The Impact of Website Design and Customer Support on Customer Experience and Its Relation to Fintech Adoption Intention in Saudi Arabia

Very little research has been conducted on customer experience (CE) in fintech to date, especially in the Kingdom of Saudi Arabia. The purpose of this paper is to study the impact of website design (WD) and customer support (CS) on CE and its relation to fintech adoption intention (AI) in Saudi Arabia. Data was gathered from 296 individuals by means of a survey. After checking the validity and reliability of the results, structural equation modeling was used to test the hypotheses. The results showed that WD and CS have a significant positive impact on CE. Also, WD and CE positively influence AI in fintech, while CS does not have a direct relation. Furthermore, CE mediates both WD and CS with AI. This paper provides further conceptual understanding of antecedents and consequences of CE in fintech. Practitioners in the fintech sector could benefit from results in achieving long-term goals. There are some limitations in the study that suggest the need for further studies. of CE in fintech. It shows that CE is positively influenced by WD and CS, which will help fintech


Introduction
The term fintech is an abbreviation of the words "financial" and "technology". It is an innovation in the field of financial services that combines technology and financial service activities (Ketterer, 2017;Schueffel, 2016). Innovation and enhanced customer experience (CE) are causing customers to move away from traditional financial activities toward fintech. Accelerated by the increased use of internet services, smartphones, and cashless payments due to Covid-19, fintech has become an important dimension of the financial services industry in Saudi Arabia. According to FintechSaudi (2021a), 74% of individuals in Saudi Arabia have used at least one fintech service. In 2021, there are 82 active fintechs, with disclosed funding for the period September 2020 to August 2021 totalling more than 1350 million SAR (FintechSaudi, 2021b). Major fintech services in the Kingdom involve electronic payments, online budgeting, equity crowdfunding, insurance aggregators, robo-advisory services, and peer-to-peer lending (FintechSaudi, 2021a). Gilmore (1998, 1999) describe the fourth wave in economic development as "experiences" and the emergence of new economy as the "experience economy" (Jain, Aagja, & Bagdare, 2017). Traditional product or service value proposition is no longer adequate for reaching customers; hence, businesses must focus on creating a seamless overall experience (Carbone & Haeckel, 1994). Studies on CE in fintech are still in their infancy. And the factors affecting customers adoption of fintech services have not yet been fully clarified (Boratyńska, 2019) nor properly understood and described in the literature (Barbu, Florea, Dabija, & Barbu, 2021). Although the fintech sector is expanding and has a growing customer base, to the best of the author's knowledge few studies have analysed CE in fintech, especially in Saudi Arabia. Therefore, this represents a major research gap, which justifies investigating the impact of WD on CE, exploring how CS influences CE, outlining the relationship CE has with AI, and assessing WD and CS in relation to AI in fintech.

Importance of the Problem
This study makes a valuable contribution to the knowledge in the field, as it provides evidence from the Saudi market about the role of WD and CS in affecting CE in fintech, a new and fast-growing sector in the Kingdom and shows how AI is influenced by these variables. Practitioners in the Saudi market can benefit by designing websites and managing CS in ways which will improve CE and increase AI. Moreover, the model of WD, CS, CE, and AI presented by the author examines the Saudi fintech environment for the first time.

Website Design
WD has seen great academic and practical attention over the past two decades with the spread of internet and online services. Hence, several definitions have been created for WD. We describe it here as creating a functional and user-friendly website for customers. The objective of WD is to direct consumers to the goals set by designers (John, 1996) whether to influence visitors' trust in websites (Pelet & Papadopoulou, 2011) which is essential in fintech as it deals with financial matters, frame expectations of the website's function (Cebi, 2013), or affect behavioural intention (Atilgan & Alhussein, 2021;Dunn, Ramasubbu, Jensen, Galletta, & Lowry, 2021;Rose, Clark, Phillip, & Neil, 2012) like the intention to adopt (Rahi, Abd Ghani, & Ngah, 2020). Moreover, research indicates that WD plays an important part in converting visitors to customers (McDowell, Wilson, & Kile Jr, 2016) and is valued by customers (Lin, 2007). CE can be influenced by WD, as certain design elements can induce a negative experience (Bleier, Harmeling, & Palmatier, 2019) and bring about feelings of irritation (Hasan, 2016). In the fintech spectrum, (Rahi et al., 2020;Rahi, Yasin, & Alnaser, 2017) proven the significant relationship between WD and intention to adopt internet banking which is a fintech service.

Customer Support
CS is a mean for organizations to help their customers when facing a problem (Parasuraman, Zeithaml, & Malhotra, 2005). It is defined as set of activities that ensures product availability for trouble free use to consumers over its useful lifespan (Loomba, 1998). It can be offered in the form of live chat, helpdesks, help pages and more. The perception of communication with customers can result in enhanced experience (Song & Zinkhan, 2008). Moreover, the ability to express one's needs and information searched helps reduce the level of frustration, anxiety, and uncertainty (Mclean & Wilson, 2016). It is especially important in fintech to help overcome aspects of concern like monetary or privacy considerations to influence positive behaviours. Fintech's success over the traditional financial sector is a result of its superior and personalized CE (Lee & Teo, 2015). Aligning with that, a study by Barbu et al. (2021) highlights the relation between CS and CE in fintech.
Regarding CS influence on AI, several studies have emphasized its importance to the initial acceptance of a service (Singh & Srivastava, 2020) and behavioural intention (Javalgi & Moberg, 1997). CS also has significant impact on a customer's intention to use mobile banking (Singh & Srivastava, 2020). And helpdesks, which are a form of CS, can increase the adoption rate of new services (Riel, Liljander, Lemmink, & Streukens, 2004).

Customer Experience
CE is a relatively new concept which emerged in the mid-1980s and has been applied in business since the 1990s (Sundström & Kashyap, 2016). It is described as a psychological construct that incorporates a subjective response, following the customer's interaction with a service or a product (Rose et al., 2012). Such interactions can be direct or indirect from multiple channels during the customer's lifetime (Pour, Rafiei, Monireh, & Atena, 2021). Consumers are viewed by experiential marketers as rational and emotional human beings who care about achieving pleasurable experiences (Schmitt, 1999). The literature on CE in fintech is still scarce. Barbu et al. (2021) define CE in fintech as "a set of relevant cognitive, affective and social responses, resulting from the customer-company interaction". CE has proved its importance with CS (Barbu et al., 2021), and WD (Bleier et al., 2019). Furthermore, studies have found a strong correlation between CE and AI (Huang & Kuo, 2014;Marriott & Williams, 2018;Nasermoadeli, Ling, & Maghnati, 2013).

Adoption Intention
AI has gained researchers' attention, and several theories and models have been proposed to study it. Intention to adopt is defined as the acceptance of anything based on the person's willingness on a certain object (Jenkins & Ophoff, 2016). An individual's intention depends on multiple factors, including technology-related ones (Ali, Puah, & Arif, 2015

Research Model
The study is based on the following proposed model.

Research Hypothesis
H1: WD has a significant positive impact on CE.
H2: CS has a significant positive impact on CE.
H3a: WD has a direct impactive relation with AI.
H3b: CE mediates the relationship between WD and AI.
H4a: CS has a direct impactive relation with AI.
H4b: CE mediates the relationship between CS and AI.
H5: CE has a significant positive impact on AI.

Sampling Framework
The population of this research is the general population of Saudi Arabia who enjoy the benefits associated with technology developments. And random sampling of individuals who have used at least one fintech service during their lifetime regardless of their sex, age, income, lifestyle, or tech savviness was used for convenience and to reach as many users as possible. According to FintechSaudi (2021a), three out of four individuals have used at least one fintech service in Saudi Arabia.

Instrument Design
Qualitative data was collected by means of a questionnaire, which was initially constructed in English then translated to Arabic, and then retranslated back to English following the retroversion approach (Brislin, 1970) to ensure the equivalence of the two versions (Barbu et al., 2021). To measure the construct, according to the literature, scales were adapted to the research scope. A scale of four items was adapted from Wolfinbarger and Gilly (2003) to measure WD, three items from Parasuraman et al. (2005) to measure CS, four items from Ali and Amin (2014) to measure CE, and three items from Venkatesh et al. (2003; to measure AI. Respondents had to assess their agreement/disagreement on a five-point Likert scale, ranging from "strongly disagree" (1) to "strongly agree" (5).

Sample Characteristics
To recruit quality respondents, a filtering question was asked indicating the frequency of usage of fintech.
Depending on the answer given, the full survey was then presented. Within three weeks, 328 participants had responded with 296 being eligible for analysis as 32 respondents stated they had never used fintech which made them ineligible to complete the rest of the survey. Females made up 79.7% of respondents to the full survey while the rest 20.3% are males. The age of respondents was almost evenly distributed between 21 and 50. More than three quarters of answers came from bachelor's degree holders (see Table 1 for more details).

Reliability and Validity Measures
As mentioned previously, the scales of the construct were adopted from the literature. Three measures were taken to ensure validity and reliability of the construct. First, internal consistency reliability was measured using Pearson correlation coefficient. The correlation coefficient values between each item on WD with the total score of the same field ranged between 0.734−0.806. Similar results were found in the other dimensions (CS 0.707−0.809, CE 0.650−0.761, and AI 0.889−0.912) (see Table 2 for more details). The p-value of all the coefficients was less than 0.001. Pearson correlation coefficient values at 0.65, p < 0.001 denotes significant linear correlation (Kalliokoski, Löfvander, & Bergqvist, 2013). The closer the values to 1 the stronger the correlation (Schober, Boer, & Schwarte, 2018). So, the results were significant at 0.001 in all four variables. Thus, the fields were considered reliable for what was set to be measured. Second, to assess construct validity, Pearson correlation was employed by calculating the correlation coefficients between each field and the total score for all items of the questionnaire. Values were within the range of strong correlation (Schober et al., 2018) between 0.767 and 0.878 with a p-value of less than 0.001, meaning the results were significant. Hence, the questionnaire was considered valid. Third, a Cronbach's alpha was calculated with outcome values ranging between 0.630 and 0.882 (see Table 3 for more details). All the values were above the acceptable values of 0.7 or 0.6 (van Griethuijsen et al., 2015). Taber (2018) interprets alpha values between 0.58 and 0.97 as satisfactory.

Hypothesis Testing
The hypotheses were tested using Structural Equation  (Marsh & Hocevar, 1985). The SEM path estimates showed support for most of the hypotheses (see Table 4 for more details). WD and CS influence CE (H1, H2). WD and CE have direct impactive relationships with AI (H3a, H5). CE mediates two relationships, the first between WD and AI and the second between CS and AI (H3b, H4b). There is no support for H4a, which means that CS does not have a direct impactive relation with AI.

Discussion
Research about CE in the fintech industry is limited. And the ability to understand the factors affecting it among the target audience of Saudi Arabia is crucial for the development of the sector in the country. By knowing the specific desires of the audience using the original model presented in the study, fintech providers can focus on what people want and achieve operational efficiency. The study explored the impact of WD and CS on CE, and their relations with AI in fintech. The results indicate that fintech companies can achieve better CE through better WD and CS which corroborates the findings of previous studies (Barbu et al., 2021) for the CS and (Bleier et al., 2019) for WD. The results also reveal that fintech companies and decision makers should recognize the effect of WD and CE on AI, as WD has a positive significant influence on AI as confirmed also by Rahi et al. (2020) and Rahi at al. (2017). And CE has a positive significant impact on AI which is further confirmed by Huang and Kuo (2014), Marriott and Williams (2018), and Nasermoadeli, Ling, and Maghnati (2013). With respect to mediation affect, the study verifies the mediation of CE between WD and AI. However, although mediation of CE between CS and AI was confirmed, a direct relationship between CS and AI was not found. This study provides insight for marketers to consider the powerful effect WD and CS have on CE that leads to AI of fintech in Saudi Arabia.

Limitations and Future Studies
There were some limitations to the research that allow for further studies. For example, some participants had never used a fintech service. Also, there were far fewer male participants than females. Finally, the results may only reflect the current status of fintech in Saudi Arabia as fintech sector is changing rapidly.
Further studies could investigate a larger sample size and include more male participants. Additionally, other variables with a possible relationship with CE could be discussed, such as brand trust, website quality, or CS quality. Research could also compare intention to use with actual usage.

Implications
This study has several conceptual and managerial implications as it offers insight into the understanding of the antecedents of CE in fintech. It shows that CE is positively influenced by WD and CS, which will help fintech ijms.ccsenet.org International Journal of Marketing Studies Vol. 14, No. 1;2022 service providers to enhance their CE to achieve their long-term objectives. Another contribution relates to outcomes of CE as the results show that CE derives AI, hence there is a need to carefully monitor CE to maximize AI in the Kingdom. Further, the outcomes of the study encourage fintech service provider managers to design their websites carefully to achieve better CE. More investment should be made in the WD as it is the face of the company in the technological realm. Efficient and simple designs that facilitate navigating through the pages is required. Also, managers should assess CS teams to resolve customers' issues effectively. Additionally, Fintech providers should allocate different communication channels and an efficient complaint management system equipped with an online complaint tracking feature to enhance transparency. Furthermore, fintech service providers need to consider that CE is a powerful determinant of AI, and fintech managers must work to provide innovative techniques to improve fintech CE and continuously measure CE of their customers.

Conclusion
Fintech is the new norm for many financial activities, thus the ability to understand the factors affecting AI of fintech in Saudi Arabia will make it easier to develop the fintech sector. This study provides several key insights into factors affecting fintech AI in the Kingdom. First, WD is a key factor that positively influences AI of fintech, while CS does not have such a direct relation. Second, WD and CE are a significant determinant of AI. Third, CE mediates both WD and CS with AI.