Outlets malls of the future: an approach to customer’s expectations

Abstract

In recent years, the number of outlets has increased significantly; however, little research has been done with reference to users’ expectations about the outlets in the years to come. Our research tries to determine which expectations customers have about the future outlet mall in terms of stores, entertainment venues and technological innovation, and whether these expectations are conditioned by demographic factors. We examined a sample of 1531 respondents from an online panel survey. We used the chi-square test of independence to compare proportions among demographic characteristics. Our findings showed that there are significant differences between men’s and women’s expectations when analysing stores categories. Personal factors, like age and occupation, condition preferences over leisure activities and technological innovations. These results represent an important starting point for decision makers who wish to manage outlet malls based on a customer-centric strategy.

Keywords:

• Outlet mall
• consumer expectations
• retail
• technological innovation
• entertainment venues

JEL CLASSIFICATIONS:

• M21; M31; L81

1. Introduction

Like traditional marketplaces, shopping centres or shopping malls are ‘urban shopping areas featuring a variety of shops surrounding a usually open-air concourse reserved for pedestrian traffic’ (Shopping Malls, n.d.). Malls are seen as a one-stop shopping experience where one can visit multiple retailers and make several purchases at one location while enjoying restaurants and other entertainment. Another attraction of malls for shoppers is usually that a trip to the mall often takes the form of an outing with friends and family. Consequently, shopping malls require a specific design and layout to promote traffic (Behera & Mishra, 2017). In this context, store layout has a significant impact on consumer behaviour both in traditional and online environments (Griffith, 2005). Malls combine consumer consumption with consumer experience (Hu & Jasper, 2018), enhancing user’s satisfaction to new levels (Anselmsson, 2006).

Turnover from shopping centre retail in the EU-28 in 2016 was above 560 billion euros, where France and the United Kingdom together accounted for more than 235 billion. Spain was the fourth country in terms of turnover, with 53 billion euros (International Council of Shopping Centers [ICSC], 2017). More than 6.1 million people were employed in this industry in the EU-28 in 2016. The United Kingdom and France together employed more than 2.3 million people. In Spain, more than 530,000 people were employed in shopping centres and related operations in 2016 (ICSC, 2017).

In addition, the leading shopping malls in the United States have significantly higher sales per square foot than those of the average department store retailer. In 2017, there were approximately 116,000 shopping malls spread across the United States, whereas back in 1970, there were only 37,000 (Kearney, 2018). In Europe, as of July 2017, there were a total of 9546 shopping centres distributed in three categories: 7085 traditional shopping malls; 2214 retail parks; and 247 factory outlets¹ (ICSC, 2017). In Spain, and in terms of centre size, half of the Spanish shopping centres had an area of less than 20,000 square metres, while only 3% had an area of more than 80,000 square metres (Cushman & Wakefield, 2017).

Despite the aforementioned details, traditional malls are now in turmoil. A recent report from Credit Suisse indicated that up to 25% of US shopping malls will close in the 2018–2022 period (Easter, 2017). These figures contrast with the fact that, in 2004, there were more shopping centres than high schools in the United States (Schwartz, 2004). Both the increasing influence of digitalisation, which has augmented the existence of online shopping, digital marketplaces and e-commerce sites² (Ram, 2017), and the appearance of price-driven shopping centres, so-called outlet malls, are affecting the survival and performance of traditional malls (Coughlan & Soberman, 2005).

Differences between traditional shopping malls and outlets are basically a matter of price and offer. Reynolds et al. (2002, p. 687) define factory-outlet malls as shopping centres ‘comprised of stores operated by brand-name manufacturers of apparel, shoes, electronics, and other products’ that ‘offer an additional outlet for their inventory’. Research on traditional shopping mall users indicates that they feel that traditional malls are too expensive and that they simply do not have as much time to shop at malls as they would need. As a consequence, the number of outlets, where prices are lower and the offer is concentrated, has increased in the recent years. More specifically, outlets normally present two characteristics: (1) they are low-service manufacturer-operated shops; and (2) they are located at a significant distance from the shopping districts of major metropolitan areas (Coughlan & Soberman, 2005).

Although there is an abundance of research about how outlets have traditionally looked (Reynolds et al., 2002), there is very little said about how these outlets will function in the future, and even less about outlet users’ expectations in the years to come. We term these specific outlet malls to come as ‘outlets of the future’. We believe that both increasing digitalisation, meaning that outlet managers will leverage their technologies to interface with customers who have their own technologies and devices (Kearney, 2018), and the growing number of outlets represent a good starting point for our research. Accordingly, we will leave traditional or other shopping formats aside to concentrate on studying the outlet mall of the future. In this sense, and from the perspective of consumer behaviour, we posit that personal factors affect outlet users’ relations with the shopping centre. Consequently, our research will concentrate on determining which expectations customers have about the outlet mall under certain demographic situations, and, more specifically, expectations about the offer of outlet malls in terms of store categories, entertainment activities and technological innovation. In other words, we want to determine whether personal characteristics condition what customers expect to find in an outlet centre: which stores they would like to visit, what kind of entertainment venues are expected, and which technological gadgets that could make an outlet centre more attractive.

In order to determine the answers to our questions, we performed a descriptive analysis through an online panel survey conducted in Spain. We collected data from a sample of 1531 customers, from 18- to 60-year old, who regularly visit shopping centres. We analysed data by applying cross-tabulation, the chi-square test of independence and one-way analysis of variance (ANOVA).

The rest of this article is structured as follows. First, we review the literature to provide insights into the different personal and demographic factors that may affect outlet users’ relations with the shopping centres and the link between these factors and the main trends that will shape the future of outlet malls. This is followed by a discussion of the methodology, the presentation of the results and a conclusion with some suggestions for further research.

2. Theoretical framework and hypothesis

There is extensive literature on shopping malls and on how they interact and have been interacting with users over time. It is common to find research on how the number of malls is increasing in different parts of the world (Patil, 2019), mall consumer behaviour in terms of age (Sudha, 2020) and how an individuals’ culture affects their mall shopping behaviour (Agrawal & Gupta, 2017; Allen, 2019), among other studies. However, it is more difficult to find literature on how these shopping malls will develop in the future and, more specifically, the customers’ perceptions towards such developments. Despite some research on actual transformations of shopping malls into different kinds of facilities, such as transformations into paintball centres (Dunham‐Jones & Williamson, 2017), practitioners are some of the few that analyse trends in the outlet industry.

We will build on what are understood as the three consumer trends that will shape the future of the shopping mall: nature of the stores, entertainment and technological innovation (Ram, 2017). We will extrapolate these shopping mall trends to the particular case of the outlet mall.

The first trend is related to the nature of the stores included in a shopping mall. The users of the outlets of the future will seek experiences as well as products. Introducing visual displays and merchandise offers the users decisions (Grewal et al., 2017) that will certainly condition the future of outlet malls. In addition, stores will need to present the assortment of products in ways that are easier for customers to process (Kahn, 2017), with adequate spatial organisation of the merchandise (Nordfält et al., 2014). In the specific case of outlets, the kind of product sold is also important. In this sense, customers can expect to find sport stores, perfumeries, homeware shops, fashion shops, electronics stores, furniture stores and so forth.

The second trend is associated with entertainment, or with how to permit customer prioritisation of multisensory experiences and events over the actual acquisition of products. There is a strong link between entertainment and customer satisfaction (Neal et al., 2007). People will continue to visit the outlets to socialise, to have something to eat (for instance, the existence of restaurants in the malls has a direct influence on mall success; Haseki, 2013), and to entertain (like visiting the bowling centre or going to the cinema), among other forms of leisure. Some of these entertaining services offer outdoor cinemas or ‘big-screen’ sporting events projection; adventure-sports areas; air tunnels; children’s and adults’ mat zones; availability of virtual reality games (VRG); escape-like games; day care; playrooms and/or playgrounds for children; and so forth.

The third trend to analyse refers to the degree of technological innovation the outlet of the future presents. In the near future, it will be important to generate a more interactive retail experience. This user experience can be enhanced, for example, through the use of touch screen navigation panels, virtual reality (with or without virtual fitting rooms), augmented reality (like magic mirrors), artificial intelligence, robots, drones and even driverless vehicles (Grewal et al., 2017). In addition, other services like free Wi-Fi or apps will also need to be part of the outlet of the future design.

Once the major trends for the outlets of the future have been stated, we should not forget the main research goal of this study. The research is focused on identifying the expectations that outlet users have about the characteristics of the outlets of the future and, furthermore, whether those expectations differ according to users’ demographic characteristics. To achieve this goal, the remainder of this section is organised as follows: first, we will determine the factors that can influence consumer perceptions about outlets. Second, we will review the existing literature on consumer expectations of outlets and how these expectations are conditioned by personal factors. Finally, we will outline the hypotheses that will be evaluated in the subsequent sections.

Understanding consumer behaviour implies a thorough understanding of the behaviour of individuals as they interact with one another and their environment in a consumption context (Morales et al., 2017). Consumer behaviour is affected by various factors. If these factors change, so does consumer behaviour. ‘The demographic factors which affect consumer behaviour are: (1) age, (2) sex, (3) marital status, (4) income, (5) family background, (6) education, (7) occupation, (8) family size, (9) geographic factors, (10) psychological factors’ (Kumar, 2014, p. 1). We understand that these factors, or at least some of them, can be tracked down to check whether they affect the way outlet mall users interact; that is, how do these consumers behave with the shopping centre?

The existing literature includes several studies that relate certain demographic factors with the way consumers interact with shopping malls. For example, Prasad and Aryasri (2011, p. 1), in their analysis of stores, convenience stores, supermarkets and hypermarkets in India, determine that the ‘age, gender, occupation, education, monthly household income, family size and distance travelled to store have significant association with retail format choice decisions’. Furthermore, in their study about Hispanic consumers’ shopping orientations, Seock and Sauls (2008) examine how age and gender differences influence shopping orientations and retail store evaluation criteria. Their results revealed that males and females have different shopping orientations and apparel retail store evaluation criteria. Shopping orientation and apparel retail store evaluation criteria also varied across the age groups. Finally, Mihić and Kursan Milaković (2017), in a study conducted in Croatia, show that personal factors, with the moderating effects of gender and education, positively influence shopping enjoyment.

The way the shop is presented to customers is seen differently depending on these demographic factors. Gunaseelan and Chitra (2014) relate store ambience, merchandise attributes, service procedures, convenience and satisfaction and promotion with the impact of store operations on mall users within certain demographics. Specifically, aspects like age, gender, education, occupation, family size, marital status, monthly family income, family type and number of visits to the store influence the level of agreeability towards all the aforementioned store characteristics. Although these conclusions are limited by the local nature of the sample, which was taken from the Coimbatore district in India, these authors additionally conclude that customers are expecting more services in the retail outlets during shopping.

Conversely, although reaching similar conclusions to the papers mentioned, other authors (Nagella & Reddy, 2019) investigate the opposite; that is, there is no significant impact of store attributes on the selection of a retail outlet, and there is no association between customers’ demographic characteristics and the choice of the retail format.

Other non-demographic but psychographic factors have also been analysed in the existing literature. For instance, Tendai and Crispen (2009) explain the influence of an in-store shopping environment on impulsive buying among consumers. Existing research indicates that atmosphere affects mall loyalty and shoppers’ identification with the mall. If discriminated by gender, the sample behaves differently between females and males. In the case of male shoppers, the drivers of mall loyalty are mall atmosphere, prices and identification with the mall. With female shoppers, the drivers are the mall’s atmosphere, its physical design and the perceived quality of products and services (Haj-Salem et al., 2016).

In conclusion, the literature demonstrates the importance that the number and the nature of the stores included in an outlet mall have in the design of the outlet of the future. The growing importance of digitalisation, the variety of stores included, the products offered, and so forth will certainly condition the design and success of these outlets of the future. Furthermore, different demographic characteristics such as gender or age determine different attitudes towards the shopping centres. In this regard, males value outlet prices more than females do; on the contrary, female shoppers give a higher value than men to perceived quality of products and services. These two facts, the importance of store types and demographic differences, allow us to express our first hypothesis:

H1: The type of store expected by outlet users is conditioned by users’ demographic characteristics.

Similar comments are applicable for the next two hypotheses. Entertainment and digitalisation of outlets is key for their success, and the perception towards these variables is affected by demographic characteristics of outlet users. Consequently, we propose the following hypotheses:

H2: The kind of entertainment expected by outlet users is conditioned by users’ demographic characteristics.

H3: The kind of technological innovation expected by outlet users is conditioned by users’ demographic characteristics.

3. Methodology

As discussed, this research tries to determine which expectations customers have about the future outlet mall in terms of stores, entertainment venues and technological innovation, and whether these expectations are conditioned by demographic factors. To achieve this main goal, we conducted a research study in Spain. We collected data using an online panel survey. We followed a descriptive design for this study. This kind of research, if done with large and representative samples, achieves conclusive results and is adequate to verify hypotheses and find relationships among variables (Grande Esteban & Abascal, 2007). Using an online survey, we collected the opinion and assessments of consumers from a convenient internet panel. This kind of panel has the advantages of a relatively low cost, a rapid speed of data collection and the ability to reach a large number of respondents in subgroups of interest (Hays et al., 2015). The sample includes 1531 consumers from 18- to 60-year old (Table 1), who regularly buy in shopping centres. We used a stratified sampling method to ensure different age segments were represented within the whole sample. In this sense, although there is little difference between opting out of a probability sample and a non-probability sample (Rivers, 2013), we selected individuals of each age subgroup using random sampling.

Table 1. Sample sociodemographic characteristics (N = 1531).

	Sex		Do you have children?		Total
	Male	Female	Yes	No
Age
18–28	34	147	16	165	181
29–39	115	368	430	53	483
40–50	222	309	531	0	531
51–60	177	159	336	0	336
Occupation
Employed	468	685	1034	119	1153
Retired	38	9	45	2	47
Unemployed	30	168	176	22	198
Housewife	2	51	53	0	53
Student	10	70	4	76	80
Total	548	983	1312	219	1531

We started to develop the questionnaire by setting the research objectives in an initial script. From this script, we formulated the different questions to be posted, as well as the variable definition and the rating scale (Fernández, 2002). We mostly used closed questions. We anticipated potential customer answers to these closed questions with the existing literature. Closed questions present the advantage that they require less effort to answer (Schuman & Presser, 1981). In sum, we included in the survey questions that allow us to know the respondents’ sociodemographic characteristics, such as gender, age, marital status, educational level, occupation and parental status (Table 1); additionally, we also asked about their shopping frequency, type of store desired, innovations available and entertainment venues expected.

To evaluate the collected data, we applied cross-tabulation and chi-square analysis. This is the usual methodology employed to analyse group differences when the measurement level of all variables is nominal.

We used chi-square to analyse the expectations of the different groups in terms of age range, sex, marital status and occupation (Howitt & Cramer, 1999) towards store categories, entertainment activities and technological innovations. We measured the sociodemographic variables as categories. Consequently, we used the chi-square test of independence to compare the frequency of each category for all sociodemographic variables across the categories of stores expected, preferred entertainment and preferred technological innovation. In this sense, data do not violate any of the chi-square test assumptions, because the compared proportions belong to independent groups; that is, samples are not paired and levels of the variables are mutually exclusive (McHugh, 2013). The null hypothesis for this test is that there is no relationship between the dependent variable (stores expected, preferred entertainment and preferred technological innovation) and independent variables (sociodemographic characteristics).

Finally, and depending on respondents’ first preference towards technology and entertainment, we decided to use a one-way ANOVA to compare the mean of some of our quantitative variables, like the number of children in the family and the respondent’s age among the different groups of consumers. As our results for some categorical variables showed significant differences among means, we used the Bonferroni test as a post hoc method for multiple comparisons.

4. Results

Table 1 shows that the majority of mall visitors are over 28-year old and have children, which was an expected result (e.g., Bloch et al., 1994).

Regarding the type of shops expected to be located in an outlet shopping centre, we investigated customer expectations in 10 different categories. Some categories are more expected than others; the most dominant categories are apparel and accessories, followed by sport shops (Table 2). To our surprise, and regardless of respondent’s age, optical is a non-expected store category in an outlet centre (nearly 75% of users did not select it). Something similar happens with furniture shops (only around one fourth of the sample, 28%, expects to find furniture shops in an outlet). Accordingly, none of the dominant categories except sports presents differences among ages. This fact demonstrates the primacy of this variable, the type of stores present, over the rest of the demographic characteristics, and the importance of the existence of anchor stores in retail (Nicholls et al., 2002). Nonetheless, anchor stores have traditionally represented a significant feature of malls and seem to remain important today.

Table 2. Stores categories expected by age groups (%).

	Apparel	Sports	Fragrance	Cosmetics	Decor	Furniture	Accessories	Optical	Jewellery	Electronics
Age
18–28	97.8 ^a	65.2 ^a	58.0 ^a	57.5 ^a	43.6 ^ab	31.5 ^a	73.5 ^a	18.8 ^a	43.6 ^a	47.5 ^a
29–39	96.1 ^a	69.8 ^ac	50.1 ^ab	42.4 ^b	37.5 ^ab	25.9 ^a	76.2 ^a	21.3 ^a	34.2 ^b	42.4 ^a
40–50	94.7 ^a	75.3 ^b	48.8 ^b	39.0 ^b	43.3 ^a	31.5 ^a	75.9 ^a	26.9 ^b	30.5 ^b	45.0 ^a
51–60	92.9 ^a	74.1 ^bc	48.5 ^bc	39.6 ^b	35.1 ^b	26.2 ^a	71.4 ^a	25.0 ^ab	31.5 ^b	44.3 ^a
Gender
Men	92.0 ^a	76.3 ^a	43.2 ^a	30.1 ^a	30.3 ^a	24.5 ^a	61.1 ^a	20.1 ^a	26.8 ^a	52.6 ^a
Women	96.8 ^b	69.8 ^b	54.1 ^b	49.2 ^b	45.0 ^b	30.8 ^b	82.3 ^b	25.8 ^b	37.1 ^b	39.8 ^b
Total	95.1	72.1	50.2	42.4	39.7	28.5	74.7	23.8	33.4	44.4

Note. Data analysed with the chi-square test of independence. Values with different superscript letters in a column are significantly different (p < 0.05).

In terms of gender, there are significant differences between men’s and women’s expectations towards all the tested store categories, especially in those categories traditionally associated with gender interests like electronics in the case of men, and accessories, cosmetics, décor and jewellery stores in the case of women.

Regarding which store is expected in terms of age groups, we find significant differences in cosmetics, decor and jewellery store categories. Adults from 29- to 60-year old give less chance to find cosmetics or jewellery shops in a mall than customers in their twenties. Moreover, significant differences among ages are seen in the fragrance category, where the youngest and those in their fifties have greater expectations to find such stores. In addition, customers from 40- to 60-year old take for granted, in a higher and significantly different proportion than youngest visitors, the existence of sport stores in an outlet. Furthermore, there are some store categories like fragrance and electronics, which, though not statistically significant, are selected by approximately 50% of the sample. Consequently, H1 is supported in the sense that, although the expectations to find certain categories of stores do not present differences in terms of age groups, all the store categories are affected by gender.

We also tested different entertainment facilities: open air cinema, playgrounds and nurseries for children, adventure tracks, VRG, wind tunnels, mat parks and escape rooms (Table 3). We asked individuals to order the different entertainment facilities offered in a preference order. The dominant preference would be the one selected as the first choice.

Table 3. Dominant preference for entertainment facilities.

Order	N = 1531	%
1	Open air cinema and sport events projection	25.4
2	Playroom and playground for children	15.7
3	Multi-adventure track (zip lining, Gymkhana for adults, etc.)	13.7
4	Virtual reality games for children and adults	13.1
5	Nursery	12.2
6	Adult wind tunnel	7.8
7	Indoor trampoline park (mat park)	6.5
8	Escape rooms	5.6

Looking at the dominant preference for leisure activities, open air cinema and sport events projection are the most preferred venues in an outlet shopping centre, selected by one fourth of the sample. The next highest percentage of choice refers to the existence of playrooms and playgrounds for children, followed by multi-adventure tracks, VRG and nurseries, all of which have similar percentages. Finally, wind tunnels, mat parks and escape rooms show the lowest dominant preference.

According to gender preferences towards entertainment, none of the facilities except VRG present significant differences between men and women. VRG is clearly chosen by men compared with women (19.5% and 9.6% respectively), while a playroom for children is preferred by women (17.3% versus 12% for men).

Table 4 provides information about dominant preferences by age groups, showing significant differences among ages according to the preferred leisure activity. In this sense, and even though open-air cinema constitutes the first choice for all age groups, the results are significantly higher in the older adults’ group (51- to 60-year old) compared with middle-aged groups (29- to 50-year old).

Table 4. First entertainment choice by age groups (%).

First choice	18–28	29–39	40–50	51–60
Open air cinema and sport events projection	29.3 ^ab	21.1 ^c	23.0 ^bc	33.3 ^a
Playroom and playground for children	4.4 ^a	24.8 ^b	15.3 ^c	9.2 ^d
Multi-adventure track (zip lining, Gymkhana, etc.)	18.2 ^a	12.2 ^b	14.1 ^ab	12.8 ^ab
VRG for children and adults	11.6 ^ab	9.9 ^b	14.7 ^a	16.1 ^a
Nursery	7.7 ^a	14.1 ^b	10.5 ^ab	14.6 ^b
Adult wind tunnel	12.2 ^a	3.9 ^b	9.8 ^a	7.7 ^a
Indoor trampoline park (mat park)	3.3 ^a	8.7 ^b	7.9 ^b	2.7 ^a
Escape rooms	13.3 ^a	5.2 ^b	4.7 ^b	3.6 ^b

Note. Data analysed with the chi-square test of independence. Values with different superscript letters in a column are significantly different (p < 0.05).

Playroom and playground for children is the kind of facility preferred by the group aged 29 to 39 years (24.8%). We must remark that it is not the first choice for the other age segments. To some extent, this is an expected result, as 89% of people in this age group have children. There is a similar result for mat parks, which are preferred by visitors from 29- to 50-year old, and those with children. On the contrary, the 29- to 39-year old group shows the lowest preference for modern leisure facilities based on an out-of-home experience, such as VRG (9.9%), multi-adventure tracks (12.2%) and wind tunnels (3.9%). The last two are highly preferred by young visitors.

We find similar results if we split respondents in terms of their first entertainment choice (Table 5) and compare the mean age for each group. In this sense, those who select open air cinema, VRG and adult wind tunnel as their preferred entertainment options have the highest mean age. On the contrary, the lowest mean age appears in those groups whose first leisure choices are escape rooms and mat parks. Finally, we should remark that escape rooms, adventure tracks and wind tunnel venues are becoming the sought entertainment for young customers when visiting shopping centres. Specifically, escape rooms are significantly preferred by the youngest customers, with a significant difference compared with the oldest customers.

Table 5. Mean age by first entertainment choice.

First choice	n	M	SD	SE	95% CI lower bound	95% CI upper bound
Open air cinema and sport events projection	389	43.1 ^a	11.0	0.6	42.0	44.2
Playroom and playground for children	240	40.7	7.8	0.5	39.7	41.7
Multi-adventure track (zip lining, Gymkhana, etc.)	210	41.2	10.2	0.7	39.8	42.6
Virtual reality games for children and adults	201	43.1 ^b	10.1	0.7	41.7	44.5
Nursery	187	42.5 ^c	10.3	0.8	41.0	43.9
Adult wind tunnel	119	42.3 ^d	10.7	1.0	40.4	44.2
Indoor trampoline park (mat park)	99	40.1	8.3	0.8	38.4	41.8
Escape rooms	86	37.3 ^abcd	10.9	1.2	35.0	39.6

Note. Data analysed with one-way analysis of variance, followed by Bonferroni comparison between groups. Mean values with the same superscript letter are significantly different (p < 0.05).

In terms of marital status, there are some significant differences (p < 0.05) when comparing open air cinema among ‘not married’, married and divorced visitors. The ‘not married’ group shows a significantly larger proportion (33.5%) than the other two (24.5% and 21.5%, respectively). Something similar occurs if we introduce marital status to the escape room preference analysis. Only 3.9% of ‘not married’ visitors choose escape room as their first entertainment venue. This result is aligned with the age group preferences, as the ‘not married’ group represents the majority (74%) of the 18- to 28-year-old visitors. On the contrary, and as expected, unmarried visitors present the lowest proportion in the selection of child-like leisure activities, such as nursery (3.6%) or playroom (6.2%). There are significant differences (p < 0.05) between ‘not married’ and married, and between ‘not married’ and divorced. Married and divorced show the highest preferences for nursery (12.8% and 21.5%, respectively) and playroom (18.8% and 21.5%, respectively).

If we consider the average number of children when choosing the preferred entertainment option, we find significant differences between those selecting escape rooms and the remaining options (Table 6). These results are aligned with the previous findings in relation to age: younger consumers, who have fewer children, select escape rooms as their first option, while consumers with one or more child choose other leisure activities.

Table 6. Mean number of children by first entertainment choice.

First choice	n	M	SD	SE	95% CI lower bound	95% CI upper bound
Open air cinema and sport events projection	389	1.3 ^a	0.8	0.0	1.3	1.4
Playroom and playground for children	240	1.5 ^b	0.6	0.0	1.4	1.6
Multi-adventure track (zip lining, Gymkhana, etc.)	210	1.4 ^c	0.9	0.1	1.3	1.5
Virtual reality games for children and adults	201	1.4 ^d	0.7	0.1	1.3	1.5
Nursery	187	1.5 ^e	0.7	0.1	1.4	1.6
Adult wind tunnel	119	1.3	0.8	0.1	1.1	1.4
Indoor trampoline park (mat park)	99	1.5 ^f	0.8	0.1	1.3	1.6
Escape rooms	86	0.9 ^abcdef	0.9	0.1	0.7	1.1

Note. Data analysed with one-way analysis of variance, followed by Bonferroni comparison between groups. Mean values with the same superscript letter are significantly different (p < 0.05).

Regarding respondents’ occupation, the existence of a nursery is the only category that shows significant differences between groups. Nursery is selected as the first option by homemakers and retired people, with ratios of 22.6% and 17.4%, respectively. In comparison, 5% of students prefer this service, whereas this kind of facility is selected by 10.6% of the unemployed and 12.4% of those who are currently employed.

According to these results, H2 is supported: we found significant differences in terms of demographic characteristics for the type of entertainment expected by outlet users.

With regard to technological innovation (Table 7), free Wi-Fi becomes the first option, with a wide margin over the rest (40.1%), followed by the existence of a shopping-centre app (14.5%), and the availability of magic mirrors to virtually try on clothes (13.4%). The next category in percentage belongs to GPS tracking systems for kids, and the availability of smart lockers with home delivery options.

Table 7. Dominant preference for technological innovations.

Order	N = 1531	%
1	Free Wi-Fi	40.1
2	Shopping-centre app (special promotions, store locator, etc.)	14.5
3	High tech mirror to try on clothes without getting undressed	13.4
4	GPS tracker service for kids	9.2
5	Locker smart service to keep purchase with home delivery option	9.0
6	Touch screens to access information about stores and venues	8.0
7	Mobile device chargers	5.7

According to gender, although there are significant differences between men and women with regard to the use of a tech mirror to try on clothes (mostly preferred by women), males and females tend to agree on their preference for the other technological innovations offered in an outlet mall.

If we include age in the analysis (Table 8), technological innovation preferences do not show as many significant differences as would be expected. In this sense, free Wi-Fi is the first choice for all ages, with no differences among age groups. There is a similar result in terms of the disposal of a shopping-centre app, which is the second preferred innovation regardless of age.

Table 8. First technological innovation choice by age groups.

First choice	18–28	29–39	40–50	51–60
Free Wi-Fi	41.4 ^ab	34.0 ^a	43.9 ^b	42.3 ^bc
Shopping-centre app (special promotions, store locator, etc.)	16.0 ^a	17.0 ^a	13.2 ^a	12.2 ^a
High tech mirror to try on clothes without getting undressed	18.2 ^a	10.6 ^b	13.9 ^ab	14.0 ^ab
GPS tracker service for kids	3.9 ^a	16.1 ^b	7.5 ^a	4.8 ^a
Locker smart service to keep purchase with home delivery option	9.9 ^a	9.1 ^a	8.7 ^a	8.9 ^a
Touch screens to access information about stores and venues	3.9 ^a	5.8 ^ab	8.7 ^bc	12.5 ^c
Mobile devices chargers	6.6 ^ab	7.5 ^a	4.1 ^b	5.4 ^ab

Note. Data analysed with the chi-square test of independence. Values with different superscript letters in a column are significantly different (p < 0.05).

The main difference among age groups is GPS tracking for kids. GPS is highly preferred by the 29- to 39-year-old group. Most people in this range have children (89%). Finally, touch screens represent the kind of innovation preferred by the oldest adults (those from 40- to 60-year old), which is significantly different compared with the youngest group.

Moreover, if we compare consumers’ average age regarding their first technological innovation choice, age becomes an important demographic segmentation variable with regard to technological priorities. In this sense, and as shown in Table 9, there are significant differences between the mean age of those who select different technological innovations. For example, mobile chargers, the existence of an outlet’s app, or GPS tracking for children is preferred by younger people, whereas those who choose technological innovations that make the shopping experience easy and comfortable, such as touch screens, are part of the older age groups.

Table 9. Mean age by first technological innovation choice.

First choice	n	M	SD	SE	95% CI lower bound	95% CI upper bound
Free Wi-Fi	614	42.5 ^a	10.2	0.4	41.7	43.3
Shopping-centre app (special promotions, store locator, etc.)	222	39.9 ^ab	10.1	0.7	38.5	41.2
High tech mirror to try on clothes without getting undressed	205	42.0	10.9	0.8	40.5	43.5
GPS tracker service for kids	141	40.3 ^c	7.8	0.7	39.0	41.6
Locker smart service to keep purchase with home delivery option	138	41.2	10.1	0.9	39.5	42.9
Touch screens to access information about stores and venues	123	45.3 ^bcd	9.4	0.8	43.6	47.0
Mobile device chargers	88	39.9 ^d	10.3	1.1	37.7	42.1

Note. Data analysed with one-way analysis of variance, followed by Bonferroni comparison among groups. Mean values with the same superscript letter are significantly different (p < 0.05).

On the contrary, there are no differences in technological innovation preference among marital status categories, though some can be identified in terms of occupation. Hence, there are differences between students and the other occupation groups. Students show the highest preference for free Wi-Fi (48.8%) – an expected result due to their lifestyle – compared with workers (41.2%), retired people (30.4%), unemployed (35.4) and homemakers (30.2%). Furthermore, and in relation to the kids GPS tracking system, homemakers (28.3%) most prefer this technological innovation, with significant differences compared with workers (9.2%), retired (8.7%), unemployed (6.3%) and students (1.3%). Finally, there is a significant difference between retired people (26.1%) and the other occupation categories (all below 10%) when dealing with the preference towards a smart locker service.

Finally, H3 is partially supported, because there are significant differences between the preference of technological innovation among ages and some differences among occupation but not among marital status or gender.

5. Discussion

Our research investigated the characteristics that the outlet mall of the future will need to have in order to meet the expectations of the customers of the present. Moreover, once we determined these characteristics, we questioned whether the demographic factors of these users affect their views and preferences towards this kind of shopping centre.

We supported H1; that is, the type of store expected by outlet users is conditioned by user’s demographic characteristics. In our research, gender and age undoubtedly condition the kind of store the outlet of the future will need to have. Apparel and accessories have moved from the background to the forefront to become the anchor attraction in an outlet mall. This idea is consistent with the commercial mix that usually exists in primary shopping centres, where the aforementioned categories represent the largest number of establishments.

We also validated H2. We demonstrated that the kind of entertainment expected by outlet users is conditioned by users’ age and gender. It is clear that the shopping centre is no longer just a place for shopping. Malls must now offer a wide portfolio of entertainment services that provide customer leisure while serving as a claim for the customer’s visit. Furthermore, these additional services can be a strategic key to differentiate from other competing outlet centres. Our research also provides insights to allow business managers to prioritise the implementation of some services over others according to the age and sex of the target audience to which the outlet mall is positioned.

Regarding H3, the story is a bit different. We found differences with regard to the kind of technology innovations expected in terms of age. In terms of gender, we only found differences in the high-tech mirrors to try on clothes (magic mirrors) category. As mentioned, although we had advanced that sociodemographic characteristics could affect users’ attitude towards technology, we were unable to find statistically significant differences in terms of marital status or gender, which, in our opinion, shows that technology has become part of our daily life regardless of personal factors.

In sum, all age groups seek outlets with sport, apparel and accessory stores; open air cinemas and sport events projection; and free Wi-Fi. Specifically, people between 18- and 28-year old want to find magic mirrors. People aged 29 to 39 years prefer to find playrooms and playgrounds for children, child GPS tracking systems and nurseries; these findings highlight the importance that their family status (i.e., having children) has on their expectations about the outlet of the future. The previous characteristics also appear in the 40- to 50- and the 51- to 60-year-old groups, although with only minor importance. For these latter age groups, some new categories also show up, such as the existence of multi-adventure tracks and the availability of VRG for children and adults. We can conclude that certain demographic characteristics affect the expectations of users about the mall of the future.

6. Future research lines

We determined which features (in terms of type of stores, entertainment venues and services and technology) an outlet should have to address the likes and preferences of customers segmented by different demographic characteristics. Starting from this point, we find some lines for future research.

We concentrated on the analysis of outlets. A comparison between outlets and traditional malls could be carried out. In this sense, it would be interesting to study to what extent the outlet client expects to find the same services, leisure activities, technological level and nature of the stores available when visiting traditional shopping centres.

Another line of research would be related to premier outlets that have emerged in recent years. Product quality and the presence of top brands are the main claims of premier outlet malls, together with the opportunity to acquire products at better prices than market. Premier malls are becoming a separate category, attracting the national and international public because the type of restaurants, as well as leisure services and activities, often differ from those that can usually be found in conventional outlet shopping centres. It might be interesting to see how sociodemographic variables affect this type of outlet customer and compare the outcomes with those for traditional outlet users.

Moreover, we conducted our research prior to the COVID-19 pandemic. Understanding the impact of COVID-19, if any, in the design of the outlet of the future (like maximum capacity per store, activity or centre, among many other potential restrictions) could certainly improve our conclusions.

Finally, it would be interesting to incorporate e-commerce in the research, analysing whether this growing category is affecting consumer perceptions and expectations towards the nature of the outlet of the future.

Disclosure statement

No potential conflict of interest was reported by the authors.

Notes

1 A ‘retail park’ is a shopping area on the edge of a town or city, where there are several large stores (Retail park, n.d.), whereas an outlet or factory outlet is a store that sells goods that come directly from the factory more cheaply than those sold in retail stores.

2 Although we did not include the impact of e-commerce in our research, it has been verified that times are changing in terms of online shopping. In fact, e-commerce is projected to account for a third of retail sales by 2030, forcing shopping centre operators to start exploring what should be done with existing – and future – construction (Kearney, 2018).