Jeffrey D. Wall

Privacy Coping and Information-Sharing Behaviors in Social Media: A Comparison of Chinese and U.S. Users

Although many studies examine privacy in social media settings, few studies examine privacy issues that may arise due to characteristics of user populations. This study compares privacy issues among social media users in the United States and China. It also explores privacy issues among users with different levels of Internet addiction and different online identity perceptions. In doing so, it identifies several populations that are more susceptible to privacy violations due to their online behaviors. The study finds that U.S. and Chinese users differ in their privacy coping and information-sharing behaviors. Chinese users may be at greater risk to privacy violations because of their online behaviors. Additionally, users addicted to social media and users with different online identities may be vulnerable to privacy violations. Potential explanations for these findings are provided and directions for future research are offered.

Proposing the core contributor withdrawal theory (CCWT) to understand core contributor withdrawal from online peer-production communities

Drawing on sensemaking and emotion regulation research, the purpose of this paper is to reconceptualize core contributor withdrawal (CCW) in the context of online peer-production communities (OPPCs). To explain the underlying mechanisms that make core contributors withdraw from these communities, the authors propose a process theory of contributor withdrawal called the core contributor withdrawal theory (CCWT).,To support CCWT, a typology of unmet expectations of online communities is presented, which uncovers the cognitive and emotional processing involved. To illustrate the efficacy of CCWT, a case study of the English version of Wikipedia is provided as a representative OPPC.,CCWT identifies sensemaking and emotion regulation concerning contributors’ unmet expectations as causes of CCW from OPPCs, which first lead to declined expectations, burnout and psychological withdrawal and thereby to behavioral withdrawal.,CCWT clearly identifies how and why important participation transitions, such as from core contributor to less active contributor or non-contributor, take place. By adopting process theories, CCWT provides a nuanced explanation of the cognitive and affective events that take place before core contributors withdraw from OPPCs.,CCWT highlights the challenge of online communities shifting from recruiting new contributors to preventing loss of existing contributors in the maturity stage. Additionally, by identifying the underlying cognitive and affective processes that core contributors experience in response to unexpected events, communities can develop safeguards to prevent or correct cognitions and emotions that lead to withdrawal.,CCWT provides a theoretical framework that accounts for the negative cognitions and affects that lead to core contributors’ withdrawal from online communities. It furthers the understanding of what motivates contributing to and what leads to withdrawal from OPPC.

Contextualized Meaning Extraction: A Meta-Algorithm for Big Data Text Mining with Pragmatics

Textminingisapowerfulformofbusinessintelligencethatisusedincreasinglytoinformorganizational decisions.Currenttextminingalgorithmsrelyheavilyonthelexical,syntactic,structural,andsemantic featuresoftexttoextractmeaningandinsightfordecisionmaking.Althoughsemanticanalysisisa usefulapproachtomeaningextraction,pragmaticssuggeststhatamoreaccuratemeaningoftextcan beextractedbyexaminingthecontextinwhichthetextisrecorded.Giventhatmassiveamountsof textualdatacanbedrawnfrommultipleanddiversesources,accountingforcontextisincreasingly important.Aconceptualmodelisprovidedtoexplainhowconceptsfrompragmaticscanimprove existingtextminingalgorithmstoprovidemoreaccurateinformationfordecisionmaking.Reversing thepragmaticprocessofmeaningexpressioncouldleadtoimprovedtextminingalgorithms.The theoreticalprocessmodeldevelopedhereincanprovideinsightintothedevelopmentandrefinement oftextminingalgorithmsthatdrawfromdiversesources. KEywORDS Big Data, Business Intelligence, Context, Pragmatics, Sentiment Analysis