Business Commons^™

The Technology, Organization, And Environment Framework For Social Media Analytics In Government: The Cases Of South Africa And Germany, Brenda M. Scholtz, Khulekani Yakobi

The African Journal of Information Systems

This paper investigates factors influencing the adoption of social media analytics (SMA) for citizen relationship management (CzRM). Three real-world cases of government departments, two in South Africa and one in Germany, were investigated, and focus group discussions were conducted. The technological, organizational, and environmental (TOE) theory and qualitative content analysis guided the data analysis. The findings revealed that in all cases, staff usually conducted a manual analysis of social media and SMA had not been implemented sufficiently to realize its full potential. Insights were obtained from TOE and factors were identified that should be considered for improving the planning of …

Digital Transformation Of Smes Through Social Media, Georgette Eugenia Otoo, Raphael Amponsah, Eric Afful-Dadzie, Emmanuel Awuni Kolog

African Conference on Information Systems and Technology

This research paper explores the strategic integration of social media platforms by Small and Medium-sized Enterprises (SMEs) in Low- and Middle-Income Countries (LMICs) beyond marketing. Drawing from Resource-Based View and Dynamic Capabilities theories, the study investigates how social media enhances management, coordination, and control functions. Through five diverse case studies from Ghana, findings reveal SMEs’ innovative use of platforms like Instagram, WhatsApp, Slack, and Trello. These platforms foster efficient internal communication, customer engagement, project management, and talent acquisition. Challenges such as technical expertise and dynamic digital landscapes are identified.

Employee Attrition: Analyzing Factors Influencing Job Satisfaction Of Ibm Data Scientists, Graham Nash

Symposium of Student Scholars

Employee attrition is a relevant issue that every business employer must consider when gauging the effectiveness of their employees. Whether or not an employee chooses to leave their job can come from a multitude of factors. As a result, employers need to develop methods in which they can measure attrition by calculating the several qualities of their employees. Factors like their age, years with the company, which department they work in, their level of education, their job role, and even their marital status are all considered by employers to assist in predicting employee attrition. This project will be analyzing a …

Manufacturing “Hits”: A Data-Driven Ai Approach To Releasing A Pop Song In 2022, Jessica Birk, Madhavi Chakrabarty

Atlantic Marketing Journal

Technology is radically transforming the music industry through the use of big data, artificial intelligence and machine learning algorithms. This paper presents a pilot study that examines the impact of data-driven approaches in creating, predicting, and marketing music. A machine learning algorithm is used to determine the optimal characteristics of the most popular songs and is used as the basis for creating the next song. Next, an AI technique was used to generate the inspiration of the instrumentation and lyrics of the song. Finally, a listener survey is used to determine the trend which included the mood, preference and context …

Branding, Internationalization And Performance Of Restaurant Chains, Saarthak Bhatia, Joel Smith

Symposium of Student Scholars

Every year, restaurant companies try and find new and innovative ways to grow their business. Many companies look towards creating new brands and expanding internationally as a means of achieving this goal but do multibranding and the scope of internationalization (number of countries) have an influence on the firm performance of restaurant companies? We dive into this by looking at data from over 100 publicly traded companies to see what the results show.

The examination period for this study was 1997 to 2019. Data for 2013-2019 is still being collected. Sample selection started with 118 publicly traded restaurant firms listed …

Driving Marketing Efficiency In The Age Of Big Data: Analysis Of Subprime Automotive Borrowers, Edwin Baidoo, Ryan Matthews, Frances Ann Stott, F. Stuart Wells

Atlantic Marketing Journal

Big Data methodologies are applied to understand subprime borrowers in the U.S. automobile space. The focus on the automobile market is essential as this subsegment is responsible for directly and indirectly employing over one million people and creating payrolls in excess of $100 billion annually in the U.S. It is found in this article that if a subprime borrower is a homeowner, the probability of repaying their auto loan increases by almost 4%. However, if the borrower is renting, the likelihood of repaying their auto loan increases by nearly 1.4%. Applying Big Data in making subprime auto loans can add …

Amazon: A Maze Through China - An International Marketing Case Study, Hanane Goubil

Symposium of Student Scholars

Amazon is an e-commerce technology company best known for its fast delivering time and for being one of the tops of the big four technology companies in the United States. Despite its success in the U.S. and several countries abroad, it has struggled to succeed in China since 2004 where Alibaba and JD.com control 82% of the market. This is due to Amazon failing to compete with Alibaba and neglecting to acclimatize their online offers to appease Chinese customers’ preferences. An example of this is that Alibaba has its own payment system called Alipay, while Amazon had yet to include …

Market Research: How To Keep And Gain Customers, Chris Mccall

Symposium of Student Scholars

Customer-centered market research is essential to the creation and management of successful marketing campaigns. A company that understands their customers will be able to provide those customers with products and services that fit their needs better than the competition, and ultimately increase profits. My research focuses on a database containing customer information for a telecommunications company called Telco. Within this research, I will focus on a number of customer attributes including demographics, services provided, payment methods, contract lengths, monthly charges, and tenure with the company. Considering how these attributes relate to one another will give me a better understanding of …

Contingency Planning Amidst A Pandemic, Natalie C. Belford

Journal of Cybersecurity Education, Research and Practice

Proper prior planning prevents pitifully poor performance: The purpose of this research is to address mitigation approaches - disaster recovery, contingency planning, and business continuity planning - and their benefits as they relate to university operations during a worldwide pandemic predicated by the novel coronavirus (COVID-19). The most relevant approach pertaining to the University’s needs and its response to the coronavirus pandemic will be determined and evaluated in detail.

Contingency Planning Amidst A Pandemic, Natalie C. Belford

KSU Proceedings on Cybersecurity Education, Research and Practice

Proper prior planning prevents pitifully poor performance: The purpose of this research is to address mitigation approaches - disaster recovery, contingency planning, and continuity planning - and their benefits as they relate to university operations during a worldwide pandemic predicated by the Novel Coronavirus (COVID-19). The most relevant approach pertaining to the University’s needs and its response to the Coronavirus pandemic will be determined and evaluated in detail.

A Credit Analysis Of The Unbanked And Underbanked: An Argument For Alternative Data, Edwin Baidoo

Doctor of Data Science and Analytics Dissertations

The purpose of this study is to ascertain the statistical and economic significance of non-traditional credit data for individuals who do not have sufficient economic data, collectively known as the unbanked and underbanked. The consequences of not having sufficient economic information often determines whether unbanked and underbanked individuals will receive higher price of credit or be denied entirely. In terms of regulation, there is a strong interest in credit models that will inform policies on how to gradually move sections of the unbanked and underbanked population into the general financial network.

In Chapter 2 of the dissertation, I establish the …

A Novel Penalized Log-Likelihood Function For Class Imbalance Problem, Lili Zhang

Doctor of Data Science and Analytics Dissertations

The log-likelihood function is the optimization objective in the maximum likelihood method for estimating models (e.g., logistic regression, neural network). However, its formulation is based on assumptions that the target classes are equally distributed and the overall accuracy is maximized, which do not apply to class imbalance problems (e.g., fraud detection, rare disease diagnoses, customer conversion prediction, cybersecurity, predictive maintenance). When trained on imbalanced data, the resulting models tend to be biased towards the majority class (i.e. non-event), which can bring great loss in practice. One strategy for mitigating such bias is to penalize the misclassification costs of observations differently …

Business Analytics Programs In Business School: What Can Marketing Do?, Yanbin Tu

Atlantic Marketing Association Proceedings

No abstract provided.

Social Media And University Enrollment: Can Social Media Be Used To Raise Awareness Of University Programs?, Hannah Nicole Starnes, Kelly Green Atkins

Atlantic Marketing Association Proceedings

No abstract provided.

Financial Technology Usage 2017 Predictive Analytics Study, Alan D. Smith

Atlantic Marketing Association Proceedings

No abstract provided.

Making Good Decisions: An Attribution Model Of Decision Quality In Decision Tasks, Bethany Niese

PhD in Business Administration Dissertations

Decision-makers endeavor to obtain the decision quality which puts them in a position to reach their goals. In order to control or influence decision quality, the processes by which individuals form their beliefs must be understood. In addition, many decision makers rely on decision support technologies to help find patterns in data and make sense of the input, so these technologies must be considered in parallel with the processes.

There have been numerous studies conducted to illuminate the factors which affect decision quality, however, many of these studies focused on objective measures and factors. This approach ignores individual perception, belief, …

A Descriptive Study Of Variable Discretization And Cost-Sensitive Logistic Regression On Imbalanced Credit Data, Lili Zhang, Jennifer Priestley, Herman Ray, Soon Tan

Published and Grey Literature from PhD Candidates

Training classification models on imbalanced data tends to result in bias towards the majority class. In this paper, we demonstrate how variable discretization and cost-sensitive logistic regression help mitigate this bias on an imbalanced credit scoring dataset, and further show the application of the variable discretization technique on the data from other domains, demonstrating its potential as a generic technique for classifying imbalanced data beyond credit scoring. The performance measurements include ROC curves, Area under ROC Curve (AUC), Type I Error, Type II Error, accuracy, and F1 score. The results show that proper variable discretization and cost-sensitive logistic regression with …

An Investigation Of The Association Between Tourist Pre-Trip Planning Time And Length Of Trip, Lodging Choice, Tourist Psychographics And Demographics: An Application Of Correspondence Analysis And Cramér’S V Effect Size, James E. Stoddard, George D. Shows

Atlantic Marketing Association Proceedings

No abstract provided.

Making The Case For Global Outsourcing: Cases Of Business Complexities And Success, Alan D. Smith, Sara Krivacek

Atlantic Marketing Association Proceedings

No abstract provided.

A Product Affinity Segmentation Framework, Lili Zhang, Jennifer Priestley, Joseph Demaio, Sherry Ni

Published and Grey Literature from PhD Candidates

Product affinity segmentation discovers the linking between customers and products for cross-selling and promotion opportunities to increase sales and profits. However, there are some challenges with conventional approaches. The most straightforward approach is to use the product-level data for customer segmentation, but it results in less meaningful solutions. Moreover, customer segmentation becomes challenging on massive datasets due to computational complexity of traditional clustering methods. As an alternative, market basket analysis may suffer from association rules too general to be relevant for important segments. In this paper, we propose to partition customers and discover associated products simultaneously by detecting communities in …

A Comparison Of Machine Learning Algorithms For Prediction Of Past Due Service In Commercial Credit, Liyuan Liu M.A, M.S., Jennifer Lewis Priestley Ph.D.

Published and Grey Literature from PhD Candidates

Credit risk modeling has carried a variety of research interest in previous literature, and recent studies have shown that machine learning methods achieved better performance than conventional statistical ones. This study applies decision tree which is a robust advanced credit risk model to predict the commercial non-financial past-due problem with better critical power and accuracy. In addition, we examine the performance with logistic regression analysis, decision trees, and neural networks. The experimenting results confirm that decision trees improve upon other methods. Also, we find some interesting factors that impact the commercials’ non-financial past-due payment.

Influence Of The Event Rate On Discrimination Abilities Of Bankruptcy Prediction Models, Lili Zhang, Jennifer Priestley, Xuelei Ni

Published and Grey Literature from PhD Candidates

In bankruptcy prediction, the proportion of events is very low, which is often oversampled to eliminate this bias. In this paper, we study the influence of the event rate on discrimination abilities of bankruptcy prediction models. First the statistical association and significance of public records and firmographics indicators with the bankruptcy were explored. Then the event rate was oversampled from 0.12% to 10%, 20%, 30%, 40%, and 50%, respectively. Seven models were developed, including Logistic Regression, Decision Tree, Random Forest, Gradient Boosting, Support Vector Machine, Bayesian Network, and Neural Network. Under different event rates, models were comprehensively evaluated and compared …

Comparison Of Bankruptcy Prediction Models With Public Records And Firmographics, Lili Zhang, Jennifer Priestley, Xuelei Ni

Published and Grey Literature from PhD Candidates

Many business operations and strategies rely on bankruptcy prediction. In this paper, we aim to study the impacts of public records and firmographics and predict the bankruptcy in a 12-month-ahead period with using different classification models and adding values to traditionally used financial ratios. Univariate analysis shows the statistical association and significance of public records and firmographics indicators with the bankruptcy. Further, seven statistical models and machine learning methods were developed, including Logistic Regression, Decision Tree, Random Forest, Gradient Boosting, Support Vector Machine, Bayesian Network, and Neural Network. The performance of models were evaluated and compared based on classification accuracy, …