or not provided sufficiently to meet public demand. However, the concept of organizational failure has not been examined for the nonprofit organization. This research addresses the deficiency in the literatures of organization failure and nonprofit organizations.^ An eight category typology, developed from a review of the current literature and findings from expert interviews, is initially presented to define nonprofit organization failure. A multiple case study design is used to test the typology in four nonprofit human service delivery agencies. The case analysis reduces the typology to five types salient to nonprofit organization failure: input failure, legitimacy failure, adaptive failure, management failure and leadership failure.^ The resulting five category typology is useful to both theory builders and nonprofit practitioners. For theory development, the interaction of the failure types extends the literature and lays a foundation for a theory of nonprofit organization failure that diffuses management and leadership across all of the failure types, highlights management and leadership failure as collective functions shared by paid staff and the volunteer board of directors, and emphasizes the importance of organization legitimacy.^ From a practical perspective, the typology provides a tool for diagnosing failure in the nonprofit organization. Using the management indicators developed for the typology, a checklist of the warning signals of potential failure, emphasizing the key types of management and leadership, offers nonprofit decision makers a priori examination of an organization's propensity for failure. ^

The impact of information technology on organizations: A study of enterprise resource planning system influences on job design and organizational culture

The primary purpose of this research is to study the linkage between perceived job design characteristics and information system environment characteristics before and after the replacement of a legacy information system with a new type of information system (referred to as an Enterprise Resource Planning or ERP system). A public state University implementing an academic version of an ERP system was selected for the study. Three survey instruments were used to examine the perception of the information system, the job characteristics, and the organizational culture before and after the system implementation. The research participants included two large departments resulting in a sample of 130 workers. Research questions were analyzed using multivariate procedures including factor analysis, path analysis, step-wise regression, and matched pair analysis. ^ Results indicated that the ERP system has introduced new elements into the working environment that has changed the perception of how the job design characteristics and organization culture dimensions are viewed by the workers. The understanding of how the perceived system characteristics align with an individual's perceived job design characteristics is supported by each of the system characteristics significantly correlated in the proposed direction. The stronger support of this relationship becomes visible in the causal flow of the effects seen in the path diagram and in the step-wise regression. The perceived job design characteristics aligning with dimensions of organizational culture are not as strong as the literature suggests. Although there are significant correlations between the job and culture variables, only one relationship can be seen in the causal flow. ^ This research has demonstrated that system characteristics of ERP do contribute to the perception of change in an organization and do support organizational culture behaviors and job characteristics. ^

Ensemble fuzzy belief intrusion detection design

With the rapid growth of the Internet, computer attacks are increasing at a fast pace and can easily cause millions of dollar in damage to an organization. Detecting these attacks is an important issue of computer security. There are many types of attacks and they fall into four main categories, Denial of Service (DoS) attacks, Probe, User to Root (U2R) attacks, and Remote to Local (R2L) attacks. Within these categories, DoS and Probe attacks continuously show up with greater frequency in a short period of time when they attack systems. They are different from the normal traffic data and can be easily separated from normal activities. On the contrary, U2R and R2L attacks are embedded in the data portions of the packets and normally involve only a single connection. It becomes difficult to achieve satisfactory detection accuracy for detecting these two attacks. Therefore, we focus on studying the ambiguity problem between normal activities and U2R/R2L attacks. The goal is to build a detection system that can accurately and quickly detect these two attacks. In this dissertation, we design a two-phase intrusion detection approach. In the first phase, a correlation-based feature selection algorithm is proposed to advance the speed of detection. Features with poor prediction ability for the signatures of attacks and features inter-correlated with one or more other features are considered redundant. Such features are removed and only indispensable information about the original feature space remains. In the second phase, we develop an ensemble intrusion detection system to achieve accurate detection performance. The proposed method includes multiple feature selecting intrusion detectors and a data mining intrusion detector. The former ones consist of a set of detectors, and each of them uses a fuzzy clustering technique and belief theory to solve the ambiguity problem. The latter one applies data mining technique to automatically extract computer users’ normal behavior from training network traffic data. The final decision is a combination of the outputs of feature selecting and data mining detectors. The experimental results indicate that our ensemble approach not only significantly reduces the detection time but also effectively detect U2R and R2L attacks that contain degrees of ambiguous information.

The role of the health care organization in supporting nurses in the delivery of culturally competent care

This study was designed to explore ways in which health care organizations (HCOs) can support nurses in their delivery of culturally competent care. While cultural competence has become a priority for the federal government as well as the major health professional organizations, its integration into care delivery has not yet been realized. Health professionals cite a lack of educational preparation, time, and organizational resources as barriers. Most experts in the field agree that the cultural and linguistic needs of ethnic minorities pose challenges that individual care providers are unable to manage without the support of the health care organizations within which they practice. While several studies have identified implications for HCOs, there is a paucity of research on their role in this aspect of care delivery. Using a qualitative design with a case study approach, data collection included face-to-face interviews with 23 registered nurses, document analysis, and reports of critical incidents. The site chosen was a large health care system in South Florida that serves a culturally diverse population. Major findings from the study included language barriers, lack of training, difficulty with cultural differences, lack of organizational support, and reliance on culturally diverse staff members. Most nurses thought the ethnic mix was adequate, but rated other supports such as language services, training, and patient education materials as inadequate. Some of the recommendations for organizational performance were to provide the expectations and support for culturally competent care. Implications and recommendations for practice include nurses using trained interpreters instead of relying on coworkers or trying to "wing it", pursuing training, and advocating for organizational supports for culturally competent care. Implications and recommendations for theory included a blended model that combines both models in the conceptual framework. Recommendations for future research were for studies on the impact of language bathers on care delivery, develop and test a quantitative instrument, and to incorporate Gilbert's model into nursing research.

A quantitative approach to the organizational design problem

The span of control is the most discussed single concept in classical and modern management theory. In specifying conditions for organizational effectiveness, the span of control has generally been regarded as a critical factor. Existing research work has focused mainly on qualitative methods to analyze this concept, for example heuristic rules based on experiences and/or intuition. This research takes a quantitative approach to this problem and formulates it as a binary integer model, which is used as a tool to study the organizational design issue. This model considers a range of requirements affecting management and supervision of a given set of jobs in a company. These decision variables include allocation of jobs to workers, considering complexity and compatibility of each job with respect to workers, and the requirement of management for planning, execution, training, and control activities in a hierarchical organization. The objective of the model is minimal operations cost, which is the sum of supervision costs at each level of the hierarchy, and the costs of workers assigned to jobs. The model is intended for application in the make-to-order industries as a design tool. It could also be applied to make-to-stock companies as an evaluation tool, to assess the optimality of their current organizational structure. Extensive experiments were conducted to validate the model, to study its behavior, and to evaluate the impact of changing parameters with practical problems. This research proposes a meta-heuristic approach to solving large-size problems, based on the concept of greedy algorithms and the Meta-RaPS algorithm. The proposed heuristic was evaluated with two measures of performance: solution quality and computational speed. The quality is assessed by comparing the obtained objective function value to the one achieved by the optimal solution. The computational efficiency is assessed by comparing the computer time used by the proposed heuristic to the time taken by a commercial software system. Test results show the proposed heuristic procedure generates good solutions in a time-efficient manner.

Energy-efficient self -organization of wireless acoustic sensor networks for ground target tracking

With the developments in computing and communication technologies, wireless sensor networks have become popular in wide range of application areas such as health, military, environment and habitant monitoring. Moreover, wireless acoustic sensor networks have been widely used for target tracking applications due to their passive nature, reliability and low cost. Traditionally, acoustic sensor arrays built in linear, circular or other regular shapes are used for tracking acoustic sources. The maintaining of relative geometry of the acoustic sensors in the array is vital for accurate target tracking, which greatly reduces the flexibility of the sensor network. To overcome this limitation, we propose using only a single acoustic sensor at each sensor node. This design greatly improves the flexibility of the sensor network and makes it possible to deploy the sensor network in remote or hostile regions through air-drop or other stealth approaches. Acoustic arrays are capable of performing the target localization or generating the bearing estimations on their own. However, with only a single acoustic sensor, the sensor nodes will not be able to generate such measurements. Thus, self-organization of sensor nodes into virtual arrays to perform the target localization is essential. We developed an energy-efficient and distributed self-organization algorithm for target tracking using wireless acoustic sensor networks. The major error sources of the localization process were studied, and an energy-aware node selection criterion was developed to minimize the target localization errors. Using this node selection criterion, the self-organization algorithm selects a near-optimal localization sensor group to minimize the target tracking errors. In addition, a message passing protocol was developed to implement the self-organization algorithm in a distributed manner. In order to achieve extended sensor network lifetime, energy conservation was incorporated into the self-organization algorithm by incorporating a sleep-wakeup management mechanism with a novel cross layer adaptive wakeup probability adjustment scheme. The simulation results confirm that the developed self-organization algorithm provides satisfactory target tracking performance. Moreover, the energy saving analysis confirms the effectiveness of the cross layer power management scheme in achieving extended sensor network lifetime without degrading the target tracking performance.

Ensemble Fuzzy Belief Intrusion Detection Design

With the rapid growth of the Internet, computer attacks are increasing at a fast pace and can easily cause millions of dollar in damage to an organization. Detecting these attacks is an important issue of computer security. There are many types of attacks and they fall into four main categories, Denial of Service (DoS) attacks, Probe, User to Root (U2R) attacks, and Remote to Local (R2L) attacks. Within these categories, DoS and Probe attacks continuously show up with greater frequency in a short period of time when they attack systems. They are different from the normal traffic data and can be easily separated from normal activities. On the contrary, U2R and R2L attacks are embedded in the data portions of the packets and normally involve only a single connection. It becomes difficult to achieve satisfactory detection accuracy for detecting these two attacks. Therefore, we focus on studying the ambiguity problem between normal activities and U2R/R2L attacks. The goal is to build a detection system that can accurately and quickly detect these two attacks. In this dissertation, we design a two-phase intrusion detection approach. In the first phase, a correlation-based feature selection algorithm is proposed to advance the speed of detection. Features with poor prediction ability for the signatures of attacks and features inter-correlated with one or more other features are considered redundant. Such features are removed and only indispensable information about the original feature space remains. In the second phase, we develop an ensemble intrusion detection system to achieve accurate detection performance. The proposed method includes multiple feature selecting intrusion detectors and a data mining intrusion detector. The former ones consist of a set of detectors, and each of them uses a fuzzy clustering technique and belief theory to solve the ambiguity problem. The latter one applies data mining technique to automatically extract computer users’ normal behavior from training network traffic data. The final decision is a combination of the outputs of feature selecting and data mining detectors. The experimental results indicate that our ensemble approach not only significantly reduces the detection time but also effectively detect U2R and R2L attacks that contain degrees of ambiguous information.

Energy-Efficient Self-Organization of Wireless Acoustic Sensor Networks for Ground Target Tracking

With the developments in computing and communication technologies, wireless sensor networks have become popular in wide range of application areas such as health, military, environment and habitant monitoring. Moreover, wireless acoustic sensor networks have been widely used for target tracking applications due to their passive nature, reliability and low cost. Traditionally, acoustic sensor arrays built in linear, circular or other regular shapes are used for tracking acoustic sources. The maintaining of relative geometry of the acoustic sensors in the array is vital for accurate target tracking, which greatly reduces the flexibility of the sensor network. To overcome this limitation, we propose using only a single acoustic sensor at each sensor node. This design greatly improves the flexibility of the sensor network and makes it possible to deploy the sensor network in remote or hostile regions through air-drop or other stealth approaches. Acoustic arrays are capable of performing the target localization or generating the bearing estimations on their own. However, with only a single acoustic sensor, the sensor nodes will not be able to generate such measurements. Thus, self-organization of sensor nodes into virtual arrays to perform the target localization is essential. We developed an energy-efficient and distributed self-organization algorithm for target tracking using wireless acoustic sensor networks. The major error sources of the localization process were studied, and an energy-aware node selection criterion was developed to minimize the target localization errors. Using this node selection criterion, the self-organization algorithm selects a near-optimal localization sensor group to minimize the target tracking errors. In addition, a message passing protocol was developed to implement the self-organization algorithm in a distributed manner. In order to achieve extended sensor network lifetime, energy conservation was incorporated into the self-organization algorithm by incorporating a sleep-wakeup management mechanism with a novel cross layer adaptive wakeup probability adjustment scheme. The simulation results confirm that the developed self-organization algorithm provides satisfactory target tracking performance. Moreover, the energy saving analysis confirms the effectiveness of the cross layer power management scheme in achieving extended sensor network lifetime without degrading the target tracking performance.

Best Practices: Lessons Learned by a South Florida Non-Profit Community Based Organization while Designing and Implementing a Career Exploration Evidence Informed Framework in Urban Communities

All A’s was designed to support of the agency’s family strengthening initiatives in South Florida. All A’s uses evidence informed strategies poised to be an inclusive curriculum that teaches self-determination and adaptive behavior skills. The framework incorporates problem based learning and adult learning theory and follows the Universal Design for Learning. Since 2012, the agency has served over 8500 youth and 4,000 adults using the framework. The framework addresses educational underachievement and career readiness in at risk populations. It is used to enhance participants AWARENESS of setting SMART goals to achieve future goals and career aspirations. Participants are provided with ACCESS to resources and opportunities for creating and implementing an ACTION plan as they pursue and ACHIEVE their goals. All A’s promotes protective factors and expose youth to career pathways in Science, Technology, Engineering and Math (STEM) related fields. Youth participate in college tours, job site visits, job shadowing, high school visits, online college and career preparation assistance, service learning projects, STEM projects, and the Winning Futures© mentoring program. Adults are assisted with résumé development; learn job search strategies, interview techniques, job shadowing experiences, computer and financial literacy programs. Adults and youth are also given the opportunity to complete industry-recognized certifications in high demand industries (food service, general labor, and construction), and test preparation for the General Educational Development Test.