Service quality in mobile banking Open Access

Parasuraman et al. (1985) revolutionized our understanding of service quality with the introduction of the service quality (SERVQUAL) model, which has profoundly shaped how the banking sector approaches customer experience and satisfaction. As the industry transitions toward mobile services (Kumar et al., 2022), the importance of service quality has become even more critical in marketing (Leem and Eum, 2021). Service quality encompasses various attributes that users consider crucial in their service interactions (Zeithaml et al., 1990). This study focuses specifically on service quality in mobile banking (m-banking), consciously excluding electronic banking (e-banking) due to their distinct differences. M-banking involves banking services accessible through mobile devices such as phones and tablets via dedicated mobile applications, which enables customers to perform transactions anytime, anywhere (Shaikh and Karjaluoto, 2015; Thakur, 2014). This form of banking features unique aspects like biometric authentication, push alerts, and location-based services (Malaquias and Hwang, 2019). In contrast, e-banking encompasses a wider range of services, including online banking via desktop computers (Martins et al., 2014). Consumers differentiate among m-banking, e-banking, and traditional banking based on factors such as accessibility, convenience, security, and user experience (Koenig-Lewis et al., 2010; Laukkanen, 2007; Sreejesh et al., 2016). Within m-banking, users’ evaluations of service quality reflect their perceptions of the excellence and uniqueness of mobile content delivery (Arcand et al., 2017). Indeed, this subject has been central to recent empirical studies on m-banking (Baabdullah et al., 2019; Mostafa, 2020; Poromatikul et al., 2020; Shankar et al., 2020; Sharma and Sharma, 2019).

M-banking offers tangible benefits for both users and financial institutions (Baabdullah et al., 2019), wherein mobile devices like smartphones are used to carry out diverse financial and non-financial operations, marking a novel service within banking (Shaikh and Karjaluoto, 2015). While numerous studies have presented varied perspectives on m-banking (Baptista and Oliveira, 2015; Mohammadi, 2015; Munoz-Leiva et al., 2017; Tam and Oliveira, 2016), Shaikh et al. (2022) provide an encompassing definition. They highlight the convenience it offers, allowing users to seamlessly manage their finances on a secure platform, with flexibility and user-centric design. Noteworthily, existing reviews on m-banking have mainly focused on areas like adoption (Shaikh and Karjaluoto, 2015), payments (Alkhowaiter, 2020), and services (Kim et al., 2018; Shaikh et al., 2022). Yet, a systematic review focusing exclusively on service quality within m-banking has not been conducted.

The absence of a focused systematic review on service quality within m-banking presents both an academic and industry gap. Service quality acts as a linchpin in ensuring user satisfaction, trust, and loyalty—elements crucial for the long-term success and sustainability of m-banking platforms. As digital transactions become the norm and user expectations continue to evolve, understanding the elements of service quality becomes critical. A thorough review on this subject would not only synthesize existing findings but also identify areas where enhancements are necessary. Moreover, as financial institutions race to outpace competitors, a dedicated consolidation of knowledge on service quality in m-banking can provide a blueprint for innovation and excellence. Without such a review, there is a risk of institutions operating in silos, potentially overlooking critical insights that could shape the next generation of m-banking services. Moving beyond trend adaptation, this review seeks to outline what m-banking can and should accomplish in terms of service quality.

Systematic reviews synthesize existing research by rigorously consolidating, evaluating, and streamlining the literature (Kraus et al., 2022; Lim et al., 2022b; Paul et al., 2021). This synthesis becomes particularly vital in areas like ours, where distinct viewpoints enrich understanding, thus reducing bias and paving the way for state-of-the-art insights (Higgins and Green, 2008; Petticrew and Roberts, 2006). In addition, systematic reviews fortify the foundation upon which new inquiries can be validated (Khan et al., 2003) while also spotlighting gaps and inconsistencies that necessitate further exploration (Tranfield et al., 2003). Such reviews therefore contribute to advancing both theory and practice through their sensible establishment of current states and future directions (Donthu et al., 2021; Lim and Kumar, 2024; Mukherjee et al., 2022). Given the lack of a holistic assessment of the literature on m-banking service quality, pressing research questions (RQs) linger. Our review therefore seeks to address:

The implications of our review are manifold. Noteworthily, a systematic review offers more than an aggregation of knowledge—it serves as a critical lens of a domain (Kraus et al., 2022; Lim et al., 2022b; Paul et al., 2021). Addressing RQ1, our review offers a structured understanding of m-banking service quality research, identifying the primary theories, contexts, and methods that shape the field. This approach not only grounds the research in established knowledge but also emphasizes the defining features of the domain. As for RQ2, an in-depth exploration of constructs is essential. Service quality in m-banking is influenced by a network of antecedents, mediators, moderators, and consequences. Understanding this structure not only clarifies the factors that shape service quality but also provides insights to improve customer experience. Alas, RQ3 highlights gaps in current knowledge. Each underexplored area represents a potential avenue for researchers and practitioners. Identifying these unexplored areas allows us to extend discussions on m-banking service quality and move the field forward. Therefore, this systematic review serves as a guide, illuminating paths in the existing literature and pointing toward future directions in m-banking service quality research.

Subsequent sections of this article follow a logical trajectory. We commence by distilling the core concepts linked to m-banking service quality before we delineate the methodology underpinning our systematic review. We then proceed with our review, encompassing insights on theories, constructs, contexts, and methods to an integrated framework. We conclude by presenting a roadmap for future inquiries into m-banking service quality.

In today’s competitive business landscape, the imperative to deliver high-quality service stands out as a linchpin to gaining a competitive edge (Lin et al., 2021; Shankar et al., 2019). This is primarily because service quality fortifies customer satisfaction, bolsters loyalty, and solidifies retention, laying a strong foundation for the satisfaction-profit chain (Anderson and Mittal, 2000; Anderson, 2006; Falk et al., 2010; Heskett et al., 1994; Hogreve et al., 2022). This, in turn, explains why the concept of service quality has consistently been at the forefront of services marketing (Brady and Cronin, 2001; Caruana et al., 2000; Cronin and Taylor, 1992; Grönroos, 1984; Dabholkar et al., 2000; Parasuraman et al., 1988; Shankar et al., 2019, 2020).

Historically, researchers articulated two seminal conceptualizations of service quality: the Nordic School perspective, led by Grönroos (1984), and the American School perspective, championed by Parasuraman et al. (1985). Both these perspectives take their roots from the expectancy-disconfirmation theory (Oliver, 1977, 1980), which frames service quality as a holistic assessment of service excellence (Parasuraman et al., 1988) and as an evaluation where consumers juxtapose their expectations with perceived service outcomes (Grönroos, 1984). Emphasizing its multidimensional nature, Grönroos et al. (1993) divided service quality into three segments: technical quality (the service outcome), functional quality (the service process), and corporate image (the service brand). In contrast, Parasuraman et al. (1985) initially pinpointed 10 dimensions, mainly oriented around functional quality (i.e. access, communication, competence, credibility, courtesy, security, reliability, responsiveness, understanding/knowing the customers, and tangibles). These dimensions underwent further refinement, resulting in the widely-acknowledged five-dimension model, namely assurances, empathy, reliability, responsiveness, and tangibles (Parasuraman et al., 1988). This model laid the groundwork for the SERVQUAL instrument, which gauges service quality based on the nexus between customer perceptions and expectations.

The applicability of SERVQUAL transcends sectors, illuminating insights in domains like banking (Ali and Raza, 2017; Bhat, 2005; Kumar et al., 2009; Nair et al., 2010; Siddiqi, 2011), education (Arambewela and Hall, 2006; Yousapronpaiboon, 2014), healthcare (Ozretić-Došen et al., 2020), hospitality (Atilgan et al., 2003; Chaturvedi, 2017), retail (Finn and Lamb, 1991; Naik et al., 2010; Zhao et al., 2002), and telecommunications (Belwal and Amireh, 2018; Khan, 2010). Yet, it is vital to note the empirical inconsistencies across service sectors regarding SERVQUAL’s five dimensions, leading to calls for its refinement (Bahia and Nantel, 2000; Ladhari, 2010). Moreover, amidst critiques targeting SERVQUAL’s conceptual ambiguity around expectations, researchers introduced SERVPERF as a performance-centric measure of service quality (Cronin and Taylor, 1992).

The intellectual curiosity surrounding service quality does not only revolve around its definitions or measurement. Pioneering studies in this area proactively explore the connections of service quality with associated constructs like customer’s perceived value (Cronin et al., 2000; Fornell, 1992; Zeithaml et al., 1996), intentions (Boulding et al., 1993; Zeithaml et al., 1996), satisfaction (Anderson and Sullivan, 1993; Dabholkar et al., 2000; Taylor and Baker, 1994), and loyalty (Bell et al., 2005; Ladhari et al., 2011; Kumar et al., 2010). These relationships underline the profound influence of service quality on the wider scope of customer experience and business outcomes.

In the virtual environment, where customers interact with technology rather than humans, the foundational concepts of service quality require a rethinking (Parasuraman and Grewal, 2000; Shankar et al., 2019). Since 2000, research has predominantly pivoted to electronic service quality (e-service quality), which Parasuraman et al. (2005, p. 217) articulate as “the extent to which a website facilitates efficient and effective shopping, purchasing, and delivery.” Though many view m-banking as a subset of electronic commerce, characterized by financial and non-financial transactions via mobile communication technology (Moser, 2015; Shaikh and Karjaluoto, 2015), a clear distinction exists between e-service quality and mobile service quality (Arcand et al., 2017; Twum et al., 2022). Broadly, m-banking service quality encompasses “overall customer evaluations and judgments regarding the excellence and quality of mobile content delivery in the m-banking context” (Lin, 2013, p. 196). In its essence, it must encapsulate the enhanced opportunities the mobile context presents to marketers and consumers. Specifically, m-banking outpaces both traditional and online banking by offering superior convenience, flexibility, localization, and ubiquity (Laukkanen, 2016; Rajaobelina et al., 2021; Srivastava and Vishnani, 2021).

Over time, although e-service quality has seen extensive research, e-banking seems rather underserved (Arcand et al., 2017; Ayo et al., 2016). Furthermore, only a handful of studies have ventured into assessing service quality within m-banking (Arcand et al., 2017; Khan et al., 2021; Jin and Lim, 2021; Jun and Palacios, 2016; Mostafa, 2020; Rajaobelina et al., 2021; Shankar et al., 2019, 2020; Sagib and Zapan, 2014; Zoghlami et al., 2020). This is surprising, given m-banking’s prominence as the fastest-growing e-banking channel (Arcand et al., 2017; Shankar et al., 2020). Research focused on m-banking service quality predominantly zeros in on utilitarian service features like convenience, ease of use, privacy, reliability, responsiveness, risk, security, and usefulness (Aldiabat et al., 2022; Bui et al., 2022; Heng et al., 2019; Mahakunajirakul, 2022; Mostafa, 2020; Jun and Palacios, 2016; Srivastava and Vishnani, 2021; Tang et al., 2021; Zoghlami et al., 2020). However, a subset of studies also explores dimensions related to hedonic and social values (Arcand et al., 2017; Baabdullah et al., 2019; De Leon et al., 2020; Liébana-Cabanillas et al., 2019; Mahakunajirakul, 2022; Mostafa, 2020; Raman and Aashish, 2021; Tang et al., 2021). It is also noteworthy that m-banking service quality consistently correlates with perceived value, intention, satisfaction, trust, and loyalty with m-banking services (Arcand et al., 2017; Baabdullah et al., 2019; Bui et al., 2022; Hassan et al., 2020; Heng et al., 2019; Liébana-Cabanillas et al., 2019; Zoghlami et al., 2020).

Taken together, these insights highlight both the depth and diversity of factors influencing m-banking service quality, underscoring the need for a focused examination of its unique dimensions beyond traditional e-service frameworks. Given the range of utilitarian, hedonic, and social factors at play, as well as their consistent associations with key customer outcomes like satisfaction, trust, and loyalty, a comprehensive and systematic review of existing research can reveal gaps and opportunities for further exploration. This article now turns to a systematic review of the literature on service quality in m-banking, with a view to synthesizing current knowledge and establishing a foundation for future research.

Systematic reviews endeavor to provide a comprehensive synthesis of published studies on a specific topic (Grant and Booth, 2009; Kraus et al., 2022; Lim et al., 2022b), laying the groundwork for future research endeavors (Palmatier et al., 2018; Paul et al., 2021). More precisely, these reviews critically assess the breadth of current knowledge, distinguishing between well-established findings and unresolved questions (Popay et al., 2006). Our methodological approach followed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) protocol introduced by Moher et al. (2009). Originally conceived for healthcare reviews, the PRISMA protocol’s rigorous standards have been widely recognized and adopted in disciplines ranging from information science to marketing (Denter et al., 2023; Lim et al., 2022c; Lim et al., 2022d). This protocol systematically progresses through four stages: identification, screening, eligibility, and inclusion, which we detail below (Figure 1).

During the identification stage, we initiated our search on Scopus and Web of Science and then extended to Google Scholar to ensure comprehensive coverage, in line with established research practices (Bergmann et al., 2023; Ladeira et al., 2023). We did not restrict ourselves to a specific timeframe, opting instead to explore articles up to the end of 2022—the latest full year at the time this review was conducted. Our keyword selection, stemming from exhaustive literature reviews, encompassed the following search string: (“Mobile bank*” OR “M bank*” OR “M-bank*” OR “Mbank*”) OR (“Mobile money” OR “M money” OR “M-money” OR “Mmoney”) OR (“Mobile payment” OR “M payment” OR “M-payment” OR “Mpayment”) OR (“Mobile wallet” OR “M wallet” OR “M-wallet” OR “Mwallet”) OR (“Cell phone banking” OR “Mobile phone banking” OR “Smartphone banking” OR “SMS banking” OR “Telephone banking”) AND (“service quality” OR “SERVQUAL” OR “service performance” OR “quality of service” OR “service level”). Consistent with our objective, we channeled our attention toward articles within domains like “business, management and accounting”, “economics, econometrics and finance”, and “social sciences”. From an initial pool of 152 articles, the removal of duplicates yielded 108 unique pieces, which we subjected to further screening.

In the screening stage, we focused on article relevance. Our seasoned expertise in systematic reviews guided our selection, gravitating toward diverse journal publications irrespective of their impact factors, consistent with Lim and Rasul (2022). However, we excluded non-journal articles, as concerns about their peer review transparency could cast doubts on their validity (Paul et al., 2021). Our screening retained 73 articles.

During the eligibility stage, a language assessment led to the removal of three non-English articles, leaving 70 articles. Our focus remained on empirical qualitative and quantitative studies, resonating with the guidelines of prior research (Paul et al., 2021). We consciously excluded certain categories like reviews to prevent redundancy. While various rankings, such as the Australian Business Deans Council (ABDC) journal list, serve as benchmarks in some studies (Lim and Rasul, 2022; Lim et al., 2021), we opted for a more holistic approach, valuing the content over its source. Consequently, 71 articles remained in our curated list ( Web Appendix A and  Web Appendix B).

In the inclusion stage, we systematically coded all 71 articles, extracting details about theories, constructs (antecedents, mediators, moderators, and consequences), contexts, and methods (TCCM) (Paul and Rosado-Serrano, 2019; Paul et al., 2021). This detailed coding informed both our responses to the RQs outlined in the introduction and the creation of an integrated model. Employing content (thematic) analysis (Agyekum et al., 2019; Braun and Clarke, 2006; Kraus et al., 2022; Lim, 2025; Vaismoradi et al., 2013), we further refined our insights, achieving a commendable inter-rater agreement score of 90% (Belur et al., 2021). The subsequent sections will elucidate the findings, shedding light on the results that emerged from our analysis.

The findings of the review on m-banking service quality are organized into four main categories: theoretical, contextual, methodological, and construct. This categorization provides a comprehensive understanding of the various elements of the discourse. The theoretical perspective examines the foundational theories that underpin service quality in m-banking. The contextual perspective explores the geographical and service-related contexts that influence m-banking service quality. The methodological perspective encompasses the research approaches employed in studying m-banking service quality. Finally, the construct perspective elucidates the antecedents, mediators, moderators, and consequences of m-banking service quality, offering a detailed analysis of the factors that shape user experiences and outcomes in m-banking. This multifaceted approach ensures a detailed and finer-grained understanding of the complexities involved in m-banking service quality.

In the rich body of knowledge on m-banking service quality, our review encompasses 71 studies. These studies have variably employed 34 distinct theories to underpin their inquiries. A total of 24 studies drew upon a singular theoretical lens, 28 combined multiple theories to offer a multi-dimensional viewpoint, while 19 refrained from adopting a specific theoretical foundation. The ensuing analysis offers a consolidated view of the theoretical foundations shaping the discourse on m-banking service quality ( Web Appendix C).

User adoption and behavioral intentions. A significant chunk of the literature focuses on user adoption and the determinants of behavioral intentions. Technology acceptance model (TAM) and its evolved counterpart, the unified theory of acceptance and use of technology (UTAUT), emerge as cornerstones, with numerous studies underlining their import (Abu-Taieh et al., 2022; Alenizi, 2022; Hijazi, 2022; Tang et al., 2021). Augmenting this narrative, the theory of planned behavior (TPB) and the theory of reasoned action (TRA) probe deeper into the behavioral intentions predicting m-banking usage (Abu-Taieh et al., 2022; Yeh, 2020). The diffusion of innovation theory offers a macro lens, dissecting the spread of novel banking solutions (Mensah, 2019; Payne et al., 2018; Yeh, 2020).

Cognitive and emotional processes. Moving to the psychological perspective, theories like the elaboration likelihood model (ELM) demystify the cognitive processes users engage in when encountering persuasive m-banking messages (Zhou, 2012). Attachment theory elucidates the affective anchors users forge with their banking apps, shaping their loyalty trajectories (Rajaobelina et al., 2021). Complementing this, flow theory makes sense of the states of deep immersion that users experience during m-banking interactions (Zhou, 2013).

Service quality and user satisfaction. Centrally rooted in the quality of services, the DeLone and McLean IS Success Model offers a holistic framework for assessing m-banking success across various dimensions, including service and system quality (Ali et al., 2022; Eren, 2022; Tam and Oliveira, 2017). Specific to service quality assessments, the SERVQUAL model, electronic service quality (E-S-QUAL) model, and the mobile service quality (MSQ) model evaluate user experiences by juxtaposing expected service levels against perceived outcomes (Rajaobelina et al., 2021; Shankar et al., 2019; Zhou et al., 2021). The expectation-confirmation theory (ECT) harmonizes with this narrative, presenting a bridge between user expectations, perceptions, and satisfaction (Abbasi et al., 2022; Srivastava and Vishnani, 2021).

Social influences and external stimuli. Understanding m-banking with a societal lens, the social practice theory and the social proof theory underscore the influence of societal norms, peer behaviors, and cultural determinants on m-banking perceptions and behaviors (Alenizi, 2022; Naeem et al., 2022). Augmenting this societal viewpoint, the stimulus-organism-response (S-O-R) framework encapsulates a causal chain, wherein external cues mold user perceptions, leading to specific behavioral outcomes in m-banking (Abbasi et al., 2022; Hassan et al., 2020).

System interactions and technological alignment. Underlining the technical aspects of m-banking, complexity theory offers insights into the interrelated systems at play (Abbasi et al., 2022). The self-service technology (SST) lens centers on the user-technology interface, exploring how system features and functionalities shape user experiences (Hassan et al., 2020; Naruetharadhol et al., 2021). Reinforcing the need for alignment, the task technology fit (TTF) model emphasizes the congruence between users’ tasks and the technological tools m-banking platforms provide (Tam and Oliveira, 2016).

Reflecting on the theoretical landscape informing m-banking service quality, it becomes clear that understanding this domain requires more than straightforward assessments of service effectiveness or technology adoption. Instead, m-banking service quality is a multi-dimensional construct influenced by human behaviors, cognitive and emotional responses, service standards, social influences, and technological integration. Theories in this area highlight the interdependent roles of businesses, consumers, and technologies, emphasizing the value of interdisciplinary approaches. This range of perspectives not only enriches the field but also highlights essential insights across disciplines. As m-banking advances, these theoretical foundations offer researchers a platform to build upon, adapt, and refine, ensuring that our understanding evolves alongside the dynamic nature of m-banking itself. This review now transitions into a systematic analysis of the contextual perspectives of service quality in m-banking.

When examining the backdrop of m-banking service quality, studies span a diverse range of geographical and service-oriented contexts. This variety highlights the global relevance of m-banking and its impact across various cultures, economies, and technological infrastructures.

Asian dominance. The Asian landscape emerges as a prime research hub, housing a major fraction of the examined studies ( Web Appendix D). Leading the pack is India with 10 studies, reflecting the country’s rapidly growing digital economy and FinTech adoption. China, another Asian powerhouse, follows closely with seven studies, mapping its digital trajectory and consumer behaviors in m-banking. The Southeast Asian region is also well-represented with countries like Indonesia (five studies) and Thailand (six studies), underscoring the pivotal role of m-banking in these emerging economies. Other significant contributions from Asia encompass studies from Bangladesh, Jordan, Kuwait, Malaysia, Pakistan, Palestine, Taiwan, and Vietnam.

Middle Eastern and African insights. The Middle East and Africa provide unique perspectives, representing regions with a blend of traditional banking methods and emerging digital solutions. Countries such as Egypt, Jordan, Lebanon, Libya, Oman, Saudi Arabia, Sub-Saharan Africa, Sudan, Tunisia, and the UAE offer a confluence of cultural, economic, and technological insights that shape the m-banking arena.

Western perspectives. Representing North America and Europe are countries such as Canada, Croatia, Portugal, Spain, and the USA. These nations often portray more mature markets with advanced technological infrastructures and sophisticated user expectations. In these regions, m-banking service quality appears to be less frequently studied for several reasons. The widespread availability of m-banking services may have diminished the perceived need for extensive research. In addition, researchers in these countries may have found that existing service quality frameworks are sufficiently adaptable to m-banking. Consequently, the focus in these mature markets has shifted toward optimizing and refining established systems rather than developing new frameworks. This relative scarcity of research in Western contexts highlights a potential area for further investigation, particularly in exploring how advanced technologies and evolving user expectations can further enhance service quality in m-banking.

Mainstream m-banking. By far, the most prominent context emerging from the literature is m-banking itself, with 46 studies dedicated to its exploration ( Web Appendix E). This dominant position highlights the foundational role of general m-banking platforms in the discourse of service quality.

Specialized financial services. While traditional m-banking retains a stronghold, various specialized financial services are also making their mark. Mobile payment, with 16 studies, reflects the shifting consumer preference toward contactless and mobile-centric payment methods. This is complemented by studies focusing on digital banking, digital payment, internet banking, mobile money service, AI-enabled m-banking, e-wallet, and m-wallet. These contexts, though fewer in number compared to general m-banking, signal the evolving landscape of digital financial services.

The wide range of contexts—both geographical and service-oriented—illustrates the multi-faceted nature of m-banking service quality. This diversity reinforces the need for researchers and practitioners to adopt a holistic perspective, recognizing the unique conditions each context contributes. Addressing these distinct dimensions can enable a more comprehensive understanding of m-banking service quality, ensuring that both research and practice are adaptable to the evolving digital financial landscape.

In the exploration of m-banking service quality, a myriad of research methodologies have been employed, varying in their depth, breadth, and nature ( Web Appendix F). These methods provide a range of lenses through which the phenomenon can be understood.

Quantitative approaches predominantly dominate the m-banking service quality research, with surveys being the most prevalent method utilized in 61 studies. Such research, from studies like Zhou (2012) to the more recent works of Abbasi et al. (2022), offers a comprehensive understanding of consumer behaviors, preferences, and perceptions regarding m-banking. A singular approach using text mining and sentiment analysis has been adopted by Leem and Eum (2021), showcasing an innovative way to extract and analyze information from large textual datasets, often revealing hidden sentiments, patterns, or themes.

Qualitative methods, though fewer in comparison, offer a deep dive into specific phenomena, enabling a rich, detailed understanding of the subject matter. Several studies, such as those by Alenizi (2022), Naeem et al. (2022), and Jun and Palacios (2016), have opted for analyzing online reviews/users’ comments. These three instances provide a raw, unfiltered glimpse into real-world users’ sentiments and perspectives. Focus groups and semi-structured interviews, each applied in three studies, offer interactive platforms to garner detailed insights. Shankar et al. (2020), for instance, have extensively used qualitative techniques ranging from content analysis, critical incident techniques, in-depth and focus group interviews, to netnography. The richness of qualitative research lies in its ability to uncover underlying motives, beliefs, and attitudes, often missed in large-scale quantitative surveys.

Mixed methods approach has been adopted by a few researchers, leveraging both quantitative and qualitative techniques to offer a comprehensive understanding. For example, Tang et al. (2021) and Goplakrishnan and Ravindran (2012) combined surveys with semi-structured interviews and focus groups, respectively. Such a dual approach allows for both the breadth of quantitative research and the depth of qualitative investigation, ensuring a well-rounded exploration of m-banking service quality.

While quantitative methods, particularly surveys, remain dominant in m-banking service quality research, the selective use of qualitative and mixed methods highlights an awareness of the topic’s multi-dimensional nature. This diversity in approach reflects a growing recognition of the need for varied methodologies to capture the full range of factors shaping m-banking service quality. Such an approach ensures that insights are not only statistically robust but also contextually grounded.

We have classified antecedent factors into several clusters: user-based factors, system-success-based factors, and quality-based factors (Table 1).

Starting with the user-based factors, these encompass elements emanating from the TAM, such as perceived ease of use and perceived usefulness. Additionally, they draw from the UTAUT, including effort expectancy, facilitating condition, performance expectancy, and social influence. Notably, research has leveraged the UTAUT variables to dissect intentions surrounding the adoption of m-banking, its continued use, actual m-banking practices, satisfaction, and loyalty (Abu-Taieh et al., 2022; Baabdullah et al., 2019; Mansour, 2020; Windasari and Albashrawi, 2021). Furthermore, numerous studies utilizing TAM to decode m-banking acceptance have concurrently assessed both the perceived ease of use and perceived usefulness of m-banking services (Abbasi et al., 2022; Aldiabat et al., 2022; Mahakunajirakul, 2022; Mostafa, 2020; Naruetharadhol et al., 2021; Navavongsathian et al., 2020; Puriwat and Tripopsakul, 2017a; Srivastava and Vishnani, 2021). Intriguingly, some studies have isolated perceived ease of use for in-depth examination (Mensah, 2019; Tang et al., 2021; Zoghlami et al., 2018) while others have scrutinized it within the context of service quality (Bui et al., 2022; Jun and Palacios, 2016).

Moving on to system-success-based factors, researchers have sought to discern the roles of system quality, service quality, and information quality—the primary components of IS overall quality (Ali et al., 2022). These elements influence the adoption, sustained intent to use, real-world use of assorted m-banking services, customer contentment, loyalty, and other associated parameters. Grounded in DeLone and McLean (2003) IS success model, system quality undergoes evaluation by examining its technical traits, encompassing aspects like flexibility, interface, navigation, usability, and user-centric features (Baabdullah et al., 2019; Mouakket, 2020; Tam and Oliveira, 2016, 2017; Zhou, 2013). Information quality, on the other hand, is gauged through parameters like accuracy, consistency, pertinence, and promptness (Ali et al., 2022; Cavus et al., 2021; Geebren et al., 2021; Mouakket, 2020; Talwar et al., 2020). Service quality concentrates on the overarching backing of the IS provider, weighing factors like dependability, personalizability, promptness, reliability, and responsiveness in addressing user requirements (Ali et al., 2022; Cavus et al., 2021; Geebren et al., 2021; Mouakket, 2020; Talwar et al., 2020). Beyond the IS success model, certain investigations solely emphasize service quality, viewing it either as an independent construct (Mansour et al., 2016; Navavongsathian et al., 2020; Tew et al., 2022) or diving into its multifaceted nature (Bui et al., 2022; De Leon et al., 2020; Khan et al., 2021; Mahakunajirakul, 2022; Rajaobelina et al., 2021; Jin and Lim, 2021).

Next, in our exploration of quality-based factors, we further classify them into six comprehensive categories: adaptability-based, confidence-based, hedonic-based, interactivity-based, scheme-based, and utilitarian-based factors. The bulk of research in this domain seeks to elucidate the influence of these distinct dimensions on key outcomes such as attitudes, satisfaction, and loyalty toward m-banking adoption, among others.

Adaptability-based factors. This category encapsulates factors like compatibility, convenience, and customization. Jin and Lim (2021) identified compatibility as a foundational characteristic of m-payment services. Concurrently, customization is often examined in conjunction with its inherent ability for personalization and adaptability (De Leon et al., 2020; Hassan et al., 2020; Naruetharadhol et al., 2021) while convenience typically underscores the service’s ubiquity and seamless access (De Leon et al., 2020; Sagib and Zapan, 2014).

Confidence-based factors. Assurance, credibility, reliability, privacy, and security form the pillars of this category. Assurance, an embodiment of trust in m-banking, stems from the renowned SERVQUAL model and is an integral component of the self-service technology quality (SSTQUAL) construct (De Leon et al., 2020; Hassan et al., 2020; Naruetharadhol et al., 2021; Shankar et al., 2019). Meanwhile, credibility has earned its place as a pivotal determinant of m-banking service quality and exerts considerable influence on attitudes and satisfaction (Goplakrishnan and Ravindran, 2012; Jun and Palacios, 2016; Mansour et al., 2016). Reliability, emerging from the SERVQUAL framework (Shankar et al., 2019), underscores the consistency and efficacy of service delivery (Khan et al., 2021; Sagib and Zapan, 2014). Pertinently, in m-banking literature, privacy and security—essential facets of user information protection—are often intertwined or addressed as a cohesive dimension, emphasizing their central role in gauging user intent, satisfaction, and loyalty (Tang et al., 2021; Twum et al., 2022).

Hedonic-based factors. At the heart of this category lies enjoyment. Far from mere functional aspects of m-banking quality, enjoyment accentuates the delight and pleasure experienced during m-banking (Hassan et al., 2020; Mahakunajirakul, 2022; Naruetharadhol et al., 2021; Puriwat and Tripopsakul, 2017a), influencing attitude (De Leon et al., 2020; Mostafa, 2020) and relationship quality (Arcand et al., 2017).

Interactivity-based factors. Access, communication, empathy, responsiveness, and sociality come under this umbrella. While access and communication act as touchpoints for m-banking customer service quality, empathy and responsiveness pivot around customer care support, drawing inspiration from the SERVQUAL dimensions of m-banking service quality (Harb et al., 2022; Khan et al., 2021; Puriwat and Tripopsakul, 2017b; Reeshma and Rajkumar, 2017; Shankar et al., 2019). The sociality dimension enriches our understanding of the social benefits gained through online customer service interactions (Arcand et al., 2017; Mahakunajirakul, 2022; Puriwat and Tripopsakul, 2017a).

Scheme-based factors. Moving into scheme-based factors, we encounter aspects like aesthetics, design, interface, and tangibility. Notably, aesthetics and design primarily emerge as sub-dimensions of mobile service quality, representing both the visual and functional facets of the application (Arcand et al., 2017; Mahakunajirakul, 2022; Puriwat and Tripopsakul, 2017a). In a similar vein, studies conducted by Al-Zubi and Al-Gasawneh (2022), Puriwat and Tripopsakul (2017b), and Zhou et al. (2021) have expounded on interface design, elucidating the features integral to the execution of m-banking services and the presentation of information. Kapoor and Vij (2020) ventured further, exploring diverse design types—from visual and information designs to navigational and collaborative designs—to discern their impact on m-banking app ratings. Interestingly, many researchers have analyzed interface design within self-service technology quality (De Leon et al., 2020; Hassan et al., 2020; Naruetharadhol et al., 2021). Tangibility, meanwhile, has been assessed both as a distinct SERVQUAL dimension related to m-banking service quality in the context of customer satisfaction (Harb et al., 2022; Khan et al., 2021; Reeshma and Rajkumar, 2017; Shankar et al., 2019) and in terms of the application’s visual appeal and navigationality (Shankar et al., 2019).

Utilitarian-based factors. We classified utilitarian-based factors into three principal categories: efficiency, functionality, and practicality. Starting with efficiency, researchers have explored it as a systemic characteristic within m-payment services (Jin and Lim, 2021), as a dimension within E-S-QUAL (Shankar et al., 2019), and as an element embedded in higher-order service quality (Sagib and Zapan, 2014). Moving into functionality, this has been dissected as a dimension of SSTQUAL by multiple researchers (De Leon et al., 2020; Hassan et al., 2020; Naruetharadhol et al., 2021). Lastly, practicality has undergone examination both as a dimension of second-order m-banking service quality (Mahakunajirakul, 2022; Puriwat and Tripopsakul, 2017a) and as an independent external factor that significantly sways relationship quality (Arcand et al., 2017).

M-banking service quality research frequently integrates mediators into their models and our analysis underscores this trend. For instance, Mansour et al. (2016) identified attitude as a primary mediator influencing customers’ behavioral intentions. Similarly, Mostafa (2020) determined that users’ attitudes toward m-banking mediated the relationship between m-banking service quality dimensions—including perceived ease of use, enjoyment, usefulness, and privacy/security—and their intention to co-create value. Turning to brand attachment, Rajaobelina et al. (2021) found it mediated the indirect relationship between privacy/security, interactivity, and word-of-mouth (WOM) endorsements. Zhou (2013) highlighted the roles of flow and trust, noting their mediation effects on the relationship between information quality, service quality, and overall satisfaction. Moreover, trust has been spotlighted for its mediation between service quality and various outcomes: satisfaction (Arcand et al., 2017; Geebren et al., 2021; Jin and Lim, 2021), ongoing usage intentions (Dlodlo, 2014; Jin and Lim, 2021), and the interplay between behavioral intentions and loyalty (Hassan et al., 2020). These pivotal findings are summarized in Table 1.

M-banking service quality is influenced by a multitude of moderating variables, as substantiated by an array of academic investigations. Zoghlami et al. (2018) pinpointed that brand reputation moderates the nexus between e-loyalty and e-WOM. Complementing this, Rajaobelina et al. (2021) validated the generalizability of their model—comprising m-banking service quality, brand attachment, and e-WOM—across diverse demographics, namely gender, and between devices, namely smartphones and tablets.

Echoing similar insights, Zoghlami et al. (2020) highlighted the profound roles of age and gender as they moderate the relationship between specific m-banking service quality dimensions and e-trust. Extending this, Abu-Taieh et al. (2022) unveiled variations in behavioral intentions attributed to factors like age, education level, gender, and internet experience.

Venturing into the domain of switching costs as potential moderators, the literature presents a dichotomy. Bui et al. (2022) argued that the impact of switching costs on the dynamic between m-banking service quality, customer contentment, perceived worth, and undying customer loyalty was negligible. Contrastingly, Fan et al. (2021) championed its paramount significance. Whereas, Fan et al. (2021) illuminated that perceived switching costs, coupled with personal innovativeness—both categorized as anchoring determinants—exert negative moderating forces on the continuum from system quality dissatisfaction to the intention to transition from internet payments to m-payments. Augmenting this narrative, these researchers also uncovered that these determinants concurrently dampen the tie between the perceived superiority of alternative IT solutions and the switch intention. Additionally, an individual’s personal innovativeness markedly moderates, in a negative trajectory, the relationship between service quality discontent and the ensuing switch intention (Table 1).

The consequences stemming from m-banking service quality and its distinct dimensions exert direct and, in some instances, indirect effects. Superior service quality typically results in satisfied, loyal customers who are more likely to champion the service through WOM endorsements. Consequently, several studies have investigated customer satisfaction as an offshoot of service quality (Abu-Taieh et al., 2022; De Leon et al., 2020; Jin and Lim, 2021; Khan et al., 2021; Puriwat and Tripopsakul, 2017b; Reeshma and Rajkumar, 2017; Said et al., 2022; Zoghlami et al., 2018, 2020). Loyalty (Puriwat and Tripopsakul, 2017b; Sagib and Zapan, 2014; Zoghlami et al., 2020) and WOM (Abu-Taieh et al., 2022; Mobarak et al., 2022; Rajaobelina et al., 2021; Said et al., 2022; Zoghlami et al., 2018, 2020) also emerge as salient outcomes. Furthermore, there is a significant body of work exploring behavioral intention/intention to use (Abu-Taieh et al., 2022; Alenizi, 2022; Eren, 2022; Mahakunajirakul, 2022; Tew et al., 2022), continuance intention (Abbasi et al., 2022; Abu-Taieh et al., 2022; Dlodlo, 2014; Liébana-Cabanillas et al., 2019; Zhou, 2013), and actual usage (Alenizi, 2022; Ali et al., 2022; Payne et al., 2018; Sharma and Sharma, 2019) as direct outcomes of m-banking service quality (Table 1).

Drawing upon our comprehensive review, we have devised an integrated framework (Figure 2) anchored on the stimulus-organism-response (S-O-R) framework (Mehrabian and Russell, 1974). This framework marries insights from both services marketing and technology acceptance, underscoring how the end-user’s actual usage, as the ultimate response, stems from an array of factors (or stimuli), which subsequently undergo internal processing via another set of determinants (termed as the organism).

Stimuli. Within m-banking, stimuli comprise the m-banking app features, along with other elements curated and conveyed by the m-banking service provider. These collectively shape users’ perceptions, experiences, attitudes, and behavior toward m-banking. Our framework demarcates three primary stimuli clusters.

• (1)

  User-based factors. Drawing from TAM (encompassing perceived ease of use and perceived usefulness) and UTAUT (covering effort expectancy, facilitating condition, performance expectancy, and social influence), these factors elucidate how perceptions and expectations surrounding a technological platform—m-banking in this case—foster user adoption.

• (2)

  System success-based factors. Identified from DeLone and McLean (2003) IS success model, these pertain to system quality, service quality, and information quality.

• (3)

  Quality-based factors. Further dissected into six distinct groups, these refer to varying facets of m-banking service quality. These range from adaptability-related factors, emphasizing app compatibility, convenience, and customization to the customer’s needs (De Leon et al., 2020; Yeh, 2020), to confidence-building factors such as assurance, credibility, privacy, reliability, and security (Mahakunajirakul, 2022; Naruetharadhol et al., 2021; Hassan et al., 2020). Notably, the hedonic dimension of m-banking service quality is encapsulated by enjoyment (Arcand et al., 2017) while interactivity focuses on the interaction quality (access, communication, empathy, responsiveness, and sociality) between users and the m-banking platform (Jun and Palacios, 2016; Khan et al., 2021). In addition, aesthetics, design, interface, and tangibility touch upon the sensory appeal of the app (Lee et al., 2015), with utilitarian factors homing in on app efficiency, functionality, and practicality. Collectively, these factors serve as m-banking’s external stimuli, guiding the client’s internal processing mechanisms (the organism).

Organism. The organism component spotlights the cognitive, emotional, and experiential processes consumers undergo, serving as the critical nexus in the S-O-R chain (Sahoo and Pillai, 2017). Notably, attitude, brand attachment, flow, and trust emerge as pivotal mediators. For instance, attitudes molded by user-based, system-success, and quality-based stimuli shape user engagement with m-banking (Mansour et al., 2016). Concurrently, brand attachment illuminates a consumer’s emotional bond to m-banking (Rajaobelina et al., 2021). Flow encapsulates the euphoria of complete task immersion (Zhou, 2013), whereas trust emphasizes the consumer’s confidence in the security measures instituted by m-banking providers (Geebren et al., 2021). Given the centrality of trust, fostering it becomes paramount, as its absence can limit adoption and loyalty (Hassan et al., 2020). Therefore, m-banking operators need to astutely comprehend and harness these mediators to augment intent, usage, satisfaction, and loyalty.

Response. Culminating the S-O-R chain, consumer responses epitomize the interplay between stimuli and individual internal processes (Sahoo and Pillai, 2017). Within m-banking, outcomes span actual usage, behavioral intent, continuance intent, customer loyalty, satisfaction, and WOM endorsements. Of these, customer satisfaction emerges as pivotal, reflecting the cumulative appraisal of the m-banking service (Bui et al., 2022). Satisfied users not only persist with the service but also advocate it, engendering loyalty and buoyant WOM, whereas discontented ones may gravitate toward alternatives or even dissuade peers from adoption (Bui et al., 2022; Zoghlami et al., 2018). Behavioral intention, akin to loyalty, reflects one’s predisposition toward consistent service use (Mahakunajirakul, 2022). Meanwhile, continuance intention amplifies this sentiment, underscoring sustained m-banking adoption aspirations (Abu-Taieh et al., 2022). Ultimately, actual usage frequency captures the tangible manifestation of these intentions (Mahakunajirakul, 2022). To distill, satisfied m-banking users not only exhibit elevated loyalty and continued usage intentions but also generously disseminate positive experiences within their circles (Zoghlami et al., 2020). These outcome metrics, therefore, provide a compass to gauge m-banking providers’ efficacy and success.

This systematic review of the literature sought to address three critical questions to provide comprehensive insights into service quality in m-banking. First, we explored the theories, contexts, and methods that characterize m-banking service quality research (RQ1). This examination allowed us to understand the diverse theoretical frameworks, varied geographical and service-related contexts, and the range of research methodologies employed in studying m-banking service quality. Second, we scrutinized the constructs, including antecedents, mediators, moderators, and consequences, that impact service quality in m-banking (RQ2). Dissecting these constructs, we highlighted the complex factors and relationships that influence user experiences and outcomes in m-banking. Lastly, we identified new, unexplored areas to guide future research in m-banking service quality (RQ3). Addressing these research questions significantly expands the current knowledge base, providing both researchers and practitioners with invaluable insights into the field of m-banking service quality. This review not only synthesizes existing knowledge but also charts a path for future investigations to further refine and enhance service quality in m-banking.

Our findings deepen the intellectual terrain of m-banking service quality, casting new light on an increasingly vital subject.

An organizing framework for m-banking service quality. Through our extrapolation of the S-O-R framework to m-banking research, we provide researchers with a structured way of understanding how external stimuli (such as service quality dimensions) influence consumers’ internal cognitive and emotional states, which, in turn, guide their m-banking behaviors. This framework fosters a comprehensive understanding of the consumer journey by highlighting the complex interactions between external factors and internal processes. We therefore encourage researchers to utilize this extrapolated approach or framework to explore multifaceted relationships within m-banking contexts, moving beyond simple linear models to capture the complexities and variations of consumer behavior.

Compartmentalization of m-banking service quality. The compartmentalization of service quality facets—ranging from adaptability-based, confidence-based, hedonic-based, interactivity-based, scheme-based, and utilitarian-based dimensions—serves a dual purpose. This typology assists researchers in identifying specific constructs for further investigation, potentially leading to the development of new ones or refinement of existing ones. Such a detailed classification also empowers the examination of each facet more thoroughly, potentially uncovering new quality-based facets or refining existing facets. This typology therefore allows future research to be more targeted, thereby advancing the understanding of m-banking service quality.

Navigating theories for m-banking service quality. The pervasive use of technology acceptance theories (TAM, UTAUT) in our findings underscores their importance in examining initial adoption propensities. However, these theories leave a gap regarding the complexities of post-adoption behaviors and sustained engagement. Researchers have an opportunity and a responsibility to enrich the discourse with theories focusing on the post-adoptive phase. Theories such as expectation-confirmation theory (Lim et al., 2022a; Oliver, 1977, 1980), resource-advantage theory (Donthu et al., 2023; Hunt and Morgan, 1995), and theory of behavioral control (Lim and Weissmann, 2023) may provide deeper insights into the determinants of continued m-banking use, thus helping to bridge this theoretical gap.

Reimagining the role of internal consumer processes in m-banking service quality. Emphasizing the organism segment of the S-O-R framework highlights the significance of internal consumer processes. Attitude, brand attachment, flow, and trust, among others, are not mere mediators; rather, they are central factors that shape and are shaped by external stimuli. We advocate for a more in-depth theoretical exploration of these dynamics, whereby focusing on these mediators may be key to unlocking a richer, more profound understanding of m-banking service quality.

The varied geographical and service contexts of m-banking service quality studies reveal significant implications for both researchers and practitioners.

Adaptation to specialized services. As the landscape of digital financial services evolves, m-banking providers must be agile. The rise of specialized financial services like AI-enabled m-banking highlights that consumers are seeking more than just basic banking functions on their mobile devices. Anticipating and integrating these specialized services will be key to stay ahead of the curve.

Emerging markets as innovation hubs. The strong representation of studies from Asian and African contexts indicates the vibrant innovation and adoption landscapes in these regions. It is imperative to closely observe these markets, as they often pioneer unique solutions and strategies tailored to their unique challenges, which might be transferable to other contexts.

Tailored service design. Given the profound differences in how m-banking is perceived and utilized across different geographical contexts, a “one-size-fits-all” approach is clearly untenable. M-banking solutions designed for the Southeast Asian user, for instance, may not resonate as effectively with a North American user. This tailored design must account not just for technological sophistication, but also for local cultural, economic, and even political differences.

The variety of methodological approaches used in m-banking service quality research offers important insights for future studies. Recognizing the strengths and limitations of these methods can help researchers fill existing gaps and strengthen the quality of their work.

Addressing quantitative dominance. Our analysis shows a strong reliance on quantitative methods, especially surveys, in researching m-banking service quality. While these methods are effective for collecting data from large populations and facilitating generalizations, they may not fully capture the complexity of user experiences and perceptions. This predominance suggests that certain subtle factors influencing m-banking service quality might be overlooked. Researchers should therefore consider complementing survey-based studies with experimental designs to explore causality and gain a more in-depth understanding of user behavior. Experiments allow researchers to manipulate variables in controlled settings, helping them to identify causal relationships and test specific hypotheses about the factors influencing m-banking adoption and satisfaction.

Emphasizing qualitative techniques. Although less frequently employed, qualitative methods offer valuable insights into the complex dynamics of how users interact with m-banking services. These approaches allow researchers to explore participants’ perspectives thoroughly, uncovering motivations, attitudes, and contextual influences that quantitative methods might overlook. For example, qualitative studies can reveal how cultural norms, trust issues, or personal experiences shape user perceptions of service quality. Incorporating methods like in-depth interviews, focus groups, or ethnographic observations can enhance the understanding of user behavior and inform the development of more user-centric m-banking services. Embracing qualitative research enables researchers to generate theories grounded in actual user experiences, leading to more effective strategies for improving service quality.

Leveraging mixed-methods design. Combining quantitative and qualitative methods through mixed-methods research provides a comprehensive understanding of m-banking service quality. Mixed-methods studies can validate quantitative findings with qualitative insights, ensuring that statistical trends are supported by contextual understanding. For instance, a mixed-methods study could quantify the prevalence of certain user behaviors while simultaneously exploring the reasons behind those behaviors through qualitative interviews. This approach allows researchers to address complex research questions that neither method could answer alone. Adopting mixed-methods designs therefore enables researchers to produce more detailed and actionable findings that bridge the gap between theory and practice.

Pursuing novel methodologies. The rapid evolution of technology and data availability presents opportunities to use innovative methods in m-banking research. Leveraging big data analytics and machine learning can enable researchers to analyze large volumes of unstructured data from sources like customer reviews, social media, and transaction logs. Techniques such as sentiment analysis allow for the examination of user sentiments and opinions expressed in text data. For example, applying sentiment analysis to social media posts can reveal public perceptions of m-banking services, which, in turn, enables the identification of areas of concern and satisfaction. These approaches enable researchers to stay current with the dynamic m-banking landscape and contribute findings that are relevant and timely.

While the halls of academia are replete with theoretical discourses, it is the application of these theories in the real world that truly tests their mettle. Drawing from our review, we find a confluence of theory and practice with implications for those at the helm of m-banking.

Strategic integration of the S-O-R framework. The S-O-R framework serves not just as a theoretical model but as an operational guide for m-banking service providers. Recognizing the complexities between external stimuli, internal processes, and behavioral outcomes enables firms to craft interventions that align with customers’ cognitive and emotional processes. For example, rather than solely enhancing app features (stimuli), providers should prioritize user education to shape internal perceptions (organism), leading to increased engagement and satisfaction (response).

Granularity in service quality enhancement. Breaking down service quality into specific dimensions provides m-banking firms with a targeted approach to improvement. This is not just about improving service quality but rather knowing precisely where to allocate resources. For instance, if user feedback suggests a dip in trust, providers can focus their efforts on bolstering security features and ensuring transparency in transactions. Such precision not only optimizes resource allocation but also ensures timely redressal of service pain points.

Beyond initial adoption. While many m-banking providers might celebrate high adoption rates, our findings underscore the importance of post-adoption behaviors. Initial adoption is just the starting line. The real challenge lies in sustaining engagement, ensuring continued use, and strengthening brand loyalty. Given the gap in understanding post-adoption behaviors, practitioners would do well to invest in continuous user research, periodic feedback cycles, and iterative enhancements. This not only ensures user stickiness but also opens avenues for up-selling and cross-selling opportunities.

Deepening engagement through internal dynamics. Our emphasis on the organism component serves as a clarion call for m-banking providers to prioritize user-centric strategies. Understanding and leveraging internal consumer processes like attitude, brand attachment, flow, and trust can lead to more personalized service offerings. For instance, leveraging data analytics to understand individual user’s flow patterns can help in customizing user journeys, thereby offering a more seamless and personalized m-banking experience.

Continuous training and capacity building. Given the dynamic nature of m-banking and its reliance on technological interfaces, there is an inherent need for continuous skill upgradation, both for the end-users and for the professionals managing the service. Providers should invest in training modules, webinars, and tutorials to ensure their user base and personnel stay updated with the latest features and best practices.

Staying abreast of user sentiments. The dominance of quantitative methods in the scholarly space highlights the importance of leveraging a data-driven approach for the industry. Nevertheless, m-banking providers should also consider qualitative feedback, such as user testimonials and in-depth interviews, to glean rich insights into user sentiments and pain points, complementing their quantitative reports.

As m-banking continues to grow in adoption and sophistication, the insights from this review offer researchers and practitioners a valuable guide. Intertwining theory with practice, we present a roadmap that seeks not only for growth but also for sustained excellence in m-banking services.

Addressing the future avenues of research within the scope of m-banking service quality is of high importance. We pave the way for researchers by spotlighting areas ripe for further inquiry to bridge knowledge gaps and broaden our understanding of m-banking. These forward-looking directions empower researchers to navigate previously underexplored areas, engage in granular, context-driven analyses, and pursue new perspectives. Moreover, they shed light on various facets of the consumer experience, spanning from initial engagement to continued loyalty. In the same vein, embracing novel aspects such as cutting-edge technologies and evolving behavioral influences is crucial. Through this scholarly pursuit, we not only deepen our insights into service quality in m-banking but also fuel its theoretical and practical progression. We now turn our attention to the agenda for future research.

Our review indicates that most existing research on m-banking service quality centers on specific antecedents and consequences, such as distinct service quality dimensions, customer satisfaction, loyalty, and WOM intentions. While these factors are essential, focusing solely on them may limit the depth of understanding regarding customer behavior in m-banking contexts. To develop a more comprehensive understanding, future studies should incorporate additional critical factors that can significantly influence m-banking service quality.

Integrating variables like perceived value, perceived risk, user experience, and individual characteristics such as personality traits can offer richer insights into customer decision-making processes. For example, perceived value has been shown to directly affect customer satisfaction and loyalty (Bui et al., 2022; Dlodlo, 2014; Twum et al., 2022). Understanding how customers assess the value of m-banking services can help providers enhance features that customers find most beneficial. Similarly, perceived risk can be a substantial barrier to adoption and continued use (Fan et al., 2021; Twum et al., 2022). Identifying and mitigating these risks can allow service providers to improve trust and encourage more widespread and sustained adoption.

In addition, exploring user experience factors (Zhou, 2013) can reveal how interface design, usability, and overall interaction quality impact customer satisfaction. Individual characteristics like personality traits (De Leon et al., 2020; Zoghlami et al., 2020) can moderate how customers perceive and respond to m-banking services, suggesting that personalization strategies could enhance their engagement (Chandra et al., 2022).

Expanding the scope further, Mobarak et al. (2022) suggest incorporating variables such as hedonic motivation, perceived price, privacy concerns, technological self-efficacy, ubiquity, and usage barriers. These factors can shape attitudes, continuance intentions, and WOM behaviors. For instance, hedonic motivation reflects the enjoyment users derive from m-banking services, which can enhance satisfaction and loyalty. Addressing privacy concerns is crucial in building trust, especially given the sensitive nature of financial transactions.

Employing mediation and moderation analyses can provide deeper insights into the complexities among these variables. Mediation analysis can uncover the mechanisms through which service quality affects outcomes like loyalty and satisfaction. Moderation analysis can identify conditions under which certain effects are stronger or weaker, such as the influence of age, gender, or cultural background. Understanding these relationships enables researchers to develop more refined models that reflect real-world complexities.

Measuring outcomes like customer value co-creation behavior offers additional valuable insights (Hasan et al., 2024; Mostafa, 2020). When customers actively participate in the creation and delivery of services, they may experience higher satisfaction and stronger loyalty. Studying the impact of error management also holds potential for enriching our understanding (Aggarwal et al., 2022), wherein effective error management can turn service failures into opportunities to strengthen customer relationships.

Efforts in these directions hold significant promise because they address current gaps in the literature by acknowledging the multifaceted nature of customer interactions with m-banking services. A more holistic understanding can inform the development of tailored interventions and strategies by service providers, thereby improving customer experiences and nurturing stronger customer relationships. In this regard, capturing a greater array of factors influencing m-banking service quality would allow researchers to better contribute to theoretical advancements and provide practical insights that help organizations thrive in an increasingly competitive market. Hence:

• Future direction 1. Explore and integrate a wider range of attitudinal, behavioral, contextual, perceptual, personal, and motivational factors in m-banking service quality research, utilizing main, mediation, and moderation analyses to uncover deeper insights.

Our review reveals that most studies have focused on m-banking service quality within a single national context. Notable works by Arcand et al. (2017), Kapoor and Vij (2020), Leem and Eum (2021), Shankar et al. (2020), Sharma and Sharma (2019), and Rajaobelina et al. (2021) exemplify this trend. This geographical concentration limits the generalizability of findings, potentially overlooking cultural, economic, and regulatory factors that influence m-banking service quality in different regions.

Expanding research to include diverse national contexts holds significant promise. Replicating seminal studies across various countries can strengthen the robustness of established relationships and theories (Geebren et al., 2021; Mostafa, 2020; Shankar et al., 2020; Twum et al., 2022). Comparing findings between developed and developing nations (Geebren et al., 2021) can uncover unique consumer behaviors and preferences in m-banking, which, in turn, provides a better understanding of global m-banking service quality (Mahakunajirakul, 2022; Tran et al., 2022). This wider perspective is essential, as it accounts for cultural differences and the varying stages of m-payment system development and adoption (Liébana-Cabanillas et al., 2019; Raman and Aashish, 2021). Enhancing our grasp of these factors can lead to more effective strategies for improving m-banking services worldwide. Pursuing this direction is likely to be worthwhile because it addresses the limitations of current research confined to single-country studies. Incorporating diverse national contexts allows researchers to identify universal principles and region-specific factors affecting m-banking service quality. This comprehensive understanding can inform multinational banks and technology providers seeking to offer services that meet the needs of a global customer base. This can also reveal how cultural, economic, and regulatory environments influence customer expectations and perceptions, enabling more effective customization of services. Therefore:

• Future direction 2. Expand the geographic scope of m-banking service quality research to include diverse national contexts, particularly comparisons between developed and developing countries, to enhance the generalizability and robustness of findings.

Investigating m-banking service quality across different payment ecosystems and related mobile service sectors also offers valuable insights. Comparative analyses of mobile commerce, insurance, payments, or wallets (Mouakket, 2020; Shankar et al., 2020) can reveal transferable best practices and common challenges. Exploring the impact of emerging technologies such as AI in m-banking, as suggested by Payne et al. (2018), is particularly worthwhile. AI-powered m-banking represents the future of personalized, efficient financial services, and understanding its influence on service quality can guide both research and industry practices. Pursuits in this direction hold promise because they recognize the rapid evolution of mobile services and the integration of innovative technologies. Studying various payment ecosystems and related services can uncover synergies and distinctions that enhance service offerings. For instance, insights from mobile commerce or wallet services might inform improvements in m-banking platforms. Investigating AI’s role in m-banking is particularly timely, as AI has the potential to revolutionize customer experiences through personalization, predictive analytics, and improved security. Understanding how AI influences service quality can help organizations adapt and excel in a competitive, technology-driven market. This knowledge can drive innovation, leading to more intuitive, efficient, and secure m-banking solutions that meet emerging customer needs. Thus:

• Future direction 3. Conduct comparative analyses across different payment ecosystems and related mobile service sectors, and explore the impact of emerging technologies such as AI in m-banking, to gain deeper insights on m-banking service quality.

Understanding the diverse demographics that influence perceptions of m-banking service quality is essential, wherein a uniform approach fails to capture the varied preferences and behaviors of different user groups. Windasari and Albashrawi (2021) highlights significant differences in how gender affects loyalty in m-banking. Their findings indicate that men tend to focus on cognitive components such as system and information quality while women place more emphasis on relational aspects, particularly service quality. This gender-based divergence suggests that gender may play a crucial moderating role in m-banking user behavior. Investigating gender as a moderating factor can uncover valuable insights into how different genders perceive and prioritize aspects of m-banking service quality (Arcand et al., 2017; Liébana-Cabanillas et al., 2019; Mostafa, 2020; Puriwat and Tripopsakul, 2017a; Singh, 2022). Such insights can guide the development of more tailored and effective m-banking services that meet the specific needs of different gender groups. Specifically, going in this direction is worthwhile because it addresses the need for gender-responsive m-banking services. Understanding gender-specific preferences and concerns can enable service providers to tailor their offerings to better meet the needs of both men and women, potentially increasing user satisfaction and loyalty. Gender-based insights can therefore inform targeted marketing strategies and service improvements that resonate with each group, leading to a more inclusive and effective m-banking environment. Hence:

• Future direction 4. Examine gender as a moderating factor in future m-banking studies to uncover how different genders perceive and prioritize aspects of m-banking service quality.

Age is another critical demographic dimension influencing perceptions of m-banking service quality. Significant disparities exist in digital literacy and technology adoption across age groups. As observed by Zoghlami et al. (2020), baby boomers and Generation X often approach new technologies like m-banking platforms with caution. This suggests that age can significantly impact how users perceive and interact with m-banking services. Segmenting users by age allows researchers to examine how age-related differences in digital literacy and technology adoption affect perceptions of service quality and related constructs (Eren, 2022; Mostafa, 2020; Liébana-Cabanillas et al., 2019; Talwar et al., 2020; Tang et al., 2021). Understanding these differences is vital for designing user interfaces and services that are accessible and appealing to various age groups, thereby enhancing user satisfaction and adoption rates. Noteworthily, efforts in this direction hold promise because they recognize the diverse needs and capabilities of different age groups. Exploring how age affects user perceptions and behaviors allows researchers and practitioners to develop age-appropriate interfaces and support systems that enhance usability for all users. This can lead to higher adoption rates among older users who may be hesitant to embrace new technologies as well as better retention among younger users who have different expectations and preferences. Therefore:

• Future direction 5. Segment m-banking users by age to gain a deeper understanding of how age-related differences in digital literacy and technology adoption influence perceptions of service quality and related constructs.

Effective research requires methodologies that are tailored to the specific inquiries being pursued (Lo et al., 2020). An examination of the current literature reveals a heavy reliance on quantitative methodologies, particularly cross-sectional studies (e.g. Fan et al., 2021; Harb et al., 2022; Khan et al., 2021; Mobarak et al., 2022; Mostafa, 2020; Mouakket, 2020; Purwono et al., 2021; Rajaobelina et al., 2021; Said et al., 2022; Twum et al., 2022; Zhou et al., 2021). While quantitative research offers precision and the ability to generalize findings, it may not fully capture the depth and complexity of constructs and their interrelationships (Basias and Pollalis, 2018). Certain aspects of m-banking service quality require the rich insights provided by qualitative research methods. Recognizing this limitation, researchers like Aldiabat et al. (2022) and Naeem et al. (2022) advocate for a mixed-methods approach that combines both qualitative and quantitative methodologies. Employing mixed methods can provide a more comprehensive understanding of m-banking service quality by capturing both the breadth of quantitative data and the depth of qualitative insights. This approach allows researchers to explore not only the “what” but also the “why” behind customer behaviors and perceptions, leading to more detailed and actionable findings. Pursuing this direction holds significant promise because mixed-methods research can bridge the gap between numerical data and contextual understanding. Notably, integrating quantitative and qualitative data enables researchers to validate findings across methods, enhancing the rigor and robustness of the results. This comprehensive approach can uncover insights that inform better strategies for improving service quality, customer satisfaction, and loyalty in m-banking. This also enables a more targeted exploration of customer experiences, capturing the complexities of their interactions with m-banking services. Thus:

• Future direction 6. Adopt a mixed-methods approach, combining qualitative and quantitative research, to yield a more comprehensive understanding of m-banking service quality.

The reliance on cross-sectional studies limits understanding to a single point in time, which may not reflect changing customer behaviors and perceptions in the dynamic m-banking environment. There is a growing call for longitudinal studies that can provide insights into how customer behaviors and perceptions evolve over time (Baabdullah et al., 2019; De Leon et al., 2020; Hijazi, 2022; Mahakunajirakul, 2022; Raman and Aashish, 2021; Sharma and Sharma, 2019; Shankar et al., 2019; Windasari and Albashrawi, 2021). Longitudinal research can reveal patterns and trends that cross-sectional studies cannot detect, offering a more robust understanding of the factors influencing m-banking service quality over time. Whereas, experimental designs have the potential to strengthen the methodological rigor of m-banking research by enabling researchers to establish causal relationships rather than mere associations (Abu-Taieh et al., 2022; Windasari and Albashrawi, 2021). Experiments allow for the manipulation of variables in controlled settings, providing tangible evaluations of how changes in service quality dimensions affect customer perceptions and behaviors. This approach can lead to more definitive conclusions about the effectiveness of specific interventions or features in m-banking services. Therefore, efforts in this direction are likely to be worthwhile because they address the limitations of cross-sectional studies and observational research. Longitudinal studies can track changes over time, providing valuable information about trends and the long-term effects of interventions. Indeed, understanding how customer perceptions and behaviors evolve allows for the development of strategies that remain effective in the face of changing market conditions and technological advancements. Likewise, experimental designs can establish causality, enabling researchers to determine the effectiveness of specific changes or innovations in m-banking services. This methodological rigor can lead to more precise recommendations for practitioners, which, in turn, enhances service quality and customer satisfaction. Hence:

• Future direction 7. Employ longitudinal studies and experimental designs to provide temporally contextualized insights and tangible evaluations of service quality so as to deepen and enhance the robustness of m-banking research.

Service quality is a foundational element in marketing research, recognized for its critical role in customer satisfaction and loyalty (Brady and Cronin, 2001; Shankar et al., 2019). Widely accepted measurement tools like SERVQUAL and SERVPERF have been instrumental in assessing service quality across various industries. However, service quality is inherently contextual (Cronin and Taylor, 1992), and as Shankar et al. (2020) argue, there is a necessity for developing context-specific scales that accurately reflect the unique characteristics of different service environments.

In the digital arena, instruments like E-SQUAL have emerged as predominant measures for electronic services (Sardana and Bajpai, 2020). Despite these advancements, a significant gap exists in measuring service quality specific to the m-banking sector. Existing scales may not fully capture the distinctive features and user experiences inherent to m-banking platforms (Shankar et al., 2020). The lack of a tailored measurement instrument for m-banking service quality limits the ability to accurately assess and improve these services.

Developing a bespoke service quality scale for m-banking is therefore an imperative. Such a scale would address the specific attributes of m-banking services, including mobile interface design, security concerns unique to mobile platforms, context-aware services, and seamless integration with mobile device functionalities. Capturing these unique dimensions allows researchers and practitioners to obtain more precise and reliable assessments of service quality in m-banking.

Efforts to create a specialized measurement instrument hold significant promise for several reasons. First, doing so enhances the precision of empirical studies by ensuring that the constructs measured are directly relevant to m-banking. This relevance improves the validity of research findings and enables more meaningful comparisons across studies. Second, a tailored scale provides m-banking service providers with actionable insights specific to their services, which, in turn, facilitates targeted improvements that can enhance customer satisfaction and loyalty. Third, developing such a scale contributes to theoretical advancements by identifying new dimensions of service quality unique to m-banking, thereby enriching the discourse on service quality measurement.

Pursuing this direction is likely to be highly worthwhile. A specialized measurement instrument would fill a significant gap in the literature and equip researchers and practitioners with a tool that can drive innovation and improvement in m-banking services. As the m-banking sector continues to grow and evolve, having an accurate and reliable means of assessing service quality is crucial for maintaining competitive advantage and meeting the evolving needs of customers. This development can lead to enhanced customer experiences, increased trust, and sustained loyalty, which, when taken collectively, contributes to the success and sustainability of m-banking. Therefore:

• Future direction 8. Develop a tailored service quality scale specific to contemporary m-banking to improve the accuracy and reliability of measuring m-banking service quality.

This systematic literature review synthesizes 71 articles to answer three critical questions and provide in-depth insights into service quality in m-banking. First, we explored the theories, contexts, and methods that characterize m-banking service quality research (RQ1). Second, we examined the constructs comprising antecedents, mediators, moderators, and consequences that affect service quality in m-banking (RQ2). Third, we identified new, unexplored areas to guide future research in m-banking service quality (RQ3). Addressing these research questions, we expanded the current knowledge base, and more importantly, offered researchers and practitioners a more informed understanding of the field.

Our study identified key theoretical frameworks, important constructs, contextual variations, and various methodologies within m-banking service quality research. From our review, we developed an integrative framework that maps the relationships among service quality dimensions, consumer cognitive and emotional processes, and behavioral outcomes in m-banking. This framework enhances understanding and guides future research directions in m-banking service quality. Central to our review is the examination of factors influencing consumer reactions to m-banking services, focusing on a combination of service quality dimensions, user-centric elements, and system successes. The majority of the reviewed literature is grounded in services marketing and technology adoption paradigms. Outcomes associated with m-banking service quality include actual usage, behavioral intentions, continuance intentions, customer satisfaction, loyalty, and WOM communication.

While we take pride in our endeavor, we acknowledge the study’s limitations. At the outset, our scope was confined to peer-reviewed journal articles, excluding books and conference proceedings, which might contain valuable insights. In addition, despite our carefully crafted keyword search, the rapidly evolving landscape of m-banking may have led to inadvertent omissions. Despite these limitations, we believe that our review and the framework we have developed provide a solid foundation for future scholarly endeavors. We anticipate that this work will inspire further research that advances the understanding and practice of m-banking service quality, benefiting both academia and industry. Importantly, by bridging theoretical understanding and practical application, this work should contribute to the development of more effective and user-centric m-banking services.