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Andrea Schlegel, Rebecca Pfitzner and Joerg Koenigstorfer

limitations section. At the end, participants were thanked for participation and fully debriefed concerning the aim of the study. Participants did not receive any monetary or in-kind compensation. Participants To determine the required sample size, we considered the correlation between perceived atmosphere in

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Minjung Kim, Brent D. Oja, Han Soo Kim and Ji-Hyoung Chin

total, two parameters were added in the academic PsyCap model, all of which belong to the same first-order latent variable and have similar wording, which along with the common method utilized might have caused the correlation between residuals ( Kenny, Kashy, & Bolger, 1998 ). The measurement models of

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Yuhei Inoue, Mikihiro Sato, Kevin Filo, James Du and Daniel C. Funk

provide a procedural remedy to minimize potential sources of common method biases, which occur when an observed correlation between two variables is artificially inflated by the use of a single data source ( Podsakoff, MacKenzie, Lee, & Podsakoff, 2003 ). A separation between the collection of the

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Yonghwan Chang, Daniel L. Wann and Yuhei Inoue

( Hamann, 2012 ). Asakawa ( 2010 ) also suggested that there is a negative correlation between flow proneness and anxiety. Accordingly, and quite simply, events that evoke positive emotions are likely to produce flow regardless of the level of iTeam ID. This straightforward effect could be related to

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Kirstin Hallmann, Anita Zehrer, Sheranne Fairley and Lea Rossi

validity and reliability of the motivation, commitment, and social capital scales were assessed using confirmatory factor analysis. This procedure served to identify potential correlations between the latent constructs to address the first research question. Using the measurement model (see Table  3

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Jacqueline McDowell, Yung-Kuei Huang and Arran Caza

correlations. The square roots of the average variance extracted values for all four constructs were greater than their correlations with other constructs (see Table  2 ), indicating discriminant validity. Table 2 CR, AVE, Correlation Matrix, and Descriptive Statistics Constructs M SD CR AVE 1 2 3 4 1. AL 3

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Yonghwan Chang, Yong Jae Ko and Brad D. Carlson

loadings ranged from .68 (Arousal 1) to .98 (Arousal 3). All average variance extracted values were greater than the .50 standards, ranging from .70 (arousal) to .79 (pleasure and pride). Factor correlations were reasonably high given .64 for pleasure and arousal, .67 for arousal and pride, and .83 for

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Matthew Katz, Aaron C. Mansfield and B. David Tyler

calculated the intraclass correlation, which provides a measure of the proportion of variability in emotional support that exists between units ( Beretvas, 2009 ), or the within-cluster correlation ( Raudenbush & Bryk, 2002 ). Intraclass correlations are calculated by dividing the between-group variance (τ

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Sarah Kelly and Michael Ireland

-order correlations between mere exposure and the outcomes of brand attitude and alcohol-consumption indicators. These correlations are presented in Table  1 . These analyses are partitioned according to participants’ reporting principle exposure to sports with alcohol-brand sponsorship (AFL, cricket, rugby league

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Wonseok Jang, Yong Jae Ko, Daniel L. Wann and Daehwan Kim

). These results provide empirical evidence of convergent validity ( Hair et al., 2006 ). Moreover, squared correlations between each pair of constructs were less than the AVE value for any two related constructs ( Fornell & Larcker, 1981 ). Finally, none of the correlations between constructs was greater