Lin, Y (2024) Integrating social network analytics into operations management. PhD thesis, University of California, Berkeley, USA.
Abstract
The societal system is an intricate composition of individuals, each contributing their distinct demographics and experiences. It is not just a collection of people; it is an interconnected network that goes beyond the sum of its separate parts. Within this network, even a marginal action can have ripple effects, leading to the diffusion of behaviors, information, or, as painfully evidenced, infectious diseases. These intricate connections introduce significant challenges to the realm of operations management, including increased decision-making complexity, data overload in analysis, and a lack of theoretical guidance. All these challenges come together to form the central question that runs through my research: How can we leverage the vast wealth of data and information available to navigate this intricate societal system for more effective operational decision-making?In response to this growing need, my dissertation contributes to the intersection of social network analytics and operations management. The objective is to create a more precise reflection of our interconnected societal systems, which, in turn, enables improved decision-making across a broad spectrum of platforms. To this end, I have employed a diverse tool set. These include optimization for high-quality problem-solving, data analytics to uncover actionable insights, machine learning to enable data-driven decision-making, network and graph theory to better understand the interconnected systems, and stochastic simulation for informed evaluation, etc.The dissertation comprises three papers that each examine a different facet of integrating social network analytics with operations management. In Chapter 2, we explore the promotion optimization strategy with the consideration of the diffusion effects, drawing on extensive data from a large-scale online platform. In Chapter 3, we propose a general approximation framework to evaluate the impact of nonprogressive diffusion, delving into both its theoretical underpinnings and practical applications. In Chapter 4, we highlight the significant findings and set the stage for future research, particularly focusing on the challenges of learning user behavior within a social network with limited data availability.
| Item Type: | Thesis (Doctoral) |
|---|---|
| Thesis advisor: | Shen, Z-J M |
| Uncontrolled Keywords: | complexity; optimization; decision making; learning; operations management; problem solving; machine learning; simulation |
| Index terms: | platform, promotion, demographics, dissertation, graph theory, reflection, social network, strategy, drawing, complexity, data-driven decision-making, operations management, machine learning, problem solving, decision-making |
| Subjects: | artificial intelligence, technical documentation, systems engineering, sociology, decision analysis, professional development, management, research dissemination and communication, demography, digital design, decision-making and reasoning, theoretical framing |
| Topics: | Research Practice, Information Management, Business Strategy, Human Resources, Design Practice, Digital Applications, Urban Studies, Risk Management, Engineering Principles |
| Descriptive scope: | 4 PCTA |
N.B. Descriptive scope is a count of how many of the five facets of empirical research are indicated by the words used in title, abstract and keywords. It is not intended as a judgement on the research; merely a count of the kind of word we would expect to indicate Phenomenon, Concepts, Theoretical framing, Empirical techniques, Analytical techniques. If all five are present, then a code of “5 PCTEA” will indicate this. If you feel the coding for this record is questionable, we welcome discussion around the terms we matched or the way we categorized them. The facet you would expect may not be coded, or a facet may be coded inappropriately. This can also bear on a larger question, of which facets should be treated as defining in construction management research. Please get in touch, and we will look at it. More details here