Abstract
Background: Bottom-of-the-pyramid (BOP) consumers’ significant discretionary purchases of non-essential products, such as apparel, contribute to retail growth. In a turbulent retail environment, store atmospherics presents an innovative cost-effective solution to incorporate BOP consumers’ unique motivations for desired apparel behavioural intentions to grow market share.
Aim: This study aims to fill the knowledge gap regarding a systematic review of store atmospherics on urban BOP consumers’ apparel behavioural intentions in an emerging African market. This study also proposes various purchase antecedents to attitudes, which drive apparel behavioural intentions. The moderation effect of age is investigated to understand the change in purchase antecedents related to attitudes among different urban BOP age groups.
Setting: Interviewer-administered surveys was conducted among urban BOP consumers in Namibia’s main urban areas.
Method: A sample of 881 respondents was selected by purposive, interlocking and convenience sampling methods. Covariance-based structural equation modelling (SEM) confirmed the significant relationships between all constructs in the proposed model.
Results: The systematic link between store atmospherics and urban BOP apparel behavioural intentions is primarily because of consumers’ perceived value, trust and attitudes. Urban BOP consumers’ perceived value, anticipatory emotions and attitudes are moderated by age.
Conclusion: Overall, this study sheds light on the differences between young and old urban BOP consumers and how they differ or coincide with age groups in more affluent segments. In order to adequately segment the urban BOP market, apparel retailers should emphasise on affordability, quality, emotional goal attainment and trust in their store atmospherics.
Contribution: In the broad context of store atmospherics, this study specifically uses age categories as a lens to provide valuable insights concerning age differences in urban bottom-of-the-pyramid consumers’ perception of store atmospherics which influences their apparel behavioural intentions. Further, this study solidifies that different age categories amongst urban bottom-of-the-pyramid consumers bring forth different behavioural intentions to which marketers should adapt their store atmospheric strategies.
Keywords: store atmospherics; apparel; urban BOP consumers; attitude; behavioural intention; age.
Introduction and background
For retailers in emerging African markets, the untapped, and underserved bottom-of-the-pyramid (BOP) consumers present a tremendous market opportunity (Kumar & Gupta 2022). Prahalad and Hart have been spearheading this idea since 1999, introducing an emerging economy perspective to BOP market research. Emerging economies therefore place a high value on newer disciplines that drive BOP markets for economic development since these markets hold an enormous profit potential for retailers (Khandker 2023; Srivastava 2022). Therefore, the study advocates integrating marketing, economics and environmental psychology literature with BOP literature. It focusses on addressing the literature gap on urban BOP consumers’ behavioural intentions towards costly non-essentials like apparel rather than affordable essentials (Singh, Paul & Sharma 2022). A further related concern in BOP literature is segmenting the large BOP market to better target different urban BOP groups, as different age groups have different expectations (Khandker 2023; Tasci & Pizam 2020:1000). Accordingly, this study incorporates age as a demographic variable to improve effective BOP segmentation in emerging African markets. To provide a systematic review of BOP apparel behavioural intentions, this study suggests that retailers targeting the urban BOP segment must encourage apparel consumption through store atmospherics. This is because, in a turbulent retail environment, store atmospherics present an economical and strategic tool to incorporate consumers’ unique needs and motivations to generate desired behavioural intentions (Sitinjak, Pangaribuan & Tafriza 2019:38).
In emerging African countries, the apparel retail industry plays a significant role in economic development (Fitch Solution Group Limited 2022). Namibia’s apparel retail industry is expected to grow by 5% by 2024, making it one of the top emerging markets in the Southern African Development Community (SADC). This growth is primarily because of Namibia’s growing BOP consumer segment (52%), in comparison with other mature SADC retail markets like Botswana (46%) and South Africa (40%) (Statista 2022). Bottom-of-the-pyramid consumers in Namibia’s apparel retail industry spent nearly 5% of their discretionary income on apparel (National Planning Commission 2018). This is because BOP consumers aspire to progress up the economic pyramid by using similar products than affluent consumers (Singh et al. 2022). Therefore, Namibia and its large BOP segment has become a major economic power presenting viable opportunities for international and African retailers to take advantage of their collective size and consumer behaviours.
The BOP segment represents the world’s poorest and largest segment, receiving less than N$6000 ($347) gross monthly household income (Simpson & Lapperman 2017). This segment is comprised of urban and rural BOP consumers (Subhan & Khattak 2016). Urban BOP consumers are defined as consumers who: (1) reside or work in densely populated urban areas; (2) are relatively educated because they have access to information (e.g., media); (3) have the ability to purchase new products and services to improve their standard of living; (4) purchase products and services from both formal and informal markets; (5) have access to new technology (e.g., mobile banking or social media); and (6) are employed with fairly stable incomes which secures their discretionary purchases directed towards non-essential products, such as apparel (Srivastava 2022). In contrast, rural BOP consumers are largely illiterate, widely dispersed in rural areas, extremely poor and mostly concerned with essential products such as food and transport rather than discretionary purchases (Nickanor et al. 2018:55). However, exploring and serving the urban BOP segment is more realistic for retailers (Mathur 2018:202), since their economic contribution of continued discretionary purchases of non-essential products may sustain profitable returns for retailers and additionally stimulate inclusive economic development (Singh et al. 2022). This, in turn, may benefit urban BOP consumers, whereby their social need to distinguish their place in society from affluent consumers is met through non-essential purchases (Srivastava 2022).
Consumer behaviour literature becomes more relevant by studying consumers’ behavioural intentions. Behavioural intentions are the subjective probability that consumers will behave in a specific way in the future. Behavioural intentions predict consumers’ actual behaviour, as more variables influence intention than the actual behaviour (Ajzen 1991:183; Jain & Khan 2017:345). Researchers and retailers can improve their understanding of consumers’ specific needs and motivations by examining consumers’ behavioural intentions. Bottom-of-the-pyramid consumers use apparel to improve their self-esteem, social acceptance and belonging (Akdemir 2018:1390; Mostafa 2019:99). Therefore, understanding this low-income group’s behavioural intentions is particularly important for retailers to meet their physiological needs and offer them products that improve their quality of life (Khandker 2023).
Store atmospherics involves the personality of retailers, which is represented by the stimuli in various dimensions and elements in the store, such as the exterior, interior, layout and design, point of purchase and decoration and human variables (Turley & Milliman 2000:194). Research strongly supports that these store atmospheric dimensions and elements are perceived by consumers’ senses (Mohammad & Baharun 2016:4), which affect their emotions and feelings (Barros et al. 2019:829) and significantly influence their store choices, product choices, propensity to spend more than planned, merchandise information evaluations, shopping enjoyment, willingness to interact with sales personnel and their behavioural intentions (Sitinjak et al. 2019:38). However, existing research neglected a clear systematic review regarding the link between store atmospherics and urban BOP consumers’ apparel behavioural intentions. Significant evidence supports the probability of this link by highlighting BOP consumers’ needs for store atmospherics (Singh et al. 2022; Srivastava & Srivastava 2021:1). Firstly, BOP consumers prefer to purchase from physical stores rather than online, more so than other income segments (Hasan, Lowe & Rahman 2017:154). Secondly, with limited income, their aim is to experience the apparel physically before purchasing it. Thirdly, low literacy levels make them highly engaged, visual consumers who need more guidance from the retail experience for decision-making than more affluent segments (Singh et al. 2022). Fourthly, they are value-driven, do not trust easily and pursue emotional goals (Antoni & Basso 2016:596; Praceus & Herstatt 2017:97). Lastly, because of their position in the economic pyramid, BOP consumers are extremely driven by social requirements when shopping, specifically discretionary purchases such as apparel, to achieve their ideal selves (Srivastava 2022).
In order to understand the systematic review of the link, the researcher identified that certain BOP purchase antecedents, namely perceived value, anticipatory emotions and trust, which are associated with urban BOP consumers’ attitudes, are in part responsible for the link between store atmospherics and urban BOP consumers’ apparel behavioural intentions (Antoni & Basso 2016:596; Praceus & Herstatt 2017:97; Singh 2016:27). For urban BOP consumers, the perceived value through store atmospherics is based on quality, affordability and fulfilment of social goals. Further, urban BOP consumers anticipate that their emotional goals for reassurance of social and retail belonging are met. General mistrust is common among BOP consumers; therefore, they have to trust that the retailer will deliver a reliable, genuine and respectful experience (Antoni & Basso 2016:601; Barki & Parente 2006:56). Therefore, these urban BOP purchase antecedents serve as the key driving force for attitude formation (Barki & Parente 2006). Kumar and Nayak (2022) argue that solely focussing on attitudes to reveal behavioural intentions is sufficient as attitudes is more powerful than any other theory of planned behaviour (TPB) construct, making it the core predictor of behavioural intentions. Promoting positive attitudes among urban BOP consumers is critical to reduce the uncertainty of being able to afford high-priced products such as apparel associated with the stigma of poverty (Hasan et al. 2017:154).
Research has found that consumers’ age alters behaviour change profoundly (Kuppelwieser 2016). Therefore, as urban BOP consumers age, their attitudes towards store atmospherics change, meaning that different age groups behave differently (Khan et al. 2020a). In marketing literature, the focus on age in the context of BOP consumers has not been intensively researched, which highlights and supports the importance of this study’s focus on age. Age differences play an important role in consumer behaviour since a change in consumers’ ages results in changes in preferences, attitudes, decision-making, and buying behaviours shaped by external factors (Moon 2021). Marketers can tailor their strategies effectively by understanding these differences. From a practical standpoint, retailers should be aware that when implementing store atmospherics, they cannot treat the entire BOP segment as one segment given that consumers of different ages have different expectations of store atmospherics. For this reason, age is significant for theoretical and practical development, given that store atmospherics are evaluated differently by various urban BOP age groups. Yet, whether the link between store atmospherics and urban BOP consumers’ apparel behavioural intentions becomes stronger or weaker in terms of their age, is still not fully grasped. In order to fill this gap, this study investigates the moderating effect of age. Further investigation would contribute to a better understanding of the distinct segments in the urban BOP, which may assist to explain the change in behavioural intentions because of the use of non-essentials, such as apparel.
Against the background provided above, this study specifically focuses on age as a differential factor which influences urban BOP consumers’ perceptions of store atmospherics and apparel behavioural intentions, which has not been the focus of the European Business Review article entitled ‘Apparel behavioural intentions of urban bottom-of-the-pyramid consumers: Exploring the role of store atmospherics’ (Van Niekerk, Roberts-Lombard, & Cunningham 2023). The European Business Review article focused on a holistic overview of urban BOP consumers’ apparel behavioural intentions, by considering the role of store atmospherics. Additionally, the current article flows from the Ph.D. study of B.M. van Niekerk conducted entitled: ‘A proposed model towards the impact of store atmospherics on BOP consumers’ intentions to purchase apparel’ at the University of Johannesburg in South Africa, available from: https://hdl.handle.net/10210/504239.
Based on the above background, this study aims to: (1) systematically demonstrate the link between store atmospherics and urban BOP consumers’ apparel behavioural intentions, (2) determine prominent urban BOP purchase antecedents driving attitude formation and (3) measure the extent to which various urban BOP age groups moderate the magnitude of the relationships established. It is essential to gain insights from these aims to improve the global business models of international retailers entering emerging African markets.
The next section provides a theoretical framework regarding the constructs and grounding theories. Henceforth, the study formulates hypotheses for empirical testing. An explanation of the research methodology is followed by an overview of the research findings. Following this, theoretical and practical implications are discussed, and future research recommendations are offered.
Theoretical framework
Theory of planned behaviour
The TPB framework guided this study to provide a systematic review of how different urban BOP consumers’ age categories impact their attitude and ultimately their apparel behavioural intentions by including store atmospherics and context-specific purchase antecedents (Jain et al. 2017). The results of previous research extending the TPB with the moderating effects of age revealed that different consumer age groups impact behavioural patterns in many contexts. Thus, this study incorporates age as a moderating variable to better understand how TPB functions under a different set of circumstances when compared to previous research (Moon 2021; Wang, Yeh, Chen & Huan 2022). To further enhance the under researched explanatory power of BOP attitudes on behavioural intentions, age is contextualised within the TPB theory to better understand age in business to consumer research (such as BOP consumer research). Therefore, this study proposes that BOP consumers’ cognitive processing of store atmospherics, motivates context-specific purchase antecedents of perceived value, anticipatory emotions and trust, which contributes to their attitude formation and behavioural intentions when considering age as a moderator.
In the TPB, attitudes play a substantial role in predicting behavioural intentions as it is formulated from affective, cognitive and behavioural elements (Ajzen 1991:183). Combining attitudes with store atmospherics, attitudes give value to the environmental stimulus through consumers’ perceptions, emotions and trust (Chang & Watchravesringkan 2018:159). When context-specific antecedents directly correlating with the attitude construct are included in the TPB, these can strongly dictate behavioural intentions (Latiff, Mohamed & Mohamad 2016:14). The meta-analysis model by Kumar and Nayak (2022) summarised the effect size of each TPB construct, revealing that attitude is the core predictor of behavioural intentions and can produce behavioural intentions more than the other TPB constructs (Ajzen 1991:183). Consequently, this study focusses only on attitudes and behavioural intentions, excluding the traditional social norms and perceived behavioural constructs in the TPB. Previous TPB research has not considered store atmospherics and urban BOP purchase antecedents of perceived value, anticipatory emotions and trust as influential factors predicting behavioural intention.
In order to expand on the previously published European Business Review article ‘Apparel behavioural intentions of urban bottom-of-the-pyramid consumers: Exploring the role of store atmospherics’ (Van Niekerk, Roberts-Lombard, & Cunningham 2023), and understand the systematic nature of urban BOP consumers’ apparel behavioural intentions, this study integrates different age categories as a moderator in a TPB framework. The reason being to validate the various urban BOP consumers’ perceptions of store atmospherics and behavioural change according to the different age categories.
Hypotheses and model development
Figure 1 illustrates the conceptual model developed for the study. The study’s developed hypotheses are discussed below.
Store atmospherics, perceived value and attitudes
Moharana and Pattanaik (2018:173) and Sitinjak et al. (2019:31) pointed out that perceived value is established when consumers positively interact with the store atmospherics through the retail experience. For BOP consumers, the perceived value depends on the store’s merchandise price and quality (Lappeman et al. 2019:321). Bottom-of-the-pyramid consumers understand price as the measure of quality versus sacrifice. According to various research studies, consumers experience higher perceived value levels when their own preferences match the store atmospherics (Moharana & Pattanaik 2018:173; Ng et al. 2021:69), especially in urban BOP contexts, where value receiving is highly prioritised (Antoni & Basso 2016:597). Furthermore, when consumers receive positive perceived value from a retailer, positive attitudes are likely to be evoked (Mohammad & Baharun 2016:4). Hasan et al. (2017) explained that BOP consumers could form attitudes towards the store and subsequently make purchasing decisions if they perceive the retail store, sales personnel or merchandise as having value, such as price, quality or social acceptance. Perceived value is an antecedent to attitude (Hasan et al. 2017) as it reveals consumers’ cognitive and affective states in relation to attitude in the TPB. The following hypotheses are proposed within the TPB:
H1: Store atmospherics significantly and positively influence urban BOP consumers’ perceived value.
H2: Urban BOP consumers’ perceived value significantly and positively influences their attitudes.
Store atmospherics, anticipatory emotions and attitude
Consumers anticipate their emotions before, during or after an occurrence in the retail environment (Kim et al. 2021:424). Store atmospherics acts as a stimulus, influencing consumers’ anticipatory emotions (Barros et al. 2019:829) and enhance their shopping experience through their senses (Kapoor & Rashmi 2016:97). Consumers respond to such stimuli where their anticipatory emotions aid in decision-making (Aziz et al. 2018:4). Bottom-of-the-pyramid consumers make shopping decisions based on emotions, because improved self-esteem is an important emotional goal for them. They primarily shop for emotional goal attainment (Antoni & Basso 2016:597; Barki & Parente 2006:12). Accordingly, consumers’ attitudes depend on whether their anticipated emotional expectations are met (Kim et al. 2021:424). When BOP consumers’ emotional expectations are met through the symbolic value offered by apparel and the retail experience, they may form a positive attitude (Antoni & Basso 2016:596). Anticipatory emotions are an antecedent to attitude (Petrocelli & Whitmire 2017:924) as consumers form favourable or unfavourable attitudes based on their affective states in the TPB (Bettiga & Lamberti 2018:301). Considering urban BOP consumers’ positive emotional goal attainment with store atmospheres, anticipatory emotions were measured as a single construct. The following hypotheses are proposed within the TPB:
H3: Store atmospherics significantly and positively influence urban BOP consumers’ anticipatory emotions.
H4: Urban BOP consumers’ anticipatory emotions significantly and positively influence their attitudes.
Store atmospherics, trust and attitudes
Various store atmospheric elements contribute to the development of BOP consumers’ trust, including merchandise assortment (Lappeman et al. 2019:322), sales personnel (Antoni & Basso 2016:601), advertisements and promotional signs (Jaiswal & Gupta 2015:119). Bottom-of-the-pyramid consumers’ trust is heavily influenced by social sources, such as sales personnel and the retail environment (Jaiswal & Gupta 2015). In order to avoid being exploited by high prices and low-quality products, BOP consumers need retailers to be reliable and trustworthy (Praceus & Herstatt 2017:97). Therefore, it is argued that the social nature of store atmospherics may develop consumers’ affective trust regarding the retailers’ goodwill and competence, which fosters positive attitude formation (Abbes & Goudey 2015:1104). Trust is an antecedent to attitude (Pfeuffer & Huh 2021:49) based on consumers’ affective trust states in the TPB. The following hypotheses are proposed within the TPB:
H5: Store atmospherics significantly and positively influence urban BOP consumers’ trust.
H6: Urban BOP consumers’ trust significantly and positively influences their attitudes.
Attitude and behavioural intention
Consumers’ attitudes towards performing a behaviour strongly predict their behavioural intentions (Zhang & Cude 2018). Consumers’ general attitudes towards a behaviour are established through their salient behavioural beliefs and evaluations (Latiff et al. 2016:140). Scholars (Kumar & Nayak 2022) state that attitude is the strongest predictor of behavioural intentions in the TPB model. Similarly, attitude is the most prominent predictor of BOP behavioural intentions (Singh 2016:23). Bottom-of-the-pyramid consumers’ attitudes towards apparel influence their behaviour when apparel shows value in terms of becoming more economically integrated (Mostafa 2019:99). For instance, apparel may enhance consumers’ feeling of being socially accepted; therefore, attitude is important when analysing urban BOP consumers’ apparel behavioural intentions. Resultantly, this study developed and examined the following hypothesis:
H7: Urban BOP consumers’ attitudes significantly and positively influence their behavioural intentions.
The moderating role of age
In marketing research, age is considered a significant demographic factor (Tasci & Pizam 2020:1000), particularly in an apparel retail context (Ajitha & Sivakumar 2019:442; Tsarenko & Lo 2017:67). Different consumer age groups have varying apparel needs, wants, motivations and preferences, which tend to change as they age, resulting in different behaviours (Khan et al. 2020a). Consumers’ attitudes differ widely among age groups, which can greatly influence apparel behavioural intentions (Ajitha & Sivakumar 2019; Singh 2016). The influence of age is vital to understand in a BOP context, as the level of BOP consumers’ need for symbolism and social value attached to apparel varies among different age groups. Furthermore, BOP groups differ significantly in preferred store atmospheres based on their age (Khan et al. 2020a; Mostafa 2019:99). In order to ensure proper segmentation of the large urban BOP market (i.e., dividing the market into groups based on similar needs and wants according to different ages), different age groups should be clearly evaluated to reveal whether the link between store atmospherics and urban BOP consumers’ apparel behavioural intentions becomes stronger or weaker. This study used age as a moderator by applying four age categories (i.e., 18–27 years, 28–36 years, 37–48 years and 49–65 years). Although various generational cohorts exist (Ajitha & Sivakumar 2019), this study used the four age categories mainly to distinguish between young and old urban BOP consumers. Consequently, this study expects urban BOP consumers’ age to moderate the relationships between perceived value, anticipatory emotions, trust (urban BOP purchase antecedents) and attitude to explain the change in behavioural intentions. The following hypotheses are proposed within the TPB:
H8a: Age moderates the relationship between urban BOP consumers’ perceived value and attitudes.
H8b: Age moderates the relationship between urban BOP consumers’ anticipatory emotions and attitudes.
H8c: Age moderates the relationship between urban BOP consumers’ trust and attitudes.
Research methodology
Questionnaire and measurement
This study applied a descripto-explanatory research design, which allowed the researchers to measure the interrelationships between the constructs of concern and demonstrate causality (Hair 2017). As urban BOP consumers require guidance and advice regarding the questions in the questionnaire (Antoni & Basso 2016:596), an interviewer-administered survey was used to gather insights into store atmospherics and urban BOP consumers’ apparel behavioural intentions, and the effects of age as a moderator. Adapted scales from previous studies were used to measure and validate the constructs of the developed research model (Figure 1). Table 1 contains the items used to measure constructs and references from whom the scales have been adopted or adapted. A second-order unidimensional construct was used to measure store atmospherics based on the five dimensions of Turley and Milliman (2000:194). This study identified and included the corresponding store atmospheric elements in each dimension that BOP respondents specifically recognise at retail stores, as revealed by literature. Each questionnaire item was measured on a five-point Likert scale, with 1 being ‘strongly disagree’ and 5 being ‘strongly agree’.
| TABLE 1: Reliability and validity of the measurement model. |
Sample procedure, data collection and analysis
The target population of this study comprised of urban BOP consumers aged 18–65 years with a minimum gross monthly income of N$6000 ($347), who had purchased apparel at least once at an apparel retailer in the Khomas or Erongo region of Namibia in the last 6 months. Nearly 80% of Namibia’s urban BOP segment lives in the Khomas and Erongo regions, and apparel is one of the largest components of discretionary spending (per month) in both regions (National Planning Commission 2018; Nickanor et al. 2018:55). The participation criteria contained seven closed-ended screening questions: (1) receive less than N$6000 ($347) gross monthly household income; (2) have access to information (e.g., media); (3) have the means to purchase new products and services to raise living standards; (4) purchase products and services from both formal and informal markets; (5) have access to new technology (e.g., social media or mobile banking); (6) aged 18–65 years; and (7) purchased apparel more than once from an apparel retailer in Namibia in the past 6 months (Van Niekerk 2022). These above criteria were formulated according to the BOP definition frameworks of Simpson and Lapperman (2017) and Subhan and Khattak (2016).
As no sample frame existed, trained fieldworkers from a reliable research agency conducted the data collection face-to-face (Antoni & Basso 2016:596). Non-probability sampling was performed using purposive, interlocking and convenience sampling. Purposive sampling was executed since respondents were selected based on participation criteria. Research (e.g., Angeli et al. 2018) supports purposive sampling in conjunction with expert practitioners to access BOP consumers. Interlocking sampling was applied as respondents were chosen based on their respective populations in the two major urban areas of Namibia – the Khomas and Erongo regions. Respondents within the quota set for each region were selected using convenience sampling. The sampling methods allow the results to be generalised (Babin & Zikmund 2016). Data were collected from 881 urban BOP consumers – fieldworkers administered the questionnaire online to respondents and responses were captured in real-time using an electronic tablet. Audio recordings of respondents’ coded replies were listened to by trained fieldworkers as part of quality control. The data were only accessible by the researchers, guaranteeing confidentiality and anonymity, as it did not contain any personal information about the respondents. Data were collected over a 2-week period in 2021, where coronavirus disease 2019 (COVID-19) regulations were adhered to. Moreover, respondents were informed of their ethical rights before consenting to the study.
The data distributions were analysed using Statistical Package for the Social Sciences (SPSS) Version 25 descriptive statistics and normality test. A multivariate normality estimate was derived from the non-normal distribution of data using the maximum likelihood parameter (MLM) estimate and the Satorra-Bentler Chi-square fit index. Confirmatory factor analysis (CFA) and covariance-based structural equation modelling (CB-SEM) in Mplus Version 8.3 were used to test the psychometric properties of the adapted scales in the study’s measurement model (Muthén & Muthén 2019:533). An assessment of goodness of fit, construct reliability (internal consistency and composite reliability) and validity (convergent and discriminant validity) was performed as part of a CFA to obtain a model fit. The Hayes Process Categorical Model 1 and bias-corrected bootstrapping (intervals at a 95% confidence interval [CI]) were used to calculate the moderation effect of age (Hayes 2017:168).
Assessment of common method bias
The Harman’s single-factor test (MacKenzie & Podsakoff 2012) was applied to investigate common method bias. The results showed that the total variance explained by the single-factor solution was 35.57%, which is less than 50% of the total variance. Furthermore, item ambiguity as well as item complexity and double-barrel questions in the questionnaire were pre-tested among 60 respondents that complied with the participation criteria. No discrepancies were identified with the questionnaire pretest. Therefore, common method bias was not a concern in this study.
Ethical considerations
The ethical approval for this study was obtained from the College of Business and Economics Research Ethics Committee (CBEREC) and its sub-committees at the University of Johannesburg, ensuring that respondents’ rights were not violated during the completion of this research. 2021SCiiS050.
Results
Demographic profile and apparel patronage habits
Regarding respondents’ demographic profile, 54% of the 881 respondents were female and 46% were male. Most respondents (33%) were aged 28–36 years, had a high school certificate (55%), spoke Oshiwambo as their first language (49.7%), worked full time (36.8%), and often purchased apparel from Windhoek (Khomas region) (33.1%). Concerning respondents’ apparel patronage habits, most respondents (37.1%) said they spend approximately N$893.75 ($52.59) on apparel monthly. Furthermore, 71.7% made their apparel decisions based on basic needs, 51.2% on seasonal changes, and 50.5% on belonging. Additionally, urban BOP respondents said they purchase women’s daywear (41.1%) from department stores (61.5%).
Measurement and structural model assessment
The measurement model did not include age, as it is categorical and binary. Using CB-SEM, interrelationships were examined and measurement errors were accommodated (Strasheim 2014:31). A good fit was found in the measurement model (Satorra-Bentler χ2/df = 2.33, comparative fit index [CFI] = 0.944; Tucker-Lewis index [TLI] = 0.938; root mean square error of approximation [RMSEA] = 0.039).
In Table 1, descriptive statistics and construct reliability and validity are presented for each item. The table shows that the Cronbach’s alpha coefficients and the composite reliability scores are greater than 0.7 (Hair 2017), demonstrating construct reliability. Convergent validity was demonstrated because all factor loadings exceeded 0.5 and were statistically significant at an average variance extracted (AVE) value above 0.5. The Fornell-Larcker measure was used to assess discriminant validity. All constructs had AVE values greater than their squared correlation estimates, indicating discriminant validity. This study identified two CB-SEM measurement models based on the Bayesian information criterion approach that indicated store atmospherics as a second-order construct reflective of the five underlying dimensions (first-order factors) (Fabozzi et al. 2014:401). Therefore, multicollinearity was ruled out by AVE values greater than the squared correlation estimate and the high correlation between first-order factors within a larger construct (store atmospherics) (Fornell & Larcker 1981:38). As such, H1–H7 were validated and the structural model fit the data practically well (Satorra-Bentler χ2/df = 2.56; CFI = 0.933; TLI = 0.928; RMSEA = 0.042). A discussion follows the results of the structural model in Table 1.
From Table 2, the study supports all seven Hypotheses (H1–H7) related to the main effects of the developed research model. The causal flow between the store atmospherics, urban BOP purchase antecedents, attitude and behavioural intention constructs in the TPB reported statistically significant structural paths (p = 0.0001**). Perceived value (β = 0.896; large relationship) had the strongest relationship with store atmospherics, while trust (β = 0.375; small relationship) revealed the strongest relationship among all purchase antecedents with attitude.
Moderation effects of age
Age as a moderator in this study is multi-categorical (i.e., there are more than two groups). The age categories for this study in years were 18–27 years, 28–36 years, 37–48 years and 49–65 years. As such, the interpretive approach differs from other modes of moderation analysis. Age is the categorical predictor – whenever a categorical predictor is used in regression, the number of variables results in k–1 (Hayes 2017:168). In order to identify each age category, a unique indicator coding comprising zeros and one is assigned. A reference category of 18–27 years was created and the other categories (W1: 28–36; W2: 37–48; W3: 49–65) were compared against this category. Statistical Package for the Social Sciences Statistics 25 was used to calculate the moderation effect of age for each category using the Hayes Process Categorical Model 1. The significance of interaction effects was assessed using the bias-corrected bootstrapping intervals at a 95% CI. An interaction effect is statistically significant if it does not contain zero in the bias-corrected interval (Hayes 2017:168). Table 3 summarises the moderation effects of age.
| TABLE 3: Summary of moderation effects of age. |
Considering the different age categories, no moderation occurred between perceived value and attitude, and anticipatory emotions and attitude concerning the W1 (18–27 years vs 28–36 years) and W2 (18–27 years vs 49–65 years) age categories. No moderation for all age categories was evident between trust and attitude. As per Table 3, there are significant interaction effects between perceived value and attitude as well as anticipatory emotions and attitude considering the W3 (18–27 years vs 49–65 years) age category. Consequently, the conditional effects to examine significant moderation effects were probed further in Table 4 and Table 5 (Hayes 2017:168).
| TABLE 4: Conditional effects of perceived value on attitude at different values of age. |
| TABLE 5: Conditional effects of anticipatory emotions on attitude at different values of age. |
Significant conditional effects were found between perceived value on attitude and anticipatory emotions on attitude in terms of age as a moderator in category W3 (18–27 years vs 49–65 years). Both conditional effects become weaker as age increases. Therefore, H8a and H8b are accepted at a 95% CI. This indicates that age has a moderating effect on these relationships.
Discussion and research implications
In marketing literature, store atmospherics are extensively discussed as a method for creating desired behavioural intentions. In a turbulent retail environment, store atmospherics presents an innovative, cost-effective solution to incorporate consumers’ unique motivations for desired apparel behavioural intentions to grow market share (Sitinjak et al. 2019:38).
Therefore, the current study assists to explain this matter by providing novel insight into the systematic link between store atmospherics and urban BOP consumers’ apparel behavioural intentions by incorporating certain urban BOP purchase antecedents (perceived value, anticipatory emotions and trust) driving consumer attitude formation and urban BOP consumers’ apparel behavioural intentions overarched by the TPB. Further insights are provided regarding the extent to which various urban BOP age categories moderate the magnitude of the established links. Several managerial and theoretical implications were also derived from the findings in an emerging African market, Namibia.
Confirmation of H1 (store atmospherics → perceived value) demonstrated that BOP consumers’ perceived value regarding price and quality expectations are based on the experience received in retail stores (Barki & Parente 2006:12; Hasan et al. 2017). This solidifies the finding of Jiang, Luk and Cardinali (2018:374) from an emerging markets context, explaining that in any consumption experience, consumers’ main goal is perceived value. Evidently, insufficient financial resources prevent urban BOP consumers from affording ‘socially acceptable’ apparel values. Among the urban BOP purchase antecedents examined in the study, store atmospherics had the strongest relationship with perceived value, correlating with findings by Ng et al. (2021:69) and Salem and Salem (2018) among more affluent consumers purchasing apparel in Malaysia. In line with these authors’ findings, the positive and significant link from H2 (perceived value → attitude) established that the more value urban BOP consumers perceived to receive from a store, the greater the likelihood of favourable attitudes. This signifies that in a less developed emerging African market, urban BOP consumers show homogeneity in terms of their perceived value for apparel when store atmospherics match their preferences for value. Therefore, it is recommended that apparel retailers clearly display store signs and apparel prices with the words ‘bargain’ and ‘good deal’ to guide consumers to strengthen BOP consumers’ primary motive of perceived value.
Validating the significant and positive link of H3 (store atmospherics → anticipatory emotions) shows that store atmospherics encourage urban BOP consumers’ emotional goal attainment with apparel. This solidifies the assumption made by Antoni and Basso (2016) and Barki and Parente (2006) regarding BOP consumers’ apparel purchases. Zhang and Cude (2018) and Nell (2017) found an equivalent outcome among affluent consumers purchasing apparel. Furthermore, H4 (anticipatory emotions → attitude) is confirmed in an urban BOP consumer context. Research by Ong et al. (2021) supports this link among affluent consumers’ apparel purchases. This reveals that urban BOP consumers anticipate positive emotional goal attainment from apparel at a similar level to more affluent consumers. Therefore, to encourage apparel purchases among urban BOP consumers, apparel retailers are advised to connect emotionally with this segment by incorporating cause-related marketing into their store atmospherics. For example, bright-coloured signage retailing flip-flops to fight income inequality.
The links for H5 (store atmospherics → trust) and H6 (trust→ attitude) found that urban BOP consumers develop trust in apparel retailers before forming attitudes of trustworthiness and reliability in delivering valuable apparel. Lappeman et al. (2019:322) and Antoni and Basso (2016:601) found similar results. Accordingly, BOP consumers are homogeneous in their trust and attitudes towards non-essential products (e.g., apparel) in emerging markets. Apparel is more expensive than non-essentials; therefore, BOP consumers’ limited financial means reveal a high need for certainty in terms of trusting the store and its products (Singh et al. 2022). Moreover, among the urban BOP purchase antecedents, trust had the strongest relationship with attitude, meaning that trust is crucial in shaping urban BOP consumers’ attitudes towards retail stores. Bottom-of-the-pyramid consumers have high consumption needs for social acceptance, so their trust is developed more by sales personnel than by stores (Abbes & Goudey 2015:1114). In order build trust with urban BOP consumers, apparel retailers should train their sales personnel in, for instance, promoting ethical business practices, being friendly and joyful, resolving conflict, using language to convey similarities and build connections with consumers, assisting, treating consumers respectfully and ensuring privacy.
Confirmation of H7 (attitude → behavioural intention) affirmed that attitudes become beliefs for urban BOP consumers. The beliefs and evaluations of apparel value, price, quality and social acceptance of BOP consumers determine their attitudes towards the retail store (Hasan et al. 2017:154). Singh (2016) supports the results of H7, explaining that attitude is the most significant predictor of BOP consumers’ behavioural intentions in the TPB. In an emerging country, Panda et al. (2022:50) found a similar outcome among urban and rural BOP consumers. In order to encourage attitudes and behavioural intentions, apparel retailers should ensure their stores and apparel centre on urban BOP consumers’ basic motivations for purchasing apparel. For example, a store that is pleasant, rewarding, beneficial, supportive and offers value (quality apparel) should encourage urban BOP apparel behavioural intentions. Apparel retailers can implement these recommendations to better understand urban BOP consumers’ attitudes, formulate marketing strategies, position their stores and communicate better with their target audience (Hasan et al. 2017:154).
The links regarding the different age categories illustrate that younger urban BOP consumers’ perceived value and anticipatory emotions towards their attitudes are significantly different from those of older urban BOP consumers. H8a is supported by Tsarenko and Lo (2017:67), indicating that consumer groups buying apparel differ most in perceived value orientation based on age. As urban BOP consumers age, the perceived value driving their attitudes becomes weaker. Ajitha and Sivakumar (2019:442) supported this finding among affluent consumers, explaining that older consumers are more price- and quality-savvy as they are nearing retirement. Thus, when purchasing apparel, older consumers are increasingly looking for comfort and functionality. Therefore, H8a reveals that older urban BOP consumers tend to purchase apparel based on control over resources. In contrast, high social pressures to be fashionable, modern and unique are more important to young consumers, which causes a higher intention to purchase and much more concern with value (i.e., economical price, bargain or a good buy to be socially accepted) compared to older consumers. Therefore, young urban BOP consumers demonstrate stronger positive attitudes because they perceive merchandise prices as having more influence on appearance, availability, familiarity and brand name (Singh 2016). Ng et al. (2021) further supported the results of H8a, indicating that young consumers have become more value-orientated by further considering aspects such as product usefulness, benefits and experience. Consequently, from H8a it can be concluded that young urban BOP consumers interact more with the store atmospherics to derive perceived value, whereas older urban BOP consumers have already established a sense of perceived value from the store and within their community (Antoni & Basso 2016:603).
Furthermore, H8b shows that the anticipatory emotions that drive urban BOP consumers’ attitudes weaken as they age. Khan et al. 2020b and Singh (2016) agreed, stating that older consumers are more emotionally mature and possess more emotional control. Young consumers are more concerned with emotional gain when engaging with a retail store. The outcome of H8b is like affluent consumers (Ajitha & Sivakumar 2019), clarifying that older urban BOP consumers are more accustomed to knowing what to expect, while young urban BOP consumers are more welcoming of new experiences and emotions, particularly where store atmospherics evoke such experiences and emotions. Age was not found to moderate the relationship between urban BOP consumers’ trust and attitudes (H8c). This finding contradicts Khan et al. (2020b:5), who found that the link between trust and attitudes is higher among young consumers when experiencing the store than for older consumers. It is reasonable to argue that because urban BOP consumers perceive apparel purchases (expensive) as a high risk because of limited financial means, both young and old urban BOP consumers need trusting relationships. As such, apparel retailers can improve urban BOP market segmentation for profitability by communicating the preferred motivations of each age group they wish to target in their store atmospherics.
Conclusions, limitations and future research for apparel retailers
This study provides a deeper understanding of the link between store atmospherics and urban BOP apparel behavioural intentions in an emerging African country context. In particular, this study proposes and verifies the extent to which various urban BOP purchase antecedents may impact their attitudes, as well as the moderating role that age may play in influencing these relationships. In the sharing economy, apparel retailers may benefit from a better understanding of the differences between young and old urban BOP consumers to gain a competitive advantage. Because of financial constraints, it was not feasible to survey all urban BOP respondents geographically. Therefore, Namibian urban BOP consumers could not be generalised. Future research could focus on the similarities and differences among the urban BOP consumers purchasing apparel in other emerging African countries. It is recommended that researchers define urban and rural BOP consumers appropriately in Namibia as well as internationally. The current study focussed on an overall view of young and old urban BOP consumers. A similar study could incorporate the generational cohort theory.
Acknowledgements
Source of article
The article originates from the PhD thesis study of B.M. van Nierkerk, completed at the University of Johannesburg, in South Africa, entitled ‘A proposed model towards the impact of store atmospherics on BOP consumers’ intentions to purchase apparel’, obtained from the University of Johannesburg, South Africa, and available from: https://hdl.handle.net/10210/504239. The article further also expands on a study published in the European Business Review entitled ‘Apparel behavioural intentions of urban bottom-of-the-pyramid consumers: Exploring the role of store atmospherics.’ Available from: https://doi.org/10.1108/EBR-05-2023-0139.
This article therefore provides a more in-depth comparative exploration of the different BOP age groups and how the attitudes and behavioural intentions of urban BOP consumers change towards store atmospherics when considering the age of these consumers. This was not the primary focus of the article published in the European Business Review. As such the study established that consumers’ age do alter behaviour change in a significant manner, specifically their perceived value, anticipatory emotions and attitudes considering the 18–27 years vs 49–65 years age categories.
Competing interests
The authors declare that they have no financial or personal relationships that may have inappropriately influenced them in writing this article.
Authors’ contributions
B.M.v.N., M.R-L. and N.C. contributed equally to this article.
Funding information
The research was funded by the Wholesale and Retail SETA PhD bursary received in 2021.
Data availability
The data supporting the findings of the study are available from the corresponding author, B.M.N., upon reasonable request.
Disclaimer
The views and opinions expressed in this article are those of the authors and do not necessarily reflect the official policy or position of any affiliated agency of the authors, and the publisher.
References
Abbes, M. & Goudey, A., 2015, ‘How salespersons induce trust between consumers and retailers: The case of French well-being stores’, International Journal of Retail & Distribution Management 43(12), 1104–1125. https://doi.org/10.1108/IJRDM-06-2014-0064
Ajitha, S. & Sivakumar, V.J., 2019, ‘The moderating role of age and gender on the attitude towards new luxury fashion brands’, Journal of Fashion Marketing and Management 23(4), 440–465. https://doi.org/10.1108/JFMM-05-2018-0074
Ajzen, I., 1991, ‘The theory of planned behavior’, Organizational Behavior and Human Decision Processes 50(2), 179–211. https://doi.org/10.1016/0749-5978(91)90020-T
Akdemir, N., 2018, ‘Visible expression of social identity: The clothing and fashion’, Gaziantep University Journal of Social Sciences 17(4), 1389–1397. https://doi.org/10.21547/jss.411181
Ali, M.S.I. & Mubarak, K.M., 2016, ‘Exterior atmospherics and there impact on consumer purchasing behavior at self-serving convenience stores’, International Journal of Science and Research 5(11), 263–268.
Angeli, F., Ishwardat, S.T., Jaiswal, A.K. & Capaldo, A., 2018, ‘Socio-cultural sustainability of private healthcare providers in an Indian slum setting: A bottom-of-the-pyramid perspective’, Sustainability 10(12), 4702. https://doi.org/10.3390/su10124702
Antoni, V. & Basso, K., 2016, ‘Expectations concerning retail: A study of clothing and shoe consumers at the bottom of the social pyramid’, Revista Brasileira de Gestão de Negócios 18(62), 593–608. https://doi.org/10.7819/rbgn.v18i62.1702
Aziz, S., Bahadur, W.J., Zulfiqar, S., Sarwar, B., Chandia, K.E. & Iqbal, M.B., 2018, ‘Do emotions bring customers to an environment: Evidence from Pakistani shoppers?’, Cogent Business & Management 5(1), 1536305. https://doi.org/10.1080/23311975.2018.1536305
Babin, B.J. & Zikmund, W.G., 2016, Exploring marketing research, 11th edn., Cengage, Boston, MA.
Barki, E. & Parente, J., 2006, ‘Consumer behaviour of the base of the pyramid market in Brazil’, Greener Management International 56, 11–23.
Barros, L.B., Petroll, M.D.L.M., Damacena, C. & Knoppe, M., 2019, ‘Store atmosphere and impulse: A cross-cultural study’, International Journal of Retail & Distribution Management 47(8), 817–835. https://doi.org/10.1108/IJRDM-09-2018-0209
Bettiga, D. & Lucio Lamberti, L., 2018, ‘Exploring the role of anticipated emotions in product adoption and usage’, Journal of Consumer Marketing 35(3), 300–316. https://doi.org/10.1108/JCM-06-2016-1860
Calvo-Porral, C. & Lévy-Mangin, J.P., 2021, ‘Examining the influence of store environment in hedonic and utilitarian shopping’, Administrative Sciences 11(1), 6. https://doi.org/10.3390/admsci11010006
Chang, H.J. & Watchravesringkan, K.T., 2018, ‘Who are sustainably minded apparel shoppers? An investigation to the influencing factors of sustainable apparel consumption’, International Journal of Retail & Distribution Management 46(2), 148–162. https://doi.org/10.1108/IJRDM-10-2016-0176
Fabozzi, F.J., Focardi, S.M., Svetlozar T., Rachev, S.T. & Bala, G.A., 2014, ‘Appendix E: Model selection criterion: AIC and BIC’, in F.J. Fabozzi, S.M. Focardi, T. Svetlozar, S.T. Rachev & G.A. Bala (eds.), The basics of financial econometrics: Tools, concepts, and asset management applications, p. 448, Wiley & Sons, New York, NY.
Fitch Solutions Group Limited, 2022, Namibia consumer & retail report, North Colonnade, Fitch Solutions, London.
Fornell, C. & Larcker, D.F., 1981, ‘Evaluating structural equation models with unobservable variables and measurement error’, Journal of Marketing Research 18(1), 39–50. https://doi.org/10.2307/3151312
Guenzi, P., Johnson, M.D. & Castaldo, S., 2009, ‘A comprehensive model of customer trust in two retail stores’, Journal of Service Management 20(3), 290–316. https://doi.org/10.1108/09564230910964408
Hair, J.F. Jr., 2017, Essentials of marketing research, 3rd edn., McGraw-Hill, New York, NY.
Hasan, M.R., Lowe, B. & Rahman, M., 2017, ‘Visual cues and innovation adoption among bottom of the pyramid consumers’, Qualitative Market Research 20(2), 147–157. https://doi.org/10.1108/QMR-04-2015-0032
Hayes, A.F., 2017, Introduction to mediation, moderation, and conditional process analysis: A regression-based approach, 2nd edn., Guilford Press, New York, NY.
Jain, S. & Khan, M.N., 2017, ‘Measuring the impact of beliefs on luxury buying behavior in an emerging market: Empirical evidence from India’, Journal of Fashion Marketing and Management 21(3), 341–360. https://doi.org/10.1108/JFMM-07-2016-0065
Jaiswal, A.K. & Gupta, S., 2015, ‘The influence of marketing on consumption behavior at the bottom of the pyramid’, Journal of Consumer Marketing 32(2), 113–124. https://doi.org/10.1108/JCM-05-2014-0996
Jiang, K., Luk, S.T.K. & Cardinali, S., 2018, ‘The role of pre-consumption experience in perceived value of retailer brands: Consumers’ experience from emerging markets’, Journal of Business Research 86, 374–385. https://doi.org/10.1016/j.jbusres.2017.09.026
Kapoor, R. & Rashmi., 2016, ‘The effects of in store music on shopping behaviour in a retail setting’, Management Dynamics 16(1), 96–108. https://doi.org/10.57198/2583-4932.1078
Khan, I., Fatma, M., Shamim, A., Joshi, Y. & Rahman, Z., 2020a, ‘Gender, loyalty card membership, age, and critical incident recovery: Do they moderate experience-loyalty relationship?’, International Journal of Hospitality Management 89, 102408. https://doi.org/10.1016/j.ijhm.2019.102408
Khan, I., Hollebeek, L.D., Fatma, M., Jamid Ul Islam, J.U. & Riivits-Arkonsuo, I., 2020b, ‘Customer experience and commitment in retailing: Does customer age matter?’, Journal of Retailing and Consumer Services 5(C), 102219. https://doi.org/10.1016/j.jretconser.2020.102219
Khandker, V., 2023, ‘Two decades of the bottom of the pyramid research: Identifying the influencers, structure, and the evolution of the concept’, Management Review Quarterly 73(3), 1151–1178. https://doi.org/10.1007/s11301-022-00271-y
Kim, J.H., Kim, M., Yoo, J. & Park, M., 2021, ‘Consumer decision-making in a retail store: The role of mental imagery and gender difference’, International Journal of Retail & Distribution Management 49(3), 421–445. https://doi.org/10.1108/IJRDM-10-2019-0353
Kumar, D. & Gupta, P., 2022, ‘Social innovation for sustainability and social inclusion’, Asia-Pacific Journal of Management Research and Innovation 18(3–4), 169–177. https://doi.org/10.1177/2319510X221144657
Kumar, G. & Nayak, J.K., 2022, ‘A meta-analysis of TPB model in predicting green energy behavior: The moderating role of cross-cultural factors’, Journal of International Consumer Marketing 35(2), 147–165. https://doi.org/10.1080/08961530.2022.2070900
Kuppelwieser, V.G., 2016, ‘Towards the use of chronological age in research: A cautionary comment’, Journal of Retailing and Consumer Services 33, 17–22. https://doi.org/10.1016/j.jretconser.2016.05.016
Lappeman, J., Ransome, K. & Louw, Z., 2019, ‘Not one segment: Using global and local bop characteristics to model country-specific consumer profiles’, European Business Review 31(3), 317–336. https://doi.org/10.1108/EBR-01-2018-0027
Latiff, Z.A.B., Mohamed, Z. & Mohamad, Z.M., 2016, ‘Food labels’ impact assessment on consumer purchasing behavior in Malaysia’, Journal of Food Products Marketing 22(2), 137–146. https://doi.org/10.1080/10454446.2013.856053
MacKenzie, S.B. & Podsakoff, P.M., 2012, ‘Common method bias in marketing: Causes, mechanisms, and procedural remedies’, Journal of Retailing 88(4), 542–555. https://doi.org/10.1016/j.jretai.2012.08.001
Mathur, M.K., 2018, viewed n.d., from https://www.emerald.com/insight/search?q=Ritu%20Mehta.
McLean, G., Osei-Frimpong, K., Al-Nabhani, K. & Marriott, H., 2020, ‘Examining consumer attitudes towards retailers’ m-commerce mobile applications – An initial adoption vs. continuous use perspective’, Journal of Business Research 106, 139–157. https://doi.org/10.1016/j.jbusres.2019.08.032
Mohammad, N. & Baharun, R., 2016, ‘Effect of perceived value and personal value in affecting the attitude to purchase organic product: A literature review’, Paper presented at the International Conference on Science, Engineering, and the Social Sciences, Johor Bahru, Malaysia, May 2016.
Moharana, T.R. & Pattanaik, S., 2018, ‘Store selection in emerging markets: An Indian perspective’, Journal of Management Research 18(3), 162–175.
Moon, S.J., 2021, ‘Investigating beliefs, attitudes, and intentions regarding green restaurant patronage: An application of the extended theory of planned behavior with moderating effects of gender and age’, International Journal of Hospitality Management 92, 102727.
Mostafa, M.M., 2019, ‘Profiling kuwaiti female apparel consumers: Self-identity, social interactions, and prestige consumption’, International Journal of Sociology and Social Policy 39(1/2), 98–117. https://doi.org/10.1108/IJSSP-06-2018-0109
Muthén, L.K. & Muthén, B.O., 2019, Mplus for Windows 8.3, Muthén & Muthén, Los Angeles, CA.
Namibia Statistics Agency, 2016, Namibia household income and expenditure survey (NHIES) 2015/2016 report, Namibia Statistics Agency, Windhoek.
National Planning Commission, 2018, Namibia poverty mapping report, National Planning Commission, Windhoek.
Nell, E.C., 2017, ‘The impact of sensory environments on consumer buying behaviour: A study of visual displays and sight atmospherics’, Journal of Business and Retail Management Research 11(2), 155–164.
Ng, S.I., Jo, J.A., Lim, X.J., Lin Chong, K.L. & Latiff, K., 2021, ‘Mirror, mirror on the wall, are we ready for Gen-Z in marketplace? A study of smart retailing technology in Malaysia’, Young Consumers 22(1), 68–89. https://doi.org/10.1108/YC-06-2019-1006
Nickanor, N., Kazembe, L., Crush, J. & Wagner, W., 2018, The urban food system of Windhoek, Namibia: Africa’s supermarket revolution and urban food security in Namibia, Hungry Cities Report No. 8, Hungry Cities Partnership, Waterloo.
Ong, A.K.S., Cleofas, M.A., Prasetyo, Y.T., Chuenyindee, T., Young, M.N., Diaz, J.F.T. et al., 2021, ‘Consumer behavior in clothing industry and its relationship with open innovation dynamics during the COVID-19 pandemic’, Journal of Open Innovation: Technology, Market, and Complexity 7(4), 211. https://doi.org/10.3390/joitmc7040211
Panda, D., Mukhopadhyay, S., Saha, R. & Panigrahi, P.K., 2022, ‘Attitude of bottom of the pyramid consumers towards packaging-influenced purchase decision: An opinion mining-based approach’, Journal of Packing Technology and Research 6(1), 49–62. https://doi.org/10.1007/s41783-021-00128-x
Park, M. & Lennon, S.J., 2009, ‘Brand name and promotion in online shopping contexts’, Journal of Fashion Marketing and Management 13(2), 149–160. https://doi.org/10.1108/13612020910957680
Petrocelli, J.V. & Whitmire, M.B., 2017, ‘Emotion decoding and incidental processing fluency as antecedents of attitude certainty’, Personality and Social Psychology Bulletin 43(7), 924–939. https://doi.org/10.1177/0146167217700606
Pfeuffer, A. & Huh, J., 2021, ‘Effects of different sponsorship disclosure message types on consumers’ trust and attitudes’, International Journal of Advertising 40(1), 49–80. https://doi.org/10.1080/02650487.2020.1807229
Praceus, S. & Herstatt, C., 2017, ‘Consumer innovation in the poor versus rich world: Some differences and similarities’, in C. Herstatt & R. Tiwari (eds.), Lead market India: Key elements and corporate perspectives for frugal innovations, pp. 97–117, Springer, Cham.
Salem, S.F. & Salem, S.O., 2018, ‘Self-identity and social identity as drivers of consumers’ purchase intention towards luxury fashion goods and willingness to pay premium price’, Academy of Management Journal 23(2), 161–184. https://doi.org/10.21315/aamj2018.23.2.8
Simpson, J. & Lappeman, J., 2017, ‘An overview of the South African consumer market’, in J. Simpson & J. Lappeman (eds.), Marketing in South Africa: Consumer landscape: Concepts and cases, 4th edn., pp. 3–12, Van Schaik, Pretoria.
Singh, A., 2016, ‘Understanding purchase behaviour and analyzing marketing mix strategies: A study of Bottom of the Pyramid (BOP) consumers’, PhD thesis, Punjab Agricultural University.
Singh, D.P., Paul, J. & Sharma, P., 2022, ‘Segmenting the bottom of the pyramid consumers: Theoretical approach’, International Journal of Market Research 64(3), 397–419. https://doi.org/10.1177/14707853211055048
Sitinjak, M.F., Pangaribuan, C.H. & Tafriza, N., 2019, ‘Do store atmosphere and perceived value matter in satisfying and predicting the millennials’ behavioral intention in a café setting?’, Binus Business Review 10(1), 31–40. https://doi.org/10.21512/bbr.v10i1.5345
Srivastava, R. & Srivastava, V., 2021, ‘A study of buyer motivations at the bottom of pyramid in urban India’, Vision: Journal of Business Perspective 09722629211051441. https://doi.org/10.1177/09722629211051441
Srivastava, R., 2022, ‘Marketing at the bottom of the pyramid: A systematic literature review to set the research agenda’, Academy of Marketing Studies Journal 26(4), 1–18.
Statista, 2022, Namibia’s economy & society, viewed n.d., from https://www.statista.com/study/48468/namibia/.
Strasheim, A., 2014, ‘Testing main and interaction effects in structural equation models with a categorical moderator variable’, Management Dynamics 23(4), 31–68.
Subhan, F. & Khattak, A., 2016, ‘What constitutes the Bottom of the Pyramid (BOP) market?’, in W.H. Rizvi & H. Amir (eds.), Proceedings of the 3rd International Conference on Marketing, IBAICM 2016, Institute of Business Administration, Karachi, December 19–20, 2016.
Tasci, A.D.A. & Pizam, A., 2020, ‘An expanded nomological network of experienscape’, International Journal of Contemporary Hospitality Management 32(3), 999–1040. https://doi.org/10.1108/IJCHM-12-2018-0988
Tsarenko, Y. & Lo, C.J., 2017, ‘A portrait of intimate apparel female shoppers: A segmentation study’, Australasian Marketing Journal 25(1), 67–75. https://doi.org/10.1016/j.ausmj.2017.01.004
Turley, L.W. & Milliman, R.E., 2000, ‘Atmospheric effects on shopping behavior: A review of the experimental evidence’, Journal of Business Research 49(2), 193–211. https://doi.org/10.1016/S0148-2963(99)00010-7
Van Niekerk, B.M., Roberts-Lombard, M. & Cunningham, N., 2023, ‘Apparel behavioural intentions of urban bottom-of-the-pyramid consumers: Exploring the role of store atmospherics’, European Business Review, viewed n.d., from https://doi.org/10.1108/EBR-05-2023-0139
Van Niekerk, B.M., 2022, ‘A proposed model towards the impact of store atmospherics on BOP consumers’ intentions to purchase apparel’, PhD thesis, College of Business and Economics, University of Johannesburg, Johannesburg. https://hdl.handle.net/10210/504239.
Wang, L.H., Yeh, S.S., Chen, K.Y. & Huan, T.C., 2022, ‘Tourists’ travel intention: Revisiting the TPB model with age and perceived risk as moderator and attitude as mediator’, Tourism Review 77(3), 877–896.
Wu, J., Kang, J.-Y.M., Damminga, C., Kim, H.-Y. & Johnson, K.K.P., 2015, ‘MC 2.0: Testing an apparel co-design experience model’, Journal of Fashion Marketing and Management 19(1), 69–86. https://doi.org/10.1108/JFMM-07-2013-0092
Zhang, L. & Cude, B.J., 2018, ‘Chinese consumers’ purchase intentions for luxury clothing: A comparison between luxury consumers and non-luxury consumers’, Journal of International Consumer Marketing 30(5), 336–349. https://doi.org/10.1080/08961530.2018.1466225
|
Hide
Show all
Email this article (Login required)
