Economic rationale for visual configuration of space for rent and tenanting decision in shopping malls


Research Paper (postgraduate), 2016

92 Pages, Grade: 2.5


Excerpt


Table of Contents

List of Figures

List of Tables

Chapter I: Introduction
1 Introduction
1.1 Preamble
1.2 The characteristics of Shopping Mall as a study area
1.3 Background of the research
1.4 Research objective

Chapter II: Literature Review
2 Need for study of interdisciplinary works on Retail Management and Space Syntax
2.1 Studies on the Location and Rent Decision of Stores in a Shopping Mall
2.1.1 Significance of Tenant Mix in a Shopping Mall
2.1.1.1 Tenant mix policy
2.1.2 Strategic significance of Inter-store externality for describing and analysing positioning of stores in shopping malls
2.1.2.1 Managerial solutions for internalizing externalities
2.1.3 Configurational studies on Shopping Malls and application of the urban spatial theories
2.2 Human navigation pattern and spatial configuration
2.2.1 Space syntax measures and human navigation patterns
2.2.2 Relationship between navigation and visibility
2.2.2.1 Through Vision and its significance in Visibility Graph Analysis

Chapter III: Research Methodology
3 Research approach for achieving objectives
3.1 Bid-rent analysis
3.2 Space Syntax analysis
3.2.1 Visibility Graph Analysis
3.2.2 Agent Based Simulation
3.3 Measuring navigation pattern and behavioural factors in a shopping mall
3.3.1 Sample
3.3.2 Survey Instrument

Chapter IV: Analysis and Discussion
4 Analysis and Discussion
4.1 Establishing relationship between store area, rent per unit area and customer density
4.1.1 The relationships of decision variables for changing tenant store variables
4.1.2 The logic of tenanting decision
4.2 Analysing the syntactic logic behind configuration and navigation
4.2.1 Visibility Graph Analysis for describing configuration
4.2.2 Visibility Graph Analysis and Agent based simulation
4.3 Behavioural factors behind Navigational patterns in a shopping mall
4.3.1 Identification of behavioural factors behind response towards visibility
4.3.1.1 Description of Research variables
4.3.1.1.1 Shopping Orientation
4.3.1.1.2 Shopping Habitat
4.3.1.1.3 Attitude
4.3.1.2 Conceptual model for navigation behaviour
4.3.1.3 Demographic characteristics
4.3.1.4 Factor Analysis of the Model constructs
4.3.1.5 Model fit and Hypothesis Testing
4.3.2 Measuring Navigation Patterns
4.3.3 Navigational behaviour and shopper type
4.3.3.1 Tukey HSD Test
4.3.3.1.1 Post-hoc Tukey-HSD test results
4.3.3.2 4.2.2.2 Scheffe’ multiple comparison
4.3.3.3 4.2.2.3 Bonferroni and Holm multiple comparison

Chapter V: Conclusion
5 Conclusion

ANNEXURE I

List of Figures

Figure 1-1: New York City Plazas (The Good Plaza and the Bad one, Whyte, 1980) and their use during the lunch time; some are full of activities while some are deserted

Figure 1-2: Mehrabian & Russell Model (1974) p. 8

Figure 1-3: The complexity of the shopping mall design development process where the link is absent between Architectural function and real estate consultancy function

Figure 1-4: Mall rentals and vacancy situations in major Indian cities (Q1, 2013) (source: Cushman and Wakefield, 2013)

Figure 1-5: Demand supply situation of Indian malls (source: JLL, 2014)

Figure 1-6: Framework of the research

Figure 1-7: Architectural quality as an integration of functional, formal, technical and economic issues as per Voordt & Vrielink (1987)

Figure 1-8: Framework of the study

Figure 2-1: Trafalgar Square: Axial analysis and Movement traces before and after the design interventions. The strong relationship between integration of axial lines and movement traces is clearly visible (Source: Dursun, 2007)

Figure 2-2: Relationship between Sales, Pedestrian Movement and Spatial Configuration of a store. Adopted from Kong and Kim, 2012

Figure 2-3: Movement traces and VGA of the Tate Britain, existing situation (Source: Dursun, 2007)

Figure 2-4: Testing Three Design Proposals, Tate Britain (Source: Dursun, 2007)

Figure 2-5: The lines contributing to the through vision value for a location, highlighted grid (source: Turner, 2007).

Figure 3-1: Alonso’s (1964) bid-rent curve, rent decreases for increasing distance from the Central Business District

Figure 3-2: Separate land use pattern and their bid-rent curve with relationship to a city centre, how much different sectors of the economy are prepared to pay for land

Figure 3-3: Three kinds of spatial representations

Figure 3-4: Convex and Axial map representation (adopted from Hillier et al., 1983)

Figure 3-5: Isovist from the centre point of the spatial arrangements (top row) and visibility Graph Analysis (bottom row) of five situations, using Depth map X software tool

Figure 3-6: The process of Visibility Graph Analysis (from Turner & Penn, 1999); the first step (from left), defining isovist field of each view point, identifying overlapping area of isovist fields, calculating the overlapping areas of isovist field, result of calculated overlapping areas presented in a spectrum of colour

Figure 3-7: Reference colour spectrum for VGA output graphs

Figure 3-8: An exosomatic visual architecture agent tends to move through an environment due to repeated reselection from a field of view (Source: Turner, 2007)

Figure 3-9: Scenes of the simulated situations of shopping mall junctions shown to the respondents for recording navigational preferences

Figure 4-1: The relationship between f(A).A and A, with increasing A the value of f(A).A increases at a decreasing rate

Figure 4-2: Optimal floor area at different customer densities for a particular category of store (from Equation 8)

Figure 4-3: Optimal rent per unit area for different floor areas for a particular category of store (Equation 9)

Figure 4-4: Top: Relationship between optimal rent per unit area and optimal area of Store for 5 store types as identified in CRISIL RESEARCH, 2015 (From Table 4-1and Equation 9)

Figure 4-5: Top: Relationship between Area of Store and customer density for five store types (From Table 4-1and Equation 8)

Figure 4-6: Relationship between area of store and customer density at different values of k2

Figure 4-7: Relationship between area of store and customer density at different values of Fixed cost component (CF)

Figure 4-8: Relationship between area of store and customer density at different values of k1

Figure 4-9: Relationship between area of store and customer density at different values of density elasticity of demand (k3)

Figure 4-10: Relationship between optimal rent per unit area and optimal area for different values of area elasticity of demand (k2)

Figure 4-11: Relationship between optimal rent per unit area and optimal area for different values of maintenance cost component (CM)

Figure 4-12: Relationship between optimal rent per unit area and optimal area at different values of CF

Figure 4-13: The relationship between total revenue per store and customer density

Figure 4-14: Total revenue per store and customer density at different values of k2(area elasticity of demand)

Figure 4-15: Relationship between Total revenue per store and customer density at different values of CM

Figure 4-16: Relationship between Total revenue per store and customer density at different values of CF

Figure 4-17: Relationship between Total revenue per store and customer density at different values of k1

Figure 4-18: Relationship between Total revenue per store and customer density at different values of k3

Figure 4-19: The logic of introducing new store types in a shopping mall

Figure 4-20: Relationship between area of store and customer density for the two store types

Figure 4-21: Difference in rents per unit area of the two store types

Figure 4-22: Shopping mall typologies according to Verdil, 2009. From Left, Cartesian, Dumbbell, Tree and Hybrid

Figure 4-23: Isovist Area, Connectivity and Visual integration map (from left) of the Cartesian Mall configuration using Depthmap X

Figure 4-24: Isovist Area, Connectivity and Visual Integration map (From top) of the Dumbbell Mall configuration using Depthmap X

Figure 4-25: Isovist Area, Connectivity and Visual integration map (from left) of the Tree type Mall configuration using Depthmap X

Figure 4-26: Isovist Area, Connectivity and Visual integration map (from left) of the Hybrid type Mall configuration using Depthmap X

Figure 4-27: Summary of the mean values of the visibility graph measures of the four shopping mall typologies

Figure 4-28: Scatter plot of connectivity measure and visual integration value for (a) Cartesian System, (b) Dumbbell System, (c) Tree System and (d) Hybrid System

Figure 4-29: The movement tracks of agents released within the layouts of (a) Cartesian type (b) Dumbbell type (c) Tree type and (d) Hybrid type shopping malls

Figure 4-30: Relationship of Through Vision measures (top) and Metric Mean Straight line distance (bottom) with gate counts for Cartesian type Mall configuration

Figure 4-31: Relationship of Through Vision measures (R2= 0.770922) (top) and Metric Mean Straight line distance (R2= 0.11258) (bottom) with gate counts for Dumbbell type Mall configuration

Figure 4-32: Relationship of Through Vision measures (R2= 0.615145) (top) and Metric Mean Straight line distance (R2= 0.0035) (bottom) with gate counts for Tree type Mall configuration

Figure 4-33: Relationship of Through Vision measures (R2= 0.471691) (top) and Metric Mean Straight line distance (R2 = 0.02269) (bottom) with gate counts for Hybrid type Mall configuration

Figure 4-34: Conceptual model for navigational intention in a shopping mall (based on visibility) [F1: Shopping habitat, F2: Achievement orientation]

Figure 4-35: Theoretical Model of Navigational intention with the constructs and all research variables [F1: Shopping habitat, F2: Achievement orientation]

Figure 4-36: Final Model with standardized regression weights

Figure 4-37: Scatter plot of the regression weights of Attitude towards high visible areas and shopping habitat

Figure 4-38: Scatter plot of the regression weights of Attitude towards high visible areas and achievement shopping

Figure 4-39: Scatter plot of the regression weights of Attitude towards high visible areas , achievement shopping and shopping habitat

Figure 4-40: Scatter plot of the regression weights of Attitude towards high visible areas and intention

Figure 4-41: The layouts of the shopping mall junctions shown to the respondents. The dot denotes the point of view. STN1 at the Left and STN2 is at the right

Figure 4-42: Isovist from the vantage points of the two situations

Figure 4-43: VGA analysis results of the two situations, Isovist area (top) and Visual integration (bottom)

Figure 4-44: Scatter plot of behavioural intention scores and actual behaviour results of the two situations

Figure 4-45: Agent based simulation for movement of the two situations

Figure 5-1: Spatial decision making model of the shopping mall

List of Tables

Table 4-1: Assumptions for Five types of stores (source: CRISIL Research, 2015)

Table 4-2: Description of Constructs and research variables used in the survey

Table 4-3: Respondent Characteristics

Table 4-4: Descriptive Statistics for All Measurement Items for Each Research Variable (N=119)

Table 4-5: Summary of KMO and Bartlett's test of Sphericity

Table 4-6: The factor structure for all the research variables

Table 4-7: Summary of the Model fit results

Table 4-8: Summary of the Model fit with standards

Table 4-9: Regression weights of the Model shown in Figure 4-35

Table 4-10: Standardized Regression Weights of variables

Table 4-11: Standardized Correlation between independent variables

Table 4-12: Summary of the responses regarding navigational preferences

Table 4-13: Descriptive statistics for k=4 independent treatments

Table 4-14: One-way ANOVA of the 4 independent treatments

Table 4-15: Tukey HSD test results

Table 4-16: Scheffe' test results

Table 4-17: Bonferroni and Holm results: all pairs simultaneously compared

Chapter I : Introduction

1 Introduction

1.1 Preamble

In October 1943, after the destruction of the Commons Chamber of the British Parliament by incendiary bombs during the blitz (German word for lightning, applied by the British Press to the heavy bombing raids in 1940 and 1941), the commons debated the question of re-building the chamberthe same way it was. With Winston Churchill’s assertion, they agreed to retain the rectangular pattern of the chamber instead of changing it to a semi-circular or horse-shoe type design followed by some legislative assemblies. Churchill’s logic for supporting the rectangular shape was the belief that, the rectangular shape of the old chamber was responsible for ‘two-party system’, which signified the essence of British Parliamentary Democracy.

“We shape our buildings, and afterwards our buildings shape us.” Sir Winston Churchill[1] is reported to have used this widely used aphorism twice: first in 1924 at the Architectural Association of London and then in 1943, requesting to re-construct the bombed-out British Parliament exactly as before (the incident described at the beginning of this chapter). Churchill’s intuition regarding the influence of built space on humans no doubt deserves credit. But, a comparable belief of the influence of buildings or more specifically built environment on human behaviour is very common in the Architecture and Urban Design literature (e.g. Canter & Larkin, 1993[2] ; Lewin, 1951[3] ; Mc Andrew, 1993[4] ;Mehrabian&Russel, 1974[5] ; Spellar, 2006[6] ).

Modernist Architects of the early twentieth century perceived themselves not only as designer of buildings but also of utopian societies (e.g. Short, 1989[7] ). They tried to replace the popular notion of “God as Architect” with the myth of “Architect as God”. Le Corbusier, one of the most significant architects of that period, believed that, a “building is a machine for living in” (Le Corbusier,1931[8], quoted in Short, 1989, p. 42) and tried to represent the concept in the buildings and spaces he designed with the conviction that an Architect, as an artist, should be free from the demands of the population. This period observed a significant shift from client preferences to architectural fashion.

The utopia envisaged by Le Corbusier and his followers failed to acknowledge not only the preferences of the inhabitants of built spaces but also the impact of those built spaces on the public users: how they behave and interact in that environment (Baldwin, 1999[9] ). Many of these projects failed; abandoned and eventually demolished because of the social problems they created (e.g. Short, 1989).According to Newman (1972)[10], in case of Pruitt-Igoe, (which was supposed to provide a better environment to the inhabitants through design interventions) residents felt ignored and vented their frustration on the environment (the issue is debatable, but explains the failure of the project). Architect Minoru Yamasaki designed long communal hallways in thathousing project to serve as community gathering places. However, the hallways became isolated, defaced and unsafe. Most residents stayed locked-up in their rooms and use the corridors only when absolutely necessary. The demolition of Pruitt-Igoe (first occupied in 1954 and demolished in 1972) marked the end of modernist Architecture (e.g. Jencks, 1991[11] ).

Despite Architectural concerns with the form and functions of individual buildings; a building is capable of influencing the built environment at a much wider scale. The profound influence of built environment on human behaviour is recognised by designers and it had subsequently given rise to ideas aimed at changing the society through design interventions.The most significant and perhaps the most ambitious of them are the proposals of “cites ideales” by Claude Ledoux (1804)[12] in the eighteenth century and the proposals of Buckminister Fuller in the 1960s with the slogan:

“Reform the environment. Stop trying to reform the people, they will reform themselves if the environment is right”. (Quoted in Todd &Gigerenzer, 2012[13] p.1)

Keeping aside the boisterous claims, researchers in the field of human spatial behaviour established the relationship between the built environments and its human inhabitants in different real life situations. In studying lunch time behaviour of people in Plazas of New York City, Whyte (1980)[14] identified two types of plazas (Figure 1-1): “Good Plazas” attracted people sitting, sunbathing and talking while other plazas failed to attract people for any kind of activities except only walking across.

So, if the relationship between behavioural patterns (actual) and built spaces could have been identified beforehand, the spaces could be designed in a way to suit and accommodate the intended behaviours. Predicting human spatial behaviour, thus, is of great consequence to the designers as well as the decision/policy makers. Inspiteof various researches in the field, the attempts to ‘quantify’ the relationship between built environment and social life are very rare. Finding the unambiguous relationship between space and behavioural patterns, forecasting future usage patterns and incorporating them in the design, therefore, are challenges for the designers.

Abbildung in dieser Leseprobe nicht enthalten

Figure 1-1: New York City Plazas (The Good Plaza and the Bad one, Whyte, 1980) and their use during the lunch time; some are full of activities while some are deserted

The relationship between the Environment (both, natural and man-made) and Behaviour has been recognized for a long time. In order to emphasize the significance, psychologist Kurt Lewin (1951) argued that, Behaviours (B) are not only a function of Personal factors(P) but also of the Environment (E) where it takes place. Lewin (1951) expressed the relationship in a functional form as:

B=f(P, E)

The advent of Environmental Psychology as a discipline widened the scope of researches in the area of human spatial behaviour. The study of Environmental Psychology is concerned with analysing and interpreting the dynamic relationship between human beings and their surrounding environmental factors (e.g. Mc Andrew, 1993) and for that, as a discipline, it draws inputs from the research findings of behavioural scientists, psychologists, sociologists and ecologists who have been able to demonstrate that built and natural environment can facilitate or hinder human behaviours (e.g. Spellar, 2006; Canter, 1997[15] ).

Robson (2002)[16] suggested a good analogy of restaurant visit forexplaining navigation pattern within a built environment. When entering empty restaurants, people do not sit down at an arbitrary space; rather carefully choose their seat in relation to the surrounding architectural features of the restaurant. Movement decisions in an unfamiliar environment contain regular patterns and probably influenced by the configuration and visuo-spatial characteristics of the vantage points (e.g. Janzen, 2000[17] ; Zacharias, 2001[18] ). Several studies were done on the spatial influence on way-finding as an important behaviour influenced by the built environment/ spatial configuration (e.g. Weiner et al., 2004[19] ; Weiner &Mallot, 2003[20] ).

Most of the researches in this field of human spatial behaviour has been conducted in hospitals, schools and offices, whereas, very few focus on analyzing the effect of retail store environments on consumer behaviour (e.g. Baker et al. 1992[21] ).

As it is known that, environmental conditions can influence human behaviours, studying retail stores is an interesting area of study as influencing consumer behaviour is essential for functioning and survival of a retail store. People’s preference with places has direct implications on retail design since the target of every retailer is, naturally, to build places that consumers prefer than those of their competing retail stores. Atmospheric or environmental conditions in a retail store have an influence to such a degree that it can have an equally important effect on the consumer as the quality of product themselves. The store environment is so important because, on an average, 2/3rds of purchasing decisions are made inside stores and these are mostly unplanned purchases.

Moreover, planning for a retail store needs to redefine itself in a socially connected era as a destination. Despite a small share of sales, online sales or e-tailing is predicted to grow continuously and retailers would need to re-evaluate the use of retail floor space.

The origin of the environmental studies on retail stores stem from the work of Mehrabian& Russell (1974), though, their research was conducted in the domain of psychology and had nothing to do with retailing. They summarized previous research results in this field and established in a structural method the cause-and –effect relationship between environmental stimuli and emotional and behavioural responses.

In the Mehrabian&Russell model (Figure 1-2);the environment acts as the stimulus. The person (Organism), situated in the environment, gets influenced by the environmental stimuli and responds accordingly. Response is the outcome of the evoked emotional state. Mehrabian& Russell (1974) stated that, emotional outcomes could be reduced to three basic emotional variables: pleasure, arousal and dominance. Each variable include the opposite feelings, e.g. pleasure and displeasure; arousal and avoidance, dominance and submissiveness, and the experienced emotions can be found on a continuum between the two pairs.

In their model, Mehrabian& Russell (1974) concluded that the emotional responses caused by the three emotional stateswould be either approach or avoidance. Depending on the emotional influence, the person will either want to approach the environment or prefer to avoid it. Approach means the willingness of the subject to stay in or explore the spatial setting they are occupying. Positive feelings of pleasure and arousal will influence the approach behaviour. Feelings of dominance refer to the extent a person feels restricted or free to act in a particular setting.

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Figure 1-2: Mehrabian&Russell Model (1974) p. 8

For retailers, the most important part is to understand how different stimuli affect the consumer’s responses. The intention of most of the retailers is to increase the number of approach behaviours in their consumers. Thus, it is important to understand what factors in the environment generate pleasure and arousal, since stores that elicit feelings of pleasure are likely to be the ones where people want to spend their time and money. The optimal level of stimulation, though, depends on the individual. One can experience a store too attractive or too unattractive or optimal. Therefore, the individual arousal level follows an inverted “U” shaped curve. The higher the level of preferred arousal for an individual, the more environment stimuli the consumer will seek. Thus, there is no optimal level of stimuli that will work for each and every individual, but rather, the retailer should find a level, tolerated and preferred by the majority of the consumers.

Donnovan&Rossiter (1982)[22] used the Mehrabian & Russell approach and established the validity of the relationship between emotional states of shoppers, store environment and shopping behaviours. The empirical research suggested “emotion responses induced by in-store environments are primary determinants of the extent the individual spends beyond his/her original expectations” (Donovan&Rossiter, 1982. p.54). Donovan et al. (1994)[23], in an extension of the study of Donnovan&Rossiter (1982), found that emotional responses, induced by the store environment, can actually influence time and money spent in the retail stores.

Creating a unique shopping environment becomes even necessary for customer retention; negative shopping experience can lead to several negative consequences: most consumers do not complain when dissatisfied, they just shop elsewhere.

These factors have necessitated the growing emphasis on environmental factors in retail outlets or more specifically, on ‘Retail Atmospherics’ among researchers and management professionals. Atmospherics is the process retailers use to manipulate the physical retail or service environment to create specific mood responses from shoppers. Philip Kotler (1973)[24] introduced the concept by emphasizing the effects of atmospherics on emotions. His approach linked the study of atmospherics directly to the study of consumer behaviour. Kotler (1973) defined atmospherics as the effort to design buying environment to produce specific emotional effects in the buyer that enhance his/her purchase probability. Introduction of atmospherics is quite revolutionary as commercial spaces, conventionally, used to concentrate on products, services and sales people. But, as the difference between merchandise offered by competitive retailers is diminishing, environment play a significant role in the success of the commercial outlets.

Studies show that shop environments create ‘retail experiences’ that strongly influences consumers' purchase behaviour (e.g. Chebat&Michon, 2003[25], Mehrabian&Russel, 1974, Dennis et al. 2002[26], Newman & Patel, 2004[27], Stoel et al., 2004[28] ). It also influences the consumer’s judgements of the quality of the store (e.g. Babin& Darden, 1996[29] ). Moreover, keeping shoppers longer in stores is likely to result in increased browsing behaviour (e.g. Moye&Kincade, 2002[30], Babin&Attaway, 2000[31] ; Sherman et al. 1997[32] ), which in turn is likely to cause increased impulse purchasing (e.g. Beatty &Ferrel, 1998[33] ). Some research even suggests that up to two third of purchase decisions are made in stores (e.g. POPAI, 1998[34] ; Inman & Winner, 1998[35] ).

Furthermore, our shopping behaviour is becoming increasingly experience based. Since convenience is becoming primary basis for making a purchase decision for increasing number of consumers; retailers tend to focus on purchase pattern (e.g. Kang et al., 1996)[36]. Stores have become important places for spending leisure time (Hirschman & Holbrook, 1982[37] ; Tauber, 1972[38] ) and for enjoyingsocial experiences outside home (Richardson, 1993)[39]. Stores also offer maximum opportunity for browsing, and browsers are considered to be essential for success of a retail store (e.g. Jarboe&McDanil, 1987).[40]

Studies in the field of marketing focussed on the micro characteristics of the environment, or more specifically on the environment-emotion link. Contributions are made, for example on the influence of music on the volume of purchase made, impact of colour on approach and avoidance behaviour, effect of odour on time spent in store even on the relationship between crowding and shopping satisfaction. Turley &Milliman (2000)[41] reviewed the influence of atmospherics on consumer behaviour and concluded that, individual atmospheric elements have an effect on the behavioural responses and on the outcome of evaluations (e.g. store image, quality of merchandise etc.).

Throughout the history of research, the ‘atmosphere’ is broken into its components and analysed separately with the hope that joining the results of individual components will give the effect of the overall atmosphere. In reality, the effect of atmosphere is not the sum of the effects of it parts, it is synergetic in nature. As a result of the popular belief, most of the studies in the field of Retail Atmospherics have been done on micro environmental variables and not on the overall spatial arrangement as such. Most significant weakness of layout design studies is the lack of focus on movement and behaviour of shoppers (McColl, 1989)[42], as shoppers’way finding would affect the performance of the shopping centres (e.g. Chebat et al., 2005).[43]

1.2 The characteristics of Shopping Mall as a study area

Shopping malls can be defined in various ways and can be considered as “cathedrals of consumption”. Definitions of shopping mall from various sources are described below for understanding the characteristics of this retail format:

According to the International Council of Shopping Centres (ICSC, 2004[44], p.1), the shopping centre or mall is defined as:

“A group of retail and other commercial establishments that is planned, developed, owned and managed as a single property, with on-site parking provided. The Centre’s size and orientation are generally determined by the marker characteristics of the trade area served by the centre.”

In most of the cases the terms ‘shopping centre’ and ‘shopping mall’ are used interchangeably. Webster Dictionary defines a shopping mall as:

“A large building or group of buildings containing many different stores” (Merriam-Webster, 2004)[45]

Wikipedia describes a shopping mall as:

“A shopping mall is a modern, chiefly North American, term for a form of shopping precinct orshopping center, in which one or more buildings form a complex of shops representing merchandisers with interconnecting walkways that enable customers to walk from unit to unit. A shopping arcade is a specific form serving the same purpose.” (Wikipedia)[46]

A shopping mall can also be defined as a ‘built environment’ in the following way:

“… (That) attempts to simulate the commercial live centre of cities; artificially devised to recreate the same intensity of urban buzz (if not more) removed from the city streets” (Fong, 2003[47] ).

A shopping mall, as defined, can be considered as an urban experience in a closed environment combining a large variety of stores together encouraging movement throughout the entire centre.Shopping malls as a retail format are growing rapidly because of shopper’s inclination towards a ‘one-stop’ shopping destination driven by the better economic condition (e.g. Gilboa, 2009)[48]. However, the massive growth has led to tightened business environment and mutual cannibalization among malls (e.g. Tssai, 2010)[49].

The shoppers patronize the shopping mall to economize their shopping time by combining their purchase intentions from different categories of store and by making multi-purpose shopping trips (e.g. Leszczyc et al., 2004[50] ). Customers, in a shopping mall, shop not only for goods or services but for several experiential and emotional reasons (e.g. Babin et al., 1994[51] ; Tauber, 1972; Westbrook & Black, 1985[52] ). Retail tenants on the other hand come to shopping malls for enjoying high level of customer traffic for agglomeration of shops (e.g. Brueckner, 1993[53] ).

The success of a shopping mall is dependent on the mall configurationfor attracting and distributing customers throughout the mall and modifying their behaviour when they arrive near different stores (e.g. Kirkup&Rafiq, 1994[54] ; Shah &Mrudula, 2005[55] ; Hunter, 2006[56] ; Brito, 2009[57] ; Chebat et al, 2010[58] ). Tenant mix or composition of stores, therefore, is a significant strategic mall management decision because success of a shopping mall depends on the success of the retail tenants (else, they will simply move away from the mall) and the success of the shopping mall itself (else, it cannot sustain). Studying the economics of configuration of shopping malls is, thus, a fertile area of study.

To summarise, the characteristics of shopping mall can be listed as follows:

- The Shopping Mall is an urban experience in a closed environment with constant ambient condition and no external influence on indoor movement
- The success of a shopping mall is dependent on the movement of customers
- Economic success of individual tenant stores is paramount for its sustainability
- Tenant mix or composition of stores is a significant strategic mall management decision and rely on inter store externality
- Presence of Hedonic shopping motives along with utilitarian motives play a significant role

1.3 Background of the research

Two factors contribute significantly as motivators behind the research. One pertains to the performance of the shopping malls and other to the complexity of its design development.For assessing the performance of the shopping malls, the Indian market scenario is considered, where the modern retail formats are on the rise.

The design development of a shopping mall is complex. Once the strategic decisions like location and format of the shopping centre is finalized, the concept is briefed to the designers for accommodating the functional requirements within a spatial envelope and for making the space aesthetically pleasant. The transformation of the enclosure into an operational shopping space is achieved through the input of other players like retailer, mall management and leasing management. The process is explained with the diagram shown in Figure 1-3.

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Figure 1-3: The complexity of the shopping mall design development process where the link is absent between Architectural function and real estate consultancy function

The retail sector in India has witnessed a significant transformation in the past decade and still experiencing a rapid growth. The modern retail market heregrows twice as fast as the traditional trade. Expected compound annual growth rateis 21% for modern trade compared to 10% for traditional. But instead of the growth, the shopping malls in India suffer from high mall vacancy rates. The following figures (Figure 1-4, Figure 1-5) will illustrate the situation of mall vacancy of few major cities of India.

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Figure 1-4: Mall rentals and vacancy situations in major Indian cities (Q1, 2013) (source: Cushman and Wakefield, 2013)[59]

The right vertical axis inFigure 1-4represents asking rent in US$ /sf/year and the left axis represents mall vacancy rate in percentage of occupation of total available leasable area.

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Figure 1-5: Demand supply situation of Indian malls (source: JLL, 2014)[60]

New completions and absorptions, in terms of Gross Leasable Area (GLA), of Indian shopping malls (left vertical axis, Figure 1-5) are expressed in Million Square Feet and vacancy rates (right vertical axis)are expressed in percentage of absorption. Vacancy rates in poorly built and operated malls are as high as 20% where relatively better managed malls have vacancy rates of about 10%. The root cause for this problem is poor tenanting decision, faultyanchor placement and irrationalrental plan. A recent report in Economic Times (India), (Ghosh, 2015)[61] highlights the fact that retailers opt for high performing malls and for that they are ready to pay rent premiums. High performing malls are those which have low vacancy rates. A report from JLLM (2007)[62] identified mall management as a growing phenomenon in Indian retailing and listed tenant-mix, zoning and location of anchors as some of the most important functions of mall management. For proper functioning of a mall, therefore, rental, tenanting and location decisions should be takenon a sound scientific basis and not on a rule of thumb approach.

As discussed earlier, problem of mall vacancy is intrinsically spatial, but, the spatial intervention in the development of the shopping space is limited only to the creation of an enclosure for accommodating Gross Leasable Area (GLA). The potential of space design is not fully explored in formulating strategic decisions like tenanting and rent allocations. In practice, these decisions are under the purview of the mall management/real estate and leasing professionals and the interventions come much after the spatial design is finalized. For addressing rental and space allocation issues, ideally, the spatial design and mall management decisions should complement each other, without keeping the spatial design as an end in itself. This integration of spatial design and strategic decision making will simplify design development and in turn, address mall vacancy issues based on a scientific rationale.

1.4 Research objective

Store space allocations, store location within a planned shopping centre and even the lease pricing of an individual store have spatial as well as commercial implications. Proper understanding of the store location, store space allocation and lease price discrimination (most prevailing pricing method for stores in a shopping mall is leasing) requires interdisciplinary knowledge of retail management and architecture.

The operation of the psychological mechanism of the environment, which influences behaviour, depends on the physical characteristics of the concerned environment. The physical characteristics include the differentiation of their appearance, visual access the environment provide and the complexity of the spatial arrangement (e.g. Gärling et al., 1986[63] ; Weisman, 1981[64] ), and the significance of signage. The psychological mechanism includes sensory access, attention, memorability, behavioural affordance, affect and sociality. The behavioural outcome in this case is movement, as identified from the previous discussion.

Thephysical characteristics are embodied in the spatial layouts of the environment. The theoretical framework of Space Syntax provides an analytical tool for conceptualization and quantitative description of spatial layouts. The quantitative description of configuration is proposed to explain a variety of manifestation of environmental psychology. A wide range of theorists, including psychologists Gibson &Ittelson (1973) and planner Appleton (1975)[65] supported the importance of visibility in explaining behavioural interactions with the environment and supported by roboticistsYeap& Jefferies (1999)[66] for artificial organisms. The purpose of the research is to establish the between visual access and movement. The process is shown in the following Figure 1-6.

Abbildung in dieser Leseprobe nicht enthalten

Figure 1-6: Framework of the research

So, there are sufficient reasons to believe and ample evidences to support, that, human activity in general and retail consumer behaviour in particular, is profoundly influenced by built environment. This influence can be measured and shaped through spatial design interventions. This has necessitated researches in this particular area. In the shopping centre industry, it has been found, that, developers normally follow some rules of thumb when deciding on store locations and rent allocations in their shopping centres. A wrong approach in taking these strategic decisions may lead to economic malfunction of the entire centre. In reality, the competition among stores for location within planned shopping malls is of interest to academic researchers in the field of marketing as well as to mall management professionals. A designer’s perspective remains unexplored.

Hillier et al, (1984) asserted that, “Scientific approaches to architecture usually avoid the issue of building form, preferring to focus on function” (Hillier et al., 1984 p.61)[67]. Hillier et al. (1984) also elaborated the reason for this predilection towards functionality as the popular belief that, function is scientifically tractable whereas the form is not.

Hillier &Leaman (1976)[68] identified four distinguishable functions of a building:

- Spatial organisation of activities
- Climate regulation
- Symbolic function (material embodiment of ideas and expectations, i.e., cultural aspect of a building)
- Economic function

These functional qualities of a building (Hillier &Leaman, 1976) correspond closely to the functions identified by Norberg-Schulz (1965)[69]. The functional quality of a building can be explained as; the extent to which the built form provides a proper level of support for the envisaged activities. The functional quality depends on the level of support from spatial and physical qualities on the later three functions identified by Hillier &Leaman (1976): climatologic, cultural and economic function.

Abbildung in dieser Leseprobe nicht enthalten

Figure 1-7: Architectural quality as an integration of functional, formal, technical and economic issues as per Voordt&Vrielink (1987)

The above diagram describes architectural quality as mentioned by Voordt&Vrielink (1987)[70]. The economic quality, same as the economic function of Hillier &Leaman, 1976, is less explored in most of the Architectural studies. Shopping mall is one area where the economic sustenance is essential for the very existence of the building. The anecdote of the shopping mall Beau Monde, which could not continue as a mall due to poor configuration and eventually resurrected as a church may not be irrelevant in this regard (“Beau Monde, which failed to lure shoppers to its turrets, towers and cobblestone walkways as a mall, is being reborn…what was once the Denver’s (Denver Dry Goods) cosmetic store, filled with luxurious potions and fragrances, has become a chapel, filled with row after row of folding chairs” September 29th issue of the Denver Post, as quoted in Brown, 1999[71] ) . So, for a shopping mall to be a success, economic rationale for spatial configuration is to be studied in detail.

Retail researchers have focused on the movement of customers within shopping centres and considered metric distance as the only spatial aspect influencing rental differences (e.g. Carter &Haloupek, 2002[72] ; Carter & Vandell, 2005[73] ; Ingene&Ghosh, 1990[74] ) andthe takeaways have been targeted at retail professionals. On the other hand, space syntax method provides the configurational logic of spatial behaviours. Space syntax measures, through quantifying the configurational pattern, predict movement, (e.g. Hillier et al., 1987[75] ; Hillier et al., 1993[76] ) and influencing movement is almost all of what mall management aspire towards (as identified in retail management literatures). Among several space syntax measures, visibility in Architectural configuration has emerged as a useful tool for understanding configuration and human behaviour (e.g. Benedikt, 1979; Turner & Penn, 1999). Hölscheret al., (2012)have asserted that VGA acts as a strong predictor in navigation decision making. Other studies have suggested that VGA is best suited in enclosed spaces because of its simplistic approach compared to other methods of Syntactic Analysis (Axial Line and Convex Space) in highlighting locational differences (e.g. Fong, 2003).So, visibility Graph Analysis can describe the spatial configuration and people’s navigation through it effectively in shopping malls.

In retail management, no study was conducted with characteristics of spatial configuration (VGA measures in particular) as independent variables. Thus, there is a gap between the outcomes of the in-store-movement genre of research, which have not been actualized in spatial configuration terms and the architectural input, which, in-spite of dealing with a design-centric holistic view, remains insufficient: too few studies on space syntax measures as independent variables have been conducted in retail environments. Retail design research has the potential to bridge this gap through an understanding of retail spatial configurations (VGA)and its strategic implications. The broad framework of the study is shown in

Abbildung in dieser Leseprobe nicht enthalten

Figure 1-8: Framework of the study

The specific objectives of the study are as follows:

- To examine the effect of customer movement or the distribution of customer concentration within a shopping mall in predicting the optimum area and rent of stores and to understand the effect of customer concentration (gate counts) in tenanting decision making
- To understand the effect of visibility in predicting customer movement within a shopping mall and the role of Visibility Graph Analysis (VGA) in predicting pattern of movement or customer density distribution
- To explore the navigational behaviour of individuals in a shopping mall and to identify the role of shopping motivators behind navigational preferences and attitude towards visibility
- To consider spatial factors (metric and non-metric) and behavioural factors in rationalizing location and tenanting decision in shopping malls.

Chapter II: Literature Review

2 Need for study of interdisciplinary works on Retail Management and Space Syntax

The success of any real estate is largely dependent on its location, and is applied specifically to retail assets (e.g. Levy, 2013)[77], but surprisingly the use of techniques is not very popular in real estate research and management (e.g. Dubin et al., 1999).[78] Dubin et al. (1999) asserted that stores in a shopping mall share similar locational characteristics as houses in a housing estate, but the problem of valuation in the former case is unavailability of data and accepted techniques. A spatial statistical technique can be applied in this regard:

“Ironically, real estate as a discipline espouses the supremacy of location while employing economic tools designed for a spaceless world. Adoption of spatial statistical techniques offers the opportunity to align theoretical considerations with empirical practice.” (Dubin et al., 1999, p.90)

For developing a model of profit maximization for shopping malls, proper understanding of the strategic significance of composition of different categories of stores in shopping malls and the benefits of spatial configuration bothshould be studied in detail. The research literatures, thus, can be classified into two distinct approaches. The first category focuses on location and rent decisions of stores within a shopping mall and explains the underlying logic behind this tenanting decision, while the second genre emphasizes on syntactical values of space in predicting human navigation patterns in built environment. The purpose of this study is to integrate these two approaches for obtaining a better understanding of location, rent and tenanting decision of shopping malls.

2.1 Studies on the Location and Rent Decision of Stores in a Shopping Mall

Studies on retail store configuration have paid more emphasis on ‘inter-store externalities’ than on the ‘spatial logic’ in deciding location and rent within a shopping mall. Since 1990s, studies on shopping malls were focused on micro-economic rationale of lease-price discrimination and store space allocation, relying on the concept of inter-store externality (e.g. Benjamin et al., 1992[79] ; Brueckner, 1993; Eppli& Shilling, 1995[80] ; Pashigan&Gould, 1998[81] ). Studies of this genre attempted to identify an ‘ultimate tenant mix’ that would have emerged as a useful tool for the mall management in deciding which tenant store to be included for which location. Other researchers, on the contrary, have argued that the notion of ultimate tenant mix is a vague one (Carter & Allen, 2012[82], quoting Marlow, 1992 and Stambaugh, 1978[83] ) and there is no magic formula or hard and fast rule for finding the ‘ultimate tenant mix’; some notions are just better than others (Des Rosierset al., 2009[84] ).

2.1.1 Significance of Tenant Mix in a Shopping Mall

The theoretical works of Eaton &Lipsey (1979[85], 1982[86] ) and Stahl (1982a[87], 1982b[88] ) on the agglomeration of firms can explain the logic behind formation of planned shopping malls. Most of the studies on composition of shopping malls focused on tenant mix by developers (e.g. Dawson, 1983[89] ; Brown, 1992[90] ) and the way rents are finalized (e.g. Benjamin et al., 1990[91] ; Benjamin et al.,1992; Gatzlaff et al., 1994[92] ; Gould et al., 2005[93] ). Specific location of stores within a planned shopping centre, though, is mostly overlooked.

Tenant mix refers to the combinations of retail tenant stores, occupying spaces in a shopping mallto form an assemblage that produces optimum sales and rent for the retail stores themselves and helps achieving financial viability of the entire shopping mall (e.g. McCollum, 1988[94] ). Different category of tenant stores have different capacities of rental payment, a balanced tenant structure, is therefore, necessary to maximize the total revenue from the shopping mall. The wisdom has been reflected in The Urban Land Institute, Shopping Center Development Handbook (1977)[95] as:

“The developer must remember that all types of store cannot and should not pay the same rental per square foot... Certain types of services establishments may pay comparatively low rentals and may even be loss leaders for the center. Such tenants, however, are valuable to high rent tenants for their drawing power and for rounding out the centre’s service to the community.”

The underlying rationale behind tenant mix is described through store externalities by Wheaton (1999)[96]. Success of a tenant store partly depends on the mix of other tenant stores through comparison or complimentary shopping products. There are literatures to support that tenant mix of a shopping centre influences the selection of the centre (e.g. Bellenger et al., 1977[97] ), frequency of shopping (e.g. Stoltman et al., 1991[98] ) and shopping centre image (e.g.Finn & Louviere, 1996[99] ). Small tenants depend on spill-over of customers of the anchor tenants(e.g. Benjamin et al, 1992; Brueckner, 1993; Gatzlaffet al, 1994; Miceli et al., 1998[100] ; Pashigian& Gould, 1998) as well as from the strong brand name retailers (e.g. Miceli&Sirman, 1995[101] ) and at the same time smaller stores provide supportive services and adds variety for the entire shopping centre (Wakefield & Baker, 1998[102] ).Moreover, agglomeration of tenant stores generates positive shopping atmosphere (e.g. Burns & Warren, 1995[103] ; Wakefield & Baker, 1998; Bone & Ellen, 1999[104] ) and causes increasing returns to scale of operations (e.g. Goldstein &Gronberg, 1984[105] ; Fujita, 1989[106] ; Fujita &Thisse, 2002[107] ). This agglomeration also creates synergy in the centre (e.g. Nelson, 1958[108] ; Anikeeff, 1996[109] ), and the synergy results in operational efficiency of the entire shopping mall.

A proper mix of different retail tenants, or ‘Tenant Mix’, as it is commonly referred to, is, therefore, a strategic success factor for a shopping mall.

“A full line-up of strong and well placed traders is important to the retail tenant, whose performance is dependent on the level and type of footfall attracted. The success of individual tenants and the success of a centre as a whole are interdependent and enhanced by the cumulative synergy generated by the mix of stores” (Kirkup&Rafiq, 1994: 29)

Despite its immense strategic significance, there are very few empirical researches on the placement decisionor spatial arrangement of tenant stores in the shopping malls. Moreover, the major space users (anchor tenants) have received more attention in real estate and marketing researches, ignoring smaller tenants (McGoldrick& Thompson,1992[110] ).

The tenant-mix decision within a planned shopping centre is distinctively different from retail-mix and consists of:

- Compositional structure of tenants for the shopping centre
- Space allocation for each category of stores
- Location of tenants within the mall

These aspects are becoming increasingly important and should be taken care of at the very early stage of shopping centre design (e.g. Guy, 1994[111] ; Beyard& O’Mara, 1999[112] ). A tenant mix policy is therefore essential for design of a functional shopping mall.

2.1.1.1 Tenant mix policy

Grenadier (1995)[113] focussed on the dynamic nature of the tenant mix and mentioned that:

“A defining characteristic of real estate projects is their long-lived nature. It is the long time horizon which makes the optimal tenant mix strategy fundamentally dynamic in nature”

In spite of the strategic significance and dynamic nature of tenant-mix, developers normally follow some rules of thumb when deciding on placement and location of stores in shopping centres.

General guidelines (Rules of thumb) for locating tenant stores state that:

- Food shops should be grouped around the supermarkets and not in prime or higher rental locations
- Fashion and clothing should be grouped together and require prime locations, preferably in the central position of the mall and away from the food stores
- Service shops are located in the less attractive and low rental location of the shopping mall
- Restaurants should be positioned throughout the centre ... (Maitland, 1985[114] ; p.21)

The rules have been reviewed by several researchers. These rules are mostly descriptive and lack sound theoretical backgrounds. Some rules are even questionable regarding applicability.

For example, as identified by Brown (1992) and Carter &Haloupek (2002), jewellery stores in a mall should be located separately (or in other words, should not be clustered together). Normal wisdom, on the contrary, suggests otherwise. It is found that comparing quality and price is prevalent in jewellery item purchase process, therefore, the jewellery stores should better be clustered together instead of positioned separately.

Several researchers contributed to the tenant-mix policy of a shopping mall. Dawson (1983) considered number, nature and size of the retail tenant stores and focused on placement of those tenants relative to each other and to the points of entry to the mall.

“The relationship of tenants to each other in the centre as measured by:

- The proportion of floor space and/or number of units of each retail and service type
- The relative locations within the centre of units of different retail and service type” (Dawson, 1983, p.86)

Dawson (1983) listed few factors which are critical in designing tenant mix of a shopping centre:

- Selection of tenants should be done in such a way that the proper variety of stores are included in the shopping centre and a distinct image for the centre is established
- Affinity of shop types affects pedestrian flow within the shopping centre. The basic rule for locating stores is to maximise the interaction between stores. There are two contradictory approaches for the store placement. One view recommends the clustering of stores for ease of shopping and the other view counsels the separation of stores to maximise shopper movement. Dawson’s study showed that ‘match’ rather than ‘mix’ approach is helpful for increasing customer interchange and interaction
- Key tenants should be located at the end of a strip rather than placed together in the intermediate location
- Selection of proper range of store types is important for determining the number of stores of a particular type to maximise sales

West et al. (1985)[115] identified the characteristics of regional shopping centres and highlighted the fact that, a Shopping Centre should contain:

- At least one departmental store
- A large variety of stores which comprises of stores selling convenience goods or personal services with stores selling comparison shopping goods
Northern (1984)[116] listed few general guidelines for designing an efficient and effective tenant mix:
- A balance of retail stores has to be maintained in the shopping centre for achieving its optimum trade
- A good tenant mix should provide a reasonable level of competition with a wide range of price level and also offer an wide array of choice for the consumers
- Tenant placement and location of tenant stores should encourage pedestrian flow or circulation within the entire shopping centre
- Tenant stores with strongest pulling power should be located at the extremities of the development to ensure pedestrian flow from one end to other
- Small variety stores should be located at various intervals
- Proper utilization of shop frontage and optimal use of noticeable back space will enhance functional efficiency
- Proper tracking of the performance of the tenant’s trade to improve the tenant mix during the management stage of a completed centre

Brown (1992) studied the pedestrian movement in 250 shopping malls in Belfast and found that, the situation of closely located stores of similar types increases the likelihood of visiting other stores for a consumer when he visits one, such that “customer interchange” is “substantially greater than that between similar, spatially separated shops and contrasting outlet types in close proximity” (Brown, 1992, p.398). Brown’s findings supported the prevalent industry wisdom. The key guiding principles towards designing a proper tenant mix according to Brown (1992) can be summarised as follows:

- Stronger stores should be located at opposite ends and smaller tenant stores should be located in the intermediate positions
- Anchor stores should be placed far apart to spread the shoppers
- Cul-de-sacs should be avoided in the internal routes
- Service outlets should be located close to the ingress and egress points
- Pet shops and dry cleaners should be located away from food stores
- Food shops to be separated from outfitters

The upshots of the studies on tenant mix policy of shopping malls are mostly prescriptive, as if they provide guidelines for locating shops. They are not based on any sound scientific background that can be replicated in different situations. Even categorisationof stores are done broadly (i.e. jewellery stores, pet shops, food shops etc.), though there can be difference among the same broad category (i.e. one jewellery store may be considered as Anchor, e.g. Tanishq, while others may be considered as smaller tenants e.g. Chique, Saakshi Jewellery etc.).Tenant mix decisions are taken mostly on the basis of practical experience or through trial-and-error. The process is difficult and the knowledge gained is not generalised for future use. The Institute Real Estate Management (1990, p.78) mentioned that there is no optimal tenant mix model. Casazza&Spink (1985)[117] are of the same opinion about tenant-mix and asserted that the proper tenant mix for one can be mistake for another. Designing a tenant-mix strategy based on a scientific rationale is, therefore, of immense importance.

2.1.2 Strategic significance of Inter-store externality for describing and analysing positioning of stores in shopping malls

Theoretical studies on the internal composition of the shopping malls include studies by Brueckner (1993), Miceli et al. (1998) and Vitorino (2012)[118]. Brueckner (1993) studied the space allocation problem of a developer considering the externality among stores. The space allocation decision for a particular category of store should be based on the marginal revenue from additional square foot of area and the spill over effect (marginal increase in sales by all other stores due to externality generated by the concerned store). Space is allocated to a tenant store up to the level where net marginal revenue for adding that space equates with the marginal cost of the space less the externality term.

Brueckner’s (1993) model was based on the following assumptions:

- Developer’s intention is profit maximization
- Profit of the shopping centre equates rental fees less cost
- Rent of the tenants depend on their sales
- Sales volume of a particular store depends on the leased area of the store and other leased areas
- Sales of a tenant store increases as other stores increase in size

The model as proposed by Brueckner is:

Ri=Ri (S1, S2, S3…..Sn); δRi/ δSi>0 and δRj/ δSj>0; j≠i

Where, Si =The sales area of store i

Ri = The volume of sales achieved by store i

i = the number of stores from 1 to n

Brueckner’s analysis was later supported by Miceli et al. (1998). Miceli et al. (1998) explained the larger area of departmental store than those of other stores in the shopping centre through higher ability of that previous category of storesfor generating externality. These studies focussed on demand externality in predicting the optimum space allocation decision. But, these studies never commented on the location decision in a spatial arrangement in presence of demand externalities.

Departmental stores act as shopping destinations and provide a wide array of products; they increase the sales for the rest of the stores as well as their rent through exercising externality. Rent subsidies are provided to those that produce externality and rent premium are charged from those who ‘free ride’ on them(e.g.Pashigian& Gould, 1998; Gould et al., 2005). Pashigian& Gould (1998) asserted that:

“Mall developers internalise these externalities by offering rent subsidies to anchors and by charging rent premiums to other mall tenants” (Pashigian& Gould 1998, p.115)

Mejia &Eppli (2000)[119] grouped tenant stores into two broad categories: anchor tenants and non-anchor tenant, based on the capacity of generating externality. Gatzlaff et al. (1994) found that rent of non-anchor stores declines in response to the loss of an Anchor stores and the closure of an anchor store has a negative impact on the sales of other stores in the shopping centre (Yeates et al. 2001)[120]. Even anchor stores may negotiate “the right of approval or veto clause, which intends to give an anchor or major tenant veto power over the admission of new tenants” (Wunder, 1998, p. 30)[121] and the power to influence location of other stores (e.g. Harvard Law Review Association, 1973[122] ; Mason, 1975[123] ).

As anchor stores enjoy rent subsidies, developers have to rely largely on non-anchor stores for proper functioning of the mall. According to Benjamin et al., (1990, 1992), one component of the rent of non-anchor stores are fixed and another component increases with sales volume. Gould et al., 2005 mentioned that the rents of non-anchor stores are dependent on the sales volume, whereas the rents of anchor stores are independent of the sales volume.

The findings of Yeates et al., 2001 provided supportive evidence for the influence of departmental stores. They studied 18 regional shopping centres in Canada and found that profit of a store is dependent on the distance from the departmental store and all stores are not affected in the same manner. Bean et al., 1988[124] suggested that, tenant mix has to be suitable for maximal utilization of space, optimal internalization of interaction between tenants. So, internalizing externality is essential for successful tenant mix decision making.

2.1.2.1 Managerial solutions for internalizing externalities

Positive inter-store externalities or demand externalities are the positive effects generated by one tenant store to others without consent and compensation between the two (e.g. Eppli& Benjamin, 1994)[125]. The demand externality can also be seen as customer spill-over effect (Gatzlaff et al., 1994; Pashigian& Gould, 1998). These externalities are considered as significant in generating increased returns. There are three basic approaches (Yuo et al., 2003[126] ) to internalise the externalities that can be considered as the managerial solutions for shopping malls:

1. Pigouvian tax/subsidy
2. Coase Theorem
3. Constraint regulation

The Pigouvian tax/subsidy approach considers a mechanism of subsidy between the generator and receiver of the externalities. Under this mechanism of internalisation, the benefit receiver should pay a ‘tax’ equal to the benefit received in subsidising the benefit generation. Coase Theorem states that, by delineating the property rights of the externalities, the Pareto Optimal condition between the generator and receiver of the effect can be achieved through negotiation. High transaction costs emerge as a road-block for internalising externalities, so rules and regulations set and implemented by a third party can be considered as the best possible way to manage externalities.

A well designed tenant mix can prevent negative effects of wrong tenanting decision and enhance agglomeration economics between tenants by internalising the externalities through proper distribution of monetary and non-monetary obligations.

2.1.3 Configurational studies on Shopping Malls and application of the urban spatial theories

During the same period, when studies on inter-store externality were gaining popularity, some studies focused on circulation or movement of customers within shopping centres (e.g. Brown, 1991; Fisher & Yezer, 1993[127] ; Sim & Way, 1989[128] ). They relied on the concept of bid-rent model to explain and analyse the location decision of stores. They concluded that, as a rule, customers prefer shops that are easily accessible compared to those that are not. Customer density, therefore, decreases for shops with lesser accessibility.

Carter &Haloupek (2000) developed a model to describe the profit function of a store, and based on the model, found that the distance from the nearest exit and the distance from the nearest similar type of stores do not have any significant impact on the rents of stores, but the distancefrom the nearest vacant store is significant at 10% level and distance from the centre is significant at the 5% level.

Carter &Haloupek (2002)investigated the dispersion of stores by store types in malls and relied on the spatial economic model of (Ingene&Ghosh, 1990). They collected data from nine US malls between 1991 and 1992 and found the clustering behaviour of certain store types like apparel; stores of the other types exhibit dispersion.

Carter &Vandell (2005) and Eckert & West (2008)[129] focussed on internal composition of the shopping malls. Carter & Vandell (2005) assumed in their model the highest consumer traffic at the centre of the mall and tapering off traffic density while moving away from the centre. Based on the assumption of profit maximization, they identified that the total profit of the tenant store depend on price per unit of goods sold, quantity of goods sold, size of that store, customer traffic proportion of customer traffic that actually purchases, costs (labour, maintenance, utility cost, etc. and rents. They posited the following hypotheses:

- Rents and sales volume for non-anchor stores decreases for increasing distance from the central position of the shopping mall
- The rent of different types of stores decreases at different rates with increasing distance from the central position
- Size of non-anchor stores increases with the distance from the centre of the mall

According to the rationale of the model, stores with high sales response to customer traffic and high-price per item (e.g. jewellery stores) should be of smaller size and will be located close to the centre of the mall as they are able to bid for higher rents. Carter & Vandell (2005) tested the hypotheses from a sample of 689 leases in different shopping malls of the US, but, their assumption is applicable only for a linear symmetric mall configuration with a central entry. This simplistic technique neither takes into account the difference in location decision approach for both anchor and non-anchor stores, nor does it provide a logical framework for the tenanting decision.

Carter & Vandell (2005) adopted the distance measures of Carter &Haloupek (2000) and showed that the different store types are based on the bid-rent theory. The studies, which considered this movement component in predicting leasing and location decision, relied on central place theory (Christaller,1933[130] ; Christaller, 1966[131] ), the model as proposed by Alonso (1964)[132] or revised central place theory as adopted by Carter & Allen (2012)82for explaining customer density distribution throughout the shopping mall.

The configurational theories on shopping malls (Vandell & Lane, 1987[133] ; Pearson, 1991[134] ; Brueckner, 1993; Roulac, 1996[135] ; Brown, 1999[136] ) were evolved from and relied on the theories of urban spatial structure (Hotelling, 1929[137], Chrstaller, 1933; Lösch, 1940[138] ;Alonso, 1964)for describing customer density distribution as a significant factor for identifying locational characteristics of tenant stores.In contrast to the studies conducted in the residential and office market where rent issues were widely investigated, the study on retail rent allocations remained at the nascent stage because of the confidential nature of the required information.

The explanations for difference in customer density distribution were based on the adaptation of central place theory (Christaller, 1966) (highest density at the centre with decreasing density when moving away from it) and not on the logic of spatial arrangement/configuration. A detailed scientific investigation on therelationship between spatial configurationand indoor navigation pattern is, therefore, important forproper understanding of the spatial influence onhuman density distribution within it. Theknowledge of spatial configuration of a shopping mall then can aid considerably in improving store space allocation and tenanting decision making.

2.2 Human navigation pattern and spatial configuration

A distinction is often made between navigation (where certain knowledge of the route is assumed) and way-finding (which involves search and exploration) in the field of spatial cognition (e.g. Golledge&Gärling, 2004[139] ), but the terms are also used interchangeably in certain situations (e.g. Duckham et al. 2003[140] ). In this research too, the two terms have been used interchangeably and they convey similar connotation.

Navigation or pedestrian movement is critical in determining the traffic concentration within a built structure. A large number of models have been developed to explain pedestrian movement: i.e. queuing models (e.g. Hoogendoom&Bovy, 2004[141] ; Lovàs, 1994[142] ), transition matrix models (e.g. Helbing et al., 2001[143] ; Kurose &Hagishima, 1995[144] ), stochastic model (e.g. Ashford, 1976[145] ) and route choice model (e.g. Hoogendoom&Bovy, 2004) to name a few. They predict and simulate pedestrian movement in several critical situations like evacuation through emergency exits etc. Different from the above mentioned models, space syntax model is a configurational type model and it is a potential field of study for understanding social encounters and co-presence within a spatial arrangement.

Human concentration at any particular location in a spatial arrangementdepends on movement (movement of people to and through certain location), which in turn depends on accessibility of that particular location compared to other locations in that spatial arrangement under consideration. Hillier (1996 a)[146] explained the logic behind the natural movement (to and through) through the structure of the urban grid as:

“Natural movement is the proportion of movement on each line that is determined by the structure of the urban grid itself rather than by the presence of specific attractors or magnets”. (Hillier, 1996 a, p.161)

Hillier et al. (1987; 1993) and Hillier & Iida(2005)[147] categorised movements into two types: ‘to movement’ and ‘through movement’ and suggested that both categories are influenced by the configuration of the urban grid. They further suggested that, in a situation of agreement between configuration, movement and land use; configuration must be given causal primacy (Hillier et al., 1993). Hillier (1996 b)[148] proposed the concept of a ‘movement economy’ and suggested that, evolution of different densities are based on the characteristics of configuration and determine many aspects of land uses. It can, therefore, be concluded that, there is a strong relationship between spatial configuration and navigation.

2.2.1 Space syntax measures and human navigation patterns

The aim of space syntax research is to develop a series of techniques for analysing social meaning of spatial configurations. In other words, it is an attempt for constituting a configurationaltheory by providing conceptual understanding of people’s use of spatial arrangements. Space Syntax analysis quantifies the underlying ‘structure’of the urban grid (as mentioned in Hillier, 1996 a) or of the spatial arrangement, and therefore, is a determinant for accessibility. Space syntax theory depends on the linkages of a particular location with the entire spatial structure, called configuration, or:

“... A set of relationships among things all of which interdependent in an overall structure of some kind” (Hillier, 1996 a)

The configurational modelling of urban networks is done through breaking up of the urban layout (an abstraction of the space in two dimensions) into fewest and longest lines of sight that represent all possible routes for movement. The axial map (the map of the fewest and longest lines of sights or axial lines (e.g. Klarqvist, 1993[149], Ostwald & Dawes, 2011[150] )), thus produced, is analysed with statistical measures to determine the configurational property of the network. The measurement of accessibility of each axial line compared to the neighbouring lines is derived by counting the number of connections per segment (e.g. Hillier et al., 1993, p.35). The relationship of each axial line to the entire layout provides a measure called ‘integration’. Integration, in a way measures the mean depth of every axial line in the grid compared to other neighbouring lines (e.g. Hillier et al., 1993, p.35). Highly integrated lines have ‘shortest average trip’ length. Research results show that, there is a significant correlation between integration of an axial line and the magnitude of traffic and vehicular movement through the line (e.g. Hillier et al., 1993; Penn et al., 1999[151] ; Hillier, 1996; Peponis et al., 1997[152] ). So, spatial configuration can strongly explain human movement and measured through space syntax technique. This logic, though, contradicts with the axiom that movement density is determined by the pattern of land use.

One of the successful design practicesapplying space syntax methodology in urban context can be cited in this regard. Space Syntax Laboratory underpinned the proposal of Norman Foster in improving the network of public spaces (which was perceived as unpleasant, unsafe and dominated by traffic) in central London between Trafalgar Square and Parliament Square. Space syntax Laboratory surveyed existing pedestrian patterns, and simulated the environment which corresponded well with observed patterns. The findings of the space syntax analysis (Figure 2-1) triggered a number of key design ideas for re-interpretation of Trafalgar Square (Dursun, 2007)[153].

The designer acknowledged the input from space syntax analysis as:

“I would just mention that the sources of our proposals have an interactive relationship with each other. Many have emerged from these experiences; but they have also come out of the brief. They have resulted from our observations, but the same time here is constant crosschecking between those findings and public consultation. It is this symbiosis which demonstrates to me what a very creative tool the space syntax theory is” (Foster, 1997[154], quoted in Dursun, 2007, p. 56-6)

Abbildung in dieser Leseprobe nicht enthalten

Figure 2-1: Trafalgar Square: Axial analysis and Movement traces before and after the design interventions. The strong relationship between integration of axial lines and movement traces is clearly visible (Source: Dursun, 2007)

The logic of space syntax analysis was adopted from an urban scale and implemented in buildings. This technique can be applied to two dimensional building plans or urban layouts (depending on the nature of enquiry) to quantify the characteristics of the spatial structure or configuration, which is otherwise expressed in qualitative terms, making it difficult to establish correlation with other variables (e.g. movement, interaction).

The space syntax analysis in architectural space is done through decomposing the space in smaller spatial units and then establishing connections between these units (convex spaces). Syntactical analysis of convex mapping involves identifying and quantifying the spatial pattern: in terms of ‘connectivity’ and ‘integration’. Studies on human navigation patterns in indoor environments (e.g. Peponis et al., 1990[155] ; Haq&Zimring, 2003[156] ; Hölscheret al., 2012[157] ) suggest that human route choices in a built space are influenced by syntactic properties of space.

Kuipers and his associate researchers worked on the framework of navigational paths. Kuiperset al.,(2003)[158] found that, the expert navigators quickly identify a set of paths or ‘skeletons’ in the cognitive map when they explore a complex environment and reach the ultimate destination. The computational simulation used by Kuiper et al., (2003)illustrated that, the greater the number of ‘links’of one path compared to the others, the greater is the likelihood of usage of that path.

Hillier (1996), Turner & Penn (1999)[159], Turner et al. (2001)[160] and Choi (1999)[161] confirmed in their respective researches that patterns of visibility and accessibility are stronger predictors of movement than normal metric measures. In the space syntax literature, integration value is believed to be a potential determinant of human concentration and movement in that particular location compared to other spaces within the spatial arrangement. A higher integration value of a particular location signifies that theparticular 'street segment’ or location is highly connected with the overall spatial arrangement (building or an urban area).

Though, there is no explicit rigorous theoretical background, it relies mostly on the intuitive arguments and the empirical support that has been found (e.g. Enström&Netzell, 2008[162] ). Penn (2003)[163] suggested that the integration values capture the sense of how people cognitively perceive spatial arrangements.

The relationship between integration value and traffic movement had been investigated widely by Hillier et al. (1983)[164], Hillier et al., (1987), Hillier et al., (1993), Hillier & Hanson (1984)[165], Hillier (1988)[166], Peponiset al.,(1989)[167] and Marcus (2000)[168]. The outcomes of these researches show a high correlation, in many circumstances, mostly with local integration values.

2.2.2 Relationship between navigation and visibility

A rich body of literatures from Architecture and Environmental Psychology indicate a significant role of visual fields in experiencing built structures and shaping patterns of use of space (e.g. Benedikt, 1979[169] ; Frankl, 1973[170] ; Gibson, 1979[171] ). Frankl (1973) mentioned that our visual perception of any built structure affect our cognitive interactions with that environment and not only the aesthetic appreciation of Architecture as the common wisdom suggests. Diverse literatures advocate the different kinds of cognitive process and behaviour influenced by visibility in a built environment: visibility displays in museums to affect visitor’s movement (e.g. Peponis et al., 2004[172] ; Psarra, 2009[173] ; Stavroulaki&Peponis, 2003[174] ; Tzortzi, 2004[175] ); influence of visibility on movement between workstations and interactions between employees (e.g. Hilier&Penn, 1991[176] ; Markhede& Koch, 2007[177] ; Penn et al., 1999; Peponis et al., 2007[178] ); influence of visibility inway finding behaviour (e.g. Braaksma& Cook, 1980[179] ; Churchill et al., 2008[180] ; Lam et el., 2003[181] ; Omer &Goldblatt, 2007[182] ).

Visibility was incorporated in space syntax analysis by Benedikt (1979), initially as analysis of single viewpoints or isovists. Isovists are central to modelling geometrical properties related to mental representations (Meilinger et al., 2012[183] ) and aspects concerning geometry and movement (Batty, 2001[184] ), in addition to reflecting the local properties of space (e.g. Franz & Weiner, 2005[185] ; Stamps, 2005[186] ).

As it is impractical to consider every point in a spatial arrangement, the space is articulated into a fine grid and isovists of centre point of each cell of the grid are drawn. A graph is then developed with the cells as nodes and the existence of visibility between the cells as its edges and called Visibility Graph Analysis. Visibility Graph Analysis (VGA) was developed into a proper syntactic analysis tool through the works of Turner & Penn (1999) and Turner (2001)[187] and VGA has been defined as:

“… (A) set of points distributed symmetrically in space between which inter-visibility can be analysed through isovists.” (Abishirini&Koch, 2013[188] ).

The Visibility Graph Analysis (VGA) has been used as a tool by various researchers and scholars to study architectural spaces (e.g. Batty et al., 1998[189] ; Turner & Penn, 1999; Turner, 2001; Turner et al ., 2001; Desyllas& Duxbury, 2001[190]. Turner (2001) and Doxa (2001)[191] applied Visibility Graph Analysis in way-finding. Batty et al., (1998), Turner & Penn (1999), Turner et al., (2001) and Desyllas& Duxbury (2001) studied the relationship of Visibility Graph Analysis and pedestrian movement. When using a space, a space with higher visibility enjoys easier accessibility.From a study of a departmental store (Turner & Penn, 1999) it is seen that VGA representation yields a higher correlation than axial line method when relationship of Graph measure of Mean Depth and pedestrian movement is compared between the two approaches. Similar study by Desyllas&Duxbury (2001) showed that VGA method is superior in establishing correlation between movement and visibility in urban pedestrian space, when compared with axial line measures. The best correlation with VGA is r2= 0.625 and the best relationship with axial line is r2= 0.429. The study investigated pedestrian flow on pavement at 84 locations to identify a pattern. Parvinet al., (2007)[192] also analysed pedestrian flow and visual accessibility of a public area and found a strong relationship. Kong& Kim (2012)[193] suggested a model(Figure 2-2) to express the relationship between spatial configuration characteristics and sales, relying on the Visibility Graph Analysis of 28 stores in basement floor of a mall in Seol, Korea. It can be deciphered that, spatial configuration has the potential to influence pedestrian movement and sales.

[...]


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Title
Economic rationale for visual configuration of space for rent and tenanting decision in shopping malls
College
Indian Institute of Engineering Science and Technology, Shibpur  (Indian Institute of Engineering Science and Technology)
Grade
2.5
Authors
Year
2016
Pages
92
Catalog Number
V347134
ISBN (eBook)
9783668367326
ISBN (Book)
9783668367333
File size
4893 KB
Language
English
Keywords
economic
Quote paper
Sumanta Deb (Author)Prof. Keya Mitra (Author), 2016, Economic rationale for visual configuration of space for rent and tenanting decision in shopping malls, Munich, GRIN Verlag, https://www.grin.com/document/347134

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