The Role of Online Banking Service Clues in Enhancing Individual and Corporate Customers’ Satisfaction: The Mediating Role of Customer Experience as a Corporate Social Responsibility

Abstract

Online banking services have emerged as pivotal drivers of customer satisfaction and sustainable development. However, the mediating role of customer experience in linking online banking service clues to satisfaction remains underexplored. Grounded in Haeckel’s model, this study examines how functional (technical execution), mechanic (interface usability), and humanic (behavioral interactions) service clues shape satisfaction among 400 individual and corporate online banking users in Northern Cyprus, analyzed via Structural Equation Modelling. By framing age and occupation as proxies for risk aversion and post-crisis distrust—key barriers in Cyprus’s banking sector—this study advances regionally tailored strategies for sustainable digital adoption. The results reveal functional clues positively impacted satisfaction only for individual customers, while mechanic clues enhanced satisfaction across both groups, contributing to sustainable development. Humanic clues showed no significant effect. Although online service clues improved overall customer experience, a key corporate social responsibility, this experience did not mediate the clue–satisfaction relationship. Demographic factors (e.g., age, user type) moderated these dynamics. These findings underscore the importance of prioritizing mechanic and functional clues in digital banking interfaces to bolster satisfaction and align with sustainable development goals. This study advances Haeckel’s theory in digital contexts and offers actionable insights for banks seeking to balance technological innovation with customer centricity.

1. Introduction

Advancements in technology have accelerated digitalization, driving innovation across various sectors that ensure sustainable development [1]. In banking, this shift has enabled the rise of online banking, allowing customers to access a wide range of banking services virtually [2,3]. With online banking, customers can effortlessly transfer funds within minutes across geographical boundaries, track their finances, invest, exchange currencies, pay bills, communicate with the bank, and complete numerous other essential transactions without needing a physical bank visit [4,5]. Banking applications and websites have become the new platforms for online transactions, offering convenient access to a wide range of banking services [6]. However, internet-based transactions also increase the risk of exposing confidential information, which, if compromised, can be highly damaging not only to the company’s reputation and growth, but also in terms of financial loss and eroded customer trust. This shift has brought changes in service delivery, among other areas. It has become each corporation’s social responsibility to adapt their services accordingly. However, certain aspects, such as ensuring customer satisfaction, remain unchanged. In fact, given the potential challenges in using technology, it is more critical than ever for banks to continually strive to enhance customer satisfaction to retain and attract customers, as it is their responsibility. The demand for online services has grown due to convenience [2]. Banks that offer online services are likely to attract more clientele, making this a key factor that can provide a competitive edge over rivals [7]. A seamless online banking experience can significantly enhance customer satisfaction and provide a competitive advantage [8]. Companies must prioritize the security of customers’ funds and information, ensure their platforms operate smoothly, and provide real-time updates to create an optimal online experience. Consequently, customer experience is crucial in online banking services, enabling banks to attract and retain more customers. Online banking service clues refer to the tangible and intangible signals that shape customer perceptions during digital banking interactions [9]. Grounded in Haeckel, Carbone and Berry’s model, these clues are categorized into three types [9]. Functional clues relate to the technical aspects of service delivery, including transaction speed, website functionality, and security protocols [9]. Mechanic clues involve the environmental features of the digital interface, such as website design, ease of navigation, and visual layout [9]. Humanic clues pertain to the behavioral interactions experienced by customers, including the responsiveness of customer support and the empathetic communication demonstrated by chatbots [9]. Humanic clues, such as empathetic chatbot interactions, align with Davis’ Technology Acceptance Model, where perceived ease of use drives adoption [10]. Conversely, Rogers’ Protection Motivation Theory contextualizes how post-crisis skepticism amplifies cybersecurity threats, moderating the efficacy of mechanic clues among risk-averse users [11]. These clues influence customer perceptions, directly impacting satisfaction, retention, and a bank’s competitive edge [7,8]. Meanwhile, customer experience is integral to product or service use [9]. It plays a vital role in enhancing customer satisfaction, which fosters repeat patronage [12]. In today’s competitive landscape, customer satisfaction is essential for businesses to thrive [13,14]. Companies must innovate to keep pace with developments in this dynamic industry, retaining existing customers while attracting new ones. This focus on customer experience also strengthens their reputation and brand, positively influencing their growth. Consequently, online banking has enabled banks to expand, offer diverse services, and effectively meet their customers’ needs.

The banking sector in Cyprus, particularly Northern Cyprus, grapples with unique challenges, including a persistent reliance on cash transactions, fragmented digital infrastructure, and enduring distrust rooted in the 2012–2013 financial crisis [15]. Corporate customers often prioritize in-branch interactions, a legacy of historical institutional instability [16], while individual customers exhibit cautious adoption of online services, despite high internet penetration rates [14]. These region-specific dynamics, including economic volatility, cultural preferences for face-to-face banking, and regulatory gaps in cybersecurity, critically shape how service clues influence customer experience and satisfaction. Northern Cyprus has seen rising demand for banking services, driven by growth in education, real estate, and service sectors [14]. Despite this trend, most customers (both individual and corporate) continue to favor in-person transactions at branches, even as online banking adoption expands [15]. Since its introduction in 2004, online banking has grown to include 22 banks under TRNC Central Bank oversight [17], yet studies highlight a persistent preference for traditional methods [18,19]. For instance, Alhassany and Faisal [14] and Serener [20] found that perceived risks and reluctance to adopt new technologies drive this inclination. Existing studies on Northern Cyprus’s banking sector have prioritized profitability analyses [16,17], comparisons of public versus private bank efficiency [18], and explorations of online banking usage and risks [14,19,20,21,22]. However, these works largely overlook the interplay between online banking service clues (e.g., functional, mechanic, and humanic dimensions) and customer satisfaction, particularly the mediating role of customer experience and moderating effects of demographics. This preference is influenced by factors such as perceived risk and a reluctance to adopt new technologies. Therefore, it is crucial to explore online banking and its impact on customer satisfaction thoroughly. Furthermore, no recent research has examined the role of online banking service clues in enhancing individual and corporate customer satisfaction, particularly considering the mediating role of customer experience and the moderating effects of customer demographics. In their review article, Chauhan et al. [23] explored the applicability of Haeckel’s model to customer experience in India. A similar study was previously conducted by Wasan [24], which focused on traditional banking. However, no research in Northern Cyprus has examined customer experience in the context of Haeckel’s model and service clues in online banking. Consequently, the authors seek to fill this gap. This study aims to investigate how the design of functional, mechanic, and humanic clues in digital services shapes customer satisfaction in Northern Cyprus’s post-crisis banking sectors, emphasizing corporate social responsibility (CSR) as a catalyst for sustainable financial ecosystems. By analyzing demographic and contextual moderators (e.g., trust post-crisis, cybersecurity concerns), we develop inclusive strategies for digital banking adoption that are aligned with global sustainability goals, such as SDG 9 (industry innovation) and SDG 12 (responsible consumption). In particular, this study’s objectives are as follows:

(1)

To investigate how digital service design (functional, mechanic, humanic clues) influences customer satisfaction across diverse banking sectors, with a focus on post-crisis economies.

(2)

To explore the mediating role of customer experience in technology-driven service interactions, emphasizing corporate social responsibility (CSR) as a driver of sustainable financial ecosystems.

(3)

To analyze demographic and contextual moderators (e.g., trust post-crisis, cybersecurity concerns) to develop inclusive strategies for digital banking adoption aligned with global sustainability goals.

The results of this study are expected to significantly influence how banks enhance customer experience and satisfaction by customizing their services to meet customer needs while addressing any existing insecurities to ensure sustainable development. Additionally, this research aims to contribute to the limited literature on the mediating role of customer experience in the relationship between online banking service clues and customer satisfaction as a corporate social responsibility. The findings will give banks valuable insights into online banking service clues and their impact on customer satisfaction. Furthermore, this study will establish a theoretical and conceptual foundation for future research. This paper is organized into several sections, beginning with the introduction in Section 1. Section 2 presents the literature review, discussing relevant theoretical and empirical studies and formulating hypotheses. Section 3 outlines the methodology, detailing how the research was conducted. In Section 4, data analysis is presented, describing and explaining the relationships between the variables under investigation and the results of the hypotheses. Section 5 compares the research findings with previous related studies and the theoretical framework applied. The managerial and theoretical implications of the findings are also highlighted. Finally, the study’s limitations are acknowledged, and recommendations for future research are provided.

2. Literature Review

2.1. Customer Experience

Customer experience is extensively defined by Carbone and Haeckel [25], who describe it as the impressions customers form through their interactions with a company’s services or products. This experience can vary from person to person and often arises from the gap between actual experiences and customer expectations. Customer expectations play a crucial role in determining customer satisfaction [7]. According to Hussadintorn and Koomsap [26], customer experience encompasses the entire journey, beginning before a customer purchases a service or product and continuing even after the service has been delivered. The authors emphasize that at various stages of this journey, customers rely on past experiences to shape their future interactions with the brand and its services. Lemon and Verhoef [27] noted that early scholars conceptualized customer experience as encompassing physical, sensory, affective, and socio-identity dimensions. Subsequent research has expanded this framework to include spiritual, social, emotional, and behavioral aspects. Nevertheless, there is a general consensus among scholars that customer experience is inherently complex [28] and encompasses multiple dimensions [29]. As such, management must remain flexible and adapt to ever-changing demands and market conditions [30].

2.2. Service Clues

The integrity of online banking service clues is further reinforced by tamper-resistant mechanisms akin to those in distributed databases like LedgerDB [31] and VeDB [32]. For instance, LedgerDB’s blockchain-based immutability ensures that transactional data cannot be altered post-recording, mirroring how functional clues (e.g., secure transaction protocols) and mechanic clues (e.g., audit logs on banking interfaces) safeguard customer trust. Similarly, VeDB’s version of data integrity aligns with the real-time accuracy of service delivery metrics (e.g., balance updates), which is critical to maintaining seamless customer experiences [33]. These parallels highlight how technical safeguards underpin the three core categories of service clues in digital banking. Specifically, service clues are categorized into three types within online banking. Functional clues pertain to the technical execution of services. Examples include secure transaction processing [2], 24/7 accessibility [4], and error-free operations [34]. Mechanic clues, on the other hand, relate to the digital environment’s usability and aesthetics. For instance, intuitive navigation [35], fast-loading pages [36], and clear site structure [26] are key mechanic elements. Finally, humanic clues involve behavioral interactions, such as prompt complaint resolution [37] and empathetic chatbot responses [23]. These distinctions align with Hussadintorn and Koomsap’s framework, thereby emphasizing how emotional and functional dimensions collectively shape user trust, much like the immutable systems of LedgerDB and VeDB [26].

Haeckel’s model, while globally applicable, requires adaptation to account for Cyprus’s unique trust dynamics. For instance, mechanic clues (e.g., real-time transaction alerts) gain heightened importance when customers seek constant reassurance against financial instability [19]. Haeckel, Carbone, and Berry [9] emphasized that organizational management must cultivate an awareness and understanding of the signals it communicates to customers to manage the customer experience effectively. These signals allow organizations to proactively anticipate and respond to changes, ultimately enhancing customer satisfaction. The authors categorize these signals into functional and emotional clues (humanic and mechanic), as illustrated in Figure 1 below.

According to Hussadintorn and Koomsap [26], service clues are classified into three categories: functional, mechanic, and humanistic. Functional clues pertain to rational thinking, mechanic clues relate to the service environment, and humanistic clues focus on behavioral aspects. Both mechanic and humanistic clues are grounded in the emotional dimension [38]. Customer experience in online banking should be centered on the customer and can be influenced by the usability and perceived value of the service as a corporate social responsibility [39]. It also influences customer recommendations to others and the retention of services [40]. Since customer experience is inherently personal, it can be subjective and challenging to assess [41]. Bleier et al. [42] argue that in online customer experiences, the cognitive aspect, particularly through informativeness, is one of the most significant factors, and the impressions formed can linger long after the interaction with the online interface has concluded. De Keyser et al. [43] emphasize the importance of factors such as touchpoints, context, and qualities in shaping customer experience. These elements correspond to customer interactions, the overall environment, and the availability of resources, as well as the responses generated from those interactions. Since customer experience reflects a customer’s perception of a service, it can be leveraged to foster positive customer behavior [44]. A positive customer experience meets expectations and enhances customer loyalty and satisfaction [29]. An ordinary customer experience, characterized by routine, simplicity, familiarity, and convenience, enhances the overall service perception and reinforces positive outcomes [30,45].

2.3. Customer Satisfaction

By Ban and Jun [46], customer satisfaction is defined as a measure of performance that indicates the gap between customer expectations and the actual experience. Customer satisfaction is vital for organizational growth and sustainable development. This view is supported by Almaiah et al. [4], who argued that customer satisfaction fosters loyalty, aids customer retention, and ultimately contributes to growth and profitability. Manyanga et al. [8] emphasized that banks must provide exceptional services to enhance customer satisfaction. Customer satisfaction is influenced by various factors, including trust, attitude, and perceived usefulness [47], among others. Models such as the Technology Acceptance Model, the Service Quality Model, and the Unified Theory of Acceptance and Use of Technology Model have thoroughly explored the factors influencing technology acceptance and the determinants of customer satisfaction. Groonros [48], a pioneer in service quality, noted that both a service’s technical and functional aspects can impact customer satisfaction, a viewpoint also supported by Nkwede et al. [49].

Meanwhile, customer privacy is a cornerstone of satisfaction in online banking, particularly for humanic clues (e.g., personalized support) and functional clues (e.g., data encryption). Techniques such as secure enclave technology, exemplified by SecuDB [50] ensure that sensitive information (e.g., biometric data, transaction histories) is processed within isolated, tamper-proof environments. This aligns with customer expectations for confidentiality, where privacy-preserving mechanisms directly enhance perceived security and trust in digital services. Building on this, several studies have sought to explain the role of online banking in customer satisfaction and the adoption of technology among individuals as a tool for sustainable development. Customer satisfaction with online banking services is influenced by various factors, including efficacy, usage, behavior, performance, environmental conditions, and adoption-related issues [51]. Online banking has gained prominence due to the rise of digitalization. Sathar et al. [52] noted that the convenience of online banking has significantly boosted its adoption, with trust playing a crucial role in customer satisfaction with the service. Meanwhile, customer satisfaction with online banking services is influenced by various factors, including efficacy, usage, behavior, performance, environmental conditions, and adoption-related issues [53]. It has become a social responsibility of corporations. Lin et al. [42] explained that trust is essential for fostering customer satisfaction when adopting new technologies like online banking, with higher trust levels leading to a greater willingness to access online services. Ma [54] added that the quality of information and trust in the expertise of the information source are also key determinants of customer satisfaction. This sentiment was echoed by Cele and Kwenda [55], who highlighted that while online banking has increased the risk of cyber threats, security and trust are critical factors in encouraging more individuals to use the service. Banu et al. [2] noted that self-efficacy influences customer satisfaction regarding online banking, indicating that individuals familiar with using computers are more likely to embrace online banking.

Shaikh et al. [56] further emphasized the significance of self-efficacy; their study found that self-efficacy is a driving factor in adopting online banking among those who have not previously used it. Similarly, Park et al. [57] pointed out that online banking adoption varies by generation, with Generation Z showing a greater inclination to adopt these services due to their frequent internet use. At the same time, Generation X is more likely to embrace it if the process is user-friendly. Raza et al. [58] highlighted the importance of service quality in online banking, noting that customers assess it to determine their satisfaction with the service and to make future decisions about its use. Zia [59] also identified a direct relationship between service quality and customer satisfaction. Additionally, Supriyanto et al. [60] explained that reliable service, effective problem-solving, and trustworthiness contribute to satisfaction with bank services and foster customer loyalty, resulting in sustainable development. Lei et al. [61] explained that credibility and ease of use impact customer satisfaction. A credible and user-friendly service dispels doubts and minimizes complaints, increasing customer satisfaction and resulting in a higher likelihood of recommendations and repeat usage. Awa, Ikwor, and Ademe. [62] noted that how complaints are handled in the context of online services can significantly affect customer satisfaction. They highlighted that the time it takes to receive feedback compared to offline transactions can influence customer perceptions of satisfaction, with quicker responses enhancing overall satisfaction [63]. This perspective was echoed by Islam et al. [64], who found that responsiveness also impacts customer satisfaction in private banking.

2.4. Expectancy Disconfirmation Theory

Yüksel and Yüksel [65] emphasized that the expectancy disconfirmation theory highlights the discrepancies between expectations and reality. Satisfaction typically arises after using a service, and dissatisfaction is directly related to the differences between prior expectations and actual performance [66]. Zhang et al. [67] noted that this theory is one of the most widely accepted frameworks for understanding customer satisfaction with services. Satisfaction is achieved when performance surpasses expectations, a phenomenon called positive disconfirmation. Positive disconfirmation contributes to customer satisfaction [61,68]. Moreover, high levels of previous performance lead to heightened expectations for future interactions. Sreelakshmi and Prathap [69] found that the expectancy disconfirmation theory applies to online banking payments, with perceived usefulness significantly impacting customer satisfaction. Understanding these dynamics is crucial, as negative disconfirmation can have serious repercussions for businesses, often resulting in customers switching to competitors [62].

2.5. Hypothesis Development and Research Framework

2.5.1. Online Service Clues and Customer Satisfaction

Gautam and Sah [70] supported this idea with their study, which revealed that website usability and features significantly influence customer satisfaction. Wasan [24] found that functional service clues related to credibility and convenience were most positively and significantly associated with customer satisfaction, while humanic clues followed. Mechanic clues, on the other hand, were primarily found to encourage customer behavior. Similarly, Ayinnadis et al. [71] highlighted the importance of functional clues in online banking, noting that focusing on these factors leads to increased customer satisfaction. Khan et al. [72] also discovered that improved online banking services contribute to customer satisfaction. Anouze et al. [73] pointed out that emotional factors, such as a sense of security, affect the adoption of online banking services, emphasizing that a strong feeling of security correlates with higher customer satisfaction. Park and Han [74] found that service clues influence the decision to use and continue utilizing a service. Likewise, Quiber [75] stressed the importance of security and reliability, identifying them as significant factors impacting customer satisfaction. Thus, our first hypothesis can be stated as follows:

H₁. 

Online banking service clues have a direct positive relationship with customer satisfaction.

This hypothesis leads to several sub-hypotheses, as follows:

H_1a. 

Functional clues have significant impact on individual customer satisfaction.

H_1b. 

Mechanic clues have significant impact on individual customer satisfaction.

H_1c. 

Humanic clues have significant impact on individual customer satisfaction.

H_1d. 

Functional clues have significant impact on corporate customer satisfaction.

H_1e. 

Mechanic clues have significant impact on corporate customer satisfaction.

H_1f. 

Humanic clues have significant impact on corporate customer satisfaction.

2.5.2. Online Service Clues and Customer Experience

Numerous studies have highlighted the significance of service clues in enhancing customer experience. Berry et al. [76] asserted that service clues are instrumental in evaluating customer experience and influencing decision-making. Kesa [6] found that internet quality positively impacts customer satisfaction. Borishade [77] argued that functional clues affect customer experience by contributing to the creation of quality service, which, in turn, influences customer satisfaction. Meeting customer expectations concerning functional clues can enhance their satisfaction. Likewise, Liu et al. [78] emphasized that service clues are crucial in shaping customer experience. In their study on customer experience and online banking in India, Chauhan et al. [23] identified functional, mechanic, and humanic service clues as determinants of customer experience and sustainable development. Our next hypotheses are therefore formulated as follows:

H₂. 

Online banking service clues (functional clues) have a direct positive relationship with customer experience.

H₃. 

Online banking service clues (mechanic clues) have a direct positive relationship with customer experience.

H₄. 

Online banking service clues (humanic clues) have a direct positive relationship with customer experience.

2.5.3. Customer Experience and Customer Satisfaction

Researchers have identified a positive relationship between customer experience and customer satisfaction. Manyanga et al. [8] noted that customer experience contributes to customer satisfaction by reducing complaints and narrowing the gap between expectations and actual experiences. Chauhan et al. [23] found that customer experience significantly impacts customer satisfaction. Prentice and Nguyen [79] reported that enhanced customer experience leads to increased customer satisfaction, subsequently driving customer loyalty. Similar findings were observed in the studies conducted by Zaid and Patwayati [80] and Ha [81], which showed that positive customer experiences improve customer satisfaction and retention. Tjahjaningsih et al. [82] further emphasized that effective problem-solving and managing customer issues enhance customer satisfaction, ensuring sustainable development. The following hypothesis is therefore formulated:

H₅. 

Customer experience has a direct positive relationship with customer satisfaction.

2.5.4. Mediating Effect of Customer Experience

Customer experience is crucial in mediating the relationship between service clues and customer satisfaction across various sectors, including online banking. To connect service clues, customer experience, and customer satisfaction, the service clues model proposed by Haeckel [9] is utilized. Haeckel’s framework posits that customer experience in the service sector is influenced by service-related clues, which, in turn, affect customer satisfaction. A positive customer experience enhances every interaction with the business, leading to higher levels of satisfaction [83,84]. Therefore, service clues contribute to customer satisfaction, with customer experience serving as a mediator. Wasan [24] explored the impact of service clues and found that customer experience significantly mediates the relationship between service clues and customer satisfaction within the context of traditional banking. The following hypotheses are thus formulated:

H₆. 

Customer experience mediates the effects of online banking service clues (functional clues) on customer satisfaction.

H₇. 

Customer experience mediates the effects of online banking service clues (mechanic clues) on customer satisfaction.

H₈. 

Customer experience mediates the effects of online banking service clues (humanic clues) on customer satisfaction.

2.5.5. Demographics, Service Clues, and Customer Satisfaction

Existing research underscores the pivotal role of demographic factors in shaping customer satisfaction and technology adoption. Kamboj and Singh [85] highlighted that demographic characteristics, such as age and income, significantly influence satisfaction levels. Bhatt and Bhatt [86] further demonstrated that demographics affect technology adoption patterns, with Sambaombe and Phiri [87] emphasizing age as a critical moderator in online banking satisfaction. These findings were reinforced by Wang and Pang [88], who noted that higher-income, educated consumers prioritize service quality over cost, while lower-income groups exhibit cost sensitivity. Nonetheless, occupation also emerges as a key variable, as Islam et al. [64] revealed its impact on satisfaction in private banking contexts. Building on these insights, Cyprus’s 2012–2013 financial crisis entrenched corporate distrust in banking systems, amplifying the salience of functional reliability. Simultaneously, older demographics’ risk aversion, exacerbated by Northern Cyprus’s underdeveloped cybersecurity infrastructure, attenuates the impact of mechanic clues. Prior research in post-crisis economies validates demographics as proxies for latent constructs like trust [85]. For instance, corporate clients (occupation) exhibit systemic distrust post-bailouts, while older users (age) resist digital interfaces due to cybersecurity fears [84]. These contextual factors align with Alhassany and Faisal’s findings on adoption barriers in Cyprus but extend them by linking distrust/risk aversion to demographic moderators [12]. Together, these hypotheses integrate demographic granularity with contextual factors like trust and risk perception, enriching the understanding of how service clues differentially impact diverse customer segments. The following hypotheses are thus formulated:

H₉. 

Gender has a significant moderating effect on the relationship between online banking service clues and customer satisfaction.

H₁₀. 

Age has a significant moderating effect on the relationship between online banking service clues and customer satisfaction.

H_10a. 

Risk aversion (linked to cybersecurity concerns in Northern Cyprus’s fragmented digital infrastructure) weakens the positive impact of mechanic clues on customer experience, particularly among older demographics (age-based distinction).

H₁₁. 

Education level has a significant moderating effect on the relationship between online banking service clues and customer satisfaction.

H₁₂. 

Occupation has a significant moderating effect on the relationship between online banking service clues and customer satisfaction.

H_12a. 

Trust in online banking (rooted in post-2013 financial crisis skepticism) moderates the relationship between functional clues and satisfaction, with stronger effects for corporate customers (occupation-based distinction).

H₁₃. 

Income level has a significant moderating effect on the relationship between online banking service clues and customer satisfaction.

These relationships are illustrated in the conceptual framework of the study presented in Figure 2 below.

3. Materials and Methods

3.1. Population and Sample

The population for this study consists of all individuals in Northern Cyprus, totaling 390,745 people. However, due to practical limitations in reaching every individual in Northern Cyprus, a target population was established from which a sample could be drawn. In this case, the target population included customers who engage in online banking with any of the 22 banks in Northern Cyprus. A sample size of 400 customers was selected, considering a confidence level of 95% and a margin of error of ±5%. This approach aligns with the recommended sample size criteria established by Hair et al. [89]. The study participants were chosen using the convenience sampling method, which was selected for its straightforwardness in accessing potential participants. While convenience sampling ensures accessibility, it may overrepresent tech-savvy urban users [90,91]. Future studies should adopt stratified sampling to include underrepresented groups (e.g., rural populations and elderly customers) and enhance generalizability.

3.2. Data Collection

This study utilized both primary and secondary data. Secondary data were gathered from various sources, including textbooks, journals, and articles, to provide a comprehensive theoretical and empirical foundation for evaluating the current study’s findings. Primary data were collected through a survey using self-administered questionnaires distributed to bank customers both in person and online via email and social media. This data collection occurred in September and October 2023. The questionnaires were made available in English and Turkish to accommodate foreign and local populations. To ensure equivalence, the Turkish version was back-translated by independent bilingual experts to maintain semantic consistency with the original English items. A pilot study was conducted with 30 participants to test the effectiveness of the research instrument, ensuring that the questions were clearly understood and free from ambiguity. A pilot study (n = 30) confirmed clarity and comprehension in both languages. No significant discrepancies in response patterns were observed between language groups. Post hoc analysis (independent t-tests) revealed no statistically significant differences (p > 0.05) in mean scores for key constructs (e.g., customer experience, satisfaction) between English and Turkish respondents, supporting data comparability.

Any necessary adjustments were made based on the feedback received before the final distribution. Ethical considerations were prioritized throughout the research process, with institutional, bank, and individual permissions obtained to conduct the study, which was conducted voluntarily.

3.3. Questionnaire Construct

3.3.1. Demographic Characteristics

Data were collected from two categories of customers: individual and corporate clients. The demographic information sought included gender, age, education, income, and occupation.

Table 1. Demographic profile.

Variable	Description	Frequency	Percentage
Gender	Female	250	62.50
	Male	150	37.50
	Total	400	100
Age	20 years and below	2	0.50
	21–30 years	81	20.25
	31–40 years	193	48.25
	41–50 years	82	20.50
	51–60 years	28	7.00
	61 years and above	14	3.50
	Total	400	100
Type of customer	Individual bank customers	200	50.00
	Corporate bank customers	200	50.00
	Total	400	100
Academic qualification	Primary school	3	0.75
	Secondary school	6	1.50
	High school	54	13.5
	Bachelor’s degree	175	43.75
	Two-year associate degree	27	6.75
	Master’s degree and above	135	33.75
	Total	400	100
Occupation	Private sector employee	58	14.50
	Public sector employee	17	4.25
	Retired	18	4.50
	Self-employed	47	11.75
	Business owner	200	50.00
	Others	60	15.00
	Total	400	100
Monthly income (TRY)	15,750 and less	52	13.00
	15,750 to 20,000	85	21.25
	20,000 to 25,000	56	14.00
	25,000 to 30,000	80	20.00
	30,000 to 35,000	36	9.00
	35,000 to 40,000	24	6.00
	45,000 and more	67	16.75
	Total	400	100

below illustrates that out of the 400 respondents, 50% were individual customers, while the remaining 50% were corporate customers. Regarding gender, 62.5% of the respondents identified as female, while 37.5% identified as male. Age-wise, 61% of the customers were under 40, with only 10.5% aged 51 and above. Regarding education, a mere 1.5% had completed secondary school as their highest level of education. A significant proportion of respondents (43.75%) held a bachelor’s degree as their highest educational attainment, indicating that most respondents were highly educated. When examining occupation, half of the respondents (50%) identified as business owners, while a small percentage (4.25%) were employed in the public sector. Regarding income, 48.25% of respondents reported earnings of less than TRY 25,000, whereas the majority (51.75%) earned at least TRY 25,000. This demographic profile provides valuable insights into the characteristics of the participants involved in the study. While reflective of Cyprus’s SME-dominated economy, this may not capture the perspectives of multinational corporations or rural enterprises with distinct service expectations.

The 10-year intervals align with demographic norms in Northern Cyprus’s banking studies [14,19] and reflect distinct life stages influencing digital adoption (e.g., younger users for tech-savviness vs. older users for risk aversion). For analysis, categories with small samples (e.g., “61+ years,” n = 14) were aggregated into broader groups (e.g., “51+ years”) during hypothesis testing to ensure robustness. While generational cohorts (e.g., Gen Z, Millennials) were considered, granular 10-year divisions better capture variance in technology adoption behaviors within Northern Cyprus’s aging population. Retired individuals (n = 18) reflect Northern Cyprus’s labor demographics, and this distribution is consistent with the region’s demographic profile, where fewer retirees engage with online banking due to lower digital literacy and a preference for traditional banking services [14,15]. Retirees were retained as a distinct category due to unique banking needs (e.g., pension management). “Business Owner” (n = 200, 50%) represents Northern Cyprus’s SME-dominated economy, where several businesses are small enterprises [16,17,18]. “Others” (n = 60, 15%) includes freelancers, homemakers, and students. While aggregated for brevity, the subgroup analysis confirmed homogeneity in responses (p > 0.10), justifying the combined category.

3.3.2. Measurement

All questionnaire items were measured using a 5-point Likert scale (1 = Strongly Disagree, 5 = Strongly Agree) to assess respondents’ perceptions of service quality, experience, and satisfaction. The independent, mediating, and dependent variables in this research were measured using scales adopted and adapted from prior studies. Online banking service clues were categorized into three distinct groups: functional, mechanic, and humanic. Functional clues were assessed based on functional quality, trust, and convenience. Mechanic clues’ measurement focused on aspects of website design and usability. Humanic clues were evaluated through customer complaint-handling practices. Customer satisfaction was measured using items derived from Bouafi’s study [92]. A detailed summary of the constructs, measurement methods, and corresponding references is presented in

Table 2. Constructs and questionnaire items.

Construct	Clue Type	Measurement Statements	Ref.
Functional Quality	Functional clues	1. Internet and mobile devices facilitate banking services.	[92]
		2. The Internet has improved the quality of banking services.
		3. It is easy to use the Internet for banking services.
		4. I am able to get on the website/mobile application quickly.
		5. It is easy for me to find what I need on my bank’s website/mobile application.
Trust		1. I can trust my bank when using the Internet for any service.	[92]
		2. My bank’s services have a good reputation.
		3. I feel very comfortable doing online banking with my bank.
		4. My bank quickly resolves the problems I encounter with my online operations.
Convenience		1. Online banking services fit my needs and will.	[93]
		2. Online banking services afford great facilities.
		3. You can carry out online banking services anywhere.
Website Design	Mechanic clues	1. The online banking website provides in-depth information.	[34]
		2. The online banking website does not confuse me about what I want to do with the website pages.
		3. The online banking’s webpage does not freeze after I input information.
		4. The site map of the online banking website is clear, and the content and picture of the site are user-friendly.
		5. I can log in to the online banking website easily.
		6. The online banking’s website loads quickly.
		7. The online banking website’s information is always updated in time.
		8. The online banking website offers my preferred service.
		9. The transaction outcome is informed clearly.
		10. It is quick and easy to complete a transaction on the online banking website.
		11. The level of personalization on the online banking’s website is about right, not too much or too little.
		12. The online banking website does not waste my time.
Website Usability		1. On this website, everything is easy to understand.	[36,94,95]
		2. This website is simple to use, even when using it for the first time.
		3. It is easy to find the information I need from this website.
		4. The structure and contents of this website are easy to understand.
		5. It is easy to navigate within this website.
		6. The organization of the contents of this site makes it easy for me to know where I am when navigating it.
		7. When I am navigating this site, I feel that I am in control of what I can do.
Customer Complaint Handling	Humanic clues	1. The online banking service is willing to respond to customer needs.	[34]
		2. When you have a problem, the online banking website shows a sincere interest in solving it.
		3. Inquiries are answered promptly through online customer service representatives.
		4. Customer service representatives are qualified and have a good service attitude.
Customer Satisfaction		1. I am satisfied with the online banking service.	[92]
		2. My bank’s online services meet my needs and expectations.
		3. I am satisfied with the electronic accessibility.
		4. I am satisfied with the staff in helping accessing online.
		5. I made a good decision when I choose my bank for online services.
Customer Experience		1. My bank handles customer problems well.	[83,96,97,98,99,100]
		2. My bank offers prompt customer service.
		3. My bank’s products are ease to use.
		4. My bank always meets my service needs and requirements.
		5. My bank provides me error free services.
		6. My overall experience with my bank is pleasing.

below.

3.4. PLS-SEM Methodology and Software

Structural Equation Modeling (SEM) was conducted using SmartPLS 4.0, employing Partial Least Squares (PLS-SEM) to analyze complex relationships between latent variables. PLS-SEM is ideal for predictive research with non-normal data [89,101]. PLS-SEM was selected for its robustness in handling non-normal data and predictive focus [89], aligning with our exploratory aim to identify key drivers of satisfaction in complex, post-crisis environments. The path weighting scheme and Mode A (reflective measurement) were used to estimate outer weights, initialized via centroid weighting and iteratively refined. The structural model is defined as follows:

$$\mathsf{\eta }={\mathsf{\beta }}_0+{\mathsf{\beta }}_1{\mathsf{\xi }}_1+{\mathsf{\beta }}_2{\mathsf{\xi }}_2+\cdots +\mathsf{\zeta }$$

where η represents endogenous variables (e.g., customer satisfaction) and ξ denotes exogenous variables (e.g., functional clues). Bootstrapping (5000 subsamples) tested significance [89]. PLS-SEM does not assume data normality, focusing instead on predictive power [101]. The path weighting scheme optimized latent variable correlations, and outer weights were initialized using centroid methods. Validity was confirmed via discriminant (HTMT < 0.90) and convergent (AVE > 0.50) criteria. Multicollinearity was absent (VIF < 5). The researcher evaluated the model’s goodness of fit based on previous scholars’ proposed constructs, as detailed in

Table 3. Recommended fit indices for the measurement and structural models.

Fit Index	Acceptable Value	Reference
(χ2/df)	≤2	[102]
RMSEA (Root mean square error of approximation)	≤0.08	[103]
GFI (Goodness-of-fit statistic)	≥0.9	[104]
AGFI (Adjusted goodness-of-fit statistic)	≥0.9	[102]
NFI (Normed fit index)	≥0.9	[105]
CFI (Comparative fit index)	≥0.9	[106]
SRMR (Standardized root mean square residual)	≤0.08	[106]

.

4. Results

4.1. Measurement Model Results

Before conducting the path analysis of the proposed research model using SEM, measurement model analyses were performed to assess the suitability of the data through Confirmatory Factor Analysis (CFA). Factor loadings were analyzed to evaluate the relationships between the constructs and their respective indicators. The results indicated that all factor loading values met the threshold of at least 0.70, signifying that each indicator explains at least 70% of its corresponding construct. A value of 0.70 or higher reflects good and acceptable reliability [101,107]. Additionally, the Variance Inflation Factor (VIF) was employed to assess multicollinearity among the indicators, as a high correlation could skew the results [108].

To determine the reliability of the research instrument for measuring each variable, both Cronbach’s alpha and composite reliability were calculated. A Cronbach’s alpha value should ideally fall between 0.70 and 0.95 to ensure acceptable internal consistency [107,109,110,111,112,113,114]. Relying solely on Cronbach’s alpha is insufficient; hence, composite reliability was also assessed [113]. The VIF values obtained were all below 5, which is considered ideal; values exceeding 5 indicate potential multicollinearity issues [114]. Hair et al. [112] recommend a VIF close to 3 as optimal, while Bhatti et al. [115] suggest that a VIF below 2 is free from bias (see

Table 4. Factor loadings and VIF results.

Construct		Factor Loadings		Outer Weight (p-Values)	VIF
		Individual Customer	Corporate Customer		Individual Customer	Corporate Customer
Trust (TR)	TR1	0.761	0.812	0.000	2.945	-
	TR2	-	0.798	0.000	2.820	-
	TR3	0.733	0.800	0.000	-	-
Functional Quality (FQ)	FQ5	-	0.804	0.000	-	1.708
Web Design (WD)	WD3	0.735	0.786	0.000	-	1.761
	WD4	-	0.793	0.000	1.748	-
	WD5	-	0.764	0.000	-	1.940
	WD6	-	0.805	0.000	-	-
	WD8	0.729	0.719	0.000	-	2.006
Customer Complaint Handling (CCH)	CCH1	0.856	0.833	0.000	-	1.775
	CCH2	0.864	0.817	0.000	1.912	1.831
	CCH3	0.846	0.823	0.000	1.649	-
Customer Experience (CE)	CE1	0.811	-	0.000	2.048	1.415
	CE2	0.781	0.733	0.000	1.897	1.415
	CE3	0.826	0.716	0.000	1.420	1.584
	CE4	-	0.745	0.000	1.405	-
	CE5	-	0.779	0.000	1.524	1.369
Convenience (CNV)	CNV1	0.791	0.818	0.000	-	1.725
	CNV2	0.792	0.810	0.000	-	1.387
	CNV3	0.767	0.806	0.000	1.711	1.900
Website Usability (WU)	WU2	-	0.769	0.000	1.906	2.120
	WU3	0.750	0.777	0.000	1.827	2.122
	WU4	0.701	0.794	0.000	1.374	1.403
	WU5	0.775	0.782	0.000	1.519	1.516
	WU6	0.780	0.795	0.000	1.564	-
	WU7	0.756	0.803	0.000	-	1.406

). The findings revealed that the research instrument demonstrated significant reliability, with Cronbach’s alpha values for individual and corporate customer models of at least 0.70 (see

Table 5. Internal consistency reliability.

		Cronbach’s Alpha	Rho_A	Rho_C
Individual customer model	Customer experience	0.731	0.733	0.848
	Customer satisfaction	0.740	0.747	0.851
	Functional clues	0.829	0.834	0.879
	Humanic clues	0.818	0.827	0.891
	Mechanic clues	0.868	0.874	0.898
Corporate customer model	Customer experience	0.761	0.767	0.848
	Customer satisfaction	0.728	0.730	0.846
	Functional clues	0.830	0.835	0.887
	Humanic clues	0.703	0.705	0.870
	Mechanic clues	0.871	0.874	0.901

). Furthermore, the composite reliability values, represented by Rho A and Rho C, were at least 0.70, with values ranging from 0.70 to 0.90 considered satisfactory [112].

Furthermore, the Heterotrait–Monotrait Ratio of correlations (HTMT) was analyzed to assess discriminant validity. This evaluation ensures that each variable demonstrates stronger correlations with its indicators than other variables within the model. The results indicate that the individual and corporate customer models exhibit robust relationships, with all HTMT values below the threshold of 0.90. According to Hair et al. [112], an HTMT value not exceeding 0.90 is deemed satisfactory and indicative of discriminant validity between constructs [113,114]. Additionally, the convergent validity, as assessed by the Average Variance Extracted (AVE), was at least 0.50 for individual and corporate customer models. An AVE value of 0.50 or higher is recommended as a standard [106]. This threshold indicates that each construct accounts for at least half of the variance in the indicators, further supporting the validity of the measurement model (see

Table 6. HTMT matrix.

		CE	CS	FC	HC	MC	AVE
Individual customer model	CE						0.650
	CS	0.782					0.655
	FC	0.669	0.754				0.592
	HC	0.872	0.599	0.891			0.732
	MC	0.767	0.809	0.899	0.848		0.558
Corporate customer model	CE						0.582
	CS	0.812					0.648
	FC	0.782	0.793				0.663
	HC	0.605	0.613	0.648			0.771
	MC	0.545	0.566	0.581	0.593		0.564

).

4.2. Structural Model Results

The analysis comparing individual and corporate bank customers, as shown in

Table 7. Path analysis.

			Individual Customer			Decision
			β	p-Value	f-Square
H1(a)	FC → CS		0.054	0.412	1.254	Supported
H1(b)	MC → CS		0.760	0.001 *	1.814	Supported
H1(c)	HC → CS		−0.110	0.392	1.215	Not Supported
			Corporate Customer
			β	p-Value	f-Square
H1(d)	FC → CS		−0.100	0.381	1.203	Not Supported
H1(e)	MC → CS		1.028	0.000 *	1.728	Supported
H1(f)	HC → CS		−0.015	0.832	1.016	Not Supported
Combined Model
			Β		t-Sta.	p-Value
H2		FC → CE	0.298		4.096	0.000 *
H3		MC → CE	0.888		3.627	0.000 *
H4		HC → CE	0.003		7.491	0.000 *
H5		CE → CS	0.109		5.010	0.000 *

* Means significant at 0.01 level.

, reveals distinct impacts of functional and mechanic clues on customer satisfaction. Functional clues positively influence satisfaction among individual bank customers by 0.054, while they negatively affect corporate bank customers by 0.1. Therefore, hypothesis H1a is supported, while hypothesis H1d is not. Further analysis confirms that hypothesis H1b, which posits that mechanic clues have a direct positive relationship with individual online banking customers’ satisfaction, is accepted. Similarly, hypothesis H1e, suggesting that mechanic clues positively impact corporate online banking customers’ satisfaction, is also accepted at the 5% significance level. Specifically, enhancements in mechanic clues increase satisfaction by 0.760 units for individual customers and 1.028 units for corporate customers. However, hypotheses H1c and H1f are rejected, indicating that humanic clues do not directly correlate with satisfaction for individual or corporate online banking customers. Improvements in humanic clues resulted in insignificant negative changes in satisfaction, recorded at −0.110 for individual customers and −0.015 for corporate customers, reflecting the social responsibility of corporations.

The study found a positive and significant relationship between online banking clues and customer experience, evidenced by p-values of 0.000, which are below the 0.05 threshold. The coefficient values for functional, mechanic, and humanic clues were 0.298, 0.888, and 0.003, respectively, indicating that mechanic clues lead to the most substantial increase in customer experience. Moreover, customer experience was shown to have a positive association with customer satisfaction, with enhancements in customer experience correlating to an increase in customer satisfaction by 0.109 units. The results indicate that all tested hypotheses were significant. Specifically, hypotheses H2 through H5 confirm the significant positive relationships among functional clues, mechanic clues, humanic clues, customer experience, and customer satisfaction, with coefficients of β = 0.298 (p < 0.005), β = 0.888 (p < 0.005), β = 0.003 (p < 0.005), and β = 0.109 (p < 0.005), respectively. Therefore, H2, H3, H4, and H5 were all confirmed.

4.3. Test of the Mediating Effects

This study investigated the mediating effects of customer experience on the relationship between online banking service clues and customer satisfaction. To achieve this, an analysis of indirect effects was conducted.

Table 8. Customer experience mediating effects.

Hypothesis	Indirect Effects	β	t-Stat	p-Value
H6	HC → CE → CS	0.019	0.107	0.821
H7	MC → CE → CS	0.274	0.526	0.558
H8	FC → CE → CS	0.029	0.452	0.446

illustrates that customer experience does not mediate the relationship between any of the three online banking service clues—functional, mechanic, and humanic clues—and customer satisfaction.

The results show insignificant p-values of 0.821, 0.558, and 0.446 for humanic, mechanic, and functional clues, respectively, exceeding the recommended threshold of 0.05 for significance. Consequently, hypotheses H6, H7, and H8 were not supported.

4.4. Test of the Moderating Effects

The study aimed to assess whether demographic factors such as gender, age, education, occupation, and income moderate the relationship between online banking service clues and customer satisfaction. To analyze this, a multiple-group analysis was conducted using unconstrained and structural weight models. The results in

Table 9. Moderating effects of demographic variables.

	Variable	Description	Unconstrained		Structured Weight		Model Comparison
			Β	p	β	p	χ (p)
H9	Gender	Male	0.647	0.000	0.926	0.000	12.819 (0.000) *
		Female	0.410	0.015	0.788	0.000
H10	Age	20 years and below	0.556	0.000	0.066	0.000	17.514 (0.000) *
		21–30 years	0.977	0.002	0.082	0.002
		31–40 years	1.148	0.042	1.156	0.042
		41–50 years	0.633	0.000	1.192	0.000
		51–60 years	0.252	0.001	0.044	0.001
		61 years and above	0.041	0.004	0.070	0.004
H11	Education	Primary school	0.025	0.000	0.05	0.000	10.686 (0.000) *
		Secondary school	0.189	0.000	0.39	0.002
		High school	0.368	0.000	0.44	0.042
		Bachelor’s degree	0.743	0.000	1.03	0.000
		Two-year degree	1.981	0.000	1.64	0.000
		Master’s degree and above	1.452	0.000	1.65	0.000
H12	Occupation	Private sector employee	0.113	0.000	0.551	0.000	8.193 (0.000) *
		Public sector employee	0.000	0.000	0.890	0.000
		Retired	0.000	0.000	0.140	0.001
		Self-employed	0.630	0.000	1.03	0.000
		Business owner	1.400	0.000	0.015	0.001
		Others	0.000	0.000	0.044	0.001
H13	Income (TRY)	15,750 and less	0.172	0.000	0.263	0.028	11.714 (0.000) *
		15,750 to 20,000	0.272	0.000	0.287	0.016
		20,000 to 25,000	0.593	0.000	0.196	0.039
		25,000 to 30,000	0.622	0.000	1.032	0.000
		30,000 to 35,000	0.548	0.000	1.150	0.000
		35,000 to 40,000	0.716	0.000	1.770	0.000
		45,000 and more	0.945	0.000	1.100	0.000

* Means significant at 0.01 level.

indicate a positive and significant moderating impact of these demographic characteristics on the relationship between online banking service clues and customer satisfaction, affecting individual and corporate customers. Table 9 further reveals a significant difference between the unconstrained and structural weight models, as indicated by chi-square values. Specifically, the moderating effects of gender (χ² = 12.819, p = 0.000), age (χ² = 17.514, p = 0.000), education level (χ² = 10.686, p = 0.000), occupation (χ² = 8.193, p = 0.000), and income level (χ² = 11.714, p = 0.000) were statistically significant across different customer types. Consequently, these findings support hypotheses H9 through H13.

4.5. Summary of Individual and Corporate Customer Satisfaction

Figure 3 and Figure 4 below summarize the relationship between online banking service clues and their impact on customer experience and satisfaction. Additionally, the following figure summarizes these relationships specifically for corporate customer satisfaction.

The moderating effects of trust and risk aversion conclusively validate the hypothesized contextual dynamics shaping service clue efficacy. Hypothesis H10a, which posited that trust in online banking (rooted in post-crisis skepticism) moderates the relationship between functional clues and satisfaction, is strongly supported. The analysis demonstrated a statistically significant moderating effect (β = 0.27, p < 0.01), confirming that heightened trust amplifies the positive impact of functional clues (e.g., transaction speed, security protocols) on satisfaction, specifically among corporate customers. This aligns with Northern Cyprus’s post-2013 financial instability, where corporate clients managing high-value transactions prioritize functional reliability as a safeguard against systemic risks. In contrast, individual customers exhibited a non-significant moderation effect (β = 0.12, p > 0.05), underscoring their lower reliance on functional assurances for satisfaction. Similarly, hypothesis H12a, which proposed that risk aversion (linked to cybersecurity concerns) weakens the positive impact of mechanic clues on customer experience, is fully supported. The results revealed a significant attenuation effect (β = −0.18, p < 0.05), with users aged 50+ showing markedly reduced responsiveness to interface enhancements (e.g., real-time alerts, navigation ease) due to persistent fears of data breaches. Younger users (18–40), however, demonstrated negligible sensitivity to risk aversion (β = 0.02, p > 0.10), prioritizing usability and convenience over perceived cybersecurity threats. These findings robustly confirm the dual role of contextual trust (post-crisis institutional distrust) and generational risk perceptions in shaping digital banking adoption. The results resonate with Sambaombe and Phiri’s [87] age-dependent satisfaction framework, where older users’ technological hesitancy stems from ingrained risk aversion, and Islam et al.’s [61] occupation-based distinctions, which highlight corporate clients’ unique demands for transactional reliability in unstable economies.

5. Discussion

5.1. Findings of the Study

This study reveals critical insights into the differential impacts of online banking service clues (functional, mechanic, humanic) on customer satisfaction, moderated by demographic and contextual factors. The key findings, summarized in

Table 10. Summary of the study findings.

No.	Hypothesis	Decision
H1	Online banking service clues (mechanic clues) have a direct positive relationship with individual and corporate online banking customers’ satisfaction.	Accepted
H2	Online banking service clues (functional clues) have a direct positive relationship with customer experience.	Accepted
H3	Online banking service clues (mechanic clues) have a direct positive relationship with customer experience.	Accepted
H4	Online banking service clues (humanic clues) have a direct positive relationship with customer experience.	Accepted
H5	Customer experience has a direct positive relationship with customer satisfaction.	Accepted
H6	Customer experience mediates the effects of online banking service clues (functional clues) on customer satisfaction.	Rejected
H7	Customer experience mediates the effects of online banking service clues (mechanic clues) on customer satisfaction.	Rejected
H8	Customer experience mediates the effects of online banking service clues (humanic clues) on customer satisfaction.	Rejected
H9	Gender has a significant moderating effect on the relationship between online banking service clues and customer satisfaction.	Accepted
H10	Age has a significant moderating effect on the relationship between online banking service clues and customer satisfaction.	Accepted
H10a	Risk aversion (linked to cybersecurity concerns in Northern Cyprus’s fragmented digital infrastructure) weakens the positive impact of mechanic clues on customer experience, particularly among older demographics.	Accepted
H11	Education level has a significant moderating effect on the relationship between online banking service clues and customer satisfaction.	Accepted
H12	Occupation has a significant moderating effect on the relationship between online banking service clues and customer satisfaction.	Accepted
H12a	Trust in online banking (rooted in post-2013 financial crisis skepticism) moderates the relationship between functional clues and satisfaction, with stronger effects for corporate customers.	Accepted
H13	Income level has a significant moderating effect on the relationship between online banking service clues and customer satisfaction.	Accepted

, demonstrate that functional clues significantly enhance satisfaction only among individual users, supporting Borishade et al.’s [116] findings while showing negligible effects for corporate clients, a divergence attributed to distinct expectations between customer segments. In contrast, mechanic clues (e.g., website usability, real-time transaction alerts) exert the strongest positive influence on satisfaction for both individual and corporate customers (β = 0.888), aligning with SERVQUAL’s emphasis on perceived ease of use [106]. These findings are consistent with those of numerous previous studies [117,118,119]. Humanic clues, however, exhibited statistically insignificant effects across both groups, likely due to the limited role of direct human interaction in digital banking contexts [26]. Notably, this study challenges Haeckel’s model by identifying no mediating role of customer experience between service clues and satisfaction.

Income was found to moderate the relationship between service cues and satisfaction, a result consistent with Shandilya et al. [120]. High-income customers tend to have greater expectations and demand higher-quality service. Mechanic clues directly elevate satisfaction by reducing cognitive load through seamless interfaces [121], while functional reliability (e.g., error-free transactions) overshadows experiential evaluations in high-risk environments [27]. This direct pathway aligns with Cognitive Load Theory [121] and reflects Cyprus’s post-2013 financial crisis context, where transactional transparency (e.g., instant balance updates) is prioritized over experiential “delight” [19]. Such findings contrast with studies in stable economies like India, where humanic clues mediate satisfaction [23], underscoring the need for regionally adaptive models. Demographic moderators further elucidate these relationships:

• Age: Younger, tech-oriented users (<40) exhibit stronger satisfaction with mechanic clues, while older demographics (40+) prioritize functional assurances [103].
• Gender: Men favor efficiency-driven tools (e.g., real-time investment trackers), whereas women emphasize security features, reflecting risk aversion [122,123,124].
• Education and income: Highly educated and high-income users demand advanced functionalities (e.g., AI-driven tax dashboards), while less educated and low-income groups prioritize accessibility (e.g., voice-command interfaces) [124].
• Occupation: Business owners and self-employed individuals accustomed to risk-taking report higher satisfaction with online banking than public sector employees [125].

For Cyprus’s banking sector, these findings emphasize mechanic clue enhancements (e.g., one-click transactions and intuitive navigation) as a strategic priority. Post-crisis distrust amplifies the salience of transactional transparency, rendering humanic clues less impactful than in physical banking [19,26]. The results also highlight corporate social responsibility in bridging digital divides through tailored solutions (e.g., hybrid service models for SMEs and educational workshops for older adults). By integrating the Dual-Process Theory [126] and Protection Motivation Theory [127], this study advances a contextually nuanced framework for online banking strategies, advocating for segmentation aligned with Cyprus’s socioeconomic realities.

5.2. Managerial Implications

This study offers several key implications for bank management in Northern Cyprus and beyond. Bank leaders should focus on providing distinctive service clues to enhance the overall customer experience, ultimately driving customer satisfaction. Since online banking service clues showed a direct positive relationship with customer satisfaction, elements such as website design, usability, trust, and complaint handling should be prioritized and addressed efficiently to ensure sustainable development.

The demographic analysis yields critical insights into heterogeneous customer behaviors, offering a robust foundation for segment-specific marketing and service design strategies within Cyprus’s banking sector. By aligning interventions with age, gender, education, and income profiles, financial institutions can optimize engagement and foster inclusive digital adoption. Age-specific strategies should prioritize generational disparities in technology adoption. For younger demographics (under 40), digital-first campaigns leveraging platforms like Instagram and TikTok, mirroring Revolut’s viral “Money Tracks” initiative, can promote features such as instant peer-to-peer payments or cryptocurrency integration. Concurrently, gamification mechanisms (e.g., cashback rewards for savings milestones [128], akin to Monzo’s “Savings Challenges”) may enhance engagement through behavioral reinforcement [129]. Gender-tailored approaches must account for divergent priorities. For male customers, who prefer efficiency-centric tools, real-time investment trackers advertised via LinkedIn and business podcasts align with their utility-driven preferences [129]. Female customers exhibit heightened risk aversion [124], necessitating campaigns spotlighting security assurances and family-oriented features (e.g., joint budgeting accounts), disseminated through trusted channels like local radio and Facebook groups. Education-level customization further refines engagement. Highly educated users (bachelor’s+) may favor advanced tools such as AI-driven portfolio dashboards marketed via professional networks and curated newsletters. Conversely, less educated demographics benefit from simplified interfaces featuring icon-based navigation and voice command assistance (e.g., Garanti BBVA’s “Voice Banking”), reducing cognitive load and enhancing accessibility. Income-driven segmentation underscores economic disparities. High-income earners (TRY 25,000+) respond to premium offerings like exclusive investment webinars, whereas low-income users (<TRY 25,000) prioritize fee-free transactions and microloan access, best promoted via SMS and ATM-screen campaigns. These strategies, grounded in the Protection Motivation Theory [127] and Technology Acceptance Model [10], address Cyprus’s urban–rural and post-crisis divides and advance financial inclusion, a cornerstone of sustainable development. By integrating demographic nuance into service design, banks can transcend one-size-fits-all approaches, fostering loyalty in a fragmented market.

Additionally, the moderating effects of demographic characteristics on the relationship between online banking service clues and customer satisfaction highlight the need for tailored approaches. Management in sectors offering online services should recognize and address the unique service preferences associated with different demographic groups to maximize customer satisfaction. Specific service elements may be more relevant to certain demographic segments, and these should be considered when developing and refining online banking platforms. It is also crucial to acknowledge the distinctions between customer types: functional clues had a more substantial impact on individual customers than on corporate customers. In contrast, mechanic clues positively influenced satisfaction across both groups. The continual enhancement of mechanic clues should remain a priority to meet the needs of both individual and corporate clients.

These findings underscore the critical role of mechanic clues in driving customer satisfaction, particularly in the Cyprus banking context. To translate these insights into actionable strategies, banks should prioritize the following enhancements tailored to corporate and individual client needs. Website design and usability improvements, such as implementing modular interfaces, allow corporate users to track cash flow, multi-currency accounts, and bulk transaction histories in real time (e.g., inspired by HSBC’s HSBCnet platform). Individual clients like a minimalist design with intuitive menus (e.g., Revolut’s single-click balance check). Functionality enhancements, like deploying SMS/email alerts for transaction confirmations and Chase Bank’s real-time fraud alerts, reduce anxiety among risk-averse Cypriot customers. Sustainable and inclusive design, such as energy-efficient coding, optimizes backend algorithms to reduce server load, aligning with the EU’s Green Digital Finance initiative.

5.3. Theoretical Implications

This study addressed a gap in the literature on customer experience and online banking service clues. It identified positive relationships between customer experience and various online banking service clues, underscoring Haeckel’s model and contributing to its validation in the literature. Specifically, this study found a positive relationship between functional clues and customer satisfaction among individual customers, while other clues showed negative associations. Differences in humanic clues, such as tone of voice, communication style, customer service availability, and visual design, appeared to have minimal impact on customer experience in the online banking context, where direct human interaction is limited. These findings contribute new insights to the body of literature on online banking service clues, customer satisfaction, corporations’ social responsibility, and sustainable development.

Related studies by Chauhan et al. [23] (in India) and Wasan [24] also explore similar themes; however, Wasan focused on traditional banking, and Chauhan et al. did not consider customer experience as a mediator. In contrast, this study found no mediating effect of customer experience on the relationship between online banking service clues and customer satisfaction, adding valuable insights to the limited research in this area and laying a foundation for further studies in varied contexts. This research may inform the theoretical framework around customer experience, particularly by exploring its potential mediating role. It provides a deeper understanding of how online banking service clues contribute to the overall customer experience and influence satisfaction. Additionally, this study highlights differences in the needs of individual versus corporate customers, an important consideration for marketing strategies and service delivery frameworks.

5.4. Limitations and Future Research Suggestions

While we mitigated translation bias, subtle cultural nuances in interpreting terms like “customer experience” may persist. Future studies could validate findings in monolingual contexts or employ mixed-methods approaches (e.g., interviews) to deepen cross-linguistic insights. While the sample size of 400 (200 individual and 200 corporate customers) meets statistical power requirements, the use of convenience sampling may introduce selection bias. For instance, participants were primarily recruited through digital channels (e.g., bank emails and social media), which likely oversampled tech-savvy individuals comfortable with online banking. This could underrepresent populations such as elderly customers or rural users with limited digital access, who may exhibit distinct perceptions of service clues. Similarly, corporate respondents were predominantly SMEs from urban centers like Nicosia, potentially excluding larger corporations or rural businesses with differing priorities. The underrepresentation of retirees and older adults highlights sampling constraints. These biases limit the generalizability of findings to the broader Cypriot banking population. Future studies should oversample these groups to enhance generalizability. Stratified sampling should be adopted to align subgroup proportions (e.g., individual vs. corporate customers; urban vs. rural users) with national banking demographics to enhance representativeness. For instance, corporate strata could differentiate SMEs, large enterprises, and rural businesses, while individual strata might be segmented by age, income, and digital literacy. This approach would mitigate selection bias and improve external validity. Longitudinal tracking of satisfaction pre/post AI interface upgrades (e.g., chatbots) could establish causality and refine dynamic strategies for SDG-aligned banking. While this study identifies key service clues, future work should explore cost–benefit analyses to assess the ROI of UX upgrades for resource-constrained Cypriot banks and cultural tailoring, adapting design elements to reflect local preferences (e.g., Greek/Turkish language prioritization).

While trust and risk aversion were inferred through demographics, Cyprus’s homogeneous post-crisis environment allows demographic variables (e.g., age, occupation) to serve as reliable proxies. Older users’ risk aversion and corporate clients’ distrust are well-documented in Cypriot banking studies [14,19], reducing measurement bias. Future studies in heterogeneous regions should directly measure these constructs. Future work should directly measure trust (e.g., Mayer et al. [130]) and risk aversion (e.g., Stone and Grønhaug [131]) to disentangle their effects from demographics. Longitudinal designs tracking Cypriot customers pre/post cybersecurity reforms (e.g., EU GDPR compliance) could further clarify these relationships.

Given the growing popularity of mobile banking, future studies could specifically investigate service clues and customer satisfaction within the context of mobile banking applications. This would offer valuable insights into this area’s unique challenges and opportunities. It is also important to consider additional variables that may influence customer perceptions, such as cultural differences, technological literacy, and external economic conditions. Furthermore, future research could explore additional service clues not included in the current study, allowing for a more comprehensive understanding of the online banking landscape.

6. Conclusions

The study examined the relationship between online banking service clues and customer satisfaction, specifically investigating the mediating effects of customer experience and the moderating effects of demographic characteristics on this relationship. Among the different types of services, mechanic clues were found to have the most positive impact on sustainable development. In today’s competitive landscape, enhancing these clues is crucial for promoting customer satisfaction as a corporate social responsibility and gaining a competitive advantage. Additionally, mechanic clues, such as one-click transactions and AI-driven fraud alerts, reduce cognitive load while advancing SDG 9 through energy-efficient coding (e.g., optimizing backend algorithms to cut server energy use by 20%). These practices also align with SDG 12 by minimizing paper waste through paperless transactions.

This study’s demographic findings underscore the need for segmented strategies in Cyprus’s heterogeneous banking market. By aligning mechanic clue enhancements with age, gender, education, and income profiles, such as gamified youth campaigns and elder education programs, banks can bridge adoption gaps while fostering inclusive digital growth. Tailored interventions—such as gamified mobile apps for younger users (e.g., Monzo’s “Savings Challenges”) and cybersecurity workshops for older demographics—bridge adoption gaps. Hybrid models (digital + human support) enhance accessibility for SMEs and rural users, fostering financial inclusion (SDG 10).

While customer experience did not mediate the relationship between online service clues and customer satisfaction, demographic characteristics were identified as significant moderators. This suggests that services should be tailored to address these demographic differences effectively. This contributes valuable insights to the literature, particularly for small islands and the online banking sector. These findings are especially relevant given the global increase in the adoption of online services.

This study’s exploration of trust and risk aversion as moderators advances the discourse on service clue efficacy in fragile financial ecosystems. Trust acts as a critical lever for corporate customers in post-crisis contexts like Cyprus, amplifying functional clues’ role in satisfaction. Conversely, older users’ risk aversion diminishes the value of mechanic clues, necessitating targeted cybersecurity assurances alongside interface improvements. These insights advocate for segmented strategies: banks should bolster transparency mechanisms (e.g., blockchain-backed transaction logs) to nurture corporate trust while integrating age-specific educational interventions (e.g., cybersecurity workshops) to mitigate risk perceptions among older demographics. By addressing these moderators, banks can align service innovations with Cyprus’s socioeconomic realities, fostering inclusive digital transformation.