Presentation of the Extended Technology Acceptance Model in Sports Organizations

Abstract

Background. In recent decades, information technology has become a vital component of various aspects of our lives. The use of information technology in different domains has made the analysis of the level of its acceptance/rejection a significant factor in organizations. Objectives. The aim of this study was to illustrate the application of the extended technology acceptance model in sports organizations. Methods. The participants consisted of 350 employees selected from across various Iranian sports organizations. The data were collected according to the perceived ease of use, perceived usefulness, attitude (AT), intention to use (IU), technology self-efficacy (TSE), technology anxiety, perceived enjoyment, and user satisfaction for each variable in the study model. A panel of experts determined the face and content validity of the experiment. The Cranach's alpha coefficient was used to determine the validity. Results. The results showed that AT, self-efficacy, PU, EOU, and user satisfaction have a meaningful effect on the intention to use information technology. The highest effect was related to AT, and the lowest to user satisfaction. Conclusion. It can be claimed that when people have a more positive AT toward the use of information technology, they will exhibit beliefs or excitements, which makes it more viable for them to perceive information technology positively, and consequently increase the intention to use it.

The Intention to Use Fitness and Physical Activity Apps: A Systematic Review

“…Another relationship of perceptions and the most studied is between PEOU and PU. Although all the existing relationships were significant, they had different levels of relationship: for example, three studies had a low influence of PEOU on PU with values lower than 0.30 [43,51,61], while Mohammadi and Isanejad [57] found that PEOU had an influence on lower-middle on PU [Fisher's Z = 0.41 (0.30; 0.54)]. Other studies found a high or very high influence between these two variables [Fisher's Z ≥ 0.66].…”

“…The first relationship between the variables PE and PEOU was found in four different studies showing results of different degree of relationship, although all significant. In the study by Mohammadi and Isanejad [57], the effect size was found to indicate a low-medium influence of PE on PEOU [Fisher's Z = 0.39 (0.2; 0.50)], Chiu and Cho [61] found a medium influence [Fisher's Z = 0.49 (0.35; 0.63)], while Byun et al [45] found a medium-high influence of PE on PEOU [Fisher's Z = 0.67 (0.55; 0.79)] and a very high influence of enjoyment in students on ease of use [Fisher's Z = 1.02 (0.89; 1.15)]. Another relationship of perceptions and the most studied is between PEOU and PU.…”

“…Analyzing the sample used in the different studies, there is a total of 16,025 subjects with an average sample of 843.42 subjects per study, with the Ndayizigamiye; Kante, and Shingwenyana study [54] having the smallest sample (n = 139) and Wei, Vinnikova, Lu and Xu study [55] having the largest sample with a total of 8840 subjects. Approximately a half of the studies (n = 8) used university students as a sample, followed by studies that considered users of sports applications (n = 4) and other studies took as their general population [54][55][56], a population of sports consumers [45], employees of a sports organization [57] and members of a fitness community [44,58]. Most studies had a higher proportion of females than males (n = 9), followed by studies that had parity in the sample (n = 5), four studies had a higher proportion of males while one study did not indicate the gender distribution of the sample [34].…”

“…The analysis indicated that mainly the study population are young subjects between 20 and 29 years old and all are over 18 years old except Lee, Kim and Wang [45] which also included 17-year-old subjects. Li, Liu, Ma and Zhang [46] sampled subjects over 25 years of age, while Huang and Ren [60] and Mohammadi and Isanejad [57] were at least 30 years old. Regarding the type of App evaluated, six studies evaluated the intention to use diet and fitness applications [30,44,51,52,55,59], another five studies evaluated sports information Apps [42,43,53,57,62], two studies measured the intention of fans to use the sports team app [1,45], health and fitness app [56,61], or fitness [58,60] and one study evaluated a social fitness-tracking app [63] and mHealth related to promote physical activity [54].…”

“…The most common method of analysis used was a content analysis by structural equations using the AMOS statistical package (n = 9) and the rest of the Partial Least Square studies. Seven studies also performed a correlation analysis of the data [54][55][56][57][58][59]62], and eight studies performed descriptive analysis in addition to content analysis [1,42,51,[55][56][57][58]61].…”