ROLE OF TRUST ON THE DETERMINANTS OF CONSUMER ONLINE BUYING BEHAVIOUR AMONG DISTANCE LEARNING CENTRE (DLC) POSTGRADUATE (PG) STUDENTS IN NIGERIA

This study aimed to investigate the relationship between perceived risk and social influence on consumer online buying behavior among postgraduate students in Ahmadu Bello University (ABU), Zaria. The study also explored the moderating effect of trust on this relationship. The sample size was determined using simple random sampling, and data was collected through a cross-sectional research design. 397 useful responses were used for the main analysis, and SmartPLS 3 was used to analyze the data and test the study's hypotheses. In the direct relationship, the study found that perceived risk and social influence had insignificant relationships with online buying behavior. Additionally, the study found that trust had a significant moderating effect on the relationship between perceived risk and social influence on online buying behavior. online retailers should make efforts to build trust with their customers to increase online buying behaviour. One way to build trust is to ensure customer privacy and security. Online retailers should take measures to protect their customers' personal information and ensure secure transactions to increase trust in their website.


Introduction
The advancement in new technology has commenced a new era of electronic commerce known as e-commerce, which is viewed as the trading of information, goods and services mostly via the internet, leading to the growth of online shopping or eshopping (Ekwueme & Nehemiah, 2017).
Information communication technology has made a tremendous transformation from the traditional way of shopping to the modern online way of shopping via the internet using several social media networks and different websites (Ekwueme & Nehemiah, 2017).
Online shopping is defined as the notion of buying and selling goods over the internet.
From the viewpoint of the seller, it is the attempt on the seller's part to attract and persuade the prospect to conduct the purchase decision-making process and DOI: https://doi.org/10.57233/gujeds.v4i1.10ensure satisfaction and loyalty.From the viewpoint of the buyer, online purchase behaviour is the degree to which consumers access, browse, shop transact and repeat the behaviour, Sahney, Ghosh, and Shrivastava, (2013) have seen a change in the consumers' mindset of purchase made from a physical store to online buying, the industry has witnessed the ever-increasing volumes of online transactions.Despite the growth of online shopping in Nigeria, the penetration rate is low as a result of the risk associated with online buying (Nasidi et al., 2021).For most Nigerians, the problem of transfers or sending money to an unknown entity you have never seen or encountered physically is strange.This is bound to be terrible for online stores in the country (Nwankwo, Kanyangale & Abugu, 2019) and hence, the significance of this number to the domain of behavioural intention research cannot be overemphasized.
Customers are driven by functional and hedonic motives, they like to search the internet and search products and services, they often find themselves with a sense of discomfort, apprehension and skepticism when it comes down to the actual physical and monetary exchange (Sahney, Ghosh & Shrivastava, 2013).The basic underlying issue here is the lack of trust, especially with regard to financial and personal information.
Trust is a feeling of mutual acceptance between two parties; it develops out of continuous physical interaction and leads to long-term acceptance and commitment.So, the important issue that needs to be addressed is ''trust'' among the seller-buyer, the lack of which often acts as an impediment in the trial and adoption of the virtual market concept (Lee & Turban, 2001).
Although, online buying has become an important part of individuals life especially the university students, there are numerous problems and challenges encountered by online retailers such as managing the uncertainty of technological problems, solving the logistical difficulties of order execution and delivery, reducing consumers fears regarding safety and privacy breaches, building and developing trust amongst others (Vijayasarathy, 2003) and competition from the rivals.Therefore, it becomes more difficult for online retailers to attract and retain consumers who would engage in online shopping and which makes research in this area to become scarce.
In previous studies, postgraduate students have been found to be frequent users of technology and likely to buy products online and participate in online purchasing, as a Gusau Journal of Economics and Development Studies (GUJEDS), Vol 4 (1), December, 2023ISSN: 3027-1126 (Online); 2786-9695 (Online) ©Authors 142 result, postgraduate students were chosen as the target sample (Delafrooz, Paim, Haron, Sidin, & Khatibi, 2009).The reason for choosing these categories was informed by their perceived level of education and knowledge of the internet (Inegbedion et al., 2016).In addition, Bruin & Lawrence, (2000) stated that today's university students represent a significant part of the online buying consumers and a long-term potential market.Similarly, Nwankwo et al., (2019) posits that university students are a potential market for Nigerian businesses using the online channel to shop for goods and services.
In view of the gaps highlighted above, the current study seeks to examine the effect of perceived risk, and social influence as determinants of consumer online buying behaviour.The study aims at investigating the role of trust in enhancing the effect of the determinants of consumer online buying behaviour in Nigeria.
This study intends to trust as a moderating factor of perceived risk and social influence on consumer online buying behaviour.
Based on the highlighted problems, the study In line with the research objectives, the following hypotheses were used as perceived risk and social influence have no significant relationship with consumer online buying behavior.Trust has no significant moderating effect on perceived risk and social influence on consumer online buying behavior.

Behaviour
Consumer buying behaviour refers to what persuade customers toward deciding to purchase a product or service (Emamdin et al., 2020).According to Lake, (2009), consumer behaviour is when an individual realizes that he has a need, the psychological process starts from the consumer decision process.Through this process, the individual sets out to find ways to fulfil the need he has identified.That process includes the individual's thoughts, feelings, and behaviour.When the process is complete, the consumer is faced with the task of analyzing and digesting all the information, which determines the actions he will take to fulfil the need.
Online buying which is otherwise known as online shopping involves a user accessing internet to search, select, buy, use, and dispose of goods and services, in satisfying his or her needs and wants (Shoki et al., 2014).Internet or web browser is a mechanism that people use to buy and sell goods and services to the target customers.In this present period, devices, such as desktop computers, laptops, tablet computers and even smartphones are gadgets that are found by most individuals which whenever connected to the internet can make a person to have access to a web site, hence, can shop online at his/her convenience (Sarkar & Lahiri, 2018).

Concept of Perceived Risk
One of the key elements in online buying behaviour is risk (Pappas, 2016).Risk is defined as an attribute of an alternative decision reflecting the variance of its possible outcomes (Moshrefjavadi et al., 2012).As Dholakia, (2001) suggests, perceived risk can be involved in all purchase decisions, particularly when we think that the outcome is uncertain.In online shopping, there are consumers with low risk avoidance profile in which they engage themselves into buying through the internet rather than using the traditional means to purchase their products (Juan, 1999).

Concept of Social Influence
Social Influence was considered to be most important driver for online shopping in recent studies by (Jin, Lim et al., 2014)

Theoretical Framework
Theoretical frameworks that will guide the study is the theory of planned behavior (TPB) which will be relevant to this study.This theory is a classical theory used by researchers to explain human behaviour towards adoption of information technologies (Singh et al., 2020).The ability of TPB to predict human behaviour has led to its application in several research fields, including online retailing (Pappas, 2016), since it is considered to be one of the most widely used models for the explanation and prediction of individual behavioural intention and acceptance of Information Technology (Usman & Kumar, 2020).

Theory of Planned Behavior (TPB)
Theory of Planned Behaviour (TPB) explains behaviours over which individuals have incomplete voluntary control (Ajzen, 1991).
Intentions to perform behaviours of different kinds can be predicted with high accuracy from attitudes toward the behaviour, subjective norms, and perceived behavioural control; and these intentions, together with perceptions of behavioural control, account for considerable variance in actual behaviour (Fishbein & Ajzen, 1975 (Ajzen, 1991).This relationship is justified by the TPB as discussed.

RESEARCH METHODOLOGY
The study adopted quantitative research design using a cross sectional survey method.
The Disagree" to '5' for "Strongly Agree.PLS SEM 3.2.7 was used for data analysis

Data Presentation and Analysis
The data were analyzed using SPSS and Smart PLS3.2.9.A total of 507 questionnaire were distributed among the sample population and 457 were returned, which constitutes a response rate of 84.5%.2017), all the remaining items are reliable to measure their respective reflective latent constructs.
Convergent validity is the extent to which the construct converges in order to explain the variance of its items (Hair et al., 2014).Therefore, the criteria for measuring CV are the average variance extracted (AVE) for all the items for each construct (Hair et al., 2017).Under this, the minimum accepted AVE is 0.5 or higher (Hair et al., 2020).That is an AVE of 0.5 or higher indicates that the construct explains 50% or more of the variance of the items that make up the construct (Hair et al., 2017).

Discriminant validity
For assessing discriminant validity (Hair et al., 2017), this study used the Fornell-Lacker criterion, which states that a reflective construct has discriminant validity if the square root of its average variance extracted (AVE) is greater than its correlation with any other reflective construct in the same model (Fornell & Larcker, 1981).The logic behind this method is that if a reflective construct's AVE is greater than its correlation with other constructs, it suggests that the construct shares more variance with its associated indicators than with any other construct in the model, making it distinct from other constructs (Hair et al., 2017).Henseler et al. (2015) have found that this criterion is not effective when the indicator loadings on a construct vary only slightly.Instead, Henseler et al. suggest using the Heterotrait-Monotrait (HTMT) ratio of correlations to assess DV.This method compares the mean of item correlations against the construct correlation, and has been shown to outperform both crossloadings and the Fornell and Lacker criteria (Hair et al., 2017).The threshold values for establishing HTMT should be less than or equal to 0.85 (Henseler et al., 2015) or 0.90 (Franke & Sarstedt, 2019).Table 4.3 shows that DV is established since the serial mean of all the constructs is below the lower benchmark of 0.85.

Assessment of Structural Model
This includes testing the research hypotheses to determine R2 and effect size (f2).As described in the methodology section, bootstrap analysis was used to analyze both the main and moderating hypotheses.
The standard bootstrapping procedure was employed using 5000 bootstrap samples for 397 cases to assess the significance of the path coefficients of both direct and moderating relationships (Hair et al., 2014;Hair et al., 2011;Hair et al., 2012;Henseler et al., 2009).The results of this analysis are presented in Table 4.4.On the other hand, statistical analysis indicates a positive but insignificant relationship between social influence on OBB (β=0.043,p.>0.05).The two hypotheses were not significantly related to online buying behaviour.This means that for H1, the more the risk perceived in online buying, the less people will engage in online buying behaviour.H2 states that people will not engage in online shopping despite the positive reviews they see from family and friends.

Test of hypotheses for Moderating effects
To test for moderation, the first step is to assess the impact of the independent variables, along with the moderator, on the dependent variable.Next, all the independent variables and their interaction terms should be included.It is only when these interaction terms are found to be statistically significant that the moderating effect can be considered present (Ramayya et al., 2018;Esposito Vinzi et al., 2010;Hair Jr. et al., 2013).The analysis for moderation effect is presented in Table 4.5.In Table 4.5, the focus was on examining the significance of the interaction term, which indicates the moderating effect of the moderator on the relationship between the independent and dependent variables.The bootstrapping analysis presented in Table 4.5 revealed that there is a significant moderating effect on OBB when trust interacts with perceived risk (t = 6.16, p = <0.001) and social influence (t=2.35,p = <0.05).This result is also consistent with the theoretical and conceptual meaning of the variables and in line with the prior assumption of the current study.All hypotheses were rejected.

Simple Plot Analysis
The Smart PLS provides a visual representation of the moderation analysis through a simple plot analysis.The central line in the plot corresponds to the direct relationship between the independent and dependent variables, while the lower and upper lines represent low and high levels of the moderator variable, respectively.In

Discussions of Findings
The main objective of this research was to examine the effect of perceived risk, and social influence, on consumer online buying behaviour.Also, to examine the moderating effect of trust in the relationships thereof.
Therefore, in line with the research objectives, the following hypotheses will be discussed in this section in relation to the findings of the study.The bootstrapping analysis presented in Table 4.5 revealed that there is a significant moderating effect on OBB when trust interacts with perceived risk (t=6.16,p0.05), and social influence (t=2.35,p<0.05).The finding that trust has a significant moderating effect on the relationship between perceived risk, and social influence, on OBB is consistent with prior research on the topic.
For instance, a study by Kim and Stoel (2004) found that trust has a moderating effect on the relationship between perceived risk and online purchase behaviour.Similarly, a study by Gefen et al. (2003) found that trust moderates the effect of social influence on online purchase behaviour.

Managerial Implications
The findings of this study have several practical implications for businesses and policymakers in Nigeria.The study reveals that perceived risk and social influence are not predictors of online buying behavior.
Therefore, businesses in Nigeria should focus on reducing the level of risk associated with online buying, encouraging customers to share their experiences and recommendations on social media, businesses can create a positive perception of their brand, which can lead to increased sales.

Future Research
Although, this study provides some useful questions were raised as to what extent does perceived risk and social influence affects consumer online buying behaviour among Postgraduate students in ABU Zaria?And to what extent does trust moderates the effect of perceived risk and social influence on consumer online buying behaviour?The main objective of this research is to examine the moderating effect of trust on perceived risks and social influence on consumer online buying behaviour.The specific objectives are to; assess the extent of the effect of perceived risk and social influence on consumer online buying behaviour among Postgraduate students in ABU Zaria and examine the extent of how trust moderates the effect between perceived risk, social influence and consumer online buying behaviour among Postgraduate students in ABU Zaria.

Figure 2
Figure 2.1Research Framework programmes in the University, as these are the only programmes enrolled in DLC.The total number of students for the of this study was determined using Yamane (1973) formula with 95% confidential level at 5% level of precision from the 8 course.Therefore, the calculation of sample size was given in the following equation.n = N / 1+N (e)2 Where n = sample size N = population e = error of the sampling or level of significant Thus, N = 3,765, e = 0.05 and n =? n= 3,765/1+3,765(0.05)2n= 3,765/10.4125n=362.sample size was increased by 40 percent to cover up for the missing questionnaire.This will also take care of other unavoidable errors such as incorrect filling and failure of some respondents to return questionnaire (Isreal, 2013).Therefore, in this study the number of questionnaire was increased by 40 percent (145) which makes the total questionnaire to be 507.This study employed probability sampling method because in a probability sampling method an element in the population has the privilege of having equal chance of being selected in the sampling process (Asika, 2010).Systematic sampling technique was used to select respondents in each postgraduate course.The list of the students in each course serves as sampling frame.The proportionate sample size allocated to each course was used to get the sampling interval (K).K= sampling frame/allocated sample size.The first respondent from each course was randomly selected using simple random sampling technique by balloting, and then subsequent selection was based on sampling interval.The questionnaire was distributed to the respondents that offer all the PG courses in DLC, A. B. U. Zaria.The apportionment of questionnaires was be based on the number of respondents in each course.Primary data was acquired from research participants through the use of administered online survey questionnaire conducted with the assistance of research assistants.It is divided into two major sections in the survey questionnaire.A 5-point Likert-type scale was used, with numbers ranging from '1' for "Strongly and Compiled by Author, 2023 ***P value <0.01, **P value<0.05*P-value<0.1

Figure 4 .
Figure 4.1 & 4.2, the three lines represent the relationship between SI (y-axis) and OBB (xaxis) at different levels of the moderator trust.

Figure 4 .
Figure 4.2 PR*trust on OBB insights in the link between the aforementioned variables and online buying behaviour, However, there are several limitations to this study that should be acknowledged.Firstly, the study population was the use of DLC PG students alone, other students were not included as part of the study population.Secondly, the study only focused on one country, Nigeria, which may limit the applicability of the findings to other countries or regions.Thirdly, the study only focused on two variables and their relationships, which may not capture the complexity of the online buying behaviour phenomenon.Gusau Journal of Economics and Development Studies (GUJEDS), Vol 4 (1), December, 2023 ISSN: 3027-1126 (Online); 2786-9695 (Online) ©Authors 156 For future research, it is recommended that other population of students are used.Additionally, future research should consider cross-cultural differences in online buying behaviour to expand the scope of the study and which can be used for comparative study.Moreover, researchers can consider other variables that may influence online buying behaviour, such as perceived enjoyment, product variety, and delivery speed.Finally, future research can use qualitative research methods, such as interviews or focus groups, to complement the findings of qualitative study.Vivian, A. M. (2010).A comparison study of online shopping behaviours of Nigerian students in Sheffield and Nigeria.THE UNIVERSITY OF SHEFFIELD.

Table 4 .
4 Size and Significance of the Path Coefficients of Direct Relationships

Table 4 .
4 reveals that the results of the statistical analysis indicate that perceived risk is found to have a negative and insignificant relationship with OBB (β= 0.041, p>0.05).