By integrating the theories of social support, social identification and consumer stick- iness into the specific context of live streaming sales, we develop a conceptual model which demonstrates how perceived emotional support, perceived informational support, perceived financial support, perceived affectionate support and perceived social network support separately impacts consumer-streamer identification, and in turn, how consumer- streamer identification impacts consumer-streamer stickiness and consumer-brand sticki- ness, respectively (Figure 1). Table 1. The descriptive demographic characteristics of the respondents (n = 280). Table 1. Cont. Table 2. Scale items of constructs with their sources. Table 3. Construct Reliability and Validity Assessment. Notes: SFL = Standardized Factor Loading; CR = composite reliability; AVE = average variance extracted; « = Cronbach’s alpha. SFL is significant at the 0.001 level. Table 3. Cont. Notes: Values along the diagonal in boldface represent the square roots of the AVE. Table 4. Correlation and Square Root of the AVE. Table 5. Heterotrait-Monotrait (HTMT) Ratio of Correlations. Notes: CSI = Consumer-Streamer Identification; PES = Perceived Emotional Support; PIS = Perceived Informa- tional Support; PFS = Perceived Financial Support; PAS = Perceived Affectionate Support; PSNS = Perceived Social Network Support; CSS = Consumer-Streamer Stickiness; CBS = Consumer-Brand Stickiness; 8 = Standardized Path Coefficients; £2 = Effect Size of Path; R? = Coefficients of Determination; Q? = Stone-Geisser’s Q?; ** p < 0.01, ** 9 < 0.001. Table 6. Results of PLS Path Analysis. Figure 2. Results of PLS Path Analysis. Notes: ** p < 0.01, *** p < 0.001; ns: nonsignificant.
