How pre-adoption expectancies shape post-adoption continuance intentions: An extended expectation-confirmation model

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How pre-adoption expectancies shape post-adoption continuance intentions: An extended expectation-confirmation model Anil Guptaa, Anish Yousafc, Abhishek Mishrab,* a

Faculty of Business Studies, University of Jammu, India Indian Institute of Management (IIM), Indore, India c ICFAI Business School, Hyderabad, IFHE University, India b

A R T I C LE I N FO

A B S T R A C T

Keywords: Pre-adoption expectancy Post-adoption perceived security Post-adoption self-efficacy Perceived user interface quality User satisfaction Continuance intention

Extant research examines the factors that cause the initial adoption of digital technologies, like mobile wallets, with limited focus on post-adoption behaviours. This work proposes a novel extended expectation–confirmation model which explores the impact of pre-adoption expectancies and confirmation on post-adoption satisfaction and continuance intentions. The model also explores the roles played by the post-adoption factors like perceived user interface quality, perceived security and self-efficacy. The findings indicate that pre-adoption performance/ effort expectancies impact consumption-driven confirmation, which in turn affects the post-adoption perceived usefulness, post-adoption perceived security, and user satisfaction. Further, satisfaction, post-adoption self-efficacy and post-adoption perceived usefulness are found to be strong antecedents of the user’s continuance intention. The framework contributes to the extant research by integrating both pre- and post-adoption constructs that determine post-adoption continuance intentions. The framework also guides the M-wallet application developers to enhance user satisfaction and continuance intentions by meeting their pre-adoption expectations through consumption-driven confirmation, in order to stay relevant in an extremely competitive mpayments business.

1. Introduction The integration of wireless telecommunication, smartphone and banking systems has created digital payment ecosystems, gradually replacing the conventional paper currency (Sharma, Mangla, Luthra, & Al-Salti, 2018). With the evolving technological landscape, governments, especially those of emerging economies, are giving increasing impetus to digital payments. As a result, in India, for example, the contribution of digital payments to the overall economy is expected to increase from 8.2 % in 2018 to 14.8 % by 2021, with the market size forecasted to register exponential growth of 67.10 % for the next five years (PR Newswire, 20181 . Globally, mobile wallet M-wallet transactions, an important ingredient in the digital payments ecosystem, were valued at US$594 billion in 2018 and are expected to grow to US $3,142 billion by 2022, at a growth rate of 32 % (Zion Market Research, 20192).

M-wallets facilitate digital payments, as well as store sensitive information for membership details, loyalty cards, debit/credit cards and encrypted shopping accounts (Dahlberg, Mallat, Ondrus, & Zmijewska, 2008; Slade, Williams, & Dwivedi, 2013). Despite the relative maturity of M-wallets, research in this domain is limited to initial adoption/rejection (Kalinic, Marinkovic, Molinillo, & Liébana-Cabanillas, 2019; Malaquias & Hwang, 2016; Oliveira, Thomas, Baptista, & Campos, 2016; Sharma et al., 2018; Singh, Srivastava, & Sinha, 2017; Singh, Sinha, & Liébana-Cabanillas, 2020; Slade, Williams, Dwivedi, & Piercy, 2015; Zhou, 2013). No study has specifically examined post-adoption implications for the technology, despite M-wallets being a means of regular use for many people. The application stores of smartphone platforms have multiple M-wallets with a variety of back-end technologies, yet only a few create the necessary user engagement and resultant business profitability (Bhattacharya & Anand, 2017; de Luna, Liébana-Cabanillas, Sánchez-Fernández, & Muñoz-Leiva, 2019).

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Corresponding author. E-mail addresses: [email protected] (A. Gupta), [email protected] (A. Yousaf), [email protected] (A. Mishra). Report available at: https://www.prnewswire.com/news-releases/india-mobile-wallet-market-size–analysis-2018-2023-market-registered-double-digit-valuegrowth-with-a-cagr-of-67-10-during-review-period-of-2013—2017–300700990.html 2 Report available at: https://www.globenewswire.com/news-release/2019/07/10/1880730/0/en/Global-Share-of-Mobile-Wallet-Market-to-Surpass-3-142-17Billion-by-2022-Zion-Market-Research.html 1

https://doi.org/10.1016/j.ijinfomgt.2020.102094 Received 23 April 2019; Received in revised form 31 January 2020; Accepted 31 January 2020 0268-4012/ © 2020 Elsevier Ltd. All rights reserved.

Please cite this article as: Anil Gupta, Anish Yousaf and Abhishek Mishra, International Journal of Information Management, https://doi.org/10.1016/j.ijinfomgt.2020.102094

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acceptance model (TAM; e.g. Shin, 2009; Shaw & Kesharwani, 2019) or the UTAUT (e.g. Slade, Williams, & Dwivedi, 2014; Phutela & Altekar, 2019; Sivathanu, 2019; Singh et al., 2020), with only a few using the diffusion of innovation theory (e.g. Zhou, 2013; Kapoor, Dwivedi, & Williams, 2015). In the post-adoption paradigm, a key variable is continuance intention. Zhou (2013) was the first to examine post-adoption usage of the m-payment technology and suggests that trust, flow and satisfaction are key determinants of a user’s continuance intention. Next, some studies leveraged the ECM to examine the drivers for continuance intention for maturing technologies similar to M-wallets, like M-applications, e-learning, e-books and social messaging applications (Ooi & Tan, 2016; Zadvinskis, Chipps, & Yen, 2014). Subsequently, in their review of 191 articles, Nabavi, Taghavi-Fard, Hanafizadeh, and Taghva (2016)) note that various theoretical lenses are prominent in the research on continuance intention, with the ECM developed by Bhattacherjee (2001a) the most popular. However, they admit that none of these studies was conducted in the m-payments domain. Since then, some studies have examined continuance intention towards digital payments and the effects of key antecedents, like trust transfer (e.g. Cao, Yu, Liu, Gong, & Adeel, 2018), technology affordance (e.g. Pal, Herath, & Rao, 2019) and confirmation of expectations (e.g. Chen & Li, 2016; Kumar, Adlakaha, & Mukherjee, 2018; Humbani & Wiese, 2019). With few studies using the ECM to underpin M-wallet postadoption research, this presents an extant research opportunity.

The current work proposes an extended expectation–confirmation model (EECM) that integrates elements of the unified theory of acceptance and use of technology (UTAUT), a pre-adoption model, and the expectation–confirmation model (ECM), a post-adoption model, to examine the factors that influence the users’ post-adoption continuance intention (CINT) for an M-wallet (Venkatesh, Thong, Chan, Hu, & Brown, 2011). The EECM proposes a few additional pre- and postadoption variables to the ECM, namely pre-adoption performance expectancy (PrPEE), pre-adoption effort expectancy (PrEFE), post-adoption self-efficacy (PoSEFF), perceived user interface quality (PUIQ) and post-adoption perceived security (PoPSEC). The study was conducted with recent M-wallet adopters (adopted in the last 3 months) and frequent users, using once or more a week. The study was conducted in India, a fast-growing market, where the digital payments are expected to reach US$184 billion by 2024, with more than 80 % of urban Indians using such platforms (Vohra & Hazra, 2018; Techsci Research, 20193). The results suggest that pre-adoption performance/effort expectancies from M-wallets have a significant effect on the consumptiondriven confirmation of those expectations (CONEXP), which in turn impacts post-adoption self-efficacy, perceived security, perceived usefulness (PoPU) and user satisfaction (USAT). Additionally, the perceived user interface quality significantly affects the post-adoption selfefficacy and post-adoption perceived usefulness, the post-adoption perceived security impacts user satisfaction, and the post-adoption selfefficacy influences continuance intention for M-wallets. The conventional paths of the ECM are also established, except the impact of postadoption perceived usefulness on satisfaction. In line with few such works unifying pre- and post-adoption paradigms, this study contributes theoretically by integrating elements from two seminal theories to examine the impact of pre-adoption expectations from M-wallets on post-adoption perceptions/behaviours (Obal, 2017; Venkatesh et al., 2011). The work by Venkatesh et al. (2011) is the only one, to the best knowledge of the authors, that transcends across the two adoption phases by combining the UTAUT and ECM. As a contribution to this domain, the EECM not only supplements the basic UTAUT and ECM with additional pre- and post-adoption variables but also presents a more parsimonious sequential model, over existing ones, to examine the effect of pre-adoption expectations on post-adoption outcomes. The findings also help the application designers to understand both pre- and post-adoption factors that drive the continued post-adoption usage of M-wallets. The remainder of this paper is organised as follows. The next section presents the literature review and the EECM framework. Next, we discuss the research methodology, the results and the theoretical/managerial implications. The paper concludes with the limitations/future directions.

2.2. Pre- & post-adoption frameworks In the pre-adoption domain, the UTAUT has been proposed by Venkatesh, Morris, Davis, and Davis (2003) as an extension to TAM. UTAUT suggests that the adoption of technology is driven primarily by performance expectancy, effort expectancy, social influence, and facilitating conditions. The model itself has underpinned subsequent works examining the adoption of a variety of such technologies (e.g. Williams, Rana, Dwivedi, & Lal, 2011; Thakur, 2013; Slade et al., 2015). In the recent years, the UTAUT has also been widely applied to examine the acceptance of mobile/mobile-based technologies (Phutela & Altekar, 2019; Singh et al., 2020; Sivathanu, 2019; Slade et al., 2014; Zhou, Lu, & Wang, 2010). As technologies become more personal, the UTAUT, over the years, has been modified by adding individual variables like emotions, motivations, habits, perceived enjoyment, trust, privacy, convenience, gender, age, use-voluntariness, and prior experience as determinants to adoption (Lee, Lin, Ma, & Wu, 2017; Martins, Oliveira, & Popovič, 2014; Slade et al., 2015; Venkatesh, Thong, & Xu, 2012). Across these extensions, the UTAUT retains performance and effort expectancies as the two key antecedents to pre-adoption behavioural intention. However, the UTAUT remains a pre-adoption framework with limited examination on post-adoption attitudes/behaviours. Though some studies (e.g. Praveena & Thomas, 2013; Hsiao, Chang, & Tang, 2016; Adapa & Roy, 2017; Alalwan, 2020) attempt to use such a pre-adoption model in a post-adoption domain by modifying the “intention-to-use/behavioural-intention” to “continuance intention”, the meaning remains ambiguous (Ambalov, 2018). Further, these works have limited identification of the intervening factors that relate the pre-adoption expectations to post-adoption continual engagement (Joo, Park, & Shin, 2017). In the post-adoption domain, based on expectation–confirmation theory (ECT; Oliver, 1980), Bhattacherjee (2001a) proposed the ECM, which contains three variables: confirmation of expectations, postadoption perceived usefulness and user satisfaction. The ECM maps consumers’ confirmation against their overall consumption experience, as well as suggests the effects of these experiences on continuance intention and post-adoption perceived usefulness (Bhattacherjee, 2001a). With emergent technologies like M-wallets, when even regular users are evaluating the fast-evolving product, the desired level of basic

2. Literature review & the proposed framework 2.1. Mobile wallets In an extensive literature review, Dahlberg, Guo, and Ondrus (2015)) observe: “consumer adoption has become the largest category of mobile payment research… Authors have continued to use well-established adoption theories” (p. 274). Given that an M-wallet is a continuous means of post-adoption use and is a relatively emergent technology, the post-adoption dynamics for M-wallets represent a rather unattended research gap (Mallat, 2007). Synthesising the findings of five recent review works on digital payments (Patil, Rana, & Dwivedi, 2018; Patil, Dwivedi, & Rana, 2017; Patil, Rana, Dwivedi, & Abu-Hamour, 2018; Harris, Chin, & Beasley, 2019), it is found that most of the previous studies in this domain have either used the technology 3 Report available at: https://www.techsciresearch.com/report/india-mobilewallet-market/3796.html

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the transactions is a key concern and the security protocols are the primary enablers of post-adoption consumption experiences for Mwallets (Kumar et al., 2018; Libaque-Sáenz, Wong, Chang, Ha, & Park, 2016). Similarly, users invest more with an M-wallet after the initial adoption only when it minimises their cognitive load during consumption, and the perceived user interface quality is one such mechanism (Chatterjee & Bolar, 2019; Mishra, 2016). Finally, a user’s post-adoption revision of perception of their own capabilities, reflected as post-adoption self-efficacy, in further using a technology that involves financial risks, also determines their post-adoption engagement (Compeau & Higgins, 1995; Hsu & Chiu, 2004).

expectations are largely shaped by the primary attributes of other such smartphone applications. Hence, for such technologies, when the actual performance during consumption exceeds the primary expectations, be it in terms of performance or ease of use, users confirm the expectations and continue further engagement with the technology (Gorla & Somers, 2014; Shen, Li, & Sun, 2018). Since its inception, the ECM has been applied in various contexts, including digital textbooks (Joo et al., 2017), social networking (Lin, Featherman, & Sarker, 2017), health informatics (Shin, Lee, & Hwang, 2017), messaging (Oghuma, LibaqueSáenz, Wong, & Chang, 2016), online banking (Susanto, Chang, & Ha, 2016) and e-governance (Veeramootoo, Nunkoo, & Dwivedi, 2018). The ECM, despite its strong theoretical underpinning, has been found to be parsimonious in explaining post-adoption dynamics; hence, various researchers in different contexts have added variables to better explain continuance intention, for example, self-efficacy (Hsu & Chiu, 2004), trust (Kang, Hong, & Lee, 2009), attitudes (Liao, Palvia, & Chen, 2009), ease of use (Thong, Hong, & Tam, 2006), loyalty (Bhattacherjee, 2001a), playfulness (Lin, Wu, & Tsai, 2005), social norms (Kim, 2010), prior behaviour (Limayem & Cheung, 2008) and perceived behavioural control (Liao, Chen, & Yen, 2007). However, there is limited convergence towards a concrete post-adoption engagement model, with limited efforts to integrate the pre- and post-adoption paradigms (Lu, Cui, Tong, & Wang, 2019; Obal, 2017; Venkatesh et al., 2011). Given that M-wallets are financial applications facilitated with modern smartphone technologies, it is critical to examine the entire adoption process, where the pre-adoption expectations shape the postadoption perceptions/behaviours (Zhou, 2011). The most prominent effort in this domain is by Venkatesh et al. (2011) where they integrate the focal belief variables of the UTAUT, namely perceived usefulness, effort expectancy, social influence, facilitating conditions and trust, into the ECM by measuring their pre-use, disconfirmation, and post-use values at two points in time. Further, they examine the effects of these variables on pre/post-use attitude, post-use satisfaction and post-use continuance intention. However, the model misses the role of key variables like the perceived quality of the user-interface, user self-efficacy and the perceived security of the application (their items of trust do not reflect security directly), especially relevant in the case of novel technologies like M-wallets. Additionally, the examination of the effects of pre-use beliefs on the beliefs of disconfirmation, as well as the postuse beliefs, and the mixed effects of each of those beliefs of pre-use/ post-use attitude, satisfaction and continuance intention, makes the model challenging to interpret. Thus, there is a clear need to propose a novel model which is not only parsimonious and intuitive but also includes contextual variables relevant to the application. On the lines of Venkatesh et al. (2011), this study extends the ECM by integrating the primary antecedents of UTAUT, namely pre-adoption performance and effort expectancies, and adding post-adoption contextual factors, namely perceived user interface quality, post-adoption perceived security, and post-adoption self-efficacy. In the UTAUT, preadoption performance expectancy is equivalent to pre-adoption perceived usefulness and reflects the pre-purchase perception of payment performance enhancement over conventional methods, in form of convenience, quickness, and effectiveness of payments using M-wallets (Venkatesh, Morris, Davis, & Davis, 2003). Similarly, pre-adoption effort expectancy is equivalent to pre-adoption perceived ease-of-use and indicates how easy it will be to learn a specific M-wallet for a user before adoption (Venkatesh et al., 2003). Post-adoption, confirmation indicates the perceived consumptiondriven validation of the user’s basic expectations from the primary attributes of the M-wallet (Bhattacherjee, 2001a). Satisfaction represents the psychological outcome of the post-consumption emotions in relation to the consumer’s expected feelings about the experience with an M-wallet (Westbrook & Oliver, 1991). Continuance intention reflects a post-adoption stage when the M-wallet usage transcends conscious processing to evolve as a part of the user’s regular payment mechanism (Bhattacherjee, 2001a). Beyond these ECM constructs, the security of

2.3. Hypotheses The primary paths of the proposed EECM framework align with the ECT (Oliver, 1980), which indicates that the consumers first develop expectations prior to consumption, referred to by pre-adoption performance/effort expectancy. Next, they evaluate the product performance during consumption and benchmark it to the initial expectations, measured by confirmation. Following such confirmation, the consumers evoke satisfaction which determines their continuance intentions for the future (Obal, 2017). The confirmation of expectations, where consumers compare the level of product/service performance with an evaluative standard shaped by their pre-purchase expectations, validates the adoption decision (Joo et al., 2017; Bhattacherjee, 2001b; Ng, 2020; Obal, 2017; Oghuma, Libaque-Saenz, Wong, & Chang, 2016; Westbrook & Oliver, 1991). This implies that people compare their pre-usage expectations for performance, envisaged as performance expectancy in the UTAUT, with an M-wallet’s actual performance through its various functions like digital payments execution, secure information storage, convenience of usage, power efficiency, connectedness, integration with other relevant applications, operating system compatibility, merchant acceptance, and usage rewards (Oghuma et al., 2016; Susanto et al., 2016; Venkatesh et al., 2011). If the actual consumption performance is aligned with the initial expectations, users realize positive confirmation (Nam, Baker, Ahmad, & Goo, 2019). Further, higher are the performance expectations, greater will be the resultant confirmation, in case such expectations are met (Joo et al., 2017; Sørebø, Halvari, Gulli, & Kristiansen, 2009). Hence, we hypothesize: H1. Pre-adoption confirmation.

performance

expectancy

positively

affects

The theory of diffusion of innovation indicates that the more complex a new product is, the slower is its adoption (Brown & Venkatesh, 2005; Venkatesh et al., 2011). Similar concerns exist with the adoption of M-wallets for people who are not adept with using the smartphone technology or associated protocols for making digital payments (Lim, Kim, Hur, & Park, 2019; Oghuma et al., 2016; Shareef, Baabdullah, Dutta, Kumar, & Dwivedi, 2018). In the UTAUT, the effort expectancy implies the perception where people believe they can easily use a novel technology/product (Venkatesh et al., 2011). Thus, consumers would compare their pre-use perceptions of ease-of-use, based on general beliefs or own/vicarious experiences with digital technologies, to their actual consumption of an M-wallet, creating a positive confirmation if the expectations are met (Joo et al., 2017; Obal, 2017). Additionally, higher such effort expectancies create greater resultant confirmation, if those expectations are met (Joo et al., 2017; Sørebø et al., 2009). Hence, we hypothesize: H2. Pre-adoption effort expectancy positively affects confirmation. The previous discussion, as well as contemporary works (e.g. Lu, Wei, Yu, & Liu, 2017; Tam, Santos, & Oliveira, 2018; Susanto et al., 2016; Yuan, Liu, Yao, & Liu, 2016), indicate that an M-wallet’s performance is evaluated based on prior basic expectations, driven by the user’s own/vicarious prior experiences (Hossain & Quaddus, 2012). The 3

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expectancies confirmed, but users are more likely to perceive a higher self-capability, referred to as post-adoption self-efficacy, to use Mwallets in more meaningful ways (Alalwan et al., 2015; Venkatesh et al., 2011). Thus, we posit:

post-adoption satisfaction is primarily an outcome of the alignment between such expectations and confirmation through meaningful consumption experiences, a primary tenet of the ECM (2001b, Bhattacherjee, 2001a). The meta-analytic work on ECM by Ambalov (2018) also establishes the general relationship between confirmation and user satisfaction. Therefore, we posit:

H6. Confirmation positively affects post-adoption self-efficacy. Any technology leap entails the evolution of users’ interactions with an application, occasionally creating physical and cognitive barriers (Chang & Chen, 2008; Oghuma et al., 2016). Such barriers are also due to major changes in the user interface, affecting its overall perceived ease of use, ease of learning, flexibility and clarity (Gefen & Straub, 2000). The interface, a primary facilitator of human-computer interaction, helps users to achieve salient outcomes, concomitant to their information-processing capabilities, and, in turn, augments the technology’s post-adoption perceived usefulness (Hong, Lin, & Hsieh, 2017; Luo & Chea, 2018). The user interface, through continual evolution over time, facilitates interactivity with a system and has a significant impact on users’ utilitarian values, which are related to achieving their functional goals (Foroughi, Iranmanesh, & Hyun, 2019; Kim, Hwang, Zo, & Lee, 2016; Mishra, 2016). Research by Oghuma et al. (2016) and Wixon and Todd (2005) indicates that perceived user interface quality is a user’s object-based belief about a system’s ease of interaction, while post-adoption perceived usefulness is a behavioural belief about the consumption-driven performance of the system, with the former enabling the latter. Hence, we posit:

H3. Confirmation positively affects user satisfaction. A user’s expectations of usefulness, indicated by performance expectancy in the UTAUT, represent the belief that a product/service with certain attributes will provide the expected performance benefits (Hossain & Quaddus, 2012). Customers display post-consumption delight when their pre-adoption usefulness expectations are exceeded/ met during consumption (Lee, 2010). In the ECM, the perceived usefulness is proposed as a post-adoption construct reflecting the behavioural belief in a product’s/service’s usefulness, after the assimilation of expected and actual consumption values (Bhattacherjee, Perols, & Sanford, 2008; Wixom & Todd, 2005). For confirming/disconfirming experiences, users with initial usefulness expectations will adjust their post-adoption perceived usefulness upwards/downwards (Thong et al., 2006). The relationship between confirmation and post-adoption perceived usefulness has been validated in similar contexts, such as desktop services (Huang, 2019), mobile applications (Tam et al., 2018), M-shopping (Shang & Wu, 2017) and M-banking (Susanto et al., 2016; Yuan et al., 2016). Therefore, we posit: H4. Confirmation positively affects post-adoption perceived usefulness.

H7. Perceived user interface quality positively affects post-adoption perceived usefulness.

The uncertainty associated with novel high-involvement payment technologies induces privacy/data security concerns (Casalo, Flavián, & Guinalíu, 2007; Slade et al., 2015; Khalilzadeh, Ozturk, & Bilgihan, 2017). The facilitators of digital payments need to create security mechanisms to protect customers from third-party attacks (Hoffmann & Birnbrich, 2012; Khalilzadeh et al., 2017). The ex-ante perceived risks are mitigated once the actual usage experiences confirm the safety provisions, thereby improving the overall post-adoption perceived security (Apanasevic, Markendahl, & Arvidsson, 2016; Bhattacherjee & Barfar, 2011; Mallat, 2007). Post-adoption perceived security is the degree to which a customer, post-consumption, believes that a particular digital payment with safety features like digital seals, secure payment gateways, password-protected transactions and data encryption provides adequate data security (Shin, 2009). Thus, the confirmation from an M-wallet consumption is expected to have a positive impact on its post-adoption perceived security, indicating enhanced confidence in its data protection capabilities (Chen & Li, 2016; Oghuma et al., 2016; Pal et al., 2019). Thus, we posit:

The perceived user interface quality constitutes the overall usability, which includes the perceived ease of use of an M-wallet, driven by its visual, navigational, kinaesthetic and information design, and has a primary role in creating meaningful experiences (Belanche, Casaló, & Guinalíu, 2012; Ku & Chen, 2015; Mishra, 2016; Oghuma et al., 2016). Thus, the user interface of an M-wallet should be intuitive, userfriendly, aesthetically pleasing and ergonomic, not only to reduce the user’s cognitive load, but also enhance the perceived user self-capability in deploying the system for productive interactions (Liu, Du, & Tsai, 2009; Mishra, 2016; Oghuma et al., 2016; Wixom & Todd, 2005). Therefore, we posit: H8. Perceived user interface quality positively affects post-adoption self-efficacy. Post-adoption perceived usefulness is a subjective belief that current consumption of a specific product/service increases the user’s consumption performance, and is a key determinant of satisfaction with a technology (Davis, Bagozzi, & Warshaw, 1989). In the current context, post-adoption perceived usefulness reflects an individual’s assimilated belief that the M-wallet helps them to become more effective in making digital payments and gaining usage-based rewards (Kumar, Adlakha, & Mukherjee, 2018; Lu et al., 2017). Thus, the post-adoption perceived usefulness of M-wallets serves as a motivator for users to pursue the expected as well as newly-defined end goals, which, in turn, creates user satisfaction (Karjaluoto, Shaikh, Saarijärvi, & Saraniemi, 2019; Shankar & Datta, 2018). Thus, we posit:

H5. Confirmation positively affects post-adoption perceived security. Self-efficacy is defined as the “judgments of ones’ own capabilities to organise and execute courses of action required to attain designated performances” (Bandura, 1986, p. 362). Bandura (1986) observes that self-efficacy is a significant predictor of user responses towards unfamiliar stimuli. In the context of digital payments, self-efficacy indicates the user’s self-confidence in performing key financial tasks after adoption (Gupta & Arora, 2017; Hasan, 2006). In the UTAUT, while effort expectancy can be a barrier to the initial adoption/use of technology, the perceptions of post-adoption self-efficacy are only developed after hands-on consumption experiences (Venkatesh & Davis, 1996). Through confirming consumption experiences, a user’s perception about his/her own capability to use the product would be adjusted upwards, and the user’s self-esteem and sense-of-accomplishment would be enhanced (Compeau & Higgins, 1995; Alalwan, Dwivedi, Rana, Lal, & Williams, 2015). For example, during usage, consumers can evaluate the design of the M-wallet, various aspects of its built-in interactivity, and the protocols to ensure seamless and secure digital transactions. Based on such experiences, not only are their pre-adoption

H9. Post-adoption satisfaction.

perceived usefulness positively affects user

The post-adoption perceived security of an M-wallet, a primary attribute that generates belief of financial safety, gives a customer the confidence to execute various financial transactions through the technology (Lim et al., 2019; Oghuma et al., 2016; Shareef et al., 2018; Susanto et al., 2016). A higher user perception of an M-wallet’s in-built security features, providing that belief, enables satisfaction with the overall payment experiences, as well as the product itself (de Luna et al., 2019; Chawla & Joshi, 2019; Oliveira et al., 2016; Liu, Ben, & Zhang, 2019). Thus, we posit: 4

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H10. Post-adoption satisfaction.

perceived

security

positively

affects

M-wallets that people evaluate during consumption being dynamically evolving, due to the emergent nature of the technology, the first two types of confirmation measurements are rendered infeasible. Hence, as a parsimonious option, the perceived consumption-driven confirmation can be measured at the overall product-level as a post-use only rating (Bhattacherjee, 2001a). Thus, the items for confirmation were adopted from Bhattacherjee (2001a). The items for post-adoption perceived security and post-adoption perceived usefulness were adapted from Chang and Chen (2009) and Zhou, Lu and Wang (2010), respectively. The items for satisfaction came from Cho (2016), those for continuance intention and perceived user interface quality were drawn from Bhattacherjee (2001a); 2001b) and Flavián, Guinaliu, and Gurrea (2006)), respectively, and the items for self-efficacy were borrowed from Susanto, Chang and Ha (2016). A five-point Likert scale was chosen, as it not only offers more validity to the scale compared to three-/four-point scales but also provides adequate discriminable choices to the respondent (Asún, Rdz-Navarro, & Alvarado, 2016). The content validity of the questionnaire was ensured, first by three professors with related research experience and, second with a pre-test with ten respondents. An online survey was administered by a marketing research firm using its proprietary online data collection tool. The population of the study consisted of people who had installed an M-wallet application on their smartphones in the last three months and use the application at least once a week. A retrospective period of three months was taken to prevent any recall errors for pre-adoption expectations, while the chosen usage frequency is that of 80 % of M-wallet users in India (Sudman & Bradburn, 1973; Vohra & Hazra, 2018). The 850 qualifying members of the panel of the research firm were approached by e-mail. Of those, 755 respondents showed interest, and 716 of those provided completed questionnaires, causing the overall response rate to be 84.23 %. The entire dataset was collected in the three months from April to June 2018. The sample characteristics are shown in Table 1. The statistical analysis was performed with partial least squares structural equation modelling (PLS-SEM) using SmartPLS 3.0 software (Ringle, Wende, & Will, 2005). The data was found to be slightly nonnormally distributed, with skewness ranging from -1.54 to +1.32 and kurtosis ranging from -0.32 to +4.87 (Kline, 2011). As PLS-SEM is less sensitive to non-normality compared to covariance-based SEM (CBSEM), as well as gives more-robust results with lower sample sizes, the use of PLS-SEM was more appropriate (Hair, Black, Babin, & Anderson, 2009). A two-step procedure, starting with assessing the psychometric properties of the measures, followed by the hypothesis evaluation, was undertaken (Anderson & Gerbing, 1988). The dataset was randomly split into two equal parts of 358 respondents, with the first part used for the measurement model and the second used for structural model evaluations, in order to ensure empirical rigour (Bagozzi & Yi, 1988). Given that the total population of M-wallet users in India was estimated to be 73.9 million in 2018, the data collection year, and that the sample size for each analysis was less than 5 % of the population, the sampling error for each dataset at the 95 % significance level was estimated to be an acceptable value of 5.17 % (Behani, 2019; Kosar, Bohra, & Mernik, 2018).

user

The extant literature on technology acceptance, including UTAUT, validates the important role of perceived ease of use and perceived usefulness as salient antecedents of M-wallet adoption (Kumar, Adlakha, & Mukherjee, 2018; Karjaluoto et al., 2019). From an ECM perspective, however, Bhattacherjee and Lin (2015); Oghuma et al. (2016) and Foroughi, Iranmanesh and Hyun (2019) argue that the postadoption effect of perceived ease of use may decline over time. In contrast, post-adoption perceived usefulness has a sustained impact on technology continuance intention, as consumers’ instrumental behaviours are independent of the timing of such behaviours (Bhattacherjee, 2001a). Hong, Lin et al. (2017) observe that the utilitarian value of any product/service, an indicator of its post-adoption perceived usefulness, serves as a significant cause of continuance intention with the technology. There is sufficient evidence in the context of the ECM’s application to digital technologies that post-adoption perceived usefulness should create a positive continuance intention for M-wallet users (e.g. Kumar, Adlakha, & Mukherjee, 2018; Karjaluoto et al., 2019; Shankar & Datta, 2018; Chen, Yang, Zhang, & Yang, 2018; Joo, So, & Kim, 2018). Therefore, we posit: H11. Post-adoption perceived usefulness positively affects continuance intention. Extant research supports the argument that higher self-efficacy creates user confidence, which in turn enhances usage intention for applications enabling e-learning (Hayashi, Chen, Ryan, & Wu, 2004; Roca, Chiu, & Martínez, 2006), social networking (Wang, Xu, & Chan, 2015), online banking (Susanto et al., 2016) and general e-services (Hsu & Chiu, 2004). Thus, for high-involvement technologies like M-wallets, the user’s post-adoption self-efficacy shaped by a positive confirmation is expected to have an important role in determining their increased self-belief of exploring newer features/functions of the technology to achieve novel outcomes, resulting in enhanced continuance intentions (Bhattacherjee et al., 2008; Hsu & Chiu, 2004; Sharma, Sharma, & Dwivedi, 2019). Therefore, we posit: H12. Post-adoption intention.

self-efficacy

positively

affects

continuance

A consumer’s continuance with a product/service, like an M-wallet, is primarily an outcome of satisfaction with its consumption, defined as “the post-choice evaluative judgment” of the overall performance (Westbrook & Oliver, 1991, p. 84). A user’s satisfaction with an Mwallet determines whether the individual will keep using it as the primary vehicle for digital payments (Wixom & Todd, 2005). Multiple works, using the ECM as a theoretical underpinning, have validated the relationship between satisfaction and continuance intention across similar contexts, including mobile applications (Hsiao et al., 2016; Tam et al., 2018), e-governance (Valaei & Baroto, 2017; Veeramootoo et al., 2018), e-retail (Hsiao, 2018), massive open online courses (Joo et al., 2018), e-books (Joo et al., 2017) and banking/payment services (Chen & Li, 2016; Susanto et al., 2016; Yuan et al., 2016). Therefore, we posit: H13. User satisfaction positively affects continuance intention.

4. Results

Based on the hypotheses, the EECM framework is depicted in Fig. 1.

4.1. The measurement model (the first dataset) 3. Research methodology First, the absence of common method bias, when both the independent and dependent variables are collected from the same source, was established using the Harman one-factor and marker variable tests (Lindell & Whitney, 2001). Next, the convergent validity was examined by assessing the factor loadings, composite reliability (CR) and average variance extracted (AVE; Hair et al., 2009). It was found that all of the item loadings exceeded the recommended value of 0.70, the CR values exceeded the recommended value of 0.70 and the AVE values exceeded

A survey instrument was developed with the measurement items chosen from multiple scales and adapted to the context. A panel of three experts in the M-wallet industry selected and tweaked the relevant items. The items for pre-adoption performance and effort expectancies were borrowed from Venkatesh et al. (2003). As suggested by Bhattacherjee (2001a), there are three ways to measure confirmation: objective, inferred and perceived. With the pre-specified attributes of 5

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Fig. 1. The proposed extended expectation–confirmation model.

Discriminant validity was further assessed using Fornell and Larcker (1981) criterion, and the square root of the AVE for each latent variable was found to be greater than its corresponding correlation coefficient with other variables, as shown in Table 3. Additionally, the upper-diagonal values represent the heterotrait–monotrait (HTMT) ratio of correlations, and as all of them were below 0.85, discriminant validity was concretely established (Kline, 2011).

the cut-off point of 0.50, thereby indicating that the latent variable explained sufficient variance of its own indicators (Chin, 1998; Hair et al., 2009). The results are shown in Table 2. The second step was to check for discriminant validity, which implies that each latent variable is distinct from other such variables in the model. This was initially checked by comparing the factor loading of each item on its primary construct with its cross-loading on other model constructs. Table 2 indicates that the absolute values of the peak crossloadings did not exceed the primary loadings for any of the items.

Table 1 Sample profile (sample-size:716). Characteristics

Category

Category for PLS-MGA

Frequency

Per cent

Gender

Male Female Below 20 20-30 30-40 Above 40 High School or below Graduation Post-graduation and above Service Self-employed Not working 3 months 3 - 6 months 6 -12 months More than 12 months Third-party Smartphone manufacturer Bank Any Other Online shopping Traditional shopping Both < 20 % of transactions 20–30 % of transactions 30–40 % of transactions 40–50 % of my transactions > 50 % of my transactions 1 2 3 4 or more

Male Female Low-Age Low-Age High-Age High-Age Low-Education High-Education High-Education Employed Self/Unemployed Self/Unemployed Recent Recent Experienced Experienced Third-Party/Manufacturer Third-Party/Manufacturer Bank/Others Bank/Others Online Traditional/Both Traditional/Both Some Some Most Most Most Low Low High High

414 302 114 270 184 148 137 492 87 384 276 56 112 158 194 252 216 212 224 64 242 52 422 190 166 124 152 84 624 69 15 8

57.82 42.18 15.92 37.71 25.70 20.67 19.13 68.71 12.15 53.63 38.54 7.82 15.64 22.07 27.09 35.20 30.17 29.61 31.28 8.94 33.80 7.26 58.94 26.54 23.18 17.32 21.23 11.73 87.15 9.63 2.09 1.12

Age (years)

Education

Occupation

Using M-wallets since

Type of M-wallet used

M-wallet(s) used for

Frequency of using M-wallets (% of transactions)

Frequency of using M-wallets (uses per week)

6

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Table 2 Item descriptive, loadings and cross-loadings (first-dataset). Construct

Item (Code)

Mean (SD)

Factor Loading

Peak Cross Loading

PrPEE AVE = .631 CR = .872

Before downloading, I expected the M-Wallet to be useful for making digital payments (PrPEE1)

3.22 (0.86) 3.23 (0.87) 3.71 (0.89) 3.93 (0.74) 3.37 (0.87) 3.67 (0.79) 3.61 (0.81) 3.60 (0.78) 3.87 (0.84) 3.72 (0.84) 3.82 (0.72) 4.14 (0.88) 3.94 (0.80) 4.12 (0.85) 3.61 (0.84) 3.38 (0.88) 3.68 (0.84) 3.85 (0.83) 3.55 (0.96) 3.80 (0.69) 3.91 (0.66) 4.24 (0.71) 4.16 (0.75) 4.02 (0.84) 3.90 (0.75) 3.85 (0.85) 3.70 (0.75) 3.87 (0.85) 3.88 (0.88) 3.72 (0.88) 3.88 (0.84)

0.832

0.418

0.828

0.398

0.800

0.476

0.710

0.422

0.701

0.393

0.957

0.456

0.899

0.376

0.924

0.462

0.830

−0.247

0.877

−0.198

0.758

−0.356

0.831

0.440

0.879

0.444

0.755

0.404

0.851

0.378

0.727

0.471

0.882

−0.441

0.770

0.479

0.734

0.472

0.719

0.376

0.711

0.392

0.834

0.394

0.895

−0.327

0.864

−0.283

0.876

−0.233

0.884

0.281

0.880

−0.285

0.839

−0.296

0.864

−0.295

0.812

0.454

0.796

0.380

Before downloading, I expected the M-Wallet to improve my payment efficiency (PrPEE2) Before downloading, I expected the M-Wallet to improve my payment convenience (PrPEE3) Before downloading, I expected the M-Wallet to enable me make payments quickly (PrPEE4)

PrEFE AVE = .764 CR = .927

Before downloading, I expected myself to be skilful is easily using the M-Wallet (PrEFE1) Before downloading, I expected using the M-Wallets to be easy to use (PrEFE2) Before downloading, I expected that learning to use the M-Wallet to be easy for me (PrEFE3) Before downloading, I expected my interaction with the M-Wallet to be clear and understandable (PrEFE4)

CONEXP AVE = .678 CR = .863

My basic experience of using the M-wallet is better than what I had expected (CONEXP1) The basic services provided by the M-wallet is better than what I expected (CONEXP2) Overall, most of my basic expectations from using the M-wallet were confirmed (CONEXP3)

PoPU AVE = .678 CR = .863

The M-wallet is an effective mechanism for cashless transactions (PoPU1) The M-wallet provides value by offering discounts/cash-back offers (PoPU2) The M-wallet saves time and effort by making payments faster (PoPU3)

PoPSEC AVE = .676 CR = .871

The M-wallet ensures safe transactions (PoPSEC1) The M-wallet can prevent illegal access (PoPSEC2) I feel safe making transactions through this M-wallet (PoPSEC3)

PUIQ AVE = .537 CR = .823

Every feature/function of the M-wallet is easy to understand (PUIQ1) The amount of information displayed in the M-wallet is appropriate (PUIQ2) The M-wallet provides accurate information and functions that I need (PUIQ3) The visual display/design of the M-wallet is good (PUIQ4)

PoSEFF AVE = .745 CR = .898

I have the capability to use this M-wallet for making payments (PoSEFF1) I have the confidence to use this M-wallet for making payments (PoSEFF2) I feel comfortable to pay through this M-wallet (PoSEFF3)

USAT AVE = .773 CR = .911

My overall experience of the M-wallet is satisfying (USAT1) My overall experience of the M-wallet is pleasant (USAT2) My overall experience of the M-wallet is delightful (USAT3)

CINT AVE = .687 CR = .898

I intend to continue using this M-wallet (CINT1) I will keep using this M-wallet as regularly as I do now (CINT2) My intention is to continue using this M-wallet than using any alternative means (CINT3) I intend to increase my use of this M-wallet in the future (CINT4)

SD: Standard Deviation.

Birnbrich, 2012). From the values of average AVE and average R2 in Table 3, the GoF is calculated to be 0.504 (√ (0.678 * 0.374)), indicating reasonable model fit. The psychometric properties of the constructs were again evaluated using the CR value, AVE value, convergent validity and discriminant validity, as for the measurement model, followed by the examination of the hypothesised relationships. The R2 values, reflecting the variance explained by the endogenous

4.2. The structural model (the second dataset) To overcome the absence of fit indices to evaluate the overall model (like those in CB-SEM), Tenenhaus, Esposito, Chatelin, and Lauro (2005)) propose a unique goodness-of-fit (GoF) index. A GoF value higher than 0.25 reflects good overall model fit and is calculated using the formula: GoF = √ (average AVE * average R2) (Hoffmann & 7

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Table 3 Discriminant validity (first-dataset) and model fit (second-dataset). MEASUREMENT MODEL

PrPEE PrEFE CONEXP PoPSEC PoPU PoSEFF PUIQ USAT CINT

STRUCTURAL MODEL

PrPEE

PrEFE

CONEXP

PoPSEC

PoPU

PoSEFF

PUIQ

USAT

CINT

AVE

0.794 0.360 0.413 0.336 0.667 0.393 0.517 0.431 0.467

0.372 0.874 0.363 0.237 0.258 0.433 0.620 0.340 0.342

0.513 0.443 0.823 0.534 0.498 0.583 0.570 0.794 0.636

0.418 0.279 0.693 0.822 0.451 0.501 0.469 0.551 0.522

0.629 0.325 0.680 0.618 0.789 0.457 0.443 0.503 0.485

0.470 0.512 0.728 0.616 0.599 0.863 0.527 0.611 0.645

0.623 0.748 0.771 0.629 0.624 0.685 0.733 0.604 0.529

0.508 0.391 0.787 0.676 0.649 0.718 0.770 0.879 0.648

0.558 0.393 0.792 0.637 0.630 0.758 0.681 0.756 0.829

0.630 0.758 0.670 0.667 0.598 0.762 0.553 0.764 0.702 Average AVE 0.678

R2

0.177 0.283 0.225

0.660 0.523 Average R2 0.374

Note: The square-root of AVE is on the diagonal; lower-diagonal values are inter-construct correlations; upper-diagonal values are HTMT ratio of correlations.

Fig. 2. Structural model (second-dataset).

constructs; the Stone–Geisser (Q2) values, indicating the predictive relevance of the model; and the respective effect sizes for each individual exogenous construct, represented by f2 and q2, are shown in Fig. 2 and were found to be satisfactory (Hair, Hult, Ringle, & Sarstedt, 2016). The predictive relevance (Q2) was estimated using the blindfolding procedure in SmartPLS 3.0 with an omission distance of 7 (Hair et al., 2016). The standardised root-mean-square residual (SRMR), indicating the overall model fit, was found to be 0.075 (the composite model) and 0.079 (the common factor model), both of which were below the cut-off point of 0.08 (Hair et al., 2016). From the path values and their respective significances in Fig. 2, it is evident that all of the hypotheses, except H9, were supported. Additionally, the structural model was tested across various user groups, as shown in Table 1, using PLS-MGA (multi-group analysis). As PLS-MGA can handle a maximum of two groups, such groups were created using the combination scheme depicted in the table. Interestingly, for each of the paths, none of the t-values for path difference between the two groups for a categorical variable was found to be significant at the 95 % level of significance, indicating no inter-group model variations. Table 4 summarises the results of the hypotheses.

Table 4 Hypotheses summary. Hypothesis

Description

Result

H1 H2 H3 H4 H5 H6 H7 H8 H9 H10 H11 H12 H13

PrPEE positively affects CONEXP PrEFE positively affects CONEXP CONEXP positively affects USAT CONEXP positively affects PoPU CONEXP positively affects PoPSEC CONEXP positively affects PoSEFF PUIQ positively affects PoPU PUIQ positively affects PoSEFF PoPU positively affects USAT PoPSEC positively affects USAT PoPU positively affects CINT PoSEFF positively affects CINT USAT positively affects CINT

Supported Supported Supported Supported Supported Supported Supported Supported Not Supported Supported Supported Supported Supported

4.3. Discussion & implications This work proposes a novel model, the EECM, which provides insights into the factors that determine a user’s post-adoption satisfaction and re-engagement with M-wallets, based on the pre-adoption performance and effort expectations, and confirmation of those during consumption. Using the UTAUT and ECM theories as underpinnings, this 8

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work proposes a unique model, the EECM, developed as an extension to the traditional ECM. The EECM integrates pre-adoption expectancies from the UTAUT to explain the post-adoption satisfaction and continuance intention more effectively for maturing technologies like Mwallets (Bhattacherjee, 2001a; Lin et al., 2005). While various works in the past have attempted to extend the ECM by adding variables to the core model (e.g. Hsu et al., 2004; Bhattacherjee et al., 2008; Kang et al., 2009; Liao et al., 2009; Thong et al., 2006; Kim, 2010; Limayem & Cheung, 2008; Liao et al., 2007), the results have been largely mixed. Additionally, most of the information systems literature examines the initial acceptance of any product/service, supported mostly by preadoption theories, with limited focus on the post-adoption consumption process, which is crucial for the commercial success of technologies that have moved beyond initial adoption. Though some adoption studies (e.g. Adapa & Roy, 2017; Hsiao et al., 2016) attempt to extend the effect of pre-adoption antecedents, like expectations/social influence/facilitating conditions, on post-adoption behaviours, like satisfaction/continuance intention, the route from pre-adoption expectations to postadoption experience/engagement remains unclear (Ambalov, 2018). An important work on this interaction of pre- and post-adoption paradigms, UTAUT and ECM, is that by Venkatesh et al. (2011) who develop post-adoption meanings of pre-adoption UTAUT constructs; however, as discussed earlier, their model, through rigorous, is difficult to visualize with three separate beliefs, pre-use, disconfirmation and post-use, with mixed effects of each on pre-use/post-use attitude, satisfaction and continuance intention. The EECM creates a clear distinction between the pre- and post-adoption phases, offers novel postadoption contextual variables (perceived security, self-efficacy and perceived user interface quality), and serves as a model which is more parsimonious. The overall framework, hence, provides rigorous insights about both pre-adoption and post-adoption factors that influence continuance intention for digital technologies like M-wallets. Second, this paper re-examines the role of post-adoption perceived usefulness in the EECM, as distinct from pre-adoption perceived usefulness of the adoption theories. Extant research on the ECM has defined post-usage perceived usefulness as the efficiency/effectiveness of a product/service in fulfilling a customer’s needs, once consumption happens (Bhattacherjee & Premkumar, 2004). The concept has been equated with post-usage expectations or the utilitarian value derived from a product/service. Given the proximity of post-adoption perceived usefulness to other constructs in the ECM, the association between postadoption perceived usefulness and satisfaction has changed over the years. While perceived usefulness was proposed to affect satisfaction in the original ECM (Bhattacherjee, 2001a), Bhattacherjee, Perols and Sanford (2008) dropped the direct connection, creating a relationship anomaly (Hossain & Quaddus, 2012). This work integrates the tenets of the original ECM and proposes that post-adoption usefulness is not only impacted by pre-adoption perceived usefulness, measured by performance expectancy, through confirmation, but also has a positive effect on post-adoption continuance intention. While the absence of an effect of post-adoption perceived usefulness on user satisfaction helps to resolve the above-mentioned anomaly by aligning with the findings of Bhattacherjee, Perols and Sanford (2008), this work contrasts with previous studies that have suggested the existence of this relationship in various contexts (e.g. Bhattacherjee et al., 2008; Susanto et al., 2016; Hossain & Quaddus, 2012). Third, the additional variable in the EECM in the form of perceived user interface quality was found to be a strong antecedent to the postadoption perceived usefulness and self-efficacy. This finding supports works on user interface design that propose various facets of the user interface, be it visual appeal, ergonomics, functional capabilities, or information correctness and timeliness, as important for upward adjustment of perceived usefulness and perceived self-capability for using the technology, post-adoption (e.g. Mishra, 2016). The EECM, thus, clarifies the role of user-interface in the post-adoption dynamics, an area with a limited investigation, and integrates an important

work introduces relevant pre- and post-adoption variables (pre-adoption performance expectancy, pre-adoption effort expectancy, postadoption perceived security, perceived user interface quality and postadoption self-efficacy) that have not been examined by previous researchers in the context of M-wallets. The findings indicate that pre-adoption expectancies (performance/ effort) drive confirmation when the product’s consumption experience is aligned with the expectations (Joo et al., 2017; Obal, 2017; Venkatesh et al., 2011). This implies that the extent of confirmation from an M-wallet is largely anchored on the pre-adoption expectancies, which when higher, leads to greater consumption-driven confirmation and post-adoption outcomes, like enhanced perceived usefulness and user satisfaction (Susanto et al., 2016; Tam et al., 2018). A positive confirmation also has a positive impact on the post-adoption perceived security of an M-wallet (Bhattacherjee & Barfar, 2011; Shang & Wu, 2017; Tam et al., 2018). This indicates that confirming consumption experiences, aligned with the pre-adoption expectations, implies a reinforcement of the post-adoption perceived security of an M-wallet, mitigating any risk-perceptions towards the product (Martins et al., 2014; Oghuma et al., 2016). Further, confirmation has also a strong positive impact on post-adoption self-efficacy, suggesting that a positive experience with the M-wallet enhances the user’s self-esteem and sense of accomplishment and results in higher perceived self-capability of using the M-wallet (Alalwan et al., 2015; Compeau & Higgins, 1995). Such enhanced post-adoption self-efficacy was found to affect future continuance intention (Roca et al., 2006; Wang et al., 2015). The perceived user interface quality serves as an important enabler of the post-adoption perceived usefulness and self-efficacy. This means that the post-adoption usefulness of an M-wallet is still strongly determined by its interactivity shaped through the interface, which drives perceived ease of use and empowers the advanced functionalities of the technology. The user interface also impacts post-adoption self-efficacy, which implies that the intuitive design of an M-wallet application, with aesthetic appeal, ergonomics, functional capability and useful information, leads to an upward adjustment of user’s own capabilities to consume the technology (Mishra, 2016; Oghuma et al., 2016). Interestingly, post-adoption perceived usefulness was not found to have any impact on user satisfaction, a finding in contrast to the basic tenets of ECM (Hong, Lin et al., 2017). It can be inferred that the confirming consumption experiences are the primary drivers of postadoption perceived usefulness and user satisfaction, but the two are unrelated desirable outcomes for M-wallets. The study results indicate that post-adoption perceived usefulness strongly affects the continuance intention for M-wallet applications, supporting extant works (Hong, Lin et al., 2017; Susanto et al., 2016). Combining the results of these hypotheses, it can be argued that it is important for users to perceive an M-wallet application to be highly useful, once they consume it, for continual engagement, even if they may not be completely satisfied with all its features (Ha, Kim, Libaque-Saenz, Chang, & Park, 2015; Oghuma et al., 2016). Next, post-adoption perceived security was found to be a factor that enhances user satisfaction. Thus, consumption-driven validation of various security attributes at the back end of an M-wallet, such as data encryption, security assurances and designated third-party certificates, play a positive role in reassuring customers of data safety and create satisfaction (Casalo et al., 2007; Oghuma et al., 2016). Finally, results indicate that user satisfaction has a positive impact on continuance intention, which reiterates that as long as consumers’ post-adoption evaluative judgements of an M-wallet are positive, they will re-engage with it (Hsiao, 2018; Wixom & Todd, 2005). The model characteristics remain unchanged across various user characteristics, indicating the general robustness and universal application of the EECM. 4.4. Theoretical implications The current study provides four theoretical contributions. First, this 9

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infrastructure, leading to ever-improving Internet and smartphone penetration rates, the percentage of people who engage in digital payments or use such applications is still much lower compared to that in developed economies. Future researchers could replicate this study in other developing and developed economies and examine the model dynamics across the two types of economies, where technologies like M-wallets are in different lifecycle stages. Second, besides smartphones, many users deploy a variety of devices, such as tablets, feature phones, personal computers and even smart televisions, where the experience of digital payment applications may be completely different, especially in terms of variables like user interface quality, self-efficacy and perceived security. Therefore, the general findings regarding the EECM cannot be assumed to apply to all types of smart devices. The model could be enriched by including device-specific attributes in the measures of variables like user interface quality and perceived security. Third, this work conducted a limited investigation of the roles of user variables, as listed in Table 1, in the overall post-adoption evaluation and continuance intention for an M-wallet. The multi-group analysis of these demographics and behavioural variables returned insignificant effects. However, other individual factors, like socio-economic classification, lifestyle, attitude towards digital products and personality, can be instrumental in driving overall user satisfaction and continuance intention; hence, future research should examine the impacts of these additional factors as moderators/control variables. Finally, this study was conducted with the cross-sectional research design, but since the objective was to understand the effect of expectations on post-adoption dynamics, a longitudinal design, as employed by Venkatesh et al. (2011), would have been more appropriate. This is because, besides recall error, the current consumption experiences may cause the respondents to adjust their ratings for the pre-use expectations. Future researchers can attempt to divide the data collection process of this work across two or more points-of-time and re-examine the results.

implication from the user interface design literature into the ECM (e.g. Hong, Tai, Hwang, Kuo, & Chen, 2017). Finally, the EECM empirically demonstrates the importance of postadoption self-efficacy and how an M-wallet application can not only offer financial convenience to users but also meet their need for selfgrowth, supporting their consequent continuance intention. This specific result adds to extant research that has examined the psychological components for users that enhance technology acceptance and consumption (e.g. Lee, Ham, & Kim, 2013; Hasan, 2006; Hsu & Chiu, 2004; Susanto et al., 2016). Further, the indirect significant relationship between the pre-adoption effort expectancy and post-adoption self-efficacy implies that consumption experiences and resulting confirmation can help adjust a user’s own perceived capabilities of using novel technologies (Alalwan et al., 2015; Venkatesh et al., 2011). Hence, postadoption self-efficacy needs to be studied in greater detail as an evolving concept, as it is anchored in the theory of human needs which change with every user experience and play a significant role in determining the long-term success/failure of technology products/services. 4.5. Practical implications From a managerial perspective, the EECM provides useful insights. First, application developers need to understand the importance of the pre-adoption performance and effort expectations and the post-adoption confirmation of those expectations for M-wallets. It is evident that an M-wallet that fulfils the pre-adoption expectations and provides confirmation during consumption enhances post-adoption perceived usefulness, in the form of the functional objectives of executing digital transactions and securing usage rewards, and overall satisfaction/continuance intention. It may be prudent for managers to amplify such preadoption expectations, both in terms of performance and ease-of-use and meeting them during consumption, such that the post-adoption engagement is stronger. Second, post-adoption perceived security, which itself is shaped by prior expectations and confirmation of those, is a key driver of user satisfaction; thus, application designers should emphasise the provision of the latest security services to ensure users’ trust and overall satisfaction. They should incorporate security features in the form of digital seals, secure payment gateways, password-protected transactions and data encryption, which should also be regularly updated and actively communicated with the user. Third, the application developers must focus on providing an easy and user-friendly application interface. A key objective of M-wallet developers should be to create an ergonomic user interface that offers useful and relevant information, looks eye-catching and has an intuitive navigation design. Such interface design, shaped by principles like consistency, simplicity, modularity, cognitive directness, feedback, system messages, readability and customisability, enhances the ease of interaction, in turn delivering meaningful experiences for the M-wallet user. Finally, the role of post-adoption self-efficacy in driving continuance intention indicates that application developers need to emphasise the message of “made-for-you” when marketing their applications. With high-technology high-involvement products, high preadoption effort expectancy may become a barrier to people adopting and regularly consuming the products. Thus, M-wallet application developers should promote their applications in a way that not only promises but also enhances the post-adoption self-efficacy of using the application effectively.

5. Conclusion The application stores of most smartphone software platforms have multiple M-wallets, each with different back-end technology. However, some of these applications are more popular – they are downloaded and used more than the others are; thus, it is critical for M-wallet brands to understand the factors that create continued engagement beyond adoption. Through an elaborate framework, the EECM, the current work provides a hierarchical mechanism for consumers developing continual engagement with a specific M-wallet, driven by the preadoption expectations and post-adoption perceptions of security features, user interface quality and self-efficacy. With the emphasis on both pre- and post-adoption stages, this work aims to shift the focus of academic researchers and practitioners from the initial adoption of maturing digital technologies to their impact on post-adoption consumption dynamics.

CRediT authorship contribution statement Anil Gupta: Conceptualization, Methodology. Anish Yousaf: Data curation, Writing - original draft, Investigation. Abhishek Mishra: Visualization, Supervision, Software, Validation, Writing - review & editing.

4.6. Limitations and future research directions Acknowledgements This study had a few limitations. First, the respondents of this study were those are recent but frequent users of M-wallets in India. While India, a developing economy, is gradually improving its IT

The authors would like to thank Research Now SSI for their support in collecting data for this study. 10

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Dr Anil Gupta is working as Senior Assistant Professor in The Business School and School of Hospitality and Tourism Management (SHTM), University of Jammu, Jammu (India) since 2005. His research interests include sports marketing, tourism marketing, celebrity endorsements, cross-cultural management, entrepreneurship and community-based tourism. He has to his credit several publications in reputed international journals. He has also co-edited two books on Travel and Tourism Management and Cross-Cultural Management: Practice & Research. Dr Anish Yousaf is working as Assistant Professor in ICFAI Business School, Hyderabad. His research interests include Customer-Based Brand Equity Assessment and Brand Building Process especially for spectator sports, sports marketing, and retail management. He has published several papers in the area of sponsorship and its impact on brands. Dr Abhishek Mishra is an Associate Professor in the Indian Institute of Management, Indore. He did his PhD in the area of Product Design and its implications on Brand Equity from IIM Lucknow. He has publications in leading marketing journals, like Journal of Brand Management and his research interests are in the area of New Product Development, Product and Brand Management and Fuzzy Sets and Systems.

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