The performance of shopping malls has become increasingly complex in today’s dynamic retail environment, where competition, consumer preferences and market trends are continually evolving. At the heart of a shopping mall’s success lies its tenant mix, which is the strategic combination of various retail stores, service providers and entertainment options within the mall. The tenant mix is not merely about filling retail space; it is a deliberate process aimed at creating a balanced, appealing and synergistic environment that attracts consumers, enhances their shopping experience and drives overall mall performance.

In a market economy, retail tenants cluster by type and by location. The result is a hierarchy of centres offering a mix of goods and services appropriate to the market area. This occurs because different goods and services have different trade areas and minimum purchasing power requirements. Central place theory helps describe, explain, and predict changes in the area or purchasing power of a region. A good market analysis will use those factors identified in the theory to select potential tenants (Anikeeff, 1996).

The retail landscape has undergone significant changes in recent years, driven by the rise of e-commerce, shifts in consumer behaviour and the growing demand for experiential shopping. These changes have placed new pressures on shopping malls to adapt their tenant mix strategies to stay competitive and relevant. A well-curated tenant mix can differentiate a mall from its competitors, draw in diverse consumer groups and boost both foot traffic and sales. Conversely, a poorly managed tenant mix can lead to decreased customer satisfaction, lower occupancy rates and diminished financial returns.

This research study explores the concept of retail tenant mix and its critical impact on shopping mall performance. It aims to provide a comprehensive understanding of the factors that influence tenant mix decisions, the theoretical framework that underpin these strategies and the practical implications for shopping mall management. By examining the relationship between tenant mix and key performance indicators such as foot traffic, sales per square foot, customer satisfaction and rental income, the report brings out the significance of strategic tenant selection and placement in driving the success of shopping mall.

Factors Influencing Tenant Mix

• Target Demographics: Understanding the demographics of the local area is crucial. The tenant mix should align with the preferences and spending power of the target audience.
• Market Trends: Staying up-to-date with retail trends, such as the rise of e-commerce or the demand for experiential retail, can influence tenant selection and placement.
• Competition: Analysing competitors in the area helps in curating a tenant mix that offers something unique and attractive to customers
• Location: The location of the shopping centre impacts the tenant mix. For example, a mall in a tourist area may focus on luxury and souvenir shops, while a suburban shopping centre might prioritize family-friendly stores.

Theoretical Background

Conceptual Foundation

The tenant mix within a shopping mall is a strategic component that directly affects the mall’s overall performance. Tenant mix refers to the deliberate selection, arrangement and management of different types of tenants - retailers, service providers and entertainment options - to create a balanced and appealing environment for shoppers. This section explores the conceptual and theoretical foundations that underpin tenant mix and its impact on the performance of shopping malls.

Tenant Mix

Tenant mix is the deliberate composition and arrangement of tenants within a shopping mall, aimed at creating a synergistic environment that maximizes foot traffic, enhances the shopping experience and increases the mall’s profitability. The concept involves the selection of tenants based on their appeal to target demographics, the variety and complementarity of their offerings and their potential to drive sales for the entire mall.

Key aspects of tenant mix include:

• Variety: Ensuring a diverse range of retail categories, such as fashion, electronics, dining and entertainment, to cater to different consumer preferences.
• Anchor Tenants: Large, well-known retailers or entertainment venues that attract significant foot traffic and serve as a draw for smaller tenants. Anchor tenants were seen to be the most important factor of determining tenant mix and the image of a shopping centre (Kyriazis & Cloete, 2018).
• Complementarity: Positioning tenants with complementary products or services near each other to encourage cross-shopping and longer visits.
• Zoning: The careful planning of mall tenants, frequently grouping related or complimentary businesses in one group to make it easier for customers to navigate and improve their overall shopping experience.

Shopping Mall Performance

Shopping mall performance refers to the effectiveness of a mall in achieving its strategic and financial objectives. Key performance indicators (KPIs) for malls include:

• Foot Traffic: The number of visitors to the mall, which is a primary driver of retail sales.
• Sales per Square Foot: A measure of the productivity of retail space within the mall.
• Occupancy Rates: The percentage of rentable space that is currently occupied by tenants.
• Rental Income: The revenue generated from leasing retail space to tenants.
• Customer Satisfaction: The overall experience of shoppers, which can influence repeat visits and customer loyalty.

Theoretical Foundations

Retail Agglomeration Theory

Retail agglomeration theory posits that the clustering of retail stores within a specific geographic area, such as a shopping mall, creates a more attractive destination for consumers. This concentration of stores leads to increased foot traffic and sales as shoppers are drawn to the variety and convenience offered by multiple retailers in one location.

Key Principles

• Economies of Scale: Retailers benefit from shared costs and increased efficiency by being located near other stores.
• Increased Foot Traffic: A well-designed tenant mix that leverages retail agglomeration can attract a larger number of visitors to the mall.
• Cross-Shopping: The proximity of complementary stores encourages shoppers to visit multiple retailers during a single trip, increasing overall sales.

Shopping Behaviour Theory

Shopping behaviour theory examines the factors that influence consumers’ decisions about where and how they shop. Understanding these factors is crucial for developing an effective tenant mix which meets the requirements and inclinations of the mall’s target audience.

Key Elements

• Convenience: A mall that provides a large selection of goods and services in one place is more likely to draw customers than one that requires them to visit several different businesses.
• Variety-Seeking: Consumers often seek variety in their shopping experiences, making a diverse tenant mix appealing.
• Experiential Shopping: With the rise of experiential retail, where consumers seek not just products but also entertainment and leisure experiences, a tenant mix that includes entertainment and dining options is increasingly important.

Resource-Based View (RBV) Theory

The resource-based view (RBV) suggests that a firm’s resources and capabilities are critical to achieving a competitive advantage. In the context of shopping malls, the tenant mix is seen as a key resource that can differentiate the mall from its competitors and drive superior performance.

Key Insights

• Unique Tenant Mix as a Resource: A distinctive and well-curated tenant mix can be a valuable resource that enhances the mall’s attractiveness and competitiveness.
• Sustained Competitive Advantage: By continuously adapting and refining the tenant mix to align with consumer trends and preferences, a mall can maintain a competitive edge in the marketplace.

Network Theory

Network theory explores how relationships within a network influence outcome. In a shopping mall, the network consists of the interconnected tenants, where the success of one tenant can positively impact the performance of others.

Key Insights

• Interconnected Success: The success of anchor tenants or popular stores can generate increased foot traffic for neighbouring stores, creating a positive network effect.
• Centrality of Anchor Tenants: Anchor tenants are central to the mall’s network, attracting visitors who may also shop at smaller or less prominent stores.
• Synergistic Relationships: Effective tenant placement can create synergies where the combined effect of the tenants is greater than the sum of their individual contributions.

Impact on Shopping Mall Performance

The theories outlined above illustrate how a well-planned tenant mix can significantly impact the performance of a shopping mall. Rightly selecting and placing of tenants would result in:

• Increased Foot Traffic: A diverse and complementary tenant mix, enhanced by retail agglomeration, attracts more visitors to the mall.
• Higher Sales: The combination of convenience, variety and experiential offerings encourages shoppers to spend more time and money within the mall.
• Improved Occupancy Rates: A successful tenant mix that drives foot traffic and sales makes the mall a more desirable location for retailers, leading to higher occupancy rates.
• Enhanced Rental Income: With higher demand for retail space, mall owners can command higher rents, thereby increasing rental income.
• Greater Customer Satisfaction: Right tenant mix meets the diverse requirements and inclinations of shoppers enhances customer satisfaction, leading to repeat visits and customer loyalty.

The conceptual and theoretical foundations of tenant mix and its impact on shopping mall performance highlight the importance of strategic tenant selection and arrangement. By applying theories such as retail agglomeration, shopping behaviour, the resource-based view, and network theory, mall managers and developers can better understand how to optimize tenant mix to drive foot traffic, increase sales, and enhance overall mall performance. A well-executed tenant mix is crucial for ensuring the long-term success and competitiveness of shopping malls in an increasingly complex and dynamic retail environment. The variety and quality of the tenant mix within a shopping centre is a key concern in shopping centre management. Tenant mix determines the extent of externalities between outlets in the centre, helps establish the image of the centre and, as a result, determines the attractiveness of the centre for consumers. This then translates into sales and rents (Yuo et al., 2004).

The shopping centre industry is become increasingly competitive, and the retail landscape is dynamic and demanding (Jakom et al., 2024). Developing advance plans has become crucial for shopping malls to stay ahead of the competition. As consumer needs continue to grow, malls must plan and improve their tenant mix strategy in order to have a customer-driven approach. Since the Two Rivers Mall is one of the most desired shopping mall developments, the research aims to determine how tenant strategy is affected by product (mall) design, promotional methods, and retailing. The study also looked at the mall's positioning by various stakeholders and analysed the difficulties it faces in finding the best tenant mix.  The research's conclusions and recommendations will help Two Rivers Mall enhance its tactics in order to attain the ideal tenant mix.

Previous models of mall tenant composition have concentrated on typical anchor and non-anchor businesses that provide comparable goods (Leung et. al., 2024). A new category of retailers called as “specialty stores” that provide experiential consumption is introduced in this article in response to the shifting tastes of contemporary consumers who are looking for unusual and enjoyable experiences. In the contemporary retail climate, we revisit the tenant optimization dilemma that mall owners confront using a dynamic game model that considers the trade-off between the costs of competition and the advantages of agglomeration. Our research indicates that the ideal tenant mix and developers' rent income are significantly impacted by specialty shops. For huge retail complexes that serve modern customers, this article offers insightful information on the ideal tenant mix.

Shopping districts, where retail properties are concentrated, have long existed in urban areas (Zhang et al., 2023). Diverse retail tenants are said to enhance the high street shopping district's appeal and image. Since fragmented ownership is a defining feature of these districts, individual property investors have no control over the tenant mix. By researching high street shopping areas in the Netherlands, researchers hope to investigate the connection between retail rates and tenant mix. In order to identify high street shopping areas, granular data on the spatial distribution of retail professions is used. The tenant mix within each shopping area is measured using specific data on the number of retail locations and the SBI sector categorization. Researchers discover that retail rents are higher in shopping districts with a larger tenant mix than in areas with a smaller tenant mix.

One of the main concerns in the construction and management of retail centres is the tenant mix (Wu et al., 2023). A well-designed tenant layout can improve customer satisfaction and boost revenue. Therefore, it is essential to carefully evaluate retail compatibility and rent while creating the tenant mix. Anchor and non-anchor tenants’ rent income was the primary factor taken into account by developers. However, a lot of research overlooked how anchor tenants’ externality affected non-anchor renters’ rent. Furthermore, there is insufficient research on the connection between various tenant types and retail suitability under the behavioural preferences of customers. The optimization techniques currently in use are also ineffective, poorly integrated and unable to quickly investigate design choices. In light of the rent and retail compatibility objective, the goal of this work is to automatically generate and optimize tenant mix layouts. To assess the success of the proposed designs, a bi-objective model comprising the rent and retail compatibility objectives is first created. The externality of anchor stores on the rent of non-anchor stores in the shopping centre is measured by the rent objective function. The link between consumer behaviour preferences and retail kinds is measured by the retail compatibility function. This research then suggested a generative mechanism that includes decision-making, scheme optimization, and parametric design. According to the case study's findings, the optimized plan performs better than the original plan in terms of meeting the goals of retail compatibility and rent. Tenant mix layouts are automatically generated and optimized by the suggested generative method, which also significantly increases design process efficiency.

In the literature on retailing and consumer marketing, the topic of achieving a balanced tenant mix has long been discussed (Xu et al., 2022). Additionally, from the perspective of marketing mix planning, mall performance gains from the diversity of tenants. In this research, the impact of retail tenant mix planning on mall rents is being empirically studied using a revealed preference approach. Using a cross-disciplinary methodology, this study develops the Island-Species-Area-Energy model to examine the framework for shopping mall marketing and management. The 129 largest malls in the United Kingdom are the source of the empirical data. The findings suggest that there is an equilibrium between the retail tenant mix and mall size and shopping district purchasing power. The overall retail rents will suffer from any departures from the tenant mix equilibrium. Retail rents are also found to be influenced by five factors: anchorage, leasing strategy, locational convenience, building quality, and tenant mix equilibrium. The effects of the tenant mix on retail rents are empirically analyzed using the biogeography theory to show that there is an equilibrium between the tenant mix and retail marketing planning. These findings contribute to the current corpus of knowledge in marketing from two perspectives. The second is the creation and evaluation of a five-factor model for mall performance that includes the management mix and marketing.

In the study by Zafira & Gamal (2020), the literature on the relationship between shopping centre tenant mix and rental pricing is reviewed. Finding out factors which influences the optimum tenant mix which further influences the rental cost is the goal of research. Aspects such as tenant mix have an impact on how visitors to shopping centres perceive architecture. A shopping centre’s rental price and shopping experience may both be improved by having the ideal tenant mix. Prior research has only been conducted in Western and first-world nations regarding rental costs and tenant mix. There is currently no way to measure rental pricing for Indonesian retail centres depending on the amount of tenant mix because this study has never been carried out in Indonesia. Tenant mix factors include the number of units, shopping center size, average unit area, number of categories, and number of brands, according to the findings of this research review. As a result, the Indonesian retail mall would have a standard for measuring rental pricing at the tenant mix level. This quantification technique is applicable to Indonesian retail observers and shopping center managers. Researchers might expand on this work in the future and look at the regression analysis of the relationship between tenant mix and retail center rental pricing. The results of this literature research show that the features of the tenant mix include the number of units, average unit space, shopping center size, number of categories, and number of brands. As a result, the rental prices in the tenant mix level of the shopping centre in Indonesia would be quantified. Researchers may expand on this work in the future and look at the regression analysis of the relationship between tenant mix and retail centre rental pricing.

Research Gap

Although existing literature acknowledges the relevance of tenant mix in enhancing shopping mall performance, there is limited empirical evidence identifying which specific tenant mix attributes most significantly influence sales, footfall and customer satisfaction. Furthermore, prior studies often overlook the impact of evolving consumer behaviour, regional market characteristics and the comparative strategies of developers and investors. Most analyses treat tenant mix as a static factor, with insufficient focus on how ongoing adjustments and strategic changes influence long-term mall performance. Although tenant mix selection has been a concern for shopping mall managers, no best way or strategy is offered as solving this problem (Burnaz & Topcu, 2011). In order to bridge these research gaps, the present research work has been undertaken.

Based on the above literature the following hypotheses were proposed.

• Variety has a positive relationship with the performance of shopping malls.
• Anchor Tenants are positively related to the performance of shopping malls.
• Complementarity is positively related to the performance of shopping malls.
• Zoning is positively related to the performance of shopping malls
• Convenience is positively related to the performance of shopping malls.
• Experiential Shopping is positively related to the performance of shopping malls.

Research/Hypothesized Model

The study examines the impact of retail tenant mix on performance of shopping malls. The hypothesized model is presented in the following figure.

Figure 1: Research/Hypothesized Model

Objectives of the study

1. To investigate the perceived elements that affect the choice of retail tenant mix
2. To ascertain the predictive nature of the tenant mix that would influence mall performance,
   3. To suggest a thorough outline which will help mall management or retailers to better understand as to how malls may be improved, identify their specific areas of concern, and recommend steps to improve the shopping experience.

Research Design

Locale of study:  Bangalore in Karnataka is chosen as the locale for the study.

Data collection: Both primary and secondary data was collected for the study.  Primary data was collected using the structured questionnaire and interview schedules. Secondary data was collected from different websites, articles published in various journals of national and international repute, published reports and previous research studies.

Research Methodology

The study employed a descriptive research methodology. A convenient sample of 180 respondents was selected, and 162 of them gave valid responses, yielding an 90% response rate. The questionnaire used to gather primary data was cited in the study. The survey included 26 items and five demographic characteristics and elements that are related to retail tenant mix and purchase intention and frequency of visit. Quantitative data was produced by the study, coded, and imported into Statistical Packages for Social Scientists (SPSS) for analysis through the use of descriptive statistics and correlation analysis.

Table 1 - Frequency and percentage distribution of the respondents' demographic characteristics

From table 1 we see that, maximum numbers of respondents are females (85, 52%) followed by males (77, 48%). Moreover, bulk of participants (50 at 31%) are between the ages of 36 and 45, while those Above 54 years have the fewest participation (24 at 15. Maximum numbers of respondents are having monthly income between 75001 to 100000 (46, 28%) followed by monthly income between 50001 to 75000 (41, 25%). Regarding Life Style, maximum numbers of respondents are Convenience-Seeking (58, 36%), followed by Luxury & Status-Seeking (34, 21%). Least number of respondents are Budget-Conscious (20, 12%).

Maximum numbers of respondents are under graduates (61, 38%) followed by post graduates (34, 21%). Least number of respondents are in school level. Regarding family size, maximum numbers of respondents are having family with children, who are less than 6 years old (56, 35%) followed by Couple with children living their own (44, 27%). Least number of respondents are having family having adult children (who are 18 years and older) (26, 16%). Maximum numbers of respondents are House wives (46, 28%) followed by working professionals (32, 20%). Least number of respondents are retired people (24, 15%).

Reliability test for the data collected for the present study is depicted in table 2 and the result is satisfactory and the values are under the acceptable range. The reliability test for the data gathered for this study is displayed in Table 2, and the results are satisfactory and fall within the allowed range.

Table 2 - Reliability Statistics

Table 3 - Reliability of the Factors of Retail Tenant Mix

Factors of functional attributes has the greatest alpha value (α = 0.843), followed by Tenant Variety (α = 0.812)

Table 4 - Reliability of the factors of performance of shopping malls

Factors of sales per square foot has the greatest alpha value (α = 0.831), followed by Rental Income (α = 0.827)

Table 5 - Showing descriptive statistics of the factors influencing Retail Tenant Mix

According to Table 5, the respondents agreed on the factors influencing retail tenant mix, with a composite mean of 6.2853. Of the listed indicators, the highest ranking (weighted mean score of 6.9956) went to Complementarity. Anchor Tenants came in second (weighted mean of 6.8924); Zoning came in third (weighted mean of 6.6856).

Table 6 - Showing descriptive statistics of the factors influencing Performance of shopping malls

According to Table 6, the respondents agreed on the factors influencing Performance of shopping malls, with a composite mean of 6.3540. Of the listed indicators, the highest ranking (weighted mean score of 6.9802) went to Sales per Square Foot. Rental Income ranked second (weighted mean of 6.6904) while Customer Satisfaction ranked third (weighted mean of 6.5793).

Table 7 - Correlation between factors of Tenant Mix and Performance of shopping malls

The relationships between factors of tenant mix and performance of shopping malls were analysed and presented in the above table. Table shows Pearson’s Correlation coefficients with alpha at .01 level. Since p-value is less than 0.01, for all the factors, all the hypotheses were accepted. Hence the relationship between the factors of tenant mix and performance of shopping malls is statistically significant. These statistically significant correlations suggest that these factors influence performance of shopping malls. Hence, we can conclude that

• H₁: Variety has a significant impact on performance of shopping malls.
• H₂: Anchor Tenants of the employees have a significant impact on performance of shopping malls
• H₃: Complementarity has a significant impact on performance of shopping malls
• H₄: Zoning has a significant impact on performance of shopping malls
• H₅: Convenience has a significant impact on performance of shopping malls
• H₆: Experiential Shopping has a significant impact on performance of shopping malls

Discussion

The main objective of this study is to investigate the impact of the factors of tenant mix on the performance of shopping malls. The results of this study confirm the findings of Chebat et al. (2010) by concluding that convenience has a favorable and substantial link with shopping mall performance. This study also finds that anchor tenants have a strong positive association with mall performance, which is in line with Damian et al. (2011). Additionally, zoning has had a strongly favorable impact on mall performance, which supports the findings of Chebat et al. (2010). The frequency of visits and hedonistic value were also shown to be significantly positively correlated, which is consistent with Wakefield & Baker's (1998) findings. Furthermore, the study's findings also indicated that mall advertising significantly improves shopping mall performance, a conclusion that Anselmsson (2006) supports. Last but not least, the study's findings confirm Wakefield & Baker's (1998) assertion that there is a substantial correlation between mall performance and the retail tenant mix.

Theoretical and Practical Implications

This study offers a theoretical insight in determining the factors of retail tenant mix in Karnataka's retail shopping mall environment, given the propagation of shopping malls in Bangalore area. The findings demonstrated that, in theory, each of the following factors-variety, anchor tenants, complementarity, zoning, convenience, and experiential shopping-can have a favourable effect on how frequently customers visit the mall. Furthermore, the study finds that Bangalore shoppers' purchase intentions are positively impacted by the frequency of their visits. As a result, this study has given mall managers and retailing managers valuable insights. The species‐abundance distributions of the five large‐scale malls are found to be closely in track with a geometric distribution as commonly found in ecology (Xu et al., 2022). Insights into how clients might pay for the pleasant service of a chosen destination can be given via the quantitative assessment of a perceived supplier. The significant pleasant characteristics may be improved and the supplier can be more beneficial with the assistance of these discoveries, together with the business performance. These findings provide a valuable resort management model (Ramalakshmi, 2020).

The administration of the shopping centre is mostly focused on keeping the patrons loyal while also drawing in new ones. Consequently, the aforementioned findings have indicated that mall management should employ the several characteristics mentioned above as a means of influencing and attracting customers' inclination to attend and use the mall. For instance, when developing retail strategies to draw in customers, mall management should prioritize the following elements: variety, anchor tenants, complementarity, zoning, convenience, and experiential shopping.

Limitations

While conducting this survey, a number of restrictions were found. One of the key drawbacks is that, because the study's scope was restricted to Bangalore, it might not be applicable to other regions of Karnataka. Additionally, it was discovered that the majority of survey participants were between the ages of 36 and 45. The dominance of younger shoppers over older ones may introduce bias into this. Second, the participants in this study were chosen at random, irrespective of their race; yet, some respondents could opt to respond to the survey questions in languages other than Kannada.

The results of this study demonstrate that one of the key factors influencing shopping mall success is an ideal retail tenant mix. In addition to increasing customer foot traffic, a well-balanced mix of anchor businesses, specialized shops, service outlets, and entertainment venues also lengthens customer stays and boosts total expenditure. According to the study, a mall's performance is influenced by its tenant mix through increased consumer satisfaction, enhanced brand positioning, and long-term competitive advantage. Additionally, lifestyle choices, changing consumer trends and demographic alignment influences the effective tenant mix. Dynamic and complex retail tenant mix business has an important effect on the success of shopping malls and other retail businesses. By carefully selecting and managing tenants, industry participants can create vibrant retail environments that attract customers, boost sales, and enhance the financial performance of retail assets. The ability to innovate and modify tenant mix strategies in response to shifting consumer preferences will be essential to sustaining success in the competitive retail sector. Malls that consistently modify their tenant mix in response to consumer demands and market conditions stand a better chance of achieving increased occupancy rates, improved sales results, and sustained profitability. In order to guarantee the mall's long-term success, tenant mix management should be viewed as a dynamic, data-driven process that integrates market research, consumer behaviour analysis, and strategic leasing.

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