دانلود مقاله تصویر حمایتی و ایجاد ارزش در ورزش های الکترونیکی
چکیده
مقدمه
چارچوب مفهومی و فرضیه ها
روش ها
نتایج
بحث
منابع
Abstract
Introduction
Conceptual framework and hypotheses
Methods
Results
Discussion
References
چکیده
بازی های ورزشی الکترونیکی می توانند صنعت ورزش را به جلو سوق دهند و حمایت مالی بهترین راه برای جلب مشارکت مصرف کنندگان این ورزش جدید است. هدف از این مطالعه بررسی تأثیر تصویر حمایتی و مشارکت مصرف‌کننده در فعالیت‌های مصرفی هم‌آفرینی بر پاسخ حمایت‌های طرفداران (که با متغیرهای علاقه، قصد خرید و تبلیغات شفاهی ارائه می‌شود) در ورزش‌های الکترونیکی است. چهار متغیر پیشین، تصویر حمایتی را ایجاد می‌کنند (یعنی فراگیر بودن ورزش، صداقت اسپانسر، نگرش به اسپانسر و شناسایی تیم). برای اهداف این پژوهش از رویکرد کمی استفاده شده است. حدود 445 پرسشنامه توسط طرفدارانی که ورزش های الکترونیکی را در اسپانیا تماشا می کنند پر شد. اینها با استفاده از مدل‌سازی معادلات ساختاری حداقل مربعات جزئی (PLS-SEM) تحلیل می‌شوند. نتایج نشان می‌دهد که اگر اسپانسر بخواهد تصویر حامی مالی خود را تغییر دهد و بر پاسخ حمایت مالی تأثیر بگذارد، سوابق حامیان فاکتورهای مهمی هستند و همچنین می‌توان از مشارکت برای بهبود پاسخ‌ها استفاده کرد.
توجه! این متن ترجمه ماشینی بوده و توسط مترجمین ای ترجمه، ترجمه نشده است.
Abstract
E-sports games can drive the sports industry forward and sponsorship is the best way to engage consumers of this new sport. The purpose of this study is to examine the effect of sponsorship image and consumer participation in co-creation consumption activities on fans’ sponsorship response (represented by the variables interest, purchase intention and word of mouth) in e-sports. Four antecedent variables build sponsorship image (i.e., ubiquity of sport, sincerity of sponsor, attitude to sponsor and team identification). A quantitative approach is used for the purposes of this study. Some 445 questionnaires were filled in by fans who watch e-sports in Spain; these are analyzed using partial least squares structural equation modeling (PLS-SEM). The outcomes show that sponsor antecedents are crucial factors if a sponsor wants to change their sponsorship image and influence sponsorship response, and that it is also possible to use participation to improve responses.
Introduction
The evolution of e-sports shows that this sector has been growing exponentially during recent years (Cristòfol et al., 2020, García and Murillo, 2020, Hamari and Sjöblom, 2017), to the extent that e-sports have become a global phenomenon today, with tournaments boasting million-dollar prizes and sponsors, online and television shows, and many different kinds of competitive e-sports having come into existence (Cristòfol et al., 2020). The literature about e-sports is scarcer than on traditional sports or videogames, which have been examined in great detail (Cristòfol et al., 2020, Hamari and Sjöblom, 2017). Indeed, there are some discrepancies among experts regarding the definition of terms in this sphere. One of the meanings of e-sports found in the theory encompasses all those activities in which people hone their mental and physical skills using information and communication technologies (Wagner, 2006). But this definition is not deemed sufficiently clear by some authors, who consider that Wagner (2006) does not establish the limits between e-sports and traditional sports (García and Murillo, 2020, Hamari and Sjöblom, 2017, Witkowski, 2012). According to Witkowski (2012), the key feature of e-sports is that they involve physical activities that players develop together with non-human actions and things, whereas Hamari and Sjöblom (2017) focus on the electronic support in their definition. Meanwhile, García and Murillo (2020) identify interest and participation as the main differences between traditional sports and e-sports.
Results and analyses
The data was analysed to check the reliability and validity of the measures. First, the initial factor structure was corroborated and analysis performed of how each item relates to latent constructs (see Table 4). Falk and Miller, (1992) propose retaining manifest variables with loadings that exceed 0.55, but Henseler, Ringle, and Sinkovics (2009) recommend a benchmark of 0.7, i.e. 50% of the variance of the manifest variable is related to the component. All of the loadings exceed 0.74 for these items and load more highly on their respective construct than on others. To test the significance of model estimates, the t-statistics were computed using 5,000 bootstrap re-samples (Hair et al., 2011). In addition, the internal consistency was assessed using three measures: Cronbach’s alpha, composite reliability (CR) and the average variance extracted (AVE). Nunnally and Bernstein (1994) suggest a high internal consistency when Cronbach’s alpha is higher than 0.7. The value that is recommended for CR is higher than 0.65 (Steenkamp & Geyskens, 2006), and a value at least equal to 0.5 is recommended for AVE (Fornell & Larcker, 1981). As shown in Table 4, all the coefficients of the reflective measures in the study exceed these minimum recommended values.
H1a: Ubiquity of sponsor has a positive effect on sponsorship image.
H1b: Sincerity of sponsor has a positive effect on sponsorship image.
H1c: Attitude to sponsor has a positive effect on sponsorship image.
H2: Team identification has a positive effect on sponsorship image.
H3a: Sponsorship image has a positive effect on interest.
H3b: Sponsorship image has a positive effect on purchase intention.
H3c: Sponsorship image has a positive effect on word of mouth.
H4a: Participation has a positive effect on interest.
H4b: Participation has a positive effect on purchase intention.
H4c: Participation has a positive effect on word of mouth.