This study investigates the effect the 2008 economic crisis had on the relationship between CEO compensation and firm performance measures for S&P 500 financial companies. The findings assist S&P 500 financial companies to better determine compensation levels for CEOs by accounting for performance as well as account for the most recent valley in the economic cycle. The study uses a database of CEO compensation data for S&P 500 financial firms from both before and after the crisis. The database also contains firm performance data for the respective firms and years. The relationship is explored using separate multiple regression models, then comparing the strength of relationship in 2007-2008 and 2011-2012. The results find a significant difference in Salary amounts from before and after the crisis. The p-stat and t-stat values the study uses in determining the significance of variables find the only significant variable tested to be the one representing the difference in Salary amounts from before and after the crisis. Compared to other studies on similar topics, Revenues are decidedly less important in the S&P 500 financial sector than they are for other scopes of study as a whole. The study also discovers an alarming disconnect between stock and investor returns and compensation amounts.

In data mining, classification is a task to build a model which classifies data into a given set of categories. Most classification algorithms assume the class distribution of data to be roughly balanced. In real-life applications such as direct marketing, fraud detection and churn prediction, class imbalance problem usually occurs. Class imbalance problem is referred to the issue that the number of examples belonging to a class is significantly greater than those of the others. When training a standard classifier with class imbalance data, the classifier is usually biased toward majority class. However, minority class is the class of interest and more significant than the majority class. In the literature, existing methods such as data-level, algorithmic-level and cost-sensitive learning have been proposed to address this problem. The experiments discussed in these studies were usually conducted on relatively small data sets or even on artificial data. The performance of the methods on modern real-life data sets, which are more complicated, is unclear. In this research, we study the background and some of the state-of-the-art approaches which handle class imbalance problem. We also propose two costsensitive methods to address class imbalance problem, namely Cost-Sensitive Deep Neural Network (CSDNN) and Cost-Sensitive Deep Neural Network Ensemble (CSDE). CSDNN is a deep neural network based on Stacked Denoising Autoencoders (SDAE). We propose CSDNN by incorporating cost information of majority and minority class into the cost function of SDAE to make it costsensitive. Another proposed method, CSDE, is an ensemble learning version of CSDNN which is proposed to improve the generalization performance on class imbalance problem. In the first step, a deep neural network based on SDAE is created for layer-wise feature extraction. Next, we perform Bagging’s resampling procedure with undersampling to split training data into a number of bootstrap samples. In the third step, we apply a layer-wise feature extraction method to extract new feature samples from each of the hidden layer(s) of the SDAE. Lastly, the ensemble learning is performed by using each of the new feature samples to train a CSDNN classifier with random cost vector. Experiments are conducted to compare the proposed methods with the existing methods. We examine their performance on real-life data sets in business domains. The results show that the proposed methods obtain promising results in handling class imbalance problem and also outperform all the other compared methods. There are three major contributions to this work. First, we proposed CSDNN method in which misclassification costs are considered in training process. Second, we incorporate random undersampling with layer-wise feature extraction to perform ensemble learning. Third, this is the first work that conducts experiments on class imbalance problem using large real-life data sets in different business domains ranging from direct marketing, churn prediction, credit scoring, fraud detection to fake review detection.

Effective utilization of donor resources for maternal health remains a challenge in Ghana. The purpose of this descriptive multiple case study was to identify strategies and processes that recipient partners use to improve the utilization of maternal health program funds. Harrod and Domar's aid-to-investment-to-growth model, Collier's game theory, and Martens' agency theory on aid effectiveness informed the conceptual framework of the study. The study included face-to-face semistructured interviews with 7 program and project managers from 7 UNFPA recipient institutions in Ghana. Data analysis involved assembling, rearrangement, categorizing, and interpreting the data. Member checking and methodological triangulation of interview data with evidence from administrative documents of the 7 recipient institutions occurred to assure the validity of this study's findings. Three themes emerged: clearly identifying and effectively implementing program and project budget support mechanisms, implementing robust aid effectiveness management processes, and utilizing effective project management practices. Findings indicated institutional capacity strengthening, developing and using control mechanisms, and mitigation of funds disbursement delays and activity implementation delays as derivative pathways for maximizing utilization of maternal health program funds. The findings provide potential lessons for similar organizations' improving funds utilization by project management practitioners to sustain or increase donors' interest and mitigate development programs' funding gaps. Implications for social change include the potential for maternal mortality reduction to improve the wellbeing and quality of life of rural, poor, and marginalized women and children in Ghana.

Increasing turnover rates are costly to businesses, causing problems with workloads and workflow. The annual resignation rate in the United States has approached 25%, which small business owners cannot afford. Guided by the Herzberg 2-factor theory, the purpose of this descriptive case study was to explore what strategies some small business owners have used to reduce voluntary employee turnover in the United States. Data saturation was achieved after conducting semistructured interviews and document reviews with 4 small business owners in southeast North Carolina who have been in business for at least 5 years and have not experienced any voluntary employee resignations within the past year. Data interpretations from the interview data were derived via an inductive analytic coding process; these interpretations were then triangulated with emergent themes derived from small business owners' policies, procedures, and personnel manuals. Participants noted the need for training, equitable employee compensation, a professional work environment, and open effective communication as the top contributing factors to reducing voluntary employee turnover. The small business owners indicated the use of professionalism contributes to a positive work environment and recognized education as a factor of voluntary employee turnover. Social implications include the potential to decrease voluntary employee turnover in small businesses, thus contributing to the retention of skilled employees, reducing unemployment, and decreasing revenue losses.

The corporate character value structure consists of ethical values applied in a business setting arranged in a two dimensional matrix presented here as the Corporate Character Ethical Value Matrix, or CC-EVM. The two matrix dimensions are: behavior-types defined as either (1)custodial or (2)proactive; and behavior targets (1)task, (2)consideration-specific, directed toward a specific relationship, or (3)consideration-general, directed at generalized relationships or the organization. The current research developed the matrix to define and classify the six values presented by The Character Counts Coalition’s (1993) as core “pillars” of character: trustworthiness, responsibility, respect, caring, fairness and citizenship. The theoretical background for this matrix was built from the organizational trust and organizational citizenship behavior (OCB) literatures, and the business ethics literature. The study tested the uniqueness of these six constructs using items developed from established measures that were combined as one instrument with items developed based on Character Counts Coalition statements. Factor analysis of student (n=324) responses explored the existence of theorized dimensions underlying the established trust and OCB measures. Item reduction eliminated items failing to discriminate between factors, and five factors emerged. The first factor contained items from McAlister's (1995) cognitive-based trust measure and Van Dyne, Graham, and Dienesch’s (1994) obedience measure. The second and third factors contained items from Van Dyne et al.’s advocacy and loyalty measures respectively. The fourth and fifth factors expressed concern for friends and country, and contained items developed from the Character Counts Coalition. Reliable (alpha >.80) scales from the factor items allowed further testing for inferences about the scales validity using personality and demographic measures. Findings show support for the behavior-targets dimension of the CC-EVM. The first factor corresponded to the task target. The advocacy and loyalty measures corresponded to the consideration-specific and consideration-general targets. The friends and country scales failed to exhibit predicted relationships. The five measures were regressed against measures provided by an insurance agency industry sample (n=112) of organizational commitment and shared ethical values. The strongest relationship emerged between consideration-general (loyalty) and organizational commitment. No support emerged for the behavior-types dimension. Implications for researchers and practitioners are discussed.

Since the widespread use of computers in business and industry, a lot of research has been done on the design of computer systems to support the decision making task. Decision support systems support decision makers in solving unstructured decision problems by providing tools to help understand and analyze decision problems to help make better decisions. Artificial intelligence is concerned with creating computer systems that perform tasks that would require intelligence if performed by humans. Much research has focused on using artificial intelligence to develop decision support systems to provide intelligent decision support. Knowledge discovery from databases, centers around data mining algorithms to discover novel and potentially useful information contained in the large volumes of data that is ubiquitous in contemporary business organizations. Data mining deals with large volumes of data and tries to develop multiple views that the decision maker can use to study this multi-dimensional data. On-line analytical processing (OLAP) provides a mechanism that supports multiple views of multi-dimensional data to facilitate efficient analysis. These two techniques together can provide a powerful mechanism for the analysis of large quantities of data to aid the task of making decisions. This research develops a model for the real time process control of a large manufacturing process using an integrated approach of data mining and on-line analytical processing. Data mining is used to develop models of the process based on the large volumes of the process data. The purpose is to provide prediction and explanatory capability based on the models of the data and to allow for efficient generation of multiple views of the data so as to support analysis on multiple levels. Artificial neural networks provide a mechanism for predicting the behavior of nonlinear systems, while decision trees provide a mechanism for the explanation of states of systems given a set of inputs and outputs. OLAP is used to generate multidimensional views of the data and support analysis based on models developed by data mining. The architecture and implementation of the model for real-time process control based on the integration of data mining and OLAP is presented in detail. The model is validated by comparing results obtained from the integrated system, OLAP-only and expert opinion. The system is validated using actual process data and the results of this verification are presented. A discussion of the results of the validation of the integrated system and some limitations of this research with discussion on possible future research directions is provided.

Although relational database systems have met many needs in traditional business applications, such technology is inadequate for non-traditional applications such as computer-aided design, computer-aided software engineering, and knowledge bases. Object-oriented database systems (OODB) enhance the data modeling power and performance of database management systems for these applications. Response time is an important issue facing OODB. However, standard measures of on-line transaction processing are irrelevant for OODB . Benchmarks compare alternative implementations of OODB system software, running a constant application workload. Few attempts have been made to characterize performance implications of OODB application design, given a fixed OODB and operating system platform. In this study, design features of the 007 Benchmark database application (Carey, DeWitt, and Naughton, 1993 ) were varied to explore the impact on response time to perform database operations Sensitivity to the degree of aggregation and to the degree of inheritance in the application were measured. Variability in response times also was measured, using a sequence of database operations to simulate a user transaction workload. Degree of aggregation was defined as the number of relationship objects processed during a database operation. Response time was linear with the degree of aggregation. The size of the database segment processed, compared to the size of available memory, affected the coefficients of the regression line. Degree of inheritance was defined as the Number of Children (Chidamber and Kemerer, 1994) in the application class definitions, and as the extent to which run-time polymorphism was implemented. In this study, increased inheritance caused a statistically significant increase in response time for the 007 Traversal 1 only, although this difference was not meaningful. In the simulated transaction workload of nine 007 operations, response times were highly variable. Response times per operation depended on the number of objects processed and the effect of preceding operations on memory contents. Operations that used disparate physical segments or had large working sets relative to the size of memory caused large increases in response time. Average response times and variability were reduced by removing these operations from the sequence (equivalent to scheduling these transactions at some time when the impact would be minimized).

Decreasing employee turnover among small businesses is a problem because employee turnover is costly and adversely affects business owners' ability to gain and maintain a competitive advantage. The purpose of this multiple case study was to explore strategies that small business owners use to decrease voluntary employee turnover to remain competitive. The target population was composed of small business owners located in the southeastern region of the United States who used strategies to decrease voluntary employee turnover. The conceptual framework for the study was Herzberg's 2-factor theory. Data were collected from semistructured interviews with 4 small business owners and review of company documents. Yin's 5 phases of analysis were used to analyze data and information. Five themes emerged from data analysis: caring and clean work environment, pay, rewards and recognition, supervision, and training and advancement. The implications of this study for positive social change include the potential to help small business owners and leaders implement strategies to decrease employee turnover and increase revenue to remain competitive. Stable small businesses can lead to social change by creating jobs to strengthen communities and local economies.

Despite using computerized merchandise control systems in retail, the rate of stockouts has remained stagnant. The inability to satisfy customer needs has caused a loss of 4% in potential revenue and resulted in dissatisfied customers. The purpose of this qualitative multiple case study was to explore cost-effective inventory control strategies used by discount retail managers. The conceptual framework that grounded the study was chaos theory, which helped identify why some business leaders rely on forecasting techniques or other cost-effective strategies as an attempt to prevent stockouts. The target population was comprised of discount retail managers located throughout northeast Jacksonville, Florida. Purposeful sampling led to selecting 6 retail managers who successfully demonstrated cost-effective inventory control strategies for mitigating stockouts. Data were collected through face-to-face semistructured interviews, company websites, and company documents. Analysis included using nodes to identify similar words and axial-coding to categorize the nodes into themes. Transcript evaluation, member checking, and methodological triangulation strengthened the credibility of the findings. Five themes emerged: (a) internal stockout reduction strategies, (b) external stockout reduction strategies, (c) replenishment system strategies, (d) inventory optimization strategies, and (e) best practices for inventory control. This study may contribute to positive social change by improving inventory management, which may reduce demand fluctuations in the supply chain and reduce logistics costs in the transportation of freight thereby leading to improved customer satisfaction.

The U.S. Federal Government had a turnover of more than 3.6 billion employees in 2018. The purpose of this secondary data analysis was to use data drawn from the Federal Employee Viewpoint Survey to determine if there were a statistically significant relationship between job satisfaction, employee engagement, and turnover intention among U.S. Federal Government employees. The population for this study consisted of 598,003 individuals surveyed in 2018. The multiple linear regression results revealed a statistically significant relationship between job satisfaction, employee engagement, and turnover intention, F(2, 563,432) = 33,273, p <.001, R² = .106. Herzberg’s motivationhygiene theory and Adams’s equity theory were used as frameworks for this study. The study can be extended to more specific branches of the U.S. Federal Government. The study could impact social change by allowing human resource managers to change strategies related to retention to decrease turnover and retain knowledge in the U.S. Federal Government. Retention efforts could be translated to other industries to create long-term employment and increase overall employee job satisfaction.