### Table 4: Network coding in information diffusion: a

2003

Cited by 6

### Table 1 Performance of diffusion tensor registration according to intensity information

"... In PAGE 8: ... The results of this evaluation are displayed in Fig. 3 and summarized in Table1 . When performing registration of artificially deformed data sets, TC showed significantly increased performance in EDIV, MSE, and OVL (P H11021 0.... ..."

### Table 3: Mean Usage Characteristics and Diffusion Properties of Information Types Information Type News Discussion t-statistic Usage Characteristics amp; Diffusion Properties Number of Words 3235 4168 -

2007

"... In PAGE 27: ... We then tested whether words in each category differed significantly across these dimensions by conducting t-tests of the differences of means across information types and dimensions. Table3 lists the mean usage characteristics and diffusion properties of both event news and discussion topics. Our t-tests demonstrate that these information types differ significantly across all dimensions of interest related to their use and diffusion.... In PAGE 33: ....1. Estimation of the Diffusion of Information We first tested the diffusion of all types of information through the firm. Table3 presents the results of logistic regression and hazard rate model estimates of the likelihood of receiving information and the rate at which different types of information diffuse to different users. Model 1 presents the results of the logistic regression estimating factors that influence the likelihood of receiving information.... In PAGE 34: ... Table3 . Drivers of Access to Information Model 1 Model 2 Dependent Variable: Word Received Rate of Receipt Specification (Coefficient Reported) Logistic (Odds Ratio) Hazard Model (Hazard Ratio) Demography1 Gender Dummy (Male = 1) 1.... ..."

### Table 1. Empirical Studies of IT Diffusion

1992

"... In PAGE 12: ...ndividuals in organizations or organizations as a whole. Similarly to Cooper and Zmud (1990, p. 123) information technology is defined here as any system, product or process whose underlying technology base is composed of computer or communications software or hardware. Figure 2 below maps the eighteen studies to the IT Diffusion Framework; Table1 provides a high-level summary of each study. [INSERT FIGURE 2 THEN TABLE 1 ABOUT HERE] The four subsections below use the IT Diffusion Framework as a device to structure a discussion of major results and implications arising from the eighteen studies.... In PAGE 12: ...1 Individual Adoption of Type 1 Technologies Five studies examined individual adoption or use of Type 1 technologies. The technologies included a text editor, a wordprocessing package, spreadsheet software, graphics software, personal computers and an expert system (see Table1 ). These technologies qualify as independent-use technologies since they were intended to facilitate self- contained tasks performed by individual users.... ..."

Cited by 30

### Table 10. Other Research on Internet Diffusion

2001

"... In PAGE 28: ... Rather than constraining or pre- scribing the use of any particular data, the GDI framework encourages researchers to consider any available sources. OTHER INTERNET DIFFUSION WORK We are now in a position to characterize other research work that has tried to measure the status of Internet diffusion in various countries ( Table10 ). These may be grouped into four categories: (1) studies grounded in traffic patterns and data collection from the Internet itself, (2) studies based on survey research and statistical samples, (3) estimates and derived indexes that are based on self- assessment or syntheses of other studies, and (4) quantitative modeling approaches.... In PAGE 28: ... These may be grouped into four categories: (1) studies grounded in traffic patterns and data collection from the Internet itself, (2) studies based on survey research and statistical samples, (3) estimates and derived indexes that are based on self- assessment or syntheses of other studies, and (4) quantitative modeling approaches. Table10 is a representative but not necessarily exhaustive list of these approaches and references where more information may be found about them. Press [1997b] presented one of the first survey articles about on-going measure- ment techniques.... ..."

Cited by 8

### Table 3. Drivers of Access to Information Model 1 Model 2

2007

"... In PAGE 27: ... We then tested whether words in each category differed significantly across these dimensions by conducting t-tests of the differences of means across information types and dimensions. Table3 lists the mean usage characteristics and diffusion properties of both event news and discussion topics. Our t-tests demonstrate that these information types differ significantly across all dimensions of interest related to their use and diffusion.... In PAGE 28: ... 27 Table3 : Mean Usage Characteristics and Diffusion Properties of Information Types Information Type News Discussion t-statistic Usage Characteristics amp; Diffusion Properties Number of Words 3235 4168 - Potential Diffusion Events 245280 320470 - Realized Diffusion Events 65145 9344 - Number of Emails 236.21 17.... In PAGE 33: ....1. Estimation of the Diffusion of Information We first tested the diffusion of all types of information through the firm. Table3 presents the results of logistic regression and hazard rate model estimates of the likelihood of receiving information and the rate at which different types of information diffuse to different users. Model 1 presents the results of the logistic regression estimating factors that influence the likelihood of receiving information.... ..."

### Table 6.6 are attained with t = 0, which means that neighbours are notified immediately of each status change. Then diffusion scheduling uses between 5 and 10 times the number of allocation messages of the hierarchical algorithm, gradient scheduling uses an order of magnitude more. Mad Postman closely resembles diffusion scheduling in all its message usage. However, the load information messages make up the majority of all messages: for diffusion, 80 %, for gradient, 95 %. When t is chosen so large that no explicit load messages are sent, speed-up decreases typically 10 to 40 % for diffusion and 5 % for gradient scheduling. In some cases, the number of allocation messages increases (no more than a factor two), and that leads to a speed-up comparable with t = 0.

### Table 2 summarizes the sampling distributions of the three estimators of the diffusion

"... In PAGE 20: ... If, however, the information about intraday volatility that is revealed by the range but not by absolute or squared returns is useful in the estimation of the model, the sampling properties of the range- based quasi-maximum likelihood estimator could well dominate the sampling properties of the exact maximum likelihood estimator for absolute returns. 14 Comparing the third row of each panel in Table2... In PAGE 21: ...bsolute return as volatility proxy. First, the range-based parameter estimates are more accurate. Second, even for the same parameters values, the approximate normality of the log range yields a more efficient volatility extraction. With this in mind, we summarize in the last two panels of Table2 (and in the last column of Figure 2) the sampling distributions of the average extraction error , which is 1 T j T t apos;1 ( ln Ft amp;ln Ft ) an estimator of the expected extraction error , and the average squared extraction E [ ln Ft amp;ln Ft ] error , which is an estimator of the expected squared extraction error 1 T j T t apos;1 ( ln Ft amp;ln... In PAGE 22: ... Now we discuss the results for a smaller sample size of T = 500 observations and a larger sample size of T = 5000 observations. We show the results for T = 500 in Table 3; they are qualitatively identical to those in Table2 . Quantitatively, however, the relative performance of the quasi-maximum likelihood estimator with the log absolute return as volatility proxy, which was already poor with T = 1000 observations, is much worse with T = 500 observations.... In PAGE 22: ... D We present the results for T = 5000 in Table 4. Qualitatively, they are again identical to the results in Table2 ; quantitatively, the comparative performance of the quasi-maximum likelihood estimator with the log absolute return as volatility proxy is improved in some respects,... ..."

### Table 2 summarizes the sampling distributions of the three estimators of the diffusion

"... In PAGE 18: ... If, however, the information about intraday volatility that is revealed by the range but not by absolute or squared returns is useful in the estimation of the model, the sampling properties of the range- based quasi-maximum likelihood estimator could well dominate the sampling properties of the exact maximum likelihood estimator for absolute returns. 14 Comparing the third row of each panel in Table2... In PAGE 19: ...bsolute return as volatility proxy. First, the range-based parameter estimates are more accurate. Second, even for the same parameters values, the approximate normality of the log range yields a more efficient volatility extraction. With this in mind, we summarize in the last two panels of Table2 (and in the last column of Figure 2) the sampling distributions of the average extraction error , which is 1 T j T t apos;1 ( ln Ft amp;ln Ft ) an estimator of the expected extraction error , and the average squared extraction E [ ln Ft amp;ln Ft ] error , which is an estimator of the expected squared extraction error 1 T j T t apos;1 ( ln Ft amp;ln... In PAGE 20: ... Now we discuss the results for a smaller sample size of T = 500 observations and a larger sample size of T = 5000 observations. We show the results for T = 500 in Table 3; they are qualitatively identical to those in Table2 . Quantitatively, however, the relative performance of the quasi-maximum likelihood estimator with the log absolute return as volatility proxy, which was already poor with T = 1000 observations, is much worse with T = 500 observations.... In PAGE 20: ... D We present the results for T = 5000 in Table 4. Qualitatively, they are again identical to the results in Table2 ; quantitatively, the comparative performance of the quasi-maximum likelihood estimator with the log absolute return as volatility proxy is improved in some respects,... ..."

### Table 11 Alternative Factor of Information Quality 3.23 Trendy Content 3.24 Social Concern 3.25 Diffusion 3.26 Brand Image 3.27 lateness of content 3.28 Understandability 3.23 3.24 3.25 3.26 3.27 3.28

2006