Introduction
The proliferation of Mobile banking, referred to in this project as m-banking, has transformed financial services, enabling consumers to conduct transactions conveniently through mobile devices. Despite the advantages, m-banking adoption rates vary significantly. Adoption is the early stage usage or uptake of a technology. My dissertation aims to bridge critical gaps in understanding how different factors (according to literature trends and through novel contributions) and device-specific attributes shape m-banking adoption. The central research question addressed in this dissertation is: “What are the factors that influence mobile banking adoption across different user segments?”
The current chapter is a holistic introduction to the dissertation. To answer the main research question, I conducted three distinct yet interconnected studies. These studies, presented in chronological order in the following chapters, are:
- The Inner Workings of Mobile Banking Adoption: A Systematic Literature Review of Intrinsic Factors — An SLR study of a specific group of adoption factors defined for the first time in this study as “intrinsic factors”.
- The Relationship Between Mental Health and Mobile Banking Adoption: Evidence from Canada — An analysis of the impact of mental health and related psychological factors on m-banking adoption.
- The Device Divide: Unpacking Mobile Banking Adoption Differences for Smartphones and Smart Wearable Devices — An investigation into the role of device type (smartphone vs. smart wearable) in shaping adoption behaviors.
Theoretical Contributions
This dissertation makes several contributions to the field of technology adoption and financial services research.
- My research extends the understanding of factors in m-banking adoption by providing a useful categorization in two levels.
- My research is the first to look at mental health and its related concepts in m-banking adoption, integrating mental health considerations into behavioral technology models.
- My research is the first to consider device differences in a comparative setting in m-banking, specifically looking at smartphone and smart wearable devices. The results show that device impacts Trust, Perceived Security, and Perceived Value of m-banking. Therefore, even established theoretical frameworks such as the utaut Venkatesh & Davis (2000) and tam Davis et al. (1989) could be improved by integrating device specific measures.
Practical Contributions
- I use text-mining techniques to extract topics and classify articles in a systematic literature review study, providing a structured approach for future research.
- I conduct a comparative study, the first of its kind, between different devices and their impact on m-banking adoption factors. For Practical Contributions, the insights from this dissertation help financial institutions as well as researchers on many frontiers.
- The results of the first study could be used as a reference for future scholars to conduct more directed research by finding context-specific theories, factors and methodologies.
- I highlight various gaps in the literature and offer suggestions for future researchers.
- My categorization of adoption factors helps clarify these latent ideas, improving understanding for both practitioners and researchers.
- The results from Chapter the second study show that Social Media is one of the best platforms for marketing for banks.
- From the same study, I highlight the various ways banks could promote and engage consumers with various mental health concerns.
- From the results of Chapter the third study, I offer actionable strategies for financial institutions to tailor their digital banking services based on device-specific adoption behaviors.
0.1 The Inner Workings of Mobile Banking Adoption:
0.1.1 A Systematic Literature Review of Intrinsic Factors
Despite extensive research on m-banking adoption, a clear framework categorizing user-specific influences is still missing.
After investigating the m-banking adoption literature, I found that uniform definitions for factors 1 influencing m-banking adoption was lacking. Furthermore, there was an over-reliance on only a few factors and theoretical frameworks. This prompted the undertaking of the SLR study in study 1. To address the problem of lack of uniform definitions, I categorized adoption factors based on context and impact. This way, using group/category membership help identify similarities between factors. Thus making definitions more cohesive.
Several studies support the idea that categorization helps in defining and understanding concepts more easily. Research suggests that categorization plays a crucial role in defining concepts, moving from a classical view (fixed definitions) to a probabilistic view where categorization helps in making sense of concepts based on shared characteristics Medin (1989). Categorization enables more effective organization and processing of information which are essential for learning new concepts (zentallCATEGORIZATIONCONCEPTLEARNING2002?).
Technology adoption factors are complex and often belong to multiple categories. Because of this, studies have never explicitly categorized factors across the board. I adopt a broad approach to classifying factors, focusing on two key dimensions of decision-making based on how they relate to the user: Intrinsic and Extrinsic. In the context of m-banking adoption, intrinsic factors discuss how individuals evaluate an m-banking application (Often shortened to “app”) internally — based on perceptions, goals, pressure felt from other people’s judgment, and emotions. Extrinsic factors, on the other hand, refer to the apps’ measurable features, such as performance and functionality. Since extrinsic factors are easy to quantify and experienced similarly across the board, they are also straightforward to study. Thus, I focused on intrinsic factors. Because intrinsic factors relate to so many different inner processes (mentioned above), I further categorized them into four main groups:
- Perceptive, which are based on beliefs and perceptions.
- Personal, which are based on individual motivations and traits.
- Social, which are based on the impact of others on the decision-maker.
- Psychological, which are based on based on cognitive, emotional and mental processes.
This categorization provides a useful context-specific grouping. Following this, a systematic search of the m-banking literature for intrinsic factors was carried out. Scientific articles gathered were given themes using text-mining techniques – specifically, lda for Topic Modeling. Some of these themes matched my categorization, as well. I also extended prior SLR studies by using statistical techniques – specifically, anova – to mathematically validate my findings. My results provide a strong empirical foundation for future researchers to do context-focused investigation.
The Outcome of this study is to help enhance the understanding of intrinsic factors, highlight underutilized methodologies, and identify research gaps. Additionally, I highlight the dominance of certain theoretical models while advocating for greater exploration of under-studied theories and methods. My findings show notable geographical and study-design biases, particularly longitudinal research in developed nations. My categorized framework helps scholars identify intrinsic factors, relevant theories, and research gaps, which promotes targeted future research.
For practitioners and policymakers, I recommend designing emotionally engaging apps, ensuring transparent risk communication, and educating users on safe practices. These steps enhance m-banking adoption and effectiveness.
0.2 The Relationship Between Mental Health and Mobile Banking Adoption: Evidence from Canada
Following the work of the previous study, I focused on intrinsic factors. One of the factors I found to be under-explored in the literature was mental health.
Psychological factors are comparatively less-studied across the m-banking literature Venkatesh et al. (2012), Zou et al. (2023), and Tiwari et al. (2021). I verified this further doing a quick preliminary search on Web-of-Science. Using the following search query returned 1,067 studies with no filters set on the results:
(“mobile banking” OR “mbanking” OR “m-banking”) AND (“adoption”)
When changing the search query to find studies specifically on psychological factors, the total number of studies returned were 157, which is about 14% of the total. The new search query was:
(“mobile banking” OR “mbanking” OR “m-banking”) AND (“adoption”) AND (“affective” OR “psychological” OR “affect based” OR “affect-based” OR “emotional” OR “cognitive” OR “mental” OR “personal”)
Using the same search queries in Scopus, 753 results were returned for the first, and 101 in the second search (13.4%). It is clear that only a small portion of the literature in m-banking adoption is focused on psychological factors. In this study, I investigated the direct and moderated effect of mental health on m-banking adoption. Moderators were extracted from theories in technology adoption or from literature related to mental health: RS, SD, and SNS. I used a fixed effect logistic regression model grouped based on Canadian provinces following the cluster sampling design of my dataset. The results showed that mental health significantly and negatively affects m-banking adoption: better mental health outcomes were associated with lower likelihood of m-banking adoption. I did not find sufficient evidence for the moderating effects.
0.3 The Device Divide:
0.3.1 Unpacking Mobile Banking Adoption Differences for Smartphones and Smart Wearable Devices
Moving on from focusing only on smartphones, I decided to do a comparative study considering newer devices used in m-banking. One of the findings of my SLR study from Section “refintro-section-p2 was that few comparative studies exist in general, and studies that focus on various tools used for m-banking are increasingly important. In this chapter, I examined the nuances of m-banking adoption across two mobile devices: smartphones and smart wearable devices. I investigated the impact of the following factors: trust, perceived security, perceived value (time savings), and demographic variables. Demographic factors are important as numerous studies identify these (e.g., age, gender, income, and education) as key factors influencing m-banking adoption Chawla & Joshi (2018), Ly & Ly (2022).
The results from this chapter are device-specific insights. This study also refines existing technology adoption models. Additionally, to the best of my knowledge, this is the first study to compare behavioral differences between smartphone and smart wearable users in the context of m-banking. I found that trust impacts smartphone users more strongly, while wearable users prioritize time efficiency. Users perceived smartphones as more secure. Demographic factors such as age, education, and gender exhibited varying influences based on device type as well.
0.4 Some Questions and Answers
Why not talk about “use” but “adopt”? Adoption is a more comprehensive term that encompasses the entire process of integrating a new technology into daily life, while use may imply a more superficial interaction.
What’s been done before in the litearture?
What did I do that was novel?
What insights I gained from this dissertation that were new?
How did I fill the literature gaps?
Why is it that categorization of the factors helps in understanding the factors better?
How exactly did I categorize the factors?
Who does this categorization help?