Carlos Alvarez College of Business Faculty Research

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Recent Submissions

Now showing 1 - 20 of 56
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    Understanding and Analyzing COVID-19-related Online Hate Propagation Through Hateful Memes Shared on Twitter
    (Association for Computing Machinery, 2024-03-15) Vishwamitra, Nishant; Guo, Keyan; Liao, Song; Mu, Jaden; Ma, Zheyuan; Cheng, Long; Zhao, Ziming; Hu, Hongxin
    Recent studies regarding the COVID-19 pandemic have revealed the widespread propagation of hateful content during this period. While significant research has focused on COVID-19-related online hate in text (e.g., text-based tweets), the role of memes in propagating online hate during the pandemic has been largely overlooked. Memes are a popular mechanism used by Internet users to convey their thoughts and opinions on a variety of topics. However, memes have emerged as an important mechanism through which ideologically potent and hateful content spreads on social media platforms. In this work, we focus on investigating the role of memes in the propagation of online hate during the COVID-19 pandemic. We first collect a novel dataset of 4,001 COVID-19-related hateful memes and their replies over a 3-year period from Twitter. Then, we carry out the first large-scale investigation into the impact of these memes on Twitter users, by studying the psychological reactions of Twitter users to these memes using various text analysis methods. We find that COVID-19-related hateful memes have a significantly greater negative impact on Twitter users in comparison to text-based hateful tweets, and increasing negativity towards such memes over the 3-year period. Our new dataset of COVID-19-related hateful memes and findings from our work pave the way for studying the dissemination and moderation of COVID-19-related online hate through the medium of memes.
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    International student mobility to China: The effects of government scholarship and Confucius Institute
    (SAGE Publications, 2023-11-07) Lien, Donald; Miao, Liqin
    Government scholarship plays an active role in attracting foreign students and promoting higher education exports. As a culture and education platform, Confucius Institute is also likely to affect the number of foreign students in China. Using the data from 188 countries over the 2003-2018 period, we find globally both attract more international students to China. In addition, government scholarship has stronger impacts on degree-seeking students whereas Confucius Institute affects non-degree-seeking students more. At the continental level, government scholarship remains effective, particularly for degree-seeking students. Confucius Institute, however, display opposing impacts for different continents. As the number of the Institute increases in a country, there will be more foreign students if the continent is of higher income, geographically more distant from China, or culturally less exposed to China; and vice versa. Globally and for most continents, we observe Confucius Institute affects the positive effect of Chinese government scholarship. The results offer policy implications for government scholarship allocation decisions.
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    Lean Start-Up in Settings of Impoverishment: The Implications of the Context for Theory
    (SAGE Publications, 2023-10-24) Bruton, Garry D.; Pryor, Christopher; Cerecedo Lopez, Jose A.
    We examine the application of “lean start-up” in impoverished non-Western contexts. Specifically, we focus on settings of impoverishment in which individuals earn less than $3.65 per day. We focus on how two attributes of these contexts—institutional differences relative to mature economies and resource constraints—affect entrepreneurs’ implementation of lean start-up principles. By focusing our conversation on five components of lean start-up (search for opportunities, business modeling, validated learning, minimum viable products, and the decision to persevere/pivot), we describe how the conditions faced by impoverished entrepreneurs outside the West in impoverished settings present hurdles to some practices of lean start-up while encouraging other practices. We also offer ways entrepreneurs can adapt lean start-up to fit the conditions they face. In addition to advancing our understanding of lean start-up, this article also joins recent work that has critiqued the Western orientation of many management theories and practices and especially their application to people outside the West, where assumptions may not carry over due to institutional differences and resource constraints.
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    Big Data Analytics, Data Science, ML&AI for Connected, Data-driven Precision Agriculture and Smart Farming Systems: Challenges and Future Directions
    (Association for Computing Machinery, 2023-05-09) Han, David; Rodriguez, Mia
    Big data and data scientific applications in the modern agriculture are rapidly evolving as the data technology advances and more computational power becomes available. The adoption of big data has enabled farmers and producers to optimize their agricultural activities sustainably with cutting-edge technologies, resulting in eco-friendly and efficient farming. Wireless sensor networks and machine learning have had a direct impact on smart and precision agriculture, with deep learning techniques applied to data collected via sensor nodes. Additionally, internet of things, drones, and robotics are being incorporated into farming techniques. Digital data handling has amplified the information wave, and information and communication technology have been used to deliver benefits to both farmers and consumers. This work highlights the technological implications and challenges that arise in data-driven agricultural practices as well as the research problems that need to be solved.
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    How Does Regional Social Capital Structure the Relationship Between Entrepreneurship, Ethnic Diversity, and Residential Segregation?
    (SAGE Publications, 2023-09-15) Cordero, Arkangel M.; Lewis, Alexander C.
    The extant theory posits that ethno-racial diversity promotes entrepreneurship by increasing the novelty of information and perspectives available for recombination in a region. This view presupposes the flow of novel information among potential entrepreneurs. Yet, we know comparatively little about how regional social structures (e.g., collective social capital) that affect information flows condition this relationship. We build on the sociological literature to theorize how the interplay between collective social capital and residential segregation moderates the relationship between ethno-racial diversity and entrepreneurship. We test, and find empirical support for, our hypotheses among all registered new ventures started in the United States between 1990 and 2018.
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    The Communication of Justice, Injustice, and Necessary Evils: An Empirical Examination
    (SAGE Publications, 2021-09-22) Thornton-Lugo, Meghan A.; Rupp, Deborah E.
    The prevailing approach to studying justice in the workplace has focused on recipients and observers of justice. This approach, however, fails to consider the experience of other parties including those who communicate justice. To understand the experience of communicating fairness, we investigated how justice, injustice, and necessary evils differentially affect guilt and stress. In addition, we explored how communicating bad news compares to these experiences. Across two studies, we found evidence showing that guilt and stress were affected by what was being communicated, such that injustice and necessary evils provoked more guilt and stress than justice. These findings highlight how justice broadly affects communicators psychologically and physiologically.
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    How Main Street Drives Wall Street: Customer (Dis)satisfaction, Short Sellers, and Abnormal Returns
    (SAGE Publications, 2020-10-21) Malshe, Ashwin; Colicev, Anatoli; Mittal, Vikas
    Although previous studies have established a direct link between customer-based metrics and stock returns, research is unclear on the mediated nature of their association. The authors examine the association of customer satisfaction and abnormal stock returns, as mediated by the trading behavior of short sellers. Using quarterly data from 273 firms over 2007–2017, the authors find that short interest—a measure of short seller activity—mediates the impact of customer satisfaction and dissatisfaction on abnormal stock returns. Customer dissatisfaction has a more pronounced effect on short selling compared with customer satisfaction. In addition, customer satisfaction and dissatisfaction are more relevant for firms with low capital intensity and firms that face lower competitive intensity. The results show that a one-unit increase in customer satisfaction is associated with a .56 percentage point increase in abnormal returns, while a one-unit increase in customer dissatisfaction is associated with a 1.34 percentage point decrease in abnormal returns.
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    Influential Factors and the Realization Mechanism of Sustainable Information-Sharing in Virtual Communities from a Knowledge Fermenting Perspective
    (SAGE, 2020-11-25) Zhang, Meng; Gao, Yang; Sun, Minghe; Bi, Datian
    Little is known about sustainable information-sharing in virtual communities, although it is increasingly recognized as a useful information-sharing tool. The aim of this study is to explore the influential factors and the realization mechanism of sustainable information-sharing in virtual communities. Starting from the similarity between biological fermentation and the information-sharing process in virtual communities, the present study creatively introduces the knowledge fermenting theory used in the analysis. Six factors influencing sustainable information-sharing in virtual communities are first identified based on this theory, which include sharing bodies, interactive topics, communication mechanism, supporting technology, communication environment, and platform scale. The relations among these six factors are then analyzed using the Decision-Making and Trial Evaluation Laboratory (DEMATEL) method. The results indicate that the factor of sharing bodies has the strongest influence on other factors and the factor of interactive topics receives the most influences from the other factors. On this basis, the realization mechanism of sustainable information-sharing in virtual communities is elaborated from the following four aspects: the four stages of the information-sharing realization, the guide role of “strain,” the catalytic role of “enzyme,” and the effect of environment. The results indicate that sustainable information-sharing in virtual communities is a process of spiral evolution. Finally, recommendations are given to virtual community managers, users, and business firms.
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    A DNA algorithm for the job shop scheduling problem based on the Adleman-Lipton model
    (Public Library of Science (PLOS), 2020-12-02) Tian, Xiang; Liu, Xiyu; Zhang, Hongyan; Sun, Minghe; Zhao, Yuzhen
    A DNA (DeoxyriboNucleic Acid) algorithm is proposed to solve the job shop scheduling problem. An encoding scheme for the problem is developed and DNA computing operations are proposed for the algorithm. After an initial solution is constructed, all possible solutions are generated. DNA computing operations are then used to find an optimal schedule. The DNA algorithm is proved to have an O(n2) complexity and the length of the final strand of the optimal schedule is within appropriate range. Experiment with 58 benchmark instances show that the proposed DNA algorithm outperforms other comparative heuristics.
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    A New Chaotic Starling Particle Swarm Optimization Algorithm for Clustering Problems
    (Hindawi, 2018-08-19) Wang, Lin; Liu, Xiyu; Sun, Minghe; Qu, Jianhua; Wei, Yanmeng
    A new method using collective responses of starling birds is developed to enhance the global search performance of standard particle swarm optimization (PSO). The method is named chaotic starling particle swarm optimization (CSPSO). In CSPSO, the inertia weight is adjusted using a nonlinear decreasing approach and the acceleration coefficients are adjusted using a chaotic logistic mapping strategy to avoid prematurity of the search process. A dynamic disturbance term (DDT) is used in velocity updating to enhance convergence of the algorithm. A local search method inspired by the behavior of starling birds utilizing the information of the nearest neighbors is used to determine a new collective position and a new collective velocity for selected particles. Two particle selection methods, Euclidean distance and fitness function, are adopted to ensure the overall convergence of the search process. Experimental results on benchmark function optimization and classic clustering problems verified the effectiveness of this proposed CSPSO algorithm.
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    An Extended Clustering Membrane System Based on Particle Swarm Optimization and Cell-Like P System with Active Membranes
    (Hindawi, 2020-01-31) Wang, Lin; Liu, Xiyu; Sun, Minghe; Qu, Jianhua
    An extended clustering membrane system using a cell-like P system with active membranes based on particle swarm optimization (PSO), named PSO-CP, is designed, developed, implemented, and tested. The purpose of PSO-CP is to solve clustering problems. In PSO-CP, evolution rules based on the standard PSO mechanism are used to evolve the objects and communication rules are adopted to accelerate convergence and avoid prematurity. Subsystems of membranes are generated and dissolved by the membrane creation and dissolution rules, and a modified PSO mechanism is developed to help the objects escape from local optima. Under the control of the evolution-communication mechanism, the extended membrane system can effectively search for the optimal partitioning and improve the clustering performance with the help of the distributed parallel computing model. This extended clustering membrane system is compared with five existing PSO clustering approaches using ten benchmark clustering problems, and the computational results demonstrate the effectiveness of PSO-CP.
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    A Density Peak Clustering Algorithm Based on the K-Nearest Shannon Entropy and Tissue-Like P System
    (Hindawi, 2019-07-31) Jiang, Zhenni; Liu, Xiyu; Sun, Minghe
    This study proposes a novel method to calculate the density of the data points based on K-nearest neighbors and Shannon entropy. A variant of tissue-like P systems with active membranes is introduced to realize the clustering process. The new variant of tissue-like P systems can improve the efficiency of the algorithm and reduce the computation complexity. Finally, experimental results on synthetic and real-world datasets show that the new method is more effective than the other state-of-the-art clustering methods.
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    Robust Optimization for Multiobjective Programming Problems with Imprecise Information
    (Elsevier, 2013) Hassanzadeh, Farhad; Nemati, Hamid; Sun, Minghe
    A robust optimization approach is proposed for generating nondominated robust solutions for multiobjective linear programming problems with imprecise coefficients in the objective functions and constraints. Robust optimization is used in dealing with impreciseness while an interactive procedure is used in eliciting preference information from the decision maker and in making tradeoffs among the multiple objectives. Robust augmented weighted Tchebycheff programs are formulated from the multiobjective linear programming model using the concept of budget of uncertainty. A linear counterpart of the robust augmented weighted Tchebycheff program is derived. Robust nondominated solutions are generated by solving the linearized counterpart of the robust augmented weighted Tchebycheff programs.
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    Identification of Tomato Disease Types and Detection of Infected Areas Based on Deep Convolutional Neural Networks and Object Detection Techniques
    (Hindawi, 2019-12-16) Wang, Qimei; Qi, Feng; Sun, Minghe; Qu, Jianhua; Xue, Jie
    This study develops tomato disease detection methods based on deep convolutional neural networks and object detection models. Two different models, Faster R-CNN and Mask R-CNN, are used in these methods, where Faster R-CNN is used to identify the types of tomato diseases and Mask R-CNN is used to detect and segment the locations and shapes of the infected areas. To select the model that best fits the tomato disease detection task, four different deep convolutional neural networks are combined with the two object detection models. Data are collected from the Internet and the dataset is divided into a training set, a validation set, and a test set used in the experiments. The experimental results show that the proposed models can accurately and quickly identify the eleven tomato disease types and segment the locations and shapes of the infected areas.
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    Turing Universality of Weighted Spiking Neural P Systems with Anti-spikes
    (Hindawi, 2020-09-17) Ren, Qianqian; Liu, Xiyu; Sun, Minghe
    Weighted spiking neural P systems with anti-spikes (AWSN P systems) are proposed by adding anti-spikes to spiking neural P systems with weighted synapses. Anti-spikes behave like spikes of inhibition of communication between neurons. Both spikes and anti-spikes are used in the rule expressions. An illustrative example is given to show the working process of the proposed AWSN P systems. The Turing universality of the proposed P systems as number generating and accepting devices is proved. Finally, a universal AWSN P system having 34 neurons is proved to work as a function computing device by using standard rules, and one having 30 neurons is proved to work as a number generator.
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    GPU-Based Parallel Particle Swarm Optimization Methods for Graph Drawing
    (Hindawi, 2017-07-30) Qu, Jianhua; Liu, Xiyu; Sun, Minghe; Qi, Feng
    Particle Swarm Optimization (PSO) is a population-based stochastic search technique for solving optimization problems, which has been proven to be effective in a wide range of applications. However, the computational efficiency on large-scale problems is still unsatisfactory. A graph drawing is a pictorial representation of the vertices and edges of a graph. Two PSO heuristic procedures, one serial and the other parallel, are developed for undirected graph drawing. Each particle corresponds to a different layout of the graph. The particle fitness is defined based on the concept of the energy in the force-directed method. The serial PSO procedure is executed on a CPU and the parallel PSO procedure is executed on a GPU. Two PSO procedures have different data structures and strategies. The performance of the proposed methods is evaluated through several different graphs. The experimental results show that the two PSO procedures are both as effective as the force-directed method, and the parallel procedure is more advantageous than the serial procedure for larger graphs.
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    An Improved Apriori Algorithm Based on an Evolution-Communication Tissue-Like P System with Promoters and Inhibitors
    (Hindawi, 2017-02-19) Liu, Xiyu; Zhao, Yuzhen; Sun, Minghe
    Apriori algorithm, as a typical frequent itemsets mining method, can help researchers and practitioners discover implicit associations from large amounts of data. In this work, a fast Apriori algorithm, called ECTPPI-Apriori, for processing large datasets, is proposed, which is based on an evolution-communication tissue-like P system with promoters and inhibitors. The structure of the ECTPPI-Apriori algorithm is tissue-like and the evolution rules of the algorithm are object rewriting rules. The time complexity of ECTPPI-Apriori is substantially improved from that of the conventional Apriori algorithms. The results give some hints to improve conventional algorithms by using membrane computing models.
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    The Moderating Effect of a Golden Parachute on the Association between CSR and Firm Value: Does Gender-Driven Innovation Matter?
    (2023-03-20) Okafor, Collins E.; Ujah, Nacasius U.; Cho, Eunho; Okafor, Winifred U.; James, Kevin L.
    We revisit the debate on whether a firm's corporate social responsibility (CSR) activities enhance firm value. Research on related topics has produced mixed results suggesting a need to further investigate factors that directly or indirectly impact the CSR–firm value association. To this end, we examine if a firm's adoption of a golden parachute (GP) moderates the relationship between CSR and firm value. We also investigate if diversity-based innovation as it pertains to the gender of executives reveals any difference in the CSR–firm value relation. Using a sample of 11,065 firm-year observations of publicly traded US firms from 2007 to 2016, we find that CSR activities are significantly and positively associated with firm value. More importantly, our study shows that for US firms that issue GPs, this severance pay strengthens this positive relationship, suggesting that CEOs with a GP engage in more value-enhancing innovative CSR projects than their counterparts without it. This finding supports the conflict resolution theory and the resource-based view of the firm. A test to examine if the gender of the corporate executives alters their behavior towards CSR when the GP protects them shows an inverse relationship between female executives and CSR–firm value association. This interesting finding lends credence to related theories suggesting that women in male-dominated fields may feel pressured to conform to the stereotype of women as less competent than men and may adopt traditionally masculine behaviors to counteract this stereotype. As they climb the corporate leadership ladder endowed with a GP, the stereotype threat may still prevail, adversely affecting the CSR–firm value outcomes. These results remain robust after a series of sensitivity tests.
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    Identifying Flow Patterns in a Narrow Channel via Feature Extraction of Conductivity Measurements with a Support Vector Machine
    (2023-02-08) Yang, Kai; Liu, Jiajia; Wang, Min; Wang, Hua; Xiao, Qingtai
    In this work, a visualization experiment for rectangular channels was carried out to explore gas–liquid two-phase flow characteristics. Typical flow patterns, including bubble, elastic and mixed flows, were captured by direct imaging technology and the corresponding measurements with fluctuation characteristics were recorded by using an electrical conductivity sensor. Time-domain and frequency-domain characteristics of the corresponding electrical conductivity measurements of each flow pattern were analyzed with a probability density function and a power spectral density curve. The results showed that the feature vectors can be constructed to reflect the time–frequency characteristics of conductivity measurements successfully by introducing the quantized characteristic parameters, including the maximum power of the frequency, the standard deviation of the power spectral density, and the range of the power distribution. Furthermore, the overall recognition rate of the four flow patterns measured by the method was 93.33% based on the support vector machine, and the intelligent two-phase flow-pattern identification method can provide a new technical support for the online recognition of gas–liquid two-phase flow patterns in rectangular channels. It may thus be concluded that this method should be of great significance to ensure the safe and efficient operation of relevant industrial production systems.
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    Positive Clinical Outcomes for Severe Reported Pain Using Robust Non-Addictive Home Electrotherapy—A Case-Series
    (2023-02-15) Bajaj, Anish; Han, David; Elman, Igor; Thanos, Panayotis K.; Dennen, Catherine A.; Badgaiyan, Rajendra D.; Bowirrat, Abdalla; Barh, Debmalya; Blum, Kenneth
    The North American opioid epidemic has resulted in over 800,000 related premature overdose fatalities since 2000, with the United States leading the world in highest opioid deaths per capita. Despite increased federal funding in recent years, intended to address this crisis, opioid overdose mortality has continued to increase. Legally prescribed opioids also chronically induce a problematic reduction in affect. While an ideal analgesic has yet to be developed, some effective multimodal non-opioid pharmacological regimens for acute pain management are being more widely utilized. Some investigators have suggested that a safer and more scientifically sound approach might be to induce "dopamine homeostasis" through non-pharmacological approaches, since opioid use even for acute pain of short duration is now being strongly questioned. There is also increasing evidence suggesting that some more robust forms of electrotherapy could be applied as an effective adjunct to avoid the problems associated with opioids. This 4-patient case-series presents such an approach to treatment of severe pain. All 4 of these chiropractic treatment cases involved a component of knee osteoarthritis, in addition to other reported areas of pain. Each patient engaged in a home recovery strategy using H-Wave(R) device stimulation (HWDS) to address residual extremity issues following treatment of spinal subluxation and other standard treatments. A simple statistical analysis was conducted to determine the change in pain scores (Visual Analogue Scale) of pre and post electrotherapy treatments, resulting in significant reductions in self-reported pain (p-value = 0.0002). Three of the four patients continued using the home therapy device long-term as determined by a post-analysis questionnaire. This small case-series demonstrated notably positive outcomes, suggesting consideration of home use of HWDS for safe, non-pharmacological and non-addictive treatment of severe pain.