Investigating the Impact of Shopping Mall Brand Image Components on Visit Intention and Consumer Behavior through Attitudes towards the Mall with the Moderating Role of the Social Environment

Introduction

Over the past two decades, shopping centers have faced increasing competitive pressure from new and developed shopping centers and even other types of retail outlets (Rabani et al., 2012). In the past, many of these centers did not prioritize creating and developing a brand, and their primary marketing tool was focusing on prices and costs to achieve a better market position relative to competitors (Perry & Spilker, 2011). Today, many organizations, companies, and shopping centers striving to achieve an exceptional position and unique advantages in the competitive market are aware of the importance of providing superior services to satisfy users by offering services that exceed their expectations (Holbik, 2011). Organizations that seek to maintain the quality of their services do so by continuously reviewing customers' perceptions of service quality, identifying the causes and roots of service delivery methods, and taking appropriate actions to improve service quality. Donovan et al. (2004) believe that identifying customer expectations is the most critical step in the process of customer satisfaction. In recent years, leading shopping centers worldwide have begun to discover and create brands that act as a potential source for achieving competitive advantage (Perry & Spilker, 2011).

Therefore, in the current conditions, it is necessary for the builders, officials, and developers of these centers to adopt a comprehensive brand management approach and create qualitative innovations (Jara & Cliquet, 2012) in the features of shopping centers to create a correct brand image in customers' minds. This enables them to attract more individuals compared to competitors and, with the help of branding tools, subsequently provide fundamental and superior values to customers, creating profound and long-term impacts. Ultimately, this can lead to higher performance compared to competitors through increased sales and sustainable profitability (Perry & Spilker, 2011). Hence, a marketing manager or a decision-maker needs to determine which evaluation criteria are used by consumers, what perceptions consumers have of different options within each criterion, and the relative importance of each criterion to the consumer (Hawkins et al., 2004).

Many consumer decisions are based more on emotional reactions to a brand or its overall impact on them (Hawkins et al., 2004, p. 466). Previous research has variously referred to the relationship between attitudes and behavioral intentions. Fishbein introduced behavioral intention as one of the outcomes of attitude (Fishbein, 1987). Meanwhile, Hawkins et al. consider an individual's tendency to exhibit a specific reaction towards a product or activity as one of the components of attitude (its behavioral component) (Hawkins et al., 2004, p. 303). Whether behavioral intention is an outcome of attitude or a component of it, if a consumer feels positively about something, the likelihood of forming a behavioral intention increases (Ranjbarian et al., 2004).

Consumers, from an experiential perspective, base their choices on their overall emotional response to an alternative. In other words, they take a holistic approach in their choice and select the alternative they have the most positive feelings towards (Mowen & Minor, 2000, p. 306). The Theory of Reasoned Action posits that behavioral goals and intentions are shaped based on a combination of attitudes towards a specific behavior (such as social or normative beliefs about the correctness of a behavior or an individual's motivation to comply with normative beliefs). For example, a person may have a positive attitude towards ordering cake before dinner at a restaurant. However, whether they actually order the cake depends on how well they believe doing so aligns with normative beliefs. These issues may influence their intention, and ultimately, they may not order the cake. Although the components of attitude (cognitive, affective, and behavioral) are usually consistent, the degree of compatibility or consistency among cognitive, emotional, and behavioral criteria may decrease under certain factors' influence (Roosta & Batahai, 2006, p. 306).

Actual behaviors reflect an individual's intentions within the context of situations where those behaviors occur (Hawkins et al., 2004, p. 303). Many theorists believe that behavioral intention, compared to attitude, is the primary determinant of behavioral performance. The results of a study by Kay and Shannon (2012) highlight the significant role of behavioral intentions as a mediating variable in the attitude-behavior relationship. Another result of this study indicates that customers' evaluations of the functional dimensions of shopping center features can also create satisfaction and ultimately influence their purchase intentions. Shopping center managers can help their customers through their behavioral intentions; for example, creating a favorable atmosphere in these centers can stimulate purchase intentions.

Retailers should note that the presence of shopping companions (friends or family members) can influence the individual. This companionship takes the individual out of their routine process and heightens emotional signals, increases feelings of being evaluated and controlled, and generally affects shoppers' emotional attachment to shopping centers. These findings introduce new topics for discussion in this field and should receive more attention from both managerial and theoretical perspectives (Chebat et al., 2014). In situations where shopping occurs with others, individuals spend more time in shopping centers, make more purchases (in terms of quantity and money), and the range of shops visited may vary depending on the companions. This influence varies not only compared to solo shopping but also depending on the type of shopping companions (friends or family) (Chebat et al., 2014). The social environment, as one of the five situational factors influencing consumer decisions, includes the presence of others, their personality traits, their explicit roles, and interpersonal reactions (Belk, 1975). In this study, we only examine the moderating effect of the presence of others as one of the social environment factors (Zhang et al., 2006). Previous research shows that encouraging customers to visit and shop with others benefits retailers (Chebat et al., 2014), highlighting the need to consider this moderating variable in all relationships examined in this study. Thus, this research aims to investigate the impact of shopping mall brand image components on visit intention and consumer behavior through attitudes towards the mall, with the moderating role of the social environment.

Methods and Materials

In terms of purpose, this research is applied, and in terms of classification based on method, it is descriptive, survey-based, and correlational. To collect information on the literature and background of the research, the library method was used, which includes reviewing existing and related theses and research, studying related articles and books in Persian and English, and using the internet. To examine and test the research hypotheses, a field method was used. Therefore, a questionnaire was designed and used for data collection, consisting of 30 questions that measure the variables of the study. This questionnaire was distributed among 618 female customers of shopping centers in Tehran, using simple random sampling. The questionnaire also included four questions about demographic characteristics, including age, education level, marital status, and income. A five-point Likert scale was used in designing this questionnaire. To determine validity, the questions were processed in multiple stages and approved by several university professors and marketing experts.

For analyzing the questionnaires, both inferential and descriptive statistical methods were used. Descriptive statistics include frequency tables and mean, while inferential statistics involve structural equation modeling, including confirmatory factor analysis and path analysis. The software packages used for data analysis were SPSS and Smart PLS.

Findings and Results

Based on the data collected from the questionnaires, 39.2% of the respondents were single, 17.5% were married without children, and 43.3% were married with children. Among the respondents, the highest educational level was a bachelor's degree, comprising 33.3%, while the lowest educational level was a doctorate, comprising 4.9%.

The majority of the respondents, 34%, were between the ages of 25 to 35 years. The least frequent age group was those over 55 years, making up 8.3% of the respondents. Most respondents, 44.2%, had an income level between 2 million to 5 million, and the least frequent income level was between 500 thousand to 1 million, comprising 4.7% of the respondents.

Table 1 presents the descriptive statistics for each of the research variables, including the mean, standard deviation, variance, skewness, and kurtosis.

To assess the homogeneity of the items for each variable, the homogeneity or unidimensionality test is used. If the factor loadings are above 0.7, the homogeneity test (i.e., the items for a variable are completely homogeneous) is confirmed.

In Cronbach's alpha test, values above 0.9 are excellent, values above 0.8 are very good, values above 0.7 are good, values above 0.6 are acceptable, and values above 0.5 are weak (George & Mallery, 2003, p. 231). To assess suitability, the correlation of the items for a variable in the model is tested using CR. If CR values are above 0.7, the item correlations for a variable in the model are suitable. To assess generalizability, the items for a variable in the model are tested using communal reliability. If Communality values are above 0.5, the generalizability of each item for a variable is suitable.

The alpha values for the research variables are above 0.7, indicating good correlations between the items for these variables. Additionally, all variables have CR values above 0.7, indicating suitable item correlations for all variables in the model. Furthermore, all variables have Communality values above 0.5, indicating suitable generalizability for each item.

According to the results, the factor loadings for all items are significant (greater than 1.96) and the factor loading is greater than 0.5. The AVE values for all variables are greater than 0.5, and the CR values for all variables are greater than the AVE values. Additionally, the square root of AVE for all variables is greater than the correlation between the latent variable and other latent constructs, confirming convergent and discriminant validity.

As shown in Table 4, the goodness of fit index value is 0.64, indicating a strong overall fit for the structural model. This means that the inner model has sufficient power to test the hypotheses and the test results can be statistically reliable.

Based on the inner model derived from the hypothesis testing, we examine the confirmation or rejection of the research hypotheses. We use the significance coefficient (t-statistic) to confirm or reject the hypotheses. If the t-statistic is greater than 1.96 or less than -1.96 (at the 5% error level), the hypothesis is confirmed and a significant relationship between the two latent variables is obtained.

In Table 5, the results of partial least squares analysis for the research hypotheses are considered, and all direct paths between the constructs are statistically significant. As shown in Figure 1, the brand image impacts consumers' attitudes towards the shopping mall (β = 0.82, p < 0.01). Additionally, consumers' attitudes towards the shopping mall significantly affect their visit intention (β = 0.67, p < 0.01) and consumer behavior (β = 0.49, p < 0.01). The impact of consumers' attitudes towards the shopping mall on visit intention is slightly stronger than the impact of visit intention on consumer behavior (β = 0.45, p < 0.01).

For testing H1, the t-value for the impact of brand image on consumer attitudes towards the shopping mall was 17.31. The t-value for this parameter is greater than 2.58. Therefore, the null hypothesis is rejected with 99% confidence. Hence, the brand image impacts consumer attitudes towards the shopping mall.

For testing H2, the t-value for the impact of consumer attitudes towards the shopping mall on visit intention was 11.38. The t-value for this parameter is greater than 2.58. Therefore, the null hypothesis is rejected with 99% confidence. Hence, consumer attitudes towards the shopping mall impact visit intention.

For testing H3, the t-value for the impact of consumer attitudes towards the shopping mall on consumer behavior was 5.38. The t-value for this parameter is greater than 2.58. Therefore, the null hypothesis is rejected with 99% confidence. Hence, consumer attitudes towards the shopping mall impact consumer behavior.

For testing H4, the t-value for the impact of visit intention on consumer behavior was 5.44. The t-value for this parameter is greater than 2.58. Therefore, the null hypothesis is rejected with 99% confidence. Hence, visit intention impacts consumer behavior.

The results showed that when both mediators are considered simultaneously, the total indirect effect is statistically significant (β = 0.95, p < 0.01). Specific indirect effects (path: brand image - attitude towards shopping mall - consumer behavior: 0.40) and (path: attitude towards shopping mall - visit intention - consumer behavior: 0.30) and (path: brand image - attitude towards shopping mall - visit intention - consumer behavior: 0.25) were statistically significant at the 0.01 significance level.

In general, a moderating variable is a qualitative (e.g., gender, religion, social class) or quantitative (e.g., age, income) variable that affects the direction and/or strength of the relationship between two other variables. In correlation research, a moderating variable is a third variable that affects the zero-order correlation between two other variables. This effect can reduce the level of correlation or change its direction.

The diagrams below show the role of the moderating variable in the relationships between exogenous and endogenous variables according to the research hypotheses.

Figure 1

Moderator Variable Model in Standard State

Based on the model obtained from testing the hypotheses related to the moderating variable, we examine the confirmation or rejection of these hypotheses. We use the significance coefficient (t-statistic) to confirm or reject the hypotheses. If the t-statistic is greater than 1.96 or less than -1.96 (at the 5% error level), the hypothesis is confirmed and a significant relationship between the two latent variables is obtained.

Discussion and Conclusion

Based on the findings from the theoretical and field studies conducted in this research, several recommendations can be provided. These recommendations are organized into two main categories: practical suggestions and suggestions for future researchers. Each of these items is presented below.

Given the confirmation of the impact of brand image on consumers' attitudes towards shopping malls and considering that individuals' behavior is triggered by images, this topic is highly significant for marketing theorists who strive to understand consumer behavior. According to Keller (1993), a positive brand image can be established by linking strong and unique brand associations with consumers' memories of the brand through marketing campaigns. In this regard, before a consumer can respond positively to a branding campaign, brand awareness and understanding must be achieved. If consumers are aware of the brand, the company can spend less on brand extension while achieving higher sales. Shim and Eastlick (1998) defined buyers' attitudes towards shopping malls as their attitudes towards various shopping dimensions, including location, variety of stores, parking, staff behavior, price, quality, customer service, promotional activities, environmental appeal, pleasantness, food, and security.

Previous studies have classified brand image dimensions into: (1) accessibility, (2) in-store atmosphere, (3) price and promotion, (4) inter-category classification, and (5) intra-category classification. Therefore, marketers and brand managers can adopt appropriate strategies in each of these areas to create a strong brand image in customers' minds. Expanding on this topic, the following points can be considered:

Accessibility: Convenience and time-saving factors are sought by customers in the modern world. Customers' perceptions of convenience and the time spent affect their satisfaction and loyalty. Therefore, by providing store facilities such as store location, home delivery, parking facilities, and centralized store operations that reduce travel and shopping time, or by using modern equipment and an adequate number of operators for checkout processes, time savings for customers can be achieved.

In-Store Atmosphere: The effect of store atmosphere on customer behavior has been demonstrated by several researchers, who argue that the physical environment plays a significant role in creating the retailer's image, generating cognitive and emotional evaluations that lead to behavioral responses, and communicating the retailer's identity and image to customers (Hilgard, 2006). Thus, retailers are turning to impressive décor and attractive displays to fulfill customers' needs for a pleasant and memorable shopping experience, making shopping more entertaining. Therefore, stimuli such as the number of staff, music, store color, store temperature, store layout, and others can enhance the shopping experience.

Price and Promotion: Research has shown that customers' perceptions of store prices positively affect their satisfaction, loyalty, and continued shopping (Pan & Zinkhan, 2009). Higher prices lead to reduced purchase intentions. On the other hand, customers may choose the highest-priced brand to maximize their expected quality. While price may positively impact perceived quality, it can negatively affect perceived value and purchase intention (Chaudhuri & Ligas, 2009). Therefore, implementing the right pricing strategy is crucial.

Inter-Category Classification: Inter-category classification refers to customers' perceptions of the breadth (range) of products and services offered by stores in shopping centers (Eliashberg & Keller, 2004). Products are considered one of the most important determinants of continued shopping from retailers. The breadth (number of brands) and depth (inventory levels) of products are used to meet buyers' needs in stores. Higher product breadth not only attracts more customers but may also encourage them to buy more. High product breadth also minimizes shopping trip costs (such as time and effort) and facilitates shopping (increasing comparisons during shopping). In other words, a retailer with high product breadth in its categories increases shopping convenience for customers (Chebat et al., 2010).

Additionally, the store image concept reflects the buyer's perception of a store regarding functional and psychological features, comprising a wide range of attributes. Mackenzie's brand diamond model categorizes these attributes into tangible and intangible features, and rational and emotional benefits. Expanding on this, the following points are highlighted:

Tangible Features involve characteristics perceptible through senses and form the basis of attributes that consumers associate with brand image. These features can be physical and functional. In this study, tangible features include convenience, performance, sociability, leisure, and atmosphere. A shopping center rated highly for these features by customers is positively evaluated and is likely to receive more support from shoppers compared to lower-rated markets in the same dimensions, with customers recommending it to friends and family.

Intangible Features in this study include authenticity, heritage, reputation, innovation capability, likability, and brand personality. Intangible features of brands can be considered during evaluation, although measuring them is inherently difficult and they are understood cognitively. Some buyers may consider additional intangible features as quality indicators, while others see them as part of the overall benefit package. However, a brand ranked higher by customers in these features is more likely to be supported and recommended by them.

Rational Benefits in this study include functional value, discovery, and customer welfare. Rational brand benefits generally derive directly from their tangible features. The value created by a brand mainly comes from the functional quality of a product and its performance features. It can also include other tangible features such as after-sales service range, supplier profit margin, and order lead time. As the purchasing process is more rational than emotional, and buyers need tangible features to justify their purchase decisions, the importance of product performance features has been confirmed in various studies.

Emotional Benefits include self-connection, situational self-expression, and the congruence between self-image and product image. Many studies have shown that brand-user mental alignment significantly impacts brand evaluation. High mental congruence means that the specific brand user matches their self-image. Consumers are more likely to buy a brand that aligns with their self-image.

Emotional benefits encompass all aspects where a consumer chooses a brand based on emotional evaluation, which may not align with rational criteria. If a brand enhances self-identity, image transfer, and consumer self-belief, they will attribute emotional value to it. Consumers usually prefer brands that are congruent with their self-perception or ideal self, likely choosing brands that align with their self-concept. Consumers often assess how well brands enhance their self-concept, creating and maintaining their self-image through brand use.

Since customer relationship with the brand (as a measure of brand equity) is determined by rational and emotional evaluations of the brand, with rational evaluations determined by product quality, service quality, and price, and emotional evaluations influenced by differentiation, commitment, and trust/credibility, considering that consumers rank brands in their choice list based on these dimensions, possessing these benefits can be a competitive strength for the brand.

Confirming the hypotheses on the impact of consumer attitudes on behavioral intentions and consumer behavior, and considering that many consumer decisions are based more on emotional reactions to a brand or its overall impact on them (Hawkins et al., 2004), and if a consumer feels positively about something, their likelihood of forming a behavioral intention increases (Ranjbarian et al., 2004). Hence, shopping center managers can try to assist their customers through their behavioral intentions. For instance, creating a favorable atmosphere in these centers can stimulate purchase intentions.

Regarding the non-confirmation of the hypotheses related to the moderating role of the social environment variable, it seems that practical suggestions cannot be provided based on this study's findings. However, decisions should be made considering the theoretical literature and previous studies on some of these elements that have proven effective. Since rejecting these hypotheses cannot definitively prove these variables' non-impact, as respondents in the research setting may perceive these elements as ineffective, a revised approach is needed to examine company-specific variables and their components. Theoretically, since this study's findings are not aligned with previous results, the following perspectives can be considered:

“"From a theoretical perspective, more attention should be paid to the research execution process, considering potential existing errors through re-examination.

Align this study's results with previous ones to form a comprehensive theory, defending previous research results with less rigor.

Managerial recommendations can advise company managers not to hastily change operational strategies based on these rejected hypotheses.

When different research results are inconsistent, actions based on the findings should be taken cautiously, with attention, foresight, and thoroughness. Companies must study literature, previous research results, and provide multiple pieces of evidence on the relationship between the hypotheses' variables, considering these results academically. The necessity and reason for repeating research lie in the discrepancies found by the researcher, making further studies on rejected hypotheses feasible.

Authors’ Contributions

All authors have contributed significantly to the research process and the development of the manuscript.

Declaration

In order to correct and improve the academic writing of our paper, we have used the language model ChatGPT.

Transparency Statement

Data are available for research purposes upon reasonable request to the corresponding author.

Acknowledgments

We would like to express our gratitude to all individuals helped us to do the project.

Declaration of Interest

The authors report no conflict of interest.

Funding

According to the authors, this article has no financial support.

Ethical Considerations

In this research, ethical standards including obtaining informed consent, ensuring privacy and confidentiality were observed.