• ISSN(P) : 2708-2474
  • ISSN(E) : 2708-2482
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Adequate Access to Retail Stores for Purchase Behaviour through Sustainable Attractiveness and Purchase Intention

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Abstract

With the world's increased urbanization, the exchange process's view is fetching more efficiency in the shopping process. This trend is changing customer's intentions towards the retail stores. This article examines how the perception of access reshapes the customer's purchase decision in developing countries and how the retail stores' sustainable attractiveness intervenes in the purchase decision making. The point of sale convenience sampling technique is used with data from 669 urban retail customers' in the current study. The results have revealed that purchase intention and sustainable attractiveness of the retail store sequentially intervene relationship between the customer's access to the retail store and the purchase behaviour. Moreover, this study has also explored the vital link between purchase intention and purchase behaviour in Pakistan's urban organized retail stores. This study provides new insights for retailers and urban planners to manage the store's location for more efficient exchange and increase customer patronage..

 

Key Words

Access, Purchase Intention, Purchase Behaviour, Retail, Sustainable Attractiveness

Introduction


Location access is becoming more important in tourism, urban planning, transportation geography, and service-related fields. The service location is a strategically thought and scarcely researched area in marketing research. A good site is also a significant part of the fixed cost in the market. Moreover, it defines the retailer class and attitude towards its customer. With this, the site shapes the retail marketing mix's foundations and the retailer's customer perception. Many retailers base their profitability mainly on a good location. Furthermore, a retail location draws more and more attention to academic and action research  (Joseph & Kuby, 2016; Reigadinha, Godinho, & Dias, 2017; Sevtsuk & Kalvo, 2018). With the rapid development, economies are experiencing increased purchasing power of the middle class. Thus, these customers can patronize organized retail stores as an emerging trend in most South-East Asian countries (Aman & Hopkinson, 2010).

The customer's diverse behavioral domain has been consistently challenging the retailers to study and implement the consumer-based attractions at their stores (Hansen & Solgaard, 2005). This shift has created more space for modern organized retailing, and thus organized retailers have experienced a substantial growth of their business in the region during the last two decades.

While retail markets of South East Asia have attracted more international retailers (Aman & Hopkinson, 2010), yet the retail market remained unsaturated even in the major urban centres. With increasing competition between the developing countries' retailers, more prominent, better, and attractive stores guarantee more customers (Diehl, Herpen, & Lamberton, 2015; Swoboda, Berg, Schramm-Klein, & Foscht, 2013). While making their stores more attractive, the retailers not only concentrate on retail tenants (e.g., the product assortment and utilitarian value) but also on non-retail tenants ( e.g., parking, access, and hedonic values) to maximize the customer satisfaction for the consumption of the products (Diehl et al., 2015).

The accessibility has concentrated either on opportunity based utility (Cascetta, Cartenì, & Montanino, 2016) or the hedonic side of shopping (Jang & Kang, 2015) in literature. These concepts are more relevant to transportation and urban planning, but for explaining the purchase process, the customer based factors should be combined towards final purchase behaviour (Cascetta et al., 2016).

Most of the past research has explained the access to shopping space with the view of geographical proximity, spatial interaction, and location-based physical distance (Donovan & Rossiter, 1982; Reigadinha et al., 2017). Specifically, the retail location is successfully explained with the narrow vision of physical space and price in the location choice and the bid rent theory (Rhee & Bell, 2002; Roarty, Mcgreal, & Adair, 1998). There is a need to explain other marketing-oriented variables for location, like customer's perception of the purchase process and convenience related to access (e.g., Parking, mobility within the store (Kumar, 2014). Furthermore, the research has rarely combined access to retail stores with the ease of approach and customer maneuvering for the purchase process. The bulk of empirical studies on the location are done with statistical modelling explaining spatial proximity (Sevtsuk & Kalvo, 2018; Singla & Rai, 2018).  This study answer the question of how to store accessibility link to sustainable attractiveness to influence the purchase process of urban customers. Moreover, this study will help marketers plan their retail expansion in a developing country.

The evidence for the 'settings up of retail space in a city is dominated by research in industrially advanced economies. Moreover, past retail research has focused on location-specific elements from one store only (Eckert, He, & West, 2015; Reigadinha et al., 2017). Very few studies have mapped the agglomeration effect of the retail store (Singla & Rai, 2018; Christoph Teller & Schnedlitz, 2011) and tested the concept of proximity to the purchase process and the wisdom of shopping according to the mobility of customer (Gahinet & Cliquet, 2018). This study will address this gap by analyzing the customers' sample from eight organized retail stores in two cities' retail agglomeration. Additionally, this study links to access to attractiveness for store, intention and behavior. This approach will enable us to analyze four objectives. First, to see the intention behavior gap in the urban retail purchase. Secondly, to analyze the sequential structural relationships of access to store towards the purchase behavior. Thirdly, to explain the behavior of developing country customers for organized retail. Lastly, to explain the location convenience according to the marketing perspective.

As the past location-based research has narrowly focused the distance to explain location-based advantage (Christaller & Place, 1933) but the customer behavioral intentions were not built in these location-specific theories (Jang & Kang, 2015; Reigadinha et al., 2017). This study will contribute an addition to the current location explanation.

In this study, the introduction section explains the background research questions and the research gap. This research's first theoretical conceptualization is made through customers' mobility in retail store choice in the subsequent literature review section. Secondly, it links this to the purchase process by constructing sustainable attractiveness and purchase intention in a serial mediation process. The results section explains the result of the empirical testing of the study model. The discussion section examines the findings from practical testing. Lastly, the conclusion, implications, and limitations are discussed.

 

Literature Review and Hypothesis Development

The growing body of literature has generally focused on the macro aspect of the location, price, and physical proximity (Rhee & Bell, 2002). The earlier studies have only considered that service facilities should be near the population for easier access. With the development of urban landscape, communication and transportation, the meaning of shopping has transformed more towards the marketing aspects of the shopping space. Therefore the need for re-investigation of location access is felt (Reutterer & Teller, 2009). The following sections are explaining the conceptual foundation of this study.

 

Access to Retail Store

Over the past two decades, empirical literature in marketing and retail has found the spatial characteristics related to approachability to store i-e access and physical distance, forming very relevant aspects of planning a shopping trip (Öner, 2015; Teller & Elms, 2010; Teller & Thomas, 2008). With the urban expansion of the word, developed urban areas have thought to be more effective and efficient in space utilization. Thus the mobility of customers was subjected to location and time restrictions. Moreover, the retail agglomerations competition has also been an essential factor for planning a living in the urban world (Jang & Kang, 2015).

Several studies have investigated urban services for facilitation and ease (Guidotti, Gabrielli, Monreale, Pedreschi, Giannotti, et al., 2018). Most of them have been published with the internet view in the services, as reported by the previous studies that online retail practice has changed shopping in developed countries (Cervellon, Sylvie, & Ngobo, 2015). However, site access has been identified as necessary because the big stores at city centers prefer to locate themselves near public transport admittance points (Borgers & Vosters, 2011). The research on retail location has contributed to customer purchase behaviour (Oner, 2014). Also, the spatial predictability for increased footfall was identified as necessary by retail researchers (Babin & Babin, 2001; Joseph & Kuby, 2016)

Other studies have concluded that access to the competitor's store in the same agglomeration also affects the customer choice and planning for a specific store (Li & Liu, 2012). These competitor stores could exist in the same agglomeration or according to the physical and temporal location (Guidotti, Gabrielli, Monreale, Pedreschi, & Giannotti, 2018). The store's proximity also decreases the cost of a shopping trip (Rhee & Bell, 2002); this might increase the number of trips to a particular store, depending upon the customer's daily travel route (Susilo, Hanks, & Ullah, 2013).

Past empirical studies have mentioned that the customer's retail outlet choice is greatly influenced by the parking facility and ease of access. The accessibility also varies according to gender, and the dimensions of proximity are spatial sequential (Borgers & Vosters, 2011). Moreover, ease of access to retail stores also influences the repurchase intention. The retailers have consistently added non-retail tenants to enhance customer accessible transit to and from the store (Kyriazis & Cloete, 2018; Eckert et al., 2015).

The city and corporate developers have used their bargaining powers to control access to shopping centers, thereby maintaining long-term profitability through location planning(Eckert et al., 2015). One of the controllable macro factors in urban planning was acknowledged as parking near the crowded city centers (Hasker & Inci, 2014; Van-der-waerden, Timmermans, & De Bruin-Verhoeven, 2015).In organized retail, parking was also identified as an essential non-retail tenant for purchase behaviour (Kyriazis & Cloete, 2018; Christopher & Schnedlitz, 2011; Van-der-waerden, Timmermans, & Bruin-Verhoeven, 2015). Good, comprehensive, and close proximity parking attracts more customers to plan their purchases from the retail store (Van der waerden et al., 2015). Retailers also included the parking price in the gross total payment to the store, which increases the search cost of the products. Since the like money, transportation was also the intermediate good (according to Search Theory), its opportunity cost should be minimum (Hasker & Inci, 2014).

It has been suggested that customers have preferred short walking distances for weekly shopping, whereas long walks were also acceptable for asymmetrical purchases, including electronics (Van der waerden et al., 2015). This discussion formulates the first hypothesis of the study as;

H1: Good access to retail stores positively significantly influences sustainable attractiveness for urban retail customers.

Literature also has pointed that the layout of the retail store according to its surroundings could influence the physical approach of the customer to the store, thereby affecting the psychological thinking towards a purchase from the store (Knego, Petljak, & Vouk, 2014). A study on location access has also stressed that the customer traffic, in and around the store, also adds to shopping preference (Hillier et al., 2015).

Although modern retailers were providing home delivery and online ordering services to ease the accessibility to store offerings (Hagberg & Holmberg, 2017), physical shopping has yet to be replaced by online modes of retail purchase (Gahinet & Cliquet, 2018).

The frequency of shopping has been influenced by the choice of accessibility to the store. Huff’s model explains the physical proximity to the store by making the stores more and prominent in populated centers of the cities (Sevtsuk & Kalvo, 2018) or locating the store according to individual customer needs (Handy & Niemeier, 1997). Literature suggested that customers have preferred proximity, even if the price benefit is less, for frequent purchase trips (Jiao, Moudon, & Drewnowski, 2016). The customer transit to

H2: Good access to retail store positively significantly increase purchase intention of urban retail customers.

Customers have preferred retail outlets with easy access and at convenient proximity from their location (Gahinet & Cliquet, 2018).  According to the Theory of Patronage Preference, the store location could be explained better with accessibility, one shop stop, and updated information to the customer (Sheth, 1981). Customers from one particular demographic or ethnic background prefer to travel long distances to purchase their groceries from a specific store. Past research on the emotional need-based model of activity relationships has confirmed that the individual activities planned in a day are also correlated with other activities in the same day or upcoming activities, which are either helpful or critical activities during a short planning span. It also has confirmed that different actions in a day could not be independent (Nijland, Arentze, & Timmermans, 2013). Thus, shopping trip has served as a need to complete several social, utility, and leisure activities. This compulsion has urged retail customers to plan their shopping trips periodically over a week or month (Kumar, 2014). A few needs were satisfied, and other necessities were intended for gratification in subsequent shopping trips.

H3: Good access to retail stores positively significantly affects the purchase behaviour of urban retail customers.


Figure 1: The Conceptual Model of the Study

 


Sustainable Attractiveness

Sustainable attractiveness was the customer's general tendency to revisit the retail space (Teller & Reutterer, 2008). The ease of access to retail locations builds continuous customer interaction to generate the customers (Oner, 2014). Customers choose to visit different retail spaces according to their utility and the retailer's persistent marketing attraction (Öner, 2015). The earlier literature on location has concentrated on spatial theories were as new literature in marketing and retail was explaining the location attractiveness based on competition (Teller & Elms, 2010). The empirical studies in retail have included customers' opportunities to perform their daily tasks near the purchase location as the added attractiveness of entertainment services (Cascetta et al., 2016).

According to the Retail Gravitation Theory, proximity and access to the retail stores positively influence the store attractiveness and increase retail brand equity (Swoboda et al., 2013). The literature has also identified that the cost of travel, infrastructure, and the value of the surrounding location also adds to location attractiveness in the agglomeration. The retail stores also enhance their retailer image with the added agglomeration effect in their brand equity, which has improved the store's likelihood of purchase (Cascetta et al., 2016).

The empirical studies related to spatial importance have added that accessibility and the right brand image could increase the long-term loyalty of the customer. Moreover, the past study has also found that the customer value the brand more than the convenience of accessibility for local focal retailers (Swoboda et al., 2013). Thus, accessibility has added value to the store, especially when stores are large (Reigadinha et al., 2017). According to the Spatial Interaction Theory, Consumers might trade-off a less attractive retail location near to them or go for big offer retailers at a distance from them (Reigadinha et al., 2017). Moreover, the location attractiveness of luxury goods is dependent upon display and customer assistance in the store (Hansen & Solgaard, 2005). Access to the store was found reverent only for urban areas; countryside stores have different factors for place attractiveness (Öner, 2015). This accessibility could be enhanced by the careful design and architecture of the retail stores (Said, Gambo, & Ismail, 2016). Based on the above literature following hypothesis was developed.

H4: Sustainable attractiveness positively significantly affects the purchase behaviour of urban retail customers.

 

Purchase Intention

The causes of intentions have been widely investigated in scattered studies. Out of these purchases, preferences have got considerable attention in marketing literature, but the actual studies addressing the purchase intention in the context of collective investment have remained scarce (Jeng, 2017; Morrison, 1979). The perceived thinking of customers to purchase from a retail store was referred to as purchase intention (Chandon, Morwitz, & Reinartz, 2005). Empirical research has linked purchase intention positively to trust in seller (Lu, Fan, & Zhou, 2016), elements of the theory of planned behaviour (Sreen, Purbey, & Sadarangani, 2018), product range, store patronage, word of mouth, music (Roschk, Maria, Loureiro, & Breitsohl, 2016).

By drawing on an extensive range of sources, purchase intention could be generated during the shopping trip through the hedonic value influence or could be generated after a thorough purchase decision process through the utilitarian value (Cervellon et al., 2015; Chandon, Morwitz, & Reinartz, 2005; Christopher Teller, Reutterer, & Schnedlitz, 2008).

The approach to the store serves as a vital factor for purchase decision making. The increased technological innovations, although the access barriers, like store timings and location, are bridged through e-retailing in industrially advanced countries (Christoph Teller & Schnedlitz, 2011; Christoph Teller & Thomas, 2008). The bulk of the population in developing countries still preferred location-based retail stores with ease of access, where they can purchase on a day-to-day basis (Pantano & Dennis, 2017; Wu, 2011).

H5: Sustainable attractiveness is positively significantly related to the purchase intention of urban retail customers.

 

Purchase Behaviour

Purchase behaviour is the customer reaction at the time of the final purchase. Customers desires shape the purchase behaviour at the time of shopping (Guo & Barnes, 2011) in retail. Evaluation of purchase behaviour has linked the theoretical dimensions of cognitive ( Mao & Oppewal, 2012) and affective domains to stimulus organism and response theory (Goi, Kalidas, & Zeeshan, 2014; Roarty et al., 1998). Past studies have identified that the final purchase behaviour has changed according to time, space, and culture at all the stages of purchase decision making (Wee, Ariff, Zakuan, & Tajudin, 2012). Purchase intentions are not always translated into purchase behaviour (Morrison, 1979). Thus, the studies should have emphasized adjusted intention leading to actual purchase behaviour (Jamieson & Bass, 1989). Additionally, the store patronage behaviour of customers displays significant differences according to demographics and shopping environment (Loureiro & Roschk, 2014; Christoph Teller, Gittenberger, & Schnedlitz, 2013).

In collectivist societies, customers have shaped their purchase intention according to their social referent shape (Sreen et al., 2018). Empirical studies in marketing have highlighted the need to explore the intention behaviour link in purchase decision making (Davis, Lang, & Diego, 2014; Sreen et al., 2018). Thus, the conclusion generates the sixth hypothesis.

H6: Sustainable attractiveness and purchase intention sequentially mediate the relation between access to store and purchase behaviour of urban retail customers.

Past literature in marketing has also argued that the conversion of purchase intention into purchase behaviour is affected due to the customer reach towards the products (Chandon et al., 2005). Studies on behavioral purposes are associated with the store's location convenience, pondering better responses within a one-mile radius. Moreover, for less convenient stores, ' behavioral intentions could be converted to favorable responses with increased hedonic value (Ashley, Ligas, & Chaudhurt, 2010; Hillier et al., 2015). Thus the link of intention between approach and behaviour is hypothesized below.

H7: Purchase intention mediates the relation between access to stores and purchase behaviour of urban retail customers.

With the behavioral view in marketing, the literature has identified that the link of purchase intention to behaviour needs to be tested for better choice and to how much choice is translated into actual action (Campbell & Fairhurst, 2016; Carrington, Neville, & Whitwell, 2014; Sheeran & Webb, 2016).

Pat literature depicted that the intention to behaviour gap is broad, and it is concluded that about half of the intentions turn into actual behaviour (Carrington et al., 2014; Sheeran & Webb, 2016). The intensity of the intention has to shape up the goal and the properties of choice. Additionally, three things (initiation, maintenance, and goal pursuit) are found supportive for converting the intention into actual behaviour (Carrington et al., 2014; Sheeran & Webb, 2016). In the absence of these, the purchase process will end on intention (Jamieson & Bass, 1989).In the retail competition for customers, purchase intention paves the way for the final behaviour of ideal brands (Chandon et al., 2005). Moreover, the research in psychology has also established that the evaluation of the behaviour was most elaborate in explaining the intention and action of the individuals (Wood et al., 2016)

H8: Purchase intention positively significantly predicts the purchase behaviour of urban retail customers

 

Methodology

Most retailers judge their customers based on

historical purchase data (Dolega et al., 2016). For this research, the survey-based method was adopted. Primary data is collected through a survey questioner at the point of sales. The description of the methodology is explained below.

Population and Sample

The population selected for the current study were the urban retail customers of the developing country. The minimum education qualification was high school graduates because of the comprehension skills needed to understand and respond to the research tool.

 

Data Collection

It was impossible to collect data from all the population, so the mall intercept sampling procedure (Bellini, Cardinali, & Grandi, 2017) was adopted. The survey was conducted through the self-administer online technique (Chow-Yeng, 2012). A total of 754 individuals were approached at the point of sales in five super retail stores of Rawalpindi and Islamabad. Past research establishes that individual intention and behaviour could be judged better by questioning the behaviour effect (Wood et al., 2016).

The data is collected online with the use of smartphones at the point of sales. This technique enabled the significant reach to broader respondents (Shamsher, 2016). Moreover, the customers at different stores could be reached simultaneously. The online data collection method has also been proved more time and cost-effective. This method has also ensured the appropriate record of the respondent's perception (Chidlow, Plakoyiannaki, & Welch, 2014).

The data are collected between October 2018 and April 2019. Five data collection experts were hired to collect data from eleven chosen large retail stores of the cities. All data collectors were given access to the online questioner to record multiple responses from each store. Every outlet was visited three times after a lag of two months to collect the appropriate response over the period and eliminate any random disturbance in data collection. Each respondent was briefed about the confidentiality and anonymity of the data. Moreover, the respondent was also told about the research and answered any query while filling the online questioner.

 

Measures

The research tools were adopted from previous studies (explained in Table 1). All the items were measured by using seven-point Likert scales. The scale was distributed as "Strongly disagree" was the anchor to 1 till "Strongly Agree" was anchored to 7.

The sustainable attractiveness measures were developed by using the scale developed by teller and Thomas (2008). This scale was also used by Teller, Elms, Thomson, and Paddison (2010). In past literature, the purchase intention was reported based on desired than actual (Campbell & Fairhurst, 2016). For this study, the original scale of purchase intention was adopted from  Dodds, Monroe, and  Grewal (1991), having three items. The purchase behaviour scale was adopted from Zhang and  Breugelmans (2012); this scale was also used in the previous study in retail (Guo & Barnes, 2011). Five item purchase behaviour scale was used from the survey of Ali and Kapoor (2010) instead of a two-item scale (Guo & Barnes, 2011) for linking purchase intention to purchase behaviour comprehensively (Jamieson & Bass, 1989).

The parking scale consisted of three items and was adopted from Arentze, Oppewal, and Timmermans (2005). This scale was also used in the same capacity in other studies (Sinha, 2008; Christoph Teller, 2008). This scale measures the customer's perception of the adequacy and convenience of the parking available with the retail store.


Table 1. Factor Scores and Outer VIF Measures

Construct

Item

Description

Cronbach's α

Outer VIF

Λ

AVE

Square Root of AVE

Composite Reliability

Sustainable Attractiveness

SusATT1

You will visit this store again in the future.

0.701

1.410

0.887

0.769

0.877

0.870

SusATT2

You will visit this store again to purchase something

1.410

0.867

Parking

PKR1

There is constantly enough free parking at store

0.747

1.359

0.800

0.588

0.767

0.810

PKR2

There is different sufficient parking at your store

1.175

0.697

PKR3

The store is reached quickly and easily from parking.

1.346

0.798

Accessibility

ACCB1

Store is reached quickly

0.744

1.426

0.792

0.661

0.813

0.854

ACCB2

You can easily reach the store

1.547

0.827

ACCB3

You get to the store without problems

1.490

0.821

Purchase Intention

 

 

PI1

You would purchase from my store

0.666

1.333

0.835

0.857

0.926

0.750

PI2

You would consider buying at a current price from my store

1.333

0.862

PI3

The probability that I would consider buying from my store

 

0.821

Purchase Behavior

 

PurchBehv1

Monthly store visit frequency (per month)

0.790

1.874

0.724

0.535

0.731

0.852

PurchBehv2

Shopping trip spending (per month)

1.536

0.804

PurchBehv3

The preferred format of retail

1.743

0.710

PurchBehv4

Preferred market location

1.669

0.719

PurchBehv5

Preferred type of market

1.853

0.697

 

Data Analysis


The data collected for this study were screened with initial tests to remove any incomplete responses. The measurement and the structural model are evaluated through smartpls 3.0.0. This SEM base analysis has been chosen because of the more expandability of the complex model.


Table 2. Demographics details

Variable

Group

Frequency

% of responses

Gender

Male

379

56.83

 

Female

290

43.24

Education

High School

97

14.45

 

Graduate

396

59.19

 

Post Graduate

176

26.31

Income (Rs Per Month)

< 50000

24

03.51

50001- 100000

202

30.15

 

100000- 200000

262

39.21

 

200001- 300000

134

20.02

 

< 300001

47

07.11

 


Preliminary Analysis

The reliability and internal consistency have been predicted through the factor score loading. The low score measures (less than 0.50) were removed as they remained short of explaining the total variance in the reflective index. These factor scores were listed in table 2. One item, each from the construct of purchase intention and situational attractiveness, was removed because of a low score.

After removing the items, the structural model was re-evaluated, and results were given in Figure 2.

 

Validity and Reliability

The common method biases are taken care of during the data collection because these biases threaten to explain the correlation between the measures  (Podsakoff, MacKenzie, Lee, & Podsakoff, 2003). The dependent and independent variables were collected from the same respondents through online data collection (Sreen et al., 2018). Unrotated exploratory factor analysis of the research tool was done on SPSS 22 to check for any common method biases through Harmans One factor test (Podsakoff et al., 2003). A single factor has explained 41 % of the total instrument variance, which is less than the majority of the cut-off (50 %) range. Therefore, no standard method biases exist in the data.

The discriminant validity is checked by Fornell and Lacker (1981)  criteria, and the shared variance between the construct is less than the square root of the AVE; therefore, discriminant validity is claimed according to Table 3.

The AVE also displays the convergent validity for the constructs. The value of AVE ranged between 0.536 and 0.769. These values are more than the recommended level of 0.05 (Fornell & Larcker, 1981). The composite reliability scores for the current study this has ranged between 0.810 and 0.870 in table 4. Recent results are considerably higher than the recommended level of 0.7 in the literature (Fornell & Larcker, 1981). That has concluded the model as a valid, good-fit, and reliable model.


 

Table 3. Construct and Convergent Reliability

Construct

Composite Reliability

Average Variance Extracted (AVE)

Parking

0.810

0.588

Accessibility                                  

0.854

0.661

Sustainable Attractiveness

0.870

0.769

Purchase Intention

0.750

0.857

Purchase Behavior

0.852

0.535


The VIF score of less than five according to the literature. The VIF for the outer model was between 1.333 and 1.874.  The VIF for the inner model ranged between 1.059 and 2.06, which concluded that multicollinearity does not exist in the data. The VIF values have been displayed in table 5.


 
Table 4. VIF Value for Inner Model

 

Sustainable Attractiveness

Purchase Intention

Purchase Behavior

Parking

1.665

2.060

1.877

Accessibility

1.665

1.746

1.839

Purchase Intention

1.704

 

1.653

Age

 

 

1.054

Gender

 

 

1.059

 

According to the Square Root Mean Residual (SRMR) rule of thumb (value < 0.09) for a structural model (Hu & Bentler, 1999; Jackson et al., 2009). The current model has an SRMR score of 0.076 for the estimated model and 0.072 for a structural model. Hence considered a good fit according to SRMR criteria (Hu & Bentler, 1999)

 

Table 5. Latent Variable Correlation

 

1

2

3

4

5

Accessibility

0.813

PB

0.585

0.731

PI

0.549

0.702

0.926

Parking

0.632

0.670

0.563

0.767

Sustainable Attractiveness

0.523

0.686

0.634

0.620

0.877

Note: Square root of Average Variance Extracted is displayed diagonally. SIC= Single Item    Construct.

Figure 2: The Final Structural Model

Conclusion

Hypothesis Testing Through Direct and Indirect Effect

The study has been concluded with the interpretations of the data. The relationships among the study constructs have been explained through direct and indirect impact, t-value, and the beta coefficients were measured through smartpls, on a confidence interval equal to 95%. These values are displayed in table 6,7 and table 8.


 

Table 6. Total Effect (t-value) Beta Coefficients

 

Original Sample (O)

T Statistics (|O/STDEV|)

P Values

Accesability -> PB

0.264

7.606

0.000

Accesability -> PI

0.321

8.650

0.000

Accesability -> Sus Attrc

0.218

5.997

0.000

PI -> PB

0.425

12.794

0.000

Parking -> PB

0.494

15.547

0.000

Parking -> PI

0.360

9.902

0.000

Parking -> Sus Attrc

0.482

14.463

0.000

Sus Attrc -> PB

0.175

7.005

0.000

Sus Attrc -> PI

0.413

9.764

0.000

Table 7 displays the direct effect between independent variables and dependent variables. The effect between

 

Table 7. Total Indirect Effect (t-Value) Beta Coefficients

 

Original Sample (O)

T Statistics (|O/STDEV|)

P Values

Accessibility -> PB

0.136

6.916

0.000

Accessibility -> PI

0.090

4.914

0.000

Parking -> PB

0.153

8.068

0.000

Parking -> PI

0.199

8.057

0.000

Sus Attrc -> PB

0.175

7.005

0.000

 

Table 8. Specific Indirect Effects (t-value) Beta Coefficient

(t-Value)Specific Indirect Effects

Direct Effect

Total Effect

Accessibility -> PI -> PB

(10.324)0.098

0.166(5.177)

0.264(7.606)

Parking -> PI -> PB

(4.221)0.069

0.199(4.478)

0.494(15.547)

Accessibility -> Sus Attrc -> PI -> PB

(2.562)0.038

0.226(8.723)

0.264 (7.606)

Parking -> Sus Attrc -> PI -> PB

(5.672)0.084

0.410(3.32)

0.494(15.547)

Accessibility -> Sus Attrc -> PI

(3.336)0.090

0.231(4.431)

0.321(8.650)

Parking -> Sus Attrc -> PI

(5.692)0.199

0.161(6.668)

0.360(9.902)

Note the t value in the above table are mentioned in ( ).

 


According to table 9, for hypothesis 5 the value between accessibility and purchase behavior (β=0.264 and t=7.606) has changed its direct effect after the purchase intention was introduced as a mediator (β=0.136 and t= 6.916) from table 8. It displays a change of 0.166 in the direct effect after mediation. Thus, partial mediation has been exhibited in the results based on the current study data (Frazier, Tix, & Barron, 2004).


 

Table 9. Cross Loading

 

Accessibility

PB

PI

Parking

Sus Att

ACC1

0.792

 

 

 

 

ACC2

0.827

 

 

 

 

ACC3

0.821

 

 

 

 

PI1

 

 

0.858

 

 

PI2

 

 

0.874

 

 

PKG1

 

 

 

0.800

 

PKG2

 

 

 

0.697

 

PKG3

 

 

 

0.798

 

RBB1

 

0.724

 

 

 

RBB2

 

0.804

 

 

 

RBB3

 

0.710

 

 

 

RBB4

 

0.719

 

 

 

RBB5

 

0.697

 

 

 

SusATT1

 

 

 

 

0.887

SusATT2

 

 

 

 

0.867

 

Discussion


This study has contributed to literature in three ways. First, the work related to the non-product related study of the retail stores(Ashley et al., 2010; Christopher & Schnedlitz, 2011) leads to purchase behaviour. The mediated relationship of sustainable attractiveness between store and purchase behaviour has displayed that the customers prefer the external environmental cues to plan purchases from a particular retail store. Secondly, the contribution was the mediation of purchase intention between access and purchase behaviour. Most of the past studies have considered access as part of the external environment, not contributing a lot to the final purchase decision. This study has contributed that access to the store affected the intention building of the customer. This intention was for the collective purchase of groceries and other retail store offerings, which has a considerable influence on purchase behaviour.

Lastly, the intention to behaviour relationship contribution in the urban retail context has also enabled the addition to the literature from developed countries. In the results, it was observed that not all the intentions are turned to purchase behaviour. This insight links the current finding to  the prior research explain only about one-half of choices finds their way to behaviour (Sheeran & Webb, 2016)

In the explanation of access, the physical approach has played a significant role in shaping up the purchase and repurchase behaviour in academic research (Hillier et al., 2015). The results of this study paper have displayed that the increase of physical mobility, access to the retail store is dependent upon the physical proximity from the store and on the essential non-retail tenant like parking. Results conclude that customer mobility is linked to the generation of purchase intention, which moulds the final purchase behaviour. Thus, access to physical location adds value to physical shopping space and extends to how conveniently the customer can get to the accusation of the grocery items.

 People think of acquiring the experience if they could access physical products or services. If something was accessible, it could be acquired and consumed. Marketing literature has linked purchase behaviour to selling space-related factors that could explain customer behaviour after they are in shopping space. The purchase process, on the other hand, was more comprehensive than the selling space. Accessibility not only brings the customer inside the selling space also towards the accusation and disposal of the product.

The past location models were statistically focused only on the physical location and how the factors related to these locations (Kim, Kim, Chung, & Youn, 2011; Reigadinha et al., 2017). Retail stores act more than other utility buildings in urban planning. To explain retail stores access retail stores, the literature was expended through the dimension of sustainable attractiveness, which partially mediated the relationship between access to the store and purchase behaviour. This relation guides that good access enhances the beauty for a better shopping experience at a retail store.

In the current study, the retail store's sustainable attractiveness was judged in a mediated relationship between access to store and purchase behaviour. The results have found the partial mediation of the hypothesized relationship. The purchase behaviour could be improved in the long term by consistently making the access better than the competition in the market; moreover, the access to the store also enhances the store's image as a shopping-friendly space. This image could improve the long-term patronage and purchase intention of the customers. Although; building sustainable attractiveness was a slow and gradual process but once attained, it could keep the flow of customers steady over the years.

The incremental value added by accessibility could also add to the location value of the geographical area. This additional value would increase the hedonic and unplanned shopping trips of the customers.  The stores located near the city centers have a broader scope of accessibility.

The data of 669 customers from five organized retailers in Pakistan have examined the association between variables related to store accessibility patterns, sustainable attractiveness of the store, and purchase. We have found that stores selling large-scale grocery items within urban areas plan their stores according to the accessibility to the majority of the population. The access to the retail store, however, was dependent upon the local mode of transportation. In large city centers, retailers do not offer non-retail tenants like parking for their customers because this provision has already been taken care of by urban planning. This access also takes care of the purchase intention of the customers who cross these city centres daily as a routine.

Related to the sustainable attractiveness of retail, the stores managing the beauty according to the current trends in competition could benefit the positive purchase behaviour from their customers in terms of increased visits and more spending. Our approach to evaluating the purchase process with physical access to store and sustainable attractiveness have made a difference from previous empirical studies, which have only compared the limited encashment area to a single retail store in an agglomeration (Dolega et al., 2016; Wahlberg, 2016). Furthermore, the empirical studies also have mapped the national level stores with purchase decision making (Teller & Elms, 2010), but their competition was not direct with the market. According to the retailers, this study has focused on the context of one urban area, large enough to be competitive with each other in a city.

 

Limitations

The study was based upon a few shortcomings that could improve further research in this area. First, the study is based on the cross-sectional data design, which only captured the customers' one-time responses. The relative change in the customers' preferences can be better explained better with the longitudinal research design.

Another limitation of this study was the longitudinal research design to incorporate the effect of different weather or festival-based shopping compared to normal purchase behaviour. In future studies, it would be interesting to determine how specific intentions are shaped according to the broader preferences of purchasing groceries from one particular store.



 


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Figure 2

Arentze, T., Oppewal, H., % Timmermans, H. (2005). A Multipurpose shopping Trip model to Assess Retail Agglomeration Effects. Journal of Marketing Research, 42(1), 109-115.

Ashley, C., Ligas, M., % Chaudhurt, A. (2010). Can Hedonic Store Environments Help Retailers Overcome Low Store Accessibility? Journal of Marketing Theory and Practice, 18(3), 249-262.

Babin, B. J., % Babin, L. (2001). Seeking something different? A model of schema typicality, consumer affect, purchase intentions and perceived shopping value. Journal of Business Research, 54(2), 89-96.

Bellini, S., Cardinali, M. G., % Grandi, B. (2017). A structural equation model of impulse buying behaviour in grocery retailing. Journal of Retailing and Consumer Services, 36(February), 164-171.

Borgers, A., % Vosters, C. (2011). Assessing preferences for mega shopping centres: A conjoint measurement approach. Journal of Retailing and Consumer Services, 18(4), 322- 332.

Campbell, J. M., % Fairhurst, A. E. (2016). Reducing the intention to behaviour gap for locally produced foods purchasing: The role of store, trust, and price. International Journal of Retail % Distribution Management, 44(5), 508-523.

Carrington, M., Neville, B., % Whitwell, G. (2014). Lost in translation: Exploring the ethical consumer intention-behavior gap. Journal of Business Research, 67(1), 2759- 2767.

Cascetta, E., Cartenì, A., % Montanino, M. (2016). A behavioral model of accessibility based on the number of available opportunities. Journal of Transport Geography, 51, 45-58.

Cervellon, M. C., Sylvie, J., % Ngobo, P. V. (2015). Shopping orientations as antecedents to channel choice in the French grocery multichannel landscape. Journal of Retailing and Consumer Services, 27, 31-51.

Chandon, P., Morwitz, V. G., % Reinartz, W. J. (2005). Do Intentions Really Predict Behavior? Self-Generated Validity Effects in Survey Research. Journal of Marketing, 69(2), 1-14.

Chidlow, A., Plakoyiannaki, E., % Welch, C. (2014). Translation in cross-language international business research research. Journal of International Business Studies, 45(5), 562-582.

Chow Yeng, L. (2012). The Antecedents of Customer Loyalty in the Malaysian Retail- Shopping Setting. (February), 1-17.

Christaller, W., Christaller, G., % Place, C. (1933). Christaller ' s Central Place Theory Assumptions : 1-9.

Christopher, T., % Schnedlitz, P. (2011). Drivers of agglomeration effects in retailing: The shopping mall tenant's perspective. Journal of Marketing Management.

Davis, R., Lang, B., % Diego, J. S. (2014). How gender affects the relationship between hedonic shopping motivation and purchase intentions? Journal of Consumer Behaviour, 13(1), 18-30.

Diehl, K., van Herpen, E., % Lamberton, C. (2015). Organizing products with complements versus substitutes: Effects on store preferences as a function of effort and assortment perceptions. Journal of Retailing, 91(1), 1-18.

Dodds, W. B. W., Monroe, K. K. B. K., % Grewal, D. (1991). Effects of Price, Brand, and Store Information on Buyers' Product Evaluations. Journal of Marketing Research, 28(3), 307.

Donovan, R., % Rossiter, J. R. (1982). Store Atmosphere : An Environment Psychology Approach. Journal of Retailing, 58(1), 34-57.

Eckert, A., He, Z., % West, D. S. (2015). An empirical analysis of tenant location patterns near department stores in planned regional shopping centers. Journal of Retailing and Consumer Services, 22(1), 61-70.

Fornell, C., % Larcker, D. F. (1981). Evaluating Structural Equation Models with Unobservable Variables and Measurement Error. Journal of Marketing Research, 18(1), 39-50.

Frazier, P. A., Tix, A. P., % Barron, K. E. (2004). Testing Moderator and Mediator Effects in Counseling Psychology. 51(1),

Gahinet, M. C., % Cliquet, G. (2018). Proximity and time in convenience store patronage: Kaïros more than chronos. Journal of Retailing and Consumer Services, 43, 1-9.

Goi, M. T., Kalidas, V., % Zeeshan, M. (2014). Comparison of Stimulus-Organism- Response Framework between International and Local Retailer. Procedia - Social and Behavioral Sciences, 130, 461- 468.

Guidotti, R., Gabrielli, L., Monreale, A., Pedreschi, D., % Giannotti, F. (2018). Discovering temporal regularities in retail customers' shopping behavior. EPJ Data Science, 7(1).

Guidotti, R., Gabrielli, L., Monreale, A., Pedreschi, D., Giannotti, F., Guy, C., Kalvo, R. (2018). Discovering temporal regularities in retail customers' shopping behaviour. EPJ Data Science, 7(1), 503-518.

Guo, Y., % Barnes, S. (2011). Purchase behavior in virtual worlds: An empirical investigation in Second Life. Information and Management, 48(7), 303-312.

Handy, S. L., % Niemeier, D. A. (1997). Measuring accessibility: An exploration of issues and alternatives. Environment and Planning A, 29(7), 1175-1194.

Hansen, T., % Solgaard, H. S. (2005). New Perspectives On Retailing And Store Patronage Behavior: A study of the interface between retailers and consumers. (Z. J. Acs, Ed.). Copenhagen: Kluwer Academic Publishers.

Hasker, K., % Inci, E. (2014). Free Parking for all in Shoppng Mall. International Economic Review, 55(4), 1281-1304.

Hu, L., % Bentler, P. M. (1999). Cut-off criteria for fit indexes in covariance structure analysis: Conventional criteria versus new alternatives. Structural Equation Modeling: A Multidisciplinary Journal, 6(1), 1-55.

International, E. (2013). Retailing in pakistan. https://www.euromonitor.com

Jamieson, L. F., % Bass, F. M. (1989). Adjusting Stated Intention Measures to Predict Trial Purchase of New Products: A Comparison of Models and Methods. Journal of Marketing Research, 26(3), 336-345.

Jang, M., % Kang, C. D. (2015). Retail accessibility and proximity effects on housing prices in Seoul, Korea: A retail type and housing submarket approach. Habitat International, 49, 516-528.

Jeng, S. P. (2017). Increasing customer purchase intention through product return policies: The pivotal impacts of retailer brand familiarity and product categories. Journal of Retailing and Consumer Services, 39(August), 182-189.

Jiao, J., Vernez Moudon, A., % Drewnowski, A. (2016). Does urban form influence grocery shopping frequency? A study from Seattle, Washington, USA. International Journal of Retail and Distribution Management, 44(9), 903-922.

Joseph, L., % Kuby, M. (2016). The location types of US retailers. International Journal of Applied Geospatial Research (IJAGR), 7(4), 1- 22.

Kyriazis, A., % Cloete, C. E. (2018). The tenant mix in shopping centres: South Africa and the United Kingdom compared. Journal of Business and Retail Management Research, 12(2), 152-162.

Kim, P., Kim, W., Chung, W., % Youn, M. (2011). Using new Huff model for predicting potential retail market in South Korea. African Journal of Business Management, 5(5), 1543-1550.

Knego, N., Petljak, K., % Vouk, R. (2014). Location and Layout As Sources of Competitive Advantage of Small. Journal of International Scientific Publications, 8(1), 267- 281.

Kumar Velayudhan, S. (2014). Outshopping in rural periodic markets: a retailing opportunity. International Journal of Retail % Distribution Management, 42(2), 151-167.

Li, Y., % Liu, L. (2012). Assessing the impact of retail location on store performance: A comparison of Wal-Mart and Kmart stores in Cincinnati. Applied Geography, 32(2), 591- 600.

Loureiro, S. M. C., % Roschk, H. (2014). Differential effects of atmospheric cues on emotions and loyalty intention with respect to age under online/offline environment.

Lu, B., Fan, W., % Zhou, M. (2016). Social presence, trust, and social commerce purchase intention: An empirical research. Computers in Human Behavior, 56, 225-237.

Mao, W., % Oppewal, H. (2012). The attraction effect is more pronounced for consumers who rely on intuitive reasoning. Marketing Letters, 23(555), 339-351.

Morrison, D. G. (1979). Purchase Intentions and purchase behavior. Journal of Marketing, 43(2), 65-74.

Nijland, L., Arentze, T., % Timmermans, H. (2013). Representing and estimating interactions between activities in a need- based model of activity generation. Transportation, 40(2), 413-430.

Oner, O. (2014). Retail Location. Jönköping University.

Öner, Ö. (2015). Retail City: The Relationship between Place Attractiveness and Accessibility to Shops (No. 1055). Stockholm.

Pantano, E., % Dennis, C. (2017). Exploring the origin of retail stores in Europe: Evidence from Southern Italy from the 6th century BCE to the 3rd century BCE. Journal of Retailing and Consumer Services, 39(August), 243-249.

Podsakoff, P., MacKenzie, S., Lee, J. Y., % Podsakoff, N. (2003). Common Method Biases in Behavioral Research: A Critical Review of the Literature and Recommended Remedies. Journal of Applied Psychology, 88(5), 879-903.

Reigadinha, T., Godinho, P., % Dias, J. (2017). Portuguese food retailers - Exploring three classic theories of retail location. Journal of Retailing and Consumer Services, 34, 102-116.

Reutterer, T., % Teller, C. (2009). Store format choice and shopping trip types. International Journal of Retail % Distribution Management, 37(8), 695-710.

Rhee, H., % Bell, D. R. (2002). The inter-store mobility of supermarket shoppers. Journal of Retailing, 78(4), 225-237.

Roarty, B. O., Mcgreal, S., % Adair, A. (1998). Clustering retailers by store Comparable evidence , retail function and rental value.

Roschk, H., Maria, S., Loureiro, C., % Breitsohl, J. (2016). Calibrating 30 Years of Experimental Research : A Meta-Analysis of the Atmospheric Effects of Music , Scent , and Color. Journal of Retailing, 1-12.

Said, I., Gambo, N., % Ismail, R. (2016). An evaluation into the architectural factors attracting customers to Malaysian shopping malls. Journal of Business and Retail Management Research (JBRMR) Www.Jbrmr.Com A Journal of the Academy of Business and Retail Management, 11(1), 138- 153.

Sevtsuk, A., % Kalvo, R. (2018). Patronage of urban commercial clusters: A network- based extension of the Huff model for balancing location and size. Environment and Planning B: Urban Analytics and City Science, 45(3), 508-528.

Shamsher, R. (2016). Store image and its impact on consumer behaviour. ELK Asia Pacific Journal of Marketing and Retail Management, 7(2), 1-27.

Sheeran, P., % Webb, T. L. (2016). The Intention - Behavior Gap. Social and Personality Psychology Compass, 10(9), 503-518.

Sheth, J. N. (1981). An Integrative Theory of Patronage Preference and Behavior (No. 808). Illinois.

Singla, V., % Rai, H. (2018). Examining the Effectiveness of Huff Model in Store Performance Assessment. Jindal Journal of Business Research, 7(2), 1-13.

Sinha, P. K. (2008). Store choice behaviour in an evolving market. 32(10), 482-494.

Sreen, N., Purbey, S., % Sadarangani, P. (2018). Impact of culture, behavior and gender on green purchase intention. Journal of Retailing and Consumer Services, 41(July 2017), 177- 189.

Susilo, Y. O., Hanks, N., % Ullah, M. (2013). An exploration of shoppers travel mode choice in visiting convenience stores in the UK. Transportation Planning and Technology, 36(8), 669-684.

Swoboda, B., Berg, B., Schramm-Klein, H., % Foscht, T. (2013). The importance of retail brand equity and store accessibility for store loyalty in local competition. Journal of Retailing and Consumer Services, 20(3), 251- 262.

Christoph, T. (2008). Shopping streets versus shopping malls - determinants of agglomeration format attractiveness from the consumers' point of view. The International Review of Retail, Distribution and Consumer Research, 18(4), 381-403.

Christoph, T., Elms, J. R., Thomson, J. A., % Paddison, A. (2010). Place Marketing and Urban Retail Agglomerations : an examination of shoppers place attractiveness perceptions. Journal of Place Branding and Public Diplomacy, 44(March), 1-19.

Christoph, T., Gittenberger, E., % Schnedlitz, P. (2013). Cognitive Age and Grocery Store Patronage by Elderly Shoppers. Journal of Marketing Management, 29(3-4), 317-337.

Christoph, T., % Schnedlitz, P. (2011). Drivers of agglomeration effects in retailing: The shopping mall tenant's perspective. Journal of Marketing Management,

Christoph, T., % Thomas, R. (2008). The Evolving Concept of Retail Attractiveness: what makes retail agglomerations attractive when customers shop at them? Journal of Retailing and Consumer Services, 15(3), 127- 143.

Christoph, T., % Elms, J. (2010). Managing the attractiveness of evolved and created retail agglomerations formats. Marketing Intelligence % Planning, 28(1), 25-45.

Christoph, T., % Reutterer, T. (2008). The Evolving Concept of Retail Attractiveness: what makes retail agglomerations attractive when customers shop at them? Journal of Retailing and Consumer Services, 15(3), 127- 143.

Christoph, T., Reutterer, T., % Schnedlitz, P. (2008). Hedonic and Utilitarian Shopper Types in Evolved and Created Retail Agglomerations. The International Review of Retail, Distribution and Consumer Research, 18(3), 283-309.

Van der waerden, P., Timmermans, H., % De Bruin-Verhoeven, M. (2015). Car drivers' characteristics and the maximum walking distance between parking facility and final destination. Journal of Transport and Land Use, 10(1), 1-11.

Wahlberg, O. (2016). Small town centre attractiveness: evidence from Sweden. International Journal of Retail % Distribution Management, 44(4), 465-488.

Wee, C. S., Ariff, M. S. Bin, Zakuan, N., % Tajudin, M. N. (2012). Key Factors Affecting Consumer Purchase Intention a Study of Safe Vegetable in Ho Chi Minh City , Vietnam. Review of Integrative Business and Economics Research, 3(2), 378-397.

Wood, C., Conner, M., Miles, E., Sandberg, T., Taylor, N., Godin, G., % Sheeran, P. (2016). The Impact of Asking Intention or Self- Prediction Questions on Subsequent Behavior: A Meta-Analysis. Personality and Social Psychology Review, 20(3), 245-268.

Wu, L.-W. (2011). Beyond satisfaction: The relative importance of locational convenience, interpersonal relationships, and commitment across service types. Managing Service Quality, 21(3), 240-263.

Zhang, J., % Breugelmans, E. (2012). The Impact of an Item-Based Loyalty Program on Consumer Purchase Behavior. Journal of Marketing Research, 49(1), 50-65.