Demographic characteristics and consumer decision-making styles: Do they impact fashion product involvement?

Abstract

Consumer decision-making styles are considered as psychological and intellectual approaches that can influence the customer purchase decision and can help in explaining the behavior of customers toward fashion products. The purpose of this research is to analyze the impact of consumer decision-making styles on fashion product involvement, as well as the effect of demographic variables (age, gender, income, and education) on consumer decision-making styles and fashion product involvement. Data was gathered through an online questionnaire from a sample of 400 Egyptian customers of fashion products. Results revealed that consumer decision-making styles have a strong significant positive effect on fashion product involvement. The results also showed that all four demographic variables have a significant effect on fashion product involvement as well as on consumer decision-making styles. Research findings were discussed to reflect the researchers’ interpretation and perception of the outcomes addressed.

Keywords:

• consumer decision-making styles
• fashion product involvement
• demographic variables
• consumer behavior
• fashion industry

1. Introduction

Academics in marketing and behavioral sciences are increasingly interested in consumer decision-making styles. Most prior studies have focused on customer shopping behavior and decision-making processes that the customers exhibit when purchasing products in different environments. According to Sproles and Kendall (1986), Consumer decision-making style is a mental orientation that characterizes a consumer’s approach to making decisions. Investigating this concept is critical to marketing techniques since it influences consumer behavior and is a basis for segmenting the market (Sproles & Kendall, 1986; Walsh et al., 2001). On the other hand, consumer decisions convey to marketers whether a marketing strategy was intelligent and perceptive or badly planned and missed the market. As a result, knowing consumer decision-making is critical for both domestic and international businesses when developing strategies, especially that the increased availability of worldwide brands, variety of items, numerous retail forms, and depth of information available through the internet has complexed customer decision-making patterns (Lysonski & Durvasula, 2013; Sam & Chatwin, 2015).

Because products have varying meanings for various people, buyers develop varying ties to them. The degree and type of a person’s connections may be significantly different from those of their relatives or friends. Marketing researchers and professionals are interested in understanding customers’ diverse attachments, how they arise, are maintained, and are impacted. Marketing researchers have frequently utilized the concept of involvement to gain a better understanding of consumers’ behavior regarding possessions (Laurent & Kapferer, 1985; Mittal & Lee, 1989; Ohanian, 1990; Slama & Tashchian, 1985; Zaichkowsky, 1986).

This study investigates the impact of different consumer decision-making styles on product involvement, and the impact of the demographics such as age, gender, income and education on product involvement and consumer decision-making styles.

2. Literature review

2.1. Consumer decision-making styles

To better understand why and how consumers behave when purchasing products and services, researchers are becoming increasingly interested in studying consumers' shopping styles. It has been assumed that all customer access the market with certain basic decision-making styles. Sproles and Kendell in 1986 characterized Consumer Decision-making styles (CDMS) as a mental, cognitive approach directing consumers’ purchasing decisions. Scott and Bruce (1995) described it as an individual’s acquired routine response pattern when facing a certain consuming decision condition. Based on the consumers’ responses to decision-making style inventories, prior studies segmented consumers into relevant groups. Decision-making styles were categorized into three common approaches. The first approach is consumer typology, which classifies customers’ attitudes and motivations into several categories. The second approach is the psychographics/lifestyle, which explains a consumer’s activity, interest, and opinion to assess consumer personalities and anticipate consumer behavior. The third approach is the consumer characteristics approach, which discusses cognitive and emotional orientations. The Consumer Style Inventory (CSI), created by Sproles and Kendall in 1986, is a scale that categorizes customers based on their decision-making styles and is one of the most extensively used frameworks. CSI was one of the first attempts to systematically quantify buying styles. One of this approach’s main assumptions is that each consumer has a distinct decision-making style arising from a mix of individual decision-making characteristics (Anić et al., 2014). Sproles and Kendall verified 482 high school students in the United States and validated eight different shopping styles with the following criteria:

2.1.1. Perfectionist/high-quality conscious

Perfectionists and quality-conscious consumers, according to CSI theory (Sproles & Kendall, 1986), buy attentively and make decisions based on rigorous product evaluations (Y. A. Park & Gretzel, 2008). Such clients perform extensive research to find the greatest or highest quality product (Lysonski & Durvasula, 2013; Rezaei, 2015; Sarkar et al., 2019). They consider price as a good indicator of quality because of the positive association between price and quality. They use a utilitarian purchasing strategy because it is a task-oriented strategy that focuses on price and quality (J. E. Park et al., 2010). Consumers with high levels of perfectionism are likely to engage in more careful buying and decision-making process (Andersson et al., 2016).

2.1.2. Brand consciousness

Brand awareness/price-equals-quality style consumers are more likely to purchase premium, well-known, famous, top-selling, and broadcasted products (Alavi et al., 2016; Sarkar et al., 2019; Wesley et al., 2006). To lessen the risk involved in online purchases, brand-conscious customers choose a product that has a positive image. Such customers believe that a higher price implies a higher degree of quality (Bakewell & Mitchell, 2004). Several prior studies (Klassen et al., 2009) have demonstrated the significance of brand consciousness/customer in online purchasing. These customers are less likely to compare prices and are regarded to be less price sensitive (Ailawadi et al., 2001).

2.1.3. Novelty-fashion conscious

With their constant search for variety, novelty-fashion conscious consumers enjoy adopting and staying current with the latest styles or fashion (Lysonski & Durvasula, 2013; Y. A. Park & Gretzel, 2008). This group of buyers is enthralled and easily distracted by new products. They also engage in extravagant buying, which assists them in maintaining their social status (Haron & Chinedu, 2018).

2.1.4. Recreational/Hedonistic consumers

Individuals that enjoy shopping and seek satisfaction from the activity are referred to as recreational/hedonistic customers (Lysonski & Durvasula, 2013). These consumers view shopping as a pleasurable, leisurely and amusing activity (J. E. Park et al., 2010). To enhance their shopping experience, recreational customers usually make purchases without planning from a variety of stores and spend extended periods of time exploring merchandises (Rezaei, 2015). For recreational customers, shopping is a fun and engaging hobby motivated and affected by hedonic value and enjoyment interests (Babin et al., 1994; Peters & Bodkin, 2007).

2.1.5. Price conscious consumers

Consumers that are price conscious search for the best deal and compare prices before making a purchase (Lysonski & Durvasula, 2013; Y. A. Park & Gretzel, 2008). These customers are more interested in reduced prices and practical purchasing concerns (E. J. Park et al., 2012). Buyers who are price conscious regularly look for sales promotions to get the best bargain on products and services (Rezaei, 2015). Because price-conscious buyers place a high emphasis on pricing, it is categorized as a utilitarian decision-making style.

2.1.6. Impulsive, careless consumers

Sproles and Kendall (1986) explained that the Impulsive, careless style comprises those consumers who are making their purchasing without planning for it nor have an interest in finding other options. They are described as impulsive consumers who generally purchase spontaneously and do not think often about getting the best buys. They also do not care that much about the amount of money they pay for the purchased product nor its quality. However, they frequently feel regretful about the buys they have made (Sproles & Kendall, 1986; To et al., 2007). Impulsive buying turns out to be almost certain when the affective state of the consumer represented in his sentiments, feelings, and moods defeats his cognitive one represented in his thinking, comprehension, and translation of data (Hausman, 2000). Accordingly, the impulsive decision-making style comes out from the least cognitive exertion the consumer affords and the solid presence of responsive components, along with an escalated, unexpectedly emerging affective activity. This behavior is mostly displayed in circumstances of buying low-valued, and low-involvement products of recurring need. A buy is mostly set off at the retail location.

2.1.7. Confusion by overchoice

The “Confusion by overchoice” CDMS style (CBO) comprises those consumers who find exertion in selecting and deciding their buys. Due to the massive number of alternatives and the information overload of products available in the market, they find themselves more confused when choosing and making buying decisions (Saleh et al., 2017; Sproles & Kendall, 1986; Vokounová, 2019; Y. A. Park & Gretzel, 2008). Truta and Nitoiu (2014) emphasized that the consumers who scored high on the confused by over choice consumer-style seem likely to be emotionally unstable and encounter successive negative feelings that make it inclined to a marketing stimulus.

2.1.8. Habitual, brand-loyal purchasing

Consumers that are habitual or brand loyal have favourite brands, stores, or websites to shop at. They are loyal and stick on with their favourite brands and shops. They buy the same product alternatives or from the same providers or websites on a frequent basis. These customers’ information search and decision-making processes are based on their previous shopping experiences (Bettman & Sujan, 1987). When consumers are familiar with a product category and make a routine decision based on positive experiences, they are said to be making habitual decisions. In this case, decisions are made based on their preferred brands or stores, and there is only a tiny amount of cognitive involvement.

Furthermore, Wang et al. (2004) found that buyers who are eager to buy imported goods have high levels of impulsivenessand brand loyalty as well as quality, brand and fashion conciseness. In the traditional offline trading context, brand loyalty has been defined by several studies as having a long-term impact on purchasing behavior (Hawes & Lumpkin, 1984; Sproles & Kendall, 1986). A strong brand name not only attracts new customers, but it also keeps existing customers loyal to the brand since they are pleased with their purchase. The consumer’s purchasing intentions are positively influenced by brand orientation (Jayawardhena et al., 2007).

2.2. Product involvement

For the past three decades, involvement has been an important topic in the consumer research literature (Lesschaeve & Bruwer, 2010; O’Cass, 1996). Involvement has taken a prominent place in consumer behavior theory since it is regarded to have a significant impact on the customer decision-making process (Laurent & Kapferer, 1985). As a result, academics have investigated the impact of involvement on customer attitudes, brand choices, impressions, and so on (B. Traylor & Joseph, 1984; L. Schiffman et al., 2008).

Involvement, according to Zaichkowsky (1986), is defined as a person’s perception of the object’s importance based on fundamental needs, values and interests. While Mittal (1995) defined involvement as a person’s emotional state in relation to an item or action, which manifests as the level of interest in that item or action. Brennan and Mavondo (2000) developed the previous definitions of involvement as they defined it as an inspirational and goal oriented emotional state that governs the personal significance of a buyer’s purchase decision.

Different types of involvement were identified by researchers. Rothschild (1984) proposed three types of involvement: enduring, situational and response. According to Richins et al. (1992), the degree to which the product or object relates to the self-and/or the pleasure received from it motivates enduring involvement (also known as product involvement). Product involvement indicates a consumer’s long-term impressions of the relevance of a product category based on the consumer’s implicit wants, beliefs and preferences (De Wulf et al., 2001; Mittal, 1995; Zaichkowsky, 1986). A product category may be more or less important in people’s lives, feeling of self and interactions with the entire world (M. B. Traylor, 1981).

The level of involvement shown varies from one person to another. The majority of the literature categorizes the level of involvement as either high or low (Aurifeille et al., 2002; Barber et al., 2008; Celsi & Olson, 1988). However, medium (moderate) involvement has been utilized to designate a third level of involvement (Charters & Pettigrew, 2006). Consumers with high and low involvement are thought to act differently (Barber et al., 2008; Bei & Widdows, 1999; Lockshin & Hall, 2003) A high-involvement consumer is very concerned with the distinctions between certain brands and is willing to put massive amounts of energy in decision-making (L. G. Schiffman & Kanuk, 1991). Laurent and Kapferer (1985) found that consumers with a high level of involvement attempt to increase awaited satisfaction by going through a lengthy selection process. As a result, they are information seekers who use that information to make purchasing decisions (Barber et al., 2008). On the other hand, low involvement purchases are purchases that are not very essential to the buyer, have little relevance and little perceived riskand hence result in a very little information processing (L. G. Schiffman & Kanuk, 1991).

Fashion consumer involvement is a growing study area concerned with how essential, significant, and pertinent fashion clothing is to consumers’ lives. It is a research field devoted to studying fashion clothes consumption patterns (e.g., Auty & Elliot, 1998; Bloch et al., 2009; O’Cass, 2001, 2004). O’Cass (2004) defined fashion consumer involvement as the extent to which a customer perceives associated fashion activities as a key aspect of their life. Fashion consumer involvement is a personal and situational variable that ranges from very high involvement to very low involvement (O’Cass, 2004), depending on whether customers find the fashion clothing products they actually purchase involving or not. According to their level of fashion involvement Consumers have varying preferences for shopping stores/centers. When consumers are passionate about fashion, they are more likely to shop at department or specialized stores. They wish to fast catch up with the latest trends (Seo & Namwamba, 2014, SEO et al., 2001, 2014; Shim & Kotsiopulos, 1992). When involved with a product, consumers were supposed to reflect its meanings, desires, beliefs and characteristics (Shim & Bickle, 1994; Engel et al., 1995; Feinberg et al., 1992). Consumers aim to convey different benefits through clothing, such as social position, identity, attractiveness, role identity, style image and individualism (H. H. Park & Sullivan, 2009; Kaiser, 1997; Shim & Bickle, 1994).

3. Hypotheses development

In consumer purchasing decisions, product involvement (PI) and consumer decision-making styles (CDMS) are two of the most important constructs (Klein & Sharma, 2022). Normally, consumer behavior researchers have focused on decision-making styles as a personality feature that determines consumer purchase behavior and has a long-term impact on the purchasing decision process (Saleh et al., 2017; Sproles & Kendall, 1986; Vokounová, 2019; Y. A. Park & Gretzel, 2008). However, Scott and Bruce (1995) stated that decision-making styles are not constant and can vary between contexts and different choice circumstances. Other researchers have shown that decision-making styles are influenced not only by product type, but they are also governed partly by the involvement of consumers in a particular product (Bauer et al., 2006; Saleh et al., 2017). All these studies have focused on the impact of product involvement on different decision-making styles. Very few studies have focused on the opposite relationship which is the impact of consumer Decision-making styles on product involvement. Klein and Sharma (2022) have suggested that CDMS have an impact on Customer involvement which in its turn influence and encourage customers to participate in online buying groups. This backs with prior research suggesting that consumer involvement is a vital complicated conceptual and long-lasting intervening construct that influences the consumer purchasing decision (Bauer et al., 2006; Celsi & Olson, 1988; Goldsmith & Emmert, 1991; Park, 2007). Fashion clothing involvement (FCI) describes the level to which consumers consider fashion clothing and its related activities as a substantial part of their lives (O’Cass, 2004). Product involvement includes a wide range of product categories. Accordingly, many prior studies have typically proven fashion clothing to be a high-involvement product (Johnson et al., 2017; Seo & Namwamba, 2014; Seo, 2016). However, O’Cass (2004) and Hourigan and Bougoure (2012) clarified that consumers are not all involved to the same extent with fashion clothing products. While some consumers seem not to be involved, others seem highly involved with fashion clothing products. And this influences the consumer’s purchasing decisions (Hourigan & Bougoure, 2012). Therefore, the following hypothesis is formulated:

H1:

It is expected that consumer decision-making styles will have a positive effect on Fashion Product Involvement.

Previously, the segmentation of markets depended basically on the dissimilarity of consumers’ needs and particularly on demographic characteristics (Wedel & Kamakura, 2000). Cant and Hefer (2013) emphasized the influence of demographic differences on decision-making styles and consumers’ purchasing behavior. Certain specialized consumer activities, such as apparel shopping and purchasing, personal care goods, and electronic devices, are determined by demographics and are produced and pushed for either male or female consumers (Mokhlis & Salleh, 2009; Pol, 1991). Therefore, marketers can profile, focus, and build marketing strategies for their defined target market segments using a combination of decision-making styles and demographic factors (Hiu et al., 2001; Potgieter et al., 2013).

Besides the importance of household income, marital status, ethnicity, consumer’s lifestyle and life cycle in segmenting markets (Potgieter et al., 2013), age, gender and income are the most vital demographic factors in determining one’s decision-making processes (Mokhlis & Salleh, 2009). Previous studies revealed that gender and income help in identifying and targeting the specified market segments more easily (Darley, 1995; Meyers Levy & Sternthal, 1991). Furthermore, research has shown that gender has a substantial impact on consumer attitudes, buying decisions, and purchasing behavior (Bakewell & Mitchell, 2006; Fischer & Arnold, 1994; Jaidev & Amarnath, 2018; Van Slyke et al., 2002).

Applying on Fast Moving Consumer goods (FMCG), Yener (2014) and Shamsher and Chowdhury (2012) declared statistical significance between demographic factors and product involvement. O’Cass (2004, 2001) and Johnson et al. (2017) claimed that fashion clothing, as a high-involvement product, appeals more to females than males. Female consumers tend to have higher levels of FCI than male consumers. In general, they are more fashion conscious, more engaged in fashion, more attentive to fashion, and more willing to attempt new designs (Hourigan & Bougoure, 2012). Additionally, Hourigan and Bougoure (2012), Handa and Khare (2013) asserted that female Gen Y customers have higher levels of FCI than male Gen Y consumers. Accordingly, the following hypothesis is proposed:

H2:

It is expected that demographic variables (age, gender, income, and educational level) will have an impact on the fashion product involvement.

Although few previous studies have addressed the effect of socio-demographic variables on consumer decision-making styles (CDMS) (Bakewell & Mitchell, 2004; Mitchell & Walsh, 2004; Wesley et al., 2006; Yasin, 2009), gender was found to be the most common studied demographic variable on the reverse with age, income and education (Wesley et al., 2006; Anić et al., 2014; Anić et al. (2014) claimed that gender has no significant influence on confused by overchoice (CBO) decision-making style as well as age and income. Whilst Anic´ et al. (2010) asserted that younger females are less perfectionist oriented than younger males. In contrast, other research indicated that female consumers have higher perfectionism than males. They are more concerned with novelty and fashion, are more confused by overchoice, are more brand-conscious and hedonistic-oriented than males (Mitchell & Walsh, 2004; Yasin, 2009). Prior studies revealed that women prefer well-known brands somewhat more frequently than men. They are also more inclined to spend extra on brand names (Mitchell & Walsh, 2004; Mukherjee et al., 2012; Yasin, 2009). However, a few research claimed that this association is not as evident (Anic´ et al., 2010; Kumar & Sarangi, 2008). Walsh and Mitchell (2005) indicated that women are more interested in shopping than men. They spend more time in shopping and are more involved in detailed promotional materials. Therefore, they are more exposed to information overload and thus facing more difficulty in making a buying decision.

Weiss (2003) discovered that younger customers were more disposed to make impulsive decisions and seem to be less brand-loyal than older consumers. Whereas Anic´ et al. (2010, 2014) claimed that elder consumers seem to be less impulsive than youth as they are planning more for their buys and tend to be more economical. They specify their needs more accurately and are accordingly less vulnerable to in-store influences. Therefore, the authors confirmed the negative impact of age on impulsive, careless consumers (IMP).

Consumers with a higher level of education are more concerned about novelty (Brokaw & Lakshman, 1995; Kumar & Sarangi, 2008). Kollat and Willett (1967) suggested that education does not influence the “Impulsiveness “(IMP) CDMS style. Vipul (2010) stated that highly educated customers seem to be likely more impulsive as they have more awareness about the products and have stronger ability to search for information. Therefore, they are more influenced by store stimulus. Whereas Shukla et al. (2011) argued that less-educated customers have more impulsive buying behavior.

In 1985, Zeithaml indicated that income influences consumer behavior. Prabhavathi et al. (2014) stated that the level of income significantly influences the consumers engagement in the decision-making process in the fast-food industry. Previous research has also found that income has a favourable influence on the consumer novelty consciousness style (Goldsmith et al., 1998; Jordaan & Simpson, 2006). Consequently, the subsequent hypothesis is proposed

H3:

It is expected that demographic variables (age, gender, income and educational level) will have an impact on the Consumer Decision-making styles.

4. Study sector and data collection

Fashion represents the world’s largest fast-moving consumer goods industry. According to fashion industry statistics, clothing and textile represent the world’s fourth largest segment. Fashion United reported that the industry employs 3,384.1 million people. Its monetary value is three trillion dollars. This equates to 2% of the global Gross Domestic Product (GDP) (Fashion innovation, 2021).

Shahbandeh (2021a, 2021b) stated that the global retail sales of clothes and footwear reached US$1.9 trillion in 2019 and declined to US$1.5 trillion in 2020 (Global revenue of the apparel market, 2012–2025). However, due to the COVID-19 pandemic and enhanced online shopping, the market was predicted to grow at a compound annual growth rate (CAGR) of 20.5% by 2021 (Arsenovic, 20 April 2021). Because of the high demand for clothing and footwear, this value is expected to increase to US$2.25 trillion by 2025 (Global revenue of the apparel market, 2012–2025) and is predicted to exceed three trillion US dollars by 2030 (U.S. apparel market—statistics & facts). The United States, Chinaand Japan were the biggest clothing markets in 2020, with TJX Companies, Inditex, and H&M being the best-selling retailers. While Nike, GUCCI, and Adidas took the lead for the most valued individual brands (Global Apparel Market—Statistics & Facts). The fashion industry is continuing to expand, particularly in emerging markets in Asia-Pacific and Europe. However, the African market is the smallest in the global fashion industry (Global Apparel Market—Statistics & Facts).

According to the Consumer Market Outlook (2021), statistics showed that Women’s Apparel has the largest global market share. Egypt was the African country with the largest revenue in the Women’s Clothing sector. Egypt’s revenue reached 5.4 billion of dollars in 2020. From 2013 through 2026, global Women’s Apparel revenue will expand at a CAGR1 of 4.0%. From 2020 to 2026, the segment of Women’s Apparel in Egypt will expand by 114.6%.

Also, the Consumer Market Outlook (2021) revealed that Men’s Apparel is a significant segment of the clothing market. From 2013 through 2026, global Men’s Apparel revenue will expand at a CAGR1 of 4.2%. Within the African countries, Egypt showed the largest revenue in the Men’s Apparel sector in 2020 with $3.8 billion. From 2020 ($3.8 billion) to 2026, the Men’s Apparel sector in Egypt will rise by 114.1% ($8.2 billion). Although women continue to be the largest fashion spenders, males are leading the race in relation to market growth. Accordingly, the researchers selected the clothing fashion industry for the application of the current research.

An online survey was distributed to collect the required data. The research population comprises all Egyptian adult male and female consumers of fashion clothing. 400 full, valid, and trustworthy questionnaires were obtained. The data for the study was gathered over a fifteen-month period beginning in May 2021.

5. Measurement scales

The questionnaire consisted of three parts. The first part measured the Consumer Decision-Making Styles (CDMS) based on the scale proposed by Sproles and Kendall (1986). Accordingly, eight styles of making decisions are measured: perfectionistic, high-quality conscious (eight items), brand conscious (six items), novelty-fashion conscious (five items), recreational (hedonistic) (five items), price-conscious (three items), careless (five items), confused by overchoice (four items), and habitual (brand-loyal) (four items). The second part covered Product Involvement (four items) and was measured using Beatty and Talpade (1994) scale in addition to Kopalle and Lehmann (2001) scale. All the measures were five-point Likert scales with poles ranging from “strongly agree” to “strongly disagree”. The last part of the questionnaire entailed some items for demographics (age, gender, income and education).

6. Data analysis

6.1. The descriptive statistics

As shown in Table 1, 41.5% of the respondents were between the age of 20 and 30 years old while 27.8% were 40 years old and older. Moreover, 66.5% of the respondents were females, while 33.5 were males. For the monthly income, the results revealed that 30% of the respondents earn a monthly income that exceeds 20,000 L.E, while 24.8% earn a monthly income between 1001 and 5000LE. Concerning the educational level of the respondents the majority (48.7) % held a bachelor degree and 27.3% were undergraduate.

Table 1. Demographic characteristics of the respondents (N = 400)

Demographic variables	Frequency	%
Age groups Less than 20 21–30 31–40 > 40	45 166 78 111	11.3 41.5 19.5 27.8
Gender Female Male	266 134	66.5 33.5
Income Below 1000 1001–5000 5001–10000 10001–20000 More than 20,000	37 99 72 72 120	9.5 24.8 18.0 18.0 30.0
Education Undergraduate Bachelor degree Master’s level Doctorate degree	109 195 65 31	27.3 48.7 16.2 7.8

6.2. Convergent validity and reliability of the measures

As shown in Table 2, the Cronbach’s α was used to measure the internal consistency of the measures. The Cronbach’s α for the independent variables (Consumer decision-making styles) ranged from 0.677 to 0.826, while the dependent variable (Fashion Product Involvement) showed a Cronbach’s α of 0.695. These results indicate and confirm that the selected research scales and measures are valid and reliable to be used to serve the purpose of analyzing the research data.

Table 2. Properties of measures (convergent validity and reliability)

Research Variables	Cronbach’s Alpha value	Items
Perfectionistic, High-Quality Conscious Consumer	0.705	8
Brand Conscious, “Price Equals Quality” Consumer	0.790	6
Novelty-Fashion Conscious Consumer	0.805	5
Recreational, Hedonistic Consumer	0.739	5
Price Conscious, “Value for Money”	0.677	3
Impulsive, Careless Consumer	0.724	5
Confused by Over choice Consumer	0.706	4
Habitual, Brand-Loyal Consumer	0.826	4
Product Involvement (Dependent variable)	0.695	5

Pearson correlation and simple linear regression were used to test the first hypothesis, whereby the research variables were denoted using the symbols presented in Table 3.

Table 3. Description of research variables

Research Variables	Variable Name
(1) Consumer decision-Making Styles	Independent Variables
Perfectionistic, High-Quality Conscious Consumer	X1t
Brand Conscious, “Price Equals Quality” Consumer	X2t
Novelty-Fashion Conscious Consumer	X3t
Recreational, Hedonistic Consumer	X4t
Price Conscious, “Value for Money”	X5t
Impulsive, Careless Consumer	X6t
Confused by Over choice Consumer	X7t
Habitual, Brand-Loyal Consumer	X8t
(2) Fashion Product involvement	Dependent Variable
Product Involvement	Yt

The correlation test was used to assess the discriminant validity among the items of the measures. As shown in Table 4, the findings indicated that most of the correlations among constructs are significant at the 0.01 level. These previous results showed that there is a multicollinearity that should be considered when analyzing the research model due to the high number of independent variable subdimensions. In general, the correlation results were satisfactory in a way that can help in the illustration of the model.

Table 4. CDMS Correlation matrix

CDMS	X1t	X2t	X3t	X4t	X5t	X6t	X7t	X8t
X1t	1	0.137**	0.356**	0.408**	−0.009	−0.030	0.081	0.205**
X2t		1	0.618**	−0.015	0.413**	0.376**	0.531**	0.200**
X3t			1	0.293**	0.376**	0.252**	0.475**	0.212**
X4t				1	−0.132**	−0.038	−0.115*	−0.054
X5t					1	0.0226**	0.0441**	0.211**
X6t						1	0.364**	−0.056
X7t							1	0.183**
X8t								1

Note: **denotes significance at 0.01

*denotes significance at 0.05

To test the first hypothesis, the Pearson correlations were used. Table 5 shows the calculation of Pearson correlations between the consumer decision-making styles and the fashion product involvement. The results of Table 5 show that there is a strong significant positive correlation between the eight-consumer decision-making styles and the fashion product involvement at the 0.01 level. Therefore, consumer decision-making styles are positively related to fashion product involvement. Thus, the first research hypothesis is accepted.

Table 5. Pearson Correlations

Index	Independent Variables	Pearson correlation Coefficient
1	X1t	0.501**
2	X2t	0.436**
3	X3t	0.687**
4	X4t	0.514**
5	X5t	0.196**
6	X6t	0.199**
7	X7t	0.312**
8	X8t	0.188**

To measure the extent to which consumer decision-making styles can predict fashion product involvement the forward stepwise regression analysis was used. Table 6 shows that the model is significant with F-Ratio 167.719 at d.f=(4,395) with 0.01 significance level. R square = 62.9% which shows that consumer decision-making styles affect fashion product involvement. Accordingly, the research hypothesis is accepted. The results also show that Novelty-Fashion Conscious Consumer (X3t) had the highest effect, followed by recreational hedonistic (X4t), perfectionist high quality (X1t) and brand consciousness (X2t). While the lowest effect was Habitual, Brand-Loyal Consumer (X8t).

Table 6. Estimated model

Estimated Parameters
Parameter Test	Yt=−0.672+0.387X3t+0.339X4t+0.283X1t+0.191X2t
Model Test	F-ratio= 167.719**, d.f=(4,395), Sig= 0.000 P<0.01 R2= 62.9%, S.E.=0.479

To test the second and third hypotheses of the study, the following statistical methods were used:

1. Statistical description of data: the arithmetic Mean as one of the measures of central tendency and standard deviation as one of the measures of dispersion, in addition to establishing a confidence interval with 95%

2. T-test for two independent groups that aim to identify the extent to which there are statistical differences between two independent groups

3. One-way ANOVA which is used to identify the extent to which there are statistically significant differences between two or more independent groups by using the value of F ratio. If the significance of the F test is confirmed, one of the multiple comparison tests must be performed (Multiple comparison test—Multiple ranges test—post-Hoc test). The results of the second hypothesis are shown in table 7.

   Table 7. Fashion product involvement differences between age categories, gender, income and education

   Fashion Product Involvement	Mean	St. deviation	CI for mean with 95%
   Age Groups Less than 20 21–30 31–40 > 40	3.813 3.611 3.474 3.186	0.717 0.710 0.759 0.837	(3.598,4.029) (3.502,3.720) (3.303,3.645) (3.028,3.343)
   F- Ratio = 10.116, df. = (3.396), Sig = 0.000 P < 0.01 Sig at 0.01
   Gender Female Male	3.594 3.281	0.778 0.755	(3.500,3.688) (3.152,3.410)
   T-value = 3.840, df = 398, sig = 0.000 (p < 0.01)
   Income Below 1000 1001–5000 5001–10000 10001–20000 More than 20,000	3.697 3.628 3.706 3.356 3.260	0.740 0.653 0.678 0.865 0.835	(3.451,3.944) (3.498,3.759) (3.546,3.865) (3.152,3.559) (3.109, 3.411)
   F- Ratio = 6.204, df = (4.395), Sig = 0.000 P < 0.01 Sig at 0.01
   Education Undergraduate Bachelor degree Master’s level Doctorate degree	3.758 3.446 3.351 3.103	0.682 0.762 0.777 0.982	(3.628,3.887) (3.338,3.554) (3.158,3.545) (2.743,3.464)
   F- Ratio = 8. 0574, df = (3.396), Sig = 0.000 P < 0.01 Sig at 0.01

Table 7 shows the fashion product involvement differences between age categories, gender, income, and education. For age categories, the results confirmed the existence of statistically significant differences between the different age groups in terms of product involvement, where “F” (calculated F = 10.116), which confirms its statistical significance at the 0.01 level with df (3,396) and by conducting a Tukey test for multiple comparisons it shows that these differences lie between the responses of the age group “older than 40” with each of the age group “less than 20” and the age group between “21 and 30.” Furthermore, the statistical description confirmed that these differences are in favor of the two groups less than 20 and from 21 to 30, where the mean value of the responses of each of them was 3.813 and 3.611 compared to only 3.186 for the responses of the age group older than 40.

For gender categories, the results confirmed the existence of statistically significant differences between males and females in terms of product involvement, where “T” (calculated T = 3.840), which confirms its statistical significance at 0.01 level with df (398). The statistical description confirmed that these differences are in favor of females where the mean value of their responses was 3.594 compared to only 3.281 for the males’ responses.

For the income categories, the results confirmed the existence of statistically significant differences between the different income groups in terms of product involvement, where “F” (calculated F = 6.204), which confirms its statistical significance at 0.01 level with df (3,396) and by conducting a Tukey test for multiple comparisons it shows that these differences lie between responses of the higher income level+20000 with each of the income group “less than 1000”, the income group between “1001 and 5000” and the income group between “5001 and 10,000.” The statistical description confirmed that these differences are in favor of lower income groups, where the mean value of the responses of each of them was 3.697, 3.628, 3.706 respectively compared to only 3.260 for the responses of the income group+20000.

For the education levels, the results confirmed the existence of statistically significant differences between the different education levels in terms of product involvement, where “F” (calculated F = 8.057), which confirms its statistical significance at 0.01 level with df (3,396) and by conducting a Tukey test for multiple comparisons it shows that these differences lie between the responses of the undergraduate level with the other education levels. The statistical description confirmed that these differences are in favor of undergraduates, where the mean value of their responses was 3.758 compared to 3.446, 3.351 and 3.103 for the responses for the other education levels. These previous results show that demographic variables (age, gender, income, and educational level) have an impact on the fashion product involvement. Therefore, the second hypothesis is accepted.

To test the third hypothesis of the study, the previous statistical methods were used, the results of the third hypothesis are shown in Table 8.

Table 8. Consumer decision-making styles differences between age categories, gender, income, and education

Variables	Age				Gender
	Age Groups	Mean	St. deviation	CI for mean with 95%	Gender	Mean	St. deviation	CI for mean with 95%
Perfectionistic High-Quality Conscious Consumer	< 20	3.636	0.464	(3.497,3.775)	Female	3.7	0.563	(3.635,3.765)
	21 – 30	3.765	0.556	(3.680, 3.850)	Male	3.581	0.594	(3.480, 3.683)
	31 – 40	3.649	0.581	(3.518, 3.780)
	> 40	3.52	0.556	(3.416, 3.625)
	F- Ratio = 4.422, df= (3.396), Sig =0.005 P003C 0.01				T-value = 2.011, df =398, sig = 0.045 (P< 0.05)
Brand Conscious	< 20	3.237	0.673	(3.305,3.439)	Female	3.251	0.776	(3.157,3.344)
“Price Equals Quality” Consumer	21 – 30	3.415	0.778	(3.295, 3.534)	Male	3.345	0.773	(3.212, 3.477)
	31 – 40	3.25	0.783	(3.074, 3.427)
	> 40	3.125	0.779	(2.978, 3.271)
	F- Ratio = 3.297, df= (3.396), Sig =0.021 P003C 0.05				T-value = 1.144, df =398, sig = 0.253 (P> 0.05)
Variables	Age				Gender
	Age Groups	Mean	St. deviation	CI for mean with 95%	Gender	Mean	St. deviation	CI for mean with 95%
Novelty-Fashion Conscious Consumer	< 20	3.929	0.673	(3.727,4.131)	Female	3.798	0.795	(3.702,3.894)
	21 – 30	3.935	0.733	(3.823, 4.047)	Male	3.569	0.856	(3.422, 3.715
	31 – 40	3.646	0.724	(3.483, 3.809)
	> 40	3.369	0.938	(3.193,3.546)
	F- Ratio = 12.726, df= (3.396), Sig =0.000 P003C 0.01				T-value = 2.011, df =398, sig = 0.008 (P< 0.01)
Recreational, Hedonistic Consumer	< 20	3.316	0.761	(3.087,3.544)	Female	3.269	0.712	(3.183,3.355)
	21 – 30	3.154	0.758	(3.038, 3.270)	Male	2.843	0.684	(2.726, 2.960)
	31 – 40	3.169	0.635	(3.026, 3.313)
	> 40	2.978	0.719	(2.843,3.114)
	F- Ratio = 2.734, df= (3.396), Sig =0.043 P003C 0.05				T-value = 5.723, df =398, sig = 0.000 (P< 0.01)
Price Conscious, “Value for Money”	< 20	3.541	0.644	(3.341,3.740)	Female	3.637	0.743	(3.548, 3.725)
	21 – 30	3.735	0.724	(3.624, 3.846)	Male	3.669	0.755	(3.540, 3.798)
	31 – 40	3.671	0.81	(3.488, 3.854)
	> 40	3.544	0.734	(3.405, 3.682)
	F- Ratio = 1.850, df= (3.396), Sig =0.138 P> 0.05				T-value = 0.415, df =398, sig = 0.679 (P> 0.05)
Variables	Age				Gender
	Age Groups	Mean	St. deviation	CI for mean with 95%	Gender	Mean	St. deviation	CI for mean with 95%
Impulsive, Careless Consumer	< 20	2.898	0.537	(2.736,3.059)	Female	3.874	0.532	(2.809,2.938)
	21 – 30	2.986	0.477	(2.914, 3.060)	Male	2.863	0.485	(2.780, 2.946)
	31 – 40	2.844	0.522	(2.726, 2.961)
	> 40	2.703	0.519	(2.605,2.800)
	F- Ratio = 7.129, df= (3.396), Sig =0.000 P003C 0.01				T-value = 2.01, df =398, sig = 0.841 (P> 0.05)
Confused by Over choice Consumer	< 20	3.578	0.922	(3.301,3.855)	Female	3.494	0.852	(3.391,3.597)
	21 – 30	3.364	0.83	(3.516,3.770)	Male	3.461	0.794	(3.325, 3.597)
	31 – 40	3.462	0.732	(3.297, 3.027)
	> 40	3.221	0.809	(3.069,3.373)
	F- Ratio = 6.156, df= (3.396), Sig = 0.000 P003C 0.01				T-value = 0.380, d f=398, sig = 0.704 (P> 0.05)
Habitual, Brand-Loyal Consumer	< 20	3.406	0.613	(3.221,3590)	Female	3.543	0.642	(3.466, 3.621)
	21 – 30	3. 658	0.6	(3.516,3.750)	Male	3.595	0.601	(3.492,3.698)
	31 – 40	3.625	0.542	(3.503, 3747)
	> 40	3.432	0.703	(3.300,3.565)
	F- Ratio = 4.147, df= (3.396), Sig =0.007 P003C 0.01				T-value = 0.779, df =398, sig = 0.437 (P> 0.05)
Variables	Income				Education
	Income	Mean	St. deviation	CI for mean with 95%	Education	Mean	St. deviation	CI for mean with 95%
Perfectionistic High-Quality Conscious Consumer	< 1000	3.544	0.432	(3.400,3.688)	Undergraduate	3.701	0.509	(3.604,3.797)
	1001-5000	3.749	0.504	(3.648, 3.849)	Bachelor degree	3.673	0.576	(3.592, 3.755)
	5001-10000	3.658	0.531	(3.533, 3.783)	Master’s level Doctorate degree	3.614	0.534	(3.481, 3.746)
	10001-20000	3.658	0.631	(3.510, 3.806)		3.532	0.658	(3.291, 3.773)
	> 20000	3.625	0.602	(3.516, 3.734)
	F- Ratio = 1.144, df= (4.395), Sig =0.335 P> 0.05				F- Ratio = 0.919, df= (3.396), Sig =0.432 P> 0.05
Brand Conscious	< 1000	3.248	0.696	(3.016,3.480)	Undergraduate	3.494	0.708	(3.359,3.628)
“Price Equals Quality” Consumer	1001-5000	3.306	0.685	(3.170, 3.443)	Bachelor degree	3.221	0.804	(3.108, 3.335)
	5001-10000	3.442	0.854	(3.241, 3.643	Master’s level Doctorate degree	3.056	0.775	(2.864, 3.249)
	10001-20000	3.301	0.82	(3.108,3.494) (3.024,3.307)		3.393	0.646	(3.155, 3.630)
	> 20000	3.165	0.784
	F- Ratio = 1.510, df= (4.395), Sig =0.199 P> 0.05				F- Ratio = 5.236, df= (3.396), Sig =0.001 P003C 0.01
Variables	Income				Education
	Income	Mean	St. deviation	CI for mean with 95%	Education	Mean	St. deviation	CI for mean with 95%
Novelty-Fashion Conscious Consumer	< 1000	3.941	0.552	(3.575,4.124)	Undergraduate	4.086	0.651	(3.963,4.210)
	1001-5000	3.828	0.783	(3.672, 3.984)	Bachelor degree	3.656	0.809	(3.542, 3.771)
	5001-10000	3.919	0.754	(3.742, 4.097)	Master’s level Doctorate degree	3.48	0.837	(3.273, 3.688)
	10001-20000	3.703	0.895	(3.493,3.913)		3.348	0.974	(2.991,3.706)
	> 20000	3.457	0.855	(3.302,3.611)
	F- Ratio = 5.477, df= (4.395), Sig =0.000 P003C 0.01				F- Ratio = 12.566, df= (3.396), Sig =0.000 P003C 0.01
Recreational, Hedonistic Consumer	< 1000	3.173	0.716	(2.934,3.412)	Undergraduate	3.246	0.695	(3.114,3.378)
	1001-5000	3.313	0. 660	(3.181, 3.445	Bachelor degree	3.101	0.741	(2.996, 3.205)
	5001-10000	3.061	0.645	(2.910, 3.213)	Master’s level Doctorate degree	3.095	0.739	(2.912, 3.279)
	10001-20000	3.131	0.745	(2.956, 3.306)		2.936	0.734	(2.666,3.205)
	> 20000	2.995	0.802	(2.850,3.140)
	F- Ratio = 2.825, df= (4.395), Sig =0.025 P003C 0.05				F- Ratio = 1.812, df= (3.396), Sig =0.144 P> 0.05
Price Conscious, “Value for Money”	< 1000	3.586	0.59	(3.389,3.782)	Undergraduate	3.676	0.693	(3.544,3.807)
	1001-5000	3.65	0.744	(3.501, 3.789)	Bachelor degree	3.727	0.711	(3.626, 3.827)
	5001-10000	3.653	0.741	(3.479, 3.827)	Master’s level Doctorate degree	3.426	0.823	(3.222, 3.630)
	10001-20000	3.699	0.895	(3.489, 3.910)		3.516	0.82	(3.215, 3.817)
	> 20000	3.631	0.684	(3.507, 3.754)
	F- Ratio = 0.167, df= (4.395), Sig =0.955 P> 0.05				F- Ratio = 3.111, df= (3.396), Sig =0.026 P003C 0.05
Variables	Income				Education
	Income	Mean	St. deviation	CI for mean with 95%	Education	Mean	St. deviation	CI for mean with 95%
Impulsive, Careless Consumer	< 1000	3.076	0.395	(2.944,3.208)	Undergraduate	2.985	0.684	(2.893,3.078)
	1001-5000	2.897	0.507	(2.796, 2.998)	Bachelor degree	2.85	0.526	(2.776, 2.925)
	5001-10000	2.925	0.474	(2.814, 3.036)	Master’s level Doctorate degree	2.757	0.51	(2.630, 2.883)
	10001-20000	2.894	0.524	(2.771,2.836)		2.826	0.513	(2.638,3.014)
	> 20000	2.737	0.551	(2.637,2.836)
	F- Ratio = 3.884, df= (4.395), Sig =0.004 P003C 0.01				F- Ratio = 3.065, df= (3.396), Sig =0.028 P003C 0.05
Confused by Over choice Consumer	< 1000	3.757	0.715	(3.518,3.995)	Undergraduate	3.752	0.844	(2.592,3.913)
	1001-5000	3.553	0.819	(3.390,3.716)	Bachelor degree	3.415	0.838	(3.297,3.534)
	5001-10000	3.563	0.878	(3.356, 3.769)	Master’s level Doctorate degree	3.304	0.805	(3.105, 3.503)
	10001-20000	3.504	0.825	(3.310,3.697)		3.339	0.587	(3.124,3.554)
	> 20000	3.281	0.824	(3.132,3.430)
	F- Ratio = 3.178, df= (4.395), Sig =0.014 P003C 0.05				F- Ratio = 5.738, df= (3.396), Sig =0.001 P003C 0.01
Habitual, Brand-Loyal Consumer	< 1000	3.318	0.561	(3.131,3505)	Undergraduate	3.491	0.571	(3.382,3.599)
	1001-5000	3. 452	0.601	(3.332,3.572)	Bachelor degree	3.603	0.637	(3.513,3.693)
	5001-10000	3.722	0.626	(3.575, 3869)	Master’s level Doctorate degree	3.654	0.635	(3.496, 3811)
	10001-20000	3.563	0.651	(3.410,3.716)		3.347	0.709	(3.087,3.607)
	> 20000	3.627	0.631	(3.513,3.741)
	F- Ratio = 3.743, df= (4.395), Sig =0.005 P003C 0.01				F- Ratio = 1.812, df= (3.396), Sig =0.144 P> 0.05

Table 8 shows the consumer decision-making styles differences between age categories, gender, incomeand education. The results of the impact of age on consumer decision-making styles confirmed the existence of statistically significant differences between the different age groups for all styles except for Price Conscious consumer. where “F” (calculated F ranged from 2.734 till 12.726), which confirms its statistical significance at 0.01 level or 0.05 level with df (3,396). By conducting a Tukey test for multiple comparisons, it shows that: For the perfectionistic High-quality conscious consumer, Brand Conscious, “Price Equals Quality” Consumer, Confused by Over choice Consumer, and Habitual, Brand-Loyal Consumer, these differences lie between the responses of the age group between “21 and 30” with the age group “older than 40”. The statistical description confirmed that these differences are in favor of the group from 21 to 30, where the mean value of the responses of each of them was 3.765, 3.415, 3.364, and 3.658 respectively compared to only 3.520, 3.125, 3.221, and 3.432 respectively for the responses of the age group older than 40. For Novelty-Fashion Conscious Consumer these differences lie between the responses of the age group “older than 40” with the other three age categories. The statistical description confirmed that these differences are in favor of the three groups less than 20, from 21 to 30 and from 31 to 40, where the mean value of the responses of each of them was 3.929, 3.935 and 3.646 compared to only 3.346 for the responses of the age group older than 40. For Recreational, Hedonistic Consumers these differences lie between the responses of the age group “older than 40” with the other three age categories. The statistical description confirmed that these differences are in favor of the three groups less than 20, from 21 to 30 and from 31 to 40, where the mean value of the responses of each of them was 3.316, 3.154 and 3.169 compared to only 2.978 for the responses of the age group older than 40. For Impulsive Careless Consumer, the first differences lie between the age group “older than 40” with each of the age groups “less than 20” and the age group between “21 and 30 and second differences lie between the responses of age group between 21 to 30 and the responses of the age group from 31 to 40.” The statistical description confirmed that first differences are in favor of the age groups “less than 20” and the age group between “21 and 30” where the mean value of their responses was 2.898 and 2.986 compared to only 2.703 for the responses of the age group older than 40. And second differences are in favor of the age group from 21 to 30, where the mean value of their responses was 2.986 compared to only 2.844 for the responses of the age group from 31 to 40.

The results of the impact of gender on consumer decision-making styles confirmed the existence of statistically significant differences between males and females for three styles only: Perfectionistic High-Quality Conscious Consumer, Novelty-Fashion Conscious Consumer, Recreational, Hedonistic Consumer where “T” (calculated T was respectively 2.011, 2.011, 5.723), which confirms its statistical significance at 0.01 level or 0.05 level with df (398). The statistical description confirmed that these differences are in favor of females where the mean value of their responses was respectively 3.700, 3.798 and 3.629 compared to only 3.581, 3.569 and 2.843 for the males’ responses. As for the rest of the styles the results confirmed that there are no statistically significant differences between males and females in terms of these styles, as it was confirmed by the value of the F-test, which did not achieve the lowest levels of significance (0.05). This was confirmed by the statistical description represented in the mean and the confidence interval for the mean, which is 95%.

The results of the impact of income variable on consumer decision-making styles confirmed the existence of statistically significant differences between the different income groups for five of the Consumer Decision-making styles: Novelty-fashion conscious, Recreational/hedonistic consumers, Impulsive careless consumer, confused by over choice and habitual brand-loyal consumers. Where “F” (calculated F ranged between 5.477 and 2.825), which confirms its statistical significance at 0.01 level or 0.05 level with df (4,395). And by conducting a Tukey test for multiple comparisons it shows that: For Novelty fashion conscious consumer these differences lie between responses of the higher income level+20000 with all of the other income groups. The statistical description confirmed that these differences are in favor of lower income groups, where the mean value of the responses of each of them was 3.941, 3.828, 3.919 and 3.703 respectively compared to only 3.457 for the responses of the income group+20000. For Recreational, Hedonistic Consumer, these differences lie between responses of income level from 1001 to 5000 with all of the other income groups. The statistical description confirmed that these differences are in favor of income level from 1001 to 5000, where the mean value of the responses of this category was 3.313 compared to 3.173, 3.06, 3.11 and 2.995 respectively for the responses of all other income group. For Impulsive Careless Consumer, these differences lie between responses of the income groups less than 1000, between 1001 and 5000, between 5001 and 10,000 and between 10,001 and 20,000 with the higher income level+20000. The statistical description confirmed that these differences are in favor of lower income groups, where the mean value of the responses of each of them was 3.076, 2.897, 2.925 and 2.894 respectively compared to only 2.737 for the responses of the income group+20000. For Confused by Over choice Consumer, these differences lie between responses of the income groups less than 1000, between 1001 and 5000, between 5001 and 10,000 and between 10,001 and 20,000 with the higher income level+20000. The statistical description confirmed that these differences are in favor of lower income groups, where the mean value of the responses of each of them was 3.757, 3.553, 3.563 and 3.504 respectively compared to only 3.281 for the responses of the income group+20000. For Habitual, Brand-Loyal Consumer, there are two differences the first lie between responses of the income groups less than 1000 and from 5001 to 10,000, with the higher income level+20000. The second differences lie between responses of income group less than 1000 with the responses of the income group from 1000 to 5000. The statistical description confirmed in the first case that these differences are in favor of income group+20000, where the mean value of their responses 3.627 compared to only 3.318 and 3.452 for the responses of the income group less than 1000 and from 1001 to 5000. In the second case these differences are in favor of income group from 5001 to 10,000, where the mean value of their responses 3.722 compared to only 3.318 for the responses of the income group less than 1000. As for the rest of the styles: Perfectionistic High-Quality Conscious Consumer, Brand Conscious, “Price Equals Quality” Consumer, and Price conscious the results confirmed that there are no statistically significant differences between income categories in terms of these styles, as it was confirmed by the value of the F-test, which did not achieve the lowest levels of significance (0.05). This was confirmed by the statistical description represented in the mean and the confidence interval for the mean, which is 95%.

The results of the impact of the education variable on consumer decision-making styles confirmed the existence of statistically significant differences between the different income groups for five of the Consumer Decision-making styles: Brand conscious, Novelty-fashion conscious, Price conscious, Impulsive careless consumer and confused by over choice. Where “F” (calculated F ranged between 3.065 and 12.566), which confirms its statistical significance at 0.01 level or 0.05 level with df (3,396) and by conducting a Tukey test for multiple comparisons it shows that: For brand conscious consumer these differences lie between the responses of the undergraduate level with the Bachelor and Master’s degree level, and between the doctorate level and the master’s level. The statistical description confirmed that these differences are in favor of undergraduates, where the mean value of their responses was 3.494 compared to 3.393 and 3.221 for the responses of the bachelor and master’s degree levels. It also confirmed that these differences are in favor of doctorate level, where the mean value of their responses was 3.393 compared to 3.056 for the responses of the master’s degree level. For Novelty-Fashion Conscious Consumers, these differences lie between the responses of the undergraduate level with the Bachelor, Master’s, and Doctorate degree level, and between the doctorate level and the master’s degree level. The statistical description confirmed that these differences are in favor of undergraduates, where the mean value of their responses was 4.086 compared to 3.656, 3.480 and 3.348 respectively for the responses of the bachelor, master’s doctorate degree levels. It also confirmed that these differences are in favor of master’s degree level, where the mean value of their responses was 3.480 compared to 3.348 for the responses of the doctorate level. For Price Conscious consumer, these differences lie between the responses of the undergraduate and Bachelor level with the Master’s and Doctorate degree level. The statistical description confirmed that these differences are in favor of undergraduates and Bachelor level, where the mean value of their responses was 3.676 and 3.727 compared to 3.426 and 3.516 respectively for the responses of the master’s and doctorate degree levels. For Impulsive Careless Consumers, these differences lie between the responses of the undergraduate level with the Bachelor Master’s and Doctorate degree level. The statistical description confirmed that these differences are in favor of undergraduates, where the mean value of their responses was 2.985 compared to 2.850, 2.757 and 2.826 for the responses of the bachelor, master’s, and Doctorate degree levels. For Confused by Over choice Consumer, these differences lie between the responses of the undergraduate level with the Bachelor, Master’s, and Doctorate degree level. The statistical description confirmed that these differences are in favor of undergraduates, where the mean value of their responses was 3.752 compared to 3.415,3.304 and 3.339 for the responses of the bachelor master’s and doctorate degree levels. As for the rest of the styles: Perfectionistic High-Quality Conscious Consumer, Recreational, Hedonistic Consumer, and Habitual, Brand-Loyal Consumer, the results confirmed that there are no statistically significant differences between income categories in terms of these styles, as it was confirmed by the value of the F-test, which did not achieve the lowest levels of significance (0.05). This was confirmed by the statistical description represented in the mean and the confidence interval for the mean, which is 95%.

These previous results show that demographic variables (age, gender, income, and educational level) have an impact on consumer decision-making styles. Therefore, the third hypothesis is accepted.

7. Discussion and conclusion

This research is an empirical study that tested the impact of consumer decision-making styles on product involvement and the effect of the four demographic variables: age, gender, income and education on both product involvement and Consumer Decision-making styles. Based on previous literature, it was assumed that consumer decision-making styles have a significant effect on product involvement and that product involvement and consumer decision-making styles differ between age, gender, income, and education categories. The following discussion shows the main insights that underline the relationship between Consumer Decision-making styles, product involvement and demographics.

The findings revealed that consumer decision-making styles have a positive effect on fashion product involvement, but the degree of involvement differs from one style to another. This means that every decision-making style can get involved in fashion differently than the other styles for example consumers who are novelty fashion conscious will be more involved in fashion because they are always looking for new designs, new colors and new brands and this increases their involvement and this result was proved by the multiple regression test results that have showed that novelty fashion conscious customers had the highest effect on involvement followed by recreational/hedonistic customers, perfectionist/high quality and brand conscious customers. These results are logical because recreational hedonistic customers are customers who are not only searching for fashion products, but they find pleasure and amusement in shopping, and they are also willing to spend long periods of time exploring products. While perfectionistic/high-quality consumers are consumers searching for the high-quality products, so their buying decisions are always based on massive research to find these products. Finally, the least effect on involvement was for the habitual brand loyal customers these customers have their favorite brand stores or websites, so they build their buying decision based on their previous experience they don’t search a lot about new brands or new fashions or new stores that’s why this style has the least effect on product involvement. These findings were supported by O’Cass (2004) and Hourigan and Bougoure (2012) who have suggested that consumers are not all involved to the same extent with fashion clothing products.

The study also examined the effect of the four demographic variables: age, gender, income and education on the fashion product involvement. The results revealed that all four variables have an effect on fashion product involvement as follows: for the effect of age the results showed the existence of statistically significant differences between the different age groups in terms of fashion product involvement, where younger customer groups are more involved than older groups and these results because of the importance young consumers give to the search about different brands especially when it comes to fashion, younger consumers collect information about fashion from their surroundings: family members, friends and online using online customer reviews especially if they are not certain about their choices. They pay too much attention to the fashion product because they want to be chic, up-to-date and attractive. They also do not have that much financial responsibilities which gave them the chance to spend more on fashion and allows them to search even for the most expensive fashion products that satisfy their needs. While the older groups of customers have other things to focus on, such as family and career advancement, although their interest in fashion still exists, but it comes in second or third place after other commitments. For them fashion products are only a way to improve their status and image.

Gender also influences fashion product involvement. The results showed that females are more involved in buying fashion products than males. This is because females are always searching for products that make them feel special. They are more aware about new trends, more alert about new designs and are ready to take the risk related to new designs and trends. These results are consistent with previous studies (Johnson et al., 2017; O’Cass, 2001, 2004) who have proven that fashion clothing is more attractive to females than males.

For the income categories the results confirmed that not all categories of income are involved with the same level in fashion products, the differences in fashion product involvement are in favor of the lower-income groups which means that lower income groups search and make more effort in buying fashion product than the higher-income groups, because they don’t have enough financial resources they cannot afford to make bad choices so they exert more effort, compare between several products and brands, consult friends and family before taking the decision to buy which make them highly involved in buying fashion products.

Finally, the results showed that education also has an effect on Fashion product involvement. The results have proven that undergraduates are more involved in buying fashion products than the higher education levels, because undergraduates have more time than all other education levels who have more responsibilities that make them less involved, while Egyptian undergraduates tend to learn about different cultures and have the chance to connect with these cultures through student exchange and trips to western countries which is the source of new designs and trends, also the emerging universal parts of culture have created a large global segment which have made these young customers aware about the latest changes in fashion.

As predicted, demographic variables found to have an impact on the Consumer Decision-making styles. The age variable seems to have an impact on all styles except for the price-conscious style, the results showed that there is no difference between the different age categories when it comes to the search for the best deals and the comparison between prices before taking the decision to buy fashion products. For the other seven styles the results confirmed the existence of significant differences between the different age groups, and all these differences were in favor of the younger groups in comparison to customers aged more than 40, which means that younger groups are more perfectionistic high-quality conscious consumers, more aware about brands and searching for new fashion products, they are hedonistic customers who enjoy shopping and see it as an amusing activity that satisfy them, they usually make their purchases without planning as we have said they enjoy shopping so during their amusement they can find good deals that attract them to take the decision to buy. They are also more confused when choosing from numerous alternatives that exist while shopping for fashion products. Finally, they are more loyal to their favorite brand, their decision to buy is built on their previous satisfying experiences.

The study has also examined the influence of gender on consumer decision-making styles, the results confirmed the existence of statistically significant differences between males and females in terms of Perfectionistic High-Quality Conscious Consumer, Novelty-Fashion Conscious Consumer and Recreational-hedonistic Consumer. All these differences were in favor of females. These results mean that females are more perfectionistic than males in taking the decision to buy fashion products, they are always searching for the newest fashion and the newest designs more than males. Finally, they enjoy shopping and see it as a very pleasant activity, they can devote long times in exploring different products, they enjoy trying products and comparing designs and colors. While for the other styles, the results showed that there is no impact of gender on these styles

The income variable seems to have an impact on five of the consumer decision-making styles: Novelty-fashion conscious, Recreational/hedonistic consumers, Impulsive careless consumer, confused by over choice and habitual brand-loyal consumer. The results confirmed the existence of significant differences between the different income groups, and all these differences were in favor of the lower-income groups, the results suggested that lower-income groups are searching more for new fashion products, they find amusement, satisfaction, and pleasure in shopping, they usually are impulsive buyers their purchases aren’t planned once they are attracted to a product, they take the decision to buy. Finally, they are more habitual buyers, they have their preferred brands, stores, and sites. They usually build their decisions based on these brands’ past performance.

Finally, the results showed that the education variable has an impact on five of the Consumer Decision-making styles: Brand conscious, Novelty-fashion conscious, Price conscious, Impulsive careless consumer and confused by over choice. The results confirmed the existence of statistically significant differences between the different education levels. The statistical description confirmed that these differences are in favor of undergraduates. The results suggested that undergraduates are more brand conscious; they are aware about different brands and spend their time in collecting information about the brands that exist in the market. Undergraduates are also searching for new trends in fashion, new designs and new colors as they are normally rebellion on traditional fashion. Moreover undergraduates because of their limited financial resources they are very price conscious trying to get the best value for money, and their purchases are usually not planned. Finally, because of the huge number of alternatives, they are more confused by the over choice.

8. Managerial implications

There are important insights and implications for marketers that can be concluded from this study. This study is one of few studies that have analyzed the effect of consumer decision-making styles on product involvement which can help in formulating marketing strategies. It is suggested that marketers use CDMS to increase customer product involvement which in turn can help in influencing the customer buying decision especially in fashion industry. Marketers also must draw up a separate strategy to deal with different consumer decision-making styles because each style has its own characteristics that require different approach to increase the involvement of these customers. Decision-making styles can help marketers segment customers because these styles can classify customers’ attitudes, motivations, psychographics, cognitive and emotional orientations. Secondly, this study has investigated the effect of demographic variables on fashion product involvement and consumer decision-making styles and has shown that demographic variables substantially affect both CDMS and fashion product involvement. Thus, it is very important for marketers to take into consideration these variables while designing their marketing strategies.

9. Limitations and future research

It should be noted that this study focused only on Fashion products in Egypt. Therefore, its results were limited to this sector. Further research may consider other sectors and other brands in the Egyptian context. Also, they may study the moderating effect of demographic variables in the relationship between CDMS and product involvement and the effect of other variables such as brand loyalty, brand availability and trust on product involvement. Furthermore, future research could study the effect of CDMS on product involvement and the impact of these variables on customer online purchases.

Disclosure statement

No potential conflict of interest was reported by the authors.