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Online Shopping Behaviours on Apparel Products in a Cameroon Context: Understanding the Relationships between Shopping Orientations, Gender, Online Information Search and Online Purchase Behaviour

Mbayong NA*

Catholic University Institute of Buea (CUIB), Buea, South West Region, UK

*Corresponding Author:
Mbayong NA
Assistant Professor
Catholic University Institute of Buea (CUIB)
Buea, South West Region, UK
Tel: (+237) 233 322 829
E-mail: napsub_2007@yahoo.co.uk

Received Date: May 11, 2017; Accepted Date: May 20, 2017; Published Date: May 27, 2017

Citation: Mbayong NA (2017) Online Shopping Behaviours on Apparel Products in a Cameroon Context: Understanding the Relationships between Shopping Orientations, Gender, Online Information Search and Online Purchase Behaviour. J Account Mark 6: 239. doi: 10.4172/2168-9601.1000239

Copyright: © 2017 Mbayong NA. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

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Abstract

Purpose: The purpose of this study is to investigate the online apparel shopping behaviours of college students in a Cameroon context which include information search and purchase behavior. Design/Methodology/Approaches: One of the non-probability sampling techniques termed convenience sampling was adopted as the sampling method for this research. As this research focuses on the population of Cameroon college/university students, the sample of this study is local college students who are currently pursuing Diploma or Degree studies. 121 responses have been gathered from college/university students who are currently pursuing diploma or degree courses at Catholic University Institute of Buea, Cameroon. Among these respondents, 46.3% are males (n=56) while 53.7% are females (n=65). Findings: The present research had yielded fruitful results to understand the relationships between shopping orientations, gender, online information search and online purchase behavior on apparel products. To note, the influences of shopping orientations and gender have been found to be more significantly impactful on online information search on apparel products. Originality/Value: This study’s research questions and methods are new to the line of Online Shopping Behaviours on Apparel Products in a Cameroon Context: Understanding the Relationships between Shopping Orientations, Gender, and Online Information Search and Online Purchase Behaviour

Keywords

Online shopping behaviours; Apparel products; Shopping orientations; Online information search; Online purchase behaviour

Introduction

“Hey, look at this dress that I brought online!” This has been one of the common conversations that can be heard from college students today. This is a reflection of the shift of shopping pattern from traditional shopping methods to online shopping among college students.

Throughout the years, there has been a massive growth in the number of unique internet users globally. Specifically among the Malaysian population, ITU [1], a professional agency which provides source of global ICT statistics reported that the number of internet users in Malaysia has been increasing steadily across the years – by 2011, it was estimated that around 59% of the Malaysian population (approximately 16 million users) are using the internet. The advancement of internet and rapid increment in the internet access has facilitated the adoption of online shopping, where information search and purchase of products and services were conducted at virtual stores. Globally, 875 million internet users have made purchases through online shopping [2]. This statistics well-reflects the growth of e-commerce, or the sales of products and services over the internet. In Malaysia, a total of RM842 millions of online retailing sales has been recorded in 2011; this figure is forecasted to reach a grand total of RM1.7 billion in 2016 [3]. Nielson [4] further reported that among all purchases over the internet, apparel-related products which include clothing, accessories and shoes are one of top three product categories which have the highest information search and purchase intention by global online consumers.

Problem statement

Among the Cameroonian internet users, it was recorded that those who aged 18-24, which largely made up by the college/University students’ population, have accessed the highest internet usage [5]. In fact, college students have contributed a big part in online shopping [6]. In the Malaysian context, Delafrooz et al. [7] recognized that college students are becoming the common and important consumer groups of internet commerce. Although the significance of college students among the online consumers groups have been well-recognized by researchers [7], little research has been done to obtain a thorough understanding on Cameroon student consumers’ shopping behavior particularly towards online apparel shopping.

Thus, the present study is interested to investigate this population segment of internet user in order to understand their online apparel shopping behaviors which include information search and purchase behavior. Findings of this research may contribute by helping online retailers to develop marketing and sales strategies which can effectively target on this group of online consumers.

Research objectives

The main research objective is to investigate the online apparel shopping behaviours of college students in a Cameroon context. Specifically, this research aims to identify:

1. Factors of information search on online apparel products of college students,

2. Factors of purchase behaviour on online apparel products of college students, and

3. The relationship between identified factors.

Literature Review

Empirical literature

Online apparel shopping behaviours: Pavlou [8] described online shopping behaviours occurs when a buyer is intending and deciding to make a purchase through online transactions. Pavlou [8] further explained that online transactions involve three processes: information retrieval, information transfer and product purchase. In relation to this, the present study attempts to study factors of the two dimensions of online shopping behaviours: information search and purchase behaviour. This section of the paper documented past researches which have studied two important factors of online shopping behaviour: (1) shopping orientations and (2) gender.

Shopping orientations & online apparel shopping behaviours: Throughout the years, many researchers have conducted studies to understand the influencing factor of shopping orientations on shopping behaviour. Shopping orientation was defined as “a consumer’s approach to the act of shopping” [9]. As it reflects the characteristics, preferences, needs and wants of the consumers, shopping orientation has been found to be one of the important factors of shopping behavior [10]. Past researches suggested that shopping behavior varies among consumers as a result of different shopping orientations adopted by the consumers [7]. Lim et al. [10] described the different behaviors practiced by consumers with different shopping orientations as:

In-home shoppers who liked to shop from home; economic shoppers who shopped around before making purchase decisions; mall shoppers who preferred to shop at malls; personalized shoppers who liked to shop where they knew the salespeople; ethical shoppers who liked to shop in local stores to promote the community; convenience shoppers who placed a premium on convenience when shopping; and enthusiastic shoppers who enjoyed shopping.

However, different sets of shopping orientations have been found to have significant effect on online apparel shopping behaviors among college students of different countries. While Seock and Bailey [6] found that the online apparel shopping behaviors of college students in the United States are orientated by shopping enjoyment, brand/fashion consciousness, price consciousness, and in-home shopping tendency, Gehrt et al. [9] reported that online shopping behaviors of individuals in India were influenced by sets of shopping orientations which include value singularity, quality at any price, and store reputation. Hence, the present study aims to explore shopping orientations which influence online apparel shopping behaviors of Cameroon college/university students.

Gender differences in online apparel shopping behaviours: Past literatures suggested that gender is a crucial factor of online shopping behaviours as gender differences exist in shopping preferences and shopping motivation. Study done by Nielsen [2] reported differences in males and females attitudes towards store shopping and online shopping behavior. It was found that women preferred store shopping which enhanced their shopping experience. While on the other hand, male participants rated more positively towards online shopping. Meanwhile, Chou et al. [11] have proposed that males and females have different online shopping behaviors as their shopping motivations were different. The results revealed that male college students tend to be motivated by utilitarian value which emphasized on shopping goal and purpose, such as cost saving and availability of information while female college students tend to be motivated by hedonic value, which emphasizes on shopping experiences, such as fantasy, happiness and enjoyment.

Gender differences in shopping orientations: Researches had not only supported that gender differences exist in online shopping behaviors among college students, shopping orientations was also found to be different for male and female college students. This was supported by a research done by Seock and Bailey [6] which has not only confirmed gender differences of college students in online apparel shopping behaviors (i.e., online information search and purchase behavior), but significant findings were also yielded for gender differences in the college students’ shopping orientations. In their study, Seock and Bailey [6] found that female college students were more motivated by shopping enjoyment, price consciousness, brand/ fashion consciousness and shopping confidence than male students.

Research theoretical framework

As mentioned in the previous section, the research conducted by Seock and Bailey [6] have identified both factors of shopping orientation and gender to have significant relationship with the two dimensions of online apparel shopping behaviours of college students, namely, information search and purchase behaviour. The present study hence adopted the theoretical framework of Seock and Bailey’s study to investigate whether similar findings can be replicated in the context of Malaysia. Supported by past literatures discussed previously, the present study proposed the following hypotheses:

• Hypothesis 1: College students’ shopping orientations will be significantly related to online information searches for apparel products.

• Hypothesis 2: College students’ shopping orientations will be significantly related to online purchases of apparel products.

• Hypothesis 3: There will be gender differences between male and female college students’ experience of online information search for apparel products.

• Hypothesis 4: There will be gender differences between male and female college students’ experience of purchases for apparel products.

• Hypothesis 5: There will be gender differences between male and female college students’ shopping orientations.

Based on these hypotheses, the current research constructed a research theoretical framework as shown in Figure 1. Founded by the research framework of Seock and Bailey [6], two independent and two dependent variables were tested in the current study. The first independent variable is shopping orientations of college students. This independent variable is studied through a Shopping Orientations Questionnaire adapted by Seock and Bailey [6]. The second independent variable is gender of college students which consists of two categories: males and females. In the current research, both of these independent variables were tested to investigate their effects on college students’ online apparel shopping behaviours, as stated in hypotheses 1 to 4. Particularly, two dependent variables were investigated: (1) online information search and (2) online purchase behaviour of college students, adapted from Seock and Bailey’s [6].

accounting-marketing-theoretical

Figure 1: Research theoretical framework.

Methodology

This section describes the sampling method, sample used, analysis method, as well as the measurement of variables in the current study.

Sampling method

One of the non-probability sampling techniques termed convenience sampling was adopted as the sampling method for this research. With this sampling method, respondents in this study are sampled by chance. In addition, respondents are approached based on availability and convenience of the researchers who are currently located in Catholic University Institute of Buea. The designed questionnaires were distributed by the researchers to college students who are currently pursuing Diploma or Degree studies at Catholic University Institute of Buea.

Study population

As this research focuses on the population of Cameroon college/ university students, the sample of this study is local college students who are currently pursuing Diploma or Degree studies. To note, the present study differentiates Diploma students from university students who are already in their Degree education level.

Sample selection

Around 130 sets of questionnaire are randomly distributed to college/university students at Catholic University Institute of Buea. Adapting from Seock and Bailey [6], some restrictions have been imposed to screen out inappropriate sample among the respondents. The respondents have to be within the age range of 18 to 22 years old, Cameroon college/university students who are currently pursuing their Diploma or Degree studies. Marital status was also one of the restrictions, for individuals with different marital status may have different shopping behaviours due to lifestyle variations; the current study only sampled single or unmarried college/university students. Among the 130 respondents approached, data of 9 respondents have been removed due to unfulfilled sample requirement. Hence, the total sample size (N) for this study is 121.

Measurement of variables

Gender: Respondents are grouped into 2 categories: males and females, based on their gender.

Shopping orientation: Shopping orientation is measured by the Shopping Orientation Questionnaire, adapted directly from Seock and Bailey [6]. There are a total of 25 items in this six point Likert-scale (1=strongly disagree; 2=disagree; 3=somewhat disagree; 4=somewhat agree; 5=agree; 6=strongly agree); these items/statements require the respondents to rate about their attitudes towards shopping for apparel products. These items will be tested through factor analysis to be grouped into various factors and their reliability is also tested (Section 4.3). Mean score will then be tabulated where higher scoring indicates greater adoption of the shopping orientations and vice versa.

Online information search and online purchase behaviour on apparel products: The two dependent variables in the current study are online information search and online purchase behaviour. The designed questionnaire is adapted directly from Seock and Bailey [6], where data for these two dependent variables were collected through 4 items (2 items for each dependent variable) on a 5-point Likert scale (1=never; 2=seldom; 3=sometimes; 4=regularly; 4=very often). Mean score is tabulated for each dependent variable, where higher score indicate more frequent experiences for online information search and online purchase behaviour on apparel products, vice versa.

Analysis method

A statistical tool or software named Statistical Package for the Social Sciences (SPSS) has been utilized to input and analyse data collected. Tests have been conducted to investigate the relationships between the mentioned variables which have been proposed in the hypotheses. The results are presented and discussed in the following sections.

Analysis and Results

Introduction

This section of the paper discusses the analysis conducted and presents findings for this research. Three main stages of analysis are presented: (1) descriptive analysis to describe demographic characteristics of respondents, (2) preliminary analysis which consists of factor analysis and reliability testing, and (3) hypotheses testing which utilizes multiple regressions and t-tests in order to test the relationships of the variables.

Descriptive analysis

121 responses have been gathered from college/university students who are currently pursuing diploma or degree courses at Catholic University Institute of Buea, Cameroon. Demographics characteristics of the respondents were presented in Table 1. This information is essential as it could provide a better understanding on the profiles of respondent studied in this research. Among these respondents, 46.3% are males (n=56) while 53.7% are females (n=65). Equal number of male and female respondents is important for this study, for gender is one of the independent variable tested in this research. As age restriction was imposed for current study, respondents did not differ much on their age, where all of the respondents are within the age range from 18 to 22.

Variable N=121
Frequency Percentage
Gender
Male 56 46.3
Female 65 53.7
Age
18-20 57 47.1
21-23 64 41
Race
Francophone 65 53.7
Anglophone 37 30.6
Equatorial Guinea 16 13.2
Others 3 2.5
Educationlevel
Freshmen 32 26.4
Sophomore 49 40.5
Junior 40 33.1
Monthly allowance/income level (FCFA)
Below 100,000 9 7.4
100,000–300,000 18 14.8
301,000–500,000 33 27.3
501,000–700,000 19 15.7
701,000–900,000 27 22.3
900,000 and above 15 12.4

Table 1: Demographic characteristics of respondents.

Mean obtained for age of the respondents of this study was 20.55. Similarly, there was also restriction set for the current education status, where the respondents must be college/university students who are currently pursuing Diploma or degree studies. Most of the respondents are in their sophomore year (49.5%, n=49) at the time when this research was conducted. Most of the respondents are Francophone (53.7%, n=65); this reflects the actual student population in Catholic University Institute of Buea which is mainly made up of Francophone students.

Demographic data of monthly allowance or income level was also gathered from the respondents. This characteristic may indicate the spending or purchasing power of the respondents. Most of the respondents fall within the range of 301,000FCFA to 500,000FCFA which represents 27.3% of the total respondents.

Preliminary analysis

In this stage, factor analysis and reliability testing were conducted to identify the indicators of shopping orientations which are present in this study. Exploratory Factor Analysis (EFA) is conducted to identify strongly correlated variables or items in the Shopping Orientation Questionnaire adapted from Soeck and Bailey [6] and group them into various factors or dimensions of shopping orientations. Among the 25 items or variables from the original Shopping Orientation Questionnaire, only 20 variables were found highly correlated and have been able to group into 6 factors. 5 variables have been removed due to:

• Q3 and Q13: These variables did not correlate highly (minimum correlation=0.3) with at least one other variables.

• Q23 and Q12: Q23 and Q12 have communalities value of 0.400 and 0.457, respectively. Communalities reflect the variables’ contribution to the factor analysis. This assumption is only met when the score is more than 0.5.

• Q10: This variable has high cross loadings in components 1 and 2 according to the Rotated Component Matrix.

After removing the 5 variables above, the final model has been tested again with factor analysis and all assumptions (i.e., Barlett test, correlation mix, overall and individual MSA and communalities) had been met.

Factor analysis: grouping factors of shopping orientations assumptions of factor analysis: All assumptions of factor analysis have been met for as shown in Tables 1.1-1.4 below, where:

• Barlett test is significant at 5% level (Table 1.1)

Kaiser-Meyer-Olkin Measure of Sampling Adequacy .731
Bartlett's Test of Sphericity Approx. Chi-Square 871.499
df 190
Sig. .000

Table 1.1: KMO & Barlett’s Test.

• 50% of the variables are correlated with each other and all variables are correlated (r>0.3) with at least one other variable (Table 1.2)

    q1 q2 q4 q5 q6 q7 q8 q9 q11 q14 q15 q16 q17 q18 q19 q20 q21 q22 q24 q25
Correlation q1 1.000 .605 .267 .293 .396 .261 .412 .192 .548 .194 .485 .396 .053 .193 .360 .304 .161 .118 .242 .163
q2 .605 1.000 .208 .275 .226 .190 .358 .314 .422 .245 .557 .508 .127 .123 .370 .245 .064 .219 .098 .203
q4 .267 .208 1.000 -.097 .114 .115 .115 -.066 .064 .622 .017 .073 -.022 .086 .121 .036 .226 .012 .019 .011
q5 .293 .275 -.097 1.000 .102 -.096 .173 -.335 .148 -.214 .267 .118 -.084 -.209 .119 -.019 -.344 .076 .114 -.109
q6 .396 .226 .114 .102 1.000 .383 .200 .086 .219 .095 .261 .030 -.103 .138 .157 .158 .094 .107 .532 .132
q7 .261 .190 .115 -.096 .383 1.000 .284 .102 .308 .172 .248 .196 -.018 .384 .198 .305 .266 .047 .283 .210
q8 .412 .358 .115 .173 .200 .284 1.000 .224 .496 .282 .373 .275 .052 .135 .334 .456 .188 .304 .378 .190
q9 .192 .314 -.066 -.335 .086 .102 .224 1.000 .262 .000 .112 .341 .137 .323 .080 .423 .244 .237 -.105 .136
q11 .548 .422 .064 .148 .219 .308 .496 .262 1.000 .164 .467 .419 .209 .100 .370 .426 .077 .186 .179 .282
q14 .194 .245 .622 -.214 .095 .172 .282 .000 .164 1.000 .205 .169 -.017 .103 .247 .102 .194 .053 .091 .030
q15 .485 .557 .017 .267 .261 .248 .373 .112 .467 .205 1.000 .589 .205 .054 .492 .208 .023 .259 .199 .106
q16 .396 .508 .073 .118 .030 .196 .275 .341 .419 .169 .589 1.000 .283 .147 .426 .344 .108 .252 .047 .204
q17 .053 .127 -.022 -.384 -.103 -.018 .052 .137 .209 -.017 .205 .283 1.000 .176 .375 .292 .063 .241 .085 .320
q18 .193 .123 .086 -.209 .138 .384 .135 .323 .100 .103 .054 .147 .176 1.000 .212 .329 .431 .022 .092 .188
q19 .360 .370 .121 .119 .157 .198 .334 .080 .370 .247 .492 .426 .375 .212 1.000 .294 .176 .213 .366 .151
q20 .304 .245 .036 -.019 .158 .305 .456 .423 .426 .102 .208 .344 .292 .329 .294 1.000 .285 .234 .153 .191
q21 .161 .064 .326 -.344 .094 .266 .188 .244 .077 .194 .023 .108 .063 .431 .176 .285 1.000 .172 .021 .181
q22 118 .219 .012 .076 .107 .047 .304 .237 .186 .353 .259 .252 .241 .022 .213 .234 .172 1.000 .148 .583
q24 .242 .098 .319 .114 .532 .283 .378 -.105 .179 .091 .199 .047 .085 .092 .366 .153 .021 .148 1.000 .202
q25 .163 .203 .011 -.109 .132 .210 .190 .136 .282 .030 .106 .204 .320 .188 .151 .191 .381 .583 .202 1.000

Table 1.2: Correlation Matrix.

• Overall MSA is more than 0.6 (Table 1.1) while all individual MSA are more than 0.5 (Table 1.3)

    q1 q2 q4 q5 q6 q7 q8 q9 q11 q14 q15 q16 q17 q18 q19 q20 q21 q22 q24 q25
Anti-image Correlation q1 .833a                                      
q2 -.320 .854a                                    
q4 -.224 -.090 .522a                                  
q5 -.193 -.139 -.074 .618a                                
q6 -.207 -.022 -.088 .026 .615a                              
q7 .068 -.012 -.050 .076 -.047 .790a                            
q8 -.060 -.060 .058 -.133 .218 -.017 .781a                          
q9 .059 -.246 .159 .045 -.159 .085 -.069 .659a                        
q11 -.304 .073 .049 .022 -.035 -.141 -.253 -.121 .827a                      
q14 .112 -.085 -.601 .312 .025 .013 -.209 .012 -.022 .598a                    
q15 -.088 -.230 .257 -.081 -.223 -.150 -.093 .208 -.130 -.156 .755a                  
q16 -.063 -.119 -.052 .023 .211 -.035 .102 -.233 -.033 .001 -.391 .842a                
q17 .083 .012 -.100 .125 .194 .200 .157 -.019 -.076 .132 -.146 -.027 .629a              
q18 -.113 .033 .023 .051 -.025 -.276 .010 -.240 .177 -.011 .027 .037 -.113 .722a            
q19 -.017 -.083 .029 -.086 .108 .048 .046 .107 -.110 -.135 -.164 -.121 -.260 -.126 .827a          
q20 -.037 .048 .037 .013 -.088 -.144 -.274 -.190 -.151 .021 .154 -.131 -.237 -.095 -.032 .831a        
q21 -.129 .083 -.153 .319 -.014 -.079 -.109 -.087 .108 .073 .008 .024 .094 -.235 -.146 -.079 .718a      
q22 .122 .054 -.063 -.139 -.035 .119 -.172 -.193 .163 .018 -.208 .007 -.011 .179 -.064 -.077 -.129 .611a    
q24 -.017 .105 .088 -.031 -.520 -.146 -.350 .179 .132 -.004 .116 -.009 -.103 .026 -.309 .047 .103 .025 .593a  
q25 -.043 -.158 .071 .156 -.024 -.137 .056 .146 -.231 .022 .231 -.083 -.222 -.132 .124 .116 -.011 -.591 -.109 .583a

Table 1.3: Anti–Image correlation (MSA).

• Communalities value is more than 0.5 (Table 1.4)

  Initial Extraction
q1 1.000 .667
q2 1.000 .666
q4 1.000 .764
q5 1.000 .630
q6 1.000 .640
q7 1.000 .511
q8 1.000 .502
q9 1.000 .688
q11 1.000 .532
q14 1.000 .775
q15 1.000 .666
q16 1.000 .649
q17 1.000 .721
q18 1.000 .630
q19 1.000 .708
q20 1.000 .545
q21 1.000 .573
q22 1.000 .803
q24 1.000 .787
q25 1.000 .742

Table 1.4: Communalities.

• No high cross loadings (Table 1.5)

  Component
  1 2 3 4 5 6
q1 .736          
q2 .788          
q4       .863    
q5 .514          
q6     .726      
q7   .503 .461      
q8 .525          
q9 .419 .553        
q11 .659          
q14       .853    
q15 .719          
q16 .675          
q17           .773
q18   .767        
q19 .420         .657
q20 .401 .566        
q21   .683        
q22         .868  
q24     .850      
q25         .815  

Table 1.5: Rotated Component Matrix.

Factors extraction: The results of the factor analysis and reliability testing are shown in Table 2 below (65.95% of the variation). Based on the Rotated Component Matrix (Table 1.5) shown above, 20 variables have been grouped into 6 factors of shopping orientations:

Variable Item Factor Loadinga Percentage of variance explained Cronbach’s Alpha
TOTAL     65.95 0.828
Shopping Pleasure     19.05 0.814
Q1 0.736    
Q2 0.788    
Q5 0.514    
Q8 0.525    
Q11 0.659    
Q15 0.719    
Q16 0.675    
       
Convenience/Time consciousness     11.75 0.693
Q7 0.503    
Q9 0.553    
Q18 0.767    
Q20* 0.566    
Q21 0.683    
       
In-home shopping tendency     9.86 0.692
Q6 0.726    
Q24 0.850    
       
Price consciousness     9.09 0.765
Q4 0.863    
Q14* 0.853    
       
Brand/Store loyalty     8.61 0.735
Q22 0.868    
Q25 0.815    
       
Brand/Fashion consciousness     7.59 0.544
Q17 0.773    
Q19 0.657    
     

Table 2: Factor analysis of apparel shopping orientation.

Factor 1: Shopping pleasure

• Q1. I enjoy shopping for clothes

• Q2. I think I’m a good clothing shopper

• Q5. I enjoy spending time browsing for clothes

• Q8. I’m able to choose the right clothes for myself

• Q11. Shopping for clothes puts me in a good mood

• Q15. I’m interested in fashion

• Q16. I feel confident in my ability to shop for clothes

Factor 2: Convenience/time consciousness

• Q7. I put a high value on convenience when shopping for clothes

• Q9. I like to buy clothes within shorter time frame

• Q18. I shop for clothes where it saves time

• Q20. I don’t like to spend much time shopping for clothes*

• Q21. I usually buy my clothes at the most convenient place

Factor 3: In-home shopping tendency

• Q6. I like to shop from home

• Q24. I like to shop for clothes by mail, telephone or the Internet

Factor 4: Price consciousness

• Q4. I pay a lot of attention to clothing prices

• Q14. I don’t mind paying high prices for clothes*

Factor 5: Brand/Store loyalty

• Q22. Once I find a brand I like, I stick with it

• Q25. I try to stick to certain brands and stores when I buy clothes

Factor 6: Brand/fashion consciousness

• Q17. A well-known brand means good quality

• Q19. I try to keep my wardrobe up to date with fashion trends

With a total Cronbach’s alpha value of 0.828 obtained through reliability analysis, there is high level of reliability and internal consistency for this research, for Cronbach’s Alpha value of 0.70 and above are considered to be acceptable for research in social science field [12].

Hypotheses testing

Multiple regressions were used to test Hypotheses 1 and 2 which proposed college students’ shopping orientations is related to online shopping behaviours on apparel products. To be specific, the independent variables are the six shopping orientations identified in previously-discussed factor analysis while the dependent variable for Hypothesis 1 is online information search and for Hypothesis 2 is purchase behaviour (Table 3).

Shopping orientations Standardized Coefficients t Sig.
Brand/fashion consciousness .122 1.352 .179
Convenience/time consciousness -.296 -2.535* .013
Brand/Store loyalty -.046 -.543 .588
Price consciousness -.040 -.539 .591
In-home shopping tendency .158 2.263* .026
Shopping pleasure .274 2.299* .023
 

Table 3: Regression analyses for online information searches.

Results for regression conducted to test Hypothesis 1 shows that the overall regression is significant at 1% level, for the F-test of 3.02 is with a significant value of 0.009. This implies that Hypothesis 1 is supported. However, the R square of 0.137 indicates that 13.7% of the variation in the shopping orientation factors is explained by the regression. However, the fit of the regression is weak, for regents [12] noted that R value within the range of 0 to 0.2 is considered as weak.

From the coefficient table above, it is concluded that 3 out of 6 shopping orientations above have significant effect on online information search at 5% level (p<0.05). It shows that convenience and time consciousness is negatively related to the search online with a t-value of -2.535 and coefficient of -0.296. In home shopping tendency has a positive influence on online information search bearing a t-value of 2.263 and coefficient of 0.158 while shopping pleasure has a t-value of 2.299 and coefficient of 0.274, also proving its positive relationship with the online information search. The coefficients table also shows that no significant relationship is found between online information search, brand/fashion consciousness, brand/store loyalty and price consciousness.

On the other hand, regression analyses for Hypothesis 2 reveals that respondents’ shopping orientations were not significantly related to their online purchase of apparel products, for R square=0.035, F=694, p<0.5. Thus, Hypothesis 2 is not supported.

To test Hypotheses 3 to 5, t-tests were used in order to check for the gender differences in online information search, purchase behaviour, and shopping orientations. Means were compared between male and female respondents in these t-tests. In all t-tests conducted, all tested variables (i.e., the six shopping orientations, online information search and online purchase behavior) have met the assumption of Levene’s Test for Equality Variances at p>0.5.

As shown in table below, results of t-test shows that there is gender difference in h online information search of college students, hence supporting Hypothesis 3. To be specific, sig is 0.001 for the dependent variable online information search, at 1% significant level. Female students search for website which sells apparels more often (mean=3.4077) than male students (mean=2.8750). On the other hand, t-test reveals that there is no significant difference between two genders of students when purchasing online (p=485). This finding is not consistent with the proposed Hypothesis 4 (Table 4).

Dependent variables Mean d.f. T-value
Male Female
Online information search 2.875 3.408 119 -3.435*
Online purchase behaviour 2.670 2.777 119 -7.00

Table 4: T-tests for online information searches and purchase behavior.

Looking at the result of t-test for Hypothesis 5 (Table 5), only two variables of the six shopping orientations has significant gender differences – they are shopping pleasure, in-home shopping tendency. The rest four factors have no significant difference between two genders in shopping orientations, for p>0.5.

Sig is 0.004 for the dependent variable of shopping pleasure, at 1% significant level. Female students enjoy significantly more (mean=4.300) than male students (mean=3.903) when shopping online; Sig is 0.002 for the dependent variable In-home shopping tendency, at 1% significant level. Female students are more (mean=3.438) likely doing online shopping from home than Female students (mean=2.804).

Dependent variables Mean d.f. T-value
Male Female
Brand/fashion consciousness 3.903 4.300 119 -2.945 *
Convenience/time consciousness 3.969 4.139 119 -1.230
Brand/Store loyalty 2.804 3.438 119 -3.209*
Price consciousness 4.1964 4.408 119 -1.071
In-home shopping tendency 4.2143 4.269 119 -0.302
Shopping pleasure 3.473 3.523 119 -0.272

Table 5: T-tests for shopping orientations.

Discussion and Recommendations

In this study, there are six shopping orientation factors that were identified through factor analysis. They are brand or fashion consciousness, convenience or time consciousness, brand or store loyalty, price consciousness, in-home shopping tendency and shopping pleasure. For Hypothesis 1, the relationship of these shopping orientations and online information search on apparel products were tested. Although shopping orientations was found significantly related to online information search, only three of the shopping orientations have significant influence on searching for information online – they are convenience and time consciousness, in-home shopping tendency, and shopping pleasure. Convenience and time consciousness has the strongest influence on searching online while in-home shopping tendency and shopping pleasure has a more positive influence on searching online for information.

Inconsistence with findings obtained by Seock and Bailey [6], convenience and time consciousness have been found to have a negative influence on online information search. This could be due to students being conscious that searching for information online will take up a lot of their time but they are willing to sacrifice the time just to gather more information of their interested purchase. Difference from the findings of Seock and Bailey [6] could be caused by the difference in culture as the students who are being tested in our study are Malaysian college students while those who were studied by Seock and Bailey are US college students. The difference in culture will produce different types of buyers. With that, marketers should ensure that the sites are easily accessible and does not take too long to load as long-loaded sites will discourage people from surfing. They would most probably just leave the site for other sites. They will also not return as recurring customers as just by thinking of the website which requires long time to load will discourage them from visiting the site.

Shopping pleasure also has a significant influence on the search for information online and they are positively related. This implies that shopping pleasures encourage them to search for information on their interested purchase online. This is in accordance to the past research by Seock and Bailey. Therefore, marketer should take advantage of the shopping pleasure that shoppers already have and further provide shoppers with pleasure by shopping at their online shopping websites such as offering timely delivery and excellent customer services by attending to emails from customers almost instantly.

The next variable that has significant relationship is the in-home shopping tendency and it has a positive influence on the search for information online. This implies that students actually enjoy shopping from home. This is an added advantage for marketers as they do not have to find ways to influence the students to shop at home but instead just to work on not further encouraging the students to shop from home. One thing they could do is to make sure that the students are satisfied with their purchase and will not have any incidences occurring that would make them stop shopping online. Marketers will have to make sure that the items being purchased by students meet the students’ expectation.

Hypothesis 2 which states that college students’ shopping orientation is significantly related to their online shopping behaviour was not supported by findings of the present study. This is probably due to when students shop online, they do not get the instant gratification, because they do not get to use it or wear it right away but have to wait for a period of time. Besides, they do not get to try or check on the quality of the apparel until they have made payment and received it [11]. Other than that, some of the online shopping site requires buyers to bear shipping costs and at times, the cost may be higher than the cost of the product they are buying. Some inexperienced student also refused to make any purchases online for the fear of being a victim of identity theft as debit or credit card or internet banking account are used for online shopping payment. Therefore, they are prone to being a victim of fraudulent cases which are likely to happen especially with sellers who are not of good background. They would hack into their accounts and personal information which are embedded in the credit cards. With that, what marketer can do is to offer cash on delivery that is to only collect payment upon having the apparels sent to the customers and customers will only need to pay should they are satisfied with the clothes purchased.

Gender differences in online shopping behaviours and shopping orientations

When comparing online information search between female students and male students, the result revealed that women tend to spend more time online in searching information about clothing than man. Zollo [13] stated that female consumers tend to spend more time on collecting information on products such as cosmetics, clothes and jewellery than male consumers. This indicates that online shop owners should spend more efforts in analysing the behaviours, preferences and habits of female consumers in order to attract them in purchasing their apparel products.

Therefore, it can be concluded that female students need more specific information of clothing, such as color, size, brand, price, popularity, recommendation from previous buyers and make comparison before buying to ensure that the buy is worth their money paid. This study will not only help the owner of the online retailing sites but also potential investors of online retailing as well as the advertisers who can consider advertising on the online shopping sites.

Consistent with Hypothesis 5, the findings of this study also showed that there is gender difference in college students in the six dimensions of shopping orientations. One of the results suggests that female students find more pleasure in shopping online as compared to male students. This may give an implication to virtual apparel store marketers that they should invest more on the design of the website in order to make it look attractive and fashionable to capture the attention and stimulate the urge of female students to make purchases. Among the things that the online stores can consider is to use appropriate and attractive model in modeling the clothes, attractive and in-trend colors for clothing sold and auto enlargement as the mouse is being hovered at the picture in order to ease the shoppers’ view of the clothing [14].

Another positively significant difference between genders lies in the in-home shopping tendency. Women feel more comfortable and secured when they shop from home as compared to men. Shopping at home also gives shoppers the comfort and not having to take the trouble to dress up and will be able to multitask. Shopping from home also gives more advantage and freedom in terms of time spent. Shopping at home allows more time being spent on searching for the perfect clothes as compared to shopping in the shopping centre itself as shoppers have to adhere to the opening hours of the stores hence having to rush while shopping [15].

On the other hand, there are no significant differences found in convenience or time consciousness, price consciousness, brand or store loyalty and brand or fashion consciousness between genders as compared to students in the United States. Seock and Bailey [6] found that students in the US of the same education level has significant difference between genders when considering convenience or time consuming, price label, loyalty to a certain brand or store, and consciousness about the brand or fashion element. Another view is, though there is no significant difference between genders, consumers is proven to be sensitive about the shopping convenience, price, brand, and fashion aspects [16].

Conclusions

The present research had yielded fruitful results to understand the relationships between shopping orientations, gender, online information search and online purchase behavior on apparel products. To note, the influences of shopping orientations and gender have been found to be more significantly impactful on online information search on apparel products. This may imply that online retailer or marketer must understand these characteristics of their consumers who are in the category of college students, in order to obtain greater competitive advantages and sales performance. Online retailers or marketers should understand that online search activities include number of websites visited by consumers before they make a purchase, types of websites searched, frequency of browsing online, the number of searches, and use of search terms or keywords [17,18].

As mentioned previously, there are some limitations in the present study. Findings obtained in this study may not be generalizable to all Malaysian college students’ population as the sample population (particularly for race of respondents) adopted by the current study did not reflect the actual composition of Malaysian college students. Furthermore, the survey should be extended to all states in Malaysia instead of just focusing on Klang Valley to have a better result from the analysis.

In short, though the current research have found meaningful findings which can aid online retailers to better understand the online shopping behaviour of Malaysian college students, further research is needed to overcome the limitations identified in this study. In addition, although the current research had identified shopping orientations practiced by Malaysian college students, more research can be conducted to investigate deeper on how these shopping orientations influence college student consumers’ online shopping behaviours. The present study recommends that this topic or research area should be extended to understand Malaysian college students’ online shopping behaviours of various products (e.g., books, flight tickets etc.) in future research.

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