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

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

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

Understanding User-Perceived Values of Mobile Streaming Service By Cognitive Mapping

So-called, cable ‘cord-cutting’ phenomenon (Tefertiller, 2018), or watching video/TV contents over streaming service is currently considered as an industry-wide trend. Deloitte reported 55 percent of household in the U.S. is subscribing to at least one video streaming service, which is worth $2.1 billion a month (Wang, 2018). On top of the web-based streaming service to watch video or TV content, mobile-based streaming services are not uncommon anyway for many content consumers. Although such streaming services are getting popular in the mobile industry, very few academic research efforts have made so far to understand the values of the mobile streaming services, perceived by contents consumers over other traditional media channels. Hence, the current study aims to investigate the user-perceived values of mobile-based streaming services through the lens of socio-cognitive method. By using the cognitive mapping as a socio-cognitive method rooted from the theory of social representations (Durkheim, 1898; Wagner el al., 1996; Jung et al, 2009; Jung, 2013), our study explores the values that are associated with the mobile streaming services. To achieve the goal, we have collected data using a web-based survey from 432 users of mobile streaming services. They were asked to provide three words or short phrases that best describe mobile streaming services they currently use. As a next step of the study, authors will code data to extract concepts and analyze them using the cognitive mapping method including similarities calculation and core/peripheral concepts identification process. Finally, the structure of the perceptual map will be interpreted by the social representation framework. We look forward to finding the structure of cognitive map based on the mobile streaming users’ perceptions, and it eventually reveals the relationships among the perceived values (e.g., core/peripheral, positive/negative elements, etc.) associated with the mobile streaming services. Potential findings of our study is expected to contribute to both practitioners and academic scholars who are involved in mobile streaming services through 1) better understanding of the values of the services appreciated by the users, and 2) thus being able to emphasize its importance in the future marketing / service development efforts

Geometrically Aligned Transfer Encoder for Inductive Transfer in Regression Tasks

Transfer learning is a crucial technique for handling a small amount of data that is potentially related to other abundant data. However, most of the existing methods are focused on classification tasks using images and language datasets. Therefore, in order to expand the transfer learning scheme to regression tasks, we propose a novel transfer technique based on differential geometry, namely the Geometrically Aligned Transfer Encoder (GATE). In this method, we interpret the latent vectors from the model to exist on a Riemannian curved manifold. We find a proper diffeomorphism between pairs of tasks to ensure that every arbitrary point maps to a locally flat coordinate in the overlapping region, allowing the transfer of knowledge from the source to the target data. This also serves as an effective regularizer for the model to behave in extrapolation regions. In this article, we demonstrate that GATE outperforms conventional methods and exhibits stable behavior in both the latent space and extrapolation regions for various molecular graph datasets.Comment: 12+11 pages, 6+1 figures, 0+7 table

Short-Term Forecasting of Satellite-Based Drought Indices Using Their Temporal Patterns and Numerical Model Output

Drought forecasting is essential for effectively managing drought-related damage and providing relevant drought information to decision-makers so they can make appropriate decisions in response to drought. Although there have been great efforts in drought-forecasting research, drought forecasting on a short-term scale (up to two weeks) is still difficult. In this research, drought-forecasting models on a short-term scale (8 days) were developed considering the temporal patterns of satellite-based drought indices and numerical model outputs through the synergistic use of convolutional long short term memory (ConvLSTM) and random forest (RF) approaches over a part of East Asia. Two widely used drought indices-Scaled Drought Condition Index (SDCI) and Standardized Precipitation Index (SPI)-were used as target variables. Through the combination of temporal patterns and the upcoming weather conditions (numerical model outputs), the overall performances of drought-forecasting models (ConvLSTM and RF combined) produced competitive results in terms of r (0.90 and 0.93 for validation SDCI and SPI, respectively) and nRMSE (0.11 and 0.08 for validation of SDCI and SPI, respectively). Furthermore, our short-term drought-forecasting model can be effective regardless of drought intensification or alleviation. The proposed drought-forecasting model can be operationally used, providing useful information on upcoming drought conditions with high resolution (0.05 degrees)