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- 저자 : Galen T. Trail (검색결과 3 건)
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Galen T. Trail,Yu Kyoum Kim,Priscila Alfaro-Barrantes 글로벌지식마케팅경영학회 2024 Journal of Global Sport Management Vol.9 No.1
Historically, researchers in the sport management area have used covariance based structural equation modeling (CB-SEM) when testing complex models. Recently, researchers have been using partial least squares path modeling (often called PLS-SEM) more frequently. The purpose of this paper was to advise sport man-agement researchers about what approach to use by comparing PLS-SEM versus CB-SEM analytical techniques on the two different types of models: a formative (composite indicator) multigroupmodel and, a formative (composite indicator) continuous interactionmodel. We collected data from individuals (N = 1155) in the New England area (USA). After testing a base model, a multigroup model, and a continuous interaction model, we feel that PLS-SEM is the better choice for sport management researchers when test-ing formative models that use a composite variable. Our research shows when and why each technique works, in addition to show-ing that PLS-SEM moderation and multigroup models with forma-tive items can work in the R statistical software.1. IntroductionHistorically, researchers in the sport management area have used covariance based structural equation modeling (CB-SEM) when testing models that were assumed to include error, have a solid theoretical framework, and were interested in “confirming” a structural model, including measurement models (e.g. Asada & Ko, 2019; Bang et al., 2019; Chang et al., 2020; Jones & Byon, 2020; Larkin et al., 2015; among many others). Recently, however, some in the sport management field have been using a variance-based estimation method, partial least squares path modeling (often called PLS-SEM), in situations where the use of CB-SEM was either difficult or PLS-SEM seemed like a more feasible analytic technique (e.g. Kim et al., 2018; King et al., 2017; Koo & Lee, 2019). However, Rigdon et al. (2017) noted, “the choice of © 2022 Global alliance of Marketing & Management associations (GaMMa)CONTACTGalen t. trail trailg@seattleu.edu Sport administration & leadership, Seattle university, Seattle, Wa, uSahttps://doi.org/10.1080/24704067.2022.2098802ARTICLE HISTORYReceived 28 September 2021Revised 6 May 2022Accepted 21 May 2022KEYWORDSCB-SEM; PLS-SEM; formative multigroup model; formative continuous interaction model; R statistical software
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권형일,Galen T. Trail 한국체육학회 2010 International journal of human movement science Vol.4 No.2
This study examined unplanned buying behavior of sport team licensed merchandise using the dual information processing system (heuristic and systematic) of Chaiken and colleagues (Chaiken, Giner-Sorolla, & Chen, 1996). Sport consumers are expected to experience positive or negative moods after they watch their team play. Participants (36 males and 24 females) were randomly assigned to three groups (positive outcome, control, and negative outcome) and positive and negative moods were induced with video clips of the games of the university football team. The results indicated that the participants in a positive mood state were able to recall less information about the licensed merchandise presented and were more likely to engage in unplanned buying behavior than ones in a negative mood state. Possible marketing implications and suggestions for future study were also discussed.
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Marcel Huettermann,Galen T. Trail,Anthony D. Pizzo,Valerio Stallone 글로벌지식마케팅경영학회 2023 Journal of Global Sport Management Vol.8 No.2
Traditional sport organizations and their sponsors are beginning to embrace esports, but the effectiveness of non-endemic sponsorships in esports remains uncertain. Esports consumers are notoriously hostile to organizations they perceive as seeking to exploit them – including the growing ranks of non-endemics seeking to capitalize on the youth- and tech-centric esports industry. The purpose of this study is to evaluate esports consumers’ perceptions of non-endemic sponsorships. We adapt a well established sport sponsorship model to the context of esports to test key relationships’ salience to sponsors. We demonstrate that non-endemic sponsors can benefit from esports team sponsorship through enhanced attitudes, perceived goodwill, and product purchase intentions. Moreover, we find that there is only a small effect of esports brand attitude on attitude toward the sponsor, yet a larger effect on perceived goodwill and product purchase intentions. This suggests that firms with limited marketing budgets can benefit from increased goodwill and purchase intentions by sponsoring emerging esports teams who provide low-cost sponsorship opportunities.
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