2021 Student Projects

Does State Ethnocentrism Influence Attitudes and Purchase Intentions formed on Locally Branded Products?
Author(s): Jillian Munoz

Program/Department(s):  Center for Customer Insights and Digital Marketing, International Business and Marketing Department, and Psychology Department

Exemplary Oral Presentation award in Business, Economics, & Hospitality Management category, CPP RSCA Conference, 2021
Runner-Up, Best Undergraduate Research Project in the Library Research Awards 2020
Represented CPP at the CSU-wide Research Competition, 2021

Faculty Mentor(s): Dr. Jae Min Jung

Abstract

Currently, many U.S. state governments have state-labeled logo programs (eg., CA Grown and Ohio Proud), which allow local businesses to affix origin information to the products they market within and outside their states (e.g. CA Grown sticker). With numerous social movements becoming more popular (e.g., Farm-To-Table and Sustainable Food Systems), the public is becoming increasingly aware of the origins and of their food. Consumers have responded positively to state-labeled logo programs, boosting local food sales significantly and drawing attention from industry leaders and academics alike. Thus, comprehensive research is necessary to understand consumer attitudes and motivations for buying local products. To this end, we first systematically search for articles on the topic from agricultural economics and marketing literature, synthesized and integrated past research, and developed a framework that will facilitate future research. Further, we investigate consumers’ attitudes and purchase intentions of the products made in their own (vs. other) states and assess factors that could influence consumers' attitudes and purchase intentions, such as state ethnocentrism. Data was collected from 528 students from two different state universities located within the United States. Results indicate that consumers have more favorable attitudes towards and greater purchase intentions for the products made in their own (vs. other) state and that such SOO effects were further moderated by the level of state ethnocentrism held by residents of the state. This research provides insights into government agencies and marketing literature by extending country-of-origin research and investigating state-of-origin effects in a novel way.

Key Words: Consumer Preference, State Ethnocentrism, Local Food, Product Labels, Product Origin

Pomona Homeless Resources and Services
Author(s): Jesus Duran, Leo Ngo, Vianney Echeverria, Feiyu Han, Johans Acosta, and Alondra Valadez Perez

Program/Department(s): Technology & Operations Management, Finance, Real Estate & Law, and Computer Information Systems

Singelyn Center for Innovative Analytics

 Faculty Mentor(s): Drs. Rita Kumar, Anthony Orlando, & Mehrdad Koohikamali

Abstract

The City of Pomona and Cal Poly Pomona students from the College of Business Administration had the opportunity to develop and work on a three-stage project to provide community resources for Pomona residents. Cal Poly students utilized reliable data sets from LAHSA Homeless Census Tracts, ESRI ArcGIS online datasets, Bureau of Labor Statistics, and the Federal Financial Institutions Examination Council to create multiple multi-layer GIS map visualizations to present Pomona's demographic levels and homeless counts broken down into their survey areas. In the first phase, the goal was to update the Pomona directory, identify those facilities that are no longer in service, and create a working excel document that was compatible with ArcGIS. Ultimately, geocode these services and create a simple web application that is easy to use. For the second phase, students had an opportunity to analyze both 2019 and 2020 point-in-time Homeless counts by survey area. Students also analyzed Pomona key demographics (i.e. median household income, total population, etc.) by survey area and council districts utilizing ArcGIS mapping and geocoding. This tool helped students quickly analyze 2020 demographics in Pomona and identify those areas that could be at risk of losing their homes during a time of crisis and require more services to keep up with demand. The final product is the simple web application that has all the resources and services the citizens of Pomona can utilize to filter through specific services, identify the quickest route, and how many services are in their area.

Key Words: Pomona, Homelessness, Resources, Services, ArcGIS

Analysis on Customer Satisfaction with Amazon Products
Author(s): Nicholas Bias, Anika Singh, Brandon Kang, & Sabrina Tu

Program/ Department(s): Computer Information Systems & Management and Human Resources

Faculty Mentor(s): Dr. Mehrdad Koohikamali

Abstract

We are analyzing a dataset on Amazon products. Amazon has more than 12 million products on its website and most of these products have hundreds or even thousands of reviews. It is difficult for a company to analyze this many reviews. We will be conducting a text mining analysis on these customer reviews to see how the customers rated the products. Based on our analysis we can provide recommendations on whether the product should be improved or not. This problem is important to investigate with text analytics and time series analytics to explore customer satisfaction with products and to see how their satisfaction changes over time with their ratings.

Key Words: Analysis, text mining, customer satisfaction, sentiment analysis, Natural Language Processing

Short Term Stock Price Prediction Using Deep Learning
Author(s): Fernando  Navarrete,Monica Feng Chen,Erinn Dockins, Nhi Tran, & Alan Wen

Program/Department(s): Graduate Program (MSBA)

Faculty Mentor(s): Mohammad Salehan

Abstract

Deep learning refers to the use of deep Neural Networks to solve complex Machine Learning problems such as computer vision, speech recognition, and text analytics. Our project intends to predict the short-term price of major Exchange Traded Funds (ETF). We used deep learning models, including Recurrent Neural Networks (RNN) and Long Short-Term Memory (LSTM), to predict the price of S&P 500 (SPY) and NASDAQ 100 (QQQ) ETFs. The project requires analysis of intraday ETF prices in 1-minute intervals. The model will generate short-term price predictions (next 1-hour) which would facilitate decision making about trading of stock options. The methods used in this project can be used to analyze any data with a sequence such as timer-series and text data.

Key Words: Deep Learning, Business Analytics, Finance, High Frequency Trading, Python

California Counties and Covid-19: A Descriptive Analysis
Author(s): Patrick Ogaz
Program/ Department(s): Computer Information Systems

Presented at the Kellogg Honors Convocation, 2021

Faculty Mentor(s): Dr. Rita Kumar

Abstract

With the growth of data and tracking, Covid-19 has been distinct in its ability to be one of the first fully tracked Pandemics in U.S. history. As a result, many companies and Government agencies ask us what the data tells us about the pandemic and how we understand it to help make decisions. This study analyzes a data set of Covid-19 data points provided from data.ca.gov and the U.S. Census. We utilize descriptive analytics on the Tableau software to visualize trends and patterns within the 58 Californian counties. The goal was to understand better what happened during this pandemic and observing how the pandemic impacted different communities. Included in this analysis are data about demographics, population, and socioeconomic, and their potential effect. In this study, we also deploy a time series dashboard that allows users to interact with the data to better understand trends and the various factors of Covid-19 in California.

Key Words: California, Covid-19, Vaccines, Dashboard, Tableau

COVID-19 Transition to Virtual Instruction Study
Author(s):Denise Zavala & Marlin Colin

Program/Department(s): Management and Human Resources

Presented at the CPP RSCA Conference, 2021

Faculty Mentor(s): Dr. Chantal van Esch

Abstract

Student evaluations of teaching have been the primary indicator in determining faculty effectiveness for promotions, tenure, and retention. Previous research has indicated that student biases transfer over to their evaluations of teaching and that generally, women receive lower teaching evaluations than men. In March of 2020, the Covid-19 pandemic caused universities across the United States to close campuses and instructors to move their courses to virtual in a matter of days. Some universities suspended the use of student teaching evaluations for that semester, but others did not. The purpose of this study is to understand if student biases showed up in ratings of how professors managed the shift to virtual instruction during Covid-19. We will administer a survey and use LIWC (natural language processing software) to evaluate if any gender bias is present. We expect findings to show that students evaluated their instructors among gendered expectations and that women will be expected to be more nurturing and understanding while men will be rated as more competent.

Key Words: COVID-19, Virtual Instruction, Faculty, Student Evaluations, Gender Bias

Investigating the Effectiveness of Undergraduate Students' Participation in Business Research
Author(s): Cailin Kuchenbecker, Billy Marquiz and Mitchell Pickering

Program/Department(s): Center for Customer Insights and Digital Marketing, Graduate Program (MBA), International Business & Marketing Department, and Computers Information Systems Department

Presented at the CCIDM Research Seminar 2020 & Global Marketing Conference 2020

Faculty Mentor(s): Jae Min Jung

Abstract

Drawing on psychology education literature and experiential learning literature in marketing, this study investigates the effectiveness of research participation (RP) and what factors influence its effectiveness. To this end, we comprehensively explain how various demographic factors, individuals’ research participation-related factors, and course-related factors affect the value of RP. In addition, we also test if course-related factors moderate the relationship between individuals’ RP related factors and RP effectiveness. Results show that individuals’ RP-related factors are most important in explaining passive RP effectiveness, followed by demographic factors. However, the results revealed that course-related factors have little impact on RP effectiveness. In addition, several course-related factors do in fact moderate the relationship. The findings contribute to experiential learning literature in marketing and provide practical suggestions for marketing and business faculty to make the most out of student RP as well as the researchers who use the student research panels in business schools.

Key Words: Value of Research Participation, Undergraduate Education, Experiential Learning