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Description
Forrest Research estimated that revenues derived from mobile devices will grow at an annual rate of 39% to reach $31 billion by 2016. With the tremendous market growth, mobile banking, mobile marketing, and mobile retailing have been recently introduced to satisfy customer needs. Academic and practical articles have widely discussed unique features of m-commerce. For instance, hardware constraints such as small screens have led to the discussion of tradeoff between usability and mobility. Needs for personalization and entertainment foster the development of new mobile data services. Given distinct features of mobile data services, existing empirical literature on m-commerce is mostly from the consumer side and focuses on consumer perceptions toward these features and their adoption intentions. From the supply side, limited data availability in early years explains the lack of firm-level studies on m-commerce. Prior studies have shown that unclear market demand is a major reason that hinders firms' adoption of m-commerce. Given the advances of smart phones, especially the introduction of the iPhone in 2007, firms recently have started to incorporate various mobile information systems in their business operations. The study uses mobile retailing as the context and empirically assesses firms' migration to this new sales venue with a unique cross-sectional dataset. Despite the distinct features of m-commerce, m-Retailing is essentially an extended arm of e-Retailing. Thus, a dependency perspective is used to explore the link between a firm's e-Retail characteristics and the migration to m-Retailing. Rooted in the innovation diffusion theory, the first stage of my study assesses the decision of adoption that indicates whether a firm moves to m-Retailing and the extent of adoption that shows a firm's commitment to m-Retailing in terms of system implementation choices. In this first stage, I take a dependency perspective to examine the impacts of e-Retail characteristics on m-Retailing adoption. The second stage of my study analyzes conditions that affect business value of the m-Retail channel. I examine the association between system implementation choices and m-Retail performance while analyzing the effects of e-Retail characteristics on value realization. The two-stage analysis provides an exploratory assessment of firm's migration from e-Retailing to m-Retailing.
ContributorsChou, Yen-Chun (Author) / Shao, Benjamin (Thesis advisor) / St. Louis, Robert (Committee member) / Goul, Michael (Committee member) / Arizona State University (Publisher)
Created2013
Description
The Pearson and likelihood ratio statistics are well-known in goodness-of-fit testing and are commonly used for models applied to multinomial count data. When data are from a table formed by the cross-classification of a large number of variables, these goodness-of-fit statistics may have lower power and inaccurate Type I error rate due to sparseness. Pearson's statistic can be decomposed into orthogonal components associated with the marginal distributions of observed variables, and an omnibus fit statistic can be obtained as a sum of these components. When the statistic is a sum of components for lower-order marginals, it has good performance for Type I error rate and statistical power even when applied to a sparse table. In this dissertation, goodness-of-fit statistics using orthogonal components based on second- third- and fourth-order marginals were examined. If lack-of-fit is present in higher-order marginals, then a test that incorporates the higher-order marginals may have a higher power than a test that incorporates only first- and/or second-order marginals. To this end, two new statistics based on the orthogonal components of Pearson's chi-square that incorporate third- and fourth-order marginals were developed, and the Type I error, empirical power, and asymptotic power under different sparseness conditions were investigated. Individual orthogonal components as test statistics to identify lack-of-fit were also studied. The performance of individual orthogonal components to other popular lack-of-fit statistics were also compared. When the number of manifest variables becomes larger than 20, most of the statistics based on marginal distributions have limitations in terms of computer resources and CPU time. Under this problem, when the number manifest variables is larger than or equal to 20, the performance of a bootstrap based method to obtain p-values for Pearson-Fisher statistic, fit to confirmatory dichotomous variable factor analysis model, and the performance of Tollenaar and Mooijaart (2003) statistic were investigated.
ContributorsDassanayake, Mudiyanselage Maduranga Kasun (Author) / Reiser, Mark R. (Thesis advisor) / Kao, Ming-Hung (Committee member) / Wilson, Jeffrey (Committee member) / St. Louis, Robert (Committee member) / Kamarianakis, Ioannis (Committee member) / Arizona State University (Publisher)
Created2018