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Multivariate latent growth modeling (multivariate LGM) provides a flexible data analytic framework for representing and assessing cross-domain (i.e., between-constructs) relationships in intraindividual changes over time, which also allows incorporation of multiple levels of analysis. Using the chapter by Cortina, Pant, and Smith-Darden (this volume) as a point of departure, this chapter discusses important preliminary data analysis and interpretation issues prior to performing multivariate LGM analyses.

This paper is the main section on quantitative data analysis. It explains the concepts at a greater detail to help non-Math/Stat scholars to understand the basics easily. Proper data analysis is critical to any research. If data are not properly analyzed, then it may give results which either cannot be properly interpreted or wrongly interpreted. This section covers univariate, multivariate analysis and then, factor analysis, cluster analysis, conjoint analysis, and multidimensional scaling (MDS) techniques.

In this article we propose a multivariate dynamic probit model. Our model can be viewed as a nonlinear VAR model for the latent variables associated with correlated binary time-series data. To estimate it, we implement an exact maximum likelihood approach, hence providing a solution to the problem generally encountered in the formulation of multivariate probit models. Our framework allows us to study the predictive relationships among the binary processes under analysis. Finally, an empirical study of three financial crises is conducted.

Purpose – This chapter compares the stability of the U.S. Dual Banking system's two bank groups, national and state banks, in light of the current financial crisis. The goal of the chapter is to answer three distinct questions: first, is there a difference in the (balance sheet) fragility between the two groups and, second, to what extent has the balance sheet fragility of both groups changed after the escalation of the financial crisis beginning in August 2007? Building on that, the third question asks to whether or not the respective regulatory agencies of both bank groups are responsible for these changes in balance sheet fragility in light of the financial crisis.

Methodology – To answer these questions the chapter uses U.S. Call Report data containing full quarterly balance sheets and P&Ls of all U.S. commercial banks over the period 2005–2008. Anecdotal evidence as well as univariate and multivariate difference-in-difference methodology focusing on the immediate pre-crisis period Q1/2005–Q3/2007 and the crisis period Q3/2007–Q4/2008 are applied.

Results – Highly significant and robust results show that, ceteris paribus, national banks reduced their potential balance sheet fragility after the escalation of the crisis in August 2007 by reducing lending and liquidity creation stronger than state banks. Anecdotal evidence supports the empirical findings. Although both FDIC and OCC did not anticipate the adverse effects of the crisis, the OCC publicly showed an earlier reaction to liquidity-related problems than the FDIC.

Originality – The chapter is the first of its kind to analyze bank fragility around the escalation of the financial crisis and the role of the regulatory agencies. The chapter holds especially interesting policy implications in the light of the current discussion about the future regulation of the banking markets.

Smart card-based E-payment systems are receiving increasing attention as the number of implementations is witnessed on the rise globally. Understanding of user adoption behavior of E-payment systems that employ smart card technology becomes a research area that is of particular value and interest to both IS researchers and professionals. However, research interest focuses mostly on why a smart card-based E-payment system results in a failure or how the system could have grown into a success. This signals the fact that researchers have not had much opportunity to critically review a smart card-based E-payment system that has gained wide support and overcome the hurdle of critical mass adoption. The Octopus in Hong Kong has provided a rare opportunity for investigating smart card-based E-payment system because of its unprecedented success. This research seeks to thoroughly analyze the Octopus from technology adoption behavior perspectives.

Cultural impacts on adoption behavior are one of the key areas that this research posits to investigate. Since the present research is conducted in Hong Kong where a majority of population is Chinese ethnicity and yet is westernized in a number of aspects, assuming that users in Hong Kong are characterized by eastern or western culture is less useful. Explicit cultural characteristics at individual level are tapped into here instead of applying generalization of cultural beliefs to users to more accurately reflect cultural bias. In this vein, the technology acceptance model (TAM) is adapted, extended, and tested for its applicability cross-culturally in Hong Kong on the Octopus. Four cultural dimensions developed by Hofstede are included in this study, namely uncertainty avoidance, masculinity, individualism, and Confucian Dynamism (long-term orientation), to explore their influence on usage behavior through the mediation of perceived usefulness.

TAM is also integrated with the innovation diffusion theory (IDT) to borrow two constructs in relation to innovative characteristics, namely relative advantage and compatibility, in order to enhance the explanatory power of the proposed research model. Besides, the normative accountability of the research model is strengthened by embracing two social influences, namely subjective norm and image. As the last antecedent to perceived usefulness, prior experience serves to bring in the time variation factor to allow level of prior experience to exert both direct and moderating effects on perceived usefulness.

The resulting research model is analyzed by partial least squares (PLS)-based Structural Equation Modeling (SEM) approach. The research findings reveal that all cultural dimensions demonstrate direct effect on perceived usefulness though the influence of uncertainty avoidance is found marginally significant. Other constructs on innovative characteristics and social influences are validated to be significant as hypothesized. Prior experience does indeed significantly moderate the two influences that perceived usefulness receives from relative advantage and compatibility, respectively. The research model has demonstrated convincing explanatory power and so may be employed for further studies in other contexts. In particular, cultural effects play a key role in contributing to the uniqueness of the model, enabling it to be an effective tool to help critically understand increasingly internationalized IS system development and implementation efforts. This research also suggests several practical implications in view of the findings that could better inform managerial decisions for designing, implementing, or promoting smart card-based E-payment system.

This chapter presents the impact of the Covid-19 pandemic on the course of studies and life career skills development of undergraduate students at the University of Macedonia, a mid-sized public Greek University. It describes a multivariate methodology research that investigated how the students, first-hand experienced the unexpected changes from face-to-face on campus to synchronous online education during the lockdown and how they coped with these changes. Change is considered and described as a main component within current life and career trajectories addressing chaotic and unpredictable circumstances while Chaos Theory of Careers (CTC) offers the theoretical background of the chapter. The research followed the mixed methods paradigm: a multilevel embedded sequential explanatory design including a participant selection model and multivariate data analysis methods. A survey (N = 621) was conducted; individual interviews and focus groups’ discussions further explained the quantitative findings. The emerging clusters of students revealed similarities in feelings, motivation, adaptation, and life career skills development. The first cluster comprised of older, digitally high-skilled students, with the required technological equipment, adaptable to change, self-regulated, strongly in favor of synchronous online education; in the second cluster were grouped the younger, digitally medium-skilled students, who regularly participated in both modalities, critically recognized the advantages of either one, feeling strongly in favor of a combination; finally, the third cluster included digitally medium-skilled students who found serious difficulty in using online platforms, dissatisfied with social isolation and distant interaction, strongly preferring face-to-face instruction, valuing direct physical contact, social connection, and networking.

This paper analyzes county-level firm births across the United States using a spatial count model that permits spatial dependence, cross-correlation among different industry types, and over-dispersion commonly found in empirical count data. Results confirm the presence of spatial autocorrelation (which can arise from agglomeration effects and missing variables), industry-specific over-dispersion, and positive, significant cross-correlations. After controlling for existing-firm counts in 2008 (as an exposure term), parameter estimates and inference suggest that a younger work force and/or clientele (as quantified using each county’s median-age values) is associated with more firm births (in 2009). Higher population densities is associated with more new basic-sector firms, while reducing retail-firm starts. The modeling framework demonstrated here can be adopted for a variety of settings, harnessing very local, detailed data to evaluate the effectiveness of investments and policies, in terms of generating business establishments and promoting economic gains.