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Tangible assets such as property, plants, and equipment are direct means by which firms produce products to meet customer demands. On the other hand, intangible assets such as human capital, R&D, and organizational capital have traditionally been associated with firm value and investment opportunities. While firms have significantly increased intangible investment over recent decades, research has been quiet on the relationship between these two types of capital. Taking a resource-based view, I suggest that while tangible assets can improve operational performance, there are synergistic operational benefits when simultaneously leveraging both tangible and intangible assets. This is to say, intangible assets help to increase the benefits of tangible assets. Utilizing a secondary data of 2,739 firms from 1990 to 2018 and simultaneous equation modeling, I find that tangible assets enable firms to improve their operational performance significantly, but at a decreasing rate. Furthermore, firms’ ability to effectively and efficiently exploit tangible assets to improve their operational performance is highly contingent on their intangible assets such as human capital and R&D. In addition, I find that the firm’s lifecycle has significant effects on the synergistic operational benefits of tangible and intangible assets. Specifically, that mature firms can obtain more synergistic benefits relative to new firms. In contrast, such synergistic benefits are not impacted by the amount of uncertainty in the operating environment. As a set, the findings of this study suggest that when firms are allocating capital to improve operational performance, jointly considering both tangible and intangible assets is key.

Recent advances in Hi-C techniques have allowed us to map genome-wide chromatin interactions and uncover higher-order chromatin structures, thereby shedding light on the principles of genome architecture and functions. However, statistical methods for detecting changes in chromatin organizations are still in the early stage. In this dissertation, we proposed two statistical methods, namely DiffGR and scHiCDiff, for differential analysis in Hi-C contact maps. The first method DiffGR detects differentially interacting genomic regions at the scale of topologically-associating domains (TADs) between two Hi-C contact maps. Specifically, we utilized the stratum-adjusted correlation coefficient (SCC) to measure similarity of local TAD regions. We then developed a non-parametric approach to identify statistically significant changes of genomic interacting regions. Through simulation studies, we demonstrated that DiffGR can robustly and effectively discover differential genomic regions under various conditions. Furthermore, we successfully revealed cell type-specific changes in genomic interacting regions using real Hi-C datasets.The second method scHiCDiff focuses on detecting differential chromatin interactions (DCIs) in single-cell Hi-C data. The three-dimensional genome organization constructed from the conventional bulk Hi-C protocol represents an ensemble based on thousands to millions of nuclei, but not the actual genome organizations in individual cells. Unlike bulk Hi-C, single-cell Hi-C enables the exploration cell-specific chromosomal structures. However, interpretation and analysis of single-cell Hi-C data is at very early stage. To characterize the significant changes between different cells at the single-cell level, we built scHiCDiff which applied non-parametric tests and parametric models in distinguishing DCIs from single-cell Hi-C data. Our evaluation proved that these methods, especially the zero-inflated negative binomial (ZINB) and negative binomial hurdle(NBH) models, can effectively detect reliable and consistent DCIs of single cells between different conditions, which better capture cell type-specific variations of chromosomal structures.