Rick’s Research Café is a monthly seminar series that gives USFSM faculty an opportunity to present their unfinished research projects. The goal is to facilitate constructive feedback from cross-disciplinary peers to help the author improve the quality of his/her work prior to publication. If you would like to present your ongoing research in this forum, please email Dr. Rick Borghesi at firstname.lastname@example.org, or call 359-4524.
Prior Research Presentations
Female and Male Business Leadership in Roman Pompeii
February 13, 2018
Abstract: Virtually all ancient sources address the leadership qualities that could be found among the most highly-placed individuals, most of them males, within a given society. This paper will test whether the same leadership traits could be found among two specific groups in Pompeii before the eruption of Vesuvius in 79 CE. A recent study of the ‘fullones’ (‘fullers’ who operated laundry facilities and are attested in a variety of archaeological and epigraphic records from Pompeii) by Miko Flohr (OUP, 2013) will be used to assess the business practices, political maneuverings, and, to the extent these can be determined, social aspirations of these business leaders. Comparisons would then be made to Eumachia, a prominent local woman whose patronage of the fullers was acknowledged in a famous statue with an inscription. Were there uniquely ‘masculine’ and ‘feminine’ styles of leadership on the local level, and did these mirror the leadership traits of more prominent members of Roman society?
Nonlinear Transformations in Research: Possible Problems and Potential Solutions
January 30, 2018
Abstract: We examined the use of nonlinear transformation of variables in a random sample of 323 articles published in six top journals during 2012-2017. Coding categories included the number of transformed variables, the type of transformation, the kinds of variables transformed, reasons provided for transforming variables, how transformed results were reported, and pre- and post-transformation analysis of variables. Common problems include insufficient justification for transforming variables, overreliance on log transformations, failure to report important information on the effects of transformation, and incomplete reporting and discussion of transformed results. Perhaps most importantly, there was frequent misalignment between statements of hypotheses, typically stated in terms of nontransformed variables, and the transformed data used to test them. We discuss the implications of these problems for science and practice and provide recommendations for addressing the issues.
Identifying and Tracing the Development of Military Cultural Norms among Veterans that Influence Civic Engagement
November 27, 2017
Abstract: Recent studies (Kawashima-Ginsberg, 2015; Yonkman, 2009) have established a link between military veterans and increased civic engagement. Nascent literature (Matthieu, 2016) also suggests that civic engagement can play a positive role in veteran reintegration. Given those insights, this study asks the following research questions: Does the U.S. military develop cultural norms among veterans that influence civic engagement? If so, what are they? How are they developed? What role do those dispositions play in influencing veterans’ civic engagement?
This two-year study integrates mixed methods over three project phases. Phase 1 will be an ethnographic exploration of the impact of military culture on veteran identity conducted with a select group of veterans in Florida. First phase data will be collected through in-depth interviews and Photovoice, a participatory photography method. Thematic constant comparative coding will be utilized to identify and trace the development of cultural norms that influence civic behavior among veterans. The second phase of the project will focus on hypotheses and survey development based on the quotes, codes, and themes gathered from the qualitative findings. During Phase 3, we will administer a national survey through Qualtrics to a wide audience of civically engaged veterans, using multivariate regression to analyze the resulting data.
October 30, 2017
9:30 to 11:30
Bhuvan Unhelkar and Lila Rajabion
Abstract: This special two-hour session will involve four modules: What is Big Data?, Data Analytics Types and Relevance in Practice, Framework for Big Data Adoption, Big Data Tools – IBM Watson and Import.IO
Corporate Social Responsibility and Firm Reputation Risk: Bettering Firm Reputational Risk through Socially Responsible Activities
October 16, 2017
Abstract: This study aims to extend the literature on CSR as insurance like in relation to firm performance. Socially responsible activities may provide insurance against reputational crises for firms. Specifically, a positive relationship is found between CSR and reputational risk demonstrating an additional reason why pursuing socially responsible activities makes fundamental sense for managers. This study also fills a gap in the literature by examining CSR in a multilevel model and examining an alternative data source to measure CSR beyond traditional KLD data.
Educational Data Mining: Connecting the Dots
April 11, 2017
Dr. Giti Javidi
Abstract: The application of data mining (DM) techniques with information produced in educational context has originated an emerging discipline called Educational Data Mining (EDM). This new perspective focuses on the development and application of methods to explore educational data to bring a better understanding of students’ behavior, performance and career decision making. With this purpose, this presentation will provide a general overview of the motivations behind EDM and a discussion on the current state of research in this area and some pending issues for future research.
Exploring the Role of Partners on Commitment to Romantic Relationships
March 22, 2017
Dr. Anthony Coy
Abstract: The investment model of commitment is one of the most widely researched theories on close relationships. Based on interdependence theory, the investment model proposes a framework modeling the outcome of repeated interactions within romantic relationships. However, this model is generally applied only at the individual level, rather than a dyadic (couple) level. By offering a dyadic model of romantic relationships, the present research examines the notion that partners have a direct effect on each other’s commitment with a specific focus on investments – or the time, money, and other aspects of life that partners contribute to their relationship. Findings from three studies will be presented including preliminary findings from the USFSM Couples Study which acts as a capstone study to this line of research.
Marketing Strategies to Generation Z: Hotel Industry
February 21, 2017
Dr. Lila Rajabion
Abstract: For recent years marketers across all industries have been obsessed with millennials. They have focused on how to reach them and build connections with their brands. But perhaps one of the recent population groups that have caused confusion for most marketers is Gen Z. Despite its importance to businesses based on population numbers and spending power, there is yet to be a well-explained theory that explains marketing for this market. Based on the characteristics that have been identified as defining this generation, the hospitality industry stands to benefit a lot from this market. This paper builds on this recognition and seeks to review the relevant literature and identify the marketing approaches that are needed in the hospitality to effectively capture this market. The literature will also be compared and contrasted from the occurrence on the ground which will be based on two case studies corroborated by secondary data. The findings and conclusions will aid in the formulation of recommendations for future researchers as well as for industry practitioners.
Swarm Intelligence and Machine Learning
December 9, 2016
Dr. Tad Gonsalves
Abstract: A swarm is a large number of homogenous, unsophisticated agents that interact locally among themselves and their environment without any central control or management. The collective behavior of self-organized, but decentralized natural or artificial systems that leads to the solution of complex problems is called Swarm Intelligence. The individuals that make up the swarm are often extremely simple agents that lack memory, intelligence or even awareness of one another. By following simple rules like sticking together and avoiding collision, they give rise to a form of emergent intelligence. This talk will focus on the application of Swarm Intelligence techniques in diverse areas like business, engineering, healthcare, etc. Machine learning relies on multi-layer neural networks and the backpropagation algorithm in learning a model from sample data and making predictions. We will see how Swarm Intelligence techniques can be applied to train the neural nets. We will also see some examples of deep learning in which image recognition is performed by convolutional nets.
Architecting Service Intelligence in the Big Data World
December 2, 2016
Dr. Keith Sherringham
Abstract: Keith Sherringham (BSc. Hons, FACS) has over 15 years of experience in realizing business outcomes through the business application of ICT. Delivering for executive and senior management in a range of blue chip clients across industry sectors, Keith is also a winner of the Consensus IT Professional Award and has designed award winning software. As a noted author and speaker on the business application of ICT, Keith has guest lectured at various universities in Australia and overseas. He is a company director, director for not for profits, as well as a mentor to CEOs and boards within not for profits.
Enterprise Architecture in Digital Transformations
November 22, 2016
Dr. Tushar Hazra
Abstract: Enterprise Architecture (EA) is the technical fabric of the enterprise. However, EA also transcends technology and moves into business space. Therefore, EA needs to be discussed in an integrated, holistic manner to form the basis for a business transformation. For example, the technologies of Big Data, Mobile and Cloud computing are all highly disruptive technologies that require a fine balance between their business and technical aspects as the organization moves forward. EA is treated as an integrated Architecture comprising technologies, business, frameworks, people, quality and governance of the organization. This talk focuses how such an EA can be used by an organization to absorb the impact of the aforementioned disruptive technologies.
Firms’ Propensity to Report Cash Flow and Earnings Surprises of Divergent Signs
November 17, 2016
Dr. Carlos Jimenez-Angueira
Abstract: Nonnegative (negative) cash flow surprises help generate nonnegative (negative) earnings surprises; hence, the two surprises are generally expected to have the same sign. We document firms’ propensity to report surprises of opposing signs and investigate conditions under which firms beat cash flow forecasts but miss earnings forecasts. Firms are more likely to do so when: adverse valuation consequences are less severe; analyst following of cash flows vis-à-vis earnings is large; analysts forecast extreme accruals; analysts downwardly revise cash flow but not earnings forecasts; firms are in financial distress; firms have inflated balance sheets; and earnings but not cash flows decrease.
Dynamical Conflict Management in Teams and Work Groups
November 2, 2016
Dr. Jay Michaels
Abstract: Successful team performance is critical in the classroom, government, and workplace. While much is known about specific factors that contribute to team success versus struggle, to include group members’ similarities, knowledge possessed, and personality factors, there is less known about how group members’ interactions evolve over time and lead to various performance related outcomes. Thus, the goal of the present research is to understand how different team dynamics are associated with more versus less successful group performance.
Working with (Rae) Yunzi Tan (University of Baltimore) and Urszula Strawinska-Zanko (Nova Southeastern University), I will present preliminary results from an initial field study that tracked how conflict management styles evolved in three groups of graduate students who worked on a comprehensive project in a class during the course of a semester. After providing an overview of key concepts from dynamic systems theory that are relevant to understand the types of patterns we considered in our data, I review a variety of initial results from our rich, longitudinal data set. In particular, we interestingly found that absence of competitive tensions between group members and reliance on cooperative conflict management strategies alone were not defining characteristics of more successful groups. Rather, the most successful team exhibited flexibility in the application of various conflict management strategies coupled with gradually diminishing reliance on a competitive strategy. These results fit with Losada’s (1999; Mathematical and Computer Modeling) finding that greater interaction and psychological complexity within and between group members defined higher performing teams.
Assessing the Relationship between Entrepreneurial Skills and Inmate Transformation
October 12, 2016
Drs. Jessica Grosholz and Jean Kabongo
Abstract: Over the last decade, the interest in inmate behavior and the reentry process has increased in various business, academic, government, and community circles. In 2014, close to 637,000 prisoners returned to communities throughout the country. Of those released, over half will be back in prison within five years. Research shows that one of the most consistent predictors of recidivism is the lack of stable, quality employment. In order to combat the revolving door of our criminal justice system, it becomes imperative to better prepare inmates for employment opportunities post-prison. This task becomes ever more difficult, as correctional jurisdictions are faced with constrained budgets, with treatment and/or rehabilitation programs experiencing the brunt of these restrictions. Research on prison-based education supports that prison education equips inmates with the intellectual, cognitive, and life skills necessary for successful reintegration into society. Despite the plethora of evidence suggesting that prison education and job training programs in prison are beneficial for inmates, very little research has examined the effect of entrepreneurial training on various outcomes. Because entrepreneurship training provides an alternative career path as opposed to working under supervision, it is more likely to transform prisoners’ attitudes toward themselves, their current situation, and others, thus molding their behaviors in prison and making them opt to live lawfully when they get out of prison. Logic would suggest, then, that self-employment represents a practical method for some prisoners to reenter the labor market. Ultimately, this program evaluation fills a gap in current research and seeks to understand how entrepreneurial skills influence an inmate’s transformation in terms of his behavior, criminal thinking, self-control, and entrepreneurial aptitude. To assess these objectives, we are teaching a 10-week, intensive entrepreneurship course to inmates at Hardee Correctional Institution. We are also conducting focus groups with the students, collecting pre-test and post-test surveys, and engaging in participant observation. Findings generated from this program evaluation will contribute to as well as help expand the scholarship on entrepreneurship programming in correctional facilities. Information gathered from participants will also yield pertinent knowledge about the influence of these programs on inmate behavior.
This study remains in the beginning stages, and we have abundant additional measures that we have yet to fully analyze. We are beginning the next cycle of the project with colleagues in Germany to examine team performance outside of the classroom and have plans to carry out additional field studies as well as experimental research. Thus, ideas and suggestions for strengthening our research will be most welcome.
Integrating the SMAC-Stack (Social-Mobile-Analytics-Cloud) in Big Data Strategies for Agile Business
October 5, 2016
Dr. Bhuvan Unhelkar
Abstract: Social media, Mobile, Analytics and Cloud (SMAC) – is a quartet of technologies that builds on the interconnected (and even inseparable) nature of human endeavours. These technologies operate in a much broader and dynamic eco-system of the business than a singular system. Therefore, Agile appears to be the right glue to bind these technologies together so as to produce value to the business. In fact, the way I see the business making full use of SMAC is through an acronym in the reverse CAMS (Composite Agile Method and Strategy)  . CAMS remains my core contribution to Agile as it recognizes and provides an approach that is not just a method but also an organizational strategy. The underlying theme of CAMS is balance; balance in the way Agile is applied to the business eco-system. This, in turn, has great value in the world of Big Data. The Technologies and Analytics of Big Data are able to provide value to business only when they are applied holistically across the entire business. A strategic approach to Big Data is required. This presentation outlines such a strategic approach (Based on Big Data Framework for Agile Business – BDFAB) that uses the fundamentals of CAMS in order to render a business Agile.
New Analytic Tools for Engaging Big Data: Pattern Recognition
September 21, 2016
Dr. Ehsan Sheybani
Abstract: Noise, data dimension, and fading of big datasets can have dramatic effects on the performance of the pattern recognition for decision systems. Any of these parameters alone or their combined result can affect the patterns of output for a business intelligence system. As such, total elimination of these parameters could also be damaging to the final outcome, as it may result in removing useful information that can benefit the decision making process. Experts in the field agree that it is more beneficial to remove noise and/or compress data at the node level. This is mainly stressed so that the low power, low bandwidth, and low computational overhead of system node constraints are met while fused datasets can still be used to make reliable decisions. We have developed computationally low power, low bandwidth, and low cost filters that will remove the noise and compress the data so that a decision can be made at the node level. This wavelet-based method is guaranteed to converge to a stationary point for both uncorrelated and correlated sensor data. Presented here is the theoretical background with examples showing the performance and merits of this novel approach compared to other alternatives.