Applications of Business Analytics in Marketing: Joint Modeling of Correlated Multivariate Outcomes

In this dissertation I develop a general regression methodology for mixed multivariate outcomes. This methodology extends the generalized linear mixed model paradigm (glmm) to allow for correlated multivariate normal random effects across regression equations for differing outcomes. This methodology, referred to as joint modeling, is particularly useful in business and marketing applications where multiple outcomes of varying data type must be analyzed simultaneously with regression. I apply joint models to binary and continuous measures of customer loyalty in a large multinational survey of car owners. Survey respondents’ word-of-mouth and desire to switch brands were used as proxies for attitudinal loyalty and behavioral loyalty and were modeled as a function of product-related attributes, service-related attributes, marketing activities, and overall satisfaction of both their current car and alternatives together. My findings provide insights into customer loyalty in the context of both experience based loyalty and image based loyalty as well as cross-cultural consumer behavior and confirm the mediating role of satisfaction. Furthermore, I find that brand evaluation based on experience with the current brand, and alternative brand evaluations based on image both significantly affect customers’ overall satisfaction levels with varying degrees of impact. The study also identifies a significant moderating effect of culture between product-related attribute performance, service-related attributes performance, marketing activities, and satisfaction. The association between functional attribute performance and satisfaction is found to be stronger in collectivistic cultures than more individualistic cultures. A second study focuses on gaining a better understanding of the interplay between price promotion and consumption of both hedonic and utilitarian retail grocery items. A joint model relating three key outcomes, loyalty, cross-buy, and trip revenue was fit with price promotion, consumption type, and consumer demographic characteristics as explanatory variables. The findings indicate that in-store deal use is associated with significant store loyalty, variety-seeking behavior, and trip revenue for both hedonic and utilitarian goods. More interestingly, we find that coupon use for utilitarian goods is negatively associated with store-loyalty, cross-buy (variety- seeking), and trip revenue

Does IT Improve Revenue Management in Hospitals?

In this study, we examine the question of how the adoption of IT systems influences revenue management in hospitals. We posit that IT plays a vital role in enhancing revenue by increasing net patient revenue and decreasing the uncompensated care ratio. Using unique datasets from various proprietary resources, we test the relationships between IT (clinical and business) investment and revenue management performance using dynamic panel data models with the generalized method of moments (GMM). Empirical results generally support our hypotheses. We found that both clinical and business IT investment have short-term and long-term effects on boosting net patient revenue and that clinical IT investment has a short-term contemporaneous effect on reducing the uncompensated care ratio. Moderation analyses suggest that: (1) larger hospitals tend to utilize business IT systems better in facilitating revenue management through both channels over the long run, but not necessarily using clinical IT; and (2) for-profit hospitals outperform their nonprofit counterparts when it comes to managing revenues through clinical IT; however, no interaction effect with business IT was found. This paper contributes to the literatures on the business value of IT investment and healthcare IT in the fields of information systems, revenue management, healthcare administration. We conclude this paper by discussing theoretical and managerial implications

The impact of digital innovation on the innovation of traditional industry

We propose a study that applies the new set of logic of digital innovation as a theoretical lens to investigate the indirect effect of digital innovation of social media on the innovation in relevant traditional industries

The lncRNA landscape of breast cancer reveals a role for DSCAM-AS1 in breast cancer progression.

Molecular classification of cancers into subtypes has resulted in an advance in our understanding of tumour biology and treatment response across multiple tumour types. However, to date, cancer profiling has largely focused on protein-coding genes, which comprise <1% of the genome. Here we leverage a compendium of 58,648 long noncoding RNAs (lncRNAs) to subtype 947 breast cancer samples. We show that lncRNA-based profiling categorizes breast tumours by their known molecular subtypes in breast cancer. We identify a cohort of breast cancer-associated and oestrogen-regulated lncRNAs, and investigate the role of the top prioritized oestrogen receptor (ER)-regulated lncRNA, DSCAM-AS1. We demonstrate that DSCAM-AS1 mediates tumour progression and tamoxifen resistance and identify hnRNPL as an interacting protein involved in the mechanism of DSCAM-AS1 action. By highlighting the role of DSCAM-AS1 in breast cancer biology and treatment resistance, this study provides insight into the potential clinical implications of lncRNAs in breast cancer

PARP-1 regulates DNA repair factor availability.

PARP-1 holds major functions on chromatin, DNA damage repair and transcriptional regulation, both of which are relevant in the context of cancer. Here, unbiased transcriptional profiling revealed the downstream transcriptional profile of PARP-1 enzymatic activity. Further investigation of the PARP-1-regulated transcriptome and secondary strategies for assessing PARP-1 activity in patient tissues revealed that PARP-1 activity was unexpectedly enriched as a function of disease progression and was associated with poor outcome independent of DNA double-strand breaks, suggesting that enhanced PARP-1 activity may promote aggressive phenotypes. Mechanistic investigation revealed that active PARP-1 served to enhance E2F1 transcription factor activity, and specifically promoted E2F1-mediated induction of DNA repair factors involved in homologous recombination (HR). Conversely, PARP-1 inhibition reduced HR factor availability and thus acted to induce or enhance BRCA-ness . These observations bring new understanding of PARP-1 function in cancer and have significant ramifications on predicting PARP-1 inhibitor function in the clinical setting

Assessment by multivariate analysis of groundwater–surface water interactions in the Coal-mining Exploring District, China

This paper applies for cluster analysis and factor analysis theory to statistically analyze environmental isotope (δ18O,δ2H, 3H, 14C) and water chemistry (K+, Na+, Ca2+, Mg2+, HCO3, SO42, Cl-) test data from different water bodies in the coal-mining exploring district. The result shows that groundwater can be clustered into four categories, namely GA, GB, GC and GD classes. Deep karst groundwater and spring were grouped into GA class, and the contour map of the second-factor scores shows that karst water and spring of the GA group is in the same area, indicating the same recharging source from the northern mountainous area. Deep fissure water was clustered into GC class with the lowest second-factor scores, and cation exchange plays a central role, then did not detect tritium with 14C of lower levels, indicating the late Pleistocene rainfall recharging. Shallow pore water and surface water were clustered into GB class with the high third factors scores, indicating surface water leakage recharging. The water samples of GD class have the highest three factors score, pointing out that the shallow pore water and surface water were polluted. The results of this study provide a scientific basis for assessing groundwater circulation mechanism in the coal-mining exploring district.  Evaluación de las Interacciones entre Agua Super cial y Agua Subterránea a Través del Análisis Multivariante en el Distrito de Exploración Carbonífera en China ResumenEste estudio utiliza la teoría del análisis de grupos y del análisis factorial para examinar estadísticamente la información de pruebas al isótopo ambiental (δ18O, δ2H, 3H, 14C) y a la química del agua (K+, Na+, Ca2+, Mg2+, HCO3-, SO42-, Cl-) en diferentes cuerpos de agua en el distrito de exploración carbonífera. El resultado muestra que el agua subterránea puede ser agrupada en cuatro categorías, nombradas Clase GA, Clase GB, Clase GC y Clase GD. El agua subterránea del karst profundo y el agua de manantial fueron agrupadas en la Clase GA; el mapa topográfico de los marcadores de segundo factor muestra que el agua del karst y el agua de manantial del grupo GA se encuentran en la misma área, lo que indica que tienen la misma fuente de recarga, en la región montañosa al norte del distrito. El agua de las suras profundas fue agrupada en la Clase GC con los marcadores más bajos de segundo factor y donde el intercambio de cationes es determinante; no se detectó tritio con los bajos niveles de 14C, lo que indica una recarga por lluvia en el Pleistoceno tardío. El agua poco profunda y el agua superficial fueron agrupadas en la Clase GB, con los mayores marcadores de tercer factor, lo que indica una recarga por vertido superficial. Las muestras de agua de la Clase GD tienen los mayores marcadores de los tres factores, lo que señala que las aguas poco profundas y las superficiales están contaminadas. Los resultados de este estudio proveen una base científica para la evaluación del mecanismo de circulación del agua subterránea en el distrito de exploración carbonífera

Measuring capital with spatial media: How online popularity on Instagram shapes land value patterns in Seoul

The recent proliferation of locative mobile devices and location‐based services has sparked geographers' interest in how such ‘spatial media’ reshape individual's mobility, spatial experiences, and perceptions. Notably, the urban and digital geography literature has highlighted the relationship between spatial media, gentrification, and urban redevelopment processes, suggesting the potential of spatial media to generate new capitalist opportunities and increase the profitability of urban spaces. In this context, this study examines how spatial media reconfigure urban land value patterns, which have been primarily explained through urban forms. To this end, we employ the spatial capital model, a regression model that predicts property value patterns using variables associated with urban forms. We demonstrate that models incorporating both offline spatial configurations and online popularity outperform the spatial capital model relying solely on physical urban layouts, thereby underscoring the role of spatial media in reshaping the spatial arrangements of capital in urban centres. Using a machine learning algorithm, we construct and compare spatial capital models and regression models with variables reflecting online popularity on Instagram to estimate land prices in three neighbourhoods in Seoul: Yeonnam‐dong, Seongsu‐dong, and Gyeongridan‐gil. These regions have experienced a surge in property values following heightened visibility on Instagram since the mid‐2010s. Our findings indicate that the performance of models incorporating Instagram data exhibit superior predictive performance relative to traditional spatial capital models. Instagram‐related variables also demonstrates greater explanatory power than conventional variables related to urban forms in predicting land price change rates. Additionally, the influence of online popularity on Instagram varies across time and space, closely aligned with the phase of development in online popularity within each area. In conclusion, spatial media have increasingly shaped urban land value patterns, while the dynamics between spatial media, urban forms, and property values can vary alongside the development of online popularity in specific regions

Enhancing Spatiotemporal Traffic Prediction through Urban Human Activity Analysis

Traffic prediction is one of the key elements to ensure the safety and convenience of citizens. Existing traffic prediction models primarily focus on deep learning architectures to capture spatial and temporal correlation. They often overlook the underlying nature of traffic. Specifically, the sensor networks in most traffic datasets do not accurately represent the actual road network exploited by vehicles, failing to provide insights into the traffic patterns in urban activities. To overcome these limitations, we propose an improved traffic prediction method based on graph convolution deep learning algorithms. We leverage human activity frequency data from National Household Travel Survey to enhance the inference capability of a causal relationship between activity and traffic patterns. Despite making minimal modifications to the conventional graph convolutional recurrent networks and graph convolutional transformer architectures, our approach achieves state-of-the-art performance without introducing excessive computational overhead.Comment: CIKM 202

Improving Real Estate Appraisal with POI Integration and Areal Embedding

Despite advancements in real estate appraisal methods, this study primarily focuses on two pivotal challenges. Firstly, we explore the often-underestimated impact of Points of Interest (POI) on property values, emphasizing the necessity for a comprehensive, data-driven approach to feature selection. Secondly, we integrate road-network-based Areal Embedding to enhance spatial understanding for real estate appraisal. We first propose a revised method for POI feature extraction, and discuss the impact of each POI for house price appraisal. Then we present the Areal embedding-enabled Masked Multihead Attention-based Spatial Interpolation for House Price Prediction (AMMASI) model, an improvement upon the existing ASI model, which leverages masked multi-head attention on geographic neighbor houses and similar-featured houses. Our model outperforms current baselines and also offers promising avenues for future optimization in real estate appraisal methodologies