What drives FinTech adoption?
A multi-method evaluation using an adapted technology
acceptance model
Shubhangi Singh
Research Scholar, Amity Business School, Noida, India
Marshal M. Sahni
Digital Marketing, Amity University, Noida, India, and
Raj K. Kovid
General Management, Sharda University, Greater Noida, India
Abstract
Purpose–Considering the ubiquity of FinTech services, the study proposes a research framework to examine FinTech adoption and use from the technology acceptance perspective by adding sub-constructs of technology acceptance model (TAM), unified theory of acceptance and use of technology (UTAUT), ServPerf and WebQual 4.0. This study broadly classified these sub-constructs in three dimensions: adoption, behavior and technological and explores the relationship between these attributes. It also proposes that digital behavior (Internet experience and level of awareness) and demographic characteristics (age and gender) moderate the main relationships.
Design/methodology/approach–The measurement scale for the study is developed through iterative discussion with domain experts. The data are collected from 439 active Internet users though a digital survey and analysis were done by applying structural equation modeling and multi-group analysis.
Findings–Perceived usefulness and social influence are found to be the key determinant for behavior intention to use FinTech services, with social influence having significant negative influence. Actual use is significantly influenced by ease of use and social influence but is not determined by behavior intention and perceived usefulness. Behavioral attributes are significantly impacted by technological attributes and digital behavior. Also, age significantly affects the perception of security among older users.
Practical implications –This study will help FinTech service providers to design FinTech services considering a wide spectrum of users. More consideration should be on enhancing the usefulness and security features to create social affirmations for the use of FinTech services. This will entice users for frequent use and attract nonusers to do their first online financial transaction.
Originality/value–The study adds to the technology acceptance literature by incorporating relevant technological and behavioral attributes and investigating the moderating effect of digital behavior and demographic characteristics. It contributes to the understanding of user beliefs and perceptions about actual use of FinTech services.
KeywordsTechnology adoption, FinTech service, Adoption drivers, TAM, UTAUT, ServPerf, WebQUal 4.0 Paper typeResearch paper
1. Introduction
The financial services industry is witnessing a disruptive structural change due to multiple technological innovations. The ubiquity of these innovations known as Financial Technology (FinTech) is posing a challenge for traditional banking and financial companies (McWaters et al., 2015). Enhanced and efficient client experience is drifting away consumers from conventional payment methods to FinTech. FinTech has become an important dimension of the financial services industry because of constant innovations. There have been 12,000 start- ups and a massive global investment of US$ 19bn in the year 2015 (KPMG Report, 2017) and invested more than US$ 50bn between 2010 and 2015 (Skanet al., 2016). Academic research studies as well as global evidence support that FinTech services provide increased
An adapted technology acceptance model
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Received 30 September 2019 Revised 20 August 2020 17 September 2020 Accepted 24 September 2020
Management Decision
© Emerald Publishing Limited 0025-1747 DOI10.1108/MD-09-2019-1318
personalization, flexibility and ease of delivery of financial services (PWC report, 2017), which in turn leads to higher productivity, profitability and reach of financial services (EY, 2016).
At present, the reach of FinTech services has gone beyond e-banking and digitalization of traditional financial services. Now, the focus of the financial service industry is on the consumer perspective to successfully develop and introduce innovative technology to meet the financial needs and demands of the users. FinTech services have the potential to improve efficiency, reduce risk and contribute to the inclusive growth (RBI Report, 2018). Moreover, these technological innovations have the potential to substantially influence traditional business models of highly regulated financial services industry to offer differentiated customer experience (Leonget al., 2017). This can be easily done by using simple and easy to understand design, real-time insights and greater transparency in providing information.
Since 2015, FinTech services have seen an increase in awareness as well as adoption (EY FinTech adoption Index, 2019). The adoption of FinTech services has reached 64% across the globe, with much of the growth is driven by countries like China and India with an 87%
adoption rate (EY FinTech Adoption Index, 2019). Although, there is growth in the number of FinTech users, however, we see selective adoption of FinTech services. To put things in this perspective, though there are numerous FinTech services available at present, yet only a few of them have been successful. For example, money transfer and payment services are driving the increase in the adoption of FinTech services globally with 50% of the users (EY FinTech adoption Index, 2016). This dichotomy put forward the potential concerns or barriers for FinTech adoption. Thus, this study will investigate the drivers for actual use and acceptance of FinTech services by exploring the antecedents for consumer perception for adoption of technology. This will not only help in attracting potential users but will also help in retaining existing customers.
However, there are no conclusive research findings that determine attributes affecting FinTech acceptance to lead to the actual use of FinTech. There are a few studies investigating the barriers for acceptance and actual use of FinTech, most of which are from the perspective of customer’s behavior intention to use FinTech from the applied engineering literature (Leong et al., 2017;Chenget al., 2006;Tan and Leby Lau, 2016). Success and large-scale adoption of any technology is largely determined by the willingness of potential users to adopt technological innovation and then actually use it (Rogers, 1983). The limited research on FinTech services has induced the need for a comprehensive model explaining the key perceptions and motivations for FinTech adoption by more customers for a wide range of services.
To do so, most tested and well-established technology adoption theories are used in this study. This study proposes a framework where perception about ease of use and usefulness for FinTech services are combined with technological attributes of the FinTech interface affecting the intention and actual use of FinTech services. Because of the disruptive changes induced by FinTech in the financial service industry, this study also explores the subjective norms that influence the customers to use FinTech services. In addition, this study also incorporated some moderating variables relevant for adoption of technology innovations. It is suggested that the users with higher awareness and more digital experience tend to assign higher importance to their own attitude and perception, while users with low awareness level as well as lesser digital experience will be more affected by social influence. Considering their importance in FinTech adoption, the study proposes that the level of awareness of FinTech service and years of Internet experience may play a moderating role. Also, age and gender are included as control variables, considering their importance in the previous literature of technology adoption (Venkatesh and Morris, 2000;Boonsiritomachai and Pitchayadejanant, 2017).
To conclude and to adapt to the digital transformation in the financial services industry, there is a need to understand the needs, beliefs and perceptions of customers. Due to the lack of empirical studies explaining the effect of barriers in adoption of FinTech services, the study will contribute the literature by
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(1) Analyzing the relevance of key drivers in technological and behavioral perspectives, which are affecting the customer to decide for actual use of FinTech services.
(2) Exploring the possible differences in adoption depending on the customers’digital awareness and experience and demographic characteristics (age, gender and education) as the moderating variable in the framework.
(3) Contribute to the understanding of the users’perception about FinTech services, thereby suggesting attributes to entice nonusers to do their first online financial transaction and existing users to frequently use FinTech innovations.
This paper is further structured in different sections, where the next section presents the review of literature on technology adoption and use while explaining the effect of various factors. Based on this, the study proposed the research framework and formulated associated research hypotheses. Thereafter, steps for empirical study, data collection process and methodology are explained. Then the results and main research findings are discussed, followed by the theoretical as well as practical implications of the study along with the limitations and future research directions.
2. Review of literature and proposed hypotheses
FinTech is an umbrella term representing any technological innovation related to financial services (RBI Report, 2018). It refers to the broad range of financial services delivered and accessed through any digital channel (Heet al., 2017). As per theFinancial Stability Board’s (2017)working definition,“FinTech is any technology-enabled financial innovation resulting in new business models, applications or processes or products, affecting financial markets and institutions and provisioning for financial services.”These technological innovations have the potential to offer basic financial services as affordable, secure and convenient (Leonget al., 2017). Also, the extensive use of FinTech can boost the emerging economies by US$ 3.7 tn by 2025 (Manyikaet al., 2016).
FinTech helps in assessing the rapid development of the financial system and financial institutions. It has made consumption of financial services convenient by doing the technological advancement in fundamental services and building new applications for delivery such as making payments, saving, borrowing, managing risk and seeking financial advice (Heet al., 2017). With digital transformations in other industries, there is an increased demand for technology-based financial solutions by consumers (Saalet al., 2017). FinTech companies are meeting these consumer demands with convenient and less costly ways to transfer, borrow or invest money (Manyika et al., 2016). Now, FinTech is not limited to banking services and investment funds but is adopted by retail groups and telecom operators who are innovating to offer financial services through their existing networks. There are a number of FinTech service providers offering and improving FinTech services, and still, there is selective adoption of limited FinTech services. Thus, it is imperative to study the factors affecting adoption and use of these services.
Previous studies investigate the FinTech adoption through behavior intention to use the technology (Gaoet al., 2011;Safeenaet al., 2011;Teo and Pok, 2003;Rodrigueset al., 2016).
Despite the ubiquity of FinTech services, there is a dearth of studies on assessing FinTech adoption through actual use. Most of the prior studies investigated either behavior intention or continuous usage intention. But the relationship between behavior intention and actual use is hardly tested. The relationship between behavior intention and actual use needs to be explored as most of the studies focused on behavior intention. This gap in the literature leads to various unexplored mediating and moderating relationships between behavior intention and actual use. Thus, to investigate the relationship between behavior intention and actual use, this study
An adapted
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acceptance
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derives the theoretical framework from the TAM model (Davis, 1985;Daviset al., 1989). Most of the existing literature studies on technology acceptance use the most tested and established TAM model (Davis, 1985;Davis et al., 1989;Kimet al., 2016;Yanget al., 2012); thus, it is appropriate to define the framework on the basis of TAM for this study to investigate consumers’acceptance of FinTech services. The TAM is proven to be accurate and efficient in predicting and explaining the acceptance of information technology, Internet-based information systems, business-to-consumer e-services and online shopping (Davis et al., 1989;Adamset al., 1992;Gefen and Straub, 2003;Sujana, 2008). Therefore, as FinTech is a retail format of innovation, TAM constructs are appropriate to explore the user perception for adoption and use of FinTech services, as they are important determinants for technology use.
To investigate the antecedents of technology use, this study broadly classified the constructs in three dimensions, namely behavioral attributes, technological attribute and adoption attributes (Figure 1). The behavioral attributes of perception of ease of use and usefulness of technology are derived from the TAM model as they predict attitude and subsequent acceptance and use of technology (Davis, 1985). The TAM suggests that two beliefs about a new technology, i.e. perceived ease of use and perceived usefulness, determine a user’s initiative toward using that technology, which in turn determines their intention to use it. The TAM is validated by various studies as a robust framework to understand user’s adoption of technology in varied contexts, such as banking technology, m-commerce, email, online games, educational technology, desktop video conferencing, school information and communication technologies integration and so on (Alalwanet al., 2017;Boonsiritomachai and Pitchayadejanant, 2017;Gefen and Straub, 2003;Goguset al., 2012;Zhuet al., 2012;Birch and Irvine, 2009).
TAM 2 (a revision of TAM) (Venkatesh and Davis, 2000) and unified theory of technology acceptance and use of technology (UTAUT) (Venkateshet al., 2003) are also other popular technology acceptance models, adapted from the theory of reasoned ction (TRA) (Fishbein and Ajzen, 1975) and theory of planned behavior (TPB) (Ajzen, 1985,1991). Thus, considering the importance of these studies, the third behavioral attribute of social influence for the present study is derived from the UTAUT model (Venkateshet al., 2003). Also, the construct attitude is not used in the research framework as per the revision of TAM through TAM 2 (Venkatesh and Davis, 2000).
The technological attributes of responsiveness and security were derived from ServPerf and WebQual 4.0 (Cronin and Taylor, 1992;Barnes and Vidgen, 2002). Research suggests that security, responsiveness and social influence also significantly influence acceptance of technology (Cronin and Taylor, 1992;Venkatesh and Davis, 1996;Barnes and Vidgen, 2002;
Venkatesh and Morris, 2000).
The adoption attributes of behavior intention to use FinTech services is derived from the TAM model. Actual use is also considered a necessary precursor for the success of FinTech services and is, therefore, adopted in the present study as the dependent variable (Venkatesh et al., 2012; Oh and Yoon, 2014; AbuShanab, Pearson, 2007). Actual use which is less researched attribute as the determinant of adoption of technology is derived from the UTAUT model (Venkateshet al., 2003,2011). This addition of variables helps the present study to enhance its practical implications, as in many studies, additional variables have been introduced to increase the predictive power of TAM variables (Venkatesh and Davis, 1996;
Ha and Stoel, 2009).
This study examines the antecedents of ease of use and usefulness through a comprehensive framework weaving sub-constructs of TAM, UTAUT, ServPerf and WebQual 4.0. FinTech adoption and usage are analyzed from the perspective of technology acceptance by adding constructs for security, responsiveness and social influence as additional factors influencing both behavioral as well as adoption attributes. By adding various predictor variables for actual use in the research framework, the study aims to answer the pertinent question for FinTech service providers: what attributes enhance
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Responsiveness
Usefulness
Social Influence
Security
Technological attributes Behavioral attributes Adoption attributes
Actual Use Behavior Intention H2a
H3b
H2b
H3a H4a
H4d
H4b H4c H3c
H6a H5a
H5c
H6c
H5b
H6b
H1
Ease of Use
Figure1.Conceptualframework
An adapted technology acceptance model
consumer perceptions that make FinTech services easy, useful, secure and responsiveness?
For this, this study explores the antecedents of FinTech adoption by quantitatively investigating the drivers for adoption of FinTech services.
2.1 Adoption attributes
2.1.1 Actual use.The prominent technology acceptance and use models have supported the relationship between behavior intention and use to capture the“acceptance”(Davis, 1985;
Daviset al., 1989;Dwivediet al., 2011). Because of this, most of the current research studies are more focused on investigating behavior intention to predict use. The primary studies for technology acceptance and use are dominated by the information technology perspective (Daviset al., 1989;Venkateshet al., 2003). The main aim of this study is to understand the impact of behavior intention on actual use from the perspective of business success through actual use for FinTech services and to analyze the factors affecting the consumer perception about the offered FinTech services. This study considers actual usage as the frequency and an approximate number of times a FinTech service is used in a given period.
2.1.2 Behavior intention.With the advent of FinTech and its integration with brick and mortar financial services industry, behavior intention for use has become an important dimension to indicate the possibility of use and adoption of FinTech services by the users (Fenget al., 2014). The original TAM model (Davis, 1985) purports that behavioral intention affects the usage pattern. The pace of technological advancement in financial services, vis- a-vis level of consumer awareness also has a significant influence on a person’s behavioral intention. Moreover, the FinTech companies may not be able to reap the benefits of the innovation, or the gestation time to earn profits will increase if the technology advancement is at a higher rate than consumer awareness and use (Abbasi and Weigand, 2017). Hence, technology use and adoption have gained researchers’attention, and several theories and models have been proposed to study behavior intention (Sladeet al., 2015).
Various studies have identified that behavioral intention is influenced by many factors.
Accessibility of technology, information about its utility and usage and then the direct experience of usage of technology enable users to form stable behavior intention for continued use in the future (Kaba and Toure, 2014). Other factors like performance expectancy, effort expectancy, social influences and facilitating conditions are important factors that influence behavior intention (Venkateshet al., 2003). Behavior intention was also assessed through behavioral and technological factors with two main constructs: ease of use and usefulness (Daviset al., 1989). As the relationship between intention and actual usage in the consumer perspective is not tested in the previous literature for Fintech services, thus, the first hypothesis is proposed as follows:
H1. Behavior intention (BI) positively affects actual use (AU) of FinTech services.
2.2 Behavioral attributes
2.2.1 Perceived usefulness.According to the TAM, perceived usefulness is the degree to which one believes that using the technology will enhance his/her performance (Daviset al., 1989).
There are many studies proving the significant impact of perceived usefulness on behavior intention (Davis, 1993; Venkatesh et al., 2003; Venkatesh and Davis, 1996). Perceived usefulness is a crucial construct in determining adoption of technology (Venkatesh and Morris, 2000;Chen and Barnes, 2007). The existing literature has posited that perceived usefulness significantly influences intention to adopt technological innovations thereby impacting their actual use (Adamset al., 1992;Gefen and Straub, 2003;Laukkanen, 2017). The positive influence of perceived usefulness on behavior intention is empirically tested for mobile banking services (Tan and Lau, 2016). Thus, considering the empirical and theoretical
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results, it is posited that the higher the perceived usefulness for FinTech services, the higher will be their actual use. Considering the ubiquity of FinTech services coupled with their convenience, it will be interesting to test the influence of perceived usefulness on the behavior intention and actual use of FinTech services. Therefore, the second hypothesis is proposed as follows:
H2. Perceived usefulness (PU) positively affects actual use (AU) (H2a) and behavior intention (BI) to use (H2b) of FinTech services.
2.2.2 Perceived ease of use.Perceived ease of use is the degree to which one believes that using the technology will be free of effort (Daviset al., 1989). It is further defined as the extent of effort a technology needs to make it easy to use (Venkatesh and Davis, 2000). Also, the TAM further asserts that perceived ease of use is the key factor explaining the variance in perceived usefulness. It is posited that ease of use has a positive and direct effect on behavior intention to reuse technology services (Venkatesh and Davis, 1996,2000;Amin, 2007). This, in the long run, influences users to adopt technological services. It is also found that perceived ease of use enhances the prediction of use of technology services including Internet banking (Gounaris and Koritos, 2008) as well as other IT-related products (Adamset al., 1992;Gefen et al., 2003;Tanet al., 2014). As there are numerous variants of FinTech services, perception for ease of use can be tested for its influence on the behavior intention as well as on actual use of FinTech services. Thus, considering the previous literature and empirical studies, the third hypothesis is proposed as follows:
H3. Perceived ease of use (PEOU) positively affects actual use (AU) (H3a) and behavior intention (BI) to use (H3b) for FinTech services.
Also, it is posited that perceived ease of use positively impacts perceived usefulness (Davis et al., 1989;Venkateshet al., 2003). It is considered that even a useful technology innovation may not be used by the user if it is difficult to use. Technology is used more frequently used when the user perceives that it is effortless and easy to use. Thus, it is proposed that
H3c. Perceived ease of use (PEOU) positively affects perceived usefulness (PU) for FinTech services.
2.2.3 Social influence.Social influence is the extent of the influence of others to use a specific technology (Venkateshet al., 2012). The existing literature has established the effect of social information which acts similarly to social pressure to conform with stated behavior or opinion (Fishbein and Ajzen, 1975). The influence of social norms is much higher for disruptive innovations as it is assumed that a person consults his/her social circle about new technologies and can be influenced by information provided by them. In the absence of their own experience, people give more importance to others’opinions and perceptions about the characteristics of the technology. Also, as the offered service is new, social norms influence the attitude toward the technology. Therefore, actions, statements and attitudes of significant peers, friends and family about the use of technology are very important. Among the existing literature, social influence is the most hypothesized and tested construct of UTAUT, and there is a significant relation established between social influence and its effect on behavior intention to use (Rahiet al., 2019;Alalwanet al., 2017;Rodrigueset al., 2016;Sladeet al., 2015;
Tanet al., 2014;Venkateshet al., 2011). While some of the studies state contradictory results (Shin, 2010;Amin, 2007), may be partly due to the different mechanism of application of social influence in one’s social context. Thus, it will be interesting to test the relationship between social influence and behavior intention and use, as users modify their actions according to the actions of others to gain social acceptance. Thus, considering the need for social confirmation and accounting the social incentives related with use of FinTech services, the fourth hypothesis is proposed as follows:
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H4. Social influence (SI) positively affects actual use (AU) (H4a), behavior intention (BI) (H4b), perceived usefulness (PU) (H4c) and perceived ease of use (PEOU) (H4d) for FinTech services.
2.3 Technological attributes
2.3.1 Responsiveness.Responsiveness refers to the user perception of the effectiveness and excellence of service offered online. It refers to the accuracy of the services delivered along with clear, current and complete information available on the website. It is largely affected by website or interface performance about prompt service and assistance provided while doing financial transactions. Buyer perceptions about the nature of interaction with the service provider are largely affected by responsiveness. It also helps customers in the absence of awareness of technology use or any unforeseen event requiring effective information. Thus, effective communication is the key factor determining the perceived quality of the online service; thus, responsiveness is considered to be a major element of determining the service quality (Parasuramanet al., 1985;Cronin and Taylor, 1992). It influenced user assessment for perceived usefulness and ease of use of the technology and indirectly affects the user adoption (Gefen, 2000). It has been empirically shown to have a significant impact on the quality of service and the second strongest predictor of behavior intention and attitude toward an online interface (Wolfinbarger and Gilly, 2003). Therefore, it will be interesting to investigate the effect of responsiveness on the behavioral perception of the users. Thus, considering its effectiveness in predicting ease of use and usefulness and exploring its effect on social influence, the fifth hypothesis is proposed as follows:
H5. Responsiveness (RS) positively affects perceived usefulness (PU) (H5a), perceived ease of use (PEOU) (H5b) and social influence (SI) (H5c) for FinTech services.
2.3.2 Security.One key reason for not doing online financial transactions by users regardless of being Internet users is because of the belief about the safety of the transactions (Gefen and Straub, 2003). The security of online transactions as well as the reputation of the service provider are the key factors influencing belief about safety while doing financial transactions (Pavlou, 2003). As compared with brick-and-mortar financial services providers, security is more critical while doing online financial transactions (Reichheld and Schefter, 2000;Grewal et al., 2004). Because of the absence of face-face interaction, while using FinTech services, users feel a greater risk and uncertainty. Security is the most effective tool for reducing this risk and uncertainty (Suh and Han, 2002;Pavlou, 2003). Many prior empirical studies included security into the TAM (Gerrardet al., 2006;Laukkanenet al., 2008;Sujana, 2008). Results show security to be an antecedent of ease of use (Pavlou, 2003) and usefulness (Pavlou, 2003;Dahlberget al., 2003). The enhanced security features may also positively influence the social norms for use of FinTech services. Thus, considering the effectiveness of security in explaining the belief behavior, this study intends to explore the impact of security on the user perception about FinTech services. Thus, the sixth hypothesis is proposed as follows:
H6. Security (SC) positively affects perceived usefulness (H6a), perceived ease of use (H6b) and social influence (SI) (H6c) for FinTech services.
2.4 Moderating effects
FinTech is a disruptive innovation, and the transition from physical to digital access of financial services is largely dependent on the demographic characteristics of users such as level of education, age, gender, etc. Also, the long-term use of these services is affected by the digital behavior of the users largely analyzed by capturing their level of awareness, Internet experience, etc. There is a considerable difference between users in the level of awareness and
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familiarity with FinTech services. Users with a higher level of awareness for FinTech services and experience with Internet will have a better perception of FinTech services. These users will also value the usefulness of FinTech services. Thus, this study proposes individual differences in the level of awareness, and familiarity with Internet services may play a crucial role in the adoption and actual use of FinTech services. At present, there are no studies on the exploring the effect of level of awareness and familiarity on actual use of utilitarian services. Thus, theoretically contributing, this study proposed that awareness level for FinTech service and Internet experience acts a moderating variable and seventh hypothesis is proposed as follows:
H7. The effect of perceived ease of use and perceived usefulness on behavior intention and use of FinTech services is moderated by level of awareness (H7a) and Internet experience (H7b).
This study also considers the effect of two demographic characteristics, age and gender.
These characteristics may help in explaining the decision-making process for behavior intention and actual use of FinTech services. Thus, these are included as control variables to assess the impact as it is believed that older people are less susceptible to social pressure (Boonsiritomachai and Pitchayadejanant, 2017;Tan and Lau, 2016). Similarly, females are considered to be more affected by others’opinions. Also, more the familiarity and experience with the technology helps the users to formulate their opinion and belief about the use of technology. Thus, these factors may influence the results for exploring the influence of antecedents of behavior intention and actual use.
3. Method
3.1 Survey instrument
Before conducting the main data collection, the study did iterative discussion to (1) identify FinTech adoption drivers that emerge when doing financial transactions and (2) to refine and finalize the main survey instrument. A pool of questions was created considering the latent constructs from an in-depth review of the relevant literature about technology adoption and online banking. The items of the constructs are adapted from established and well-tested scale measuring perceived usefulness and perceived ease of use (Daviset al., 1989), social influence (Venkatesh and Morris, 2000;Venkateshet al., 2003), responsiveness and security (Cronin and Taylor, 1992;Barnes and Vidgen, 2002), behavior intention (Daviset al., 1989) and actual use (Venkateshet al., 2003). The extensive literature review establishes the content validity of the measurement scale.
To establish the face validity, iterative discussion was done with eight domain experts from academia and industry who have extensive knowledge about FinTech services and technology adoption studies. The iterative discussion was conducted on four broad dimensions: (1) number of FinTech services used and their usage frequency; (2) intention to use FinTech service; (3) belief variables for ease of use, perceived usefulness, security, responsiveness, social influence and attitude toward FinTech usage and (4) demographic information. Based on the consensus of the experts, only those items were retained which strongly associated with the chosen domain. The confusing or ambiguous items were revised.
In the final survey instrument, all variables except for demographic information and FinTech service usage frequency were assessed on a five-point Likert scale (55strongly agree and 15strongly disagree). To assess FinTech service usage frequency, the experts were requested to give their usage frequency for each service, namely“more than once a week”,“weekly”,“monthly”,“less than once a month”or“never”. All scales were measuring self-reported behavior. The respondents were also asked to name the FinTech service which is most preferred or frequently used by them. The TAM scale (Davis, 1985,1993;Daviset al., 1989) was used to measure usefulness with seven items, ease of use with eight items and
An adapted
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intention to use with three items. In total, five items from WebQual 4.0 (Barnes and Vidgen, 2002) and three items from ServPerf (Cronin and Taylor, 1992) were used to assess security, three items from UTAUT (Venkateshet al., 2003) were used to assess social influence and three items form ServPerf (Cronin and Taylor, 1992) were used to assess responsiveness. To eliminate bias due to brand association, general terms such as payment wallet, online trading, algorithm trading, etc. were used in the survey instrument.
3.2 Data collection
The final survey instrument used the measures refined and reworded through iterative discussion. A total of 850 users were invited for an online survey, who were chosen based on past usage and/or experience of the listed FinTech service. The survey was conducted by the electronic distribution of the survey questions to potential participants. The target respondents were customers of leading public and private sector banks, academicians and staff working in university or college and postgraduate students of private universities in Mumbai and Delhi. A combination of convenient and random sampling method was used, as it is difficult to obtain the sampling frame containing the details of all the FinTech users. The universities and banks were conveniently selected, and users were randomly chosen. All participants were briefed about FinTech service and relevance of this study though general description to eliminate any bias. The response rate for the survey instrument was 51.6%
with the collection of 439 responses.
4. Result
4.1 Sample characteristics
Out of 439 respondents, 148 respondents (33.7%) were in the age group of less than 30 years.
Approximately, 48% of respondents were between 30 and 50 years of age who are also incidentally the major user of FinTech services (EY, 2016). Approximately, 70% of the respondents were male, and 30% were female respondents. A total of 195 respondents (44.4%) have more than 10 years of Internet experience. The preferred FinTech service for the respondents was Internet banking with 56% of responses. The second preferred service was payment wallet service. Except for four respondents, all respondents have used at least one of the FinTech services in the last month.
4.2 Measurement model results
Exploratory factor analysis (EFA) was conducted to assess the construct validity (Kaiser, 1974) of 29 items with maximum likelihood extraction in SPSS software (v.23). For presuming correlation among FinTech adoption factors, oblique rotation method (Promax) was used. A total of three items, one item from construct ease of use and two items from construct usefulness, were omitted from further analysis as they showed poor psychometric properties and significant cross loading (Gefen and Straub, 2003). This analysis gives 26 items under the five factors extracted in EFA. Sample adequacy is established with the Kaiser-Meyer-Olkin (KMO) measure of 0.933 with significant Bartlett’s test of sphericity (p50.00). Cronbach’s alpha of the measurement model was conducted to ensure the reliability and internal consistency of each sub-scale. The acceptable value for Cronbach’s alpha is 0.7 (Hairet al., 2014;Nunnally and Bernstein, 1994). All the coefficient alpha values were higher than the threshold value of 0.7, ranging between 0.863 for responsiveness to 0.952 for social influence.
Also, there was no multicollinearity as the values for Variance Inflation Factor indicators were close to 1, and tolerance values were more than 0.1 (Gaskin, 2018). All the final extracted factors accounted for the total explained variance of 69.965%, which is considered acceptable in social sciences research (Hairet al., 2014).
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4.3 Structural model results
After confirming the measurement model, the structural model was developed to test the stated hypotheses. The structural model was tested through maximum likelihood method with AMOS (v.21). The result shows an acceptable fit for the proposed model with χ2(209)5490.831, CMIN52.348, CFI50.965, GFI50.910, AGFI50.882, SRMR50.0379, RMSEA50.055, PCLOSE50.077. The hypothesis test supports all postulated paths exceptH1,H2a,H4b,H4c andH5b(seeFigure 2for standardized path coefficients and significance level).
First, the direct determinants for adoption attributes were tested withH1examining the impact of behavior intention on actual usage of FinTech services. The direct influence of behavior intention on actual use is not significant (γ5 0.038,p> 0.05); thus,H1is not supported.
Second, the direct influence of behavioral attributes was tested. There is a positive influence of perceived usefulness on behavior intention (γ50.676,p< 0.01), supportingH2b, but its direct influence on actual use in not significant (γ5 0.089,p> 0.05), rejectingH2a.
Moreover, the positive effect of perceived ease of use is well established, and this study also supports the direct influence of perceived ease of use on actual use, behavior intention and perceived usefulness (Alalwanet al., 2017;Laukkanen, 2017). There is a significant influence of perceived ease of use on actual use (γ50.350,p< 0.01) and perceived usefulness (γ50.461, p< 0.01) and a moderate positive direct influence on behavior intention (γ50.190,p< 0.01), supporting hypotheses H3a, H3b and H3c. The hypotheses testing reveals a significant negative effect of social influence on actual use (γ5 0.113,p< 0.05) of FinTech services, suggesting partial acceptance forH4a. On the contrary, social influence positively affects the perceived ease of use (γ50.162,p< 0.01), supportingH4d. Also, the direct influence of social influence on behavior intention (γ5 0.001,p> 0.05) and perceived usefulness (γ50.026, p> 0.05) is not established, rejectingH4bandH4c.
Lastly, the direct influence of technological attributes on behavioral attributes was tested.
The analysis established the positive impact of responsiveness on perceived usefulness (γ50.164,p< 0.01) and perceived ease of use (γ50.242,p< 0.01), supportingH5aandH5b.
But no significant direct impact of responsiveness was found on social influence (γ50.005, p> 0.05), and thus rejectingH5c. Also, the results confirmed the significant direct positive effect of security on perceived usefulness (γ 5 0.335, p < 0.01), perceived ease of use (γ50.479,p< 0.01) and social influence (γ50.301,p< 0.01), strongly supportingH6a,H6b andH6c(Table 3).
Solid paths are significant. Dotted paths are insignificant
The hypothesis test explained 70% variance in behavior intention to use FinTech services explained by two behavioral attributes, perceived usefulness and perceived ease of use. A 6%
variance was explained for actual usage of FinTech services explained by perceived ease of use and social influence. Perceived ease of use, social influence, responsiveness and security constructs explained 77% variance in perceived usefulness. In total, 56% of the variance is explained for perceived ease of use through three antecedents, namely social influence, responsiveness and security.
Furthermore, given that security, responsiveness and social influence may have a direct impact on intention and actual usage of FinTech services, a modified structural model allowing these paths was estimated. All three paths are not significant; thus, the finding confirmed the proposed hypotheses.
4.4 Moderating effects
Moderating effects of awareness level and Internet experience were assessed by doing multi- group analysis, a widely used method to evaluate group differences based on behavior or demographic characteristics (Gaskin, 2018). The distinguishing criteria were based on theEY FinTech Adoption Index (2016) mechanism to determine the awareness level of the
An adapted
technology
acceptance
model
Responsiveness
Usefulness
Social Influence
Security
Actual Use Behavior Intention
Ease of Use
y = 0.164 p < 0.01
y = 0.005 p > 0.05
y = 0.026 p < 0.05
y = 0.335 p > 0.01
y = 0.301
p < 0.01 y = 0.242 p < 0.01
y = 0.479 p < 0.01
y = 0.162 p < 0.01
y = –0.113 p < 0.05
y = –0.089 p > 0.05
y = –0.038 p > 0.05 y = –0.001
p < 0.05 y = 0.461
p < 0.01
y = 0.676 p < 0.01
y = 0.190 p < 0.01
y = 0.350 p < 0.01
Figure2.Structuralmodelresults
MD
Factor/items
EFA CFA
Factor 1
Factor 2
Factor 3
Factor 4
Factor 5
Std factor loading
Construct reliability AVE
Factor 1: Ease of use 0.921 0.661
Easy to learn 0.904 0.173 0.017 0.146 0.122 0.85
Easy to browse 0.929 0.105 0.004 0.056 0.050 0.84
Easy to use 0.894 0.015 0.068 0.020 0.068 0.86
Create positive experience
0.679 0.086 0.010 0.163 0.077 0.77
Clear and comprehensible interaction
0.723 0.073 0.025 0.020 0.037 0.78
Has attractive layout and appearance
0.640 0.025 0.014 0.063 0.070 na
Easy to become skillful for use
0.723 0.039 0.015 0.071 0.056 0.77
Factor 2: Security 0.928 0.720
Has good reputation 0.127 0.687 0.027 0.093 0.075 0.78 Safe to complete
financial transaction
0.058 0.948 0.028 0.045 0.006 0.88
Gives the feeling of safety for personal information
0.036 0.870 0.028 0.070 0.067 0.81
Service is trustworthy
0.120 0.670 0.002 0.071 0.064 0.89
Has adequate security features
0.018 0.816 0.048 0.053 0.029 0.87
Factor 3: Social influence 0.952 0.868
Family and friends insist to use
0.020 0.052 0.919 0.012 0.011 0.90
Colleagues & peers insist to use
0.017 0.021 0.987 0.006 0.011 0.99
Spouse insist to use 0.001 0.045 0.909 0.046 0.002 0.90
Factor 4: Usefulness 0.893 0.676
Useful in daily life for doing financial transactions
0.193 0.171 0.031 0.515 0.021 0.81
Enhance independence for doing financial transactions
0.074 0.078 0.015 0.731 0.057 0.76
Helps to do financial transactions quickly
0.055 0.029 0.053 0.908 0.028 0.87
Increase productivity 0.031 0.062 0.005 0.872 0.013 0.84 Have necessary
knowledge to do fin.
transactions
0.094 0.188 0.177 0.416 0.060 na
Factor 5: Responsiveness 0.863 0.679
Give information about time taken to complete financial transaction
0.067 0.010 0.020 0.089 0.694 0.82
Give prompt service as promised
0.000 0.024 0.027 0.063 0.980 0.89
(continued)
Table 1.
Exploratory factor analysis and confirmatory factor analysis
An adapted
technology
acceptance
model
customers. Thus, this study classified the respondents with awareness for four or less FinTech services as less aware, and with awareness for five and more FinTech services were considered as highly aware users. This method identifies 184 respondents as less aware and 255 users as highly aware. The multi-group analysis in the structural model establishes a significant difference in perception for ease of use and usefulness.
As hypothesized, there is a significant influence of level of awareness on perceived ease of use and usefulness (Table 4). As highly aware users are abridged with many FinTech services, they are having accessibility to a multitude of options to choose a useful service. The results confirmed the influence of ease of use while deciding for the usefulness of a service.
Also, highly aware users consider security as an important dimension. It can be attributed to the fact that an aware user keeps himself/herself abridged with the current news and events as about the numerous online frauds, password protection rules, etc. This awareness gives them an understanding of the safe practices and security features of FinTech platforms.
Moreover, the results of the study posited that highly aware users may not very keen on constant digital or personal intervention by the service provider and may consider it as an intrusion to privacy or time consuming. Thus, responsiveness of the FinTech platforms for providing help may be considered constant irritation to highly aware users, in contrast to users with low awareness, who may require constant help and support to bridge the learning curve for understanding the use of the FinTech services.
Factor/items
EFA CFA
Factor 1
Factor 2
Factor 3
Factor 4
Factor 5
Std factor loading
Construct reliability AVE Helps at the time of
problem or issue
0.004 0.191 0.027 0.010 0.581 0.76
Eigen value 11.269 2.398 1.704 1.067 1.005
Variance % 35.815 21.185 5.733 4.020 3.212
Cronbach alpha 0.923 0.922 0.950 0.890 0.858
Note(s): Confirmatory factor analysis was done using AMOS (v.21) to test the measurement model on the items extracted after the EFA. One item each from ease of use and usefulness was removed from the final scale due to large standardized residual. The final set of 21 items exhibit construct reliabilities, average variance extracted (AVE) and Cronbach’s alpha exceeding the recommended standard of reliability and unidimensionality (Table 1). The AVE value greater than 5.0 of the total variance established the convergent validity. Discriminant validity denotes that each factor is different than others, and it is established if AVE is greater than the squared multiple correlation coefficient between factors (Hairet al., 2014) (Table 2).
The goodness of fit statistics are as follows:χ2(177)5429.256, CMIN52.425, CFI50.967, GFI50.914, AGFI50.888, SRMR50.0397, RMSEA50.057, PCLOSE50.045
Table 1.
Social influence Usefulness Ease of use Security Responsiveness Social influence 0.932
Usefulness 0.316 0.822
Ease of use 0.349 0.758 0.813
Security 0.295 0.733 0.672 0.849
Responsiveness 0.225 0.656 0.595 0.718 0.824
Note(s): The numbers in the diagonal is AVE of each construct. Numbers below diagonal are squared correlation coefficients between constructs
Table 2.
Convergent and discriminant validity
MD
Similarly, the moderating effect of Internet experience was investigated by first classifying the sample in two groups distinguishing them based on their years of Internet experience. The respondents with more than seven years of Internet usage were classified as users with more Internet experience, and less Internet experience were users with less than four years of Internet usage. This distinguishes the sample with 138 users with less Internet experience and 301 users with more Internet experience. The multi-group analysis in the structural model established a significant difference in perception for ease of use and usefulness.
As expected, Internet experience also plays a significant role in affecting perceived ease of use and usefulness (Table 5). With a more experienced user, the importance of ease of use is reduced as compared to a less experienced user. It is attributed to the less familiarity with the systems, as it is considered difficult for inexperienced users to navigate through websites or
Estimate
Z-score
Constraints Less aware users Highly aware users
RS→PEOU 0.439*** 0.135** 2.72***
SC→PEOU 0.317*** 0.652*** 2.897***
PEOU→PU 0.350*** 0.494*** 2.281**
Notes(s): ***p-value<0.01; **p-value<0.05
Hypothesis Hypothesized path Standardized estimate p-value Hypothesis support
H1 BI→AU 0.038 0.659 Not supported
H2a PU→AU 0.089 0.367 Not supported
H2b PU→BI 0.676 *** Supported
H3a PEOU→AU 0.350 *** Supported
H3b PEOU→BI 0.190 *** Supported
H3c PEOU→PU 0.461 *** Supported
H4a SI→AU 0.113 0.023 Supported
H4b SI→BI 0.001 0.968 Not supported
H4c SI→PU 0.026 0.292 Not supported
H4d SI→PEOU 0.162 *** Supported
H5a RS→PU 0.164 *** Supported
H5b RS→PEOU 0.242 *** Supported
H5b RS→SI 0.005 0.940 Not supported
H6a SC→PU 0.335 *** Supported
H6b SC→PEOU 0.479 *** Supported
H6c SC→SI 0.301 *** Supported
Note(s):CMIN52.376, CFI50.964, GFI 50.907, AGFI50.880, SRMR50.0386, RMSEA50.056, PCLOSE50.057
Estimate
Z-score Constraints Less Internet experience More Internet experience
SI→PEOU 0.427*** 0.093*** 4.246***
SC→PU 0.453*** 0.247*** 2.58***
PEOU→PU 0.474*** 0.281*** 2.985***
Notes(s): ***p-value<0.01
Table 4.
Moderating effect of awareness level about FinTech services Table 3.
Standardized estimates (hypothesized model)
Table 5.
Moderating effect of Internet experience
An adapted
technology
acceptance
model
applications. Also, less experienced user relies more on other perception and feedback for use of FinTech services. The more experienced user gives almost no importance to the social circle and opinions as they are equipped with first-hand knowledge about the system and decide to use the services based on their own cognitive beliefs and experiences. Moreover, users with less Internet experience gives higher priority to security features of FinTech services in comparison to a more experienced user. The lack of familiarity and knowledge about the services may make a less experienced user susceptible to doubt about the safety of the financial transactions through FinTech services.
Besides, as mentioned earlier, age and gender were used as control variables to explore any potential difference in the influence of antecedents for FinTech adoption. To investigate the moderating role of gender, multi-group analysis was done males (n5306) and female (n5133). There was no significant difference found between both the groups suggesting both males and females have similar beliefs while deciding for use of FinTech services. This can be attributed to the utilitarian characteristics of FinTech services, which demand a pragmatic and informed approach while decision-making. Therefore, it is evident from the results that gender does not alter the influence of the antecedents of FinTech use.
Finally, the moderating effect of age was assessed by a similar process. The sample was divided into two groups based on the EY FinTech adoption index (2016) where respondents with more than 40 years of age were considered older users (n5190), and respondents with age less than 40 years were considered younger users(n 5 249). The results present a significant difference for the influence of security on perceived ease of use and perceived usefulness of FinTech services. Though the security attribute significantly affects both younger and older users, the importance of security features for older users is highly evident from the results (Table 6). Older users give more importance to security constraints in comparison to younger users. This can be attributed to the fact that the older population manages a higher amount of finances through FinTech applications, and thus security becomes all the more important for them. Also, as the younger population is born after the 1980s, the same timeline of digital and Internet revolution, they are more aware, familiar and hands on with digital interfaces gives them a feeling of control for the frequently used services. Nevertheless, security has emerged as an important technological attribute affecting the perception of ease of use and usefulness.
5. Discussion
The study explores drivers for FinTech adoption by exploring technological and behavioral attributes. Previous studies do not explore the effect of technological and behavioral factors on actual use of FinTech services. This study contributes to the theory of technology adaption by proposing a direct effect of social influence along with perceived ease of use and perceived usefulness on FinTech adoption. Moreover, technological attributes are added to make the study robust for both theoretical and practical applications. These technological attributes about responsiveness and security are proven to be academically important in influencing users’behavior intention to use technology (Barnes and Vidgen, 2002;Cronin and Taylor, 1992;Parasuramanet al., 1985) and were, thus, proposed as antecedents of ease of use
Estimate
Z-score
Constraints Younger Older
SC→PEOU 0.433*** 0.706*** 2.315**
SC→PU 0.270*** 0.458*** 2.211**
Notes:***p-value<0.01; **p-value<0.05 Table 6.
Effect of age on perceived ease of use and usefulness
MD
and usefulness in the study. To test the impact of these factors, the study modified the TAM by adding these constructs. The result of this study gives support to the relevance of added antecedents in the TAM.
The original TAM asserts that actual use of technology can be significantly predicted by three belief constructs, namely perceived ease of use, perceived usefulness and behavior intention to use (Daviset al., 1989). But results of the study reveal no significant influence of behavior intention on actual use of FinTech services, which differ from prior study findings (Sladeet al., 2015;Fenget al., 2014). It may be attributed to the difference in user reaction when asked for intended use and actual use. This difference can be attributed to the positive perception for FinTech, which affects the users’intention, but it is not converted to actual use because of various practical constraints such as comfort with existing service, reputation of the service provider, etc. Also, behavior beliefs and behavior intention are more related to intended use then with actual use (Adamet al., 1992;Straubet al., 1995). It is observed that users are not very frequent in changing the utilitarian services, especially for older users (Boonsiritomachai and Pitchayadejanant, 2017; Tan and Lau, 2016). Incidentally, in this study, the majority of the users are above the age of 40 years indicating a more emphasis on other relevant factors. Moreover, it is observed that the older population is very cautious about the monetary transactions; first because of their past experience, and second because of the high-value transactions as older users are considered more well off as compared to their younger counterparts (Kaba and Toure, 2014).
This study also confirms the robustness of the TAM in explaining the effect of behavioral attributes on adoption behavior for FinTech services. Usefulness emerges as the most powerful indicator of intention to use supporting the prior TAM research while ease of use (Davis, 1985, 1993;Daviset al., 1989) and social influence are secondary determinants. The most significant influence of perceived usefulness indicates that the FinTech service providers should emphasize more on enhancing interface features for reducing task redundancies, faster information availability and lesser need for service intervention to enhance the experience of the user. Now with postmillennial generation becoming the major market for FinTech services, service providers should focus on the no-error system, as seeking response for complaints and queries is becoming troublesome and time-taking for users.
The results of the study also confirm that perceived ease of use has a significant positive relationship with actual use of FinTech services. This suggests that ultimately the ease of use and access determines the actual use. It can be said that the inoperability of the FinTech services poses a significant threat to actual use. Also, there is a moderate and positive relationship between perceived ease of use and behavior intention. These results are similar to previous technology adoption studies (Gefen and Straub, 2003;Adamset al., 1992). Also, the secondary impact of perceived ease of use can be explained with an increase in user- friendliness and less difficulty in using online systems due to their prolonged use by the users (Kaba and Toure, 2014). Also, it can be because of the omnipresence of online technology and thereby increase in competence of users while using them. It can be said that with more familiarity and experience, the ease of use may not continue to remain a significant factor in postmillennial generation users.
The added behavioral construct of social influence as an extension to the TAM and analyzed as the antecedent for ease of use, usefulness and actual use shows a significant negative impact on actual use of FinTech services. This can be attributed to the higher age group of most of the respondents as with an increase in age and experience, users are not much influenced by peer pressure for use of FinTech services (Venkateshet al., 2003). In fact, the opinion of social group is posing more of a discouragement for use of FinTech. This indicates that social group is influencing users to not use a FinTech service if it found to be troublesome, fraud or a security threat. Also, as FinTech deals with money and financial transactions, the user may be more concerned about the negative feedback from their social group rather than a positive one.
An adapted
technology
acceptance
model
This study also revealed that added technological attributes related to security and responsiveness have a significant positive influence on actual usage and were mediated through ease of use and usefulness. Security and responsiveness are not yet considered in previous TAM research in the context of FinTech services. In comparing path coefficients of antecedents of ease of use and usefulness, security emerge as the powerful indicator of perceived ease of use and perceived usefulness toward FinTech usage in comparison to responsiveness. Therefore, this study suggests improving the security features of FinTech services and to stress on responsiveness of their system and/or website. With these features, FinTech service providers can reassure their users that the services are safe to do financial transactions. They can also influence the perception of security and responsiveness of the FinTech services by emphasizing the newly added positive features in the marketing campaign. Proper training should be offered to the users on the safe usage of FinTech services. This can also improve security and enhance user confidence thereby enabling users to feel secure and comfortable in using FinTech services. However, it may be possible that with properly incorporated enhanced security features, security and responsiveness might not remain as a key concern in the future.
With respect to moderating effect of digital behavior, awareness level for FinTech services as well as Internet experience was found to have a significant influence on perceived usefulness and ease of use. Also, the gender of the respondents bears no effect on the decision- making process for use of FinTech services, but the age of the user is a strong influence on the security perception of FinTech services.
6. Conclusion
The results of the study provide several key insights into the drivers of FinTech usage. First, perceived usefulness is the key factor that positively influences intention to use FinTech services while perceived ease of use is the second significant factor. Second, social influence is a significant negative determinant of actual use while ease of use positively impacts the actual use. Third, security and responsiveness are a significant determinant for intention but is mediated through usefulness and ease of use. Security is considered the more important technological attribute deciding the perception of FinTech use. Lastly, age highly influences the security perception of FinTech services.
6.1 Theoretical and practical implications
FinTech industry is seeing the rapid changes and every day newer technologies are introduced in the market. From a consumer perspective, users need to continuously adapt to newer offerings. Thus, to achieve successful adaption and to earn business gains, FinTech service providers need to carefully understand and integrate the needs and perceptions of consumers.
Considering this, the present study contributes to the existing literature on technology adoption by considering traditional behavioral attributes (perceived ease of use, usefulness and social influence) and identifying key technological attributes (security and responsiveness) affecting customer’s decision to adopt FinTech services. This will help the FinTech service providers to understand the appropriate interface characteristics to maximize use behavior. Also, the present study investigates the possible differences in the adoption process of FinTech services due to customer’s awareness about FinTech and their Internet experience. Moreover, demographic characteristics were tested to check their effect on the proposed conceptual framework. The inclusion of digital behavior and demographic characteristics helps in a better explanation of the attributes and gives a wider scope to the study.
Also, the study posited that gender does not alter the dynamics between attributes for FinTech services within the proposed research framework, suggesting that service providers should target users independently of their gender. But age should be considered as users with
MD
age above 40 lay more emphasis on security. Moreover, the upcoming postmillennial generation poses a dichotomy for the service providers as they are more familiar with the FinTech services and show contrasting behavior from the previous generation. Thus, it can be said that there is a need for mass customization of services to attract more users, similar to the marketing initiatives of famous shoemaker Nike (Levesque and Boeck, 2017).
6.2 Limitations and future research
The findings of the study are encouraging with an explained variance of 70% for behavior intention because of added antecedents. Also, the moderating effect of awareness and Internet experience is useful in exploring the effect on behavioral attributes. However, the study has some limitations. First, the convenience sampling technique was used to include participants who are the existing Internet users, thus limiting the scope of generalizability to only existing users of any technology. Second, this study used self-reported use for actual use of FinTech, instead of observation or log-in data. Capturing the actual respondents’
behavior will enhance the predictive power of this study. The study only covers the construct of security, responsiveness and social influence. Future research may investigate issues related to information quality and website quality and its impact on intention and actual usage. Also, the present measurement instrument can be further refined in further studies to enhance its validity. Future research can be done in other product categories for generalization of results.
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