دانشگاه پیام نور فصلنامه علمی پژوهش‌های کاربردی در مدیریت ورزشی 2345-5551 15 1 (تابستان 1405) 2026 06 22 Identifying and Prioritizing Factors Influencing Sports Clubs Site Selection Using Geomarketing Approach شناسایی و اولویت‌بندی عوامل مؤثر بر مکان‌یابی باشگاه‌های ورزشی با رویکرد ژئومارکتینگ 19 38 13154 10.30473/arsm.2026.77542.4044 FA فاتح فرازیانی استادیار، گروه مدیریت ورزشی، دانشگاه پیام نور، تهران، ایران. حدیقه رحمانزاده کارشناسی ارشد، گروه مدیریت ورزشی، معلم آموزش و پرورش. Journal Article 2026 02 22 Introduction From a marketing perspective, “place” has transitioned from being merely one element of the traditional marketing mix to becoming a strategic competitive advantage. In this context, geomarketing has emerged as an interdisciplinary paradigm integrating spatial intelligence, geographic information systems (GIS), demographic analysis, and consumer behavior studies to support data-driven location decisions. Geomarketing views location not only as a physical coordinate but as a multidimensional strategic variable that influences accessibility, market demand, competitive positioning, and economic performance. Classical location theories—such as Von Thünen’s agricultural land use theory, Weber’s industrial location theory, and Christaller’s central place theory—emphasized transportation cost, market threshold, and spatial hierarchy as determinants of economic activities. Contemporary geomarketing expands these theoretical foundations by incorporating multi-criteria decision-making (MCDM) tools and spatial analytics to address complex service-sector decisions. In service-based businesses, particularly those requiring physical customer presence such as sports clubs, spatial proximity plays a decisive role in customer choice. Empirical research suggests that most fitness customers are unwilling to travel more than 15 minutes to reach a facility, reinforcing the critical importance of accessibility in site selection. In Iran, despite rapid growth in private fitness centers, location decisions are often made based on intuition, personal experience, or limited financial considerations rather than systematic spatial-market analysis. Moreover, the country exhibits significant geographic, demographic, economic, and cultural heterogeneity. Large metropolitan areas, mid-sized cities, mountainous regions, and less densely populated zones demonstrate distinct patterns of demand, infrastructure availability, and competitive intensity. Official reports indicate disparities in per capita sports space distribution across provinces, highlighting spatial inequalities in service access. However, there is currently no comprehensive, context-specific geomarketing model designed for private sports club site selection at the national level. Existing Iranian studies primarily focus on public sports facilities using GIS and AHP methods but rarely integrate market-oriented variables such as customer purchasing power, lifestyle, or competition dynamics. Furthermore, limited research has applied a hybrid Fuzzy Delphi–AHP approach to systematically identify, refine, and prioritize site selection indicators based on expert consensus. Therefore, the present study aims to fill this theoretical and practical gap by identifying, screening, and prioritizing geomarketing factors influencing optimal site selection of private sports clubs in Iran. The central research question guiding this study is: What are the key geomarketing factors affecting optimal private sports club site selection in Iran, and how should they be prioritized? The study seeks to: Systematically extract relevant indicators from literature. Achieve expert consensus using Fuzzy Delphi. Quantitatively prioritize criteria using the Analytic Hierarchy Process (AHP). Develop a weighted, context-specific geomarketing decision-support framework. The findings contribute both theoretically—by enriching geomarketing and sports management literature—and practically—by reducing investment risk and promoting balanced spatial development of sports services. Methodology This research is applied in purpose and descriptive–analytical in nature, employing a mixed-method design (qualitative–quantitative). A hybrid methodology combining Fuzzy Delphi and Analytic Hierarchy Process (AHP) was adopted to ensure both consensus-based refinement and quantitative prioritization. Stage 1: Indicator Identification A systematic literature review covering geomarketing, spatial economics, sports management, urban planning, and multi-criteria decision-making studies was conducted. Based on this review, 78 preliminary indicators were identified and classified into 10 main dimensions: Accessibility Physical/Spatial Characteristics Economic/Cost Factors Place Attractiveness Performance Criteria Population Structure & Customer Characteristics Customer Economic Factors Competitive Conditions Market Saturation Market Attractiveness Stage 2: Fuzzy Delphi Method To refine and validate the indicators, the Fuzzy Delphi method was employed. Fifteen national experts were purposively selected, including: Selection criteria included a minimum of five years of professional experience, familiarity with spatial-market analysis, and national-level perspective. A five-point linguistic scale (very low to very high) was converted into triangular fuzzy numbers. Three Delphi rounds were conducted. After each round, statistical feedback (mean and dispersion) was provided to participants to facilitate convergence. The stopping criterion was set at a mean difference of less than 0.2 between consecutive rounds. After three rounds, consensus was achieved. Five indicators were eliminated due to low consensus, redundancy, or lack of predictive relevance. Consequently, 73 indicators remained in the final model. Stage 3: Analytic Hierarchy Process (AHP) In the quantitative phase, pairwise comparison matrices were designed based on Saaty’s 1–9 scale. Experts compared both dimensions and sub-indicators. Weights were calculated using the eigenvector method. Consistency Ratio (CR) values for all matrices were below 0.1, confirming logical consistency. Final global weights were obtained by multiplying local weights (within each dimension) by global dimension weights. Data analysis was conducted using Excel and Expert Choice software Findings This research is applied in purpose and descriptive–analytical in nature, employing a mixed-method design (qualitative–quantitative). A hybrid methodology combining Fuzzy Delphi and Analytic Hierarchy Process (AHP) was adopted to ensure both consensus-based refinement and quantitative prioritization. Stage 1: Indicator Identification A systematic literature review covering geomarketing, spatial economics, sports management, urban planning, and multi-criteria decision-making studies was conducted. Based on this review, 78 preliminary indicators were identified Stage 2: Fuzzy Delphi Method To refine and validate the indicators, the Fuzzy Delphi method was employed. Fifteen national experts were purposively selected, including: 8 private sports club managers 4 sports management specialists 2 human geography experts 1 urban planner Selection criteria included a minimum of five years of professional experience, familiarity with spatial-market analysis, and national-level perspective. A five-point linguistic scale (very low to very high) was converted into triangular fuzzy numbers. Three Delphi rounds were conducted. After each round, statistical feedback (mean and dispersion) was provided to participants to facilitate convergence. The stopping criterion was set at a mean difference of less than 0.2 between consecutive rounds. After three rounds, consensus was achieved. Five indicators were eliminated due to low consensus, redundancy, or lack of predictive relevance. Consequently, 73 indicators remained in the final model. Stage 3: Analytic Hierarchy Process (AHP) In the quantitative phase, pairwise comparison matrices were designed based on Saaty’s 1–9 scale. Experts compared both dimensions and sub-indicators. Weights were calculated using the eigenvector method. Consistency Ratio (CR) values for all matrices were below 0.1, confirming logical consistency. Final global weights were obtained by multiplying local weights (within each dimension) by global dimension weights. Data analysis was conducted using Excel and Expert Choice software. Findings Dimension-Level Prioritization The AHP results indicate the following ranking of main dimensions: Table 1. Final Ranking and Relative Weights of Main Geomarketing Dimensions Based on AHP Analysis Rank Dimension Final Weight Population Structure & Customer Characteristics 0.158 Accessibility 0.142 Physical/Spatial Characteristics 0.098 Performance Criteria 0.092 Competitive Conditions 0.088 Place Attractiveness 0.085 Market Attractiveness 0.079 Economic/Cost Factors 0.071 Customer Economic Factors 0.056 Market Saturation 0.050 The first three dimensions together account for over 40% of total weight, forming the strategic triad of Customer–Accessibility–Location. Key Indicators The most influential global indicators include: Population density within catchment area (0.039) Strategic location at corner/intersection (0.032) Income and purchasing power (0.031) Age and gender composition (0.026) Distance to main roads (0.026) Availability of parking (0.023) Service diversity and quality (0.021) Expected profitability (high within performance dimension) Number of competitors (dominant in competition dimension) Security and street lighting The results emphasize demand-driven, accessibility-oriented, and service-quality-based decision logic. Discussion and Conclusion The prioritization pattern reflects a strong market-oriented perspective among national experts. The dominance of “Population Structure & Customer Characteristics” indicates that demand analysis precedes all other considerations in private investment decisions. This aligns with international findings highlighting population density and income as primary determinants of sports club distribution. However, unlike studies conducted in highly developed urban transit systems where public transport accessibility dominates, Iranian experts placed stronger emphasis on car-based accessibility indicators such as parking availability and strategic intersection location. This reflects the heterogeneous transportation infrastructure across Iranian cities. The significant weight assigned to physical/service quality suggests that competitive differentiation in Iran increasingly depends on service experience rather than mere facility size. Customers prioritize functional efficiency and service diversity over architectural grandeur. Interestingly, the relatively low weight of “Market Saturation” suggests that many Iranian regions still offer growth opportunities, unlike mature European markets where saturation strongly influences entry decisions. The elimination of certain indicators during the Delphi process strengthened the model’s predictive relevance by removing outcome-based or overlapping variables. Overall, the study confirms that geomarketing in Iran must integrate demographic demand analysis, infrastructural accessibility, competitive intelligence, and economic feasibility within a unified framework. This study developed and validated a comprehensive, context-specific geomarketing model for optimal site selection of private sports clubs in Iran. By combining Fuzzy Delphi and AHP, it transformed qualitative expert judgments into a structured quantitative decision-support framework. The findings reveal that optimal site selection in Iran is primarily driven by: 1. Customer demand characteristics 2. Ease of access 3. Physical and service-related quality The strategic triad of Customer–Accessibility–Location constitutes the core of the proposed model. Practically, the model: Reduces investment risk Supports evidence-based location decisions Promotes balanced spatial distribution of sports services Enables integration with GIS for spatial suitability mapping Theoretically, the study advances geomarketing literature by providing a national-scale, market-oriented model tailored to developing economies with spatial heterogeneity. پژوهش حاضر با هدف طراحی و اعتبارسنجی یک مدل بومی برای مکان‌یابی بهینه باشگاه‌های ورزشی بخش خصوصی در ایران بر پایه رویکرد ژئومارکتینگ انجام شد. ژئومارکتینگ با تلفیق تحلیل‌های فضایی، داده‌های جمعیت‌شناختی و رفتار مصرف‌کننده، تصمیم‌گیری داده‌محور در انتخاب مکان را امکان‌پذیر می‌سازد و مکان را به‌عنوان یک مزیت رقابتی راهبردی در شکل‌دهی تقاضا، دسترسی و سودآوری تلقی می‌کند. این مطالعه از نظر هدف کاربردی و از حیث روش، توصیفی–تحلیلی با رویکرد ترکیبی دلفی فازی و فرآیند تحلیل سلسله‌مراتبی (AHP) بود. در مرحله نخست، ۷۸ شاخص در قالب ۱۰ مؤلفه اصلی از مرور نظام‌مند ادبیات مرتبط استخراج شد. سپس ۱۵ نفر از خبرگان حوزه مدیریت ورزشی، جغرافیا و برنامه‌ریزی شهری طی چند دور دلفی فازی به ارزیابی و پالایش شاخص‌ها پرداختند که در نهایت ۷۳ شاخص در مدل نهایی باقی ماند. در مرحله کمی، وزن نسبی مؤلفه‌ها و شاخص‌ها با استفاده از AHP محاسبه شد. نتایج نشان داد مؤلفه‌های «ساختار جمعیت و ویژگی‌های مشتریان»، «سهولت دسترسی» و «ویژگی‌های مکانی/فیزیکی باشگاه» بیشترین اهمیت را دارند. همچنین شاخص‌هایی نظیر استقرار در نبش یا چهارراه، تراکم جمعیت در حوزه نفوذ، امنیت معابر، تنوع خدمات ورزشی و سودآوری مورد انتظار به‌عنوان عوامل کلیدی شناسایی شدند. مدل ارائه‌شده می‌تواند به‌عنوان چارچوبی تصمیم‌یار در محیط GIS برای شناسایی پهنه‌های مستعد سرمایه‌گذاری و توسعه رقابت‌پذیر باشگاه‌های ورزشی به کار رود.

https://arsmb.journals.pnu.ac.ir/article_13154_8e4cf09f899bb2ea4523e8cd59e1478f.pdf