Keywords

Visual Merchandising, Gen X and Y, Mannequin Display, Product Display, Window Display, Product Signage.

Introduction

Indian apparel retail assumes significance as the second largest contributor to the country’s gross domestic product (GDP). The country’s apparel market is estimated to be eighty-nine billion dollars (for the financial year 2022), with a compounded annual growth rate of 5.5%. It is expected to cross a hundred billion dollars by 2026 (Knowledge-Ridge, 2022). Additionally, it contributes 2.3% to the country’s GDP, 13% to its industrial production, 12% to exports, and 4% to the global apparel market (Rathee, 2024). The growth of the apparel and fashion market has continuously evolved over the last century. From appealing to a narrow group of upper-class clientele at the turn of the twentieth century, today, the apparel market has gained mass appeal and stands at 1.7 trillion dollars globally and is expected to touch two trillion dollars by 2030 (Statista, 2023; Soresen, 1995). However, apparel retail has undergone significant changes recently due to the advent of technology and the inter-generational changes in customer demographics (Humphrey, 2007; Reardon & Gulati, 2008; Tschirley et al., 2010).

Though e-commerce and fashion have been growing in popularity in India, apparel shopping at physical stores has been resurgent, especially post-COVID-19, considering the immersive experience it offers. It is estimated that online channels will contribute only about twenty percent of apparel sales (about 16 billion dollars), and physical stores will account for a whopping eighty percent (about 65 billion dollars) by the end of 2025. This is especially true among the older generations of X and Y customers, who currently contribute to most sales – especially those through the physical stores (Knowledge-Ridge, 2022; Tagra, 2023). Apparel sales through physical stores entail visual merchandising (V.M.) elements, which can significantly improve store attractiveness, perceived image, and positive mood among shoppers, promoting purchase intention. V.M. creates visual invitations by highlighting presentation forms, display features, fixture intricacies, and merchandise color and helps cognitive processing such that it can help improve desired customer response and satisfaction. Considering the customer category involvement and the social importance of the category, V.M. significantly influences rational and emotional/ impulsive apparel buying among shoppers (Yaoyuneyoung et al., 2014; Baek et al., 2015; Wright et al., 2006; Spies et al., 1997). However, because generational cohorts affect consumer behavior, it is reasonable to assume that V.M. will have a differential impact on Generation X and Y apparel buyers.

Considering the factors mentioned above, this study proposes to investigate the differential influence of V.M. on the rational and emotional apparel buying decisions among Generation X and Y consumers in India and their differential impact between the two groups. The study investigated the impact of four V.M. elements – Viz. Window display (W.D.), mannequin display (M.D.), promotional signage (P.S.), and product display (P.D.) on the emotional and Rational buying (E.B. & R.B., respectively) of apparel among generation X and Y customers. The study results indicate a clear intergenerational difference in the effect of V.M. on apparel shopping. Though V.M. contributes significantly to rational buying in both groups, in Generation Y, its contribution to emotional buying is significantly higher. Whereas MD contributes significantly to E.B. and R.B. among Gen-Y, it has no significant effect on Gen-X behavior. However, P.S. had a significantly higher influence on R.B. among Gen-X. The remaining V.M. elements (W.D. & P.D.) had no differential impact on apparel buying between the two generational cohorts.

The current study contributes to the body of knowledge by providing an improved understanding of how V.M. elements differentially contribute to the apparel purchasing intentions of generations X and Y. Such understanding can help apparel industry managers develop suitable merchandising strategies to attract, convert, and satisfy their targets from the two generational groups. The study limits itself to investing in the apparel buying behavior of X and Y generations, considering their significant role in the category. However, considering the changing nature of apparel retailing (fast growth of online retail) and the critical role Generation Z will play in the category as they become the dominant customers, the study warrants extensions in the future. The rest of the study is organized as follows - the second section deals with literature review and theory building, followed by the methodology section. The fourth section analyses the study results and is followed by a discussion. The last two sections deal with the implications and limitations of the study.

Literature Review

The apparel retail has seen sea changes in the recent past due to industry dynamics (especially the growth of non-European and American brands), technological developments, changes in demographics (increased participation from Gen-Z & Alpha, who are digital natives), changes in consumer preference, and growth of e-commerce (Molla-Descals et al., 2014; Verhoef et al., 2015; NRF, 2018). Such change, especially the growth of e-commerce in apparel retail, has been further aided by increased internet penetration and data traffic, diffusion of financial technologies, growth of third-party logistics, and information technology advancements, resulting in intense rivalry between traditional and modern retail (physical vs online) (Goswami & Khan, 2015; Kaushik & Dhir, 2019). Such a scenario has prompted the brand to adopt the 'phygital' strategy – a twin strategy of being physical and digital at the same time to retail apparel. However, apparel's critical role in signaling user personality and social status has ensured that the physical retailing format retains significance. It is noticeable that physical retailing has rebounded post-COVID-19 due to its ability to provide an enhanced shopping experience through a multi-sensory experience, lacking in online formats (Knowledge-Ridge, 2022; Tagra, 2023). V.M. is critical to providing a multi-sensory experience to shoppers, especially in a category like apparel, which entails high customer involvement. Understanding its impact on the consumer's behavior is critical to the retail success of brands. Such understanding assumed heightened significance in India, where traditional retail still accounts for more than 80% of the category retail, and most customers come from pre-digital generations (Gen-X & Y).

Visual Merchandising and Its Elements

The role of retail spaces has significantly evolved from a place to conduct business to a place that stimulates consumer buying through multi-sensory experiences. The role of V.M., a part of the store atmospherics, is very critical in the provision of such customer experiences (Underwood et al., 1973; Park et al., 2015; Ha & Lennon, 2011; Yaoyuneyong et al., 2014). Kotler (1974) defines store atmosphere as the quality of the retail space that can generate the desired response of a customer. However, Baek et al. (2015) expanded the definition of store atmosphere to visual strategies by posing and arranging products using props and backdrops to make a visual statement encompassing deeper strategies of highlighting information than a simple display of merchandise (p.35). V.M., a sub-component of store atmospherics, is a strategic tool for apparel retail brands to attract customers and communicate the brand image through various forms like floor layout, signage, interior design, product mix, and in-store promotion.

Jakhar et al. (2020) list the numerous visual merchandising dimensions and sub-dimensions, such as pictorial presentation, customization, product information presentation, and store environment. While pictorial presentation involves presentation method, mode of presentation, and location of the displays, customization comprises mannequin displays, personalization, and apparel ensemble coordination. These dimensions are not universally applicable across sectors and mediums of purchase (online/offline; electronic/physical stores) and, therefore, vary according to their specific needs and target population. Previous research has established the impact of V.M. elements like product signage, window display, lighting levels, presentation forms, sensory features of display materials, intricacies of fixtures, merchandise color, and music on consumers’ cognitive processing (Mehta & Chugan, 2013; Summers & Hebert 2001; Brakus et al., 2009; Kerfoot et al., 2003). Krishnakumar (2014) and Zhu et al. (2019) opine that accurate purchase occurs when the consumer experiences the visuals at the store in real-time and suggest that V.M. in apparel can be used through two techniques: Interior Display and Exterior Display. Both exterior components- such as window display and interior components – such as Mannequins, Product Display, and Promotional signage - are visual merchandising components responsible for creating pleasant store atmospheres and can influence consumer behaviour by stimulating purchase intention and probability.

Additionally, V.M. has been shown to influence in-store consumer participation and enhance purchase intention, favourable consumer responses, and customer satisfaction (Bakamitsos, 2000; Wright et al., 2006; Spies et al., 1997). Retailers have widely used the importance of V.M. in retailing to build brand image and improve product preference among shoppers using visual elements (Mehta & Chugan, 2013; Martin & Morich, 2011; Mohan & Ojha, 2014). However, the consumer responses to V.M. can be affected by demographics like age, culture, beliefs, and opinions across different generational cohorts and necessitates studying consumer buying behaviour by studying generational cohorts (Landmark & Sjøbakk, 2017).

Generational Cohorts

Although age is one of the most frequent approaches for customer segmentation (Bakewell & Mitchell, 2003; Dou et al., 2006; Meredith et al., 2007), scholars have lately proposed analyzing consumer behaviour through generational cohort group segmentation. It involves segmenting and labeling customers with comparable behavioural characteristics, attitudes, opinions, and ideas into the same generations (Soares et al., 2017; Noble & Schewe,2003; Yu & Miller, 2003). Neal et al. (2004) define generational cohorts as people who share similar values, views, and ideas and have shared economic and social experiences. In topical studies, generational cohorts have emerged as a marketing tool that helps explain the tectonic shifts in the buying behaviour of different consumer segments (Howe & Strauss, 2000; Moore & Carpenter, 2008; Schewe & Noble, 2000). Two of the most widely influential generational cohorts are Gen X and Y, who dominate the current employment scene and possess enormous purchasing power by age.

Generation X individuals, a.k.a. early millennial generation or late baby boomers, were born between 1965 and 1977. Generation X is significant for generational cohort studies as this age group bridges the gap between pre-digital (baby boomers) and digital (millennials) cultures (Miller & Laspra, 2017; Katz, 2017). Through their long employment, they are rich but spend relatively low incomes on apparel, preferring to spend more on needs and less on luxury items (Paulin & Riordon, 1998). They are almost disillusioned and skeptical about everything in life (Moore & Carpenter, 2008), and as a result, they are seen as disloyal to the brands available on the market (Williams, 2005; Johnson & Moore, 2001). Individuals born between 1978 and 1990 are known as Generation Y (Millenials). They are the first digital generation, which makes them significantly different in behavior compared to previous generations (Raines, 2002). They believe in networking, sharing, and cooperating due to the significant influence of social media in their lives and place a higher value on status than previous generations (De Paula, 2003; Dickey & Sullivan, 2007; Mueller et al., 2011). Gen Y is more concerned with service and is more willing to criticize and complain about poor service quality (Soares et al., 2017). They are more brand loyal and prefer hedonic experiences to physical products (Bento et al., 2018; Brown & Lubelczyk, 2018). Drawing upon the above differences between attitudes and motivations of Gen X and Y, apparel retailers and marketers must better understand and map their in-store behavior.

Consumer Buying Behaviour

Literature merges buying behaviour elements into three main types - rational, emotional/ impulsive, and compulsive- categorized based on the concerned purchase action (Kardes et al.,2011). Rational behaviour refers to buying motives based on consumers' thoughts and judgment, such as testing, observation, and comparing products concerning their durability, price, and quality. From the economic purchase behaviour perspective, the rational factor— based on the utility model— continues to dominate, wherein consumers dispassionately conduct the cost-benefit analysis to select the product with the highest utility. On the other hand, impulse buying (herein referred to as emotional) can be described as unplanned but highly involved and simultaneously impulsive (Dhurup, 2014; Rook, 1987). They are mostly unplanned, but not all unplanned purchases are emotional (Iyer, 1989). Compulsive buying is an advanced version of impulsive behaviour, wherein the occasional lapse of judgment becomes extreme and frequent, sometimes leading to substantial personal distress and debt. Thus, this study restricts its scope by investigating the effect of V.M. elements on the rational and emotional buying behaviour of GEN X and Y and excludes compulsive buying behaviour since it is majorly associated with clinical conditions and personality disorders (Hollander & Allen, 2006; Raab et al., 2011).

The physical apparel retail space capitalizes on the benefits of allowing customers to "touch and feel" products, as well as correct fit and sizing, zero shipping lead-time, and guidance and up-selling from sales personnel (Bhatnagar et al., 2000; Levin et al., 2003; Liao & Cheung, 2001). Physical retail clothes stores must function as efficiently as possible while also exciting clients in new ways in order to remain appealing and economically sustainable. Understanding consumer behaviour toward visual merchandising could be a strategic advantage in current times that is favoring e-commerce. Consumer purchasing behaviour has developed over time and now encompasses more than just the acquisition, evaluation, use, and disposal of goods and services (Bamossy et al., 2006). The process starts before product acquisition and can be influenced by practical marketing tools. One such marketing tool is visual merchandising, which is widely used by apparel retail stores and can serve to guide customers toward specific products (Hefer & Cant, 2013).

The Influence of Visual Merchandising on Consumer Behaviour across Gen X and Y

Generational consumption habits are influenced by their upbringing, shared views, and values (Williams & Page, 2011). It is widely established that the purchasing patterns, motivations, and drivers influencing consumer-purchasing decisions fluctuate between generations (Marton et al., 2019). According to Howe and Strauss (2000), a generation's traits are determined by shared beliefs and behavioural patterns rather than age. Previous research has looked at different aspects of visual merchandising (Eroglu et al., 2003) by comparing two V.M. features to see their importance. Researchers have examined the impact of goods presentation components on customers' perceptions and shopping behaviour (Yi et al., 2015). Contemporary research has argued that the availability of products on credit, a wide range of domestic and international choices, rising disposable income, and easy access to buying options (mobile applications, social media, websites, etc.) have diminished the rational buying behaviour and that individuals indulge themselves in buying, at least occasionally, on impulses and emotions, instead of necessities. Thus, there is a need to compare how different dimensions of V.M. affect customer's emotional and rational buying behaviour. Literature has numerous V.M. dimensions, and it can be difficult for a store to choose the right one because it can be costly and time-consuming. As a result, it is critical to comprehend the relative importance of a few key V.M. dimensions. However, there is a paucity of research comparing the relative impact of different visual merchandising dimensions in apparel retail contexts.

Studies have reported generational differences in the shopping behavior of consumers using multiple variables (Loureiro & Breazeale, 2016; Nash, 2019; Alaeddin et al., 2018; Jakhar et al., 2020; Widyastuti, 2018; Najafi et al., 2021). However, an intergenerational comparison of the impact of V.M. elements on generations X and Y has been desired. As a result, merchants and marketers can build in-store elements that appeal to this consumer niche based on each generation's distinct behaviours and value systems. Therefore, the study examines how visual merchandising influences consumer behaviour across generations X and Y. This study uses Mehrabian and Russell's stimulus-organism-response framework (1974) to investigate the differential impact of V.M. elements on generations X and Y. The V.M. dimensions (mannequins, store displays, promotional signage, and window displays) are the stimulus. The two generational cohorts represent the organism; the response is the rational or emotional buying they exhibit. This theory introduces the idea that a customer of a different generation may choose to behave differently toward V.M. dimensions (Stimulus). Consequently, the study proposes to identify the differential impact of the four V.M. elements on the emotional and rational apparel buying behaviour of Generation X and Y.

The study presents the following hypothesis based on the above research objective

Hypotheses

H1(a,b): There exists a significant influence of V.M. elements on emotional (H1a) and rational buying (H1b) decisions of Gen-X consumers

H2(a,b): There exists a significant influence of V.M. elements on emotional (H1a) and rational buying (H1b) decisions of Gen-Y consumers

H3a: There is a significant difference in the influence of V.M. elements on emotional buying between Gen-X and Y.

H3b: There is a significant difference in the influence of V.M. elements on rational buying between Gen-X and Y.

Methodology

Data Design

The current study is a descriptive cross-sectional causal investigation using primary data. The study was conducted among Gen X and Y apparel shoppers. The sampling criteria required the respondents to belong to one of these generational cohorts and purchase apparel from a physical store in the last six months. Such sampling criteria ensured that the survey participants helped achieve the research objectives. The study ensured the exclusion of participants from other generational cohorts and online shopping for the same reason. The study used a self-administered questionnaire to measure variables. Based on the proposed research model, a thirty-nine-item instrument was developed using standardized scales from earlier research. The list of instruments used in the study and the number of items are detailed in Table 1. All items used a five-point Likert scale of the agreement type, as proposed in the original scales. The instrument was pretested with 12 respondents meeting the sampling criteria to ensure communicative validity and comprehension. The participants comprehended the items well, necessitating no further modification to the scale items.

Table 1 Measurement Scales
S.No	Variable	No. of items	Adapted from
1	Window display	6	Eroglu et al. (2003), Mehta & Chugan (2013)
2	Mannequin Display	5	Boardman & McCormick, 2019), Mehta & Chugan (2013)
3	Promotional Signages	4	Beatty, S.E. & Ferrell, M.E. (1998), Youn, S. & Faber, R.J. (2000), Han Y.K. (1987), Weun, S., Jones, M.A. & Beatty, S.E. (1998),
4	Product Display	7	Mehta & Chugan (2013), Hussain, R., & Ali, M. (2015).
5	Rational Behaviour	6	Malone, K., Stewart, S. D., Wilson, J., & Korsching, P. F. (2010
6	Emotional Buying Behaviour	11	Donovan, R.J. and Rossiter, J.R. (1982). Mehrabian, A. and Russell, J.A. (1974

Sampling Design

Due to the sample sensitivity of covariance-based SEM, an A-Priori sample estimation, based on the proposed conceptual model, was conducted using an online sample power calculator (Soper, 2021). The model power estimation was based on an anticipated effect size of 0.5 (large), the desired statistical power of 0.95, and a probability level of 0.05 (95% CI level). The sample size estimation was for a model with thirty-nine items and six latent variables. The recommended minimum sample for effect detection was 58, and the model structure was 288, well below the final sample of 514 respondents.

The current study uses snowballing – a type of convenient non-random sampling. Such a sampling technique was adopted to achieve the specific research objective and to ensure that all respondents met the sample selection criterion. In the snowballing, the initial samples were recruited from the researchers' contacts, and the subsequent samples were recruited through a chain referral from these contacts. However, it was ensured that no more than three referrals were recruited from any source to avoid sampling biases associated with the technique (Mathew et al., 2024). Two thousand respondents were recruited through snowballing and were mailed the questionnaire (shared through their social media handles). The recruits were sent a reminder at the end of the third, eighth, and twelfth week. The potential respondents were informed about the academic nature of the survey, ensured anonymity, and informed about their right not to participate or drop out anytime during the survey. Such assurance enhanced survey participation and reduced response biases. The online survey was programmed to accept only completed responses, thus reducing non-response biases. The survey was conducted during the four months between May and August of 2023.

Data Analysis

The current study used a two-stage data analysis model using covariance-based structural equation modelling (CB-SEM) (Anderson & Gerbeing, 1988). A covariance-based model is used, as the proposed research model constructs are reflective. The first stage of the analysis involves the assessment of the measurement model and ascertaining the scale's reliability and validity. The second stage is the structural model analysis, which tests the hypothesized relations in the conceptual model. It also ascertains the inter-generational differences in the impact of the V.M. elements in rational and emotional buying situations. The model fit indices are ascertained as proposed by Hair et al., 2014.

Analysis

• Sample descriptive

Five hundred and twenty-four complete responses were received – a 25.7 percent response rate comparable to recent Indian research in apparel and fashion involving online data collection (Himanshi et al., 2024). The generational representation of the respondents was 49 and 51 percent for generations X and Y, respectively. The respondent gender distribution was nearly equal, with 52% male and 48% female respondents. The mean age of the respondents belonging to Gen X was 48 years, whereas the mean age of Gen Y was 36 years. 20 percent of Gen X and approximately 19 percent of Gen Y belonged to the below five lakhs annual income category. 29 of Gen X and 30.11 percent belonged to the 5 to 10 lakhs annual income category. Only 13.7 percent of respondents of Gen X and 11 percent of Gen Y belonged to the income category of above 15 Lacs.

• Measurement Model, Validity, and reliability

The usage of CB-SEM requires data normality. However, data normality assumptions were impossible since the sampling technique was non-random. Data normality was assessed by calculating the skewness and kurtosis for all 39 items used in the survey. The skewness for all items was well within the limit of +2 to -2 and Kurtosis +3 to -3, indicating the applicability of parametric tests like CB-SEM (Hair et al., 2010) (Table. 2). Post assessment of the data normality, the data was further subject to Confirmatory Factor Analysis (CFA). All items loaded well into their respective constructs (factor loading range between 0.707 and 0.0834), and there were no issues of significant cross-loading of the items (where the difference between the loadings on the two factors was more than 0.200 for all thirty-nine items), leading to retention of all the items in the final analysis. The convergent validity of the measurement model was estimated using the strength of the factor loading and cross-loadings (all loadings above .70 and all cross-loading differences more than 0.200), construct reliability (more than 0.70 for all constructs), and the average variance extracted (more than 0.50 for all constructs) (Hair et al., 2014; Liu & Wang, 2016; Farrell, 2010). The discriminant validity was ascertained using two criteria. The Fornell and Larcker criteria require the AVE of each concept to exceed the inter-construct correlations – which was true for the current measurement model (Table 3). The discriminant validity was also ascertained using the Heterotrait-Monotrait ratio (HTMT). All the HTMT ratios between the constructs were well below the threshold level of 0.85, indicating discriminant validity of the proposed conceptual model constructs (Fornell & Larcker, 1981; Henseler et al., 2015; Ogbeibu et al., 2018) (Table 4). The measurement model also exhibited acceptable model fit indices (χ2/df = 1.128, CFI = 0.993, SRMR=0.03, RMSEA = 0.016, and NFI = 0.981). All the inter-construct correlations were significant (p<0.01). Construct descriptive and correlations are given in Table 4.

Table 2 Measurement Model - Discriminant and Convergent Validity
Constructs & Items	Factor Loading#	Composite Reliability	AVE*
MD	.780	0.905	0.655
MD	.828
MD	.816
MD	.805
MD	.818
P.D.	.783	0.92	0.622
PD	.786
P.D.	.802
P.D.	.775
P.D.	.783
P.D.	.801
P.D.	.792
P.S.	.806	0.863	0.611
PS	.767
PS	.747
PS	.805
WD	.796	0.913	0.638
WD	.834
WD	.788
WD	.793
WD	.801
WD	.778
EB	.759	0.927	0.536
EB	.758
E.B.	.714
E.B.	.721
E.B.	.724
E.B.	.731
E.B.	.707
E.B.	.738
E.B.	.732
E.B.	.744
E.B.	.727
R.B.	.793	0.923	0.668
RB	.821
R.B.	.826
R.B.	.833
R.B.	.819
R.B.	.809

Table 3 Discriminant Validity – Htmt Ratios
	M.D.	PD	PS	WD	EB	R.B.
MD
PD	0.632
PS	0.333	0.384
WD	0.496	0.569	0.409
EB	0.451	0.51	0.368	0.499
RB	0.607	0.643	0.642	0.693	0.517

Table 4 Descriptive Statistics and Correlation Analysis
	Mean	Std. Deviation	MD	PD	PS	WD	EB	R.B.
MD	3.07	1.105	1
PD	2.97	1.073	0.631***	1
PS	3.07	1.063	0.330***	0.384***	1
WD	3.06	1.066	0.498***	0.571***	0.405***	1
EB	3.03	0.961	0.453***	0.512***	0.366***	0.503***	1
RB	3.03	1.093	0.606***	0.645***	0.643***	0.691***	0.516***	1

Biases due to common method variances are common in survey research, necessitating the following procedural and computational steps during the design and implementation of the current research. A pilot test was performed to ensure that the respondents comprehended the survey items well, thus preventing response biases. The respondents were clarified, prior to survey participation, the research objective, their right to refusal, and participant anonymity to prevent response biases. The survey did not collect identifiable personal information to encourage impartial responses. Computationally, a Harman single-factor test was performed to identify any significant common method variance (Harman, 1976). The single factor explained 38.48 percent of the variation, well below the permissible 50%, indicating an absence of CMB in the present study.

Structural Model and Hypothesis Testing

After the establishment of the reliability and validity of the measurement model, the structural analysis was conducted to test the conceptual model and the hypothesized relations. The structural analysis is done in three steps. First, the impact of the V.M. components in E.B. and R.B. were estimated for the complete sample. A separate impact assessment for the two generational cohorts followed it. Finally, a multigroup analysis was conducted to measure the differential impact of the V.M. elements on E.B. & R.M. between the two generational cohorts.

The global analysis for V.M.'s impact on emotional and rational buying returned a statistically significant influence of M.D., P.D., P.S., & W.D. on both buying among the study subjects (Table 5 & Figure 1). However, the independent impact for Gen-X indicated that M.D. had no statistically significant impact on either E.B. or R.B. The impact of the remaining V.M. elements was statistically significant on both types of buying. The independent impact analysis yielded different results for Gen-Y. For the group, M.D. significantly influenced both types of buying. However, P.D. did not significantly influence either type of buying among this generation (Table 6). Based on the test results, Hypotheses H1a, H1b, H2a, and H2c were partially accepted – i.e., certain visual merchandising elements influenced rational and emotional buying among both generations X and Y, but not all. For details of the V.M. elements significantly affecting E.B. and R.B. among the generations, please refer to Table 6.

Table 5 Results of Full Sample Regression Analysis
Predictor	Outcome	Std Beta	C.R.	R-Square
MD	E.B.	.141 *	2.517	R-Square for EB = 0.36
PD	E.B.	.230 ***	3.804
PS	EB	.134 **	2.86
WD	EB	.249 ***	4.591
MD	R.B.	.200 ***	4.698	R-Square for RB = 0.71
PD	R.B.	.185 ***	4.089
PS	RB	.370 ***	9.527
WD	RB	.337 ***	7.936

Table 6 Regression Analysis – Gen X & Y
Predictor	Outcome	Estimate	C.R.	R-Square
Generation X
MD	EB	0.001	0.02	R-Square for EB = 0.25
PD	EB	0.213**	2.768
PS	EB	0.217**	2.819
WD	EB	0.205**	2.863
MD	RB	0.08	1.59	R-Square for RB = 0.70
PD	RB	0.142*	2.592
PS	RB	0.565***	8.813
WD	RB	0.267***	5.01
Generation Y
MD	EB	0.542***	4.037	R-Square for EB = 0.52
PD	EB	0.033	0.278
PS	EB	0.046	0.788
WD	EB	0.158	1.584
MD	RB	0.44***	4.287	R-Square for RB = 0.78
PD	RB	0.078	0.85
PS	RB	0.206***	4.34
WD	RB	0.305***	3.88

Considering the significant impact demographic variables like generational cohorts can have on consumer behavior (Chow et al., 2007), a post-hoc multigroup analysis was conducted to investigate the moderating effect of V.M. components on emotional and rational buying between the two generational groups (Klesel et al., 2019). The results indicated a statistically significant difference in the influence of M.D. on the two types of buying among the groups. Whereas MD had a significantly higher impact on E.B. and R.B. among the Gen-Y, among the Gen-X, the impact was not significant on the two types of buying (Table 7). P.S. also had a differential impact on the two generations. Whereas it did not significantly influence E.B. between the two groups, its impact on R.B. significantly differed between the two cohorts. It significantly affected rational buying between both generational groups but had a significantly higher influence among Gen-X than Gen-Y (Table 7). Hypotheses H3a and H3b are partially accepted based on the multigroup moderation analysis. Please refer to Table 7 for details on the differential effect of the V.M. elements on rational and emotional buying between the two generational cohorts.

Table 7 Multigroup Analysis for Moderation
Path Name	Group X Beta	Group Y Beta	Difference in Betas	P-Value for Difference
P.D. → E.B.	0.213**	0.033	0.180	0.219
WD → E.B.	0.205**	0.158	0.047	0.703
MD → E.B.	0.001	0.542***	-0.540	0.000
PS → E.B.	0.217**	0.046	0.171	0.105
PD → R.B.	0.142**	0.078	0.064	0.607
WD → R.B.	0.267***	0.305***	-0.038	0.599
MD → R.B.	0.080	0.440***	-0.361	0.001
PS → R.B.	0.565***	0.206***	0.359	0.000

Discussion and Implications

The study results concluded that visual merchandising elements such as product display, window display, promotional signage, and mannequin display play an important role in emotional and rational buying situations. However, such impact is significantly higher on rational buying (R² of 0.71 for R.B. vs. 0.36 for E.B. for the complete sample), indicating its importance in aiding consumers' product evaluation and choice in the category. This study's results align with the study conducted by Complete Controller, USA, which found evidence that V.M. makes the retail organization more attractive to customers, increases store traffic, and promotes purchases. Carefully planned V.M. strategy was also found to boost repeat sales and customer loyalty (Complete Controller, 2018). Hence, marketers are recommended to consider improving the store V.M. to improve the store’s performance.

The study results also indicate the differential importance of merchandising elements in the buying situation. Window displays followed by product displays were most important in emotional buying situations, whereas promotional signage followed by window displays was more impactful in rational buying situations. Reynold (2021) opines that window and product display play an instrumental role in emotional buying situations, and the current study results confirm this. The author contemplates that retail buyers often make emotional purchases, and intelligently designed window and product displays might leverage the customers' mood to push them through the buying funnel. According to Hayes (2020), rational buyers look for choices to optimize utility. Hence, customers in rational buying situations may consider promotional signage offering discounts and attractive offers – a fact supported by current research results—however, Methenitis and La Poutré. (2020) opine that buyers are not always rational and might resort to tactics usually adopted by emotional buyers, even in rational buying situations. In such a scenario, the current study authenticates a result where W.D. is the second most influential V.M. factor affecting R.B. – validating the research of Hayes (2020) and Methenitis & La Poutré (2020). However, using the V.M. depends on the buying situation, and it becomes contingent on the retailer to assess the buying situation. Retailers may consider training their salespersons to probe customers to understand the exact buying situation without causing customer irritation.

The results inter-generationally conclude that P.S. most influenced Gen-X customers in emotional and rational buying situations. However, Gen-Y P.S. exerted insignificant influence in E.B. situations but was significant in R.B. situations. The findings are in congruence with the Signage Education Research, wherein the report claims that signage affects retail customers significantly and that 60% of retailers experience a surge in their sales after using or updating their promotional signage (Keenan, 2021). However, the overall importance of P.S. to Gen-Y was lower than Gen-X's, as indicated by their beta weights. Anitha and Selvaraj (2010) and Cohen (2014) found evidence that M.D. attracts customers by transmitting an aspirational look and making them aware of the latest trends. Customers perceive the color, design, and overall appearance more positively through mannequin displays, which is critical to apparel retailing. M.D. more influenced Gen-Y customers in both emotional and rational buying situations, whereas the influence was absent for Gen-X in both situations. This implies Gen-Y customers' preference for acquiring hedonic experiences over physical goods (Brown & Lubelczyk, 2018), a factor that Gen-X has overlooked.

The results indicate that P.S. and W.D. are the most critical elements of V.M. in rational buying situations for both generational cohorts. Whereas P.D., P.S., and W.D. play a significant role in emotional purchases among Gen-X customers, these three V.M. components have no role in the emotional buying of apparel among Gen-Y customers. Whereas MD critically influences both rational and emotional buying among Gen-Y, it has no influence on either among the Gen-X customers. Significant inter-generational differences in V.M. factors affecting the two buying situations are seen in the case of M.D., where it significantly influences Gen-Y behavior but not among Gen-X. There is also a significant difference in the influence of P.S. on rational buying between the two generational cohorts, and it is significantly higher among the X generation. The results imply that important visual merchandising factors such as promotional signage and mannequin displays are likely to have a transition effect across buying situations. It highlights the increasing importance of V.M. in ultra-competitive retailing, wherein they can be strategically used to improve retailing effectiveness in the apparel category. However, retailers must focus on the relevant visual merchandising factors depending on the buying situation and the targeted generational cohort. While doing so, marketers are advised not to overwhelm the customers with too many displays as it might confuse them.

The current study contributes to the literature by providing an increased understanding of the intergenerational differences in consumer behavior towards V.M. elements in apparel retailing. The study results have implications for retail practitioners in the field of fashion and apparel retailing. The results can be effectively used to develop and optimize merchandising strategy in apparel retail stores that cater to Gen-X or Gen-Y customers.

Limitations

The current study has certain limitations that primarily restrict its scope. Firstly, it investigated the effect of only four V.M. elements. However, V.M. is a broader concept, and the other elements like color, texture, layout, décor, point of focus, landscaping, etcetera were not a part of the study, limiting its scope. The second limitation was that the study included only emotional and rational buying and excluded impulse purchases, limiting the study's scope. The third limitation was that the study did not specifically look for an association between salient demographic features and visual merchandising, such as gender, occupation, and education. However, such associations are possible beyond the influence of generational cohorts. However, each limitation allows future researchers to extend the current study and make fresh contributions to the body of literature. Most importantly, the study considers only two generational cohorts, X and Y, considering their market dominance. However, as the Gen-Z enters the workforce and quickly becomes the dominant consumption cohort, there is a need to extend the study to this generation in the future.

References

Alaeddin, O., Rana, A., Zainudin, Z., & Kamarudin, F. (2018). From physical to digital: Investigating consumer behaviour of switching to mobile wallet. Polish Journal of Management Studies, 17(2), 18-30.

Indexed at, Google Scholar, Cross Ref

Anderson, J. C., & Gerbing, D. W. (1988). Structural equation modeling in practice: A review and recommended two-step approach. Psychological Bulletin, 103(3), 411–423.

Anitha, N., & Selvaraj, C. (2010, March). The effects of mannequins on consumers’ perception and shopping attitude. In International Conference on Business Administration and Information Processing (pp. 641-647). Springer, Berlin, Heidelberg.

Indexed at, Google Scholar, Cross Ref

Brown, M., & Lubelczyk, M. (2018). The future of shopping centers. online], Kearney.

Cohen, A. (2014). Mannequin size on consumers' perception of self and satisfaction with fit. 'M.A. Thesis. Available at: https://scholarcommons.sc.edu/cgi/viewcontent.cgi?article=3601&context=etd

Complete Controller (2018). Why is Visual Merchandising Important for your Retail Store?

Farrell, A. M. (2010). Insufficient discriminant validity: A comment on Bove, Pervan, Beatty, and Shiu (2009). Journal of Business Research, 63(3), 324-327.

Indexed at, Google Scholar, Cross Ref

Fornell, C., & Larcker, D. F. (1981). Evaluating structural equation models with observable variables and measurement error. Journal of Marketing Research, 18(1), 39–50.

Ha, Y., & Lennon, S. J. (2011). Consumer responses to online atmosphere: The moderating role of atmospheric responsiveness. Journal of Global Fashion Marketing, 2(2), 86-94.

Cross Ref

Hair, J. F., Black, W. C., Babin, B. J. & Anderson, R. E., 2010. Multivariate Data Analysis: A Global Perspective. 7th ed. New Jersey: Pearson Education International.

Hair, J., Black, W., Babin, B. & Anderson. R.E. (2014), Multivariate data analysis. New

Harman, H. H. (1976). Modern factor analysis. University of Chicago Press.

Hefer, Y., & Cant, M. C. (2013). Visual Merchandising Displays Effect On Consumers: A Valuable Asset Or An Unnecessary Burden For Apparel Retailers. International Business & Economics Research Journal (IBER), 12(10), 1217-1224.

Indexed at, Google Scholar, Cross Ref

Henseler, J., Ringle, C. M., & Sarstedt, M. (2015). A new criterion for assessing discriminant validity in variance-based structural equation modeling. Journal of the Academy of Marketing Science, 43(1), 115–135.

Jakhar, R., Verma, D., Rathore, A. P. S., & Kumar, D. (2020). Prioritization of dimensions of visual  merchandising for apparel retailers using FAHP. Benchmarking: An International Journal.

Keenan, M. (2021). How Retail Signage Works and Why You Should Care.

Klesel, M., Schuberth, F., Henseler, J., & Niehaves, B. (2019). A test for multigroup comparison using partial least squares path modeling. Internet Research. 29(3), 427-459.

Knowledge-Ridge. (2022). Indian Apparel Retail Poised For Ascendancy Amidst Economic Revival, available at: https://www.knowledgeridge.com/expert-views/rising-indian-apparel-retail-in-the-midst-of-economic-revival, accessed on 12 June 2023.

Liu, S., & Wang, L. (2016). Influence of managerial control on performance in medical information system projects: The moderating role of organizational environment and team risks. International Journal of Project Management, 34(1), 102–116.

Cross Ref

Martin, N., & Morich, K. (2011). Unconscious mental processes in consumer choice: Toward a new model of consumer behavior. Journal of Brand Management, 18(7), 483-505.MartinN,MorichK.Unconscious mental processes in consumer choice: Toward a new model of consumer behavior. Journal of Brand Management.2011;18(7):483–505.

Mathew, V., Santhosh Kumar P K, & Sanjeev M A. (2024). Financial Well-being and Its Psychological Determinants— An Emerging Country Perspective. FIIB Business Review, 13(1), 42-55.

Mehta, D., & Chugan, P. K. (2013). The impact of visual merchandising on impulse buying behavior of consumer: A case from Central Mall of Ahmedabad India. Universal Journal of Management, 1(2), 76-8.

Indexed at, Google Scholar, Cross Ref

Methenitis, G., Kaisers, M., & La Poutré, H. (2020). Degrees of rationality in agent-based retail markets. Computational Economics, 56(4), 953-973.

Najafi, K., Heidarzadeh Hanzaee, K., & Khounsiavash, M. (2021). Exploration of the Role of Visual Merchandising (Mannequin) on Women’s shopping Behavior through the Moderating Role of Type of Store and Customers’ Intention: An Experimental Research Approach. Journal of Business Management, 13(1), 247-272.

Nash, J. (2019). Exploring how social media platforms influence fashion consumer decisions in the U.K. retail sector. Journal of Fashion Marketing and Management: An International Journal.

Indexed at, Google Scholar, Cross Ref

Ogbeibu, S., Senadjki, A., & Gaskin, J. (2018). The moderating effect of benevolence on the impact of organizational culture on employee creativity. Journal of Business Research, 90, 334–346.

Indexed at, Google Scholar, Cross Ref

Park, H. H., Jeon, J. O., & Sullivan, P. (2015). How does visual merchandising in fashion retail stores affect consumers’ brand attitude and purchase intention?. The International Review of Retail, Distribution and Consumer Research, 25(1), 87-104.

Indexed at, Google Scholar, Cross Ref

Rathee, B,. (2024). Textile & Apparel – Invest India.

Soper, D., 2021. https://www.danielsoper.com. [Online] Available at: https://www.danielsoper.com/statcalc/calculator.aspx?id=89 [Accessed 14 April 2023].

Statista. (2023). Apparel – Worldwide, available at: https://www.statista.com/outlook/cmo/apparel/worldwide, accessed on 12 June 2023.

Tagra, D. (2023). Aggressively growing Indian apparel retail – Apparel Resources, available at: https://apparelresources.com/business-news/retail/aggressively-growing-indian-apparel-retail/, accessed 12 June 2023.

Underwood, B. , Moore, B. S. , Rosenhan, D. L. (1973) Affect and self-gratification, Developmental Psychology, 8 (2), 209– 214.

Indexed at, Google Scholar, Cross Ref

Widyastuti, P. (2018). Does visual merchandising, store atmosphere and private label product influence impulse buying? Evidence in Jakarta. Journal of Business and Retail Management Research, 12(3).

Yaoyuneyong, G., Foster, J.K., & Flynn, L.R. (2014). Factors impacting the efficacy of augmented reality virtual dressing room technology as a tool for online visual merchandising. Journal of Global Fashion Marketing, 5(4), 283-296.

Zhu, B., Kowatthanakul, S., & Satanasavapak, P. (2019). Generation Y consumer online repurchase intention in Bangkok: Based on Stimulus-Organism-Response (SOR) model. International Journal of Retail & Distribution Management, 48(1), 53-69. 

Indexed at, Google Scholar, Cross Ref

Received: 02-Apr-2025, Manuscript No. AMSJ-25-15830; Editor assigned: 03-Apr-2025, PreQC No. AMSJ-25-15830(PQ); Reviewed: 20-Apr-2025, QC No. AMSJ-25-15830; Revised: 03-May-2025, Manuscript No. AMSJ-25-15830(R); Published: 18-May-2025