Research on Public Participation in Sustainable Urbanization Process
In: Journal of politics and law: JPL, Volume 5, Issue 2
ISSN: 1913-9055
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In: Journal of politics and law: JPL, Volume 5, Issue 2
ISSN: 1913-9055
In: Journal of politics and law: JPL, Volume 5, Issue 3
ISSN: 1913-9055
In: Business research quarterly: BRQ, Volume 22, Issue 4, p. 245-256
ISSN: 2340-9444
In: Journal of enterprise information management: an international journal, Volume 33, Issue 2, p. 353-380
ISSN: 1758-7409
Purpose
The purpose of this paper is to explore the antecedents of self-disclosure intention on mobile social applications. This study integrates privacy calculus model and elaboration likelihood theory to reconcile the rational and heuristic views of privacy decision making.
Design/methodology/approach
Using a "random dialing" sampling method, an empirical survey with 913 respondents was conducted. A series of regression models were employed to test the proposed relationships. Robust checks with sub-group analysis were conducted.
Findings
Self-disclosure intention develops along a dual route including the central route and the peripheral route. When the central route predominates, social media users form their attitudes toward self-disclosure based on a rational calculus of the privacy concern and perceived rewards. When the peripheral route predominates, users perform a more heuristic evaluation of relevant informational cues (information about privacy harms, the extent of information asymmetry between users and operators) and contextual cues (flow experience, privacy disclosure of friends). Peripheral cues moderate the relationships between central cues and self-disclosure intention.
Originality/value
This paper extends the Elaboration Likelihood Model by investigating the interaction between the central route and peripheral route. The results provide alternative explanations on the renowned "privacy paradox" phenomenon.
In: Information, technology & people, Volume 32, Issue 6, p. 1679-1703
ISSN: 1758-5813
Purpose
The purpose of this paper is to facilitate understanding of how to mitigate the privacy concerns of users who have experienced privacy invasions.
Design/methodology/approach
Drawing on the communication privacy management theory, the authors developed a model suggesting that privacy concerns form through a cognitive process involving threat-coping appraisals, institutional privacy assurances and privacy experiences. The model was tested using data from an empirical survey with 913 randomly selected social media users.
Findings
Privacy concerns are jointly determined by perceived privacy risks and privacy self-efficacy. The perceived effectiveness of institutional privacy assurances in terms of established privacy policies and privacy protection technology influences the perceptions of privacy risks and privacy self-efficacy. More specifically, privacy invasion experiences are negatively associated with the perceived effectiveness of institutional privacy assurances.
Research limitations/implications
Privacy concerns are conceptualized as general concerns that reflect an individual's worry about the possible loss of private information. The specific types of private information were not differentiated.
Originality/value
This paper is among the first to clarify the specific mechanisms through which privacy invasion experiences influence privacy concerns. Privacy concerns have long been viewed as resulting from individual actions. The study contributes to literature by linking privacy concerns with institutional privacy practice.
In: International Geology Review, Volume 12, Issue 9, p. 1112-1136
In: Environmental science and pollution research: ESPR, Volume 26, Issue 2, p. 1706-1715
ISSN: 1614-7499
For a decade, The Cancer Genome Atlas (TCGA) program collected clinicopathologic annotation data along with multi-platform molecular profiles of more than 11,000 human tumors across 33 different cancer types. TCGA clinical data contain key features representing the democratized nature of the data collection process. To ensure proper use of this large clinical dataset associated with genomic features, we developed a standardized dataset named the TCGA Pan-Cancer Clinical Data Resource (TCGA-CDR), which includes four major clinical outcome endpoints. In addition to detailing major challenges and statistical limitations encountered during the effort of integrating the acquired clinical data, we present a summary that includes endpoint usage recommendations for each cancer type. These TCGA-CDR findings appear to be consistent with cancer genomics studies independent of the TCGA effort and provide opportunities for investigating cancer biology using clinical correlates at an unprecedented scale.
BASE
For a decade, The Cancer Genome Atlas (TCGA) program collected clinicopathologic annotation data along with multi-platform molecular profiles of more than 11,000 human tumors across 33 different cancer types. TCGA clinical data contain key features representing the democratized nature of the data collection process. To ensure proper use of this large clinical dataset associated with genomic features, we developed a standardized dataset named the TCGA Pan-Cancer Clinical Data Resource (TCGA-CDR), which includes four major clinical outcome endpoints. In addition to detailing major challenges and statistical limitations encountered during the effort of integrating the acquired clinical data, we present a summary that includes endpoint usage recommendations for each cancer type. These TCGA-CDR findings appear to be consistent with cancer genomics studies independent of the TCGA effort and provide opportunities for investigating cancer biology using clinical correlates at an unprecedented scale.
BASE
For a decade, The Cancer Genome Atlas (TCGA) program collected clinicopathologic annotation data along with multi-platform molecular profiles of more than 11,000 human tumors across 33 different cancer types. TCGA clinical data contain key features representing the democratized nature of the data collection process. To ensure proper use of this large clinical dataset associated with genomic features, we developed a standardized dataset named the TCGA Pan-Cancer Clinical Data Resource (TCGA-CDR), which includes four major clinical outcome endpoints. In addition to detailing major challenges and statistical limitations encountered during the effort of integrating the acquired clinical data, we present a summary that includes endpoint usage recommendations for each cancer type. These TCGA-CDR findings appear to be consistent with cancer genomics studies independent of the TCGA effort and provide opportunities for investigating cancer biology using clinical correlates at an unprecedented scale.
BASE
For a decade, The Cancer Genome Atlas (TCGA) program collected clinicopathologic annotation data along with multi-platform molecular profiles of more than 11,000 human tumors across 33 different cancer types. TCGA clinical data contain key features representing the democratized nature of the data collection process. To ensure proper use of this large clinical dataset associated with genomic features, we developed a standardized dataset named the TCGA Pan-Cancer Clinical Data Resource (TCGA-CDR), which includes four major clinical outcome endpoints. In addition to detailing major challenges and statistical limitations encountered during the effort of integrating the acquired clinical data, we present a summary that includes endpoint usage recommendations for each cancer type. These TCGA-CDR findings appear to be consistent with cancer genomics studies independent of the TCGA effort and provide opportunities for investigating cancer biology using clinical correlates at an unprecedented scale.
BASE
For a decade, The Cancer Genome Atlas (TCGA) program collected clinicopathologic annotation data along with multi-platform molecular profiles of more than 11,000 human tumors across 33 different cancer types. TCGA clinical data contain key features representing the democratized nature of the data collection process. To ensure proper use of this large clinical dataset associated with genomic features, we developed a standardized dataset named the TCGA Pan-Cancer Clinical Data Resource (TCGA-CDR), which includes four major clinical outcome endpoints. In addition to detailing major challenges and statistical limitations encountered during the effort of integrating the acquired clinical data, we present a summary that includes endpoint usage recommendations for each cancer type. These TCGA-CDR findings appear to be consistent with cancer genomics studies independent of the TCGA effort and provide opportunities for investigating cancer biology using clinical correlates at an unprecedented scale. Analysis of clinicopathologic annotations for over 11,000 cancer patients in the TCGA program leads to the generation of TCGA Clinical Data Resource, which provides recommendations of clinical outcome endpoint usage for 33 cancer types.
BASE