Designing a Model for the Impact of Guerrilla Marketing on Attracting Customers of Sports Stores via an Agent-Based Simulation Approach

Abstract
Various types of marketing are putting into practice in all industries across the world at this time. Additionally, numerous viewpoints and methods are being exploited by marketers to develop creativity and improve sales in such industries, with their own labels (Jensen et al., 2023); including guerrilla marketing (GM) as one of the most novel concepts. In this sense, GM can bring loads of benefits to all industries, and the sports industry is no exception here (Liao et al., 2021). Many companies, manufacturing sporting goods and supplying various sports services are currently tapping GM as a strong market penetration strategy (Fujak et al., 2018). The sports industry has been also significantly influenced by changing lifestyles, thereby devoting much attention to health issues among people and even making them give more value to leisure-time and sports activities (Scola & Gordon, 2019). Over and above promoting the sports industry, this has also led to a growing trend in other industries (Liao et al., 2021). What is more, higher standards of living have augmented purchasing power among consumers, and undoubtedly the number of sports stores are on the rise (Serazio, 2021). In this context, Yazd Province, Iran, is annually attracting lots of sports tourists during domestic and international competitions, festivals, as well as training camps for clubs and national teams in various sports fields. Accordingly, sports stores in this region are in need of proper marketing activities for their survival (Mohebi et al., 2021). Given the widespread emergence of sports stores in Yazd Province, there has been an intense competition, in comparison with the past, to attract and retain more customers, which is the most challenging issue (Tighband Jangalei, 2022). With regard to the gap in the market of sporting goods, sales ratios, the wide of variety of products, and the competitive market over the last few years, retailers have further resorted to diverse strategies to attract and retain customers, including GM (Fujak et al., 2018). Therefore, investigating the effect of GM on attracting and retaining the sports store customers was assumed as the main objective of this study. This empirical study with an exploratory research design was accordingly fulfilled using a deductive-inductive approach. In terms of the type of data, the mixed-methods (qualitative-quantitative) strategy was tapped, so it was a cross-sectional survey with resepct to data collection time and method. The statistical population consisted of university professors (namely, theoretical experts) and sports store managers (i.e., empirical experts) in Yazd Province, Iran. Snowball sampling until theoretical saturation was reached was also conducted to select the study samples (N=12) due to the high number of the experts in this field and the probability of not being familiar with all by the researcher. The criterion for selecting the empirical experts was having at least 10 years of work experience in this domain. On the other hand, the theoretical experts were selected out of the professors involved in the universities in Yazd Province, nominated for their academic works published as books and articles in the field of sports marketing. The data collection tools were interviews and questionnaires. As well, thematic analysis was initially used to explore the specialized interviews, and then the fuzzy Delphi method (FDM) was operated to prioritize the GM tools. Ultimately, the effect of GM on the attraction of the sports store customers was simulated through the agent-based modeling (ABM). In the qualitative phase of this study, the validity of the interviews was tested via the Q-sort technique, and their reliability was checked and confirmed using the Cohen’s kappa coefficient. In the quantitative phase, the validity of the questionnaires was measured by relative content validity, and their reliability was established based on test-retest reliability. MAXQDA 2020 was further utilized for thematic analysis, the fuzzy Delphi method calculations were correspondingly performed in Microsoft Excel, and the ABM was done using AnyLogic. Based on the study results, 36 primary codes, 8 sub-themes, and 4 main themes, including the cultural, human-related, technological, and customer-attraction tools, were identified, wherein the cultural tools were spotted as the most effective ones. The simulation outputs also revealed that the proposed model could properly estimate the future of attracting sports store customers in Yazd Province, Iran. Moreover, all four tools, were of utmost importance, but in varying degrees, in order to attract the customers of sports stores in this region, and their prioritization was simply to gain a better understanding of more effective GM tools. In line with the study findings, as a general policy, the managers of sports stores in Yazd Province are thus suggested to implement this research model. In this way, it is ideal to run the proposed model within a specific time period and measure its capacity to improve the effectiveness of the mentioned GM tools. If the existing model leads to an increase in the attraction of sports store customers in practice, it is then recommended to be taken into account in future periods.