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Abstract

Maintaining durable and long-lasting relationships with customers is a key factor that is widely considered by marketing practitioners and company management. Therefore, this study aims to explore and examine the factors (personal interest, trust, safety perceptions, E-transaction acceptance, and privacy concerns) influencing electronic relationship ER from the customers’ perspectives. The study selected the sample from university students (456 respondents) and was conducted in United Arab Emirates UAE, to analyze their perspectives about these factors. The study findings found significantly positive effect of all these factors on ER. And the most influential one was the personal interest followed by trust. Our research concludes by mentioning customers’ communication experiences and perceptions with their companies in order to assess their ability to meet expectations and maintain ongoing relationships. The research implications offer the marketing practitioners with insight to diversify their interaction ways with their key customers.

Keywords

Electronic relationship trust technology acceptance Partial Least Squares-Structural Equation Modeling (PLS-SEM)

Introduction

The massive economic consequences of the global recession have prompted businesses to explore new and efficient ways to manage their customers in order to maintain a competitive edge. This led the organizations to intensify their activities that include information technology adoption in several business functions. Of these functions, Customer Relationship Management (CRM) is the most important, which mainly indicates an integrated approach to manage the customer relations using a combination of essential components including people, procedures, technology and culture.¹ CRM has been widely adopted by businesses of different sizes globally as a means to meet a key objective of developing relations between the organization and its customers.² However, the application of technologies in the CRM has also presented a birth to what is currently known as Electronic Relationship (ER) with the customer.³ The intention of technology adoption and acceptance of the modern innovative technologies generally depends on various factors including an organization’s technical capabilities. While large number of companies now are using electronic means to interact or do business directly with customer, factors affecting customer-supplier electronic relationship(ER) from customers’ perspectives need to be viewed again especially when companies tried to adapt direct digital channels with customers as claimed by Mihailova.⁴

The notion of ER goes beyond the transactions exchanges that sufficiently provide products or services to targeted customers. It also indicates a long-term relationship that utilizes advanced modern technological tools and methods to expand the understanding of customers’ behaviours, increase customers commitment and enhance satisfaction.⁵ Furthermore, ER differs from the traditional marketing relationships, which generally focus on maintaining customer relations through factors related to 4Ps such as reasonable price and quality service. The growing complexity of the different services requires greater concerns with issues of privacy and personal interest, with levels of trust in customer interactions.⁶ It is critical to focus on customer’s relationship support in the light of their technological services and interest. The current study thus sought to address and discuss the factors affecting ER and identify the critical issues and challenges that influence this topic and provide more insights with applicable recommendations for ER adoption and development in a developing context. Specifically, this study tried to answer the following question what the main factors are affecting customer-supplier ER especially from customers’ perspectives. In more details, this study aims to¹ investigate the current state of ER implementation and discuss the challenge(s) faced, as well as² identify and test practically a set of factors that affecting ER adoption.

The study importance

This paper is important while not that many papers discuss the factors affecting ER between companies and customer (B2C). Also, it’s important for companies to search and find ways how to enhance the mutual ERs and interactions among partners especially between companies and customers especially when adapting different digital technologies and tools.⁷ This study adds value to the literature also by investigating the factors affecting online buying while the majority of customers are transferring their buying to be online buying as claimed by Barbosa et al.⁸ To add more, this study will help in enhancing the companies capabilities how to leverage the digital technologies to customize and increase the interaction performance between companies and customers as highlighted by Lin and Lin.⁹ Moreover, this study investigates a set of electronic indictors such as safety perceptions, privacy concerns, E-transaction acceptance and trust, which in turn able companies to benefits from customers experience in using and practising digital platforms in doing business as mentioned by Ramasundaram et al. (2023).¹⁰ Additionally, by using new technologies to interact with customers and enhance the business with such as using Metaverse in Marketing as discussed by Dwivedi et al. (2023),¹¹ this study will help to consider some of the customers’ indicators in how to enhance such Business-to-customer (B2C) Mutual interactions within the technology era.

Literature review

Safety perceptions

During the working to enhance business metrics, the culture evaluation has been used as a measure for indirect safety. The safety culture indicates the sets of shared systems of the behaviours, values and attitudes which reflect the major practices towards organizational commitment of the safety.¹² The organizations with great safety perceptions empowers the key stakeholder like customers to identify and state the main concerns, acknowledge the possible errors and identify the required resources to promote the related efforts (Alshurideh et al., 2022).¹³ As the safety perceptions can develop the culture improvement and correlate with the great outcomes, addressing the safety perceptions of the custom (Al Kurdi et al., 2023; Alshurideh et al., 2023).^14,15 However, the results guided the growing need for education programs development that can develop the perceptions about the safety of using advanced technology and systems.¹⁶ A recent study by Arwa Zabian and Sami Qawasmeh (2022) investigated the impact of emerging technologies on workplace privacy, including electronic monitoring. The study found that safety concerns were positively related to electronic monitoring, as employees felt that monitoring could help ensure their safety in the workplace during the COVID-19 pandemic. Increasing electronic related outcomes have encouraged the organizations with several industries to identify as related customer relationship strategies to improve their safety perceptions. This led to help the marketing practitioners and management to organize and maintain relationships with all key parties of the business. However, little of the studies that examined the effecting role of the safety perceptions among the ER with the customers. Therefore, the study postulates the following research hypothesis:

H1: Safety perceptions influence positively ER.

Privacy concerns

Confidentiality is an important part to sustain a successful marketing relationship. Once the customers have trust in a company, they definitely believe about the detailed confidence for their data and will be used by the organization ethically.¹⁷ A study by Hsu et al.¹⁸ found that privacy concerns and information sharing have a U-shaped relationship. Users with privacy concerns initially do not share their private information, but eventually do so as they trust the website and its members. The relationship varies based on system evaluation perception and personal motivation levels. Therefore, businesses should prioritize protecting customer privacy and building trust in their website and members. Other studies have shown that the customers are more likely and concerned of the contexts where their personal or financial data are not sold to others without asking them for consent, whereby the organizations collect and use the customers’ information to contact them repeatedly.¹⁹ As such, a conclusion is that the customers have lower privacy concerns when the marketing relationships are practised through using their information by the companies for different purposes. The successful marketing relationship is more dependent on the customers’ perceptions of the business procedures to protect their privacy.²⁰ The current companies apply trading through the Internet and this could make them manipulate or misuse their customers’ information, and it potentially jeopardizes the relations with the customers. The research has presented that business operations like sending spam e-mails to the customers are a reason for the customers to have a privacy concern.²¹ As such, the businesses that trade by the Internet requested to better understand the online perceptions of the customers who are sensitive to particular effects and change their perceptions about the purchasing decisions. Examining the privacy concerns might be a right step of the directions towards establishing a good customer relation. Therefore, the study postulates the following research hypothesis:

H2: Privacy concerns influence positively ER.

Personal interest

The digital relationship can benefit when the customers have limited communication methods or time. Due to the interest of some people not to be involved within a physical occurrence, today’s virtual human relations can also help the business to faster build relationships and trust.²² ER building can make it easier to flirt with the internets. People can also discuss the problems or difficulties with others, and it is easier to discuss more than emails. Honesty in online communication is key. The individuals have to be more trusted and honest to make a business relationship particularly if it involves within a virtual realm.²³ This may mean not limiting or annoying others in any way. Establishing a mutually beneficial ER can be an effective approach to meet new customers, thus it is considerably affordable and faster than face-to-face interaction. The positive aspects of the ER can take the form of allowing the people to mutually interact with others.²⁴ This is significant when the people are living in different regions and are separated by far distance and they may be unable to get in touch personally. Therefore, the study postulates the following research hypothesis:

H3: Personal interest influences positively ER.

E-transaction acceptance

Although the Internet infrastructure can generate an implicit uncertainty through the separation between customers and services providers or products retailers, the unpredictability of this uncertainty about online transactions is still a crucial issue for customers.²⁵ The risks of money loss, since the customers should depend on electronic transactions and information and hence become a vulnerable factor for customers to incomplete orders or give untrue information at the retailers’ webs.²⁶ Furthermore, a risk associated with losing the privacy linked with giving personal or financial information to the companies. E-transactions are connected with the important power delegation which the customers surrender during the online transactions. In fact, the open business due to the Internet and the business transactions infrastructure and the worldwide nature increase the rates of uncertainty around online transactions, so this leads to trust as well crucial risks of e-transaction. A study suggests that individuals who perceive online trust are more inclined to use e-wallets. Additionally, trust plays a crucial role in the adoption of electronic wallets.²⁷ ER in this setting is seen as a catalyst for all parties of the transactions that can also provide the customers with their expectations and satisfy the exchange relations.²⁸ The studies in this context maintained the trust in the e-transaction as an essential element to understand the interpersonal attitudes as well socio-economic exchanges.²⁹ The importance of ER has grown in the e-commerce field due to the higher degree of uncertainty and risks that exist in most online transactions. Therefore, the study postulates the following research hypothesis:

H4: E-transaction acceptance influence positively ER.

Trust

Trust is an important factor when it comes to online transactions, in fact and according to research, it is the mediator between selected antecedents and the willingness to buy through e-commerce.²⁹ It is one of the important relationship-characteristics that increase and positively affect customer’s retention.³⁰ With the advancements of technology, people are more open to try out and almost adapt to the innovations that arise in the globe, although transactional risk was found to negatively affect trust when it comes to online payments.³¹ Many studies have been conducted to know how to acquire a consumer’s trust. For instance, Luo et al. (2020) found that a combination of virtual community and e-commerce service quality (including system quality, security assurance, product variety, and service support construct) positively affects users’ trust in online platforms that then predicted their transaction intention.

The social presence of online brand communities and the relationship between the brand and their customers positively affects online social commerce trust.³² The communication through social platforms, interactivity level, formal, or informal styles all create a level of trust in the online world. A study showed that female customers were firstly reliant on reputation and relationship building, so then this would predict their future purchases;³³ however, male customers depend on their online purchase based on their developed trust. Another factor that affects trust is the return policy of products bought online. A paper found leniency with online return policy had a significant positive impact on consumer’s trust which then positively impacts their online purchase decision.³⁴ Due to the fact that the quality of online products is not fully guaranteed and experienced, some sense of security of their purchase increases their intent to buy the perform the online transaction and get returns if otherwise unsatisfied. Customer service that provides essential information, for example, delivery estimates or in case of unexpected delays, firmly establishes a sense of trust from the customer to the company, which was found to be positively affecting their behavior and intention to use the online shopping services.³⁵ Therefore, the study postulates the following research hypothesis:

H5: Trust influences positively ER.

Method

Data collection

Data collection took place from 15^th June to 20^th February 2020 over the winter semester (2019–2020) in University of Sharjah using online surveys. The research team conducted a random distribution of 500 questionnaires. Out of 456 questionnaires were filled by the respondent, which represent about 91% response rate. Apart from that, 44 questionnaires were also excluded because of some missing values. Because of this, the number of valid questionnaires was 456. Krejcie and Morgon³⁶ suggested that these accepted questionnaires had an appropriate sample size level (the expected sampling size for 306 respondents/1500 population). There is a great difference between the sample size (456) and the minor requirements. Considering this, the sample size was evaluated using Structural Equation Modelling SEM,³⁷ which was used to confirm the hypotheses. It is also worth noting that the previous theories (based on the M-learning context) were the foundation of our hypotheses. When it comes to the evaluation the measurement model, SmartPLS Version 3 was used to conduct the final path model.

Participants

The results showed that about 49% of the respondents were male and 51% were female. And about 63% of the respondents were aged around 18–29 years and the rest of them were above 29. In terms of the academic background, there were 26% students in Business Administration, 21% in College of Engineering and Information Technology, 19% in Humanities and Social Sciences, 18% in General Education, and 16% in Mass Communication and Public Relations. The respondents mostly had university degrees alongside educated backgrounds. More specifically, the percentages of students having a bachelor degree, master degree, and a doctoral degree were 68%, 21%, and 11%, respectively. Al-Emran and Salloum³⁸ suggested that in cases where the respondents show willingness for volunteering, there can be utilization of the ‘purposive sampling approach’. When it comes to this sample, the students belonged to different universities, age groups, and educational programs and levels. Other than that, IBM SPSS Statistics version 23 was used for analyzing the demographic data.

Study Instrument

In this study, a survey instrument was suggested for validating the hypothesis. In order to measure the study’s six constructs, 22 items were further added to the survey. Table 1 presents these constructs with their respective items. To make the research more applicable, the researchers made amendments to the questions of prior research.

Table 1.

Study instrument.

Constructs	No. of items
Safety perceptions	4
Privacy concerns	4
Personal interest	4
E-transaction acceptance	4
Trust	4
E-relationship	2

Data analysis

The questionnaire was distributed by the researcher. As for the online surveys, they were given to students at universities in the UAE (N = 500), where this research has considered two different universities, that is, The British University in Dubai and University of Fujairah. It is worth noting that both these institutes have popularity in the UAE.

Survey structure

A questionnaire survey was given to the students.³⁸ This survey has three sections.

• The first section focuses on the respondents’ personal data.

• The second section presents five items that represent the general question related to M-learning systems.

• The third section consists of 15 items that deal with Service quality, and Quality of the system.

For measuring the study items, a five-point Likert Scale was considered with alternatives: strongly disagree (1), disagree (2), neutral (3), agree (4), and strongly agreed (5).

The analysis findings

Data analysis

For this study, the data analysis was conducted using the Partial Least Squares-Structural Equation Modelling (PLS-SEM) through SmartPLS V 3.³⁹ The collected data was analyzed by using a two-step assessment approach, which includes the measurement model and structural model.⁴⁰ The PLS-SEM was selected in this research for a number of factors.

First, if the given research aims to work on a current theory, the preference should be given to PLS-SEM.⁴¹ Second of all, the PLS-SEM can help with effectively handling the exploratory research that has complex models.⁴² Third of all, PLS-SEM carries out analysis on the entire model as one unit rather than making subdivisions out of it.⁴³ Lastly, PLS-SEM also provides concurrent analysis for the structural and measurement models because of which accurate measurements are generated.⁴⁴

Convergent validity

To assess the measurement model, Hair et al.⁴⁰ suggested the construct reliability (which includes Cronbach’s alpha, and Composite Reliability (CR) and validity (which includes discriminant and convergent validity). For determining the construct reliability, Cronbach’s alpha (CA) was ranged within 0.787–0.901 as given in Table 2. The threshold value (0.7) is lower than these figures.⁴⁵ The results showed that the CR values rane from 0.726 to 0.900, which exceed the threshold value.⁴⁶ Rather, researchers should use the rho (pA) reliability coefficient for evaluating and reporting construct reliability.⁴⁷ As with CA and CR, the reliability coefficient ρA should be at least 0.70 (exploratory research) and 0.80 or 0.90 (advanced research stages).⁴⁸ Table 2 also showed that 0.70 is the minimum reliability coefficient ρA of all measurement constructs. These results confirmed the construct reliability, and each construct was considered to be free from errors, ultimately (Figure 1).

Table 2.

The Measurement model.

Constructs	Items	Factor loading	Cronbach’s alpha	CR	PA	AVE
Safety perceptions	SP1	0.760	0.815	0.726	0.880	0.584
	SP2	0.859
	SP3	0.874
	SP4	0.866
Privacy concerns	PC1	0.881	0.893	0.782	0.844	0.721
	PC2	0.852
	PC3	0.856
	PC4	0.823
Personal interest	PI1	0.849	0.890	0.829	0.841	0.786
	PI2	0.854
	PI3	0.896
	PI4	0.846
E-transaction acceptance	EA1	0.724	0.788	0.839	0.847	0.840
	EA2	0.858
	EA3	0.775
	EA4	0.887
Trust	TRT1	0.845	0.890	0.830	0.846	0.744
	TRT2	0.869
	TRT3	0.876
	TRT4	0.746
E-relationship	ER1	0.789	0.787	0.801	0.809	0.766
E-relationship	ER2	0.752	0.787	0.801	0.809	0.766

Figure 1.

Conceptual model.

When it comes to the measurement of convergent validity, it is necessary to test the Average Variance Extracted (AVE) and factor loadings of the indicators.⁴⁰ Apart from that, Table 4 suggests that each factor loading value exceeded the threshold value of 0.7. Other than that, according to the Table 1 results, the AVE values ranged from 0.584 to 0.840, which are determined to exceed the ‘0.5’ threshold value. On the basis of these following results, it is possible to achieve convergent validity.

Discriminant validity

To measure discriminant validity, it was suggested to consider two criteria that include the Heterotrait–Monotrait ratio (HTMT) and Fornell–Larker criterion.⁴⁰ Table 3 findings suggest that the Fornell–Larker condition confirms the requirements because each AVE and their square roots exceed its correlation with other constructs.⁴⁹

Table 3.

Correlation matrix of measures.

	SP	PC	PI	EA	TRT	ER
SP	0.887	—	—	—	—	—
PC	0.233	0.781	—	—	—	—
PI	0.222	0.605	0.876	—	—	—
EA	0.201	0.555	0.631	0.905	—	—
TRT	0.288	0.625	0.565	0.454	0.807	—
ER	0.189	0.585	0.487	0.286	0.300	0.865

Note. SP = Safety Perceptions; PC = Privacy Concerns; PI = Personal Interest; EA = E-Transaction Acceptance; TRT = Trust; ER = Electronic Relationship.

Table 4 showed the HTMT ratio findings, which represents that the value of each construct is lower than the ‘0.85’ threshold value.⁵⁰ Because of this, there is a presence of the HTMT ratio. With the help of these findings, there is calculation of the discriminant validity. According to the analysis results, there was not a single issue related to assessing the measurement model when it comes to its reliability and validity. Because of it, the collected data can be further used for evaluating the structural model.

Table 4.

Heterotrait–Monotrait ratio (HTMT).

	SP	PC	PI	EA	TRT	ER
SP	—	—	—	—	—	—
PC	0.130	—	—	—	—	—
PI	0.122	0.702	—	—	—	—
EA	0.390	0.632	0.630	—	—	—
TRT	0.488	0.520	0.415	0.111	—	—
OT	0.296	0.410	0.239	0.138	0.536	—

Note. SP = Safety Perceptions; PC = Privacy Concerns; PI = Personal Interest; EA = E-Transaction Acceptance; TRT = Trust; ER = Electronic Relationship.

Model fit

The RMS_theta, NFI, Chi-Square, d_ULS, d_G, exact fit criteria, and Standard Root Mean Square Residual (SRMR) which show the model fit in PLS-SEM are the fit measures provided by SmartPLS.⁵¹ SRMR shows how the observed correlations are different from model implied correlation matrix (Hair et al., 2016) and <0.08 values are thought to be good model fit measures.⁵² A good model fit is considered to be > 0.90 Normative Fit Index NFI values.⁵³ The NFI ratio deals with the Chi2 value in the proposed model and the null model or benchmark model.⁵⁴ The NFI is directly correlated to the parameters and considering this, model fit indicators do not include NPI.⁴² Discrepancy between empirical covariance matrix and covariance matrix implied by composite factor model is offered by the two metrics, the geodesic distance d_G, squared Eucledian distance, and d_ULS.^42,47 RMS_theta can only be applied to the reflective models and helps with evaluating the degree of outer model residuals correlation.⁵⁴

The PLS-SEM model will improve as the RMS theta value reaches zero, with a good fit being <0.12 and poor fits being other values.⁵⁰ The relationship between each construct is evaluated by the saturated model, while the estimated model works on model structure and total effects. According to Table 5, the value of RMS_theta was 0.059. From this, it can be said that the size of the goodness-of-fit for the PLS-SEM model was appropriate for demonstrating global PLS model validity.

Table 5.

Model fit indicators.

	Complete model
	Saturated model	Estimated mod
SRMR	0.086	0.050
d_ULS	0.663	2.620
d_G	0.644	0.644
Chi-square	415.605	415.725
NFI	0.753	0.753
Rms theta	0.059

Hypotheses testing

For determining whether the structural model’s theoretical constructs are interdependent, the structural equation model alongside SmartPLS with maximum likelihood estimation was performed.⁵⁵ Accordingly, the analysis of the proposed hypotheses was completed. Table 6 also showed high predictive power of the model,⁵⁶ that is, there was 76% variance within e-relationship.

Table 6.

R² of the endogenous latent variables.

Constructs	R ²	Results
OT	0.763	High

Note. OT = E- Relationship/Online Transaction.

In Table 7, the beta (β) values, t-values, and p-values for all of the developed hypotheses have been described on the basis of the produced findings with the help of the PLS-SEM technique. There is no doubt that every researcher has supported each hypothesis. Taking into consideration the data analysis hypotheses, the empirical data supported H1, H2, H3, H4, and H5.

Table 7.

Summary of testing results.

H	Relationship	Path	t-value	p-value	Direction	Decision
H1	SP - > ER	0.420	8.333	0.013	Positive	Supported*
H2	PC - > ER	0.101	1.235	0.046	Positive	Supported*
H3	PI - > ER	0.852	25.977	0.000	Positive	Supported**
H4	EA - > ER	0.396	17.117	0.002	Positive	Supported**
H5	TRT - > ER	0.527	10.552	0.011	Positive	Supported*

Note. SP = Safety Perceptions; PC = Privacy Concerns; PI = Personal Interest; EA = E-Transaction Acceptance; TRT = Trust; ER = Electronic Relationship.

SEM approach was employed with SmartPLS having maximum likelihood estimation to find out the interdependence of various theoretical constructs of the structural model.^55,57 In this way, the proposed hypotheses were analyzed. As appeared in Table 6, the model had a high predictive power,⁵⁶ that’s, the percentage of the variance within e-Relationship is nearly 76%.

Table 7 describes the beta (β) values, t-values, and p-values for each of the developed hypotheses based on the generated results through PLS-SEM technique. It is clear that all the researchers have supported all hypotheses. Based on the data analysis hypotheses H1, H2, H3, H4, and H5 were supported by the empirical data. Safety Perceptions (SP), Privacy Concerns (PC), Personal Interest (PI), E-Transaction Acceptance (EA), and Trust (TRT) has significant effects on e-Relationship (ER) (β = 0.420, p < 0.05), β = 0.101, p < 0.05), (β = 0.852, p < 0.001), (β = 0.396, p < 0.01), and (β = 0.527, p < 0.05), respectively; hence H1, H2, H3, H4, and H5 are supported.

The findings discussion

The findings revealed that the proportion of ER of the business was relatively limited. Perceptions about privacy and interest of the customers were already stated and the organizations through their ER applications and methods updated the marketing strategies to meet the increasing changes in the people’s attitudes and perceptions. Therefore, there is an important requirement to focus and integrate further adoption among services or products offered to the customer. Currently, organizations are showing increased involvement in advanced technology practices to maintain mutual benefits with their key customers and achieve strategic goals, as indicated by the results. However, the results also confirm the importance of trust and easy communication channels to ensure the safety of electronic transactions. Additionally, the insights from our research suggest that developments in customer marketing relationships encourage businesses to adopt and use ER applications. These applications need more attention, as do the effects of the factors identified in this study. Furthermore, the study findings are consistent with the existing body of the literature,²¹ which has also confirmed the effect of the personal individual factors and revealed their significant role to differentiate the customer ER.

The study findings indicate a positive influence of trust as a key motivator for customers to largely engage with modern technologies in order to meet their expectations and make transactions easier. With confidentiality issues becoming more crucial for people, the intention to follow recommendations from trusted sources is influential in making real experiences with given judgements and accepting the ER. The confidence factor presented in the analysis was more significant than personal interests and perception factors, with the findings showing the highest bet for this factor (852). The finding for E-transaction acceptance was not surprising. The level of a company’s electronic applications and activities act as indicators to predict the likelihood of ER involvement and adoption. This may be due to the ER practices, which ultimately involves many people within different interactions with the company. The level of acceptance of this original business aspect is crucial for electronic adoption decisions. Users of ER realize the potential advantages of practicing ER applications for establishing long-term relationships with profitable customers.

Although the results for trust and e-transaction acceptance factors were statistically significant, discriminant validity analysis showed that they had low predictive power for ER. This suggests that these factors have little impact on customers’ intention to integrate with the company through ER compared to concerns about privacy and other factors.⁵⁸ This finding is in line with the literature⁶ which supports the significance of privacy maintenance and protection of personal and financial customer information, as well as the commitment of the company in these aspects. The effects of the technological factors, on other hand, support the current study findings, which are consistent with previous research.¹² In this research, all factors were considered significant determinants influencing the ER. All study factors were statistically positive and significant in the SEM analysis, as indicated by a p-value lower than the stated statistical and standard values. When ranking these factors from most to least significant, they were: personal interest, trust, safety perceptions, E-transaction acceptance, and privacy concerns.

The results suggest that personal interest is more likely to affect the ER compared to other research factors. For instance, although e-transaction acceptance had a significant effect on ER, it can be explained that both the company and the customer have a mutual interest in diversifying traditional communication methods and enhancing this issue with more advanced communication approaches for equivalent chances and trying out various applications of ER. It does not matter whether ER factors can be adopted among different sized businesses or not. The results also showed that the reliability values for all these factors were relatively high. This indicates that all factors were well-considered for successful customer ERs before planning marketing strategies and promotions. They permit the usage of customer relationships with a reliable base for long-term strategic marketing relationships. Despite the general agreement with many factors that should be considered, this does not necessarily mean that there are no other factors that need to be examined. In general, integrating common practices for relations development with customers is highly required for sustainable productive B2C processes.

Furthermore, the study results indicate that CRM plays a vital role in developing e-marketing capabilities and enhancing relationships with key stakeholders. Effective marketing management across industries requires strategic planning for long-term customer relationships, and the use of electronic methods is becoming increasingly important, particularly for service-oriented organizations. Additionally, the selected factors influencing this issue require close monitoring to keep up with changes in marketing trends and to create successful and effective relationships with customers, as well as, to encourage them to use electronic devices and keep in touch using their favourable ways. This could also broaden the scope of online marketing. Kline’s⁴⁶ study supports these findings and indicates that competitive environmental factors influence the development of e-relationship capacity. It can be inferred that factors with limited focus require further support, market orientation and inclination, industry and customer pressures with consideration of customers’ skills and awareness of using electronic devices for business interactions.

Implications and conclusion

The aim of the current study is to gain a deeper understanding of the practical insights that can contribute to the development of customer delight through ER from their perspectives. This research focuses on exploring the major elements of ERs and the respective factors that have been empirically examined in various studies. The research examines the effects, methods, and measurements of these factors to provide insights into their significance in enhancing customer satisfaction through ER. Moreover, the issue of ER in this study demonstrated high technology acceptance through the growing electronic transactions. According to the study findings and analysis, the key results of SEM approach analysis can be summarized as follows; in respect to the factor of trust, customers believe this element should be present before involving in ER with a company, as well as their privacy should be maintained and protected to last this relation. It is obvious that the customers’ personal interests factor has also been recognized in the ER, which reflects the change in people’s interests that should be met by the companies. However, this study concentrated on customers’ perspectives in universities’ contexts to define their perceptions about the most important factors that play a role in accepting involvement in ER. To explore the modern interaction methods that enhance ER, organizations nowadays struggle to adopt tools to achieve this point such as smartphone applications, which are currently being employed to improve communication and related operations with customers.

Since all marketing activities are generally aimed at promoting organizational profits and maintaining good customer relations, the current research discussed and examined the factors that influence ER from customers’ perspectives and explored their experience with this issue. In addition, the current existing literature lacks studies on the ER and related factors within the developing context, and how these factors can influence building great perceptions of the customer towards the development of interrelationship with companies. The concept of customer relationship in this study was debated based on the customer experiences and intention to accept the technology and make transactions using the advanced technological methods. Thus, this study substantially contributes to the relevant body of the literature by conceptualizing and empirically examining the factors that influence ER in UAE. The study further introduced a conceptual model supported by theory in order to achieve the stated purposes and investigate the identified issue. The research model confirmed the influencing effect of all involved factors on the ER.

From a practical perspective, this study provides valuable implications for different sectors operating in emerging contexts. In order to gain good customer focus and attention, organizations should build long-lasting relationships with customers and invest appropriately in factors that contribute significantly to their experience and satisfaction. Today’s customers expect fast, personalized, and efficient interactions with their companies, based on time-built experiences. Therefore, the findings of the study have demonstrated good customer perceptions which have positive effects on ER.

Recommendations and limitations

This empirical study provides significant contributions toward the factors influencing ER and how this issue is covered and debated through the current research field, but there are still some limitations. First, the research only focused on the customer-centric perspective and did not consider the viewpoints of employees or marketers. The study model can extend more and include the effect of ER from marketers’ perspectives as well. Second, the sample size used in this study was not large enough to generalize the results. Future studies should include larger and more diverse samples from different demographic backgrounds and professional settings. Third, the study concentrated on university students, and it would be beneficial to expand the scope to include other groups to provide a more comprehensive view. Additionally, only a limited number of factors were examined to understand ER, and future research can consider including other factors to enrich the discussions and literature in this field such as customer benefits. Lastly, a comparative analysis between different sectors in terms of ER and individuals’ perspectives, as well as the exploration of the causal relationship between these variables using a longitudinal research approach can provide more insights into this topic.

Footnotes

Declaration of conflicting interests

The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The author(s) received no financial support for the research, authorship, and/or publication of this article.

ORCID iDs

Muhammad Turki Alshurideh

Barween Al Kurdi

Haitham M Alzoubi

Shanmugan Joghee

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Factors affecting customer-supplier electronic relationship (ER): A customers’ perspective

Abstract

Keywords

Introduction

The study importance

Literature review

Safety perceptions

Privacy concerns

Personal interest

E-transaction acceptance

Trust

Method

Data collection

Participants

Study Instrument

Data analysis

Survey structure

The analysis findings

Data analysis

Convergent validity

Discriminant validity

Model fit

Hypotheses testing

The findings discussion

Implications and conclusion

Recommendations and limitations

Footnotes

Declaration of conflicting interests

Funding

ORCID iDs

References