Exploring the Moderating Role of Average Length of Stay in the Relationship Between International Tourist Arrival and Tourism Revenue: Evidence from 2000 to 2024 in Cambodia

Exploring the Moderating Role of Average Length of Stay in the Relationship Between International Tourist Arrival and Tourism Revenue: Evidence from 2000 to 2024 in Cambodia

Chumneanh Lork¹, Piseth Holl², Chanbopheak Nguon³, Samean Phon⁴

^1;2,4University of Economics and Finance, Phnom Penh, Cambodia

³Western University, Kompong Cham, Cambodia

DOI: https://dx.doi.org/10.47772/IJRISS.2025.907000307

Received: 08 July 2025; Accepted: 15 July 2025; Published: 15 August 2025

ABSTRACT

This study explores the moderating role of average length of stay (ALS) in the relationship between international tourist arrivals and tourism revenue in Cambodia, using annual data from 2000 to 2024. The research addresses four key hypotheses through correlation and multiple regression analyses. Findings provide strong support for H1, confirming a highly significant and positive relationship between international arrivals and tourism revenue (R² = 0.966, p < 0.001), with each additional visitor associated with approximately USD642 in revenue. In contrast, H2 is not supported, as ALS exhibited no significant effect on revenue (p = 0.176) and showed a weak negative correlation. The combined model in H3 significantly predicts revenue (R² = 0.967), but this predictive power is largely driven by arrivals, with ALS remaining insignificant (p = 0.458). Lastly, H4 which tested whether ALS moderates the relationship between arrivals and revenue, is not supported—the interaction term was statistically insignificant (p = 0.442), indicating that the influence of international arrivals on revenue remains stable regardless of stay duration. These results align with previous research who found that longer stays often lead to reduced daily spending and limited revenue gains. The study concludes that focusing on increasing tourist volume may yield more substantial economic benefits than extending visitor stays. It contributes to sustainable tourism policy discussions by emphasizing the need to align destination strategies with visitor behavior and economic realities.

Keywords: Tourism Revenue, International Tourist Arrival, Average Length of Stay, Regression Analysis

INTRODUCTION

The Royal Government of Cambodia (RGC) has consistently recognized tourism as a key catalyst for socio-economic growth. It stands as one of the country’s core economic sectors, playing a crucial role in employment creation, poverty alleviation, and the stimulation of related industries such as transport, hospitality, and retail. Both international and domestic tourism have substantially shaped Cambodia’s development path, supported by government-led policies and strategic infrastructure investments aimed at fostering sectoral growth. In the early 2000s, Cambodia’s international tourism was predominantly composed of visitors from Western countries, including the United States, the United Kingdom, and France. Over time, the composition of tourists shifted, reflecting broader geopolitical and economic trends in the region. By 2019, Chinese travelers had become the largest group of foreign visitors to Cambodia, followed by tourists from neighboring countries such as Thailand, Vietnam, South Korea, Japan, and Chinese Taipei. This growing dependence on regional source markets highlighted Cambodia’s increasing integration into the Asian tourism landscape. In contrast, outbound tourism from Cambodia primarily targeted Thailand and Vietnam, with smaller numbers traveling to Taiwan, China, and Malaysia—indicative of strong regional mobility. Domestic tourism also recorded significant growth, and by 2019, both domestic and international tourist numbers had reached record highs. However, the outbreak of the COVID-19 pandemic in early 2020 triggered an unprecedented disruption. International travel bans, quarantine requirements, and widespread fear of infection led to a sharp decline in Arrival. The tourism sector—heavily reliant on foreign visitors—was among the hardest hit, leading to serious economic and employment consequences. Tourism-related employment contracted significantly in 2020, with the workforce declining by over 20 percent compared to the previous year. Thousands of tourism-dependent businesses, particularly in Siem Reap province, were forced to shut down or suspend operations. The resulting job losses led many displaced workers to return to subsistence agriculture or informal labor for income. The crisis exposed the sector’s vulnerability to external shocks and underscored the need for greater resilience.

In response, the Cambodian government implemented a range of short- and long-term measures to stabilize and revive the tourism industry. Immediate interventions included tax and fee exemptions for affected businesses, direct cash support, and concessional loans aimed at helping enterprises survive the downturn. Complementary efforts were made to accelerate vaccination campaigns and provide skills training for tourism workers to enhance future capacity. For long-term recovery, the government prioritized infrastructure development and introduced innovative financing strategies. A key initiative was the Tourism Recovery Co-financing Scheme (TRCS), a collaborative public-private mechanism that allowed eligible tourism businesses to access low-interest loans through the Small and Medium Enterprise Bank of Cambodia and participating financial institutions. This initiative was designed to restore liquidity and ensure the survival and regeneration of affected enterprises. Despite the disruptions, Cambodia’s tourism sector has demonstrated remarkable resilience. A gradual return of international visitors in 2022 signaled the beginning of recovery. The sector remains a significant employer, especially for women, and continues to drive the expansion of the accommodation industry. By 2020, Cambodia had developed a vast network of hotels and guesthouses across all provinces, reflecting the increasing demand for hospitality services. The Angkor Archaeological Park in Siem Reap continues to be Cambodia’s most renowned attraction, drawing millions of tourists and standing as a powerful representation of the nation’s cultural heritage. Beyond Angkor, the country also offers a diverse range of attractions—from coastal areas like Preah Sihanouk and Kep to the mountainous regions of Rattanakiri and Mondulkiri, including a growing number of ecotourism sites. Looking ahead, the continued development of Cambodia’s tourism sector will depend on its ability to build resilience, embrace sustainability, and diversify tourism offerings to align with global trends and changing traveler preferences.

LITERATURE REVIEW

Tourism serves as a significant economic engine worldwide, contributing to revenue generation across diverse regions through its complex and interconnected dynamics. Despite its importance, the specific elements that impact revenue generation within the accommodation sector remain under-researched, leaving a gap in the understanding of crucial factors for long-term, sustainable growth. The Length of Stay (LOS) of tourists is a critical metric in tourism research, given its direct correlation with visitor expenditure and overall destination revenue. Numerous studies have explored the determinants of LOS using a variety of econometric and statistical approaches, aiming to uncover the socio-demographic, behavioral, and destination-related factors that shape tourists’ travel duration. These insights are vital for policymakers and tourism stakeholders seeking to enhance revenue generation and promote sustainable tourism practices. Past research has consistently highlighted the influence of variables such as age, income, nationality, travel purpose, type of accommodation, group size, and destination familiarity on LOS. For instance, [1] employed survival analysis to determine that income, education, travel experience, and familiarity with the destination significantly influenced tourists’ duration of stay. Similarly, using a zero-truncated negative binomial model, emphasized the importance of demographic characteristics such as age, employment status, and nationality, alongside trip-related costs and motivations [2]. Their findings underscore the multifaceted nature of LOS and the importance of tailoring tourism strategies to specific visitor profiles. In addition, [3] investigated the impact of first-time visitation and travel distance on LOS, revealing that both factors positively correlate with longer stays. These results emphasize the need for destination marketing strategies that not only attract new visitors but also encourage extended stays through enhanced travel experiences and value-added services. While a range of analytical models has been employed across these studies, there remains a gap in capturing the nonlinear relationships between influencing variables and LOS. To address this, the current study adopts the Multivariate Adaptive Regression Splines (MARS) model, which is well-suited for modeling complex, nonlinear interactions among variables. This approach offers improved predictive accuracy and allows for a deeper understanding of the drivers behind LOS, particularly within the context of Indian domestic tourism. Understanding these determinants is essential for designing targeted policies and interventions that can optimize visitor spending and promote sustainable destination management.

Although extended tourist stays are often associated with higher overall expenditures, this relationship is not universally consistent. Variations in tourism types, seasonal demand, and regional infrastructure can significantly moderate the impact of length of stay on revenue generation. The assumption that longer visits always result in increased income fails to account for the complexities introduced by the tourists’ country of origin, local economic structures, and temporal fluctuations. Furthermore, prolonged stays may exert pressure on environmental and community resources, raising concerns about sustainability. Strategic management practices are thus essential to balance economic benefits with responsible resource use. This study examines how different regions navigate these challenges, focusing on patterns of tourism and revenue management in Portugal during periods marked by economic and environmental vulnerability. While conventional frameworks such as the Length of Stay Model, Tourist Expenditure Model, Accommodation Utilization Theory, and Destination Choice Model posit that longer tourist stays are positively correlated with increased revenue due to extended use of services and elevated spending, this study critically reassesses this assumption by analyzing regional disparities in revenue performance across diverse accommodation types [4]–[6]. The research highlights that length of stay alone does not uniformly translate into higher economic returns, particularly when accounting for location-specific dynamics and operational capacities. Furthermore, the study examines how variables such as the proportion of foreign visitors and seasonal fluctuations in overnight stays influence not only short-term revenue generation but also broader indicators of economic sustainability.

Hypotheses and Research Framework

• H1:   There is a significant positive relationship between international tourist Arrival and      tourism revenue.
• H2:   There is a significant positive relationship between average length of stay and   tourism revenue.
• H3:   International tourist Arrival and average length of stay together significantly    predict tourism revenue.
• H4:   The average length of stay moderates the relationship between international Arrival   and tourism revenue.

Figure 1: Research Framework

RESEARCH METHODOLOGY

In their 2017 study, [7] utilized a descriptive survey research design. Following a similar methodological approach, the current study applies a descriptive research framework based on secondary data sourced from the Ministry of Tourism of Cambodia. The data, covering the years 2000 to 2024, includes statistics on international tourist Arrival, the average duration of stay, and tourism-related revenue.. It tests the null hypothesis that all group means are equal against the alternative that at least one group mean is different [8]. According to [9], multiple regression analysis (MR), a highly flexible system for examining the relationship of a collection of independent variables (predictors) to a single dependent variable (criterion). Moreover, Simple linear regression is a mathematical technique used to model the relationship between a single independent predictor variable and a single dependent outcome variable [10].

This method is effective in identifying the relationship between dependent and independent variables and is commonly used in both statistical analysis and machine learning to develop predictive models. Additionally, [11] stated that a p-value less than 0.05 (p < 0.05) signifies a statistically significant deviation from the baseline regression model, indicating the existence of a threshold. On the other hand, a p-value greater than 0.05 (p > 0.05) implies a lack of sufficient evidence to confirm such a threshold, suggesting that the regression model does not significantly differ from the baseline in that region. To analyze the data, Pearson’s product-moment correlation was applied [12]. Furthermore, [13] noted that a p-value above 0.05 suggests the correlation is not statistically significant, meaning there is inadequate evidence of a linear relationship between the variables, as assessed using SPSS version 26.

ANALYSIS AND FINDINGS

Table 1 shows that from 2000 to 2019, international tourism showed steady growth, with revenue rising from USD 228 million to USD 4.919 billion and Arrival increasing from 466,365 to 6.61 million. The average length of stay remained relatively stable between 5.5 and 7 days.

The COVID-19 pandemic (2020–2021) caused a dramatic decline in both revenue and Arrival—dropping by over 80–95%—while the length of stay surged to 24–30 days, likely due to travel restrictions, quarantines, and stranded visitors.

Recovery began in 2022, accelerating by 2023–2024. By 2024, Arrival surpassed pre-pandemic levels at 6.7 million, while revenue reached USD 3.63 billion. However, the average stay dropped to 5 days, indicating a shift toward shorter visits and changing travel behavior.

Table 1: Annual Tourism Indicators for Cambodia, 2000–2024

(Source: Ministry of Tourism of Cambodia)

Figure 2: Tourism Revenue 

Figure 3: International Arrival

Figure 4: Average Length of Stay (Day)

Descriptive Statistics

Table 2 reveals that over the 25-year period analyzed (2000–2024), international tourism performance exhibited substantial variation. Tourism revenue ranged from a minimum of USD 184 million to a maximum of USD 4.919 billion, with a mean of approximately USD 1.92 billion and a high standard deviation (SD = 1.37 billion), indicating considerable fluctuation due to both growth phases and external shocks, particularly during the COVID-19 pandemic.

International tourist Arrival varied from 196,495 to 6.7 million, with a mean of roughly 2.99 million and an SD of 2.09 million, reflecting a wide range in annual visitor numbers. This variability aligns with significant disruptions and recoveries observed during the period.

The average length of stay ranged from 5.0 to 30.0 days, with a mean of 8.27 days and an SD of 5.92, indicating notable year-to-year shifts. The exceptionally high values during 2020–2021 skewed the average upward, likely due to pandemic-related factors such as travel restrictions, extended quarantines, and long-stay tourism patterns.

Table 2: Summary of Descriptive Analysis

Correlation Analysis

Table 3 reveals that a very strong positive correlation between Tourism Revenue and International Tourist Arrival (r = 0.983, p < 0.01), which is statistically significant. This suggests that revenue growth is highly dependent on the number of tourists visiting the country—a logical and expected relationship. However, Average Length of Stay shows a weak negative correlation with both revenue (r = -0.279) and Arrival (r = -0.312), neither of which is statistically significant (p > 0.05). This implies that longer stays do not necessarily lead to higher revenue or more Arrival, and could reflect irregular behavior during outlier years (e.g., COVID-19).

Table 3: Pearson Correlation Matrix for Tourism Revenue, International Arrival and Average Length of Stay

Correlation is significant at the 0.01 level (2-tailed).

Hypotheses Tested

• H1: There is a significant positive relationship between international tourist Arrival and tourism revenue.

Table 4: Simple Linear Regression – Effect of International Tourist Arrival on Tourism Revenue

The results are summarized in Table 4 above, a simple linear regression was conducted to examine the extent to which international tourist Arrival predict tourism revenue. The results indicated that the model significantly predicts tourism revenue, F (1, 23) = 655.46, p < 0.001, explaining approximately 96.6% of the variance (R² = 0.966, Adjusted R² = 0.965). The unstandardized regression coefficient for international tourist Arrival (B = 642.03, SE = 25.08) was positive and statistically significant (t = 25.60, p < 0.001), demonstrating that each additional international arrival is associated with an increase of approximately USD 642 in tourism revenue. Collinearity diagnostics showed no evidence of multicollinearity, with tolerance and variance inflation factor (VIF) values equal to 1.0, suggesting the predictor variable independently contributes to the model. The findings are consistent with those of [14], providing empirical support for H1 by confirming a significant and positive relationship between international tourist arrivals and tourism revenue.

• H2: There is a significant positive relationship between average length of stay and tourism revenue.

Table 5: Simple Linear Regression – Effect of Average Length of Stay on Tourism Revenue

The results are summarized in Table 5 above; to test Hypothesis 2, a simple linear regression was conducted with average length of stay as the independent variable and tourism revenue as the dependent variable. The model was not statistically significant, F (1, 23) = 1.945, p = 0.176, indicating that average length of stay does not significantly predict tourism revenue within the studied period. The model explained only 7.8% of the variance in tourism revenue (R² = 0.078), and the adjusted R² was just 0.038, suggesting a very weak explanatory power. The unstandardized regression coefficient (B = -64,562,223.04) was negative, and the effect was not statistically significant (t = -1.395, p = 0.176). Thus, an increase in the average length of stay is not associated with an increase in tourism revenue; rather, the relationship is negative but not significant. Collinearity statistics indicated no multicollinearity concerns (Tolerance = 1.0; VIF = 1.0). Condition indices were well below the critical threshold (highest = 3.169), suggesting stable estimation. Contrary to expectations, the results do not support H2, as the average length of stay was not found to have a significant or positive impact on tourism revenue during the period analyzed. This finding is consistent with [15], who highlighted that longer stays do not always translate into increased revenue, particularly when moderated by factors such as tourist spending behavior, destination type, and seasonality.

• H3: International tourist Arrival and average length of stay together significantly predict tourism revenue.

Table 6: Multiple Linear Regression Predicting Tourism Revenue from International Tourist Arrival and Average Length of Stay

The results are summarized in Table 5 above; to examine

• H3, a multiple linear regression was conducted to determine whether international tourist Arrival and average length of stay jointly predict tourism revenue.

The overall model was statistically significant, F(2, 22) = 321.88, p < 0.001, indicating that the combination of international tourist Arrival and average length of stay significantly predicts tourism revenue. The model explained 96.7% of the variance in tourism revenue (R² = 0.967; Adjusted R² = 0.964), demonstrating an excellent model fit.

Among the predictors:

• International tourist Arrival had a strong and statistically significant positive effect on tourism revenue (B = 648.31, t = 24.33, p < 0.001, β = 0.993). This result confirms its primary contribution to predicting revenue.
• In contrast, average length of stay was not a statistically significant predictor (B = 7,120,028.12, t = 0.755, p = 0.458, β = 0.031), suggesting its influence is negligible when controlling for Arrival.

Collinearity diagnostics revealed no issues: VIF values were well below the standard threshold of 10 (both = 1.108), and condition indices were below 30, with the highest being 5.260. These results indicate no multicollinearity between the predictors.

The results support H3, showing that the combined predictors significantly explain tourism revenue, mainly driven by international tourist arrivals. This aligns with prior studies, such as [16], [17], which found that tourist volume is a stronger revenue driver than length of stay, as longer trips often involve lower daily spending.

H4: The average length of stay moderates the relationship between international Arrival and tourism revenue.

Table 7: Moderated Multiple Regression Analysis Predicting Tourism Revenue

The results are summarized in Table 7 above; to examine the moderating role of the average length of stay in the relationship between international tourist Arrival and tourism revenue, a moderated multiple regression analysis was conducted. The model included three predictors: international tourist Arrival (centered), average length of stay (centered), and their interaction term (International Tourism Arrival and Average Length of Stay).

The regression model was statistically significant, F(3, 21) = 211.030, p < 0.001, with an R² of 0.968, indicating that 96.8% of the variance in tourism revenue was explained by the combined predictors. The results suggest a well-fitting model, highlighting the significant predictive strength of the variables included in the analysis. Analysis of the coefficients revealed that international tourist Arrival had a significant and positive effect on tourism revenue (B = 589.22, SE = 80.05, β = 0.902, t = 7.361, p < 0.001). However, the main effect of average length of stay was not statistically significant (B = 831,291.10, SE = 12,448,493.02, β = 0.004, t = 0.067, p = 0.947), suggesting that variation in length of stay does not directly influence revenue when Arrival are accounted for. Most critically, the interaction term between Arrival and length of stay was not significant (B = 9.23, SE = 11.78, β = 0.091, t = 0.784, p = 0.442), indicating that the effect of international tourist Arrival on tourism revenue is not conditional on the average length of stay. Therefore, no moderation effect was observed. Multicollinearity diagnostics showed that the VIF for the interaction term was 8.87—elevated, but below the conventional threshold of 10—and the condition index peaked at 14.77, indicating acceptable multicollinearity levels for interpretation. Although the overall model was highly predictive, the interaction effect was not statistically significant. These results suggest that average length of stay does not moderate the relationship between international tourist Arrival and tourism revenue. Thus, H4 is not supported, as international arrivals consistently impact revenue regardless of stay duration. Similarly, [15] found that in Portuguese regions, longer stays reduced per-day spending, limiting their overall contribution to tourism revenue.

CONCLUSION, LIMITATION OF STUDY, AND FUTURE RESEARCH

This study investigated the relationship between international tourist arrival, average length of stay (ALS), and tourism revenue in Cambodia from 2000 to 2024, using multiple regression models. The results provide strong empirical support for H1, confirming a highly significant positive relationship between international arrival and tourism revenue. However, H2 was not supported, as ALS had no significant effect on revenue and even showed a weak negative correlation. The combined effect of international arrival and ALS (H3) significantly predicted revenue, although the influence was primarily driven by tourist volume. Furthermore, the moderating effect of ALS on the relationship between arrival and revenue (H4) was not supported. These findings suggest that increasing tourist numbers has a greater impact on economic outcomes than extending stay durations, aligning with prior research.

This study has several limitations. It relies on secondary, national-level data, which overlooks regional and seasonal variations and excludes qualitative factors like tourist spending behavior and motivations. Key variables such as tourist type, infrastructure quality, and marketing efforts were not considered. Additionally, COVID-19 anomalies may have distorted average stay and spending patterns. Future research should incorporate regional and seasonal data, apply panel or time-series methods, and include variables like tourist categories and spending types. Qualitative studies could further explain why longer stays may not always boost revenue.

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