Faculty Research

Variogram estimation plays a vastly important role in spatial modeling. Different methods
for variogram estimation can be largely classified into least squares methods and likelihood
based methods. A general framework to estimate the variogram through a set of estimating
equations is proposed. This approach serves as an alternative approach to likelihood based
methods and includes commonly used least squares approaches as its special cases. The
proposed method is highly efficient as a low dimensional representation of the weight
matrix is employed. The statistical efficiency of various estimators is explored and the lag
effect is examined. An application to a hydrology data set is also presented.

While it is crucial for organizations to automatically identify the gender of participants in product discussion forums, they may have difficulties adopting existing gender classification methods because the associations between the linguistic features used in those studies and gender type usually varies with context. The prototype system we propose to demo validates a framework for the development of gender classification that uses a more “data-driven” approach. It constantly extracts content-specific features from the discussion content. And the system could automatically adjust itself to accommodate the contextual changes in order to achieve better classification accuracy.

Senior citizens could greatly be benefited from the social support received from a community (Choi et al. 2014; Goswami et al. 2010). Social support denotes to the interaction/communication with others, verbal or nonverbal, reducing the uncertainty or enhancing the self-perception of in control of one’s own life (Albrecht and Adelman 1987). All participants of online communities are motivated by their desire of seeking social support. And such support occurs when community members form relational links among them and have interactions that intend to help (Heaney and Israel 2002). A network member can receive/send different types of social supports from/to others. Informational support transmits information and provides guidance related to the task/question a community member has (Krause 1986); emotional support expresses understanding, encouragement, empathy affection, affirming, validation, sympathy, caring and concern (House 1981; Wang et al. 2014); companionship or network support gives the recipient a sense of belonging (Keating 2013; Wang et al. 2014); and appraisal support enhances the self-evaluation of the recipient (House 1981). Studies have shown that people are usually motivated by their desire of seeking one or more types of social supports to participate in an online community (Goswami et al. 2010; Kanayama 2003; Pfeil 2007; Pfeil and Zaphiris 2009; Wright 2000; Xie 2008). And such social support can only be acquired during the interaction with others. For senior citizens, even though they can be greatly benefited from the social support received through participation, the obstacles they need to overcome in order to feel engaged could be larger than that of younger people (Charness and Boot 2009; Lee et al. 2011), especially when they come to the community for the first time. They could be easily overwhelmed by the content that has been generated by other existing members, finding it difficult to identify an appropriate member to initiate a meaningful interaction. It therefore is critical for an online community system to help senior participants identify other existing members who are more likely to supply the type of support they are seeking. While many previous studies have uncovered the variety factors, contextual (Pfeil and Zaphiris 2009; Wang et al. 2015; Xie 2008) or individual (Wang et al. 2014, 2015, 2012; Wright 1999), that impact the degree to which a senior citizen receives social support needed from an online community, it remains unclear what the characteristics of existing community members who are more likely to provide a new comer the kind of support, informational, emotional, companionship, or appraisal are. And the answer to this question may have significant academic and practical implications. This study thus proposes to fulfil the gap by utilizing data collected from a senior community website to investigate the links between the characteristics of existing senior members and the amount and the type of support they provided to new comers.

Purpose
The emergence of mobile health (mHealth) products has created a capability of monitoring and managing the health of patients with chronic diseases. These mHealth technologies would not be beneficial unless they are adopted and used by their target users. This study identifies key factors affecting the usage of mHealth apps based on user usage data collected from an mHealth app.

Design/methodology/approach
Using a data set collected from an mHealth app named mPower, developed for patients with Parkinson’s disease (PD), this paper investigated the effects of disease diagnosis, disease progression, and mHealth app difficulty level on app usage, while controlling for user information. App usage is measured by five different activity counts of the app.

Findings
The results across five measures of mHealth app usage vary slightly. On average, previous professional diagnosis and high user performance scores encourage user participation and engagement, while disease progression hinders app usage.

Research limitations/implications
The findings potentially provide insights into better design and promotion of mHealth products and improve the capability of health management of patients with chronic diseases.

Originality/value
Studies on the mHealth app usage are critical but sparse because large-scale and reliable mHealth app usage data are limited. Unlike earlier works based solely on survey data, this research used a large user usage data collected from an mHealth app to study key factors affecting app usage. The methods presented in this study can serve as a pioneering work for the design and promotion of mHealth technologies.

Xiaohui Chang associate professor of Business Analytics, doesn't hold office hours. She holds "ask me anything hours" as a part of her methods to engage, connect and show empathy for her online students. Her first essay on the Ecampus teaching journey has great tips for all of our increased interactions in the virtual space.