Now showing items 1-20 of 83


      Watson, MariEtta Joleen (Indiana State University, 2022-05)
      Optional Practical Training (OPT) is a highly valued and highly underutilized program designed to offer international students an opportunity to work in the U.S. and train in their field of study. This qualitative study collected and analyzed the narratives of three alumni of a Midwest university who completed OPT in the manufacturing engineering field. Four themes were identified in the narratives. These themes were inextricable to the premise that OPT is a deeply appreciated opportunity for F-1 students. The first theme is viewing the OPT experience as a system which includes the university, USCIS, and the employer and moreover a need to improve this system. Secondly, subjects demonstrated an acute reluctance to disclose information, an important point to keep in mind when conducting future research. The third theme is the desirability of sustained professional development. This theme was expressed through descriptions of actions and as advice for future OPT workers. Finally, subjects identified the uncertainty of new experiences as a bigger challenge than the reality of the experience. Efforts to remove barriers for these workers should include the time leading up to the action and address uncertainty.

      Short, C. Grant (Indiana State University, 2019-12)
      The respective Bodies of Knowledge (BoKs) as described by the American Society for Quality (ASQ) for Certified Quality Engineers (ASQ, 2015a) and Certified Six Sigma Black Belts (ASQ, 2015b) are quite similar, yet anecdotally, six sigma black belts are recognized and consequently rewarded more highly than are quality engineers. While Quality Engineering work is typically regarded as preventive in nature, work performed by six sigma black belts is in the realm of improvement, hence is reactive in nature. Consequently, a dichotomy exists in that preventive actions, which are less costly by their nature, are not rewarded as well as costlier reactive actions. This results in loss to the owning organization. The intent of this research is to determine the validity of the anecdotal evidence, and subsequently determine the root cause therefor. The research method was to perform a survey of managers knowledgeable in the duties of both quality engineers and six sigma black belts combined with a Delphi Study of the ASQ certification board, which develops the respective bodies of knowledge, and a comparison in salaries of the two positions, based on the ASQ salary survey for several years. The results reflect the validity of the anecdotal evidence and indicate the need for further research.

      Seifers, Harold Leon III (Indiana State University, 2019-12)
      The purpose of this study was to determine if there was a correlation between the number of reported incidences of rape and stalking on college campuses and campuses that utilize CampusClarity by EVERFI as an educational tool to “reduce risky student behavior and prevent sexual assault on your campus.” The study investigated the relationship between the dependent variables, incidences of rape and stalking, versus the independent variables: partnership, men’s population, women’s population, on-campus drug crime, on-campus alcohol crime, on-campus domestic violence, and on-campus dating violence. The 11,181 campuses that report crime data required by the Clery Act were included as data with partnership information on 186 reported partners. A multiple linear regression analyses was used to determine a correlation. The results of the study show a positive statistically significant correlation between rape and stalking with partnership. This does not necessarily mean that partnership increases the occurrences of sexual assault. It is possible that the campuses that use these tools are already different from campuses that do not utilize their tools. A lack of information on implementation could also lead to varying results, as early and repetitive implementation could lead to less assaults occurring. It is also possible that the universities that utilize these tools are producing better educated students regarding sexual assault, which means they are more likely to report incidences than students at campuses that do not provide this type of training to its students, staff, and faculty.

      Schoff, Ronald D. (Indiana State University, 2020-08)
      Worker Safety is an area of high focus. Costs and impacts associated with incidents of workplace injury or fatality can have powerful effects on the organization. Workplace leadership style studies have shown statistically significant relationships between leadership style and rates of OSHA incidence and severity. One such example is transformational leadership. Studies have been completed in various industries, including high hazard industries that confirm this positive relationship. Organized labor offers many benefits of value to the employment sector. Such benefits as higher wages and better workplace safety practices contribute to society in economic and health related ways, among others. Transformational leadership and subordinates safety have been studied in non-union settings. Prior to this study, no study had been conducted to explore if a relationship existed between the leaders’ management style of transformational leadership and incidents of safety in a workplace setting that utilized a unionized workforce. This study addressed that literature gap. Specifically, this study examined if a relationship existed between transformational leadership style and OSHA incidence and severity in a unionized high hazard public private partnership utility. The study consisted of an analysis of transformational leadership ratings of front line, non-union supervisors as rated by their union-member subordinates and OSHA incident and lost time or severity rates. The results of the study indicated that, contrary to the results of the previous non-union based studies, this study found no statistically significant relationship between transformational leadership management style and OSHA incidence and severity rates.

      Phipps, Gregory Edward (Indiana State University, 2020-12)
      This study analyzed four determinant factors attributed to the acceptance of Bonded Cellular (BC) technology and applies the tenants of Diffusion of Innovation and Technology Acceptance Models. BC “bonds” available cellular channels and transmits a multiplexed signal to a broadcaster. This study will advance the understanding of factors that may impact the acceptance of BC at the management level. Research Questions 1. Can behavioral intention (BI) to adopt BC tools be predicted by using an independent variable representing Perceived Ease-Of-Use (PEOU)? 2. Can behavioral intention (BI) to adopt BC tools be predicted by using an independent variable representing Perceived Usefulness (PU)? 3. Can behavioral intention (BI) to adopt BC tools be predicted by using an independent variable representing the Relative Advantage (RA)? 4. Can behavioral intention (BI) to adopt BC tools be predicted by using an independent variable representing Compatibility with existing operations (COMP)? Null Hypotheses 1. H01:B1=0. There is no correlation between the (PEOU) intention to adopt BC. 2. H02:B2=0. There is no correlation between the (PU) and intention to adopt BC. 3. H03:B3=0. There is no correlation between (RA) and intention to adopt BC. 4. H04:B4=0. There is no correlation between (COMP) and intention to adopt BC. Adoption of BC technology is the dependent variable, behavioral intent (BI). An Internal Review Board approved Qualtrics questionnaire was disseminated to a targeted population comprised of TV technology and media production managers. The 32 carefully constructed Likert-Scaled questions explore a manager’s current familiarity, current usage, anticipated plans and time-frames regarding the adoption of BC technology. The survey results were tabulated using IBM SPSS statistical Linear Regression analysis models that correlates aspects of DOI and TAM to examine the independent variables (IV) to arrive at a determination of the factors contributing to the behavioral intent (BI) to adopt BC technology.

      McCauley, Kathleen H. (Indiana State University, 2019-05)
      The chaotic and complex nature of the construction industry and construction projects hinders maximization of productivity. Certain aspects of lean manufacturing are adaptable to construction with the goal of improving work flow reliability; for example, measuring work flow reliability by Percent Planned Complete (PPC). As more construction projects are being built with lean methodologies, it is important to bring this knowledge into the undergraduate Construction Management (CM) programs so that new graduates have relevant knowledge of emerging trends and can serve as change agents in industry. The purpose of this study was to construct a mathematical estimation of project dynamics through a future-oriented regression model for predicting PPC at various times throughout a project. A hands-on learning activity utilizing small diameter PVC piping materials was developed to collect data for the PPC model. The hands-on activity was executed at various universities using a homogeneous sample of undergraduates in CM courses. A secondary purpose was to assess whether or not participating in the hands-on activity improved learning as compared to a control group which only experienced a lecture. The results indicated various internal and external events experienced by the students were useful in predicting PPC. There is consistent negative direction in the coefficients for the predictors TI, number of Internal events, and TE, number of External events, indicating that for every unit of increase for either TI or TE, there will be a decrease in the future PPC. Furthermore, the hands-on activity did not improve learning in CM students as no statistically significant differences were observed between the test and control groups. A number of approaches are considered for future research to build on the PPC model by utilizing different materials and components, different internal and external events, weighting these events, and collecting data from different industry stakeholders.

      Houseworth, Matthew A. (Indiana State University, 2020-05)
      This research provides the automotive collision industry empirical evidence of the effects of Lean-for-Collision Training and Development Initiatives facilitated by a targeted sample of three automotive collision repair centers. Through formal interview and review of artifacts, the findings showcased in this study are in terms of automotive collision industry metrics; a balance in cost, quality, and service delivery, specifically, vehicle cycle-time, vehicle touch-time, employee turnover, and the Return-on-Investment (ROI) of their Lean training. In addition, this research provides automotive collision centers with critical knowledge and understanding of how to successfully navigate and progress through the Framework for Six Sigma Implementation in SMEs to achieve and develop a Lean culture in order to ultimately sustain the results of Lean Six Sigma training implementation.

      Lee, Gary (Indiana State University, 2021-05)
      Satashi Nakamoto’s introduction of blockchain in 2008 initially directed the technology for the use of Bitcoin and other cryptocurrencies (Nakamoto, 2008). In recent years the technology has been identified for other use cases. Businesses are currently developing this technology to reduce or eliminate transactional costs. Along with this anticipated use, businesses are using this technology to include traceability across the supply chain. This research looks at implementing blockchain technology in supply chain traceability. Clohessy (2019) identified critical success factors for implementing blockchain, but what is not existing in the literature are the relative importance of each factor for implementation of blockchain in the supply chain. The problem for this study is that we do not know which factors have the greatest influence on implementation of blockchain in the supply chain. Blockchain is in the incipient stages of implementation and there are no developed guidelines for practitioners to follow for implementing blockchain technology in the supply chain. The purpose is to provide practitioners with a foundational model as a guideline for implementing blockchain in the supply chain for product traceability. To do this, the researcher used the critical success factors identified by Clohessy in a survey instrument administered to Association of Supply Chain Management (ASCM) members. The survey had 88 respondents but only 58 that had useable data provided about the critical success factors. There were 9 respondents who had implemented blockchain. Using the 9 respondents who had implemented blockchain, a regression model was created to correlate the critical success factors to successful implementation. Other findings from the 58 respondents were that there is a significant difference on the critical success factors between small and large organizations for implementing blockchain in the supply chain, there is no significant difference on the critical success factors between low and high revenues for implementing blockchain in the supply chain, and there is a significant difference on the critical success factors between manufacturing and service industry for implementing blockchain in the supply chain. This was a quantitative non-probabilistic study based on a convenience sample of ASCM members. After the data was collected, a stepwise regression was applied to the data, so that implementation factors are considered, to create the model. Three factors were found to create a regression model for implementing blockchain in the supply chain.

      Poe, Laura F. (Indiana State University, 2019-05)
      Credit card fraud has continued to grow despite efforts to protect financial data from data breaches of financial institutions. Data breaches of financial transactional records over the past decade have impacted millions of U.S. consumers, resulting in decreased consumer confidence in security. Banking institutions losing money due to fraud are forced to raise interest rates and increase fees to their cardholders. The costs of fraud are passed to the banking institution’s customers to offset the losses. The requisite to detect and eliminate fraud before it occurs is mutually beneficial to both the banking institution and cardholders. Credit card companies continue to focus on methods for identifying fraudulent transactions as they occur and on validating account owners. Financial institutions utilize various models to alert consumers of potential fraud on a real-time basis. Current authorization models that validate the identity of the account holders during the transaction are limited or nonexistent. Many consumers are not required to provide any form of identification or signature proving identity for minimal purchase amount. For purchases requiring validation, consumers are able to validate a transaction with a simple, unverified signature mark at a merchant terminal. The introduction of the chip card added the additional element of security but can be combined with additional user authentication methods. To provide a more secure financial transaction, identity verification as a user authentication method can be realized through biometrics, most commonly, a fingerprint and can be achieved through the use of merchant touch screen credit card terminals or mobile purchasing applications. Using a physical credit card embedded with a fingerprint positions the user authentication process at the point of sale, thus providing real-time validation of the user as the credit card account owner utilizing the biometric fingerprint as identity proof and signature. This research seeks to evaluate the biometric-enabled physical credit card in an effort to increase the level of credit card transaction security and reduce the occurrences of fraud.

      Morgan, Kay Rand (Indiana State University, 2019-05)
      Today, automotive industries and their consumers are demanding more information on electric vehicles in response to the ongoing transportation transition from conventional to electric vehicles (EVs), the problems of greenhouse gas emissions from conventional vehicles (CVs) and fossil fuel usage, and interests in green energy production and the overall environmental health of this world. This study identified four variables - fuel economy (FE), annual fuel cost (AFC), maintenance frequency (f), and maintenance intensity (I) - and used comparative statistical quality assessment and failure mode and effect analysis (FMEA), a reliability management tool, for data analysis comparing conventional and electric vehicles. The research examined samples of 1,028 CVs and 282 EVs from 2016 to 2018 models for FE and AFC assessments, and 23 CVs with 65 maintenance events and 119 EVs with 348 maintenance events from 2000 to 2016 for maintenance frequency and intensity assessments. The study used Minitab 18 with two-sample ttests at the 95% confidence level for hypothesis testing. The results found that the means for FE of CVs were not significantly different between 2016 and 2017, or between 2017 and 2018 models. For EVs, the means for FE were significantly different between 2016 and 2017 i.e. 2017 FE was better than 2016, but the means for FE of 2017 and 2018 models were not significantly different. In addition, the mean FE of EVs was overall 74.19% higher than that of CVs. Moreover, the mean AFC of EVs was significantly i.e. 47.68% less than that of CVs, revealing lower annual fuel expenses for EVs. The mean frequency and intensity of maintenance of EVs were found to be approximately the same as those of CVs. The study also found that the total number of failure processes was lowest in battery electric vehicles (BEVs) and highest in hybrid electric vehicles (HEVs). Plug-in electric vehicles (PHEVs) verified the highest risk and CVs the lowest risk in vehicle systems based on risk priority number (RPN), which was calculated from the product of severity (SEV), occurrence (OCC), and detection (DET). The maintenance cost of EVs was found to be 1.2359% of manufacturer suggested retail price (MSRP) per year, which was 4.7117% less than that of CVs for 10 years. The study results confirmed that EVs are generally better in FE with less expensive annual fuel cost and maintenance cost relative to MSRP than CVs. The results also indicate that the FE of EVs improved from 2016 to 2017 but remained relatively constant from 2017 to 2018. Consumers of both EVs and CVs therefore have to do vehicle maintenance about the same number of times per year. Among EVs, the researcher would recommend choosing BEVs over HEVs or PHEVs. Future researchers can refer to this study to inform and improve further research, vehicle designs, management of different vehicle systems, profitability of manufacturers and consumers, and the advancement of the vehicle industry. In addition, consumers can adopt the miles-per-dollar (MPD) unit conversion introduced in this study in order to calculate fuel-and-distance-related costs from miles-per-gallon (MPG) and miles-pergallon- equivalent (MPGe) values.

      Long, Joseph D. (Indiana State University, 2020-05)
      The current state of additive manufacturing (AM) is still primarily used for prototyping and modeling. However, with the increase in available materials and more advanced machines, AM is beginning to become a production process in which ready-to-ship products are being manufactured. This makes it all the more important to effectively and efficiently use the material in AM machines. One of the ways that material usage can be improved upon is more efficiently structuring the infill material that is used. This dissertation reports on the testing and statistical analysis of the compressive modulus of elasticity, the compressive proportional limit, and the maximum compressive stress of chopped carbon fiber reinforced nylon specimens manufactured on an Ultimaker 2+ 3D printer. The primary inquiry in this study is to test the hypothesis that infill designs of 2D honeycomb, 3D Truss, and 3D Gyroid infill designs will make a more efficient infill design, meaning that, it has a higher compressive modulus of elasticity, compressive proportional limit, and maximum compressive stress at equal density, than a standard grid infill design. Using the honeycomb, truss, and gyroid designs are examples of bioinspiration, or the use of design in nature for solving engineering problems.

      McCain, Heather J. (Indiana State University, 2020-05)
      Quality requirements are not easy to define. In higher education, defining quality requirements and communicating those requirements to students may be accomplished through a variety of mechanisms. Students still may not know what it takes to get a good grade on an assignment and may have to wait for an instructor to clarify the assignment. This study was conducted because students and instructors may have different opinions as to which forms of feedforward and what technology are best to convey assignment requirements. The purpose of this study was to determine effective feedforward mechanisms as well as the technology used to convey quality requirements for assignments. A Delphi Panel was utilized to identify feedforward mechanisms as well as technology currently used. A survey was conducted to quantify waste in the assignment process via statistical testing. Minitab 19 with selected T-tests were used to determine if there is a difference between students and instructors as to what feedforward mechanisms or combinations of feedforward mechanisms are preferred to effectively convey quality requirements. The study involved Master-degree seeking students and instructors as well as university resources from teaching excellence programs from three universities. Combining the information from the Delphi Panel and the survey, a model was created that using the syllabus and instructions as mechanisms to convey quality requirements for assignments. Depending on the assignment a rubric, criteria sheet, or model/sample may be used to clarify requirements. Using the web-based learning management system allows students to access information outside of the classroom and at any time. The LMS can contain written as well as video or audio recordings of assignment information. The results of this study have led to improvements in a Project Management course at the University of Kansas.

      Hayes, Melvin (Indiana State University, 2019-12)
      This paper proposes to conduct research on the vehicular ad hoc networks (VANET) area of Intelligent Transportation Systems (ITS) with a focus on investigating safety methods that will significantly reduce passenger vehicle collisions which ultimately will help to save lives and reduce property losses. Key areas of this ITS research will include highway infrastructure or wireless sensor networks (WSN) to the cloud (web service) and the cloud (web service) to highway infrastructure or wireless sensor network (WSN). In turn, the cloud (web service) will communicate with passenger vehicles as components of a highway infrastructure (WSN) to vehicle (I2V) systems and a vehicle to highway infrastructure (V2I) systems. In turn, the cloud (web service) will communicate with passenger vehicles as components of a vehicle to highway infrastructure (V2I) system and a highway infrastructure to vehicle (I2V) system. Active circuit emulation will be used as an analysis tool for this research. The cloud web service in this case, will be a database that will be connected through an IEEE802.11 broadband (Wi-Fi) gateway via a border router or a network capable application processor (NCAP) to hardware and software wireless sensor networks or a simulated wireless network. The highway infrastructure portion of this design will be the IEEE1451 standard-based wireless sensor network called wireless transducer interface modules (WTIM). These WTIMs will be responsible for disseminating information from their multitude of sensors to vehicles and/or to the cloud via NCAP routers.

      Guraja, Praveen Kumar (Indiana State University, 2022-07)
      Public higher education in the United States (US) is funded through two primary forms: one is through state higher education appropriation funds, and the other is student financial aid that is directly given to students. Increasing postsecondary full-time equivalent (FTE) enrollment and graduation rate are becoming a crucial economic priority in the US. However, only a limited study is available about whether state investment in higher education and increasing tuition charges can impact FTE student enrollment (FTEE) and graduation rate (GR) at 4-year public universities in the US. A systematic literature review was conducted for the present research to comprehend the literature gap and identify factors or variables that may affect FTEE and GR. Five independent variables (IVs): state higher education appropriations per FTE (SHEA), average undergraduate charges per FTE (AUGC), student tuition share as a percentage of per capita income (STSPCI), state higher education appropriations as a percentage of GDP (SHEAGDP), and state financial aid (SFA) per FTE were selected. The dependent variables (DVs) were full-time equivalent enrollment (FTEE) and graduation rate (GR) at 4-year US public higher education institutions. Historical US public higher education data for 50 years (each year as one dataset, n=50) between 1971 and 2020 were collected and analyzed. The multiple linear regression tool of the open-source data analytics and machine learning software was used to test the hypotheses if the independent variables were significantly related to the dependent variables. Hypothesis 1 was about FTEE, and hypothesis 2 was about GR. For FTEE, three variables were found to be significant: SHEA, AUGC, and SFA. For GR, two variables were found to be significant: SHEA and STSPCI. Hence, two data analytical models were developed involving the significant IVs: one for FTEE and the other for GR. Findings from the first model revealed that when state higher education appropriation (SHEA) funds increase, average undergraduate tuition charge (AUGC) decreases, and more student financial aid (SFA) is awarded, FTE enrollment (FTEE) increases. The second model results indicated that when state higher education appropriation (SHEA) funds increase and student tuition share as a percentage of per capita income (STSPCI) decreases, there is an increase in graduation rate (GR). These findings show how state budget cuts could impact students enrolling and graduating at public 4-year institutions in the US. State policymakers, higher education administrators, and other stakeholders could use this study to develop their customized data analytics and machine learning models and analyze their past data to better prepare themselves for future uncertainties. This study did not investigate how state funding or budget cuts a) impact full-time faculty vs part-time faculty at public higher education institutions, as data were unavailable for some of the years, and b) if any specific year impact the corresponding year of cohort. These can be investigated in future work.

      Rich, Frederick Ashburn (Indiana State University, 2020-05)
      Heavy construction equipment owners and managers have few predictive tools that can estimate wear rate of undercarriage track propulsion systems working in various soil types and changing operational conditions. Managing the timely maintenance of these track systems is critical for they represent over half of the non-fuel operating cost of the equipment fleet. Understanding the major influencing factors that impact undercarriage system wear rate can help determine the most economical time to stop a machine for track maintenance thus positively impacting the equipment’s return on investment (ROI). This research analyzed the population of track type dozers in the eastern half of North Carolina, United States of America. This region has markedly different soil types, topography and precipitation amounts making this to be an excellent study canvas. Sand percentage in the soil where the machine is working is thought to be a primary factor influencing the wear rate. In addition, other factors like precipitation, temperature, machine model, machine weight, altitude above sea level, and work type code are also considered and analyzed to determine which of these factors have significance. A regression model is developed that can be used as a predictive model to help manage this high value maintenance wear item. This research is important because the results can assist machine owners in maximizing the life of the undercarriage system in eastern North Carolina and will result in better machine maintenance decisions. In addition, this research can be utilized to accurately bid construction jobs predicting machine operating expense for each specific job site soil makeup.
    • An Analysis of Charging Practices and their Impact on Battery Degradation in North American Electric Vehicles Built Between 2010-2020

      Ferrier, Douglas William Edward (Indiana State University, 2022-05)
      Electric vehicles (EVs) are emerging as a component of the global solution to combat climate change. However, in North America, particularly in the United States and Canada, the transition away from internal combustion engines (ICE) has been slow. North America faces unique challenges due to its geographical size and population in comparison to other continents. The good news is that EV adoption is increasing within North America. Along with increased EV adoption, governments and public companies are constructing charging infrastructure to support increased consumer EV purchases. Despite increased adoption, many future and current owners throughout North American society have concerns about an electric vehicles’ key feature: the battery. Many EV owners are concerned about the battery's State of Health (SOH) – how to keep batteries healthy and use best practices to keep their range at maximum capacity. SOH is influenced by five key factors: (1) temperature, (2) charge/discharge rate, (3) charge/discharge depth, (4) cyclic charging, and (5) ending State of Charge (SOC). This study primarily focuses on data centered around charging. This dissertation examines data generated by everyday EV users and uses it to predict how charging habits affect batteries over time. Charging effects include decreasing battery SOH and capacity degradation. Lowering the SOH reduces the battery's viability for continuous use; at approximately 70% SOH the battery is 'typically' deemed End of Life (EoL). The overall range of the EV is affected by capacity degradation; as batteries degrade, the total km (or miles) available decreases. This study uses regression analysis to examine relationships and predictors of SOH, temperature, levels of charging, and SOC. The data collected and analyzed determine best practices for charging batteries at home and abroad for consumers. There were two methods for analyzing data: (1) Using EV generated data (SOH, Charger Type) saved in CSV files via a smartphone application, and (2) Analyzing consumed energy in a large dataset using a segmentation process based on equivalent SOC differences between two points in time. The current study makes use of one of the largest datasets of "real world" data ever collected from EVs in the United States and Canada, with over one million lines. Eighteen models of EVs are used to make comparisons for amounts of degradation over one year. A discussion of how these findings affect EV owners’ usage of models from 2010-2020 is included. Multiple recommendations for future studies are provided.

      Park, Dennis B. (Indiana State University, 2021-07)
      According to a survey, the healthcare industry is one of the least cloud-adopting industries. The low adoption reflects the healthcare industry's ongoing concerns about the security of the cloud. Business applications, according to another survey, are among the most vulnerable components of business information systems. Many risk assessment frameworks available today, particularly for health information applications, require significant customization before they can be used. This study created a new framework to assess cloud risks specifically for their health information applications, utilizing data-driven risk assessment methodologies to avoid surveys, interviews, and meetings for data collection. For the feasibility study, the open-source application codes were chosen from over 190 million GitHub repositories using a decision tree method, while a purposive sampling method was used to choose for a simulated patient information database from the healthcare industry. Using these methods, the researcher discovered security warnings and privacy violation suspects and subsequently converted them into quantitative measures to calculate the risks of the cloud-based health information application and a database. The significance of this study lies in the collection of data directly from applications and databases with a quantitative approach for risk calculation.

      Kirkland, David P. (Indiana State University, 2021-12)
      COVID-19 created a support problem for public universities across the United States and required that IT departments and professionals alter how they performed in 2020, and perhaps beyond. IT professionals tasked with safeguarding large amounts of data were required to shift to a teleworking posture to continue offering a similar level of service as previously expected. In addition to the technological shift that organizations experienced because of COVID-19, leadership challenges also impacted IT departments across the United States. The rapid shift of operational duties has the propensity to increase technology-related stress, due to employee perception of being successful in their role. The purpose of this quantitative, non-experimental, correlational pilot study was to examine the relationship between technostress, job satisfaction, burnout, and demographic characteristics of age, gender, and years of experience of an IT professional working in higher education. This pilot study included a convenience sample of IT professionals from a single public university in the United States and an online survey was administered to discover the impact operational shifts have on levels of technostress, job satisfaction, and job burnout. To be considered, the respondent had to meet specific criteria: (a) be an adult of at least 18 years of age, (b) work as an IT professional within the university, and (c) work for a minimum of one year as an IT professional. The sample of 116 potential respondents were emailed to request participation in the study. There were 46 survey submissions received (roughly 40% of likely respondents). Of those surveys received, there were 31 completed cases (approximately 27%), which were analyzed using multiple linear regression. Results of this study suggested there was no predictive relationship of technostress on job satisfaction. However, results did show decreased job satisfaction for demographic characteristics, such as age. Additionally, there was no overall predictive relationship of technostress on job burnout, however, results suggest that compared with people over 55, people who were between 35-44 experienced increased burnout overall.

      Hoseini, Cyrus (Indiana State University, 2020-12)
      Medicaid is the largest health insurance in the U.S. It provides health coverage to over 68 million individuals, costs the nation over $600 billion a year, and subject to improper payments (fraud, waste, and abuse) or inaccurate payments (claim processed erroneously). Medicaid programs partially use Fee-For-Services (FFS) to provide coverage to beneficiaries by adjudicating claims and leveraging traditional inferential statistics to verify the quality of adjudicated claims. These quality methods only provide an interval estimate of the quality errors and are incapable of detecting most claim adjudication errors, potentially millions of dollar opportunity costs. This dissertation studied a method of applying supervised learning to detect erroneous payment in the entire population of adjudicated claims in each Medicaid Management Information System (MMIS), focusing on two specific claim types: inpatient and outpatient. A synthesized source of adjudicated claims generated by the Centers for Medicare & Medicaid Services (CMS) was used to create the original dataset. Quality reports from California FFS Medicaid were used to extract the underlying statistical pattern of claim adjudication errors in each Medicaid FFS and data labeling utilizing the goodness of fit and Anderson-Darling tests. Principle Component Analysis (PCA) and business knowledge were applied for dimensionality reduction resulting in the selection of sixteen (16) features for the outpatient and nineteen (19) features for the inpatient claims models. Ten (10) supervised learning algorithms were trained and tested on the labeled data: Decision tree with two configurations - Entropy and Gini, Random forests with two configurations - Entropy and Gini, Naïve Bayes, K Nearest Neighbor, Logistic Regression, Neural Network, Discriminant Analysis, and Gradient Boosting. Five (5) cross-validation and event-based sampling were applied during the training process (with oversampling using SMOTE method and stratification within oversampling). The prediction power (Gini importance) for the selected features were measured using the Mean Decrease in Impurity (MDI) method across three algorithms. A one-way ANOVA and Tukey and Fisher LSD pairwise comparisons were conducted. Results show that the Claim Payment Amount significantly outperforms the rest of the prediction power (highest Mean F-value for Gini importance at the α = 0.05 significance) for both claim types. Finally, all algorithms' recall and F1-score were measured for both claim types (inpatient and outpatient) and with and without oversampling. A one-way ANOVA and Tukey and Fisher LSD pairwise comparisons were conducted. The results show a statistically significant difference in the algorithm's performance in detecting quality issues in the outpatient and inpatient claims. Gradient Boosting, Decision Tree (with various configurations and sampling strategies) outperform the rest of the algorithms in recall and F1-measure on both datasets. Logistic Regression showing better recall on the outpatient than inpatient data, and Naïve Bays performs considerably better from recall and F1- score on outpatient data. Medicaid FFS programs and consultants, Medicaid administrators, and researchers could use this study to develop machine learning models to detect quality issues in the Medicaid FFS claim datasets at scale, saving potentially millions of dollars.

      Carilli, James F. (Indiana State University, 2021-05)
      Software development projects experience very high failure rates. Due to the high cost of project failure, coupled with studies that found failure rates are closely tied to the software development method used, the purpose of this mixed methods exploratory case study was to examine the extent of perceived effectiveness of the Scaled Agile Framework (SAFe®) in software development organizations using Complex Adaptive Systems as a lens to guide the study. This research focused on the extent of perceived effectiveness of the Scaled Agile Framework® on organizational outcomes, team management, stakeholder and customer management, management of emerging requirements and overall organizational agility. Three organizations took participated from Retail, Government and Logistics industries. Each organization transitioned from the Waterfall method to SAFe®. In all three cases, the participants reported the transition to SAFe® helped improve strategic alignment, facilitate business / IT coordination, increase speed of delivery, improve software quality, and reduce rework by applying Lean-Agile principles resulting in lower overall costs and reduced risk. Principle challenges included the need for change management and training to help assimilate the new structure, roles and responsibilities. Another significant challenge cited was the transition from project management measures (e.g., cost, scope, schedule, earned value) to SAFe® measures of throughput (i.e., working software) and value (i.e., prioritized features based on business value). Interactions with “non-SAFe®” organizations were cited as a concern for dependencies on other teams that could result in schedule and priority misalignment.