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- Creators: Moore, James
- Member of: Theses and Dissertations
- Status: Published
Description
In recent years, companies have been expanding their business efforts on a global scale. This project explores this expansion of American-based multinational corporations (MNCs) in Ireland, and the comparison of how their culture motivation in the workplace. We did a cultural study using Hofstede and Trompenaars' cultural dimensions of the two countries then used McClelland's Needs Theory, Equity Theory, and Herzberg's Motivation-Hygiene Theory in order to create three research questions. (1) How does the manager define success for the firm as a whole and for their employees, (2) How is the definition of success reflected in the company's corporate culture (i.e. values, norms and practices), along with how cultural values, norms and practices affect the company, and (3) How do external forces (i.e. governmental factors, workplace technology, etc.) affect the workplace environment and motivation for employees? With these we hypothesized that for research question 1, we hypothesized that from our study of Hofstede's and Trompenaars' cultural frameworks, Irish employees will show a greater tendency to favor affiliation, nAff, as opposed to a need for achievement, nAch, in American employees, according to McClelland's Needs Theory. For research question 2, we predicted that motivation would be administered through style of feedback to employees and office norms, such as autonomy, flexible hours, and work-life balance. For research question 3, we hypothesized that Ireland would have an impact from external factors such as government and technology, whereas the U.S. employees would face no clear impact. We conducted eight, qualitative interviews using a questionnaire, either in person or via video conference. The interviewees were all managers in some facet and have all had some international experience. Through the analysis of the interviews, we found that the Irish employees focused on how employees are able to help or contribute to a group (nAff), instead of looking at how the contribution of a group can be used to meet individual goals (nAch). The American companies reflected Trompenaars' definition of individualism in which employees focus on collaborating in teams, as long as individual goals are met, and benchmarked collaboration as a performance measure, tying in the need for achievement, for research question one. For the second research question, we found that employees in Ireland had a focus on teamwork in the workplace and much higher respect for work-life balance. American firms, in contrast, had a greater focus on making sure employees were contributing, meeting their goals, and getting their work done. While American firms did acknowledge work-life balance and its importance, there was a priority for coming in early and/or staying late to make sure a job got done. Findings for our third question showed that government factors did impact Ireland more, due to labor laws such as required vacation days in Ireland, and that technology had less of an impact than expected, for both countries. More importantly was our finding that the companies in Ireland were greatly impacted by the decisions made by the business executives in the United States.
ContributorsSong, Jenny Jungwon (Co-author) / Brown, William (Co-author) / Arrfelt, Mathias (Thesis director) / Moore, James (Committee member) / Department of Marketing (Contributor) / Department of Management and Entrepreneurship (Contributor) / W.P. Carey School of Business (Contributor) / Barrett, The Honors College (Contributor)
Created2018-05
Description
E-commerce has rapidly become a mainstay in today's economy, and many websites have built themselves around providing a platform for independent sellers. Sites such as Etsy, Storenvy, Redbubble, and Society6 are increasingly popular options for anyone looking to open their own online store. With this project, I attempted to examine the effects of four different marketing techniques on sales in an online store. I opened a shop on Etsy and tracked sales in connection with promotion through social media, selling products in-person at a convention, holding a holiday tie-in sale, and using price anchoring. Social media accounts were opened on Facebook, Tumblr, and Instagram to promote the shop over the course of the project period, and Etsy's web analytics were used to track which sites directed the most traffic to the shop. I attended a convention in mid-January 2016 where I sold my products and distributed business cards with a discount code to track sales resulting from being at the convention. A holiday sale was held in conjunction with Valentine's Day to look at whether holidays influenced purchases. Lastly, a significantly more expensive product was temporarily put in the shop to see whether it produced a price anchoring effect \u2014 that is, encouraged sales of the less expensive products by making them seem affordable in comparison. While the volume of sales data was too small to draw statistically significant conclusions, the project was a highly instructive experience in the process of opening a small online store. The decision-making steps outlined may be helpful to other students looking to open their own online shop.
ContributorsChen, Candice Elizabeth (Author) / Moore, James (Thesis director) / Sanford, Adriana (Committee member) / Harrington Bioengineering Program (Contributor) / Barrett, The Honors College (Contributor)
Created2016-05
Description
Only an Executive Summary of the project is included.
The goal of this project is to develop a deeper understanding of how machine learning pertains to the business world and how business professionals can capitalize on its capabilities. It explores the end-to-end process of integrating a machine and the tradeoffs and obstacles to consider. This topic is extremely pertinent today as the advent of big data increases and the use of machine learning and artificial intelligence is expanding across industries and functional roles. The approach I took was to expand on a project I championed as a Microsoft intern where I facilitated the integration of a forecasting machine learning model firsthand into the business. I supplement my findings from the experience with research on machine learning as a disruptive technology. This paper will not delve into the technical aspects of coding a machine model, but rather provide a holistic overview of developing the model from a business perspective. My findings show that, while the advantages of machine learning are large and widespread, a lack of visibility and transparency into the algorithms behind machine learning, the necessity for large amounts of data, and the overall complexity of creating accurate models are all tradeoffs to consider when deciding whether or not machine learning is suitable for a certain objective. The results of this paper are important in order to increase the understanding of any business professional on the capabilities and obstacles of integrating machine learning into their business operations.
The goal of this project is to develop a deeper understanding of how machine learning pertains to the business world and how business professionals can capitalize on its capabilities. It explores the end-to-end process of integrating a machine and the tradeoffs and obstacles to consider. This topic is extremely pertinent today as the advent of big data increases and the use of machine learning and artificial intelligence is expanding across industries and functional roles. The approach I took was to expand on a project I championed as a Microsoft intern where I facilitated the integration of a forecasting machine learning model firsthand into the business. I supplement my findings from the experience with research on machine learning as a disruptive technology. This paper will not delve into the technical aspects of coding a machine model, but rather provide a holistic overview of developing the model from a business perspective. My findings show that, while the advantages of machine learning are large and widespread, a lack of visibility and transparency into the algorithms behind machine learning, the necessity for large amounts of data, and the overall complexity of creating accurate models are all tradeoffs to consider when deciding whether or not machine learning is suitable for a certain objective. The results of this paper are important in order to increase the understanding of any business professional on the capabilities and obstacles of integrating machine learning into their business operations.
ContributorsVerma, Ria (Author) / Goegan, Brian (Thesis director) / Moore, James (Committee member) / Department of Information Systems (Contributor) / Department of Supply Chain Management (Contributor) / Department of Economics (Contributor) / Barrett, The Honors College (Contributor)
Created2019-05