The effect of perceived convenience and perceived value on intention to repurchase in online shopping: the mediating effect of e-WOM and trust

Abstract

This article investigated the effect of perceived convenience and perceived value on intention to repurchase in online shopping. We also assessed trust and e-WOM as mediators between perceived value and repurchase intention. During March-July 2022, a sample of 298 responses were collected from consumers that use online shopping in North Macedonia. We analysed the research model using PLS structural equation modelling (SEM) and used bootstrapping technique for testing the hypotheses. The findings showed that all independent variables (perceived value, and perceived convenience, trust, and e-WOM) affected repurchase intention. Moreover, the findings revealed that trust and e-WOM mediate the relationship perceived value in its relationship with repurchase intention. Perceived convenience and value contributed significantly to repurchase intention during online shopping, and perceived value had greater impact on e-WOM. Results provide some theoretical and practical implications regarding the effects of factors that impact repurchase intention during online shopping in North Macedonia.

Keywords:

• Perceived value
• convenience
• repurchase intention
• online shopping
• e-WOM

JEL CODES:

• M30
• M31
• M37

1. Introduction

In recent years, internet technologies have enabled companies to use their webs for reaching their potential targets. But, the real challenge for companies remains their connectedness with customers. Retaining and gaining new customers require companies to improve their products and services offered to their targets (Shin et al., 2022; Wu et al., 2014). It is not enough for companies just to be present online, but to find ways how to get interconnected with their customers.

In most of the research regarding repurchase intention, many authors consider perceived convenience to affect repurchase intention in online endeavours (Jebarajakirthy & Shankar, 2021; Shankar & Rishi, 2020). Therefore, perceived value impacts positively and significantly the repurchase intention (Dlačić et al., 2014).

Previous research emphasises more transaction costs that consumers incurred during their online purchasing, but still these research studies do not explain fully what motivates consumers during repurchase intention (Wu et al., 2014; Yu & Chen, 2018). Therefore, it is crucial to get customers’ insights regarding what consumers evaluate more during their repurchase intention (Galetić & Dabić, 2021).

Although, the position of value on repurchase intention is important in previous studies (Chakraborty, 2019; Chen & Lin, 2019; Zeithaml, 1988) still there is scarce research examining perceived value and its relationship with repurchase intention during online shopping. Furthermore, some studies analysed repurchase intention on both transaction cost and value perspectives in order to comprehend their impacts on consumers’ repurchase intentions (Woodruff, 1997).

Shopping convenience seem to be very meaningful to customers during their intention to repurchase (Arya et al., 2022; Fernandes et al., 2022; Jiang et al., 2013). Convenience motivates customers during online shopping in their intention towards online shopping. Kruh et al. (2017) indicate that convenience during shopping has become among the most important reasons why consumers decide to shop online. In addition, a substantial review of literature has been conducted (Berry et al., 2002; Seiders et al., 2007) on customer convenience in a service economy, by defining convenience in service industry as the time and effort that customers preserve associated with purchasing or utilising a service. Based on this, effort and time costs incurred during the process of online shopping influence the perceived convenience in service industry. Although some literature distinguishes between goods and service convenience (Kelley, 1958), Berry et al. (2002) point out that all businesses provide services to their clients, hence convenience appear to be important to goods and services. Therefore, the primary determinants of perceived service convenience are related with non-monetary expenses that are related to time and effort.

Based on the above, this article attempts to find out about the relationship of perceived convenience dimensions with intention to repurchase products online, by proposing a research framework based on perceived convenience, and online repurchase intention. The current study uses convenience dimensions (Jiang et al., 2013), and also investigates its relationship with perceived value, e-WOM and trust.

Therefore, the following study contributes by expanding previous research by providing more theoretical and empirical evidence regarding repurchase intention in an emerging economy. Second, this article develops a conceptualisation of perceived convenience and perceived value and assesses their impact on consumers repurchase intentions and perceived value. In addition, this article adds more robust explanation of trust and e-WOM as mediators regarding perceived convenience and perceived value on repurchase intention and, hence enriching the existing literature. Lastly, since the most of previous studies address the relationship of online convenience with purchase intentions, the current study supplements by closing the gap by exploring the relationship between online perceived convenience, perceived value, trust, and e-WOM. Therefore, this study supports managers by identifying dimensions that may positively influence repurchase intentions in order to improve service delivery to customers (Hur et al., 2021).

The study follows this outline. First, it introduces the research problem in the context of North Macedonia, framing consumer repurchase intention in online settings. We then review literature to develop and present the hypotheses, explain the methodology used in the study. Then we proceed with data presentation and analysis of the findings. The final section provides some implications from research for theory and practice, and limitations before providing some useful directions for research in the future.

2. Literature review and hypotheses development

2.1. Perceived convenience

Convenience concept was first coined by Copeland (1923), who used it to describe a category of goods that consumers used to buy frequently with low involvement and at easily convenience stores. In this line, some studies have used the term convenience in order to classify products that are purchased by customers with low risk and low involvement in their buying process (Bucklin, 1963; Brown, 1990; Copeland, 1923). Additionally, convenience saves consumers’ time and effort, which speeds up their intention to repurchase (Seiders et al., 2005).

The convenience concept has been used in marketing since it integrates both goods and services and needs to be analysed more thoroughly (Berry et al., 2002). The convenience concept initially found to be used with the convenience that preserved customers’ effort and time during their purchasing of goods (Farquhar & Rowley, 2009; Yale & Venkatesh, 1986). Thus, convenience studies have pointed out that consumers’ convenience has been linked to all products, whether they be tangible or intangible that preserve consumer’s effort and time during the process of shopping (Berry et al., 2002). The convenience dimensions, time and effort are found to be very consistent in previous research and was used as a convenience notion of products and services that reduced the non-monetary price (Kelley, 1958; Kotler & Zaltman, 1971).

The convenience concept apart from its focus to products has gotten attention to service convenience attributes (Jiang et al., 2013). According to Berry et al. (2002) most of researchers have linked convenience while distinguishing consumer’s interest in preserving their time and their effort during their intention to repurchase products. The convenience concepts used in this study are based on Jiang et al. (2013) constituting the dimensions below.

2.1.1. Access convenience

Access as a convenience factor characterises the ease and speed of reaching a retailer (Seiders et al., 2000). In retailing sector, access convenience is a very significant element, since it provides the consumer an opportunity to access an online service (Duarte et al., 2018). In contrast to physical retailers, consumers in an online environment can shop from different locations. According to King and Liou (2004), the access convenience dimension is thought to be as the most vital aspect in consumers’ perceptions in online shopping.

2.1.2. Search convenience

Search convenience applies to the convenience during the process of identifying and searching for a product or a service by consumers during their repurchase intention. According to Beauchamp and Ponder (2010) define search convenience as how easily and how fast consumers identify and select products during their purchase intention.

The Internet has provided companies with new ways of using different tools to improve their communication by providing useful information for their clients using their websites, paid ads, or any other form of social media (Duarte et al., 2018). Consumers benefit from these tools since it prevents them from wasting and reducing their time (Beauchamp & Ponder, 2010; Shankar & Rishi, 2020) and spending much less effort for escaping travel to physical stores (Seiders et al., 2000).

2.1.3. Evaluation convenience

Evaluation as a convenience factor means the degree of availability of products that can be evaluated by potential consumers. Jiang et al. (2013) associates evaluation convenience with various presentation contents that are easily understood, such as, texts, videos on websites of companies. When companies engage in creating good contents, consumers have clear picture about products with less required time and spent effort. Information and website content positively influence consumers’ opinion about products (Chen & Wells, 1999). In this line, Elliott and Speck (2005) states that all product information relates to product characteristics, accuracy, amount of information, info graphs, audio and video. Therefore, websites that provide product information which facilitates the process of locating, utilising that information in a timely manner, satisfies customers (Kim & Gupta, 2009).

2.1.4. Transaction convenience

Transaction convenience can be referred to consumer perception in avoiding time and effort during any online transaction with a company. Transaction convenience is defined as the ease and the speed of effecting transactions (Seiders et al., 2005), and amending transactions (Beauchamp & Ponder, 2010). Consumers’ value online paying that is easy and without any extra effort. Online users search for rapid and easy transactions due to the nature of online buying (Srinivasan et al., 2002) and are more likely to buy online when the transaction process itself is less complicated and risk-free (Dekimpe et al., 2020). Therefore, according to (Jiang et al., 2013) transaction convenience refer to customer’s time and effort incurred during the process of fulfilling a transaction.

2.1.5. Possession and post purchase convenience

Possession as a convenience element means the perceived effort and time required by consumers to gain what they want from the company. possession convenience means the money and time spent by consumers to get the desired product (Jiang et al., 2013), and how easily and at what pace consumers can attain their desired products (Seiders et al., 2000). Moreover, possession convenience relates to the money and time that consumers must invest in order to obtain their desired possessions (Jiang et al., 2013). Therefore, with online stores, consumers have to wait for product delivery, time delivery and safe product shipment before the product is in their possession (Jiang et al., 2013). In other hand, the post-purchase convenience is very important for consumers because they need to contact the company for any eventual after-sale service. Nowadays, the post-purchase convenience is very crucial for consumers since they face many obstacles while they need to return products bought online (Berry et al., 2002). Therefore, the positive perceived online convenience is reached when consumers handle successfully a failed service with less time and effort. Based on the above, we come with the following hypotheses:

H1: Perceived convenience positively impacts repurchase intention

H2: Perceived convenience positively impacts perceived value

2.2. Perceived value

Perceived value is the very reason why consumers decide to purchase online because of the little effort they make (Sharma & Klein, 2020). Perceived value as a concept is very important in marketing since consumers are attracted by products that exert perceived value to them. Perceived value is based on the value that customers perceive for a product or service (Zeithaml, 1988), or when a consumer compares the benefits and costs perceived from a marketing offer (Lovelock, 2001; Hasani & Zeqiri, 2015). Customers’ perceived value can be explained from different viewpoints. Perceived value provides consumers benefits, for example, Kuo et al. (2009) considers that besides benefits, perceived value means also money and quality for customers. According to Bishop (1984) value is created when consumers spent less on products. A lot of studies point out that perceived value is positively related to repurchase intention (Kuo et al., 2009; Wang et al., 2004; de Morais Watanabe et al., 2020). Therefore, the value may be characterised in terms of cheap price, what customers desire from the goods, the quality received for the money, and what is gotten for what has been provided (Rahab et al., 2015). Thus, we posit the following hypothesis:

H3: Perceived value has a positive impact on repurchase intention

Perceived trust is thought to play a more significant role in online market settings compared to traditional offline markets because of the perceived risk and uncertainty that may be present in the online shopping context (Kim et al.,2017). Consumer perceived value leads to consumer engagement and consumer involvement in the online process of shopping. Consumer perceived value is strongly correlated with perceived trust, and this relationship in turn has a significant link with consumers’ intentions to engage in online shopping. (Sharma & Klein, 2020). The findings reveal that confidence in the website significantly increased visitors’ intentions to make purchases there (Chen, 2012). Additionally, trust is a crucial determinant of consumer behaviour and an essential component of online shopping success. Therefore, we hypothesise the following:

H4: Perceived value has a positive impact on trust

Perceived value refers to what consumers receive from a product or service, and how they evaluate the utility of that offer (Rouibah et al., 2015). Consumers rely more on information received from friends and companions during their communication, due to the fact that they are considered more candid compared to commercial ads, henceforth, people have started to trust more word of mouth (Ismail & Changalima, 2022; Palalic et al., 2021). Consumers are more likely to use e-WOM and spread bad words to others if a product or service does not deliver what it was expected to deliver to them (Talwar et al., 2021), Therefore, using e-WOM to spread negative words effects negatively the performance of the company, henceforth, evaluating negative WOM by companies is a very important issue to tackle (Chen & Zhang, 2022). On contrary, using e-WOM to spread positive words enhances brand credibility (Banerjee & Sreejesh, 2022). Therefore, we hypothesise the following:

H5: Perceived value has a positive impact on e-WOM

2.3. Trust

Trust is a very important factor in online shopping because customers are separated from products and salespersons. This makes online shopping riskier due to eventual monetary and other losses that may occur. Trust and risk are crucial factors in determining customer behaviour in online settings (Chen, 2012; Sharma & Klein, 2020). Consumers are reluctant with online transactions because they lack confidence (Jarvenpaa et al., 2000). Because during online transactions, consumers are faced with perceived risk and uncertainty involved in online shopping, consumer’s trust with these transactions plays a crucial role in the online market than in physical markets (Head & Hassanein, 2002). Trust helps consumers avoid the reluctancy during their repurchase intention in an online environment. Thus, consumers would buy products from online stores they trust in order to reduce uncertainty and eventual risk during online shopping. A lot of research on online shopping has revealed that customers’ intentions to make purchases from an online store are positively influenced by their trust in the retailer (Chae et al., 2020; Lien et al., 2015; Ponte et al., 2015). Therefore, we come with the following hypothesis.

H6: Trust is positively related with repurchase intention

2.4 E-WOM

Consumers when they want to buy products online, in most cases they look for online reviews and comments from other consumers’ experiences before they decide to purchase products from online stores. Therefore, the consumer power lies in e-WOM, where online reviews and experiences from previous customers empower the online shopper (Park et al. 2011). e-WOM stands for a statement made form a customer about a product or a company (Handi et al., 2018). e-WOM statements can be positive or negative regarding customer experience with the product or the company. Thus, consumers can use various online tools to spread their opinions to other consumers that may affect their intention to repurchase products. Previous studies reveal that e-WOM is an important factor apart from other factors and is positively related with the repurchase intention. Sweeney et al. (2014) noted that services compared to physical goods, are more difficult to evaluate because of the intangibility nature of services. Therefore, consumers rely more on online word of mouth before any decision-making process when they need to repurchase any service. Moreover, positive and a negative e-WOM is very much related to repurchase intention (Sweeney et al., 2014; Liang et al., 2018; Sampat & Sabat, 2021). Therefore, we posit the following hypothesis:

H7: A positive significant relation exists between e-WOM and repurchase intention

Based on the above we propose the following research concept. Figure 1 presents the conceptual research framework of this study.

Figure 1. The research framework concept.

Source: Authors.

3. Method

3.1. Data collection and scales

The hypothesised relationships were analysed from the data collected from an online survey. The original survey scales were in English, and then translated into Albanian and Macedonian languages because of the respondents from North Macedonia. The online questionnaire was pretested by sending the link by mail to some respondents in order to check for any eventual mistakes or misunderstandings. The structured questionnaire was designed in two sections. The first section dealt with demographic profiles of respondents, and the second section with dimensions proposed in the model. Respondents were expected to evaluate the dimensions by using 5-point Likert scales, by indicating the scale of their agreement with the statements concerning the dimensions in the proposed model. The items used in dimensions were developed from the literature review. Items from convenience dimension were developed from Jiang et al. (2013) and Benoit et al. (2017) comprising of 17 items, perceived value with 3 items from De Toni et al. (2018), e-WOM with 3 items from Kajtazi and Zeqiri (2020), trust dimension with 9 items from Raman (2019), and Doney and Cannon (1997), and repurchase intention items from Toska et al. (2022).

3.2. Sample

The structured questionnaire was distributed using Google forms using a convenience sample technique. A sample of 298 responses were collected from consumers that use online shopping. Research was conducted during March-July 2022. Demographic profiles of participants in the study are shown in Table 2.

Table 2. Demographic profile.

Age		Frequency	Percent
	20-30	198	67
	31-40 41-50 50+	63 25 12	21 8 4
Monthly incomes		Frequency	Percent
	Up to 300EU	96	32
	310-500EU	78	26
	501-700EU	56	19
	More than 7001EU	68	23
Any Bank card holder		Frequency	Percent
	Yes	298	100
	No	0
Online experience		Frequency	Percent
	Negative	72	24
	Nor negative nor positive	63	21
	Positive	163	55

Source: Authors.

3.3. Data analysis

The obtained data was analysed using SPSS 26 statistical software and Smart PLS version 3.3.9 for carrying partial least square SEM analysis for assessing the measurement model and the bootstrapping technique for assessing the structural model. Initially, measurement model was used to evaluate the construct reliability and validity, then the structural model assessed the significance relationships of the proposed hypotheses (Table 1).

Table 1. Construct items.

Constructs	Code items	Source
Access convenience	AC1. I can buy products from online stores at any time AC2. I can order products online from wherever I am AC3. The website is always accessible	Jiang et al. (2013); Benoit et al. (2017)
Search convenience	SC1. I can find the desired products quickly	Jiang et al. (2013); Benoit et al. (2017)
	SC2.It is easy to understand the information and navigate the website of the online store
	SC3. It is easy to follow product classification
	SC4. A variety of products are available in the online store
Evaluation convenience	EC1. The online store describes the characteristics of the products	Jiang et al. (2013); Benoit et al. (2017)
	EC2. The online store provides enough information to identify different products
	EC3. The online store describes the product in words and pictures
Transaction convenience	TC1. I can shop online without difficulty	Jiang et al. (2013); Benoit et al. (2017)
	TC2. The online store offers flexible payment methods
	TC3. The online store enables online product payment in a simple and convenient way
Post purchase convenience	PPC1. All the products I ordered have arrived	Jiang et al. (2013); Benoit et al. (2017)
	PPC2. Prices have been identical to those on the online invoice
	PPC3. The products were delivered undamaged to me
	PPC4. Products were delivered on time
Trust	Trust1. I trust the information on the website of the online store
	Trust2. I trust the online store as it offers me convenient options for returning items.	Raman (2019); Doney and Cannon (1997)
	Trust3. I trust the online store as it offers a guarantee for my purchases
	Trust4. I trust the online store as it seems safe
	Trust5. The online store website protects my credit card information
	Trust6. I trust the online store as it keeps my personal information safe
	Trust7. I trust the administrators of the online store that will not misuse my personal information
	Trust8. The online store protects information about my behaviour as a consumer
	Trust9. I believe in the support provided by the customer service employees
e-WOM	WOM1. I leave positive comments with others about this online store	Kajtazi and Zeqiri (2020)
	WOM2. I recommend this online store to anyone who seeks my advice
	WOM3. I encourage friends and others to buy goods from this online store
Perceived value	PV1. The prices of products and services available in the online store are lower than in the physical store	De Toni et al. (2018)
	PV2. Overall, the value I get from shopping at the online store justifies the money and effort
	PV3. In general, it is very convenient for me to buy from the online store
Repurchase intention	RI1. I intend to visit this online store again in the future	Toska et al. (2022)
	RI2. I will continue to repurchase from this online store

Source: Authors.

3.3.1. Measurement model

This analysis (measurement model) evaluates the quality of the constructs in the study which commences with evaluating the factor loadings, followed by construct reliability and construct validity assessment (Emini & Zeqiri, 2021), before assessing the hypothesised model.

Convergent validity as a test is used to assess the closeness of the items and how much they are related to each other in a construct. The convergent validity tests analyse factor loadings, AVE (average variance extracted), Cronbach’s alpha, and composite reliability (Rahman et al., 2015). The analysis in Table 3 reveals that Cronbach’s alpha values of all dimensions vary (0.724 to 0.399), showing those results being above the proposed threshold of 0.60 (Ursachi et al., 2015), which is recommended in social sciences research. In addition, the values of the composite reliability range (0.841 to 0.949) are above the proposed threshold of 0.70. Moreover, the AVE (average variance extracted) values vary from (0.645 to 0.904), denoting that those values are over the suggested threshold of 0.50, recommended by Fornell and Larcker (1981). Based on the results presented in Table 3, convergent validity was reached (Henseler, 2017).

Table 3. Construct items loadings.

Constructs	Items	Factor loadings	Mean	STDEV	VIF
Access convenience	AC1	0.835	4.057	0.855	1.621
	AC2	0.809	4.077	0.968	1.627
	AC3	0.842	4.007	0.875	1.535
Search convenience	SC1	0.804	4.074	0.877	1.645
	SC2	0.841	4.020	0.870	2.104
	SC3	0.822	4.017	0.869	2.199
	SC4	0.743	4.233	0.836	1.646
Evaluation convenience	EC1	0.897	3.755	0.947	2.309
	EC2	0.903	3.732	0.891	2.502
	EC3	0.834	4.081	0.840	1.802
Transaction convenience	TC1	0.805	4.101	0.896	1.385
	TC2	0.824	3.859	0.923	1.759
	TC3	0.853	4.094	0.793	1.811
Post purchase convenience	PPC1	0.819	4.107	0.949	1.885
	PPC2	0.822	4.319	0.770	1.851
	PPC3	0.809	4.221	0.838	1.738
	PPC4	0.762	3.668	1.084	1.543
Trust	Trust1	0.799	3.631	0.979	2.256
	Trust2	0.798	3.725	0.965	2.709
	Trust3	0.819	3.597	1.026	3.470
	Trust4	0.847	3.661	0.946	3.293
	Trust5	0.771	3.617	0.980	2.830
	Trust6	0.845	3.654	0.982	4.044
	Trust7	0.848	3.631	0.951	4.628
	Trust8	0.856	3.745	0.876	4.253
	Trust9	0.786	3.809	0.848	2.550
e-WOM	WOM1	0.891	3.839	0.868	2.536
	WOM2	0.924	3.886	0.819	3.042
	WOM3	0.899	3.782	0.932	2.428
Perceived value	PV1	0.764	3.530	1.150	1.513
	PV2	0.823	3.691	0.982	1.587
	PV3	0.812	3.681	1.050	1.299
Repurchase intention	RI1	0.951	3.916	0.779	2.871
	RI2	0.950	3.859	0.803	2.871

Source: Authors.

3.3.2. Factor loadings

Factor loadings denote the extent of the correlation coefficient of an item with a given variable in the correlation matrix. The loadings values can vary from −1.0 to +1.0, where items that have higher loading values denote a higher correlation of that item with a given factor (Pett et al. 2003). In our study, all items had factor loadings above the value threshold (0.50) as suggested by Hair et al. (2016). Henceforth, no items needed to be removed, as it is shown in Table 3.

3.3.3. Indicator multicollinearity

In order to test the issues related with the multicollinearity of indicators, Variance Inflation Factor (VIF) statistic was utilised (Fornell and Bokstein, 1982). When VIF values are below 5, then there are not any multicollinearity issues (Hair et al., 2016). Results in Table 3 reveal that all the VIF values for the indicators for each of the indicators in this study are below the suggested threshold.

3.3.4. Reliability analysis

Reliability is referred to with consistent results. When a scale is measured repeatedly and when it produces the same results, then the scale is seen as being reliable. Reliability, according to Mark (1996), represents the degree to which a measure is consistent and stable. The measure can be consistent when it provides us with the same results or findings if used again and again.

The Cronbach alpha and Composite reliability (CR) are usually used as the two most common methods for checking the scale reliability.

4. Results

The study used partial least squares (PLS-SEM) to analyse the proposed research model. We used structural equation modelling (SEM) to assess both models, measurement and structural model. The measurement model analyses provide information concerning construct validity, such as convergent and discriminant validity.

4.1. Convergent validity

In order to find out about convergent validity, we checked the outer loadings and the average variance extracted (AVE) values to find out how close items converge while measuring the same construct (Ramayah et al., 2018; Zeqiri et al., 2022). Hence, the convergent validity test is used to provide information regarding Cronbach’s alpha, composite reliability, as well as AVE and the factor loadings (Sarstedt et al., 2019; Zeqiri et al., 2022). When the AVE values are greater than or equal to the suggested threshold value of .50, then the convergent validity is established as suggested by Fornell and Larcker (1981). The Cronbach’s Alpha ranged from .724 to .939 whereas Composite Reliability statistics ranged from .841 to .949 as can be seen in Table 4. Based on the obtained results, we can conclude that both indicators of reliability are over the required threshold of .70 (Hair et al., 2017). Therefore, all constructs established reliability. In addition, the AVE values in this study vary from 0.646–0.909, denoting that all values are above the recommended threshold, which is more than 0.50. Thus, convergent validity statistics in this study results that all the constructs higher values of recommended AVE value threshold. Table 4 reveals the AVE value for each of the dimensions.

Table 4. Construct reliability.

Construct	Cronbach’s alpha	Composite reliability	AVE
Access convenience	0.774	0.868	0.687
Evaluation convenience	0.852	0.910	0.772
Perceived value	0.724	0.841	0.639
Post purchase convenience	0.817	0.879	0.645
Repurchase intention	0.893	0.949	0.904
Search convenience	0.818	0.879	0.646
Transaction convenience	0.770	0.867	0.685
Trust	0.939	0.948	0.671
e-WOM	0.890	0.931	0.819

Source: Authors.

4.2. Discriminant validity

Conversely to convergent validity, discriminant validity shows the extent to which dimensions are unrelated or different in the construct. According to Bagozzi et al. (1991) state that discriminant validity is achieved when the construct measures do not correlate highly to each other. Therefore, discriminant validity provides evidence about the extent to which construct measures are (highly) or are not (highly) correlated with each other. In addition, Fornell and Larcker (1981) pointed out that the criteria for establishing a discriminant validity occur when the square root of AVE for the construct is greater than its correlation with all other constructs. Therefore, this study reveals that the AVE square root for all constructs is bigger than its correlation with other constructs (Table 5).

Table 5. Discriminant validity.

	AC	EC	PV	PPC	RI	SC	TC	Trust	e-WOM
AC	0.829
EC	0.531	0.878
PV	0.354	0.379	0.799
PPC	0.412	0.457	0.325	0.803
RI	0.452	0.509	0.588	0.458	0.951
SC	0.584	0.545	0.263	0.491	0.397	0.804
TC	0.512	0.565	0.338	0.590	0.439	0.581	0.828
Trust	0.453	0.528	0.395	0.513	0.55	0.465	0.498	0.819
e-WOM	0.379	0.466	0.505	0.414	0.633	0.329	0.442	0.554	0.905

Source: Authors.

4.3. Validating higher order constructs

Perceived convenience was the higher-order construct used in this study based on five lower-order constructs: access, search, evaluation, post-purchase, and transaction convenience. To establish a higher-order constructs validity, we should assess outer weights and loadings, t-statistics, p-values, and VIF. Based on the obtained results, the outer weights are significant (Hair et al., 2017). In addition, outer loadings are greater than the .50 recommended threshold value for each of the lower-order constructs (Sarstedt et al. 2019). In the end, in order to check the collinearity issues, we assessed the VIF values. Table 6 denotes that all VIF values are less than the suggested value of 0.05 (Hair et al., 2016). Therefore, based on all assessments, all criteria for the HOC validity are met.

Table 6. Higher order construct validity.

HOC	LOCs	Outer weight	T statistics	P values	Outer loadings	VIF
Convenience	Access	0.261	11.852	0.000	0.772	1.735
	Search	0.216	10.158	0.000	0.795	1.946
	Evaluation	0.288	15.810	0.000	0.802	1.760
	Transaction	0.251	12.346	0.000	0.823	2.067
	Post purchase	0.254	10.943	0.000	0.746	1.643

Source: Authors.

4.4. Structural model

The PLS-SEM was used to analyse the obtained empirical data in order to assess the hypothesised relationships and validate the proposed model and hypotheses. Figure 2 shows the structural equation modelling (SEM) and the results from path analysis, and the R² values for the constructs. The results in Figure 2 show that the R² of repurchase intention (RI) is 0.558, denoting that the combined effect of trust, perceived convenience, perceived value and e-WOM accounted for 55.8% of the RI. Therefore, based on the results, repurchase intention is explained and predicted by the above-mentioned factors with 55.8% variance.

Figure 2. Structural equation modelling.

Source: Authors.

4.5. Hypotheses testing

Bootstrapping technique was used to assess the structural equation model by getting the r- square, beta values, and t-values, in order to test the hypotheses proposed in this study (Hair et al., (2017). Table 7 provides evidence about hypotheses and based on the results all seven hypotheses were supported.

Table 7. Hypotheses testing.

	Hypotheses	Path coefficient	STDEV	T statistics	P values
H1	Perceived convenience -> Perceived value	0.422	0.054	7.892	0.000
H2	Perceived convenience -> Repurchase intention	0.224	0.063	3.369	0.001
H3	Perceived value -> Repurchase intention	0.286	0.062	4.720	0.000
H4	Perceived value -> Trust	0.394	0.052	7.531	0.000
H5	Perceived value -> e-WOM	0.504	0.056	9.057	0.000
H6	Trust -> Repurchase intention	0.136	0.063	2.177	0.030
H7	e-WOM -> Repurchase intention	0.293	0.062	4.842	0.000

Source: Authors.

H1 evaluated whether perceived convenience (PC) had a significant positive relationship with perceived value (PV). The results showed that PC had a significant effect on PV (B = 0.422, t = 7.892, p < .000). Therefore, we support H1. Perceived convenience (PC) showed to have a positive impact on Repurchase intention (RI) (B = 0.224, t = 3.369, p < .001), thus supporting H2. The result also showed a positive relationship between perceived value (PV) with repurchase intention with an effect (B = 0.286, t = 4.720, p < .000). Henceforth, H3 is supported. In addition, H4 evaluated whether the perceived value (PV) was positively related to trust. The results revealed that PV had a positive significant impact on trust (B = 0.394, t = 7.531, p < .000). Thus, H4 was supported. Moreover, H5 evaluates the impact of PV on e-WOM. The results revealed that PV impacts e-WOM (B = 0.504, t = 9.057, p < .000), in support of H5. The results also revealed that trust had a positive effect on repurchase intention (RI) (B = 0.136, t = 2.177, p < .000), supporting H6. Finally, H7 evaluated whether e-WOM had an impact on repurchase intention. Table 7 revealed that e-WOM had a positive relationship with repurchase intention (RI) (B = 0.293, t = 4.842, p < .000).

4.6. Mediation effect

The proposed model analysed the mediation effect of perceived value, e-WOM, and trust. As provided in Table 8, the findings showed that e-WOM mediates the effects of perceived value on repurchase intention (B = 0.151, t = 4.210, p < .000). In addition, results revealed that trust does not mediate the relationship between perceived value and repurchase intention (B = 0.054, t = 1.879, p < .060). Furthermore, perceived value mediates the relationship between perceived convenience and trust (B = 0.167, t = 4.315, p < .000), and finally perceived value mediates the relationship between perceived convenience and e-WOM (B = 0.213, t = 4.913, p < .000). Since the direct effects of predictor were significant, we can conclude that the mediators partially mediated the relationship of predictors and the observed variables.

Table 8. Mediation effects.

Mediation effect	Path coefficient	STDEV	T statistics	P values
Perceived value -> e-WOM -> Repurchase intention	0.151	0.036	4.210	0.000
Perceived value -> Trust -> Repurchase intention	0.054	0.029	1.879	0.060
Perceived convenience -> Perceived value -> Trust	0.167	0.039	4.315	0.000
Perceived convenience -> Perceived value -> e-WOM	0.213	0.043	4.913	0.000

Source: Authors.

5. Discussion and conclusion

5.1. Theoretical contributions

The findings of this research exhibit some useful insights regarding the role that perceived convenience and perceived value have on repurchase intention. In addition, this research enhances the existing theoretical literature by providing an original framework that investigates how perceived value and trust are related to consumer intention to repurchase. As pointed out by other research perceived convenience and perceived value are very crucial regarding the decision-making repurchase products online (Kuo et al., 2009; Wang et al., 2004; de Morais Watanabe et al., 2020). This study provides more evidence regarding the understanding of factors that drive repurchase intention during online shopping. First of all, this study addresses some important research issues in the context of online shopping. It explains the relationship between perceived convenience in the context of online shopping and eventual implications on repurchase intention.

Results showed that customers value products they purchase frequently, with low involvement, and in an easily convenient shopping environment. Moreover, our findings support previous research that convenience during purchasing process satisfies the ability of the customer to realise his or her intent since it conserves customers’ time and effort during their purchasing of goods (Yale & Venkatesh, 1986; Farquhar & Rowley, 2009), and thereby facilitating repurchase intention (Seiders et al., 2005).

Secondly, trust seems to be very important in an online shopping process. Based on the empirical evidence, this study provides more evidence to the existing literature as found out by other research studies that trust is positively related to perceived value (Kim et al.,2017; Sharma & Klein, 2020; Chen, 2012) and to customer’s intention to repurchase. In addition, perceived value arising from online shopping convenience and online trust affect customers repurchase intention. Thus, customers are more inclined to make online purchases from the stores they trust. The findings in this research are in line with many previous studies that analysed online shopping context, revealing that customers’ intentions to make purchases from an online store were positively influenced by their trust in the retailer (Chae et al., 2020; Lien et al., 2015; Ponte et al., 2015).

Importantly, perceived convenience is translated to providing more perceived value to consumers, that consumers buy products with low risk and low involvement, conserving customers’ time and effort and thereby increasing their perceived value during their ability to fulfil their intention to buy online, while perceived value has the greatest influence on e-WOM. Our findings support previous studies that perceived value has an impact on e-WOM (Rouibah et al., 2015; Ismail &Changalima, 2022; Palalic et al., 2021; Talwar et al.,2021; Chen &Zhang, 2022; Banerjee & Sreejesh, 2022).

This study also investigates how search, access, evaluation, transaction, and post-purchase convenience affect perceived convenience in an online shopper repurchase intention and shows that evaluation, transaction, and search convenience in this high-order construct (perceived convenience) significantly affect repurchase intentions by providing more empirical evidence to the theoretical part. Therefore, consumer ability to evaluate products online, the ease of transaction, and the search for product information such as company websites, etc., contributed more to repurchase intention during online shopping. Consumers gain lots of benefits from these tools since they do not waste time and not spending much effort in the process of decision-making (Beauchamp & Ponder, 2010; Shankar & Rishi, 2020; Seiders et al., 2000).

Another contribution from this research relies on the fact that the conceptualisation of this study explores the mediating role of trust and E-WOM at the same time with perceived convenience, perceived value and repurchase intention. Therefore, this makes this research among the first studies exploring the mediation effect of trust and E-WOM on online repurchase intention.

5.2. Managerial implication

This study offers some additional insights to marketing managers and companies in order to improve their marketing activities during online repurchase intentions of their customers in the following directions.

First of all, it is very important to enhance perceived convenience to their customers, i.e., marketing managers and companies need to create for their clientele a more convenient shopping environment that is very meaningful to them during their decision to repurchase products (Jiang et al., 2013). Perceived convenience during online shopping is seen as very crucial for creating a real positive value for customers in their intention towards online shopping. This evidence is also supported by a previous study done by Kruh et al. (2017) that revealed that convenience during shopping is among the main reasons why consumers intend to shop online. Therefore, it is imperative for companies that expect to sell products using online tools to develop convenience strategies that save customers’ effort and time, for example, securing wide-ranging and innovative approaches to customers to realise their process of online shopping. In this way, managers can use certain strategies in order to promote convenience by providing detailed information about their marketing offer.

Second, companies and managers should know that the more convenience is perceived by customers, such as evaluation, transaction, and search convenience by customers, then customers are more likely to repurchase and use e-WOM and eventually recommend the product to other customers. In addition, our findings provide interesting insights for managers. For example, the possibility of evaluation of product information was found to be as the most important factor that determines perceived convenience for customers during their online shopping. Moreover, the results revealed that access and transaction were very essential for customers. Therefore, managers should assure providing their customers with easily accessed information regarding their offer. Specifically, marketing managers should pay attention on shared information on company websites, or any social media platforms, and along with search engine optimisation to contain valuable information for their customers, and not just posting information, but information that represents value for customers. Therefore, the findings from managerial perspective offer managers some important insights regarding convenience dimensions and what dimensions and factors to improve in order to provide customers more convenience and value in their intention to repurchase products from their online stores and thereby enhance trust and e-WOM.

5.3. Limitations and further research

Like other studies, this study acknowledges some limitations since it collected data only from customers and not analysing any industry specifically. Therefore, the obtained results are general customer perceptions about regarding factors that might enhance the intention to repurchase online. In addition, a larger sample could produce different and more robust results.

Although the focus of the research was to explore the relationship between perceived convenience, perceived value, and repurchase intention which is limited to only perceived benefits during repurchase processes. Therefore, future studies should focus on other factors, for example, new research can be expanded to combine a more wholesome model embedding both dimensions of perceived benefits versus perceived risks. In addition, since our study explored products entailing as a concept both goods and services, using a multigroup analysis can offer more consumer insights concerning factors that contribute to repurchase intention.

Our research revealed that evaluation convenience contributed more to the perceived convenience dimension, surpassing access and search from the convenience dimension. Therefore, these results recommend some future directions for research. First, we provide some clues to further research to focus on information posted in an online context and environment, since customers value the content of information.

Moreover, we found out that certain mediators, like trust or e-WOM, strengthened the relation of perceived convenience, and perceived value with repurchase intention, in that way, using some other moderators may trigger some other insightful results. For example, using internet penetration and online service usage as moderators to find out if they moderate repurchase intention during online shopping.

Disclosure statement

No conflict of interest has been reported by the authors.

Additional information

Funding

The authors extend their appreciation to the Deanship of Scientific Research at Imam Mohammad Ibn Saud Islamic University (IMSIU) for funding and supporting this work through Research Partnership Program no.RP-21-10-04.