[결과] 숫자로 말하기
분석결과를 정리하기 위한 표현들입니다.
[1]
Regarding the effect of lagged channel-specific information stock, our results indicate that information stock accumulated through direct type-in (0.61) is the most effective in increasing search utility, followed by search engines (0.34).
[2]
Although display/referral is the least popular channel according to Table 5, information stock accumulated via this channel is still quite effective in making a website the entry site.
[3]
Direct channel (0.39) is the most effective channel in enticing consumers to visit beyond the entry site, followed by search (0.25) and email (0.24).
[4]
Table 2 outlines the statistics of user activity for mobile adopters in the first weeks.
[5]
As is evident from Table 2, women, on average, send far fewer votes (23.99 versus 30.97 for men).
[6]
In addition, note that women are far less likely to initiate explicit contact via sending a message (1.87 unique conversations initiated by women versus 4.7 conversations by men).
[7]
On the received side as per Table 3, women receive four times the number of votes than men (131.56 versus 33.65).
[8]
Table 2 and 3, the descriptive statistics show that women achieve a significantly higher number of matches for women are received matches.
[9]
More than 75% of matches for men are "matches sent," that is, matches initiated by the men, while less than 25% of matches for women are "matches sent."
[10]
We find that total matchs increase by 21.2% per week for males and by 64% per week for females.
[11]
Therefore, the most popular login hour for user A is 1 p.m. and that hour accounts for 75% of all user A’s logins in the week, 5 p.m. is the second most popular hour for user A and accounts for the balance of user A’s logins in the week.
[12]
This distribution allows us to calculate the Gini coefficient for user A of 0.9375 and thus demonstrates the diversity of different login hours of the day for user A.
[13]
In particular, the number of times the focal user viewed a profile of a user of a different race (ViewOtherRaceSent) increased by 85.3% per week for females and by 127% per week for males.
[14]
In Model 2, the relationship between system modularity and perceived risk was negative and significant (β = –0.265, t = –4.526, p < 0.01), suggesting that system modularity helps mitigate the perceived risk of the digital supply chain system.
[15]
The test statistics had a significant value of 1.988 (p < 0.05), suggesting that perceived risk mediates the relationship between system modularity and SC digitization.
[16]
The main effect of system modularity on SC digitization when perceived risk was not included (in Model 1) was positive and significant (β = 0.154, t = 2.102, p < 0.05). In contrast, the direct effect of system modularity on SC digitization when perceived risk was included (in Model 2) was not significant (β = 0.016, t = 0.625, p > 0.1). This suggests that the relationship is fully mediated by perceived risk, supporting H1.
[17]
The results, however, did not support H2. Specifically, in Model 2, the relationship between ITDR and perceived risk was not significant (β = 0.026, t = 0.353, p > 0.1).
[18]
A t‑test indicated no significant difference between these two cases (t = 0.33, p > 0.1).
[19]
Table 6 indicates that this interaction term had a positive and significant effect on SC digitization (β = 0.274, t = 2.247, p < 0.05).
[20]
Model 4 indicates that this interaction term had a negative and significant effect on perceived risk (β = –0.307, t = –3.185, p < 0.01), suggesting that system modularity and IT-DR reinforce each other in mitigating perceived risk.
[21]
In addition, Model 4 indicates that the effect of this interaction term on SC digitization became smaller and insignificant (β = 0.071, t = 1.613, p > 0.1) when perceived risk was added. This suggests that perceived risk fully mediates the positive effect of this interaction term on SC digitization. Therefore, H3 is supported.
[22]
As the full model (Model 4) in Table 6 indicates, the coefficient of risk propensity on SC digitization was positive and significant (β = 0.315, t = 2.318, p < 0.05), which implies that even in the presence of perceived risk, organizations with risk-seeking propensity still pursue higher levels of SC digitization.
[23]
The coefficient of managerial aspiration was positive and significant (β = 0.179, t = 2.518, p < 0.05). This suggests that when decision makers expect a large benefit of SC digitization, even if they perceive risk, they are willing to take the risk and adopt digital supply chain systems. This finding is in line with...
[1]
Regarding the effect of lagged channel-specific information stock, our results indicate that information stock accumulated through direct type-in (0.61) is the most effective in increasing search utility, followed by search engines (0.34).
[2]
Although display/referral is the least popular channel according to Table 5, information stock accumulated via this channel is still quite effective in making a website the entry site.
[3]
Direct channel (0.39) is the most effective channel in enticing consumers to visit beyond the entry site, followed by search (0.25) and email (0.24).
[4]
Table 2 outlines the statistics of user activity for mobile adopters in the first weeks.
[5]
As is evident from Table 2, women, on average, send far fewer votes (23.99 versus 30.97 for men).
[6]
In addition, note that women are far less likely to initiate explicit contact via sending a message (1.87 unique conversations initiated by women versus 4.7 conversations by men).
[7]
On the received side as per Table 3, women receive four times the number of votes than men (131.56 versus 33.65).
[8]
Table 2 and 3, the descriptive statistics show that women achieve a significantly higher number of matches for women are received matches.
[9]
More than 75% of matches for men are "matches sent," that is, matches initiated by the men, while less than 25% of matches for women are "matches sent."
[10]
We find that total matchs increase by 21.2% per week for males and by 64% per week for females.
[11]
Therefore, the most popular login hour for user A is 1 p.m. and that hour accounts for 75% of all user A’s logins in the week, 5 p.m. is the second most popular hour for user A and accounts for the balance of user A’s logins in the week.
[12]
This distribution allows us to calculate the Gini coefficient for user A of 0.9375 and thus demonstrates the diversity of different login hours of the day for user A.
[13]
In particular, the number of times the focal user viewed a profile of a user of a different race (ViewOtherRaceSent) increased by 85.3% per week for females and by 127% per week for males.
[14]
In Model 2, the relationship between system modularity and perceived risk was negative and significant (β = –0.265, t = –4.526, p < 0.01), suggesting that system modularity helps mitigate the perceived risk of the digital supply chain system.
[15]
The test statistics had a significant value of 1.988 (p < 0.05), suggesting that perceived risk mediates the relationship between system modularity and SC digitization.
[16]
The main effect of system modularity on SC digitization when perceived risk was not included (in Model 1) was positive and significant (β = 0.154, t = 2.102, p < 0.05). In contrast, the direct effect of system modularity on SC digitization when perceived risk was included (in Model 2) was not significant (β = 0.016, t = 0.625, p > 0.1). This suggests that the relationship is fully mediated by perceived risk, supporting H1.
[17]
The results, however, did not support H2. Specifically, in Model 2, the relationship between ITDR and perceived risk was not significant (β = 0.026, t = 0.353, p > 0.1).
[18]
A t‑test indicated no significant difference between these two cases (t = 0.33, p > 0.1).
[19]
Table 6 indicates that this interaction term had a positive and significant effect on SC digitization (β = 0.274, t = 2.247, p < 0.05).
[20]
Model 4 indicates that this interaction term had a negative and significant effect on perceived risk (β = –0.307, t = –3.185, p < 0.01), suggesting that system modularity and IT-DR reinforce each other in mitigating perceived risk.
[21]
In addition, Model 4 indicates that the effect of this interaction term on SC digitization became smaller and insignificant (β = 0.071, t = 1.613, p > 0.1) when perceived risk was added. This suggests that perceived risk fully mediates the positive effect of this interaction term on SC digitization. Therefore, H3 is supported.
[22]
As the full model (Model 4) in Table 6 indicates, the coefficient of risk propensity on SC digitization was positive and significant (β = 0.315, t = 2.318, p < 0.05), which implies that even in the presence of perceived risk, organizations with risk-seeking propensity still pursue higher levels of SC digitization.
[23]
The coefficient of managerial aspiration was positive and significant (β = 0.179, t = 2.518, p < 0.05). This suggests that when decision makers expect a large benefit of SC digitization, even if they perceive risk, they are willing to take the risk and adopt digital supply chain systems. This finding is in line with...
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