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DETERMINANTS OF CONTINUANCE INTENTION TO USE ONLINE SHOPS IN NIGERIA

FUNMILOLA OLUBUNMI OMOTAYO

Africa Regional Center for Information Science (ARCIS), University of Ibadan, Nigeria

Tel: +2348034051814

Email: lolaogunesan@yahoo.com

ROFIAT OMOTOPE ADEYEMI

Africa Regional Center for Information Science (ARCIS), University of Ibadan, Nigeria

Email: adeyemirofiat2@gmail.com

Visit for more related articles at Journal of Internet Banking and Commerce

Abstract

Online shopping creates opportunities for businesses to reach consumers globally and directly. Evidences have revealed that many consumers who search different online shops abandon their purchase intentions or may not continue to use the online shops due to one reason or the other; hence, the need to carry out a study to investigate the factors that influence continuance intention to use. This study therefore, investigated the determinants of continuance intention to use online shops among students of two federal universities in Nigeria. Survey design was adopted for the study. 593 copies of questionnaire were distributed to students who were selected by disproportionate stratified random sampling technique from the two universities, while 455 copies were retrieved and considered suitable for analysis, translating to 76.7% response rate. The study found that perceived usefulness, perceived ease of use, subjective norms, perceived enjoyment, perceived site quality, convenience, prior experience, shopping habit and trust determined continuance intention to use online shops by the students. However, perceived security and gender did not determine continuance intention to use online shops. The study concludes that the students had the intention to continue to patronise online shops. Findings from this study would assist e-retailers in knowing the factors that determine continuance use of online shops in order to meet the expectations of the youths who constitute the highest proportion of online shoppers in Nigeria.

Keywords

Continuance Intention; E-Commerce; Online Shopping; Nigeria; University Students

Introduction

Electronic commerce is one of the essential characteristics in the Internet era. Electronic commerce, especially online shopping, is a growing phenomenon all over the world, particularly common in countries where highly developed ICT infrastructure, such as the Internet, is available for marketing activities. Online shopping is, however, gradually gaining prominent also in developing countries such as Nigeria, becoming a buzz that is permeating every marketing activity. Online shopping is the activities of searching, buying, selling products and providing services online [1]. Online shopping involves all purchasing activities on the Internet which range from information search to actual purchase. These processes of purchasing products and services through the Internet are similar to what is obtained in the traditional shopping activities. The proliferation of the Internet, smart mobile devices and mobile money have facilitated the adoption and use of online shopping and thereby the growth of e-commerce, providing easier access and promoting convenience shopping across mobile application platforms. The ease of access to the Internet has actually been identified as one of the factors encouraging the adoption and growth of online shopping [2,3].

Online shopping has continued to change the way business are carried out ever since its evolution. Its development has brought advantages to the society, individuals, organisations, industries and governments. There are many benefits associated with this mode of shopping. To the buyers or consumers, it allows them place a purchase order at any time. It also helps reduce transaction costs for participation or exchange in the market. It gives the comfort of been able to make 24 hours transactions without requiring the physical interaction with the business organisation. It also allows purchases and sales to be performed from the comfort of homes or working places. Buyers also have the opportunity of buying or selling any product at any time. In addition, buyers can have quick and continuous access to information on different websites at the click of a button. To the seller, online shopping helps increase revenue, reduce operation and maintenance costs, reduce purchase and procurement costs, raise customer loyalty and retention, reduce transportation costs, develops customer and supplier relationships, improves speed of the process of selling, and helps develops company image and brand. Hence, to attract and retain consumers, e-retailers design offer shoppers certain benefits that cannot be offered by brickand- mortal (physical) stores, such as promoting user-friendly websites, offering useful products, thereby ensuring consumers get values for their money. This helps attract a growing number of consumers to online shopping [4,5].

Studying online shopping behaviour of consumers has been one of the most important research areas giving recognition in the past decades. Although, ecommerce is at an advanced stage in the developed countries of the world, online shopping trend in Nigeria is not as advanced as it is in these countries. However, the interest in online trading in Nigeria is on the rise even as many consumers are adopting the method of shopping. Many retailers in Nigeria are now using online retailing to attract the customers, which gives consumers the opportunity of having various online options from which to choose and buy from. In addition, with more people becoming computer literate and open to adopting ICT usage, and the proliferation of smart phones, e-commerce is gradually gaining popularity among many Nigerian.

Several researches have been conducted to investigate various factors influencing adoption and use of online shops, however, few of these studies have investigated the factors that influence users’ continuance intention to use or continuous usage. It is important to note that adoption and use of a technology may not translate into continuous usage. Online shopping involves a number of activities such as information search, website browsing/navigation, ordering, payment, customer service interactions, delivery, post-purchase problem resolution, and satisfaction with purchases [6]. It is actually when a customer is satisfied that he/she will want to repeat a purchase. Studies have revealed that many consumers who search different online shops abandon their purchase intentions, that is, may not proceed to actual purchase [1]. Some users may also abandon continuous use of online shops after the first use. There is therefore the need to investigate the factors that make people use online shops continuously, that is, the factors that make buyers or consumers go back to make purchases on online shops over and over. This could help online business owners understand the factors that encourage consumers to remain loyal to them. Continuance patronage of online shops is important, because customers who fail to return reduce the shop’s customer base and its revenues, and may well require substantial expenditure to attract them back. Thus, this research is mainly focused on use of online shops and repurchases intention, which has rarely been addressed. The study identifies the determinants of continuance intention to use online shops among students of two Nigerian universities. The terms online shopping, electronic shopping and Internet shopping are used interchangeably in this study. The following research questions were answered:

• What are the different types of online shops used by the students?

• How frequent do students use online shops?

• How long has students been using online shops?

• What kind of products do students buy from online shops?

• What are the factors that determine students’ continuance intention to use online shops?

• What are the challenges faced by students in using online shops?

Literature Review, Research Model and Hypotheses

Nigeria has been one of the fastest growing countries in the world in terms of ICT with the introduction of global system for mobile communication in 2001; however, online shopping did not gain ground until 2012. There are a number of online shops operating in Nigeria, among which are Jumia, Konga, Fouani, Jiji, OLX, Slot, Dealdey, Kaymu, among others. Review of literature reveals many factors that motivate buyers to visit online shopping websites continually. These studies were carried out in various countries of the world with minimal focus on Nigeria. Bhattacherjee [7] examined intention to continue using information systems (IS) by customers of the online banking division of one of the largest national banks in the United States. The results suggested that users’ continuance intention was determined by their satisfaction with IS use and perceived usefulness of continued IS use. Bhattacherjee [8] also examined the antecedents of online brokerage users’ intention to continue using business-toconsumer e-commerce services. The result shows that consumers continuance intention was determined by their satisfaction with initial service use, their perceived usefulness of service use, and the interaction between perceived usefulness and loyalty incentives for service use. Koufaris [9] conducted a study to test customer intention to repurchase on online bookstores. The result shows that shopping enjoyment and perceived usefulness were positively significant to customers’ intention to return to visit online bookstore. Lee et al. [10] found that trust and low transaction costs increased customer loyalty towards Internet stores and these factors varied according to the level of involvement with the product purchased through the stores.

In addition, Kim et al. [11] examined the effect of consumers’ trust on consumer expectations and satisfaction in Korea. The study found that both consumer’s trust and expectation had positive influences on consumer's satisfaction which also had a positive influence on the intention to repurchase. Chiang and Dholakia [12] found that convenience and product type were two major forces driving consumers to shop on the Internet. Chung and Lee [13] examined the factors influencing repurchase intention in Internet shopping malls. The result shows that demographic factors, product perceptions, customer service, perceived ease of use, site image, promotion, perceived consumer risk, personal characteristics and Internet communications environments were positively related with repurchase intention. However, perceived consumer risk which had a negative relationship with re-purchases intention. Liang and Lai [14] also examined the effect of design quality on consumer choice of online bookstores and found that the quality of e-store design had an effect on the consumer purchase decision. Consumers were more likely to shop at well-designed websites. The results also showed that design quality is as important as product price to make customers visit and purchase again. Moreover, Atchariyachanvanich et al. [15] examined what kept Japanese online customers repurchase through the Internet. The result shows that both extrinsic benefits (savings, time and money) and intrinsic benefits (pleasure, novelty, and fashion involvement) had significant positive effects on customers’ intention to continue purchasing through the Internet. The result also shows that confirmation, satisfaction, perceived usefulness, perceived incentives, and customer loyalty were the key factors underlying the customers’ intention to continue purchasing items through the Internet. In Thailand, Wangpipatwong et al. [16] investigated the fundamental factors that influenced citizens’ continuance use of egovernment websites. The results reveal that perceived usefulness and perceived ease of use of e-government websites and citizen’s computer selfefficacy directly enhanced citizen’s continuance use of e-government websites.

Lim et al. [17] investigated online search pattern and buying behaviour in Malaysia. Findings reveal that respondents who had online purchase experiences had higher intention to make online purchase in the future. The study found no gender difference in terms of the frequency online search and purchase made over the Internet. Al-Maghrabi et al. [18] examined the determinants of customer continuance of online shopping in Saudi Arabia, most especially the reason why many consumers who searched different online retail sites abandoned their purchase intentions. The study found that perceived usefulness, enjoyment, and subjective norms determined e-shopping continuance use. Praveena and Thomas [19] attempted to find the effect of perceived enjoyment on the TAM model to explain the continuance use of Facebook. The result revealed that perceived enjoyment was a strong determinant of attitude towards using Facebook in the study, and that perceived enjoyment influenced attitude more than the original TAM variables-perceived usefulness and perceived ease of use. Assensoh-Kodua [20] investigated the factors that determine the continuance intention of people to use online social network for business transactions and found that perceived trust, social norm and user satisfaction are determinants for the continuance intention of use.

Li [21] established a concept model and discussed how utilitarian values (perceived ease of use and perceived usefulness), social values (satisfaction and trust) and the hedonic value (perceived enjoyment) directly and indirectly influenced customers’ repurchase intention in the context of online shopping in China. The results showed that perceived usefulness, online customers’ satisfaction and perceived enjoyment had significantly positive impact on online customers’ repurchase intention. Moreover, the study established that, compared with utilitarian factors, the hedonic factor (perceived enjoyment) had a stronger positive impact on repurchase intention. BrezavšÃ„ek et al. [22] investigated the main factors influencing the adoption and continuous utilisation of statistical software among university social sciences students in Slovenia. The results show that statistical software self-efficacy, computer attitude, statistics anxiety, statistics learning self-efficacy and statistics learning directly influenced perceived usefulness or perceived ease of use.

Reviews of the literature also reveal that many theories/models have been used to study the continuance use of various technologies, among which are: the Theory of Reasoned Action (TRA), the Theory of Planned Behaviour (TPB), Technology Acceptance Model (TAM), Expectation Confirmation Model (ECM), Unified Theory of Acceptance and Use of Technology, among others. Some variables were identified from these theories to understand continuance intention to use online shops in the Nigerian setting. The variables are perceived usefulness, perceived ease of use, subjective norms, perceived site quality, perceived enjoyment, shopping habit, perceived security, convenience, prior experience/satisfaction, trust, and demographic variables (gender). The conceptual model is shown in Figure 1.

internet-banking-and-commerce-framework

Figure 1: Conceptual framework.

Perceived Usefulness (PU)

PU is the degree to which a person believes that using a particular system would enhance his or her job performance [23]. In this study, PU is defined as the extent to which the students believe that online shopping will enhance their purchase activities. A website is useful if it delivers services to a customer and meets customers’ delivery expectations. It is presumed that the students would continue using an online shop if the shop is able to meet the expectations of the buyer. Studies such as Al-Maghrabi et al. [1,18], Atchariyachanvanich et al. [15], Li [21] and Wangpipatwong et al. [16] have identified PU as a significant factor influencing online customers’ intention to repeat online purchases. It is therefore hypothesised that:

H1: Perceived usefulness of online shops determines students’ continuance intention to use.

Perceived Ease of Use (PEOU)

PEOU is “the degree to which a person believes that using a particular system would be free of effort” [23]. PEOU is the perception of minimum efforts expected by users to deal with a given system. PEOU is considered an essential condition to determine user readiness to accept or adopt certain technology [23]. In this research, PEOU relates to the students beliefs that online shopping is easy to use and do not requires much effort. Hence, the more the students believe that online shops are not complex to use, the more likely they are going to use it continuously. Previous research, such as Chung and Lee [13], Priyanka and Ramya [24] and Wangpipatwong et al. [16] have found positive relationship between PEOU and the acceptance and continuous use of online shopping. Therefore, this study proposed that:

H2: Perceived ease of use of online shops determines students’ continuance intention to use.

Subjective Norms (SN)

Subjective norm is defined as an individual’s perception of whether people important to the individual think the behavior should be performed [25]. It is the perceived pressure to perform a behaviour that comes from observing what important others say or do [26]; a person’s perception of the social pressures that are put on him or her to perform a behaviour. Some studies (e.g. [27]) found no significance of SN on intentions, while others [28] did. Shim et al. [29] found that SN had marginal significant influence on e-shopping intentions. Other studies such as Al-Maghrabi et al. [1], Assensoh-Kodua [20] and Priyanka and Ramya [24] found that SN determined online shops continuance use. Another hypothesis is thus proposed:

H3: Subjective norms of students determine continuance intention to use online shops.

Perceived Enjoyment (PE)

In the context of online shopping, PE is the customer’s perception that by shopping online he or she will have fun. Ulaan et al. [30] mentioned that young people tend to get a feeling of pleasure while doing their activity through internet such as browsing for a product via retailers’ website, and once they get it, it will affect their intention to shop online. Customers can have fun searching and buying products online, which makes online retailers give importance to this hedonic factor when planning to develop their websites. Several studies [1,9,19,21,30,31] have found that perceived enjoyment had a significant positive effect on online customers’ purchase and repurchase intention. For instance, Al-Maghrabi et al. [18] identified enjoyment as one of the factors that explained the level of continuance intentions towards e-shopping in Saudi Arabia, while Li [21] found that the hedonic factor (perceived enjoyment) had a stronger positive impact on repurchase intention. Hence, hypothesis four is proposed:

H4: Perceived enjoyment of online shops determines students’ continuance intention to use.

Perceived Site Quality (PSQ)

Online shopping web site quality refers to overall consumer perceptions of the excellence and effectiveness of an e-retailer's product and/or service offering through its virtual store [6]. Website quality is unique and unanimously seen as a vital factor during the initial online purchase stage. An e-vendors site with user-friendly search and navigation functions provide users with a better sense of control over their online shopping experience which in turn may translate into positive feelings about the competence of the vendor. A well-designed and organised web interface can stimulate the students’ interest to further explore shopping sites and reduce the cost and time required when searching for product information. Li and Zhang [32] explained that a variety of factors related to website quality have been demonstrated to significantly influence consumers’ online shopping attitudes and behaviour. Better website quality can guide the consumer’s complete transactions smoothly and attract them to revisit Internet store. In contrast, worse quality would hinder their online shopping moves. Liang and Lai [14] found that the quality of e-store design had an effect on the consumer purchase decision. Thus, it is hypothesised that:

H5: Perceived site quality of online shops determines students’ continuance intention to use.

Perceived Security (PS)

Online security perception is defined as the degree to which person believes that the online vendor or website is secure. It is the extent to which the students believe that the Internet is secure for transmitting sensitive information (e.g. debit card or personal information). Because of a lot of security issues associated with the use of the Internet, online shoppers would usually prefer a secured platform to carry out shopping activities. Hence, security would be considered as a considerable issue when transferring important information like debit/credit card details. Salisbury et al. [33] found security as a greater influence on intent to purchase using the Web than ease and utility of purchasing products. Aggarwal and Rahul [34], Chang and Chen [35], Pilík and Juřičková [36], Yulihasri [37] identified security as one of the factors that motivated customers to use online shops, therefore, it is hypothesised that:

H6: Perceived security of online shops determines students’ continuance intention to use.

Convenience (CON)

Convenience is considered one of the most important factors driving online shopping. Unlike traditional shopping, the distinct characteristic of online shopping is its convenience which has been found to be the major motive for consumers to shop electronically. Donthu and Garcia [38] found that people who shop online tend to seek convenience. Accordingly, Li et al. [39] found that customers who liked to purchase from online stores were less experience oriented and more convenience oriented, and that the customers regarded convenience as the most important factor for making purchase decisions. Chiang and Dholakia [12], Asunmaa [40], Diao [41], Pham et al. [42], Meixian [43], Wang et al. [44] found that convenience was among the factors that influenced consumer intention to continue engaging in online shopping. Jarvenpaa and Todd [45] study found that convenience was the single most salient benefit of online shopping. Burke [46] also found that convenience was the most frequently cited reason for consumers to engage in online shopping in the United States. Another hypothesis is thus proposed:

H7: Convenience of using online shops determines students’ continuance intention to use.

Prior experience/Satisfaction (PES)

In online shopping, consumers evaluate shopping experience and their perceptions of product related information, payment conditions, delivery terms, the service existing, risk, security/privacy, navigation, and entertainment [47]. The more experienced the customers are with online shopping and the more satisfied they are with their shopping, the more likely to purchase again and again. Satisfaction enhances consumer perception towards the usefulness, ease of use and decrease the perceived risk related to online shopping. When a customer gets experienced by the Internet it will adjust his/her perception to shop online. Many studies [15,20,21,24,34,47,48] have found that prior experience or satisfaction with purchases have significant influence of consumers intention to repurchase. Rehman et al. [48] suggested that satisfaction was one of the important factors affecting repurchase intention of online customers. Jarvelainen [49] found prior online shopping experience to have a very significant effect on the purchasing channel choice both directly as well as indirectly, and even more on the intention to select the Internet as the purchasing channel. Lin and Lekhawipat [50] investigated the effects of online shopping experience and habit on online repurchase intention and found that online shopping previous experience can be considered a key driver for customer satisfaction and repurchase intention. Jiradilok et al. [51] found that customer satisfaction led to online purchase intentions for experienced and inexperienced online purchasers in Thailand. It is therefore hypothesised that:

H8: Prior experience/satisfaction with online shops determines students‘ continuance intention to use.

Shopping Habit (SH)

Shopping habit is the extent to which people perform behaviours automatically because of learning. Habit has been operationalised in two distinct ways; Kim et al. [52] viewed habit as prior behavior, while Limayem et al. [53] viewed habit as the extent to which an individual believes the behaviour to be automatic behavioural response triggered by a situational stimulus in which the individual is not necessarily aware of the behaviour. Individuals with the habit of visiting online shops (i.e., the behavioural response) instead of a physical outlet will, automatically without further consideration visit the shops when he/she has shopping needs (i.e., situational stimulus). Barnes and Guo [54] examined a kind of shopping behavior that consumers spend noticeable amount of money for shopping from the Internet. The result indicated that consumers’ habits, external and instinct motivators had great effect on shaping online shopping behavior. In addition, Pahnila and Warsta [55] found that shopping habitual behaviour had a significant impact on online shopping. Lin and Lekhawipat [50] found that online shopping habit influenced repurchase intention of online shoppers in Taiwan. Thus, another hypothesis is postulated:

H9: Shopping habits of students determines continuance intention to use online shops.

Trust (TR)

The importance of trust in human interactions cannot be overemphasised. Trust is viewed as a set of specific beliefs dealing primarily with benevolence, competence and integrity of another party. Understanding consumer trust on social commerce is vital, as the credibility of information posted directly affect consumer's purchase intentions. Consumer trust towards the seller and products/services offered can be influenced by the user generated contents, reviews and ratings. Consumer trust continues to be a key issue impeding the proliferation of Internet shopping [56] as some degree of trust is essential in environments perceived to be risky, as the case of online shopping [57]. McKnight and Chervany [58] stated that trust is central to interpersonal and commercial relationships because it is crucial wherever risk, uncertainty, or interdependence exist. In face-to-face transactions, customers build trust based on physical interactions and human mannerisms of vendors; hence, people are less likely to make purchases from individuals perceived to be dubious, even on a first-time basis. However, online customers do not enjoy such benefits of human interaction and can only base their perceptions on vendors’ websites. Research indicates that consumers often check online, reviews other consumer's comments before buying a product to reduce the risk involved [59]. Hence, the need for trust in online environments is as important (or more important) as it is in physical interactions. Several researchers [11,20,34,47,60-62], found perceived trust as a determinant of intention and continuance intention of using online services. It is therefore proposed:

H10: Trust in online shops determines students’ continuance intention to use.

Gender

In general, gender has been the most influential factor affecting Internet usage observed in the recent years [63]. Since the early days of the Internet, gender gap has existed in using the Internet, and it is particularly evident for online shopping. Many studies, such as [64-66], have established that gender differences exist in online shopping. Ling and Yazdanifard [67] also concluded that gender role plays a significant role in online consumer behaviour. Bae and Lee [68] found a significant gender gap in consumer’s shopping behaviour which was related to difference in online shopping behaviour between men and women. Some other studies [69-71] have shown that men purchase and spend more money online than women. Khare and Rakesh’s [72] study shows that Indian men had a more positive attitude towards online shopping compared to women. However, Akman and Mishra [73] found no significant difference in using the Internet between men and women employees in Turkish organisations. Another hypothesis is also proposed that:

H11: Gender of students determines continuance intention to use online shops.

Continuance Intention to Use

Intentions represent desires, wishes or willingness or self-instructions to behave in a certain way, and capture the motivational factors that influence behaviour [74]. Intentions are indications of how hard people are willing to try, and of how much of an effort they are planning to use in order to perform the behaviour. As a general rule, the stronger the intention to engage in behaviour, the more likely a behaviour will be performed. Behavioral intention is an indication of an individual's readiness to perform a given behavior. It is assumed to be an immediate antecedent of behavior [74]. In TPB, intention is based on attitude toward the behavior, subjective norm, and perceived behavioral control, with each predictor weighted for its importance in relation to the behavior and population of interest. Many factors have been found to determine behavioural intention. In this study, continuance intention to use is measured by asking respondents to rate their intention on a scale of 1-4 on these four items:

(1) “I will frequently do my shopping online”;

(2) “I will strongly recommend others to shop online”;

(3) “I intend to continue to buy things online”;

(4) “I intend to continue to buy things online in the future”.

Materials and Methods

The location of the study is Oyo and Kwara states, Nigeria. The population comprised postgraduate students (PGS) in two federal universities located in the two states: University of Ibadan (UI) and University of Ilorin (UNILORIN). The study adopted a survey research design. Disproportionate stratified sampling was used to select the respondents from 19 faculties with a population of 15,712 postgraduate students in UI, and 13 faculties with a population of 3995 postgraduate students in UNILORIN, thus giving a sample size of 393 in UI and 200 in UNILORIN. In total, 593 PGS were selected for the study. A structured questionnaire was used to collect data. The questionnaire was carefully designed to comprise close ended questions that can be easily understood and answered in order to reduce non-response rate. Some items in the questionnaire were developed by the researchers, while some were adapted from validated instruments developed Al-Maghrabi et al. [1], Gefen et al. [56], McKnight et al. [75], Amoroso and Gardner [76], Javadi et al. [77], and Agif et al. [78]. The questionnaire has two parts. Part A captured demographic information from the respondents, while part B collected data on the variables adopted for the study. A 4-point Likert scale was used to measure the response from “1”=Strongly Disagree (SD) “2”=Disagree (D) “3”=Agree (A) “4”=Strongly Agree (SA). A pilot study was conducted to test the reliability of the instrument. The overall Cronbach Alpha is 0.931, which indicates a strong reliability of the instrument. Table 1 presents the Cronbach Alpha analysis of the items.

Table 1: Cronbach alpha results of the constructs.

Variable No of items Cronbach Alpha
PU 4 0.708
PEOU 8 0.902
SN 4 0.677
PE 6 0.872
PSQ 6 0.903
PS 4 0.635
CON 4 0.803
PES 3 0.789
SH 4 0.762
TR 4 0.559
Continuance intention 4 0.684

Data was collected primarily by the researchers with the assistance of two research assistants between February and March 2018 and lasted three weeks. Five hundred and ninety three copies of questionnaire were distributed to the respondents, however, 455 (304 copies at UI, and 151 copies at UNILORIN) were retrieved and considered suitable for data analysis, translating to 76.7 percent return rate. The study followed ethical guidelines that guide the conduct of social science research. The respondents’ rights for confidentiality and privacy were taken into consideration in the process of designing the instrument and during the data collection processes. Respondents were also given the free will to choose whether or not to participate in the study. Frequency and percentage distributions were used for descriptive statistics while linear regression, multiple regression and Chi-Square analyses were performed to test the hypotheses. Regression analysis was used because it helps to estimate the influence or effect of a variable on another variable.

Results

Descriptive Analysis

The demographic characteristics of respondents are analysed and presented by frequency counts and percentages as shown in Table 2.

Table 2: Profile of the respondents.

Variable UI (n=304) UNILORIN (n=151) Total (n=455)
Gender
Male 138 (45.4%) 88 (58.3%) 226 (49.7%)
Female 166 (54.6%) 63 (41.7%) 229 (50.3%)
Total 304 (100.0%) 151 (100.0%) 455 (100.0%)
Degrees
Bachelor Degree 0 (0.0%) 0 (0.0%) 0 (0.0%)
Post graduate Diploma 6 (2.0%) 13 (8.6%) 19 (4.2%)
Masters 287 (94.4%) 132 (87.4%) 419 (92.1%)
M.Phil. 1 (0.3%) 1 (0.7%) 2 (0.4%)
M.Phil./Ph.D. 10 (3.3%) 5 (3.3%) 15 (3.3%)
Total 304 (100.0%) 151 (100.0%) 455 (100.0%)

The table shows that most of the respondents (50.3%) were females while (49.7%) were males. In UI, the females constituted the majority (54.6%), while the males were 45.4%. In UNILORIN, the males were more represented (58.3% males compared with 41.7% females). The result also shows that most of the respondents were Master’s students (92.1%). The background information of the respondents is presented in Table 3.

Table 3: Background information of respondents on online shopping.

Variable UI (n=304) UNILORIN (n=151) Total (n=455)
Frequency of students that has purchased online
Yes 304 (100.0%) 151 (100.0%) 455 (100.0%)
No 0 (0.0%) 0 (0.0%) 0 (0.0%)
Total 304 (100.0%) 151 (100.0%) 455(100.0%)
Number of years of shopping online
Less than a year 105 (34.5%) 61 (40.4%) 166 (36.5%)
2-5 years 159 (52.3%) 74 (49.0%) 233 (51.2%)
Over 5 years 40 (13.2%) 16 (10.6%) 56 (12.3%)
Total 304 (100.0%) 151 (100.0%) 455 (100.0%)
Number of hours of shopping online
0-2 hours 184 (60.5%) 84 (55.6%) 268 (58.9%)
2-4 years 87 (28.6%) 53 (35.1%) 140 (30.8%)
4-6 hours 21 (6.9%) 11 (7.3%) 32 (7.0%)
Above 6 hours 10 (3.3%) 1 (0.7%) 11 (2.4%)
Missing values 2 (0.7%) 2 (1.3) 4 (0.9%)
Total 304 (99.3%) 149 (98.7%) 451 (92.8.0%)
Goods purchased (multiple option)
Phones and tablets 151 (49.7%) 225 (49.0%) 376 (82.6%)
TVs and electronics 46 (15.1%) 14 (9.3%) 60 (13.2%)
Fashion 128 (42.1%) 60 (39.7%) 188 (41.3%)
Computing 36 (11.8%) 10 (6.6%) 46 (10.1%)
Home and office appliances 45 (14.8%) 13 (8.6%) 58 (12.7%)
Groceries 11 (3.6%) 3 (2.0%) 14 (3.1%)
Baby and kids accessories 12 (3.9%) 8 (5.3%) 20 (4.4%)
Games and consoles 16 (5.3%) 10 (6.6%) 26 (6.3%)
Automobiles 2 (0.7) 3 (2.0%) 5 (1.1%)
Health and beauty 57 (18.8) 18 (11.9%) 75 (16.5%)
Other categories 3 (1.0%) 1 (0.7%) 4 (0.9%)
Online shops used
Jumia 234 (77.0%) 99 (65.6%) 333 (73.2%)
Konga 123 (40.5%) 59 (39.1) 182 (40%)
Kaymu 7 (2.3%) 1 (0.7%) 8 (1.8%)
EBay 23 (7.6%) 8 (5.3%) 31 (6.8%)
Jiji 46 (15.1%) 19 (12.6%) 65 (14.3%)
Aliexpress 66 (21.7%) 28 (18.5%) 94 (20.7%)
Amazon 26 (8.6%) 9 (6.0%) 35 (7.7%)
Dealdey 10 (3.3%) 1 (0.7%) 11 (2.4%)
Others 9 (3.0%) 14 (9.3%) 23 (5.1%)
Frequency of using online shops
Daily 18 (5.9%) 8 (5.3%) 26 (5.7%)
Weekly 69 (22.7%) 25 (16.6%) 94 (20.7%)
Fortnightly 73 (24.0%) 32 (21.2%) 105 (23.1%)
Monthly 136 (44.7%) 71 (47.0%) 207 (45.5%)
Semi-annually 6 (2.0%) 12 (7.9%) 18 (4.0%)
Yearly 2 (0.7%) 3 (2.0%) 5 (1.1%)
Total 304 (100.0%) 151 (100.0) 455 (100.0%)

All the students were using online shops. Majority (51.2%) had been shopping for 2-5 years, while majority (30.8%) spent average of 0-2 hours on online shops. The major products that have been purchased by the majority of the students (82.6%) are phones and tablets. In addition, most of the students (73.2%) at the two universities used JUMIA online shop. The students used the online shops majorly every month, which shows that they frequently patronised online shops.

Test of Hypotheses

This section presents the result of the test of the twelve hypotheses. All hypotheses stated were tested in null form, posing the assumption that a significant relationship does not exist between the independent and dependent variables. The hypotheses in the alternative forms assume that significant relationships exist between the concerned variables. The level of significance was pre-set to 5 percent; if p obtained <0.05, the null hypothesis was rejected, while the null hypothesis was not rejected if p obtained >0.05.

H1: Perceived usefulness of online shops does not determine students’ continuance intention to use.

Table 4 presents the results of the test of hypothesis 1.

Table 4: Results of PU and Continuance intention to use online shops.

Model Unstandardized Coefficients Standardized Coefficients T Sig.
B Std. Error Beta
UI
(Constant) -29.150 16.172   -1.803 0.072
PU 0.833 0.046 0.723 18.192 0.000
N=302; df=1; F ratio=330.943; p=0.000; R=0.723; R Square=0.523; Adj. R square=0.521
UNILORIN
(Constant) -6.692 9.388   -.713 0.477
PU -.003 .029 -.008 -.103 0.018

Table 4 presents the results at the two universities. The results show that the relationship was significant at the UI (β=0.723, p=0.000), and UNILORIN (p=0.018). This means that PU is a determent of continuance intention to use online shops by the students. It was also found that PU accounted for 52.3% of the variance in UI students’ continuance intention to use online shops (R2=0.523), and 53.0% at UNILORIN (R2=0.530).

H2: Perceived ease of use of online shops does not determine students’ continuance intention to use.

Table 5 presents the results for hypothesis 2.

Table 5: Results of PEOU and Continuance intention to use online shops.

Model Unstandardized Coefficients Standardized Coefficients T Sig.
B Std. Error Beta
UI
(Constant) -27.883 14.540   -1.918 .056
PEOU .481 .022 .784 21.920 .000
N=302; df=1; F ratio=480.506; p=0.000; R=0.784; R Square=0.614; Adj. R square=0.612
UNILORIN
(Constant) 1.466 5.640   .260 .795
PEOU .430 .026 .800 16.298 .000

The results show a positive and significant relationship between PEOU and continuance intention to use online shops at the two universities (UI: β=0.784, p=0.000) and UNILORIN: β=0.800, p=0.000). Hence PEOU determined continuance intention to use online shops by the students. PEOU accounted for 61.4% of the variance in UI students’ continuance intention to use online shops (R2=0.614), and 64.1% at UNILORIN (R2=0.641).

H3: Subjective norms of students do not determine continuance intention to use online shops.

Table 6 presents the results for hypothesis 3.

Table 6: Results of SN and Continuance intention to use online shops.

Model Unstandardized Coefficients Standardized Coefficients T Sig.
B Std. Error Beta
UI
(Constant) -29.636 15.396   -1.925 .055
SN .825 .041 .753 19.893 .000
N=302; df=1; F ratio=395.729; p=0.000; R=0.753; R Square=0.567; Adj. R square=0.564
UNILORIN
(Constant) -7.509 6.668   -1.126 .262
SN .991 .082 .703 12.058 .000

Table 6 reveal a positive and significant relationship between SN and continuance intention to use online shops at the two universities (UI: β=0.753, p=0.000; UNILORIN: β=0.703, p=0.000). Hence, the SNs of the students determined their continuance intention to use online shops. SN of the UI students accounted for 56.7% of the variance in continuance intention to use online shops (R2=0.567), and also 56.7% at UNILORIN (R2=0.567).

H4: Perceived enjoyment of online shops does not determine students’ continuance intention to use.

Table 7 presents the results for hypothesis 4.

Table 7: Results of PE and Continuance intention to use online shops.

Model Unstandardized Coefficients Standardized Coefficients T Sig.
B Std. Error Beta
UI
(Constant) -35.489 14.460   -2.454 .015
PE .651 .030 .785 22.052 .000
N=302; df=1; F ratio=486.307; p=0.000; R=.785; R Square=0.617; Adj. R square=0.616
UNILORIN
(Constant) -5.882 6.657   -.884 .378
PE .497 .041 .704 12.096 .000

The results shows a positive and significant relationship between PE and continuance intention to use online shops (UI: β=0.785, p=0.000; UNILORIN: β=0.704, p=0.000). Hence, it is stated that PE determined continuance intention to use online shops by the students. In addition, PE accounted for 61.7% of the variance in UI students’ continuance intention to use online shops (R2=0.617), and 49.5% at UNILORIN (R2=0.495).

H5: Perceived site quality of online shops does not determine students’ continuance intention to use.

Table 8 presents the results for hypothesis 5.

Table 8: Results of PSQ and Continuance intention to use online shops.

Model Unstandardized Coefficients Standardized Coefficients T Sig.
B Std. Error Beta
UI
(Constant) -33.531 13.616   -2.463 .014
PSQ .670 .028 .813 24.238 .000
N=302; df=1; F ratio=587.472; p=0.000; R=.813; R Square=0.660; Adj. R square=0.659
UNILORIN
(Constant) -4.058 9.215   -.440 .660
Perceived Site Quality .095 .036 .210 2.627 .010

The results show positive and significant relationships at each of the universities (UI: β=0.813, p=0.000; β=0.210, p=0.010). This implies that the PSQ of the online shops is a determinant of continuance use of by the students. PSQ accounted for 66.0% of the variance in students’ continuance intention to use online shops at UI (R2=0.660), and less than 50% at UNILORIN (R2=0.441).

H6: Perceived security of online shops does not determine students’ continuance intention to use.

Table 9 presents the results for hypothesis 6.

Table 9: Results of PS and Continuance intention to use online shops.

Model Unstandardized Coefficients Standardized Coefficients T Sig.
B Std. Error Beta
UI
(Constant) -33.021 13.110   -2.519 .012
PS 1.021 .040 .828 25.643 .000
N=302; df=1; F ratio=657.557; p=0.000; R=0.828; R Square=0.685; Adj. R square=0.684
UNILORIN
(Constant) -6.629 9.372   -.707 .480
PS -.012 .116 -.009 -.105 .916
N=149; df=1; F ratio=.011; p=0.916; R=0. 009; R Square=0.000; Adj. R square=-0.007
The Two Universities combined
(Constant) 6.568 .071   92.312 .000
PS -4.722E-5 .001 -.002 -.032 .975

Table 9 shows that there is a significant relationship between PS of online sites and students’ continuance use of online shops at UI (β=0.828, p=0.000), but not at UNILORIN (p=0.916). This implies that the PS is a determining factor for continuance intention to use online shops by the students of UI but not but not at UNILORIN. Furthermore, PS accounted for 68.5% of the variance in students’ continuance intention to use online shops at UI (R2=0.685). The analysis was done for the two universities combined and it was found that the relationship is not significant (p=0.975).

H7: Convenience of using online shops does not determine students’ continuance intention to use.

Table 10 presents the results for hypothesis 7.

Table 10: Results of CON and Continuance intention to use online shops.

Model Unstandardized Coefficients Standardized Coefficients T Sig.
B Std. Error Beta
UI
(Constant) -37.185 14.274   -2.605 .010
CON .991 .044 .791 22.503 .000
N=302; df=1; F ratio=506.397; p=0.000; R=0.792; R Square=0.627; Adj. R square=0.625
UNILORIN
(Constant) -3.708 6.673   -.556 .579
CON .496 .041 .703 12.055 .000

A positive and significant relationship was found between CON and continuance intention to use online shops by the students (UI: β=0.791, p=0.000; UNILORIN: β=0.791, p=0.000). Hence, CON is one of the factors that determined the continuance intention to use online shops by the students. CON accounted for 62.7% of the variance in students’ continuance intention to use online shops at UI (R2=0.627), and 49.4% at UNILORIN (R2=0.494).

H8: Prior experience/satisfaction with online shops does not determine students’ continuance intention to use.

Table 11 presents the results for hypothesis 8.

Table 11: Results of PES and Continuance intention to use online shops.

Model Unstandardized Coefficients Standardized Coefficients T Sig.
B Std. Error Beta
UI
(Constant) -36.635 14.272   -2.567 .011
PES 1.321 .059 .792 22.513 .000
N=302; df=1; F ratio=506.397; p=0.000; R=0.792; R Square=0.627; Adj. R square=0.625
UNILORIN
(Constant) -2.781 6.659   -.418 .677
PES .496 .041 .705 12.118 .000

The results show that PES is a determinant of continuance intention to use online shops at the two universities, as the relationship is positive and significant (UI: β=0.792, p=0.000, and UNILORIN: β=0.705, p=0.000). Moreover, PES accounted for 62.7% of the variance in students’ continuance intention to use online shops at UI (R2=0.627), and a little below 50% at UNILORIN (R2=0.496).

H9: Shopping habits of students does not determine continuance intention to use online shops.

Table 12 presents the results for hypothesis 9.

Table 12: Results of SH and Continuance intention to use online shops.

Model Unstandardized Coefficients Standardized Coefficients T Sig.
 B Std. Error Beta
UI
(Constant) -34.155 14.280   -2.392 .017
SH .992 .044 .792 22.509 .000
N=302; df=1; F ratio=506.662; p=0.000; R=.792; R Square=0 .627; Adj. R square=0.625
UNILORIN
(Constant) .600 7.746   .077 .938
SH .330 .039 .570 8.474 .000

The result shows a positive and significant relationship between the students’ SH and continuance intention to use online shops (UI: β=0.792, p=0.000; UNILORIN: β=0.570, p=0.000). Hence the null hypothesis is rejected. This means that the shopping habits of the students determine their continuance intention to use online shops. In addition, SH accounted for 62.7% of the variance in students’ continuance intention to use online shops at UI (R2=0.627). However, the contribution of SH to continuance intention to use online shops at UNILORIN is low, just about 33% (R2=0.325).

H10: Trust in online shops does not determine students’ continuance intention to use.

Table 13 presents the results for hypothesis 10.

Table 13: Results of TR and Continuance intention to use online shops.

Model Unstandardized Coefficients Standardized Coefficients T Sig.
B Std. Error Beta
UI
(Constant) -21.961 15.857   -1.385 .167
TR .695 .037 .737 18.957 .000
N=442; df=1; F ratio=359.354; p=0.000; R=0.737; R Square=0.543; Adj. R square=0.542
UNILORIN
(Constant) .340 7.292   .047 .963
TR .270 .027 .633 9.970 .000

Table 13 shows a significant relationship between TR and continuance intention to use online shops at the UI (β=0.737, p=0.000), and also at UNILORIN (β=633, p=0.000). Therefore, the null hypothesis is rejected. Hence, TR in the online shops is a determinant of continuance intention to use. Moreover, TR accounted for only 54.3% of the variance in students’ continuance intention to use online shops at UI (R2=0.543), and below 50% at UNILORIN (R2=0.400).

H11: Gender of students does not determine continuance intention to use online shops.

Chi-square was used to analyse hypothesis 11. The results are presented in Table 14.

Table 14: Gender of students and continuance intention to use online shops.

Gender The two Universities Combined UI UNILORIN
Male 223 (50.2%) 138 88 (58.3%)
Female 221 (49.8%) 166 63 (41.7%)
Missing values 11 (2.4%) 0 (0.0%) 0 (0.0%)
Total 455 (100.0%) 0 (0.0%) 151 (100.0%)
  Df=11; Sig=0.315; χ2=12.678; r=0.07 Df=32; Sig=0.986; χ2=17.028; r=-0.314 Df=10; Sig=0.519; χ2=9.137; r=-0.805

Table 14 shows no significant relationship between gender of students and continuance intention to use online shops at both universities and at the universities separately (p=0.315, 0.986, 0.519 respectively). Hence, the gender of the students did not determine their continuance intention to use online shops.

H12: There is no predictive joint relationship between the independent variables and students’ continuance intention to use online shops.

The results of the multiple regression for hypothesis 12 are presented in Table 15.

Table 15: Joint effect of independent variables on dependent variable.

Model Unstandardized Coefficients Standardized Coefficients Beta T Sig
  B Std. Error      
The Two Universities combined
(Constant) 3.482 .290   12.016 .000
PU 7.185E-005 .000 .011 .263 .793
PEOU 001 .001 .064 1.498 .135
SN -.001 .001 -.027 -.663 .508
PE -.002 .001 -.049 -1.179 .239
PSQ .001 .000 .074 1.798 .073
PS -.001 .001 -.024 -.598 .550
CON .204 .041 .224 4.967 .000
PES .308 .036 .389 8.587 .000
SH .001 .001 .056 1.3316 .189
TR -5.995E-005 .000 -.008 -.198 .843
N=443; df=10; F ratio=16.850; p=0.000; R=0.529; R Square=0.280; Adj. R square=0.264
UI
(Constant) -19.502 .11.138   -1.751 .081
PU -.009 .078 -.008 -.118 .906
PEOU -.195 .089 -.317 -2.190 .029
SN .207 .070 .189 2.945 .003
PE -.008 .190 -.010 -.042 .967
PSQ .962 .143 1.167 6.736 .000
PS 1.980 .190 1.605 10.439 .000
CON -2.465 .424 -1.969 -5.814 .000
PES 5.16 .021 1.366 .365 .715
SH 5.276 .000 .019 .005 .996
TR .241 .042 .255 5.784 .000
Notes: N=303; df=8; F ratio=136.696; p=0.000; R=0.887; R Square=0.778; Adj. R square=0.782
UNILORIN
(Constant) -15.293 9.634   -1.587 .115
PU -.001 .078 -.004 -.131 .896
PEOU .364 .089 .679 19.345 .000
SN .1.856 .070 1.315 1.012 .313
PE 1.660 .190 2.352 .946 .346
PSQ .003 .143 .007 .208 .835
PS .003 .190 .002 .064 .949
CON -2.079 .424 -2.947 -1.208 .229
PES 9.715 .079 2.948 .936 .351
SH -.186   -.321 -5.712 .000
TR -.004 .042 -.010 -.130 .897

The result in the Table 15 reveal that the joint effect of PU, PEOU, SN, PE, PSQ, PS, CON, PES, SH and TR on continuance intention to use online shop is significant (R=0.529, p=0.000<0.05). The result of the study further showed that 28.0% of the variance in students’ continuance intention is attributed to the joint effect of the independent variables (R2=0.280) when the two universities were combined. However, when taken singly, the result of the study showed that only CON (β=0.224, p=0.000<0.05) and PES (β=0.389, p=0.000<0.05) have significant relationship with students’ continuance intention to shop online. Table 15 also shows a significant relationship between the joint effect of the independent variables and continuance intention to use online shops by UI students (R2=0.778, p=0.000>0.05), which shows that 77.8% of the variance in students’ continuance intention is attributed to the joint effect of all the variables. Individually, the result shows that PEOU (β=-0.317, p=0.029<0.05), SN (β=0.189, p=0.003<0.05), PSQ (β=1.167, p=0.000<0.05), PS (β=1.605, p=0.000<0.05), CON (β=-1.969, p=0.000<0.05) and TR (β=0.255, p=0.000<0.05) have significant relationships with students’ continuance intention to shop online. The results also show a significant relationship between the joint effect of the independent variables and continuance intention to use online shops at UNILORIN (R=0.930, p=0.000>0.05), and that the independent variables accounted for 86.4% of the variance in students’ continuance intention to use (R2=0.864).

Discussion of Findings

The study found that many of the students at the two universities were using online shops. Findings from the study found that PU is a determinant of continuance intention to use online shops by postgraduate students at the two universities. The construct accounted for about 50% variance in the students’ continuance intention to use online shops at the two universities. PU is a strong correlate of user acceptance of technology and should not be ignored by those attempting to design or implement successful systems. Many studies have proved that users are driven to adopt an application primarily because of the functions it performs for them, while several studies, for examples [1,15,16,18,21] have been able to establish the importance of PU in determining use and continuance use of online shops.

PEOU has a positive and significant relationship with online shops continuous use at each of the universities. PEOU accounted for a relatively high variance in use by the students at the two universities. This finding conforms to the findings of many studies on technology or system use [1,13,16,18]. PEOU of a technology or system is considered an essential condition to determine user readiness to accept or adopt certain technology [23]. The easier it is to use a technology the more people tend to use it. Ordinarily, users are driven to adopt an application primarily because of its usefulness and secondarily, for how easy or hard it is to get the system to perform those functions. However, difficulty of use can discourage adoption of an otherwise useful system. The direct effect of PEOU on usage intention has also been observed in some research to increase over time [79].

The study also found that SN is a determinant of online shops continuance use at the two universities even though the contribution is marginal (56.7%). Subjective norms, which is also referred to as social pressure, is an immediate variable of intentions toward performing behaviour. It represents the person’s significant referents approval of behaviour. When people believe that most respected others would expect them to perform a behavior or are performing the behavior, they are more likely to engage in the behaviour [26,80]. Previous studies, such as [20,81] found that SN determined online shops continuance use.

PE has been found by many studies [9,19 21,47,81,82] as a key determinant of using online shops as also found by this study. PE determined continuance use at each of the universities. Young people tend to be technology savvy because they get a feeling of pleasure while using technologies or surfing on the Internet, especially when browsing through online shops either for window shopping or actual shopping. This feel of enjoyment could affect their continuance intention to use the online shops. Ulaan et al. [30] equally found that PE had a positive influence on intention to shop online by students of Sam Ratulangi University, Manado.

In addition, the finding of this study reveals that PSQ is also one of the determinants of continuance intention to use online shops by students at the two universities. This finding is in agreement with some other previous studies such as Sevim and Eroğlu-Hall [83] which found the design of the website to have impact on intention of online shopping among online shoppers in Turkey. The more the online sites could provide comfort in searching and obtaining products, accessibility of products, and the ease of comparative shopping and search for the preferred products, the more consumers would be pleased to continue using the sites.

PS was found to be significant only among UI students. PS actually accounted for a high variance in students’ continuance intention to use online shops (R2=0.685) at the university. This factor may not be so significant in UNILORIN considering the fact that most online shops allow cash on delivery service in Nigeria. Shoppers pay when goods/services are delivered thereby avoiding transferring money through the Internet. This may have contributed to the reduction of the students’ worries about the security of online shopping. Some other studies [84,85] have also found that PS was not a significant factor influencing online purchases in their studies.

Convenience is another significant factor determining continuance intention of online shopping. CON accounted for 62.7% of the variance in students’ continuance intention to use online shops at UI, but the contribution is a bit lower at UNILORIN (49.4%). Online shopping provides the convenience of shopping anywhere and anytime. Unlike traditional shopping, the distinct characteristic of online shopping is its convenience which has been found to be the major motive for consumers to shop electronically by many studies [24,45,86]. It is not surprising that this factor was found to be a determining factor influencing continuance intention to use online shops in this study. The more online shops can provide comfort in searching and obtaining products, the more consumers would be pleased and hence continuous patronage.

Prior experience/satisfaction could enhance consumer perception towards the usefulness, ease of use and decrease the perceived risk related to online shopping. PES with the purchase of a product from online stores could lead to repeat purchasing and growing usage of such products or services; hence, continuous use of the online shops from which the product was bought. PES has been found to be one of the variables influencing the intention to buy products online in this study as well as many previous studies [48,49]. Hence, it is imperative for online stores to understand customers’ needs in order to ensure a functioning and easy websites and useful products or services. This is needed to ensure customers have good experiences and are satisfied with the online sites as well as the products bought.

This study also examined the influence of the students shopping habits on continuance use of online shops and found that the SH of the students determined their continuance use of online shops at the two universities. While the contribution of SH to continuance intention to use online shops at UI is high (62.7%), that of UNILORIN is low, just about 33% (R2=0.325). This conforms to the findings of Lin and Lekhawipat [50] and Pahnila and Warsta [55]. Individuals with the habit of visiting online shops, automatically without further consideration, visit the shops continuously when he/she has shopping needs. In Gefen [87] study, habit alone explained a large proportion of the variance of continued use of a website among experienced online shoppers at a leading business school in the Mid-Atlantic region of the US, and also predicted PU and PEOU among them.

Trust is a critical factor in any relationship since the essence of trade is establishment of mutual trust. Even though TR accounted for only 54.3% of the variance in students’ continuance intention to use online shops at UI, and just 40.0% at UNILORIN, trust has a significant role in terms of realisation of shopping, as well as continuity of the relationship, as buyers and sellers implement the transactions without seeing each other as in the case of online shopping. Trust is actually the vital key to building customer loyalty and maintaining continuity in buyer-seller relationships [83]. If an online vendors could do more to gain customer’s trust, then users would be more inclined to purchase via their websites. It is not out of place for this study to find that trust is one of the determinants of continuance intention to use online shops by the university students in Nigeria because of the importance of trust in e-commerce transactions. Most users of online transactions feel fairly confident to engage in online transactions because of the trust that the disclosure of private information and details of their debit/credit card would not be compromised. A consumer must have some element of trust in online vendors’ sites before a decision to purchase online is made.

This study found that the gender of the students is not a determinant of their continuance intention to use of online shops. This result is contrary to the findings of past research, but supports the findings of Akman and Mishra [73] which also found no significant difference in using the Internet between men and women employees in Turkish organisations. Lim et al. [17] also found no gender difference in terms of the frequency of online search and purchases. From reviews of literature [88], it can be observed that there are mixed results with respect to the influence of gender on technology adoption. Even though gender has been attributed as a significant variable in explaining technology acceptance behaviour of humans, however, in few contexts, gender does not play a significant role in determining the intention of accepting or using technology. For instance, the findings of Zhang and Prybutok [89] confirmed that women reached parity with men on online shopping. Lastly, this study found that the joint effect of the independent variables on continuance intention to use online shop is significant (R=0.529, p=0.000<0.05), and accounted for 28.0% of the variance in students’ continuance intention to use online shops, and that 77.8% of the variance in UI students’ continuance intention is attributed to the joint effect of all the variables, while 86.4% of the variance is observed at UNILORIN.

Conclusion

This study concludes that the students had the intention to continue to patronise online shops. The study also concludes that all the variables, except perceived security (even though only at one university) and gender, determined students’ continuance intention to use online shops at the two Nigerian universities. This study has been able to give a clear picture of the factors that online shops need to put into consideration to maximise continued patronage of their products. The study, therefore, recommends that online retailers should leverage on these factors in ensuring continuous patronage of their products and services. They should also ensure the consistency of providing good quality products in order to guarantee consumers’ satisfaction. The study identified a limitation in that the study was conducted among students of only two universities, hence may not be generalisable to all students in Nigeria. Further studies could be expanded to include other universities in Nigeria. Further research could also be carried out to examine other predicting factors affecting continuance intention to use online shops. In addition, other data collection methods such as focus group discussions and interview could be used in combination with the questionnaire to obtain a robust and more revealing data.

References

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