Exploring user behaviours on mobile technologies combined with payment functions during the COVID-19 pandemic

Exploring user behaviours on mobile technologies combined with payment functions during the COVID-19 pandemic Yuyang Zhao (D2015046) A thesis submitted in partial fulfilment of the requirements for the degree of Doctor in Information Management. October 2021 NOVA Information Management School Universidade Nova de Lisboa Information Management II Doctoral Programme in Information Management Exploring user behaviours on mobile technologies combined with payment functions during the COVID-19 pandemic Professor Fernando Bação, Supervisor III Doctoral Programme in Information Management Copyright © by Yuyang Zhao All rights reserved IV Doctoral Programme in Information Management Abstract With the extensive spread of smart mobile devices, mobile technologies and services have revolutionised and pervaded significantly in most aspects of human life, such as social communication, commerce, entertainment, etc. Various industries have integrated services and products with mobile financial transaction technologies, facilitating the payment services combined with various mobile applications. The wide adoption of mobile transactions has increased the efficiency of transaction processes, met the expectations of customers and the requirements of enterprises, and supported the social-economic development in different scenarios, especially under the pandemic situation. Understanding mobile device users’ perceptions and behaviours on mobile technologies combining payment functions under the COVID-19 pandemic situation has reinforced the need to embark on a deeper investigation of customer behaviours during the pandemic. For these reasons, this study contributes to the advancement of knowledge and implementation methods for a better understanding of the determinants of customers’ behavioural intentions of using mobile technologies combined with payment functions in a total of seven separate studies. The investigation begins with a systematic literature review on mobile payment studies presented in chapter two. This research is augmented by investigating users’ continuance usage intention of mobile payments under the COVID-19 pandemic in chapter three. The fourth chapter analyses the determinants of continuance usage intention of food delivery apps during the pandemic. Chapters five and six present two theoretical development studies about the Unified Theory of Acceptance and Use of Technology (UTAUT) and UTAUT2, respectively. The seventh chapter investigates customers’ psychological shopping processes via live-streaming shopping apps during the pandemic lockdown period. In epistemological terms, this study involved conjoint positivist and interpretivist research in behavioural information systems research. A qualitative research method was applied in chapters two, five and six, and a quantitative research method was implemented in the third, fourth and seventh chapters. The main theoretical foundations applied and validated in three empirical studies were UTAUT and UTAUT2. V Doctoral Programme in Information Management Specifically, chapter three integrates UTAUT with Mental Accounting Theory, the fourth chapter combines UTAUT with the Expectancy Confirmation Model, and chapter seven integrates UTAUT2 with the Stimulus-Organism-Response framework and Flow theory. This study found that performance expectancy, social influence, and trust significantly affect users’ behavioural intentions in all three empirical studies. Customers’ mental cognitions, such as perceived benefits, satisfaction, flow and perceived value, positively formulate users’ behavioural intention in the three studies, respectively. Hedonic motivation and flow significantly influence users' behavioural intention when mobile technologies contain payment and entertainment features. Moreover, this study contributes several theoretical and practical implications. This study facilitates the advancement of knowledge of mobile technologies adoption through three verified theoretical frameworks and two proposed developed theoretical models and appropriate measurement methods. Meanwhile, this study supports relevant stakeholders in mobile technologies, enterprises, policymakers, service providers, and marketing departments with valuable findings and discussions for comprehensively understanding the determinants of customers’ behaviours on mobile technologies combined payment function. Keywords: mobile technologies combined payment function; mobile payment; food delivery apps, live-streaming shopping apps, customer behaviours; Unified Theory of Acceptance and Use of Technology (UTAUT); UTAUT2; Mental Accounting Theory (MAT); Expectancy Confirmation Model (ECM); Stimulus-Organism-Response framework (SOR); Flow theory VI Doctoral Programme in Information Management Publications List of publications resulting from this dissertation. Published or accepted papers (Journal articles & Conference proceedings): Zhao, Y. and Bacao, F. (2021) ‘How Does the Pandemic Facilitate Mobile Payment? An Investigation on Users’ Perspective under the COVID-19 Pandemic’, International Journal of Environmental Research and Public Health,18(3):1016. DOI: https://doi.org/10.3390/ijerph18031016 Zhao, Y. and Bacao, F. (2020) ‘What factors determining customer continuingly using food delivery apps during 2019 novel coronavirus pandemic period?’, International Journal of Hospitality Management, Elsevier, 2020, Aug, 91, 102683. DOI: https://doi.org/10.1016/j.ijhm.2020.102683 Zhao, Y. and Bacao, F. (2020) ‘A comprehensive model integrating UTAUT and ECM with espoused cultural values for investigating users' continuance intention of using mobile payment’, 2020 3rd International Conference on Big Data Technologies. September 2020, 155–161. DOI: https://doi.org/10.1145/3422713.3422754 Zhao, Y. and Bacao, F. (2020) ‘Theoretical Development: Extending the Flow Theory with Variables from the UTAUT2 Model’, 2020 IEEE 6th International Conference on Computer and Communications (ICCC), Chengdu, China, 2020, 2427-2431, DOI: https://doi.org/10.1109/ICCC51575.2020.9345049 Papers (submitted or under review): Zhao, Y. and Bacao, F. (2021) ‘Systematic Literature Review: user perspective on mobile payment adoption.’ VII Doctoral Programme in Information Management Zhao, Y. and Bacao, F. (2021) ‘How does gender moderate customer intention of shopping via live-streaming apps during the COVID-19 pandemic lockdown period.’ VIII Doctoral Programme in Information Management Acknowledgements I am grateful to Prof. Fernando Bação, my supervisor on this dissertation, for always being available, for all the guidance, support, help, patience, and encouragement that significantly contributed to the quality of all studies. A great appreciation for everything. I thank Prof. Pedro Simões Coelho for the guidance and support of the fundamental knowledge of structural equation modelling, which contributed to all the studies' statistical methods for the models’ evaluations. I appreciate Prof. Paulo Miguel Ferreira Rita and Prof. Marco Painho for the guidance of a systematic and complete research process, which supported and enriched my research skills. I am obliged to NOVA IMS for providing the perfect facilities and knowledge that supported me through my research journey. A special thanks to my parents, Liang and Zhongqing, my wife and daughter, Polina and Mira, respectively, for the support, care, and motivation they have provided me during these years. To all my sincere thanks. IX Doctoral Programme in Information Management Table of contents 1. CHAPTER 1 - INTRODUCTION .................................................................................................. 1 1.1. INTRODUCTION ..................................................................................................................................................... 1 1.2. ADOPTION MODELS .............................................................................................................................................. 3 1.3. RESEARCH FOCI ..................................................................................................................................................... 4 1.4. MAIN OBJECTIVES ................................................................................................................................................. 5 1.5. METHODS .............................................................................................................................................................. 7 1.6. RESEARCH PATH .................................................................................................................................................... 9 2. CHAPTER 2 - SYSTEMATIC LITERATURE REVIEW: USER PERSPECTIVES ON MOBILE PAYMENT ADOPTION ........................................................................................................ 10 2.1. INTRODUCTION .................................................................................................................................................. 10 2.2. METHODOLOGY ................................................................................................................................................. 12 2.2.1. Systematic Literature Review ................................................................................................................ 12 2.2.2. Formulating research questions .......................................................................................................... 12 2.2.3. Identifying relevant studies ................................................................................................................... 13 2.2.4. Selecting studies ........................................................................................................................................ 15 2.2.5. Data extraction and synthesis ............................................................................................................... 16 2.3. REPORTING AND RESULTS ................................................................................................................................. 24 2.3.1. Distribution of publication years ......................................................................................................... 24 2.3.2. Distribution of target regions ............................................................................................................... 25 2.3.3. Distribution of key terms ........................................................................................................................ 26 2.4. DISCUSSION ....................................................................................................................................................... 29 2.4.1. Different factors involved in previous studies ................................................................................. 29 2.4.2. Theoretical frameworks applied in previous studies .................................................................... 35 2.4.4. Obstacles in previous studies ............................................................................................................... 39 2.5. CONCLUSION ..................................................................................................................................................... 42 2.5.1. Implications ................................................................................................................................................. 42 2.5.2 limitations and recommendations ....................................................................................................... 43 3. CHAPTER 3 - HOW DOES THE PANDEMIC FACILITATE MOBILE PAYMENTS? AN INVESTIGATION ON USER PERSPECTIVES UNDER THE COVID-19 PANDEMIC .................. 44 3.1. INTRODUCTION .................................................................................................................................................. 44 3.2. THEORETICAL BACKGROUND ............................................................................................................................ 46 3.2.1. M-Payment and Its Utilisation Under the COVID-19 Pandemic .............................................. 46 3.2.2. Mental Accounting Theory (MAT) ....................................................................................................... 48 3.2.3. Unified Theory of Acceptance and Use of Technology (UTAUT) ............................................. 49 1 Doctoral Programme in Information Management 1. Chapter 1 - Introduction 1.1. Introduction With the significant development of information technology, smart mobile devices have been adopted ubiquitously. Various mobile applications and services have developed sharply, revolutionising the telecommunication industry (Baptista and Oliveira, 2016). Since mobile devices involved financial transaction functions, the global business climate had changed dramatically from traditional social commerce to online commerce towards mobile commerce. According to a WorldPay report, mobile payments accounted for 22% of the global points of sale spending in 2019, and this percentage will increase to 29.6% in 2023 (WorldPay, 2020). The wide adoption of mobile payment has facilitated financial transactions for paying goods, services, and bills anywhere, anytime and for anyone (Di Pietro et al., 2015). Mobile payment, as an innovative contactless financial transaction technology, has been widely applied in various business industries such as banking, catering, entertainment. Meanwhile, mobile device users’ consumption habits have significantly changed. Especially since the COVID-19 pandemic broke out in December of 2019, maintaining social distance and lockdown measures have been applied globally. Contactless payments have been adopted significantly. For example, in the catering industry, 41.6 % of residents preferred mobile delivery services to purchase food and daily supplies during the COVID-19 pandemic in China (Meituan research institute, 2020). Investigating the determinants of customer behaviours on mobile technologies combining payment functions has aroused increasing attention from both academic and practical aspects. Although some previous studies facilitated the understanding of user’s adoption intention and actual usage of mobile payments in different contexts (Di Pietro et al., 2015; Ramos-de-Luna et al., 2016; Liébana-Cabanillas et al., 2018; Cao and Niu, 2019), the determinant variation and theoretical evidence of mobile payment adoption were still insufficient (Dahlberg et al., 2015). Previous research has been especially sparse considering users’ perceptions and behavioural intention of using mobile applications with 2 Doctoral Programme in Information Management payment functions corresponding to specific environmental conditions. Thus, investigating customer behaviours is essential for relevant stakeholders to understand technology’s performance to develop better marketing and business strategies to optimise users’ experience and meet customers’ requirements opportunely. Furthermore, the main motivational factors for the current research are listed as follow. 1) Despite some previous studies having investigated various specific antecedents of mobile payment technology adoption, the research scenarios were insufficient because of the variation of cultural backgrounds (Baptista and Oliveira, 2015), which indicates the necessity for exploring the new constructs and novel interactions of variables to contribute knowledge advancement. 2) Previous studies on mobile payment adoption’s variation and theoretical evidence of different perspectives in a specific situation were limited (Dahlberg et al., 2015). Especially under emergency conditions, namely the COVID-19 pandemic, investigating the factors affecting users’ continuance intention of using mobile payments can support new insights into how emergency conditions affect users’ perceptions and behaviours. 3) Payment functions have been integrated into various mobile technologies. Food delivery apps (FDAs), as emerging catering service mobile applications, facilitate customers’ dining experiences anytime and anywhere. FDAs met customers’ daily requirements and widened catering enterprises’ operational range, especially during the COVID-19 pandemic. Therefore, investigating customers’ continuance usage intention of FDAs under the COIVD-19 scenario is valuable for knowledge advancement in new technology usage under an emergency environment. 4) The theoretical frameworks applied in previous mobile payment technology adoption studies were insufficient to explain users’ behaviours from technological and mental perceptions conjointly (Marinkovic et al., 2020). For example, as the engagement cognition, flow reflects the customer’s immersive experience (Hossain and Zhou, 2018). Thus, theoretical frameworks require adjustment and modification to comprehensively analyse users’ technological perceptions and mental cognitions in various environmental backgrounds and involve cultural moderating effects to relevant theoretical development. 3 Doctoral Programme in Information Management 5) Live-streaming shopping apps (LSSAs), as a relatively new emerging mobile commerce technology combining social entertainment and e-commerce features, have rarely been explored in previous studies. Different ages and gender play a role in customers’ psychological shopping processes via LSSAs under specific environmental conditions (such as the pandemic lockdown situation). These factors should be investigated to bring new insights into explaining customer behaviours on mobile payment technology. 1.2. Adoption models Myriad adoption models have previously been implemented in new technology adoption literature, such as the Diffusion of Innovations theory (DOI) (Rogers, 2003), Theory of Reasoned Action (TRA) (Fishbein and Ajzen, 1975), Theory of Planned Behaviour (TPB) (Ajzen, 1991), Technology Acceptance Model (TAM) (Davis, 1989) and Unified Theory of Acceptance and Use of Technology (UTAUT) (Venkatesh et al., 2003). wherein TAM and UTAUT are probably the most widely applied and validated models evident in many empirical studies on consumer behaviour on adopting new technologies (Kim, Mirusmonov and Lee, 2010; Koenig-Lewis et al., 2015). Specifically, Venkatesh et al. (2003) proposed UTAUT to investigate users’ behavioural intention to use new information technology systems to reflect social cognition theory. UTAUT incorporated four fundamental determinants, performance expectancy, effort expectancy, social influences, and facilitating conditions as an extension of the TAM model, which has been modified with different external variables or integrated with other theoretical frameworks to explore users adopting new technologies in various contexts (Oliveira et al., 2014; Khalilzadeh, Ozturk and Bilgihan, 2017; Marinkovic et al., 2020). Di Pietro et al. (2015) integrated TAM, DOI and UTAUT to investigate the determinants of mobile payment adoption. Moreover, UTAUT2, an advanced version of UTAUT, designed by Venkatesh, Thong and Xu (2012), overcame the weaknesses of UTAUT by including additional variables and moderators to predict users’ motivation and behaviour on information technology. UTAUT2 has also been revised by extending or subtracting variables or models to accurately analyse users’ adoption intention of information technology in various particular 4 Doctoral Programme in Information Management situations. For example, excluding price value and adding privacy to investigate mobile payment adoption (Morosan and DeFranco, 2016), adding culture moderators to predict mobile banking adoption (Baptista and Oliveira, 2015), and integrating the theory with the DOI model to investigate mobile payment adoption (Oliveira, 2016). 1.3. Research foci The principal foci of this paper are summarised and presented in Figure 1.1. as follows: • understanding the main determinants of customers’ continuance usage intentions of mobile payment and food delivery apps under the COVID-19 pandemic; • developing an adoption framework with theoretical support; investigating customers of different ages and genders’ psychological shopping processes via live-streaming shopping apps during the pandemic lockdown period, Meanwhile, the study’s target is to focus on individual levels of behaviour on mobile technologies combined payment function. Figure 1.1. Research focuses Mobile technologies combining payment functions interact information communication with electronic financial transactions (Dahlberg et al. 2015), which are embedded in various services to support customers’ financial transaction requirements, such as mobile banking, food delivery apps, and live-streaming shopping apps. This thesis proposes a specific adoption model in each chapter to investigate customer behaviours on Theoretical development Integrating technological and psychological constructs and cultural moderators COVID-19 pandemic condition Mobile payment continuance adoption Food delivery apps contiuance adoption COVID-19 pandemic lockdown condition Live-streaming apps usage Under age and gender moderating effects 5 Doctoral Programme in Information Management mobile technologies combining payment functions in different patterns, scenarios, and samples to contribute relevant knowledge advancement. A total of six separate studies are presented in Figure 1.2. Wherein four cover the mobile payments arena, one investigates the food delivery apps aspect, and one delves into the live-streaming shopping apps field. Specifically, UTAUT (Venkatesh et al., 2003) was integrated with different frameworks and theories for specific scenarios, such as the Mental Accounting Theory (MAT) (Thaler, 1985), Expectancy Confirmation Model (ECM) (Bhattacherjee, 2001), Hofstede’s Cultural Values (Hofstede, 1980), and the Task-Technology Fit model (TTF) (Goodhue and Thompson, 1995). UTAUT2 (Venkatesh et al., 2012) was combined with Flow theory (Csikszentmihalyi, 1975), and the Stimulus-OrganismResponse framework (SOR) (Mehrabian and Russell, 1974). Figure 1.2. List of studies and theoretical frameworks 1.4. Main objectives This study aimed to understand the main determinants of customer behaviours on mobile technologies with combined payment functions, including continuance usage intentions of mobile payment and food delivery apps during the COVID-19 pandemic, users’ psychological processes, moderated by age and gender, of shopping via live-streaming shopping apps during the pandemic lockdown period. Each research •Systematic literature review of mobile payments' adoption •Continuace usage under the COVID-19 pandemic (UTAUT + MAT) •Theoretical development of UTAUT (UTAUT + ECM+culture moderator) •Theoretical development of UTAUT2 (UTAUT 2+Flow theory) Mobile payments •Continuace usage under the COVID-19 pandemic (UTAUT + ECM+TTF) Food delivery apps •Shopping intention under age and gender moderating effects (UTAUT2 + SOR + Flow theory) Live-streaming shopping apps 6 Doctoral Programme in Information Management objective was addressed in different studies and are presented in separate chapters. The second chapter consists of a systematic literature review of mobile payment adoption studies, which generated the main determinants of mobile payment adoption, identified the popular theoretical frameworks applied in previous studies, and summarised the obstacles in mobile payment adoption studies. This chapter provides a general theoretical foundation and research guidance for supporting the remaining studies in the following chapters. The third chapter investigates the main determinants of mobile device users’ intention to continuously use mobile payments under the COVID19 pandemic by integrating the UTAUT and MAT theoretical models with additional perceived benefits, security, and trust constructs. Mobile payments provide individuals with a contactless transaction method under the highly contagious COVID-19 pandemic condition. The determinants of technology adoption under emergencies have barely been explored heretofore; this chapter explores how the pandemic has influenced individual use behaviour. The fourth chapter researches individuals’ continuance usage intention of food delivery apps under the COVID-19 pandemic. As a specific emerging online-to-offline mobile technology with payment functions, food delivery apps have been widely adopted. These apps provide two-way beneficial catering delivery services in rescuing catering enterprises and satisfying customers’ technological and mental expectations during the COVID-19 pandemic. A comprehensive model integrating UTAUT, ECM and TTF with the trust factor was proposed in this chapter to support understanding of customers’ behaviours. The fifth chapter develops a theoretical framework by integrating UTAUT and ECM with the trust construct and Hofstede’s cultural values to contribute towards theoretical development. This chapter aims to build up an adoption model under the cultural moderating effects to analyse users’ behavioural intention from technological and mental perceptions conjointly to understand individual behaviours of technology usage comprehensively. The sixth chapter presents a theoretical development of UTAUT2 by combining a new mediator, flow theory, and an additional variable, 7 Doctoral Programme in Information Management satisfaction, to explore the main antecedents of using mobile technology. Flow, as a mediating variable, represents an individual’s immersive experience and engagement with a particular technology, which is influenced by the user’s technological perceptions, towards affecting their mental cognition and adoption intention. The proposed adoption model contributes a theoretical foundation for relevant study to predict the main drivers of technology adoption from a users’ perspective. The seventh chapter proposes a modified Stimulus-Organism-Response framework, which extends stimulus based on UTAUT2 and trust, a proposed organism based on flow theory. It assumes perceived values and behavioural intention as responses to investigate customers’ psychological shopping processes via live-streaming apps under the moderating effects of age and gender during the pandemic lockdown period. This chapter provides a new insight that an individual’s psychological process of using an emerging entertainment mobile payment technology under a specific environmental condition is moderated by gender. As a concluding chapter, the eighth chapter summarises the principal findings of the previous chapters and generates the most important contributions of this study. 1.5. Methods This study involved positivist and interpretivist research in behavioural information systems research conjointly. Specifically, chapters two, five and six applied interpretivism qualitative research methods, which aimed to gain in-depth insights and empathetic understanding of the state-ofthe-art knowledge from previous studies and theoretical developments based on the weaknesses of the adoption model in previous studies. On the other hand, chapters three, four and seven broached positivism, which aimed to discover human behaviours in information system research. These chapters were conducted using a quantitative research method. Moreover, this study applied the observation method in the interpretivist research to summarise the subjective discoveries and implemented an online survey method in the positivist research to generate the objective phenomenon of human behaviours in information systems. A structural approach was applied in the positivist research, 8 Doctoral Programme in Information Management including identifying the research topic, constructing the research model, proposing hypotheses and validating the research model by collected data. The theoretical frameworks and the quantitative approach in the positivist research are presented as follows. Chapter three integrates UTAUT (Venkatesh et al., 2003) with Mental Accounting Theory (MAT) (Thaler, 1985) to establish the theoretical framework. MAT explained that personal desires affect cognitive processes towards bearing on psychological processes for valuing a specific technology which should be considered in the voluntary usage environment (Alghamdi, 2018). For the data collection, a cross-sectional online survey with a five-point Likert scale measurement for all constructs’ indicators was applied to collect data through the most popular Chinese mobile social media platform, WeChat, in a three-week period during the COVID-19 pandemic, from 11 March 2020 to 31 March 2020. A total of 739 valid data was accepted for evaluating the theoretical framework using a covariance-based structural equation modelling technique through a two-step approach (Anderson and Gerbing, 1988), including validating the measurement model and testing the structural model. Chapter four combines UTAUT (Venkatesh et al., 2003) with the Confirmation Model (ECM) (Bhattacherjee, 2001) and Task-Technology Fit model (TTF) (Goodhue and Thompson, 1995) to propose the theoretical framework. ECM explained users’ satisfaction and continuance behaviour of information systems via three dimensions, performance expectancy, confirmation and satisfaction (Bhattacherjee, 2001). TTF presented the degree of fitness between tasks and technology to assist in performing individual daily tasks and utilising technology (Goodhue and Thompson, 1995). Equivalent data collection and validation methods were applied in this chapter to understand the determinants of customers using food delivery apps during the COVID-19 pandemic. Data collection was conducted in China from 23 March 2020 to 12 April 2020. A total number of 532 valid answers were accepted to validate the theoretical framework. Chapter seven integrates UTAUT2 (Venkatesh et al., 2012) with the Stimulus-Organism-Response framework (SOR) (Mehrabian and Russell, 1974) and Flow theory (Csikszentmihalyi, 1975) to investigate individuals’ psychological process of shopping via live-streaming shopping apps 9 Doctoral Programme in Information Management during the COVID-19 pandemic lockdown period. A seven-point Likert scale cross-sectional online survey was applied in China from 9 August 2020 to 6 September 2020 through Wenjuan.com (a Chinese online survey platform) and WeChat. A total of 374 valid answers were applied for assessing the theoretical model. Moreover, to distinguish the moderating effects of age and gender, a multi-group analysis was implemented to evaluate the path coefficients across each age and gender subsample. 1.6. Research Path This research consists of six interrelated studies associated with mobile technologies that combine payment functions, namely mobile payment, food delivery apps and live-streaming shopping apps, addressed from the second to the seventh chapters separately. Two of them have already been published in Q1 and Q2 international journals with a double-blinded review process. Another two were published in international conference proceedings with a double-blinded review process and indexed in Scopus. The remaining two studies are under review. The majority of the chapters were published in international journals and qualified international conference proceedings with a double-blinded review process to guarantee the quality of the research. The summary of the current stage of each study is presented in Table 1.1. The final chapter at the end of this dissertation summarises the main conclusions of each study. Table 1.1. Current stages of studies Chapter Study name Current stage 2 Systematic Literature Review: user perspective on mobile payment adoption Under review 3 How does the pandemic facilitate mobile payment? An investigation on users’ perspectives under the COVID-19 pandemic Published in the International Journal of Environmental Research and Public Health (Q2) 4 What factors have determined customers’ continuous use of food delivery apps during the 2019 novel Coronavirus pandemic? Published in the International Journal of Hospitality Management (Q1) 5 A comprehensive model integrating UTAUT and ECM with espoused cultural values for investigating users' continuance intention of using mobile payment Published in conference proceeding of the 2020 3rd International Conference on Big Data Technologies 6 Theoretical Development: Extending the Flow Theory with Variables from the UTAUT2 Model Published in conference proceeding of the 2020 IEEE 6th International Conference on Computer and Communications 7 How does gender moderate customer intentions of shopping via live-streaming apps during the COVID-19 pandemic lockdown period Under review 10 Doctoral Programme in Information Management 2. Chapter 2 - Systematic Literature Review: user perspectives on mobile payment adoption 2.1. Introduction Mobile payment (M-payment) was defined by Dahlberg et al. (2008) as ‘the payment method for goods, services, and bills with a mobile device by taking advantage of wireless and other communication technologies. With the increasing compatibility of M-payment services and ubiquitous coverage of internet-based communication networks, financial transactions via mobile devices are leading us to move towards a cashless world, which will be worth more than £3.5 trillion by 2023, following a growth of 33.8% CAGR between 2017-2023 (Merchantsavvy, 2019). M-payments supported users’ daily transactions and protected the development of the social economy during the pandemic, especially in 2020, despite the global economy suffering seriously because of the COVID-19 pandemic. For example, the number of transactions made by mobile payments was 22.4 million in the first quarter of 2020 in China, up 187% from the year 2019 (China Banking and Insurance News, 2020). The percentage of M-payment users in China had increased from 73.5% in June 2019 to 85.3% in March 2020 and reached 86.0% in June 2020 (CNNIC, 2020). As an efficient cashless transaction pattern, M-payment plays a vital role in various industries, such as banking, e-commerce, hospitality, and entertainment. It provides two-way benefits for both consumers and merchants by providing fast, safe and convenient financial transactions. This phenomenon became even more prevalent during the Covid-19 lockdowns, which were decreed by governments worldwide. Despite various previous research focusing on mobile payment adoption in various scenarios (Slade et al. 2013; Koenig-Lewis et al., 2015; Oliveira et al.,2016; Lu et al., 2017; Park et al., 2018), a comprehensive systematic literature review on mobile payment adoption had not been sufficiently analysed. This scenario required an investigation to understand users’ behaviours on M-payment better to support relevant knowledge extraction. The purpose of this paper is to systematically 17 Doctoral Programme in Information Management Studies Title Theo. basis Key Factors Loc. No. Key outcomes Obstacles (Baptist a and Oliveir a, 2015) Understanding mobile banking: The unified theory of acceptance and use of technology combined with cultural moderators UTAUT2 Performance expectancy; Effort expectancy; Social influence; Facilitating conditions; Hedonic motivation; Price value. Hofstede’s cultural moderators. Mozam bique. 252 This study integrated UTAUT2 and Hofstede’s cultural values and found that Individualism/collectivism, Uncertainty avoidance, Long/short term, Power distance have positively moderating influences on acceptance of mobile banking. Culture; region; server (Boonsi ritomac hai and Pitchay adejana nt, 2018) Determinants affecting mobile banking adoption by generation Y based on the Unified Theory of Acceptance and Use of Technology Model modified by the Technology Acceptance Model concept UTAUT; TAM Performance expectancy; Effort expectancy; Social influence; Facilitating conditions; Security; Selfefficacy; Hedonic motivation Thailan d 480 This study combined ATM and UTAUT to determine the mediating effect of hedonic motivation being impacted by facilitating conditions, security, and self-efficacy, which conjointly affected behavioural intention on mobile backing adoption. Age (Cao and Niu, 2019) Integrating contextawareness and UTAUT to explain Alipay user adoption UTAUT Performance Expectancy; Effect Expectancy; Social Influence; Perceived Risk; Ubiquity; Context; User Adoption. China 614 This study integrated contextawareness and UTAUT and stated that performance expectancy mediated ubiquity and user adoption. Content has an insignificant positive impact on performance expectancy; effect expectancy. Security (Chawl a and Joshi, 2019) Scale Development and Validation for Measuring the Adoption of Mobile Banking Services TAM Ease of use; Attitude; Lifestyle; Convenience; Efficiency; Trust India 283 The study integrated TAM with attitude, lifestyle, trust, convenience and efficiency, and found that trust, attitude, and lifestyle significantly impacted behaviour intention. Age; region; sample size; culture (Chen and Li, 2017) Understanding Continuance Intention of Mobile Payment Services: An Empirical Study IT continuance theory Post perceived usefulness; Disconfirmation of pre-perceived usefulness; Post perceived risk; Disconfirmation of pre-perceived risk; Trust and Satisfaction. China 243 Satisfaction had positive impacts on trust and continuance intention. Preperceived usefulness positively affected satisfaction and postperceived usefulness. Pre-perceived risk negatively impacted user satisfaction and positively affected post-perceived risk. Trust had a positive impact on post-perceived usefulness and a negative impact on post-perceived risk. Habit; region (Cocosi la and Trabels i, 2016) An integrated valuerisk investigation of contactless mobile payments adoption Theory of perceived value-risk Utilitarian; Enjoyment; Social; Perceived risk (time; social; psychological and privacy); Perceived value; Canada 289 This study integrated value-risk perception as a significant factor in adopting NFC payments with smartphones in Canada. The results also presented that utilitarian and enjoyment values had a positive impact on user motivators. Psychological and privacy risks had the most negative impact. User experience (Daştan and Gürler, 2016) Factors Affecting the Adoption of Mobile Payment Systems: An Empirical Analysis TAM Perceived Reputation; Environmental Risk; Perceived Trust; Perceived Usefulness; Perceived Ease of Use; Perceived Mobility; Attitude. Turkey 225 Perceived Trust, Perceived Mobility and Attitude had positive effects on the adoption intention. Perceived trust was positively related to perceived reputation and negatively related to environmental risk. However, perceived usefulness and perceived ease of use do not affect adoption intention. Sample size; region (Kervil er, Demou lin and Zidda, 2016) Adoption of in-store mobile payment: Are perceived risk and convenience the only drivers? Theory of perceived value Perceived benefit (utilitarian, hedonic, and social); Perceived risks; Experience; Spillover effects French 363 This paper measured perceived benefits from utilitarian and hedonic, and social aspects. Financial and privacy risks were key drivers for the French consumer adopting mobile payment. Perceived Enjoyment 18 Doctoral Programme in Information Management Studies Title Theo. basis Key Factors Loc. No. Key outcomes Obstacles risk has a significant negative impact on the intention of adoption. (Di Pietro et al., 2015) The Integrated Model on Mobile Payment Acceptance (IMMPA): An empirical application to public transport TAM; DOI; UTAUT Usefulness; Ease of use; Security; Attitude; Compatibility; Users’ behaviour Italy 439 Usefulness, Ease of use and Security have a positive effect on usage intention. Usefulness is simultaneously influenced by Ease of use, towards influencing Compatibility and Attitude on adopting mobile services. Age; use experience; culture; region (Gu, Lee and Suh, 2009) Determinants of behavioural intention to mobile banking. TAM Social Influence; System Quality; Facilitating, Conditions; Selfefficacy; Familiarity with Bank; Situational Normality; Structural Assurances; Calculative-based Trust; Perceived Usefulness; Perceived Ease of Use; Trust. South Korea 910 This paper applied Trust-associated TAM and found that trust and ease of use affected perceived usefulness. Self-efficiency had the strongest impact on perceived ease-of-use, which in turn affected behavioural intention through perceived usefulness. Trust as a strong indicator could increase behavioural intention. User experience; Sample size (Haider et al., 2018) Exploring Gender Effects in Intention to Islamic Mobile Banking Adoption: an empirical study ATM Perceived financial cost; Perceived usefulness; Social norms; Perceived credibility; Perceived selfexpressiveness; Gender Pakista n 243 Male intention was significantly impacted by perceived usefulness and perceived self-expressiveness. Female intention was significantly impacted by perceived credibility. However, with perceived financial cost and social norms, no significant gender differences existed. Region; Age (Hamid i and Safaree yeh, 2019) A model to analyse the effect of mobile banking adoption on customer interaction and satisfaction: A case study of mbanking in Iran CRM Interaction; Affective commitment; Satisfaction; Trust; Loyalty; Profitability; Involvement; Number of visits; Willingness to revisit Iran 243 User’s satisfaction and profitability were the most significant indicators for user interaction. Sample size (Zhu, Lan and Chang, 2017) Understanding the Intention to Continue Use of a Mobile Payment Provider: An Examination of Alipay Wallet in China ELM Source credibility; Perceived usefulness; Perceived integration; Trust; Subjective norm; Competitors’ marketing efforts China 332 Source credibility, perceived usefulness, and perceived integration were internal factors determining continuance intention through trust. Competitors’ marketing efforts and subjective norms, as two external factors, negatively and positively impacted continuance intention, respectively. Trust (Hossai n and Zhou, 2018) Impact of m-payments on purchase intention and customer satisfaction: perceived flow as a mediator SOR Usefulness; Emotion; Security; Perceived flow; Customer satisfaction China 350 Satisfaction has the most significant positive impact on mobile payment adoption. Perceived flow, as a mediator, affected satisfaction, consequently influenced purchase intention. Enjoyment (Jenkin s and Ophoff, 2016) Factors influencing the intention to adopt NFC mobile payments – A South African perspective TAM Security concerns; Privacy concerns; Trust concerns; Perceived risk; Perceived value; Social influence; Perceived ease of use; Perceived financial resources. South Africa 331 Social influence had the most significant influence on perceived value. Security and privacy concerns had significant positive influences on perceived risk but negative impacts on adoption. Age; Sample Size 19 Doctoral Programme in Information Management Studies Title Theo. basis Key Factors Loc. No. Key outcomes Obstacles (Johnso n, et al., 2018) Limitations to the rapid adoption of Mpayment services: Understanding the impact of privacy risk on M-Payment services DOI Perceived ease of use; Relative advantage; Visibility; Perceived security; Privacy risk; Ubiquity; Trialability. USA 270 Ease of use, relative advantage, visibility, and perceived security had positive effects on adoption intention. Ubiquity and trialability positively influenced security, while concern over privacy risks negatively influences perceptions of security. Sample size (Kaita warn, 2015) Factor influencing the acceptance and use of M-payment in Thailand: a case study of AIS mPAY rabbit UTAUT Personal factors; Performance expectancy; Effort expectancy; Social influence; Facilitating condition; Attitude; Switching cost; Convenience; Privacy. Thailan d 222 Convenience and attitude are the most significant determinants of adoption intention. Privacy of personal information and the switching cost were also negative effects of usage intention. Region (Kapoo r, Dwived i and Willia ms, 2013) Role of innovation attributes in explaining the adoption intention for the interbank mobile payment service in an Indian context TAM Relative Advantage; Compatibility; Complexity; Trialability; Observability; Cost. India 323 This paper added Cost indicator to TAM and found that relative advantage, compatibility, complexity and trialability had significant positive effects on users’ adoption intention. However, observability had a poor impact on behavioural intention. Culture; region (Khalil zadeh, Ozturk and Bilgiha n, 2017) Security-related factors in extended UTAUT model for NFC based mobile payment in the restaurant industry UTAUT Facilitating condition; Selfefficacy; Attitude; security; Trust; Utilitarian; Performance expectancy; Effort expectancy; Social influence; Hedonic; Risk. USA 412 Security and trust positively impacted customers' adoption intention, while perceived risk had a negative effect. Effort expectancy, hedonic and utilitarian performance expectancy and attitude had direct and indirect impacts on adoption intention, respectively. Sample size, industry; culture; use experience (Kim, Mirusm onov and Lee, 2010) An empirical examination of factors influencing the intention to use mobile payment TAM Innovativeness; M-payment Knowledge; MPS characteristics Mobility; Reachability; Compatibility; Convenience; Perceived Usefulness; Perceived Ease of Use. South Korea 269 Perceived ease of use and perceived usefulness were the most significant indicators of usage intention. Perceived ease of use had insignificant effects on Individual differences, convenience, and reachability. Compatibility has insignificant effects on perceived usefulness and perceived ease of use. Furthermore, M-payment knowledge had a greater effect on perceived ease of use than personal innovativeness. Actual usage (Koeni gLewis et al., 2015) Enjoyment and social influence: predicting mobile payment adoption TAM; UTAUT Perceived usefulness; Perceived ease of use; Perceived risk; Perceived enjoyment; Social influence. France 316 This study combined TAM and UTAUT and found that perceived ease of use had no significant effects on perceived usefulness and intention to use. Meanwhile, perceived enjoyment had significant effects on perceived ease of use and usefulness. Social influence reduces perceived risk. Age; gender; use experience; culture; region (Konga rchapat ara, 2018) Factors Affecting Adoption versus Behavioural Intention to Use QR Code Payment Application TAM Perceived Usefulness; Perceived Ease of Use; Perceived Credibility; Behavioural Intention; Perceived SelfEfficacy Thailan d 275 This study added the self-efficacy indicator to TAM to illustrate that perceived usefulness, ease of use, and credibility significantly positively affected behavioural intention. Ease of use was affected by perceived self-efficacy when adopting QR payment. Sample size; culture 20 Doctoral Programme in Information Management Studies Title Theo. basis Key Factors Loc. No. Key outcomes Obstacles (Lee and Wong, 2016) Determinants of Mobile Commerce Customer Loyalty in Malaysia E-S-QUAL model Efficiency; System availability; Fulfilment; Privacy; Satisfaction; Trust; Commitment. Malays ia 214 Efficiency had the strongest influence on satisfaction, which in turn affected customer loyalty. Commitment had a stronger influence on customer loyalty than satisfaction and trust. Region; Culture; Enjoyment (Liéban aCabanil las and LaraRubio, 2017) Predictive and explanatory modelling regarding the adoption of mobile payment systems TAM Perceived usefulness; Perceived Ease of Use; Perceived security; Comparability; Subjective norms; Individual mobility. Spain 191 Perceived usefulness and perceived security had the most significant impacts on adopting a mobile payment system. Perceived security, comparability, subjective norms, and individual mobility positively impacted perceived usefulness and ease of use. Region, (Liéban aCabanil las et al., 2018a) Predicting the determinants of mobile payment acceptance: A hybrid SEM-neural network approach TAM Perceived usefulness; Perceived ease of use; Trust; Mobility; Customisation; Customer involvement. Serbia 224 This paper explored determinants of users adopting mobile payment through TAM and neural network analysis and found that perceived usefulness and security had the most significant effects on acceptance intention. Region; server; age; gender; use experience (Liéban aCabanil las et al., 2018b) To use or not to use, that is the question: Analysis of the determining factors for using NFC mobile payment systems in public transportation TAM Convenience; Effort expectancy; Perceived trust; Service quality; Social value; Satisfaction; Perceived risk. Malaga 180 Satisfaction, service quality, effort expectancy, and perceived risk were determining factors of the continuance intention of using NFC mobile payment systems in public transportation. Region; culture; server; age; gender; use experience (Liéban aCabanil las et al. 2017) Intention to use new mobile payment systems: A comparative analysis of SMS and NFC payments TAM Subjective norms, Perceived ease of use, Perceived usefulness, Attitude, Perceived security. Spain 287 This paper proposed Mobile Payment Acceptance Model in New Electronic Environments and identified the attitude, connected usefulness and ease of use, had the most significant effect on adoption intention. Perceived security had a significant impact on the intention of adoption. server, age, gender, use experience (Liéban aCabanil las, Sánche zFernán dez and MuñozLeiva, 2014a) Antecedents of the adoption of the new mobile payment systems: The moderating effect of age TAM, TRA, UTAUT External influences; Social influence; Subjective Norms; Trust; Risk; Ease of use; Attitude; Usefulness. Spain 201 2 This paper involved the attitude variable into the TAM adoption model to investigate the different ages adopting mobile payment. The results show that younger people easily adopt new techniques than older age users. Usefulness and external influence, based on social image and subjective norms, significantly impact adoption intention. However, risk has a negative impact. Sample size, server (Liéban aCabanil las, Sánche zFernán dez and MuñozLeiva, 2014b) The moderating effect of experience in the adoption of mobile payment tools in Virtual Social Networks: The mPayment Acceptance Model in Virtual Social Networks (MPAM-VSN) TRA; TAM; UTAUT External influences; Ease of use; Usefulness; Attitude; Trust; Risk. Spain 201 2 This study proposed a behavioural model (named MPAM-VSN) and found that previous experience reduced associated risk, improving the perception of usefulness and encouraging use, which positively affected intention of usage. Sample size, age, gender, use experience (Lu et al., 2017) How do post-usage factors and espoused cultural values impact mobile payment continuation? TAM; UTAUT; ECM Social influence; Privacy; Mobility; Privacy protection; Usefulness; Satisfaction. China 724 Privacy protection and social influence beliefs had significant impacts on continuous usage intentions. Satisfaction, indicated by mobility, had a positive impact on intention. Uncertainty avoidance affected perceived social influence culture 21 Doctoral Programme in Information Management Studies Title Theo. basis Key Factors Loc. No. Key outcomes Obstacles and mobility. Power distance served as an antecedent of perceived privacy protection. (Moros an and DeFran co, 2016) It's about time: Revisiting UTAUT2 to examine consumers’ intentions to use NFC mobile payments in hotels UTAUT2 Performance expectancy; Effort expectancy; Social influence; Facilitating condition; Hedonic motivation; Habit; Privacy; Perceived security. USA 794 Performance expectancy, hedonic motivations, habit, and social influences impacted intentions to use NFC mobile payment in an American hotel. Region, server (Mun, Khalid and Nadaraj ah, 2017) Millennials' Perception of Mobile Payment Services in Malaysia TAM Perceived Usefulness; Perceived Ease of Use; Social Influence; Perceived Credibility. Malays ia 300 Usefulness had the most significant positive impact on consumer intentions to use mobile payment services in Malaysia, followed by ease of use, social influence and credibility. Competitiv e (Muñoz -Leiva et al., 2017) Determinants of intention to use the mobile banking apps: An extension of the classic TAM model TAM Perceived Usefulness; Perceived Ease of Use; Perceived Risk; Perceived Trust; Social Influence; Attitude. Spain 103 Attitude significantly determined the intention of adopting mobile banking. Usefulness and risk had positive and negative effects on mobile banking app adoption, respectively. Actual use, sample size, loyalty (Oliveir a et al., 2014) Extending the understanding of mobile banking adoption: When UTAUT meets TTF and ITM UTAUT; TTF; ITM Performance expectancy; Effort expectancy; Social influence; Facilitating conditions; Technology characteristics; Task characteristics; Task technology fit; Firm reputation; Personal propensity to try; Structural assurances. Portuga l 194 Facilitating conditions and behavioural intentions directly affected mobile banking adoption in Portugal. Initial trust, performance expectancy, technology characteristics, and task technology fit had total effects on behavioural intention. Server, use experience (Oliveir a et al., 2016) Mobile payment: Understanding the determinants of customer adoption and intention to recommend the technology UTAUT; DOI Performance expectancy; Effort expectancy; Social influence; Facilitating conditions; Hedonic motivation; Price value; Innovativeness; Compatibility; Perceived technology security. Portuga l 301 This study combined UTAUT2, DOI and PTS. It illustrated that compatibility, perceived technology security, performance expectations, innovativeness, and social influence have significant direct and indirect effects on the adoption and recommendation of mobile payment. Trust, risk, age, culture, (Pal, Vanijja and Papasra torn, 2015) An Empirical Analysis towards the Adoption of NFC Mobile Payment System by the End User TAM Personal Innovativeness; NFC Payment Knowledge; User Mobility; Reachability; Compatibility; User Convenience; Perceived ease of Use; Perceived Usefulness Thailan d 270 perceived ease of use was the most significant indicator effect on intention of adoption. Experience positively affected ease of use; however, use mobility had a negative impact on ease of use. Trust, security, financial, policy, gender 22 Doctoral Programme in Information Management Studies Title Theo. basis Key Factors Loc. No. Key outcomes Obstacles (Park et al., 2018) Examining the role of anxiety and social influence in multibenefits of mobile payment service ATM; MAT Technology anxiety; Social influence; Convenience benefit; Economic benefit; Information security benefit; Enjoyment benefit; Experiential benefit; Social benefit; Attitudes. USA 361 This study integrated TAM and MAT and found that convenience, enjoyment, economic benefits, and attitudes positively impacted adoption intention. In contrast, experiential benefit was a negative determinant. Culture, region, sample size, age, gender, trust, use experience (Pham and Ho, 2015) The effects of productrelated, personalrelated factors and attractiveness of alternatives on consumer adoption of NFC-based mobile payments TAM; DOI Perceived ease of use; Compatibility; Perceived risk; Trialability; perceived cost; Additional values of NFC mobile payments; Attractiveness of alternatives; Innovativeness in new technologies; Absorptive capacity, Trust. China 402 This paper combined the TAM and DOI frameworks with personalrelated factors to investigate NFC mobile payment adoption. Usefulness was the most significant antecedent of adoption Intention. Perceived risk had a significant negative impact on adoption. sample size, culture, (Ramos de Luna et al., 2018) Mobile payment is not all the same: The adoption of mobile payment systems depending on the technology applied TAM; Perceived ease of use; Perceived usefulness; Subject norms; Perceived Security. Attitude, Intention of use. Spain 168 As the essential factor, attitude and usefulness positively impacted users’ intention to adopt mobile payment. Meanwhile, ease of use and perceived security had a great impact on intention to use. Sample size, region age, gender, use experience (Ramos de Luna et al., 2016) Determinants of the intention to use NFC technology as a payment system: an acceptance model approach TAM Perceived compatibility; Perceived usefulness; Subjective norms; Perceived ease of use; Personal innovation in IT; Individual mobility; Perceived security; Attitude. Spain 191 Attitude, subjective norms and innovation were essential determinants of the adoption intention of NFC mobile payment. Use experience, server, security, culture, Region (Riskin anto et al., 2017) The Moderation Effect of Age on Adopting EPayment Technology TAM Perceived Ease of Use; Perceived Usefulness; Attitude. Indones ia 532 This study found that adoption intention was moderated by age. Perceived ease of use and perceived Usefulness were the most significant antecedents of mobile payment adoption. Sample size, age (Shank ar and Datta, 2018) Factors Affecting Mobile Payment Adoption Intention: An Indian Perspective TAM Perceived ease of use; Perceived usefulness; Personal innovativeness; Self-efficacy; Subjective norm; Trust. India 381 Perceived ease of use, perceived usefulness, trust and self-efficacy had a significant impact on adoption intention. Personal innovativeness and self-efficacy had significant impacts on perceived ease of use. Personal innovativeness and subjective norm had a significant impact on perceived usefulness. Age, sample size, culture (Shao et al., 2018) Antecedents of trust and continuance intention in mobile payment platforms: The moderating effect of gender DOI Mobility; Customisation; Security; Reputation; Trust; Perceived; Risk; Gender. China 740 Security was the most significant antecedent of customers’ trust, followed by platform reputation, mobility and customisation. Customers’ trust, in turn, is negatively associated with perceived risk and positively associated with continuance intention. Age, region, use experience, trust 23 Doctoral Programme in Information Management Studies Title Theo. basis Key Factors Loc. No. Key outcomes Obstacles (Sharm a and Sharma , 2019) Examining the role of trust and quality dimensions in the actual usage of mobile banking services: An empirical investigation IS success model Service quality; Information quality; System quality; Trust; Intention to use; Satisfaction. Omani 227 Satisfaction mediated the relationship between service quality, information quality and trust with intention, which had the most significant effect on actual usage. Sample size, culture, actual adoption (Shin, 2009) Towards an understanding of the consumer acceptance of mobile wallet UTAUT Perceived usefulness; Perceived ease of use; Attitude; Social influence; Perceived security; Trust; Self-efficacy. USA 296 Perceived usefulness and ease of use were key elements of attitude on adopting mobile wallets. Attitudes and intentions are influenced by perceived security and trust. Sample size, security, social (Siyal et al., 2019) Predicting Mobile Banking Acceptance and Loyalty in Chinese Bank Customers TAM Resistance to change; Perceived risk; Awareness of service; Perceived benefit; Perceived Usefulness; Perceived ease of use; Attitude. Chines e 200 This paper explained mobile banking adoption connected with the loyalty factor. Resistance to change, perceived risk and low awareness of services, and perceived benefits had significant impacts on loyalty, in turn influencing adoption intention. Age, region, sample size, user experience (Slade et al., 2015) Exploring consumer adoption of proximity mobile payments UTAUT Performance expectancy; Effort expectancy; Social influence; Facilitating conditions; Habit; Price value; Hedonic motivation; Perceived risk; Trust in provider. UK 244 This research implemented UTAUT2 with trust and risk constructs and found that performance expectancy remained the strongest predictor, followed by habit, hedonic motivation, and social influence. Personal innovativen ess (Sripala wat, Thong mak and Ngram yarn, 2011) M-banking in metropolitan Bangkok and a comparison with other countries TAM; TPB Device barrier; Perceived risk; Lack of information; Perceived financial Cost; Subjective norm; Perceived usefulness; Perceived ease of use; Self-efficacy. Thailan d 195 Perceived usefulness and lack of information were two main factors of M-banking adoption. This paper recommended attractiveness of alternatives, convenience, context, compatibility, expressiveness, mobility, privacy, speed of transaction, system quality, technology anxiety, and trialability should be explored in future research. Region, rural (Theod ora et al., 2010) Predicting the Adoption of Mobile Transactions: An Exploratory Investigation in Greece TAM, TRA M-transactions execution; Perceived usefulness; Perceived ease of use, Familiarity with technology. Greece 392 Perceived usefulness and familiarity are the most significant variables of adoption intention. Familiarity had a significant impact on perceived usefulness. Trust, financial (Ting et al., 2016) Intention to Use Mobile Payment System: A Case of Developing Market by Ethnicity TPB Perceived usefulness; Perceived ease of use; Trust; Perceived safety; Interpersonal influence; External influence; Selfefficacy; Facilitating condition; Attitude; Subjective norm; Perceived behavioural control. Malays ia 311 Attitude, subjective norm, and perceived behavioural control positively affected mobile payment adoption by implementing the TPB framework in the Malaysian market. Meanwhile, belief also had a positive effect on the intention to use mobile payment systems Region, sample size 24 Doctoral Programme in Information Management Studies Title Theo. basis Key Factors Loc. No. Key outcomes Obstacles (Wu, Liu and Huang, 2016) Exploring User Acceptance of Innovative Mobile Payment Service in Emerging Market: The Moderating effect of diffusion stages of WeChat Payment in China Consumer Response System Model Positive emotion; Perceived risk; Perceived usefulness; Diffusion stages. China 484 Positive emotion has a strong negative impact on perceived risk and a positive impact on perceived usefulness. positive emotion and perceived risk have significant positive and negative impacts on acceptance intention at the market introduction stage rather than market growth. Region, user emotion (Yan et al., 2009) Factors that affect mobile telephone users to use mobile payment solutions. TAM; TPB Perceived usefulness; Perceived ease of use; Trust; Peer influence; Perceived price level. Malays ia 120 Trust and peer influence were the most significant antecedents of mobile payment adoption. server (Zhou, 2013) An empirical examination of continuance intention of mobile payment services IS success model; Flow theory System quality; Information quality; Service quality; Trust; Flow; Satisfaction. China 195 Service quality had significant effects on trust, flow and satisfaction of mobile payment continuance adoption. Trust affected flow, in turn, affected Satisfaction. region (Zhou, Lu and Wang, 2010) Integrating TTF and UTAUT to explain mobile banking user adoption TTF; UTAUT Task characteristics; Technology characteristics; Task technology fit; Performance expectancy; Effort expectancy; Social influence; Facilitating conditions. China 250 Performance expectancy, task technology fit, social influence, and facilitating conditions significantly affected users’ adoption intention of M-banking. region TAMtechnology acceptance model; UTAUT-Unified Theory of Acceptance and Use of Technology; TPB-Theory of planned behaviour; MATMental Accounting Theory; DOI-Diffusion of innovation, TTF-Task technology fit model; SORStimulus-Organism-Response framework; ELMElaboration likelihood model; ECM-expectancy confirmation model; E-S-QUAL model-Efficiency, system availability, fulfilment, and privacy dimensions on service quality. CRM-customer relationship management; TRA-Theory of Reasoned Action 2.3. Reporting and results This stage of the current study consists of summarising and reporting the results (Brereton et al., 2007; Kitchenham and Charters, 2007). The first part presents a descriptive analysis of the 61 selected papers. The distributions of basic information of previous works of literature were analysed and visualised by MS Excel and VOSviewer software. Specifically, publication years, target regions and key terms of selected studies were summarised and analysed for better understanding the relevant research trend. 2.3.1. Distribution of publication years The publication years’ distribution of select studies is summarised in Table 2.2. The result shows that the previous studies related to mobile 25 Doctoral Programme in Information Management payment adoption from a user’s perspective have a continuingly growing trend. From the publication years’ distribution, it can be concluded that mobile payment adoption on an individual level has become a popular topic and has gained increasing attention from various researchers. Table 2.2. Publication years of selected studies Years 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020 No. of Studies 3 3 1 1 2 4 7 11 10 11 5 3 2.3.2. Distribution of target regions The 27 countries involved in the selected 61 papers are summarised and formulated to a graphic heat map presented in figure 2.5. The colour of each county, from green to red, represents the number of articles from low to high—the top 10 target countries with the number of works of literature are presented in Table 2.3. Specifically, previous research has paid more attention to Asian and European areas. Five of 10 countries are located in Asia, and 3 of them are located in Europe. At the top of the list was China, with 10 papers, which is not surprising considering China represents the largest amount of payment transactions. Spain was the second target region, explaining the increasing attention on mobile payments in the European area. The following list of target countries, Thailand, the USA and Malaysia, provides an appropriate context for the current study. Figure 2.4. Target regions distribution 26 Doctoral Programme in Information Management Table 2.3. Top 10 target countries with number of relevant studies Regions No. of studies China 10 Spain 8 USA 7 Thailand 5 Malaysia 4 India 3 France 2 Portugal 2 South Korea 2 Brazil 1 2.3.3. Distribution of key terms The titles and abstracts of the selected 61 works were extracted and saved as a .ris (Research Information System) file and analysed by VOSviewer. The network visualisation of key terms associated with strength has been segmented into 2 clusters in figure 2.6, wherein the red cluster on the left involves the terms of adoption factors. The terms ‘Adoption’, ‘Factor’, ‘Mobile payment’ and ‘Intention’ are the most recurring terms linked with key factors of user’s payment adoption terms, such as ‘Usefulness’, ‘Ease of Use’, ‘Trust’ and ‘Risk’ as prevalent adoption factors. Furthermore, the green cluster on the right consists of the technique aspect related to the research targets. For example, the terms ‘Technology’ and ‘Model’ as two main terms with the highest occurrence are connected to adoption technique terms, such as ‘Technology Acceptance Model’ and ‘Unified theory’(UTAUT) to analyse the target terms, like ‘Customer’ ‘Acceptance’ and ‘Mobile banking’. The overlay visualisation of key terms is presented in figure 2.7. In this overlay visualisation, the colour of a node represents the influence strength of the node. The nodes with the highest values, such as ‘SEM’ and ‘Consumer’ as the key analysis tool and key research target, had the highest overlay values with a high impact on other nodes. The density visualisation of key terms is presented in figure 2.8. The colours of nodes in the density visualisation indicate how nodes were 33 Doctoral Programme in Information Management using mobile payments in China. Therefore, trust integrates external influence with personal feelings and has been measured by three items, flexibility, mobility and efficiency, affecting users' behavioural intention to adopt mobile payments. 2.4.1.5. Perceived Risk Perceived risk was identified by Cocosila and Trabelsi (2016) as an obvious obstacle to adopting new technology. Perceived risk’s negative influence on the behavioural intention added a solid power to predict users’ behaviours on adopting mobile payments (Daştan and Gürler, 2016; Kerviler, Demoulin and Zidda, 2016; Chen and Li, 2017; Khalilzadeh, Ozturk and Bilgihan, 2017). Slade et al. (2015) found that perceived risk had a strong negative effect on the behavioural intention of mobile payment users. Moreover, the measurement of perceived risk was grouped into four independent variables, time risk, social risk, psychological risk and privacy risk. Psychological and privacy risks had the most negative impacts on users’ adoption intention (Cocosila and Trabelsi, 2016). Moreover, perceived risk always interacts with trust, forming a salient foundation for evaluating users’ mental perceptions of mobile payments. Investigators such as Daştan and Gürler (2016) found perceived risk had significantly negative effects on trust and the adoption intention of mobile payments. Likewise, Pham and Ho (2015) determined that perceived risk was a strong predictor of users’ behavioural intention, which was negatively associated with performance expectancy. As a significant negative antecedent of behavioural intention, perceived risk should be measured by interacting with other factors, trust, flow, security, etc. (Cocosila and Trabelsi, 2016; Muñoz-Leiva, Climent-Climent and Liébana-Cabanillas, 2017; Shao et al., 2018). 2.4.1.6. Security Perceived security is defined as the degree to which a customer believes that using a particular technology will be secure (Shin, 2009). Security is a critical factor that leads users to adopt mobile payments (Zhu, Lan and Chang, 2017; Di Pietro et al., 2015; Hossain and Zhou, 2018). LiébanaCabanillas and Lara-Rubio (2017) found that perceived security had a significant positive impact on mobile payment adoption in Spain. 34 Doctoral Programme in Information Management Meanwhile, security had a significantly negative interaction with perceived risk (Jenkins and Ophoff, 2016). Johnson et al. (2018) discovered that ubiquity and trialability positively influenced perceived security, while privacy risks negatively influenced security. Therefore, security not only directly affects users’ behavioural adoption but also its interactions with other variables, such as trust, perceived risk, attitude, social influence, determining users adopting mobile payments conjointly (Shao et al., 2018). 2.4.1.7. Other factors According to the different mobile payment adoption scenarios, various factors were selected or modified to be applied in several different theoretical frameworks to investigate the antecedents of users’ behaviours (Baptista and Oliveira, 2015; Pham and Ho, 2015). The following factors have attracted increasing focus in previous studies, which provided insight for further research. Hedonic motivation is defined as ‘the fun or pleasure derived from using a technology (Venkatesh et al., 2012, p. 161). As a primary variable in UTAUT2, hedonic motivation complimented consumers' experience, and it was demonstrated as the second strongest predictor of behavioural intention in UTAUT2(Venkatesh et al., 2012). Moreover, hedonic motivation had significant interactions with other variables. Slade et al. (2015) associated hedonic motivation with perceived enjoyment in mobile payments adoption, which was formulated by consumers’ innovativeness and novelty-seeking. Hedonic motivation was significantly influenced by self-efficacy, facilitating condition and security, affecting perceived ease of use and usefulness (Koenig-Lewis et al., 2015). Despite Boonsiritomachai and Pitchayadejanant’s (2018) finding that hedonic motivation is negatively associated with transaction system security, mobile payment applications increasingly involve entertainment functions, which indicate the importance of considering hedonic motivation as one of the antecedents when customers adopt mobile payment technologies with entertainment feature. Habit was defined by Venkatesh et al. (2012) as ‘the tendency to automatically use a technology as a result of learned behaviour’. Previous literature has shown that habit significantly impacted 35 Doctoral Programme in Information Management behavioural intention, sometimes even higher than performance expectancy (Chawla and Joshi, 2019). Habitual responses may cause users to bypass a cognitive process. For example, mobile payment systems have developed slower in countries with well-established credit systems, mainly in European countries and the United States. However, they have developed faster in nations with less developed credit systems, such as those in Asia and Africa (Okazaki, 2006; Ghezzi et al., 2010; Kapoor, Dwivedi and Williams, 2013; Nickerson, 2013). Despite traditional payment methods being available for a long time before mobile payments, mobile payments have become a new popular trend in the current global business environment. Individuals from different regions with different cultural backgrounds have developed assorted transaction habits using traditional payment methods, including mobile payments (Slade et al., 2015; Chen and Li, 2017). Therefore, considering habit to correspond with the culture and payment development for future mobile payments adoption studies is necessary based on the transactional environment. Cultural characteristics have significantly impacted users’ adoption propensity by affecting constructs of social influence, attitude, usefulness, and behavioural intention. (Khalilzadeh, Ozturk and Bilgihan, 2017). Cultural influence is an antecedent of perceived social influence and mobility, and perceived privacy protection (Lu et al., 2017). Likewise, cultural values were considered moderators in formulating users’ adoption intention of mobile banking (Baptista and Oliveira, 2015). However, cultural factors are primarily mentioned in limitations or recommendations in previous studies (Pham and Ho, 2015; Ramos-deLuna, Montoro-Ríos and Liébana-Cabanillas, 2016; Park et al., 2018; Shankar and Datta, 2018). Thus, cultural influence should be considered a moderator or antecedent to interact with other variables to investigate mobile payments adoption. For example, culture may formulate individual consumption or payment habits when users adopt mobile payment technology. 2.4.2. Theoretical frameworks applied in previous studies Various theoretical frameworks were implemented in the selected studies to investigate new technology adoption, such as the Diffusion of Innovations Theory (DOI) (Rogers, 2003), Theory of Reasoned Action 36 Doctoral Programme in Information Management (TRA) (Fishbein and Ajzen, 1975), Theory of Planned Behaviour (TPB) (Ajzen, 1991), Technology Acceptance Model (TAM) (Davis, 1989) and Unified Theory of Acceptance and Use of Technology (UTAUT) (Venkatesh et al., 2003). Kim, Mirusmonov and Lee (2010) and KoenigLewis et al. (2015) summarised that TAM and UTAUT were probably the most widely adopted and validated models in various empirical studies concerning consumers’ adoption of new technologies, which is in accordance with the results displayed in table 2.1 showing that TAM, UTAUT and UTAUT2 were the three most applied theoretical foundations in the 61 previously selected studies. Moreover, most theoretical models have been modified with additional variables for various situations. Furthermore, the comprehensive models combined with different theoretical frameworks have become a new orientation for new technology adoption. Therefore, the two main adoption models TAM and UTAUT and comprehensive models, are critically discussed in the following three parts. 2.4.2.1. Technology acceptance model (TAM) TAM was the most extensively applied in the information systems field to analyse new technology adoption (Abrahão, Moriguchi and Andrade, 2016). Specifically, perceived usefulness and ease of use were two main factors influencing users' decisions to accept and actually use a technology (Davis, 1989). Moreover, in the studies related to mobile payments, many articles extended TAM with different constructs (Gu, Lee and Suh, 2009; Anthony and Mutalemwa, 2014; Riskinanto, Kelana and Hilmawan, 2017; Flavián, Guinaliu, and Lu, 2020; Hassan and Wood, 2020). However, TAM has limited ability or flexibility in explaining the adoption of new ICT for general consumers (Jung, 2014). Therefore, many researchers recommend that TAM be extended or incorporated with additional variables to strengthen the explanatory power of TAM. For example, Chawla and Joshi (2019) extended TAM with trust, convenience and efficiency as additional factors to analyse users adopting mobile banking in India. Daştan and Gürler (2016) extended TAM with perceived reputation, environmental risk, trust, perceived mobility, and attitude. They found that perceived trust, perceived mobility and attitude had positive effects on mobile payment service adoption in Turkey. Gu, Lee and Suh (2009) proposed trust-associated with TAM to 37 Doctoral Programme in Information Management investigate users’ adoption intention of mobile banking in South Korea and discerned that trust was a strong indicator and the interaction between trust and perceived ease of use had a positive impact on perceived usefulness. Therefore, TAM has been widely applied and modified with other indicators such as trust and social influence to optimise model performance as the most classical adoption model. 2.4.3.2 Unified Theory of Acceptance and Use of Technology (UTAUT) UTAUT, proposed by Venkatesh et al. (2003), as an extension of the TAM model, has been developed to incorporate four determinants: performance expectancy, effort expectancy, social influences, and facilitating conditions. Wherein the first two determinants are similar to perceived usefulness and perceived ease of use in TAM (Kongarchapatara, 2018). Abrahão, Moriguchi and Andrade (2016) applied UTAUT and found that performance expectation, effort expectation and social influence had significant positive impacts on mobile payment adoption in southern Brazil. Meanwhile, they involved perceived risk and perceived cost as additional factors to improve the UTAUT model performance. Many researchers have modified and extended the UTAUT model with other variables to investigate mobile payments adoption. Kaitawarn (2015) extended the UTAUT model with attitude, switching cost, convenience and privacy and personal factors to investigate mobile payments adoption in Thailand. Khalilzadeh, Ozturk and Bilgihan (2017) extended UTAUT and found the negative impact of perceived risk and positive influences of security and trust, respectively, on customers' usage intention on mobile payments in restaurants. Moreover, the moderators have been involved in UTAUT for different research purposes. Wu, Tao, and Yang (2008) included education as a moderator to investigate users adopting 3G mobile telecommunication. Shin (2009) applied social influence and self-efficacy constructs as moderators in UTAUT to analyse mobile wallet adoption in the U S. Khalilzadeh, Ozturk and Bilgihan (2017) integrated UTAUT with the security moderator and validated the model in different countries to determine the model explained roughly 70% of the variance in behavioural intention. In addition, UTAUT2, as an advanced version of UTAUT, involved additional variables and moderators in predicting behavioural intention and actual usage, wherein age, gender, and 38 Doctoral Programme in Information Management previous experience were original moderators (Venkatesh, Thong and Xu, 2012). Baptista and Oliveira (2015) extended UTAUT2 with Hofstede’s cultural moderators and confirmed the model’s performance by investigating customers’ adoption intention of mobile banking in Mozambique. Therefore, UTAUT and UTAUT2, as advanced adoption models, achieved a better performance in explaining users’ behaviour. Moreover, the additional variables, such as personal factors, perceived risk, trust and security, also significantly improved the performances of UTAUT and UTAUT2. (Baptista and Oliveira, 2015; Koenig-Lewis et al., 2015; Morosan and DeFranco, 2016; Khalilzadeh, Ozturk and Bilgihan, 2017; Marinković et al., 2020). 2.4.3.3 Comprehensive models A comprehensive model is generated by one adoption model integrated with another adoption model or theory to analyse users’ behaviours on mobile payments adoption. According to the summary of selected studies in table 2.1, comprehensive models have been applied widely (Morosan and DeFranco, 2016; Liébana-Cabanillas, Ramos de Luna and MontoroRíosa, 2017; Liébana-Cabanillas, Molinillo and Ruiz-Montañez, 2018; Hamidi and Safareeyeh, 2019). To be more specific, TAM and UTAUT, as basic adoption frameworks, have been broadly integrated with other theorical frameworks, such as DOI (Pham and Ho, 2015; Di Pietro et al., 2015; Oliveira et al., 2016), TRA (Theodora et al., 2010 LiébanaCabanillas, Sánchez-Fernández and Muñoz-Leiva, 2014) TPB (Yan et al., 2009 Sripalawat, Thongmak and Ngramyarn, 2011), TTF (Zhou, Lu and Wang, 2010; Oliveira et al., 2014), etc. The comprehensive models performed better in terms of explanatory power than a single model in mobile payments adoption (Pal, Vanijja and Papasratorn, 2015). Yan et al. (2009) integrated the TAM and TPB models with extra variables, peer influence and perceived price level to explain mobile phone users adopting mobile payments in Malaysia. Lu et al. (2017) integrated TAM, UTAUT and ECM and found privacy protection and social influence beliefs had significant impacts on users’ continuous usage intentions. Oliveira et al. (2016) generated UTAUT2 models with DOI variables, innovativeness, compatibility and perceived technology security to investigate the determinants of users adopting and recommending mobile payment systems in Portugal. Sripalawat, Thongmak and Ngramyarn 39 Doctoral Programme in Information Management (2011) combined TAM and TPB and found that perceived usefulness and lack of information were two main factors affecting mobile banking adoption in Thailand. Theodora et al. (2010) extended TAM with TRA and analysed the adoption of mobile transactions in Greece. Zhou, Lu and Wang (2010) integrated TTF and UTAUT and affirmed that task characteristics and technology characteristics determined performance expectancy towards affected individual performance and actual utilisation of mobile banking in China. Therefore, as an efficient analysis model, a comprehensive model combines different models, factors or moderators to optimise the model’s performance, which has been highly suggested for future research in particular situations. 2.4.4. Obstacles in previous studies Based on the different research purposes, target regions, sample sizes and theoretical frameworks in the selected literature, the obstacles were collected from the limitation section in previous studies and summarised in three dimensions, including internal, external and implemental aspects. The obstacles were collected and summarised by the quotation management software ATLAS. Ti v7. Each dimension was divided and identified as specific obstacles, such as internal obstacles including ‘habit’ (Chen and Li, 2017), ‘age’ (Al-jabri, 2012), ‘user experience’ (Siyal et al., 2019), ‘loyalty’ (Muñoz-Leiva, Climent-Climent and LiébanaCabanillas, 2017), etc. External obstacles consisting of ‘policy’ (Pal, Vanijja and Papasratorn, 2015) and ‘culture’ (Park et al., 2018). Implemental obstacles contemplating factors such as ‘language’ and ‘sample size’ (Anthony and Mutalemwa, 2014). Specifically, a total of 101 quotations were quoted as ‘obstacles’ from the 61 selected studies’ fulltext review, which are summarised and sorted by occurrences in figure 2.9, and the relationship network of obstacles is presented in figure 2.10. The most mentioned obstacles, such as region limitation, sample size, age limitation and cultural limitation, are highly recommended by previous researchers for future studies on technology adoption (Riskinanto, Kelana and Hilmawan, 2017; Ramos de Luna et al., 2018; Shao et al., 2018). 40 Doctoral Programme in Information Management Figure 2.8. Summary of obstacles with occurrences from selected papers Figure 2.9. Relationship network between obstacles from selected studies 41 Doctoral Programme in Information Management 2.4.4.1 User internal obstacles Internal obstacles were generated by users’ internal determinants and empirical application limitations, consisting of habit, loyalty, personal innovativeness, trust issues, emotion, financial consideration, continuance adoption, age limitation, gender and user experience by ATLAS. Ti v7. Specifically, the result presents that ‘Age limitation’, ‘Trust’, ‘Use experience’ and ‘Habit’ were the most mentioned limitations in previous studies. Specifically, age limitation was suggested 19 times in the selected papers (Al-jabri, 2012; Shao et al., 2018; Siyal et al., 2019). Previous researchers suggested that customers’ age should be considered moderators to be incorporated into a theoretical model to explain the behavioural differences in various scenarios (Shao et al., 2018). Meanwhile, use experience was suggested as a moderator to apply in exploring post-adoption or continuing usage of mobile payment (Chen and Li, 2017; Zhu, Lan and Chang, 2017). Meanwhile, consumption experience may change users’ payments habit towards affecting their adoption intention (Al-jabri, 2012; Baptista and Oliveira, 2015). 2.4.4.2. External obstacles External obstacles were summarised by external antecedents of mobile payment adoption and were divided into competitive, culture limitation, policy, security, server quality by ATLAS. Ti v7. Wherein, the cultural obstacle was most mentioned with 18 quotations in previous studies. Kapoor, Dwivedi and Williams (2013) investigated users adopting interbank mobile payments in India and suggested integrating cultural and region factors with the TAM adoption model for better model performance. Baptista and Oliveira (2015) suggested that an adoption model should involve a broader range of samples in different countries, environments with different cultural backgrounds for future mobile payment adoption research. 2.4.4.3. Implemental obstacles Implemental obstacles were defined as the empirical barriers and limitations during the research implementation process, including 42 Doctoral Programme in Information Management language, location and sample size. Regional limitation was the main obstacle mentioned in previous works of literature. According to the variety of transaction method situations in different regions, mobile payments developed faster in nations with immature credit systems than in countries with mature credit card or check payment systems (Ghezzi et al., 2010; Kapoor, Dwivedi and Williams, 2013; Nickerson, 2013). Thus, cross-regions and cross-culture comparisons were most highlighted and recommended in previous research on investigating mobile payments adoption (Koenig-Lewis et al., 2015; Baptista and Oliveira, 2015; Di Pietro et al., 2015; Junadi and Sfenrianto, 2015; Hamidi and Chavoshi, 2018; Shankar and Datta, 2018; Sharma and Sharma, 2019). 2.5. Conclusion 2.5.1. Implications This paper followed the systematic literature review process to review previous studies related to mobile payments adoption from a users’ perspective. This paper was organised in a five-step systematic literature review process to answer the research questions about the distribution and trend of the relevant literature, factors and adoption models applied in research, and the obstacles mentioned in previous studies. A total of 61 studies was selected, the data was visualised and extracted by VOSviewers, statistically analysed by Excel, and qualitatively analysed by ATLAS. Ti. The results of this paper indicate that previous mobile payment adoption studies mainly focused on Asian and European countries with an increasing trend worldwide. The prevalent factors were analysed and summarised from previous studies, including perceived usefulness, perceived ease of use, social influence, trust, perceived risk, security, hedonic motivation, culture and habit. Moreover, the interactions between factors require more attention in future research. Furthermore, this paper also found out that comprehensive models had higher performance than using a single adoption model. The internal, external and implemental obstacles were summarised from previous studies to indicate that future research should consider specific backgrounds and scenarios for particular research purposes. The reviewing process in this paper contributed a theoretical framework for relevant researchers to 49 Doctoral Programme in Information Management 3.2.3. Unified Theory of Acceptance and Use of Technology (UTAUT) UTAUT was developed by Venkatesh et al. (2003). It consists of performance expectancy, effort expectancy, social influence and facilitating conditions as determinants of behavioural intentions to use a new technology system (Venkatesh et al., 2003). UTAUT has been applied in various technology adoption contexts. It has been revised with additional variables to explain users’ behavioural intentions (Cao and Niu, 2019). For example, Khalilzadeh et al. (2017) integrated security-related factors with the UTAUT model and validated that security and trust strongly affect customers’ adoption intentions of NFC M-payments in the restaurant industry. Marinković et al. (2020) modified the UTAUT model with extra variables (perceived trust and satisfaction) to evaluate customers’ usage intentions of M-commerce. Moreover, UTAUT has also been integrated with other models to evaluate users’ behavioural intentions (Oliveira et al., 2014; Di Pietro et al., 2015). Di Pietro et al. (2015) integrated TAM, DOI and UTAUT to verify M-payment adoption intentions. Oliveira et al. (2014) integrated UTAUT with the initial trust model and task–technology fit model to investigate users’ behavioural intentions of adopting mobile banking in Portugal. However, UTAUT focuses on technological expectations rather than mental expectations, which weakly explains users’ expectations determining their intention of technology (El-Haddadeh et al., 2019). Thus, it is necessary to integrate UTAUT with MAT to explain users’ technological and mental perceptions complementarily on usage intention of M-payments during the COVID-19 pandemic. The development of hypotheses and research models is illustrated in the following section. 50 Doctoral Programme in Information Management 3.3. Development of Hypotheses and Research Model 3.3.1. Revisiting the MAT 3.3.1.1. Perceived Benefits (PBs) According to MAT, when consumers perform a particular behaviour, they tend to evaluate a possible beneficial outcome (Thaler, 1985). Perceived benefits represent users’ perceptions of the functional benefits of Mpayment services, which determine their decisions of adoption (Park et al., 2018). Perceived benefits support a better understanding of users’ mental perceptions of adoption intentions in various technologies, such as online shopping (Forsythe et al., 2006) and mobile banking (Siyal et al., 2019). Meanwhile, perceived benefits have been identified as multidimensional benefits, including utilitarian, hedonic and social values, which are determined by social influence and technology uncertainty (Kerviler et al., 2016; Park et al., 2018). However, few studies focus on the perceived benefits of technology characteristics corresponding to a particular condition. Specifically, in a pandemic situation, social distancing is an efficient way to decrease COVID-19 transmission risk among people (WHO, 2020b; Wilder-Smith and Freedman, 2020). Compared with traditional payments, the contactless characteristic of Mpayments supports users in maintaining social distancing to avoid direct and indirect contacts from cash or point of sale terminals during a transaction process. This aspect allows users to formulate their opinions on the perceived mental and physical benefits of personal safety and provides convenience and utility when using M-payment technology as a financial transaction method in the COVID-2019 pandemic. Thus, perceived benefits are considered a mental factor influencing the users’ adoption intentions of M-payments during the COVID-19 pandemic, expressed as the following hypothesis. H1: Perceived benefits positively affect the behavioural intention to adopt M-payments during the COVID-19 pandemic. 51 Doctoral Programme in Information Management 3.3.2. Revisiting UTAUT 3.3.2.1. Performance Expectancy (PE) Performance expectancy is defined as an individual’s perception in terms of the use of an information system facilitating the completion of a task and work performance (Venkatesh et al., 2003). Performance has been conceptualised by using attributes related to the system’s efficiency, speed and accuracy in task completion (Venkatesh et al., 2012). Especially during the COVID-19 pandemic, users show more concern toward payment efficiency and accuracy. Concretely, in the M-payment adoption aspect, performance expectancy has significantly positive effects on users’ adoption intentions in various contexts(Di Pietro et al., 2015; Pham and Ho, 2015; Mun et al., 2017; Liébana-Cabanillas et al., 2018). Therefore, when users perceive M-payments as a useful way to accomplish their transactions during the pandemic, they will choose Mpayment instead of traditional payment. Accordingly, this paper proposes the following hypothesis. H2: Performance expectancy positively affects the behavioural intention to adopt M-payments during the COVID-19 pandemic. 3.3.2.2. Effort Expectancy (EE) According to UTAUT, effort expectancy is referred to as “the degree of ease associated with the use of the system” (Venkatesh et al., 2003, p.450). Effort expectancy influences users’ attitudes toward adopting Mpayments (Ramos de Luna et al., 2018), revealing an even greater influence than performance expectancy (Pal, Vanijja and Papasratorn, 2015). Specifically, Liébana-Cabanillas et al. (2018) found that effort expectancy is the most significant factor affecting users’ intentions of using NFC M-payment systems in public transportation. Moreover, effort expectancy has also been verified to positively impact performance expectancy in various technology adoption contexts (Ramos de Luna et al., 2018; Alalwan et al., 2017; Di Pietro et al., 2015). Therefore, the following hypotheses are proposed. H3: Effort expectancy positively affects the behavioural intention to adopt M-payments during the COVID-19 pandemic. 52 Doctoral Programme in Information Management H4: Effort expectancy positively affects the performance expectancy to adopt M-payments during the COVID-19 pandemic. 3.3.2.3. Social Influence (SI) In terms of UTAUT, the definition of social influence is “the degree to which an individual perceives that important others believe he or she should use the new system” (Venkatesh et al., 2003, p.451). Slade et al. (2015) explained that it is an underlying assumption that users prefer to consult their social network to reduce any anxiety arising from uncertainty. Especially during the COVID-19 pandemic, recommendations and suggestions from important, relevant people are more important for individuals’ decisions and actions. From previous studies, social influence has been widely tested in the different contexts of its impact on usage intention of mobile technologies (Slade et al., 2015; Kerviler et al., 2016; Khalilzadeh et al., 2017; Jenkins and Ophoff, 2016; Mun et al., 2017). Morosan and DeFranco (2016) presented that social influence significantly affects the intention to use M-payments; Kerviler et al. (2016) illustrated that social influence plays a considerable role in explaining users’ intentions to use M-payments. Moreover, as a determinant for formulating users’ attitudes, social influence significantly affects the perceived multi-benefits of users regarding using M-payment services (Park et al., 2014). Thus, relevant hypotheses are proposed as follows. H5: Social influence positively affects the behavioural intention to adopt M-payments during the COVID-19 pandemic. H6: Social influence positively affects the perceived benefits of adopting M-payments during the COVID-19 pandemic. 3.3.2.4. Trust (TR) Trust is defined as users’ willingness to expect a positive outcome of a technology’s future performance and a subjective belief that the service provider will fulfil their obligations (Gefen, 2000). Meanwhile, the COVID19 pandemic has brought uncertainty and social pressure to individuals’ daily transaction processes. Trust in M-payment platforms can increase the likelihood of users using them to make contactless M-payments 53 Doctoral Programme in Information Management rather than traditional payments ( Zhu et al., 2017; Marinković et al., 2020). Zhu et al. (2017) validated that trust has the most significant effect on the behavioural intention to use M-payments. Meanwhile, many studies have also verified the effect of trust significantly determining users’ usage intentions of M-payments (Zhou, 2013; Gao et al., 2015; Zhu et al., 2017; Shao et al., 2018). Zhou (2013) modified a trust-based adoption model and found that trust has significant direct and indirect impacts on the behavioural intention to use M-payments. Moreover, trust has also been validated as an additional variable of UTAUT, which positively influences performance expectancy, consequently affecting user behavioural intentions to use M-payments (Khalilzadeh et al., 2017). Similar results have been supported by other studies (Alalwan et al., 2017), including trust against perceived risk and uncertainty when adopting new technology (Khalilzadeh et al., 2017; Shao et al., 2018). Moreover, perceived risk combines uncertainty with the seriousness of the potential outcome (Kerviler et al., 2016), which negatively influences the perceived multidimensional benefits (Park et al., 2018). Thus, it can be summarised that trust positively impacts perceived benefits, which has also been supported by Khalilzadeh et al. (2017). Therefore, this study proposes the following hypotheses. H7: Trust has a positive effect on the behavioural intention to adopt Mpayments during the COVID-19 pandemic. H8: Trust has a positive effect on performance expectancy to adopt Mpayments during the COVID-19 pandemic. H9: Trust has a positive effect on perceived benefits to adopting Mpayments during the COVID-19 pandemic. 3.3.2.5. Perceived Security (PS) Perceived security is defined as “the degree to which a customer believes that using a particular M-payment procedure will be secure” (Shin, 2009, p. 1346). In terms of conducting a financial transaction, lack of security— perception of security against the risk associated with mobile transactions—is one of the most frequent reasons for users refusing to adopt M-payments (George and Sunny, 2018). Previous studies have proved that perceived security is an essential factor determining whether users will adopt M-payments (Di Pietro et al., 2015; Hossain and Zhou, 54 Doctoral Programme in Information Management 2018; Liébana-Cabanillas et al., 2018). Johnson et al. (2018) found that perceived security has the most significant positive impact on users' intention to adopt M-payments. Moreover, perceived security significantly increases users’ trust by protecting users from transactional uncertainties and risks (Xin et al., 2013; Khalilzadeh et al., 2017). Shao et al. (2018) verified that security is the most significant antecedent of customers’ trust towards affecting usage of M-payments in both male and female groups. Therefore, perception of M-payments' perceived security, considered an extra variable of UTAUT, is a crucial guarantee for establishing users’ trust in using M-payment under a pandemic. Accordingly, this study proposes the following hypotheses. H10: Perceived security positively affects the behavioural intention to adopt M-payments during the COVID-19 pandemic. H11: Perceived security has a positive effect on trust to adopt Mpayments during the COVID-19 pandemic. 3.3.3. Research Model Based on the above hypotheses, all measurement items were adapted from previous studies (Venkatesh et al., 2012; Kerviler et al., 2016; Khalilzadeh et al., 2017; Park et al., 2018; Shankar and Datta, 2018; Shao et al., 2018; Cao and Niu, 2019; Tang et al., 2020) and have been reasonably modified to correspond to the research purposes to explain the mental and technological factors affecting users’ behavioural intentions with regard to adopting M-payments under the COVID-19 pandemic. Specifically, users’ adoption intention of M-payment under the COVID-19 pandemic is conjointly determined by the variables from the revised UTAUT model (for explaining users’ technological perceptions) and perceived benefits (as the variable of MAT, representing users’ mental cognitions and psychological acceptance of using M-payment under pandemic conditions). The questionnaire is presented in Appendix A with Chinese translation. Moreover, this study revises the UTAUT model, integrating performance expectancy, effort expectancy and social influence with additional variables, perceived security, trust and perceived benefits from MAT to establish a research model, depicted in Figure 3.1, with the proposed hypotheses relations. 55 Doctoral Programme in Information Management Figure 3.1. Research model with proposed hypotheses relations. 3.4. Methodology 3.4.1. Measurement In order to validate the proposed conceptual model and examine the research hypotheses, an online questionnaire survey was designed and applied to data collection. Specifically, the questionnaire consisted of two parts. The first part contained respondents’ demographic data with closeended questions consisting of gender, age, education, occupation and Mpayment experience. The second part was developed by implementing constructs and items from previous hypotheses, consisting of 27 measurement items as indicators to explain perceived benefits, performance expectancy, effort expectancy, social influence, trust, perceived security and behavioural intention. In order to reduce confusion and save time for the participants (Babakus and Mangold, 1992; 56 Doctoral Programme in Information Management Bouranta et al., 2009), a five-point Likert scale (from 1 to 5, representing “strongly disagree” to “strongly agree”) was applied to represent the items of each construct. The main survey target of this research was smartphone users who used or intended to use M-payment services in China during the COVID-19 pandemic. In order to avoid the impact of culture and language differences, the questionnaire was translated into the Chinese language by a professional translator and then reverse translated into English, followed by confirmation of the translation equivalence. The questionnaire data were collected from a Chinese social media platform, named WeChat, over a three-week period during the height of the COVID-19 pandemic in China, from 11 March 2020 to 31 March 2020. 3.4.2. Data Demographic Characteristics According to the N: q rule proposed by Jackson (2003), an ideal sample size-to-parameters ratio would be higher than 20:1 (samples: variables) (Jackson, 2003); therefore, the sample size of this study should be larger than 140. This study dispatched a total of 1000 online questionnaires via WeChat; 864 data were collected on 1 April. After removing the answers with missing values, a total of 739 valid questionnaires were accepted, achieving a final response rate of 73.9%. According to the guideline from Ryans (1974), the Kolmogorov–Smirnov test was applied to verify the sample's nonresponse bias by comparing the groups between males and females. The demographic distribution of the sample was 45.74% male and 54.26% female; 53.86% of participants were in the age bracket between 21 and 30; 61.71% of participants held bachelor’s or college degrees (this group is more active on social media and so more likely to respond to the questionnaire); employees and students were the two main groups of participants, with percentages of 43.03% and 23.68%, respectively; 56.16% of total responses used M-payments at least one time per day and 93.78% at least one time per one week during the COVID-19 pandemic, which is in accordance with a report from Ipsos (2020) expressing that the penetration rate of M-payments among mobile Internet users in China (those who have used M-payments in the last three months) is 96.9% (Ipsos, 2020). The reason for this high rate of adoption of M-payments during the pandemic can be summarised as follows. Firstly, based on the restrictions by the Chinese Government 57 Doctoral Programme in Information Management (China banking and insurance news, 2020), due to daily transactions using contact being restricted during the COVID-19 pandemic, people tended to complete the transactions in a contactless way. Secondly, according to the suggestions and recommendations by the government and WHO (2020b), avoiding contact among people is an efficient way to reduce the transmission risk of COVID-19. Thus, M-payments have been widely adopted by customers and retailers for general transactions. Thirdly, M-payment apps were applied to track users’ health statuses during the pandemic, such as Alipay Health Code with a colour code (green, yellow or red) being assigned to indicate users’ health statuses. Therefore, M-payments were dramatically adopted by smartphone users in China not only to support daily transactions but also to confirm their health statuses during the COVID-19 pandemic. Specific sample demographics are listed in Table 3.2. Table 3.2. Demographic distribution of the sample. Measures Items N % Gender Male 338 45.74% Female 401 54.26% Age <21 170 23.00% 21–30 398 53.86% 31–40 80 10.83% 41–50 29 3.92% >50 62 8.39% Education High school and lower 66 8.93% Bachler or college 456 61.71% Master 194 26.25% PhD and above 18 2.44% Other 5 0.68% Occupation Student 175 23.68% Employee 318 43.03% Public Servant 47 6.36% Retiree 47 6.36% Unemployed 6 0.81% Freelancer 65 8.80% Other 81 10.96% Experience At least 1 time per 1day 415 56.16% At least 1 time per 1 week 278 37.62% At least 1 time per 2 weeks 37 5.01% At least 1 time per 1 month 7 0.95% Never use during the COVID-19 pandemic 2 0.27% 3.5. Data Analysis The covariance-based structural equation modelling (CBSEM) technique was conducted for quantitative data analysis. SPSS 17 and AMOS 22 were applied in this study through the two-step approach suggested by Anderson and Gerbing (1988), including validating the measurement 58 Doctoral Programme in Information Management model and testing the structural model. The maximum likelihood estimation was conducted in the model assessment. 3.5.1. Measurement Model A measurement model aims to assess fitness between indicators and latent variables. Exploratory factor analysis (EFA) was applied to examine the construct reliability, and a standard method factor analysis, confirmatory factor analysis (CFA), was applied to assess the convergent and discriminant validity of the measurement model. All seven hypothesised latent constructs in the CFA model were allowed to covary and were determined by related measurement items as reflective indicators. Construct reliability was tested by Cronbach’s alpha. As presented in Table 3.3, all Cronbach’s alpha values of the latent variables are in the range of 0.807 to 0.897, all exceeding the 0.70 limits suggested by Nunnally and Bernstein (1994), which means that construct reliability has been demonstrated. Table 3.3. Item loadings and Cronbach’s alpha of structures. Factors Items Loadings Cronbach’s Alpha Performance Expectancy (PE) PE1 0.810 0.888 PE2 0.850 PE3 0.792 PE4 0.812 Effort Expectancy (EE) EE1 0.813 0.897 EE2 0.854 EE3 0.806 EE4 0.843 Social Influence (SI) SI1 0.805 0.894 SI2 0.829 SI3 0.805 SI4 0.854 Perceived benefits (PBs) PB1 0.719 0.807 PB2 0.828 PB3 0.751 Perceived Security (PS) PS1 0.773 0.848 PS2 0.850 PS3 0.800 65 Doctoral Programme in Information Management Furthermore, M-payments involve sensitive and personal data; therefore, it is necessary to ensure the reliability and credibility of M-payment platforms for securing transactions and protecting personal information (Oliveira et al., 2016). Moreover, based on the security, trustworthiness and reliability of M-payment platforms, users can accept the records of their transaction times and locations during the pandemic to be utilised by governments and health institutions to track contacts among payment processes, for monitoring, updating and reporting the pandemic transmission status. Accordingly, users can clearly and opportunely be made aware of the virus infection situation among them, which positively influences their intentions to use M-payments during the COVID-19 pandemic to reduce the infection risk. However, Hypothesis 3 was rejected in this study, which means the easiness of understanding and handling M-payment systems does not directly impact a user’s behavioural intentions to adopt M-payments during the COVID-19 pandemic. Similar results are supported by previous M-payment studies (Yuan et al., 2016; Liébana-Cabanillas et al., 2018). The main reason for this result is because users have become accustomed to smartphone functions and become more skilful through their previous utilisation of various applications on smartphones (Chopdar and Sivakumar, 2019). Meanwhile, under the COVID-19 pandemic, user behaviour is determined more by other perceptions related to personal safety, such as reliability, utility, security, trustworthiness and benefits, which can provide multidimensional supports for protecting transaction processes during a pandemic. Thus, the easiness of using M-payments is a less critical or surmountable factor determining users’ adoption intentions during the pandemic. 3.7. Theoretical and Practical Implications 3.7.1. Theoretical Implications This study contributes three main theoretical implications. First, this study was empirical and examined the factors affecting users’ adoption intentions of M-payments under the pandemic situation, which is absent from evaluations of previous studies. Consequently, the study dramatically enriched the literature on technology adoption during a 66 Doctoral Programme in Information Management pandemic. Specifically, this study illustrates a worthwhile direction to understand users’ adoption intentions by not only examining users’ perceptions from technological perspectives but also assessing users’ mental expectations. Moreover, users’ technological and mental perceptions of technology are significantly influenced by emergency situations. Therefore, this study provides a future insight for relevant research to analyse new technology adoption from technological and mental perspectives conjointly and corresponding with the specific situation, especially for emergency situations. Second, this study integrated the UTAUT model with perceived benefits from MAT and two extra variables, perceived security and trust, which significantly contribute to the emerging literature's theoretical development and framework coordination on information technology adoption. Simultaneously, this study demonstrates a substantial contribution to the theoretical expansion of UTAUT and MAT by initially proposing and verifying new causal paths (PB → BI, SI → PB, TR → PB, TR → PE, TR → BI and PS → BI) and rejecting the path EE → BI for investigating the interactions of variables in the new comprehensive model. Therefore, the integrative research approach presented in this study can serve as a beneficial and valuable reference to modify and evaluate new adoption models for investigating novel technology adoption. Third, this study initially focused on technology characteristics corresponding to the pandemic situation as a potential antecedent determining users’ mental and technological perceptions. Specifically, the contactless feature of M-payments avoids contacts during transaction processes and maintains social distancing, which improves the perceived multidimensional benefits of the users and optimises their experience of using M-payments under the pandemic situation. Meanwhile, based on the disaster status of the COVID-19 pandemic, effort expectancy became less important than other variables for determining whether users would adopt M-payments. Thus, it is essential to consider whether a particular technology’s features can influence users’ interpretations of the perceived mental and technological benefits corresponding to particular situations or conditions to explain technology adoption in an emergency situation comprehensively. 67 Doctoral Programme in Information Management 3.7.2. Practical Implications Moreover, four main practical implications are demonstrated in this study. First, the current research enhances the existing knowledge of the adoption intention of M-payments in an emergency situation and enriches the understanding of how a pandemic changes users’ payment habits. It suggests that a pandemic might bring suffering to people or society. Furthermore, it can also facilitate the development of new technology that can bring benefits to individuals, organisations and society to survive in the emergency situation, which is valuable for relevant stakeholders to consider the pandemic scenario to establish appropriate business strategies. Second, this study could be valuable to start-up companies, policymakers, government bodies and private service providers interested in M-payment services. M-payments have become increasingly popular and provide useful services for efficient transaction processes, particularly in emergency conditions. In the context of a pandemic, Mpayments can increase personal safety perception and maintain the stable development of business. Based on the findings of this study, as well as providing an easy-to-use operating application, relevant stakeholders should initially recognise the importance of M-payments in formulating users’ perceived benefits and design system attributes accordingly under the pandemic situation. Meanwhile, M-payment service providers should guarantee transactions' compatibility, efficiency, and security to meet customers’ requirements and match their lifestyles. In addition, enhancing the publics' impression of M-payments and stimulating a positive word-of-mouth social effect would improve the technology providers’ reputation in different situations. Third, this study supports new technology providers with a comprehensive understanding of customer adoption intentions, which is determined by conjoint technological and psychological perceptions. Consequently, relevant stakeholders should focus on taking advantage of the features of technology (such as the contactless characteristic of Mpayments) corresponding to its benefit to a particular situation (such as avoiding direct or indirect contacts to decrease COVID-19 transmission risk) in terms of maintaining service quality, reliability and efficiency to 68 Doctoral Programme in Information Management meet consumers’ physical and mental concerns and optimise their experience, thereby increasing acceptance among the target population. Finally, the findings and results of this study could be applied as references for other online-to-offline (O2O) service industries in a pandemic situation. Relevant businesses could utilise the results to develop appropriate strategies that combine the benefits of technology characteristics with users’ technological perceptions and mental expectations to expand markets to adapt to different emergency situations and build better customer bases. 3.8. Limitations and Future Research There are several limitations inherent in this study that need to be acknowledged. Firstly, the data collection was restricted to China during a particular period of the COVID-19 pandemic; the results may not be generalised to different countries and various situations. Future studies should replicate this model, collect data from different nationalities, and consider specific benefits corresponding to particular situations. Furthermore, the research model can be examined through cross-cultural studies to understand the variations in different cultural backgrounds better. Secondly, there were limited variables and interactions of the variables analysed in this study—e.g., the variables selected in this study were mainly from a technology adoption aspect. Future research can put more effort into integrating the relations between variables, such as social influence affecting perceived security (Khalilzadeh et al., 2017) and use technological indicators with the variables from a health and risk aspect. Meanwhile, in order to gain a deeper understanding of the mental and technological factors affecting adoption intentions with regard to novel technology, future research can incorporate research models with other variables, such as a cultural moderator, satisfaction, etc., which are also recommended in previous studies (Baptista and Oliveira, 2015; Hassan and Wood, 2020; Hossain and Zhou, 2018; Hamidi and Safareeyeh, 2019). Thirdly, as the data collection period was limited, and that data were homogeneously distributed and collected through WeChat (a mobile social media application in China), in this study, the data collection 69 Doctoral Programme in Information Management process is recommended to chronically and integrally cover the users from different areas (urban and rural areas) over a different period of using M-payment in various patterns (online and offline surveys). Finally, there was no distinction between the types of M-payment patterns (such as SMS, NFC and QR), M-payment platforms (such as Apple pay, Samsung pay, WeChat pay, and Alipay) and patterns of the electronic transaction (such as electronic transaction via computer, electronic transaction via mobile device). Therefore, a future study can focus on distinguishing the different payment methods or payment platforms of Mpayment techniques in accordance with specific research objectives. 3.9. Conclusion In conclusion, we proposed a theoretical adoption model integrating UTAUT with perceived benefits from MAT and two additional variables, trust and perceived security, to appropriately explain the mental and technological factors affecting users’ behavioural intentions of adopting M-payments during the COVID-19 pandemic. This research model provided extensive explanatory power when explaining that users’ payment habits have changed due to the influence of the pandemic and that adoption intentions of M-payments were determined by technology perceptions and mental expectations conjointly. Performance expectancy, perceived benefits, social influence, trust and perceived security are significant in facilitating users’ adoption intentions of Mpayments during the COVID-19 pandemic. Specifically, the contactless characteristic of the M-payment technique is beneficial in maintaining social distancing and protecting personal safety under a pandemic. This study also explored new causal relationships and found that perceived benefits are significantly determined by social influence and trust. Moreover, performance expectancy is influenced by effort expectancy and trust towards explaining users’ behavioural intentions of using Mpayments during the COVID-19 pandemic. Furthermore, this study provides several significant theoretical and practical contributions on investigating novel technology adoption in a particular situation, which contributes to the knowledge and understanding of the extension of the UTAUT application, explaining that users’ payment habits have changed because of the pandemic and 70 Doctoral Programme in Information Management adoption intention of M-payments is determined by users’ technological perceptions and mental expectations. In addition, this study recommends that researchers and relevant stakeholders focus on a particular characteristic of M-payments that corresponds with the pandemic, which can influence the user's perceived mental and technological benefits. Understanding users’ behaviours is an efficient way to analyse new technology adoption and develop an appropriate strategy for optimising users’ experiences. 71 Doctoral Programme in Information Management 4. Chapter 4 - What factors have determined customers’ continuous use of food delivery apps during the 2019 novel Coronavirus pandemic? 4.1. Introduction Mobile devices have been widely adopted, and their use has sharply increased worldwide. According to a report from the Global Association of Mobile Operators, global mobile phone users exceeded 5.1 billion in 2020; among them, over 1.2 billion users are accounted for in China (GSMA, 2020). Meanwhile, various mobile services are significantly developed and implemented in different industries. Food delivery apps (FDAs) as online-to-offline mobile services have recently gained popularity offering two-way benefits for catering enterprises and customers by providing convenient and efficient online order and offline delivery services. Statista Reports (2019) illustrated that FDAs revenue in China (38.4 billion US dollars (USD)) generated more than one-third of global FDAs revenue (95.4 billion USD) in 2018. Moreover, global FDAs revenue increased to 107.4 billion USD in 2019 (Statista Reports, 2019) and are expected to exceed 164.5 billion USD by 2024, expanding at a CAGR of 11.4% during 2019-2024 (Imarc, 2020). Meanwhile, the 2019 novel coronavirus (COVID-2019) erupted as a serious global pandemic from the end of 2019 and reached the whole of China in February 2020, then progressively expanded worldwide (Tang et al., 2020). According to a report from the World Health Organisation (WHO), until 21 May 2020, there were a total of 4,904,413 globally confirmed cases of COVID-19 infections and 323,412 deaths (WHO, 2020a). During the COVID-19 crisis, wearing a mask in public, social distancing, self-isolating, and other self-protection actions have been highly recommended by the WHO (2020b) to avoid direct and indirect contacts among people to reduce the risk of the COVID-19 transmission 72 Doctoral Programme in Information Management (Wilder-Smith and Freedman, 2020; Tang et al., 2020). Moreover, because fewer customers intend to use public services, the traditional catering industry has suffered dramatically during the COVID-19 pandemic. According to the data of iiMedia Research (2020), in China, the revenue of the catering industry was 419.4 billion yuan (59.2 billion USD) from January to February 2020, which decreased 43.1% year on year. 95.0% of the interviewed catering businesses' stores' revenue decreased significantly during the COVID-19 epidemic period (iiMedia Research, 2020). On the other hand, despite the negative influence of the COVID-19 significantly affecting the supply and demand of the catering industry, it has changed the consumption habits of residents and accelerated the transformation of catering enterprises from traditional in-store service to online-to-offline service for surviving in the pandemic situation and maintaining sustainable development. According to a report from the Meituan research institute (2020), there were 71.7% of 15263 participants using FDAs from the end of February to the beginning of March 2020, and 41.6% of residents preferred using online-to-offline delivery services to purchase daily supplies during the COVID-19 pandemic period in China. Likewise, iiMedia Research (2020) illustrated that 78% of responded Chinese traditional catering enterprises transferred their business to third-party FDAs (Ele.me, Meituan Waimai and Baidu Waimai). Compared to before the COVID-19 pandemic outbreak, the catering enterprises registered on FDAs have dramatically increased 63.1% in China, and 70% of the surveyed restaurants will continue to operate and increase investment in FDAs after the COVID-19 epidemic. Moreover, according to the business registration data from Tianyancha (2020), there were 106,000 new enterprise registrations related to food delivery services from January to May 2020, up 766% from the same period in 2019. The Chinese online food delivery market's estimated scale will exceed 91.8 billion USD in 2020 (iiMedia Research, 2020). Therefore, during the COVID-19 pandemic, the "internet + restaurant" modes of FDAs not only met the requirements of catering enterprises but also satisfied customers' demands on convenient and efficient food supplies and personal safety concerns (Liu and Wang, 2016). Accordingly, factors motivating users to use FDAs continuously under the COVID-19 pandemic situation are essential for relevant stakeholders to 73 Doctoral Programme in Information Management understand customers' requirements and expectations. In terms of FDA adoption, customers consider performance expectancy as the main determinator to adopt a relevant service (Yeo et al., 2017; Roh and Park, 2019). Moreover, easiness and quality of service, convenience, social influence and satisfaction are also considerable antecedents of intention to adopt FDAs (Yeo et al., 2017; Cho et al., 2019; Correa et al., 2019; Ray et al., 2019; Roh and Park, 2019). Meanwhile, in terms of continuance usage of information technology, performance expectancy, effort expectance, social influence, and satisfaction are essential for formulating users' continuance usage intention (Gao et al., 2015; Yuan et al., 2016; Alghamdi et al., 2018; Chopdar and Sivakumar, 2019; Marinković et al., 2020). Furthermore, in order to evaluate factors affecting users' continuance intention of using information technology, Chong (2013) extended the Expectancy Confirmation Model (ECM), and Marinković et al. (2020) modified the Unified Theory of Use and Acceptance of Technology model (UTAUT), they found that trust also has a significant impact on users' continuance usage intention. Meanwhile, Yuan et al. (2016) combined ECM with the Technology Acceptance Model (TAM) and the Task-Technology Fit model to explain that users' continuance usage intention is determined by perceived tasktechnology fit and confirmation. However, few prior investigations have focused on factors affecting FDAs' continuance usage, especially under pandemic conditions. Consequently, the purposes of this study are to fulfil the gap of factors determining users' intention to use FDAs during the COVID-19 period continuously and support FDA relevant stakeholders to understand customers' perceptions and behaviours for efficiently developing business strategies better. Therefore, this paper attempts to establish a comprehensive model integrating variables from ECM, UTAUT and the Task-Technology Fit model, including performance expectancy, effort expectancy, social influence, trust, perceived tasktechnology fit, confirmation and satisfaction, to investigate the factors affecting users' continuance usage intention of FDAs during the COVID19 pandemic. 74 Doctoral Programme in Information Management 4.2. Theoretical background and hypotheses development 4.2.1. Food delivery apps (FDAs) FDAs, as an emerging online-to-offline mobile technology, provide a channel between catering enterprises and customers by integrating online order and offline delivery services. FDAs can be categorised into two patterns (Ray et al., 2019). First, the restaurants themselves, such as KFC, Domino's and Pizzahut etc. Second, the third-party intermediary platforms, such as Uber Eats, Zomato, Ele.me Meituan Waimai and Baidu Waimai, are more popular and have been widely adopted in China (Roh and Park, 2019). Moreover, to adapt and overcome the COVID-19 pandemic, the contactless delivery process is applied in China, which delivers food to the gates of customers without direct contact. Meanwhile, FDAs also involve daily supplies delivery service for customers. These additional services establish multi-way benefits in efficiently maintaining social distancing during the COVID-19 pandemic, enriching service range and reducing the spatio-temporal interval of sales and consumptions processes (Liu and Wang, 2016). Therefore, the quality of FDA services significantly impacts users' perceptions. Several previous studies have focused on various factors affecting users' intentions to adopt FDAs. Yeo et al. (2017) emphasised post-usage usefulness and perceived convenience motivation as significantly affecting customers' behavioural intentions to adopt online food delivery services. Moreover, Roh and Park (2019) modified TAM with the moral obligation moderator and found that usefulness, compatibility, and subjective norms are significant determiners in the intention of online food delivery service adoption. He et al. (2018) illustrated that satisfaction is associated with food quality and service efficiency, significantly affecting online food delivery service adoption. Meanwhile, Elvandari et al. (2018) found that order conformity, quality of delivery, food quality and costs are the most significant attributes affecting the intention of using online food delivery services. Furthermore, Ray et al. (2019) implemented the uses and gratifications theory and validated that customer experience, ease-of-use, 81 Doctoral Programme in Information Management H3: Effort expectancy (EE) positively affects the continuance intention (CI) of using FDAs during the COVID-19 pandemic. H4: Effort expectancy (EE) positively affects performance expectancy (PE) towards continuously using FDAs during the COVID-19 pandemic. H5: Effort expectancy (EE) positively affects satisfaction (SA) towards continuously using FDAs during the COVID-19 pandemic. Social Influence (SI) According to UTAUT, social influence (SI) is defined as the degree that users gain willingness from others’ (e.g. families, friends and colleagues) encouragement that they should use a certain technology (Venkatesh et al., 2003). Related to this study, SI has been validated as significantly determining users' intention to use an online-to-offline delivery service (Roh and Park, 2019). Moreover, from the continuance intention of using a mobile technology aspect, SI as an important variable in UTAUT has a significant impact on users' intentions to continue using mobile technologies (Lai and Shi, 2015). This angle has been supported in various aspects, such as mobile social network sites (Zhou and Li, 2014), shopping apps (Chopdar and Sivakumar, 2019) and mobile payment systems (Zhu et al., 2017). Furthermore, SI not only directly determines users' continuance intention but also indirectly formulates users' intention to continuously use mobile technology by affecting their satisfaction (Hsiao et al., 2016). Marinković et al. (2020) revised UTAUT to confirm that SI significantly affects users' satisfaction towards continuance intention of using mobile technology. Therefore, the following hypotheses are proposed in this study: H6: Social influence (SI) positively affects continuance intention (CI) of using FDAs during the COVID-19 pandemic. H7: Social influence (SI) positively affects satisfaction (SA) towards continuously using FDAs during the COVID-19 pandemic. Trust (TR) Trust (TR) is defined as a state of individual faith regarding intentions, and prospective actions will follow the appropriate behaviour of integrity 82 Doctoral Programme in Information Management and ability (Gefen, 2000; Grazioli and Jarvenpaa, 2000). According to this study which focuses on continuance intention of using FDAs during the COVID-19 pandemic, trustworthiness can significantly formulate users' mental expectation to believe FDAs can provide reliable service (Cho et al., 2019), which means users perceived the higher accumulation of trustworthiness from FDAs, and the higher willingness to continuingly use FDAs. Moreover, trust has been validated as an additional UTAUT variable, representing personal mental perceptions reflecting users' perceived security against uncertainty and risk, which has a significant influence in formulating users' behavioural intention (Khalilzadeh et al., 2017; Shao et al., 2018). Meanwhile, trust has been confirmed as a crucial predictor determining users' continuance usage intention towards mobile technology (Hung et al., 2012; Zhou, 2013; Gao et al., 2015). Furthermore, trust was demonstrated as a significant positive antecedent of satisfaction of mobile technology adoption, such as mobile banking (Liébana-Cabanillas et al., 2016), mobile websites (Zhou, 2011b) and mobile commerce platforms (Gefen et al., 2000; Jarvenpaa et al., 2003). Likewise, in continuance usage studies, trust is also positively associated with satisfaction towards formulating users' continuance intention of using mobile technology (Chen and Li, 2017). Moreover, the positive influence of trust is not only on continuance intention but also on satisfaction to explain users' behaviour in continuance usage of mobile technology (Dlodlo, 2014). Accordingly, this study involves trust as an extra UTAUT variable and proposes the following hypotheses: H8: Trust (TR) positively affects continuance intention (CI) of using FDAs during the COVID-19 pandemic. H9: Trust (TR) positively affects satisfaction (SA) towards continuously using FDAs during the COVID-19 pandemic. 4.2.3.2. Revisiting the Task-Technology Fit model Perceived task-technology fit (TTF) The Perceived Task-Technology Fit (TTF) is a crucial factor summarised from the task-technology fit model, affecting users' technology adoption. Goodhue and Thompson (1995) argued that the higher fitness between the performance of a technology and users’ tasks and requirements, the 83 Doctoral Programme in Information Management higher the probability of adopting the technology. In the ongoing COVID19 pandemic context, TTF represents the characteristics and advantages of FDAs that users can conveniently order food or daily supplies anytime at any self-isolation location via FDAs; meanwhile, contactless offline delivery is monitored and managed by FDA platforms to ensure the quality and efficiency of the service. Therefore, TTF significantly formulates users' technological and mental expectations towards continuously using FDAs during the COVID-19 pandemic. TTF associated with ECM has a significantly positive effect on continuance intention towards mobile banking usage (Yuan et al., 2016). This result is per previous studies in information systems (Larsen et al., 2009) and Web learning systems (Lin, 2012). In addition, TTF has also been confirmed to have a significant impact on PE in technology adoption. Zhou et al. (2010) and Oliveira et al. (2014) integrated TTF with UTAUT and observed that TTF was a significant predictor in determining the PE of mobile banking adoption. TTF has been verified to have a significantly positive effect on PE determining users' continuance usage intention of mobile technology (Yuan et al., 2016). Thus, this study assumes the following hypotheses: H10: Perceived task-technology fit (TTF) positively affects continuance intention (CI) of using FDAs during the COVID-19 pandemic. H11: Perceived task-technology fit (TTF) positively affects Performance expectancy (PE) towards continuously using FDAs during the COVID-19 pandemic. 4.2.3.3. Revisiting the ECM model Confirmation (COF) Confirmation (COF) is defined as the degree of users' perceptions of an information system is congruent with their prior expectations and actual performances (Bhattacherjee, 2001). In terms of ECM, Bhattacherjee (2001) illustrated that COF is a vital factor in predicting PE and satisfaction, determining users' continuance intention of using the information system. This study refers to COF as the degree of users' confirmations of their initial expectations of FDAs, which affect PE and satisfaction towards continuance usage of FDAs during the COVID-19 84 Doctoral Programme in Information Management pandemic. Lee and Kwon (2011) validated that COF had a significant positive effect on PE and satisfaction towards users' continuance intention of using web-based services. Similar results have been verified by applying ECM in various mobile technologies' continuance usage contexts as well, like, mobile banking (Yuan et al., 2016) and mobile learning systems (Alshurideh et al., 2020). Therefore, the following hypotheses are proposed in this study: H12: Confirmation (COF) positively affects satisfaction (SA) towards continuously using FDAs during the COVID-19 pandemic. H13: Confirmation (COF) positively affects Performance expectancy (PE) towards continuously using FDAs during the COVID-19 pandemic. Satisfaction (SA) Satisfaction (SA) is defined as cumulative feelings when individual prior emotion is coupled with surrounding disconfirmed expectations (Oliver 1980). According to ECM, satisfaction refers to an overall emotion-based evaluation of an IS (Yuan et al., 2016). Users will be satisfied if perceived service performance exceeds their expectations, which leads to positive action towards continuance usage of FDAs. For example, Gao et al. (2015) found the significant influence of satisfaction towards users' continuance usage intention of mobile purchases. Moreover, satisfaction as an extra variable of UTAUT positively formulated users' continuance intentions of using information technology (Alghamdi et al., 2018). Similar findings are supported by other studies of different mobile technologies' continuance usage intention, such as mobile banking ( LiébanaCabanillas et al., 2017; Susanto et al., 2016; Yuan et al., 2016), mobile apps (Hsiao, Chang, and Tang 2016; Tam, Santos, and Oliveira 2018), mobile payment (Cao et al., 2018; Dlodlo, 2014) and mobile commerce (Marinković et al., 2020). Therefore, satisfaction as a complementary variable of UTAUT and ECM has been proposed in the following hypothesis: H14: Satisfaction (SA) positively affects continuance intention (CI) of using mobile payment during the COVID-19 pandemic. 85 Doctoral Programme in Information Management 4.2.4. Research model According to previous literature and the proposed hypotheses, the research model integrates variables from UTAUT, ECM and the TaskTechnology Fit model and presents the hypotheses paths in Figure 4.1. Figure 4.1. Research model 4.3. Methodology 4.3.1. Questionnaire development A questionnaire survey was applied to collect data to validate the conceptual model and examine the research hypotheses. The questionnaire consisted of two parts. The first part concentrated on the demographic information of respondents using close-ended questions, including gender, age, education, occupation and frequency of using FDAs during the COVID-19 pandemic. The second part comprised of 86 Doctoral Programme in Information Management constructs and items referred from pre-validated literature, consisting of 32 measurement items as indicators of variables, including performance expectancy (PE), effort expectancy (EE), social influence (SI), trust (TR), perceived task-technology fit (TTF), confirmation (COF), satisfaction (SA) and continuance intention (CI) of using FDAs, and the scale items and their references are listed in Appendix B with Chinese translation. According to the large number of measurement items, a five-point scale appears to be less confusing and less time consuming for participants to increase the response rate (Babakus and Mangold, 1992; Bouranta, 2009). Thus, all indicators corresponding to the survey constructs were measured using a five-point Likert scale, ranging from strongly disagree = "1" to strongly agree = "5". The main survey target of this research focused on smartphone users who adopted FDAs during the ongoing COVID-19 virus period in China. The questionnaire was translated into the Chinese language by a professional translator to avoid the impact of culture and language differences. The questionnaire was then reversely translated into English to confirm the translation equivalence. The questionnaire data were collected through an online survey and inquiry through Wechat (a Chinese social media platform) over a 3-week period, from 23 March 2020 to 12 April 2020. 4.3.2. Data collection and demographic distribution During the data collection period, a total of 900 questionnaires were distributed, and 713 data were collected on 13 April 2020. After scrutinising the questionnaire and removing answers with missing values, a total number of 532 valid responses were accepted, achieving a final response rate of 59.1%. The valid data were mainly collected from the Henan province, which is the third-largest population province, consisting of 6.9% of the total population in China. The sample distribution of the male and female respondent groups was compared using the Kolmogorov–Smirnov (K–S) test, and no statistical difference between them was verified (Ryans, 1974), which indicates a sample with no existing non-response bias. The demographic distribution of the sample consists of 49.62% male and 50.38% female respondents; the most significant proportion of age range is between 21 to 30 with 53.57%, which is in line with a report from Chyxx (2019) that the proportion of 87 Doctoral Programme in Information Management FDAs users are between 19 to 35 years of age was 73.9%; there are 71.80% participants with bachelor’s or college degrees because this group is more active on social media and achieve a high response rate. Meanwhile, their working and study pressures accelerate their experience of ordering food through FDAs (DCCI, 2016); employee and student are the two main groups of respondents with the percentages of 43.05% and 31.58%, respectively; there are 45.68% of total responses using FDAs at least once every three days. The specific demographic distribution is listed in Table 4.2. Table 4.2. Demographic distribution of participates Measure Item N % Gender Male 264 49.62% Female 268 50.38% Age <21 158 29.70% 21-30 285 53.57% 31-40 62 11.65% 41-50 12 2.26% >50 15 2.82% Education High school and lower 32 6.02% Bachelor’s or college 382 71.80% Master’s 107 20.11% PhD and above 9 1.69% other 2 0.38% Occupation Student 168 31.58% Employee 229 43.05% Public Servant 30 5.64% Retiree 10 1.88% Unemployed 5 0.94% Freelancer 38 7.14% Other 52 9.77% Usage Frequency At least 1 time every 3 days 243 45.68% At least 1 time per 1 week 208 39.10% At least 1 time every 2 weeks 66 12.41% At least 1 time per 1 month 11 2.07% Never used during the pandemic 4 0.75% 88 Doctoral Programme in Information Management 4.4. Data analysis The data analysis followed the two-step approach by Anderson and Gerbing (1988) by using SPSS 19 and AMOS 22 software. The first step assessed the reliability and validity of the measurement model. It was followed by examining the structural model and testing the research hypotheses. The maximum likelihood approach was implemented as the model estimation method in this study. 4.4.1. Measurement model The reliability and validity of the measurement model were assessed by exploratory factor analysis (EFA) via SPSS and confirmatory factor analysis (CFA) through AMOS. Construct reliability represents the internal consistency of survey items and was measured by Cronbach's alpha (CA). The results in Table 4.3 reveal that all CAs are in the range of 0.848 to 0.888 and all exceed 0.70 (Nunnally and Bernstein, 1994), demonstrating eligible construct reliability. Furthermore, CFA is employed to evaluate the convergent and discriminant validity of each dimension. Specifically, convergent validity refers to the high theoretical correlations of the scale items where a factor's loadings exceed 0.7 (Henseler et al., 2014). Table 4.3 confirms that all the loadings are qualified. Moreover, the complemented convergent validity is assessed by Composite Reliability (CR) and Average Variance Extracted (AVE) criteria. The CR value of 0.70 or above is deemed acceptable, and an AVE value suggested higher than the threshold of 0.5 (Fornell and Larcker, 1981). As shown in Table 4.3, the CR values of all variables range from 0.838 to 0.889. All constructs have AVE values in the range of 0.582 to 0.666. Thus, the convergent validity of the measurement model has been confirmed. In addition, discriminant validity is defined as the extent to which the indicator does not reflect other variables (Lee et al., 2007). The square root of the AVE of each latent construct should be higher than any two pairs of its inter-construct correlation to confirm the discriminant validity. Meanwhile, the values of the average variance extracted (AVE) of each variable should be greater than its maximum shared squared variance (MSV) (Hair et al., 2010). The results of the variables' MSV and square 89 Doctoral Programme in Information Management roots of AVE and correlations shown in Table 4.4 confirm the measurement model's qualified convergent validity and discriminant validity. Moreover, the model-fit was examined by the following measures: the ratio of chi-square to degrees-of-freedom (X²/ df), comparative fit index (CFI), the goodness of fit index (GFI), adjusted goodness-of-fit index (AGFI), normalised fit index (NFI), Tucker-Lewis index (TLI), root mean square error of approximation (RMSEA) and standardised root mean square residual (SRMR). All the model-fit indices of measurement model (X²/ df=1.207, CFI=0.992, GFI=0.942, AGFI=0.929 NFI=0.953, TLI=0.990, RMSEA=0.020, SRMR=0.240) respectively exceed the common acceptance levels (shown in Table 4.5), which demonstrates a reasonable fitness of the measurement model. Further, this study implemented two tests to examine the potential common method bias. First, Harman's one-factor test (Podsakoff et al., 2003) was conducted by implementing EFA in SPSS. The results present that the largest variance explained by individual factors is 44% (< 50%). Therefore, the result confirms that none of the factors can individually explain the majority of the variance. Second, a CFA was applied to assess the fitness of a single-factor model (all items as the indicators of one factor) (Malhotra et al., 2006). The results of model-fit present a poor fitness (v²/df = 6.449(>3), CFI = 0.764 (<0.9), GFI = 0.668 (<0.9), AGFI = 0.622 (<0.9), NFI = 0733 (<0.9), TLI = 0.765 (<0.9), RMSEA = 0.101 (>0.08), SRMS=0.0713 (>0.05)). Therefore, both tests confirm that no common method bias appeared in this study. Table 4.3. The factor loadings, Cronbach's alphas (CA), Composite Reliability (CR) and Average Variance Extracted (AVE) Variables Items Loading CA CR AVE Performance expectancy (PE) PE1 0.84 0.881 0.838 0.634 PE2 0.832 PE3 0.784 PE4 0.771 Effort expectance (EE) EE1 0.845 0.883 0.883 0.654 EE2 0.845 EE3 0.771 EE4 0.77 Social influence (SI) SI1 0.807 0.860 0.862 0.609 SI2 0.77 SI3 0.732 90 Doctoral Programme in Information Management SI4 0.81 Trust (TR) TR1 0.737 0.852 0.852 0.591 TR2 0.788 TR3 0.762 TR4 0.787 Perceived task-technology fit (TTF) TTF1 0.824 0.880 0.880 0.647 TTF2 0.801 TTF3 0.796 TTF4 0.797 Confirmation (COF) COF1 0.78 0.848 0.848 0.582 COF3 0.769 COF2 0.76 COF4 0.741 Satisfaction (SA) SA1 0.808 0.848 0.850 0.586 SA2 0.777 SA3 0.719 SA4 0.755 Continuance intention (CI) CI1 0.842 0.888 0.889 0.666 CI2 0.82 CI3 0.814 CI4 0.788 Table 4.4. Descriptive statistics and correlation among constructs. MSV CI PE EE SI TR TTF COF SA CI 0.610 0.816 PE 0.612 0.781 0.796 EE 0.429 0.421 0.544 0.809 SI 0.551 0.697 0.666 0.622 0.780 TR 0.575 0.758 0.700 0.549 0.742 0.769 TTF 0.612 0.781 0.782 0.530 0.642 0.699 0.805 COF 0.575 0.625 0.709 0.655 0.677 0.671 0.658 0.921 SA 0.581 0.762 0.726 0.587 0.699 0.714 0.659 0.758 0.765 Table 4.5. Models fit indices of the measurement model and structural model X²/ DF CFI GFI AGFI NFI TLI RMSEA SRMR RECOMMEND VALUE <3 >0.9 >0.9 >0.9 >0.9 >0.9 <0.08 <0.05 MEASUREMENT MODEL 1.207 0.992 0.942 0.0.929 0.953 0.990 0.020 0.0240 STRUCTURAL MODEL 1.235 0.990 0.940 0.9228 0.952 0.989 0.021 0.0265 97 Doctoral Programme in Information Management efficient pattern to explain users’ continuance usage intentions of technologies in various contexts. Moreover, four main practical implications have been demonstrated in this study. First, the current research enhances the existing knowledge and benefits of FDAs, especially in the emergency COVID-19 pandemic context. The results indicate that the benefits of the contactless delivery function of FDAs formulate users' perceptions and behaviour, together with technological and mental factors jointly affecting users' intention to use FDAs in China continuingly under the COVID-19 pandemic condition. Second, this study supports FDAs providers and catering business owners with a fundamental understanding of customer's continuance intention as driven by satisfaction, perceived task-technology fit, trust, performance expectancy, confirmation and social influence. Notably, satisfaction, as the most significant determinant of customers’ continuance usage intention, has significantly been determined by their mental and technological perception. Meanwhile, the sense of the fitness between a technology's features and users’ requirements also plays an essential role to formulate customers’ perceptions and behaviour. Consequently, relevant stakeholders should focus on taking advantage of the technology's particular characteristic or function and maintaining service quality, reliability, and efficiency to optimise users’ experience and achieve higher customers’ satisfaction, thereby increasing continuance acceptance among their target population particular situation and future development. Third, this study could be valuable for start-up companies, policymakers, government bodies, and private service providers interested in the catering industry. FDAs have become increasingly popular and gradually useful platforms for the survival of the foodservice industry in a particular emergency (the COVID-19 pandemic), as well as continuously developing after crises. This popularity is determined by customers' increasing willingness to enjoy food at home as well as self-protection during the pandemic period, which has also formulated new consumption habits for continuance usage. Finally, the findings of this study could be applied as references for other online-to-offline service industries, such as online real estate services and the online hospitality industry. Relevant businesses could utilise the findings from this study to develop appropriate strategies by integrating specific technology features with customers' technological and 98 Doctoral Programme in Information Management mental perceptions for expanding their market and building a better sustainable customer base not only in crises situations but also for future development. 4.7. Limitations and future research This section summarises three main limitations of the current study and provides relevant recommendations for future research. First, this study mainly focuses on users of FDAs in China, and the results of this study may not be generalisable to different cultures, regions and countries. Therefore, future research is recommended to pay attention to different regions or countries. Moreover, comparisons across cultures are also highly encouraged. Second, this study only conducts a short-term reflection of users' perception towards continuance usage intention of FDAs, especially in a particular situation (the COVID-19 pandemic context). According to the spatio-temporal dynamics of an individual's behaviour and intention, future research can apply longitudinal and experimental methods to explore users' perceptions in different situations, investigate causality over time, and make comparisons to more comprehensively explain users' continuance usage intentions of technology. Third, the current study does not distinguish the different FDA platforms such as Ele.me, Meituan waimai, Baidu Waimai, and Uber eats. Meanwhile, the study focuses on the customers' perspectives towards FDAs only. Consequently, the research model can be generalised to distinguish the different FDA platforms, different stakeholders, e.g., business owners, service providers, and other contexts of the online-to-offline service industry, such as online real estate platforms and online-to-offline hospitality services. 4.8. Conclusion In conclusion, at the nascent stage of FDA development, an increasing number of scholars have focused their attention on the related field. This study applies an empirical study with a high explanatory power of examining factors affecting users' continuance usage intention of FDAs during the COVID-19 pandemic, which significantly contributes to the literature of continuance adoption of information technology. Furthermore, 99 Doctoral Programme in Information Management the current study proposed a comprehensive model integrating UTAUT, ECM and the Task-Technology Fit model and investigated 532 FDA users in China by a quantitative research method. The research model consists of seven factors, performance expectancy, effort expectancy, social influence, trust, perceived task-technology fit confirmation and satisfaction, to explore the determinators of users' continuance intention of using FDAs during the COVID-19 pandemic. The measurement model demonstrates good construct reliability and sufficient convergent and divergent validity. This study concludes that customers' continuance intention of using FDAs during the COVID-19 pandemic is not only significantly determined by satisfaction but also dramatically influenced by perceived task-technology fit, trust, performance expectancy, and social influence. Moreover, it is necessary to emphasise that userperceived task-technology fit plays a crucial role to formulate users’ technological and mental perceptions when the technology’ characteristic is beneficial to a specific situation. However, this study does not find strong associations between effort expectancy with other variables (performance expectancy, satisfaction and continuance intention). In addition, this study contributes with various theoretical and practical implications. The perceived task-technology fit is an essential antecedent of UTAUT, which associates ECM to complementarily explain users’ technological and mental perceptions determining their continuance usage intention. Relevant researchers and stakeholders should combine particular technology features with users' technological and mental perceptions to analyse and understand users' behaviour and continuance intention in a specific situation integrally. 100 Doctoral Programme in Information Management 5. Chapter 5 - A comprehensive model integrating UTAUT and ECM with espoused cultural values for investigating users’ continuance intention of using mobile payment 5.1. Introduction Mobile payment (M-payment), as a burgeoning payment method, has dramatically increased in recent years, and it has been estimated to increase to 29.6% of the global point of sale in 2023 (Worldpay, 2020). Accordingly, smartphone users’ consumption habits have significantly changed. The determinants of users’ behaviours on M-payments are valuable for relevant stakeholders comprehensively understanding users’ expectations and behaviour, which had been attracted many previous studies to explain users’ behavioural intention on using M-payments (Di Pietro et al., 2015; Cao and Niu, 2019) by applying various adoption models (e.g., Technology Acceptance Model (TAM), Diffusion of Innovation Model (DOI), Unified Theory of Acceptance and Use of Technology (UTAUT), etc.). However, a limited number of researchers have focused on users’ continuance intention of using M-payments under cultural moderators’ effect. Cultural value, as a predictor and a moderator, performances as crucial information affecting consumer decisions, made by shaping perceptions and preferences of products, services and innovations on theoretical and practical aspects of technology adoption (Hofstede, 1984; Lu, Wei, Yu, and Liu, 2017; CruzCárdenas et al., 2019). Moreover, cultural values consist of the following dimensions, collectivism/individualism, masculinity/femininity, power distance, uncertainty avoidance, and long-term orientation (Hofstede, 1984). According to the updated information provided by Hofstede Insights with a 100-point scale, different countries present different cultural values. For example, the power distance score in China is 80, individualism-collectivism is 20, masculinity is 66, uncertainty avoidance is 30, and Long-term orientation is 87; the power distance score in 101 Doctoral Programme in Information Management Portugal is 63, individualism-collectivism is 27, masculinity is 31, uncertainty avoidance is 99, and Long-term orientation is 28. Furthermore, traditional adoption models (e.g., TAM and UTAUT) evaluate users’ intention determined by technological perceptions with obvious limitation of influence from users’ mental perception (Venkatesh et al., 2011). Meanwhile, the Expectancy Confirmation Model (ECM) efficiently explains users’ mental expectations by confirming and satisfying their continuance intention to use technology (Bhattacherjee, 2001). Therefore, this study aims to develop a theoretical framework by integrating UTAUT and ECM with Hofstede’s cultural value to explain users’ continuance intention of using M-payment. The paper is structured in sections as follows: literature review, development of hypotheses and proposed model; subsequently, future research; and conclusion. 5.2. Theoretical backgrounds 5.2.1. Continuance usage of mobile payment M-payment, as a contactless financial transaction method for paying goods, services, and bills by mobile devices, became a new business climate. The wide adoption of M-payments facilitates financial transactions anywhere, anytime and for anyone (Di Pietro et al., 2015). Most previous studies generally focused on M-payments initial adoption (Di Pietro et al., 2015; Cao and Niu, 2019; Kaitawarn, 2015; LiébanaCabanillas et al., 2018), there are a limited number of studies concentrated on continuance usage intention of M-payment. This paper summarises prior studies related to the continuance intention of using mobile technology in Table 5.1 with relevant theoretical frameworks and variables. Specifically, it can be summarised that UTAUT is an increasingly primary theoretical model for exploring users’ continuance intention of using mobile technology. Moreover, performance expectancy, social influence, trust and satisfaction are the most significant predictors. However, the effect of cultural values has been ignored by most previous studies. Therefore, it is necessary to incorporate cultural values to explain users’ continuance intention on M-payments. 102 Doctoral Programme in Information Management Table 5.1. Review of previous literature related to continuance intention on mobile technologies Studies Theoretical frameworks Variables Zhou, 2013 IS success model System quality, information quality, service quality, trust, flow and satisfaction Dlodlo, 2014 IS success model System quality, trust, and satisfaction Lu et al., 2017 ECM Social influence, privacy, mobility, privacy protection, mobility, usefulness and satisfaction Zhu et al., 2017 Elaboration Likelihood Model Source credibility, perceived usefulness, perceived integration, trust, competitors’ marketing efforts, and subjective norm Shao et al., 2018 DOI Mobility, customisation, security, reputation, trust and perceived risk Liébana-Cabanillas et al., 2018) UTAUT; TAM; DOI Convenience, social value, perceived trust, satisfaction, service quality, effort expectancy and perceived risk (Chopdar and Sivakumar, 2019 UTAUT2 Performance expectancy, effort expectancy, social influence, facilitating conditions, hedonic motivation, price value, habit and perceived risk Marinković et al., 2020 UTAUT Performance expectancy, effort expectancy, social influence, perceived compatibility, customer involvement, epistemic value, competitive value, trust and satisfaction 5.2.2. Unified Theory of Acceptance and Use of Technology (UTAUT) The UTAUT is an extension of the TAM model and incorporated by four fundamental determinants: performance expectancy, effort expectancy, social influences, and facilitating conditions (Venkatesh et al., 2003). Thereinto, performance expectation, effort expectation and social influence have the most significant effect on M-payment adoption (Liébana-Cabanillas et al., 2018). Moreover, UTAUT can be extended with additional variables to investigate mobile technology adoption (Kaitawarn, 2015). Furthermore, UTAUT has been integrated with other models to evaluate users’ behavioural intention (Di Pietro et al., 2015; Liébana-Cabanillas et al., 2018). In addition, UTAUT also has been implemented to analyse users’ continuance intention of using technology (Liébana-Cabanillas et al., 2018). For example, the UTAUT model integrates with perceived trust and satisfaction to evaluate customers’ continuance intention of using M-commerce (Marinković et al., 2020). 5.2.3. Expectancy Confirmation Model (ECM) The Expectancy Confirmation Model (ECM) is rooted in the expectation– confirmation theory (Oliver, 1980) to explain users’ satisfaction and continuance behaviour of information systems by three dimensions, 103 Doctoral Programme in Information Management performance expectancy, confirmation and satisfaction (Bhattacherjee, 2001). ECM has been widely applied with additional variables to explain users’ continuance intention of using mobile technology. For example, ECM has been modified with trust and proved that satisfaction and trust significantly affect the continuance intention of mobile shopping (Hung et al., 2012). Meanwhile, ECM also has been integrated with other adoption models to investigate IS continuance intention (Yuan et al., 2016). For example, combining ECM with TAM to investigate mobile technology continuation (Shang and Wu, 2017; Alshurideh et al., 2020). In addition, ECM also can be combined with espoused cultural value to analyse the factors affecting continuance intention of M-payment (Lu et al., 2017). 5.2.4. Hofstede’s cultural values Hofstede defines culture as “the collective programming of the mind which distinguishes the members of one human group from another” (Hofstede, 1980). Cultural values embody the degree to which people embrace their national culture's trust, values, and behaviours (Aparicio et al., 2016). Moreover, cultural values have been applied as moderators in various adoption models for investigating behavioural intention of technology acceptance (Srite and Karahanna, 2006). Baptista and Oliveira (2015) combined cultural moderators with UTAUT2 and found that collectivism, uncertainty avoidance, short term, and power distance have significant moderating effects on mobile banking adoption. Aparicio et al. (2016) integrated TAM and IS success model with the individualism/collectivism moderator to investigate e-learning system adoption; Tam and Oliveira (2019) integrated cultural moderators with the TTF model to explain users’ performance of mobile banking. Furthermore, cultural moderators also have been applied in adoption models to explain users’ continuance intention of mobile technologies (Lu et al., 2017; Chopdar and Sivakumar, 2019). 104 Doctoral Programme in Information Management 5.3. proposed hypotheses and research model 5.3.1. Revisiting UTAUT and ECM 5.3.1.1. Performance expectancy (PE) Performance expectancy (PE) is defined as the degree to which the users believe using a particular technology will facilitate their performance in a certain activity (Venkatesh et al., 2003). PE as an important factor significantly determines a user’s continuance intention. The higher utility users perceive from using an M-payment system, the greater continuance usage intention (Chopdar and Sivakumar, 2019; Yuan et al., 2016). Moreover, PE has also been validated to have a significant effect on satisfaction towards continuance usage intention of mobile technologies (Tam and Oliveira, 2019), like mobile banking (Yuan et al., 2016; Susanto et al., 2016); mobile commerce (Marinković et al., 2020). Therefore, the following hypotheses are proposed: H1: Performance expectancy (PE) positively affects continuance intention (CI) of using M-payments. H2: Performance expectancy (PE) positively affects satisfaction (SA) towards continuously using M-payments. 5.3.1.2. Effort expectancy (EE) Effort expectancy is defined as the degree of ease associated with users’ utilisation of a certain technology (Venkatesh et al., 2003). This aspect means perceived ease associated with using a mobile technology leads users’ to a higher intention to continuously use it (Venkatesh et al., 2011; Marinković et al., 2020). However, some studies found that EE has no significant effect on continuance intention towards mobile technology, such as mobile banking (Yuan et al., 2016) and mobile shopping applications (Chopdar and Sivakumar, 2019). Furthermore, EE is a significant predictor affecting performance expectancy and satisfaction on technology continuance usage (Kim and Malhotra, 2005). Similar results have been confirmed on mobile technology, like mobile banking (Yuan et al., 2016), mobile commerce (Marinković et al., 2020) and mobile 105 Doctoral Programme in Information Management shopping (Shang and Wu, 2017). Therefore, this study proposes the following hypotheses: H3: Effort expectancy (EE) positively affects continuance intention (CI) of using M-payments. H4: Effort expectancy (EE) positively affects Performance expectancy (PE) towards continuously using M-payments. H5: Effort expectancy (EE) positively affects satisfaction (SA) towards continuously using M-payments. 5.3.1.3. Social Influence (SI) Social influence (SI) is defined as the degree that users perceive from others (e.g., families, friends and colleagues) to encourage that they should use a certain technology (Venkatesh et al., 2003). SI has a significant positive impact on continuance intention to use mobile technologies (Zhu et al., 2017), such as mobile shopping apps (Chopdar and Sivakumar, 2019). Moreover, SI also affects satisfaction towards mobile technology continuance intention (Marinković et al., 2020). Likewise, SI has been confirmed to have both a significant effect on users’ satisfaction and continuance usage intention of mobile technology (Hsiao et al., 2016). Therefore, the following hypotheses are proposed: H6: Social influence (SI) positively affects continuance intention (CI) of using M-payments. H7: Social influence (SI) positively affects satisfaction (SA) towards continuously using M-payments. 5.3.1.4. Trust (TR) Trust (TR) originates from social psychology, as a state of individual perceived uncertain vulnerability from others, subsequently conceptualised as a faith regarding the intentions and prospective actions will follow the appropriate behaviour of integrity and ability (Gefen, 2000). Specifically, the higher trustworthiness users perceived from M-payment systems, the higher the continuance intention towards using them (Zhou, 2013; Shao et al., 2018). Trust is positively associated with the continuance usage of M-purchases (Gao et al., 2015). Furthermore, trust 106 Doctoral Programme in Information Management also strongly relates to the construct of satisfaction (Sharma and Sharma, 2019). Perceived trust has been demonstrated to significantly influence satisfaction on continuance usage of M-payment (Dlodlo, 2014), as well as mobile banking (Liébana-Cabanillas et al., 2016), mobile websites (Zhou, 2011a) and mobile commerce (Jarvenpaa et al., 2003). Accordingly, this study formulates the following hypotheses: H8: Trust (TR) positively affects continuance intention (CI) of using Mpayments. H9: Trust (TR) positively affects satisfaction (SA) towards continuously using M-payments. 5.3.1.5. Confirmation (COF) Confirmation (COF) is defined as the degree of users’ perception of an information system is congruent with their prior expectations and actual performance (Bhattacherjee, 2001). This study defines that confirmation is the degree of users’ confirmation of their initial expectations for Mpayment systems. Confirmation is a significant predictor determining performance expectancy and satisfaction (Bhattacherjee, 2001). Specifically, these results have been verified by previous studies related to continuance usage intention of various technologies, such as webbased services (Lee and Kwon, 2011), mobile banking (Yuan et al., 2016), and mobile learning systems (Alshurideh et al., 2020). Therefore, the following hypotheses are proposed: H10: Confirmation (COF) positively affects satisfaction (SA) towards continuously using M-payments. H11: Confirmation (COF) positively affects Performance expectancy (PE) towards continuously using M-payments. 5.3.1.6. Satisfaction (SA) Satisfaction (SA) is defined as cumulative feelings when individual prior emotion is coupled with surrounding disconfirmed expectations (Oliver, 1980). If perceived service performance exceeds the users’ expectations, the user will be satisfied, leading to positive actions towards continuance usage of M-payments (Dlodlo, 2014). Moreover, previous studies have 113 Doctoral Programme in Information Management 6. Chapter 6 - Theoretical Development: Extending the Flow Theory with Variables from the UTAUT2 Model 6.1. Introduction With the significant development of information technology, the global business climate has changed dramatically from traditional social commerce to online commerce towards mobile commerce in recent two decades. Examples of this burgeoning phenomenon are that Mobile payment technology has been widely adopted in various industries (Morosan and DeFranco, 2016; Hossain and Zhou, 2018; Oliveira et al. 2016); catering service has transformed from traditional eat-in to onlineto-offline order and delivery service (Zhao and Bacao, 2020); and traditional brick-and-mortar shopping has developed to online shopping towards live-streaming shopping (Wongkitrungrueng and Assarut, 2018). Customers’ consumption habits have changed by increasingly interacting with information technology (Zhao and Bacao, 2020). Thus, investigating the antecedents determining users’ adoption intention of information technologies is becoming progressively crucial for relevant business stakeholders to extend markets and manage business strategy. A variety of prior works of literature have investigated the factors influencing customers’ intention of using information technology in various contexts (Morosan and DeFranco, 2016; Hossain and Zhou, 2018; Zhao and Bacao, 2020; Wongkitrungrueng and Assarut, 2018). However, previous studies unilaterally analysed users’ adoption intention from technological perceptions or mental expectations, respectively (Wongkitrungrueng and Assarut, 2018; Venkatesh et al., 2012). Moreover, several studies involved perceived flow into adoption models as a mediating variable to illustrate customers’ mental cognition to connect the perceptions and behavioural intention (Hossain and Zhou, 2018; Chen and Lin, 2018). Therefore, the current study aims to develop the Flow theory by integrating the variables from the revised Unified 114 Doctoral Programme in Information Management Theory of Acceptance and Use of Technology 2 (UTAUT2) model as technological perceptions (Venkatesh et al. 2012), in turn formulating customers’ mental cognition and concentration (including flow and satisfaction) (Csikszentmihalyi, 1975; Pereirai et al., 2016), towards determining adoption intention. The proposed model is applied to investigate customers’ behaviour from perceptive determinants to affective engagement towards intentional reaction on new information technology adoption. In addition, in order to comprehensively understand users’ adoption intention, the following sections are addressed in this study to develop the theoretical framework: section two includes reviewing of theoretical background; section three and section four contains hypotheses and model development; section five proposes a future research demonstration; section six demonstrates the conclusion and contributions. 6.2. Theoretical backgrounds 6.2.1. Flow theory Csikszentmihalyi (1975) initially proposed the Flow theory for explaining the particular mental state of people. Flow reflects the level of concentration engaged in a certain activity, which is influenced by a loss of self-awareness, internal enjoyment, human-machine, and selfreinforcement to filter out other unrelated perceptions (Csikszentmihalyi, 1975). When users apply a specific information technology, the flow state represents their holistic feeling of total involvement and immersive experience, which is influenced by the perceptions of the technology’s features and sense of interacting with a machine (Hossain and Zhou, 2018). Meanwhile, flow is validated as significantly formulating users’ engagement in using information technology in different contexts, e.g., mobile payment systems (Zhou, 2013; Gao et al., 2015) and live streaming applications (Chen and Lin, 2018). Furthermore, flow theory has been modified in prior literature by incorporating different theoretical frameworks to investigate information technology adoption from the users’ dimension. Examples of this include integrating with the IS success model to investigate customers’ usage intention of mobile 115 Doctoral Programme in Information Management payments (Zhou, 2013); combining with TAM to explain users’ adoption intention of online games (Kim et al., 2013); and coordinating with the stimulus-organism-response framework to examine consumers’ purchase intention of mobile payment (Hossain and Zhou, 2018). Wherein, flow plays a role as a mediating variable, reflecting users’ perceptions of technological features and, in turn, determining users’ mental cognition and emotion towards formulating psychological reaction or actual behaviour, such as adoption intention, continuance usage (Zhou, 2013; Gao et al., 2015; Chen and Lin, 2018; Hossain and Zhou, 2018). Accordingly, this study defines flow as the mental cognition and affection state of customers engrossed in using information technology that is not easily disturbed by the outside world, and it contains the mediating effect that responds to users’ technological perceptions, reflecting users ’mental cognition towards determines users’ adoption intention. 6.2.2. Unified Theory of Acceptance and Use of Technology 2 UTAUT2 reflects social cognition theory, designed by Venkatesh, Thong and Xu (2012) as an advanced version of UTAUT to predict users’ adoption intention of information technology (Venkatesh et al. 2012). Moreover, the UTAUT2 model can be revised by extending or subtracting variables to analyse users’ adoption intention of information technology in a particular situation appropriately, such as excluding price value and adding privacy to investigate mobile payment adoption (Morosan and DeFranco, 2016) or adding cultural moderators to predict mobile banking adoption (Baptista and Oliveira, 2015). Moreover, the UTAUT2 model has been revised by integrating with other theoretical models to analyse the antecedents of information technology adoption comprehensively. For example, integrating with Diffusion of Innovation models to investigate mobile payment adoption (Oliveira et al. 2016); cooperating with Expectancy Confirmation Model and Task-Technology fit model to explain food delivery apps adoption (Zhao and Bacao, 2020). Therefore, the majority of users’ technological perceptions can be reflected by the variables of the UTAUT2 model, as the extension of Flow theory with synthetical determinants, to formulate users’ adoption intention of information technology. 116 Doctoral Programme in Information Management 6.3. Hypotheses development 6.3.1. Independent variables from UTAUT2 Performance expectancy (PE) PE refers to users’ perceived usefulness when using a particular technology, which can improve their performance in a certain activity (Venkatesh et al., 2012). When service provided from relevant information technology meets users’ expectations, they will tend to adopt it, which has been verified in various contexts, like mobile banking (Yuan et al., 2016), mobile payment (Di Pietro et al., 2015) and mobile internet (Venkatesh et al. 2012). Thus, PE formulates customers’ mental cognition with a positive attitude of utilisablility. Hossain and Zhou (2018) validated that when customers feel a certain technology can increase the efficiency of a particular activity, they will feel more engaged in using that technology (Hossain and Zhou, 2018). Moreover, as an affective expectation, satisfaction is partially conceptualised when users are satisfied with the performance of the service provided by the information technology (Marinković et al., 2020). Therefore, PE significantly determines customers’ satisfaction when adopting a new technology (Yuan et al., 2016; Marinković et al., 2020). Consequently, two hypotheses are formulated as follows: H1: The effect of PE positively affects flow on information technology adoption. H2: The effect of PE positively affects satisfaction on information technology adoption. Effort expectancy (EE) The definition of EE is the degree of users’ perceived easiness when they participate in a particular information technology usage (Venkatesh et al., 2012). When users’ perceive a technology has an understandable interface, operating system, and accessible service, they will formulate a positive attitude to use the new information technology. This factor means that users’ psychological cognition is formulated by perceived ease of the relevant technology, which can be summarised that customers’ perceived flow is positively influenced by EE (Hossain and Zhou, 2018; Kim et al., 117 Doctoral Programme in Information Management 2013). Moreover, EE has also been proven to positively influence PE when users tend to adopt new information technologies (Di Pietro et al., 2015; Yuan et al., 2016). In addition, satisfaction as a further cognitive reflection has also been confirmed by Marinković, Đorđević and Kalinić (2020) that significantly influenced by the perceived easiness of usage (Marinković et al., 2020). This aspect means that when users feel the new technology is easy to operate, their mental requirements will be easier to meet. The effects of EE are concluded in the following hypotheses: H3: The effect of EE positively affects flow on information technology adoption. H4: The effect of EE positively affects PE on information technology adoption. H5: The effect of EE positively affects satisfaction on information technology adoption. Social influence (SI) The definition of SI in technology adoption is “the degree to which an individual perceives that significant others believe he or she should use the new system” (Venkatesh et al., 2012). SI considerably explains users’ adoption intention of information technology (Morosan and DeFranco, 2016), which can be summarised that the recommendation and support from relevant important people can decrease the uncertainty and anxiety of using a new information technology when users are not yet familiar with it (Yuan et al., 2016). Moreover, users’ mental flow state can be formulated by users’ social recommendations and interactions (Park and Lin, 2019). Thus, SI formulates users’ attitude of accepting a new information technology and accelerates users’ engagement towards influencing users’ satisfaction (Chen and Lin, 2018; Kim et al., 2013). Therefore, this paper proposes that SI significantly affects users’ flow and satisfaction from the influence of important relevant people, shown in the following hypotheses. H6: The effect of SI positively affects flow on information technology adoption. H7: The effect of SI positively affects satisfaction on information technology adoption. 118 Doctoral Programme in Information Management Hedonic motivation (HM). Based on the concept of UTAUT2, HM is proposed as the degree of customers’ apperceptive pleasure or joy when using an information technology (Venkatesh et al., 2012). HM significantly formulates mental perception of relevant information technology, which indicates that users will emerge with a higher acceptance attitude towards impacting positive adoption intention once they acquire higher entertainment value (Chen and Lin, 2018). This result is confirmed in various information technology adoption literature, such as mobile banking (Baptista and Oliveira, 2015), mobile payment (Yuan et al., 2016; Morosan and DeFranco, 2016), and mobile shopping apps (Tak and Panwar, 2017). Meanwhile, when users assume using an information technology can bring them pleasant and enjoyable feelings by interacting with a machine, they will acquire higher levels of engagement (Wongkitrungrueng and Assarut, 2018). Thus, HM represents customers’ enjoyment, concentration and curiosity, positively formulating users’ mental state of flow (Chen and Lin, 2018). Moreover, HM is one of the antecedents of customers’ mental expectations, which in turn formulates users’ satisfaction when they tend to adopt a new information technology (Kerviler et al., 2016). Chen and Lin (2018) confirmed that entertainment and enjoyment play a considerable role in explaining users’ satisfaction (Chen and Lin, 2018). Therefore, according to previous works of literature, the following hypotheses are addressed: H8: The effect of HM positively affects flow on information technology adoption. H9: The effect of HM positively affects flow on information technology adoption. 6.3.2. Mediating variables Flow Flow describes a state of users’ mental cognition and affection that fully concentrates on participating in a particular technology or activity (Csikszentmihalyi, 1975). When customers are engaged in using information technology, if the relevant technology can fill their perceived enjoyment, relaxation and pleasure, they will totally immerse into 119 Doctoral Programme in Information Management interacting with the machine and difficultly be disturbed by outside irrelevant things (Hossain and Zhou, 2018). Thus, flow is considered a temporary experience of unawareness that shapes a positive attitude of engagement for customers, which influences their behavioural intention of adoption. Accordingly, flow directly contributes to a significant influence on the adoption intention of information technology (Zhou, 2013). Furthermore, flow establishes customers’ satisfaction, in turn affecting their adoption intention (Chen and Lin, 2018; Zhou, 2013). Customers will feel more satisfied with the service quality and information quality when immersed in a certain technology (Gao et al., 2015). Thus, the influences of flow on satisfaction and behavioural intention can be summarised in the following hypotheses: H10: The effect of flow positively affects satisfaction on information technology adoption. H11: The effect flow positively affects behavioural intention on information technology adoption. Satisfaction Satisfaction refers to customers’ general psychological cognition that believing a certain technology can bring them a positive operating experience to meet their multi-dimensional expectations (Chen and Lin, 2018; Pereirai et al., 2016). Satisfaction is positively impacted by engagement in using information technology (Wongkitrungrueng and Assarut, 2018). Thus, this study assumes satisfaction, as a mediating variable, reflects users’ technological perceptions and experience of engagement, in turn formulating users’ adoption intention of information technology, which corresponds with prior technology adoption literature in online purchase intention (Pereirai et al., 2016), mobile payment adoption (Kerviler et al., 2016; Park and Lin, 2019), and live streaming adoption (Chen and Lin, 2018). Accordingly, it can be demonstrated that satisfaction reflects customers’ technological perceptions and mental engagement towards formulating customers’ behavioural intention of adopting information technology, which generates the following hypothesis: H12: The effect of satisfaction positively affects behavioural intention on information technology adoption. 120 Doctoral Programme in Information Management 6.4. Theoretical model development Based on the above literature review and hypotheses development, Flow theory is theoretically developed by integrating four variables (performance expectancy, effort expectancy, social influence and hedonic motivation) from the revised UTAUT2 model as independent variables to measure customers’ technological perceptions. Meanwhile, facilitating conditions, habit and price value are considered to be excluded in the proposed model because these variables require incorporating the actual adoption situation of a specific information technology and sufficient usage experience, respectively (Zhao and Bacao, 2020; Baptista and Oliveira, 2015). Moreover, the mediating variables are extended with flow and satisfaction to represent customers’ engagement and mental cognitions, which reflect users’ technological perceptions (Chen and Lin, 2018; Pereirai et al., 2016; Park and Lin, 2019). Furthermore, customers’ behavioural intention is assumed as a mental reaction, influenced by their mental process of adopting information technology (Venkatesh et al., 2012). Specifically, according to the research objectives, the theoretical framework is developed based on the relevant studies with the demanded measurement constructs by extending the boundaries of flow theory with the revised UTAUT2 model to complementarily investigate external and internal antecedents determining customers’ adoption intention of information technology. The proposed theoretical model is demonstrated in figure 6.1, with relevant causal relations of the hypotheses mentioned above and a postscript of abbreviations. Moreover, all measurement items are defined based on the relevant hypotheses and modified to adapt the proposed research model as presented in the table in Appendix D with the relevant references. 121 Doctoral Programme in Information Management Figure 6.1. Research Model 6.5. Recommendations for future research This study's future research will consist of data collection, data analysis, and discussion sections to verify the proposed theoretical model and explain the factors influencing users’ adoption intention of information technology. An online questionnaire survey will be applied for data collection. Specifically, the questionnaire will be designed with two sections. The first section will involve close-ended questions for the respondents’ demographic data, including users’ gender, age range, educational background, and experience using information technology. The second section will be developed by implementing constructs and items for structural equation modelling based on previous hypotheses to explain performance expectancy, effort expectancy, social influence, hedonic motivation, flow, satisfaction and behavioural intention. A sevenpoint Likert scale (from strongly disagree = “1” to strongly agree = “7”) will measure the relevant items from the table in appendix D with 29 measurement items as indicators. Afterwards, after removing the data with missing values, the valid data will be evaluated by the Kolmogorov– Smirnov test for non-response bias (Ryans, 1974) and Exploratory Factor 122 Doctoral Programme in Information Management Analysis (EFA) will be applied by SPSS to evaluate common method bias in the dataset (Podsakoff et al., 2002). Furthermore, a covariance-based structural equation model will be applied using SPSS and AMOS through the two-step approach to evaluate the measurement model and structural model (Anderson and Gerbing, 1988). Specifically, in the measurement model, this paper will apply SPSS to implement EFA to examine the construct reliability (Cronbach’s alpha > 0.7). Meanwhile, Confirmatory Factor Analysis in AMOS will be applied to assess the convergent validity (factor loading >0.7; Composite Reliability >0.7; Average variance extracted (AVE) >0.5) and discriminant validity (square root of AVE should be greater than all correlations between any other pair of constructs) to verify the quality of measurement model. Moreover, the model fit will be assessed by the ratio of chi-square to degrees-of-freedom, comparative fit index, the goodness of fit index, adjusted goodness-of-fit index, normalised fit index, Tucker-Lewis index, and root mean square error of approximation. Afterwards, the structural model will be examined by AMOS with the maximum likelihood estimation method and bootstrapping technique. Specifically, the R² values of endogenous variables and path coefficients of the internal structure will be assessed to illustrate the explanatory power of the structural model and test the hypotheses. In addition, based on the results of the data analysis, the discussion section will evaluate the factors affecting the adoption intention of information technology to verify the quality of theoretical development and provide relevant theoretical and practical contributions based on the specific information technology. 6.6. Conclusion and contributions A theoretical development is proposed in this research by extending Flow theory with the revised UTAUT2 model to explain users’ behavioural intention of adopting information technology. The proposed model fills the gap of traditional Flow theory, only focusing on users’ mental perceptions by integrating variables from UTAUT2. Moreover, with the extension of mediating variables, flow and satisfaction, reflecting users’ mental cognition and engagement, which conjointly explains users’ adoption intention of information technology progressively. Specifically, this study 129 Doctoral Programme in Information Management Santos and Oliveira, 2020; Zhao and Bacao, 2020), in investigating customers’ behaviours on mobile technology. Furthermore, UTAUT2 involves age, gender and experience as moderators to explain the individual differences in adoption intention (Venkatesh, Thong and Xu, 2012). Moreover, UTAUT2 was applied for mobile shopping applications’ adoption by Tak and Panwar (2017), who found that hedonic motivation was the most significant antecedent, which corresponds with the current study's situation that LSSAs are entertaining mobile shopping applications. Therefore, UTAUT2 is considered the appropriate theoretical foundation for investigating users’ perceptions as a stimulus in the proposed model. 7.2.4. Flow theory Flow theory was initially proposed by Csikszentmihalyi (1975) to predict individuals’ mental engagement in a certain activity. Subsequently, Flow theory's applicability has been extended into the human-computer interaction domain to describe users’ absorption in technology (Webster et al., 1993). Specifically, flow represents users’ holistic, immersive consciousness when they concentrate entirely on a particular activity or technology; their involvement will be self-reinforced by constitutional enjoyment and engaging interactivity; in turn, their self-consciousness will become indistinct to ignore irrelevant interruptions (Csikszentmihalyi and Csikszentmihalyi, 1988). Flow has been applied as a mediator in various technology adoption studies to describe customers’ cognition and engagement for predicting users’ adoption intention (Hsu and Lu 2004; Zhou, 2013; Hossain and Zhou, 2018), especially in the fields of entertaining technologies, such as live streaming (Chen and Lin, 2018) and mobile shopping (Gao, Waechter and Bai, 2015). Flow is significantly influenced by users’ technological perceptions (Zhou, 2013; Kim et al., 2013), and mental determinants, such as emotion (Hossain and Zhou, 2018), trust (Gao, Waechter and Bai, 2015), enjoyment (Chen and Lin, 2018). Meanwhile, the combination of flow theory with other frameworks, such as the Information Systems Success Model (Zhou, 2013; Gao, Waechter and Bai, 2015), Stimulus-Organism-Response framework (Hossain and Zhou, 2018), also reasonably illustrated users’ adoption intention. Thus, Flow theory is considered a theoretical foundation for 130 Doctoral Programme in Information Management representing users’ shopping engagement via LSSAs during the pandemic lockdown period as the organism in the proposed model. 7.2.5. Moderating effects of age and gender According to the current research objectives, age and gender are proposed as moderating variables involved in the analysis process. Venkatesh, Thong and Xu (2012) initially confirmed that age and gender have moderating effects on the UTAUT2 constructs affecting users’ adoption intention. Moreover, further other works of literature have integrated age and gender as moderators within various frameworks (UTAUT, Technology Acceptance Model (TAM), Diffusion of Innovation (DOI) Theory) and confirmed age and gender significantly moderated constructs in different contexts, respectively (Venkatesh and Zhang, 2010; Liébana-Cabanillas, Sánchez-Fernández and Muñoz-Leiva, 2014; Khalilzadeh, Ozturk and Bilgihan, 2017; Riskinanto, Kelana and Hilmawan, 2017; Shao et al., 2018; Marinković, Đorđević and Kalinić, 2020). However, based on the differences in research objectives, sample targets, and involved variables in various scenarios, the moderating effects of age and gender have presented diversity in different literature. Venkatesh and Zhang (2010) validated that performance expectancy in the behavioural intention of information technology was significantly moderated by younger male users, and effort expectancy was strongly moderated by older female customers, which is contrary to the findings of Riskinanto, Kelana and Hilmawan (2017) claimed that state age had insignificant effects on perceived usefulness and ease of use on adoption intention of E-payment technology. On the other hand, LiébanaCabanillas, Sánchez-Fernández and Muñoz-Leiva (2014) complementally illustrated that social influence bore strong influence on users above 35 years old and trust was more affected by younger groups in mobile payments adoption. Moreover, Shao et al. (2018) claimed that males had a stronger moderating effect on mobility and reputation in the trust formation process of mobile payments, while females moderated customisation and security on trust more. Likewise, Pascual-Miguel, Agudo-Peregrina and Chaparro-Peláez (2015) found that the moderating effects of female customers on effort expectancy and social influence were significantly stronger than male customers on online purchase intention. In order to analyse the moderating effects of age and gender on 131 Doctoral Programme in Information Management all constructs in the proposed research model, a multi-group analysis is applied in this research, which is widely applied in previous studies for multi-group comparisons (Pascual-Miguel, Agudo-Peregrina and Chaparro-Peláez, 2015; Shao et al., 2018; Marinković, Đorđević and Kalinić, 2020). 7.3. Development of research model and hypotheses Based on the previous literature reviews, the integration of the SOR framework with UTAUT2 and Flow theory is considered the theoretical foundation to propose a comprehensive model for the investigation. Specifically, according to previous paradigms of the SOR framework application, this research extends the SOR framework by integrating variables from the revised UTAUT2 model proposed as stimulus components, roused by technological perceptions of LSSAs during the pandemic lockdown period (performance expectancy, effort expectance)(Islam and Rahman, 2017; Chen and Yao, 2018), social influence (Wu and Li, 2018), hedonic motivation (Kim, Lee and Jung, 2020) and trust (Kim, Lee and Jung, 2020). These variables reflect users’ external and internal perceptions towards inciting their further psychological cognition. On the other hand, on account of the popularisation of smartphones, proficiency in using various mobile applications and no monetary cost to operate LSSAs, original variables, facilitating conditions, habit and price value, are excluded from the UTAUT2 model, which is in accordance with previous findings respectively (Baptista and Oliveira, 2015; Slade et al., 2015; Alalwan, Dwivedi and Rana, 2017; Tam, Santos and Oliveira, 2020; Zhao and Bacao, 2020). Flow theory provides theoretical support to reflect customers’ mental cognitive and affective intermediary states of shopping via LSSAs during the COVID-19 pandemic lockdown period, which is appropriate to assume as an organism in the SOR framework (Hossain and Zhou, 2018; Kim, Lee and Jung, 2020; Zhao, Wang and Sun, 2020). Moreover, this study proposes that perceived value and adoption intention reflects customers’ psychological reactions and behaviours to constitute response elements of the SOR framework (Hossain and Zhou, 2018; Kim, Lee and Jung, 2020). In addition, age and gender are considered moderators in the theoretical model to compare the different effects of antecedents on customers’ adoption intention of LSSAs in each 132 Doctoral Programme in Information Management subgroup. The proposed research model is generalised and presented in Figure7.1 with the relevant hypotheses relations. Figure 7.1. Proposed research model 7.3.1. Stimulus components: variables from the revised UTAUT2 model Performance expectancy (PE), as a technological perception, represents users’ perceived usefulness of a certain technology that can optimise their experience in a specific technology or reinforce their performance in particular activities (Venkatesh, Thong and Xu, 2012). Moreover, users’ perceived technological features, like compatibility, service quality, information quality, and system quality, can be generalised as perceived usability of technology, represented as PE (Gu, Lee and Suh, 2009; Zhou, 2013; Di Pietro et al., 2015). Related to technology adoption, PE 133 Doctoral Programme in Information Management significantly formulates users’ mental responses, like attitude, adoption intention, and continuance usage intention, which are confirmed by prior literature, respectively (Suh and Han, 2002; Oliveira et al., 2016; Yuan et al., 2016). Accordingly, customers’ psychological cognitions are formulated by their perceptions of the satisfying usability, which indicates that PE significantly influences customers’ perceived flow when they intend to adopt new technology (Kim et al., 2013; Hossain and Zhou, 2018). Therefore, the hypothesis can be generalised as follow: H1: Customers’ performance expectancy (PE) as a stimulus positively determines the organism flow (FL) when shopping via LSSAs during the pandemic lockdown period. Effort expectancy (EE), as a technological perception, expresses that users acquire feelings of easiness from understanding, operating and interacting with a specific information technology (Venkatesh, Thong and Xu, 2012). A variety of literature has verified EE's considerable effect on customers’ attitude and behavioural intention in technology adoption research (Suh and Han, 2002; Di Pietro et al., 2015; Riskinanto, Kelana and Hilmawan, 2017). Consequently, customers’ engagement and flow experience are formulated by understandability, operability, and intractability (Hsu and Lu 2004; Kim et al., 2013). On the other hand, the influence of EE has been confirmed by Kim et al. (2013) as not only affecting flow but also performance expectancy when users adopt entertainment technology. When customers recognise that a technology is easy to access, they will tend to confirm its usability. This phenomenon has been validated in various technology adoption works of literature, such as Live streaming (Ho and Yang, 2015), mobile banking (Gu, Lee and Suh, 2009; Yuan et al., 2016; Alalwan, Dwivedi and Rana, 2017) and mobile payment (Di Pietro et al., 2015; Liébana-Cabanillas, Ramos de Luna and Montoro-Ríosa, 2017). Thus, the hypotheses related to EE are proposed as follows: H2: Customers’ effort expectancy (EE) as a stimulus positively determines the organism flow (FL) when shopping via LSSAs during the pandemic lockdown period. H3: Customers’ effort expectancy (EE) as a stimulus positively determines performance expectancy (PE) when shopping via LSSAs during the pandemic lockdown period. 134 Doctoral Programme in Information Management Social influence (SI), as an environmental perception, represents customers perceiving the influence from their relevant people, like close friends, family members and colleagues, who recommend and support them to use a certain technology (Venkatesh, Thong and Xu, 2012). Customers’ anxiety is derived from new technology’s uncertainty, which can be decreased by the influence of their close social network (Slade et al., 2015). Various technology adoption studies have involved SI in theoretical frameworks and confirmed SI is an essential antecedent in determining customers’ attitudes and behaviours (Kerviler, Demoulin and Zidda, 2016; Morosan and DeFranco, 2016; Khalilzadeh, Ozturk and Bilgihan, 2017; Chopdar and Sivakumar, 2019). Moreover, Chen and Lin (2018) claimed that the effect of SI dramatically formulated users’ mental awareness of engagement when using live streaming. Accordingly, interacting with relevant people on a specific information technology can facilitate users’ flow experience (Zhao, Wang and Sun, 2020). Hence, the current study proposes that users’ flow experience of LSSAs is positively determined by SI, which formulated the following hypothesis. H4: Social influence (SI) as a stimulus positively determines the organism flow (FL) when shopping via LSSAs during the pandemic lockdown period. Hedonic motivation (HM) was initially adapted in UTAUT2 by Venkatesh, Thong and Xu (2012), which is conducted as the internal emotional perception that users perceive enjoyment and pleasure descend from their expectation or experience of a certain information technology. The directly positive effect of HM on adoption intention has been confirmed by prior researchers who applied UTAUT2 on mobile technology adoption, e.g. mobile payment (Morosan and DeFranco, 2016) and mobile banking (Baptista and Oliveira, 2015; Alalwan, Dwivedi and Rana, 2017). Meanwhile, HM had been assumed as an antecedent also having a significant indirect effect on customers’ behavioural intention. Yeo, Goh and Rezaei (2017) claimed HM formulated attitude via convenience motivation and post-usage usefulness when customers adopt online food delivery services. Likewise, engagement, as the main characteristic of users’ flow experience, is formulated by enjoyment, curiosity, and concentration (Ghani and Deshpande, 1994; Moon and Kim, 2001). Consequently, Wongkitrungrueng and Assarut (2018) validated HM directly and indirectly (through trust), formulating users’ engagement in 135 Doctoral Programme in Information Management live streaming commerce. Furthermore, Chen and Lin (2018) illustrated that live streaming's entertainment features formulated viewers’ HM, which positively determined their mental affection of perceived value towards affected their final behavioural intention. Thus, the current study assumes that HM positively affects customers’ engagement and affection in hypothesis 5. H5: Customers’ hedonic motivation (HM) as a stimulus positively determines the organism flow (FL) when shopping via LSSAs during the pandemic lockdown period. Gefen (2000) defined trust (TR) as describing users’ subjective awareness of believing a particular technology can fulfil obligations and positively guarantee a qualified performance to meet their expectations. Specifically, under the lockdown measures of the COVID-19 epidemic, trust reflected users’ perceptions of technology characteristics, like mobility, security, etc., which correspond with perceived security against perceived risk and uncertainty conditions (Shao et al., 2018). LSSAs’ contactless online consumption functions, as a beneficial feature for the lockdown situation during the COVID-19 pandemic, formulated customers' perceived trust towards positively influenced enjoyable and practical cognitions (Khalilzadeh, Ozturk and Bilgihan, 2017). Accordingly, trust, as an essential variable to investigate users’ behavioural intention, had been integrated into various adoption models, such as UTAUT2 (Slade et al., 2015), TAM (Gu, Lee and Suh, 2009), IS success model (Zhou, 2013). Moreover, from the participation aspect, the interaction between customers and vendors on live streaming commerce facilitated users’ perceived trust in sellers and products, which in turn optimised the engagement (Wongkitrungrueng and Assarut, 2018). Therefore, this paper proposes that trust formulates users’ engagement and cognitive acceptance towards positively influencing flow (Zhou, 2013; Gao, Waechter and Bai, 2015). Meanwhile, as an antecedent of users’ utilitarian perceptions, trust positively facilitates customers’ performance expectancy stimulus (Gu, Lee and Suh, 2009; Alalwan, Dwivedi and Rana, 2017). Hence, the following hypotheses are addressed. H6: Trust (TR) as a stimulus positively determines the organism flow (FL) when shopping via LSSAs during the pandemic lockdown period. 136 Doctoral Programme in Information Management H7: Trust (TR) as a stimulus positively determines performance expectancy (PE) when shopping via LSSAs during the pandemic lockdown period. 7.3.2. Organism component: Flow (FL) Csikszentmihalyi (1975) defined flow as an individual’s cognition and affection of intrinsic absorption in a particular activity or technology. Flow experience of technology was described as users’ temporarily unawareness derived by their internal enjoyment, pleasure, engagement, and interaction with a certain technology (Gao, Waechter and Bai, 2015; Chen and Lin, 2018). Moreover, the lockdown measure provided an appropriate environment to enhance an individual’s immersive shopping experience via LSSAs at home. Various technology adoption studies have validated that flow was significantly formulated by users’ technological perceptions towards positively determining their behavioural intention of adoption or continuance usage (Zhou, 2013; Gao, Waechter and Bai, 2015; Lim et al., 2020). Consequently, flow is in accordance with the conception of an organism, which is assumed as a mediator connecting technological and environmental stimuli and responses in shopping via LSSAs. Flow has been examined by previous researchers as also having an indirect effect on users’ final responses, like adoption intention, actual usage, continuance intention, via users’ mental reflection variables, perceived value, satisfaction, attitude (Hung et al., 2010; Kim et al., 2013; Chen and Lin, 2018). Meanwhile, the effects of flow and mental reflections had been validated to determine customers’ behavioural intentions conjointly in various pieces of literature (Zhou, 2013; Gao, Waechter and Bai, 2015; Hossain and Zhou, 2018). Chen and Lin (2018) claimed that flow positively affected perceived value towards formulated customers’ usage intention of live-streaming. Therefore, the following hypotheses are proposed: H8: Customers’ flow (FL) as an organism positively determines the response perceived value (PV) when shopping via LSSAs during the pandemic lockdown period. 137 Doctoral Programme in Information Management H9: Customers’ flow (FL) as an organism positively determines the response behavioural intention (BI) when shopping via LSSAs during the pandemic lockdown period. 7.3.3. Response components: Perceived value (PV) and Behavioural intention (BI) Perceived value (PV), as defined by Zeithaml (1988), represents customers’ universal assessments of a service or technology. PV is determined by users’ perceptions of acquisition and investment. Sweeney and Soutar (2001) extended the dimensions of perceived value, including quality and price of production, customers’ emotional responses, and social influence. Meanwhile, Petrick (2002) modified behavioural price, monetary price, emotional response, quality, and reputation to emerge as other dimensions of PV. PV also represents customers’ perceived multidimensional benefits, including utilitarian, hedonic and social perspectives (Kim, Kim and Wachter, 2013; Kerviler, Demoulin and Zidda, 2016). Specifically, in this study, perceived value represents the customers’ general mental responses to shopping via LSSAs during the COVID-19 pandemic lockdown period. Perceived value has been assumed as a cognitive variable in various adoption models, such as the Expectation Confirmation Model (Hsu and Lin, 2015), Value-based Adoption Model (Kim, Chan and Gupta, 200) and Mobile user Engagement Model (Kim, Kim and Wachter, 2013), which positively determines customers’ behaviours. On the other hand, Chen and Lin (2018) confirmed that perceived value, as a conative factor, was determined by flow and, in turn, formulated users’ behaviours. Therefore, this study proposes that perceived value, as customers’ conational response, constitutes one component of the responses, which is demonstrated in the following hypothesis. H10: Customers’ perceived value (PV) positively determines behavioural intention (BI) when shopping via LSSAs during the pandemic lockdown period. 138 Doctoral Programme in Information Management 7.4. Methodology and data demographic distribution 7.4.1. Measurement Quantitative methodology is applied in this study to evaluate the proposed model. An online questionnaire survey was conducted to collect data in China, which included two parts. The first part demonstrated demographic information (consisting of gender, age and frequency of using LSSAs during the COVID-19 pandemic lockdown period) of participants with Dichotomous, Bounded continuous and Ordinalpolytomous close-ended questions; the second part consisted of a sevenpoint Likert scale (from strongly disagree = "1" to strongly agree = "7") structural questions to evaluate performance expectancy (PE), effort expectancy (EE), social influence (SI), hedonic motivation (HM), trust (TR), flow (FL), perceived value (PV) and behavioural intention (BI) with 34 measurement items referred from previous literature, shown in Appendix E with Chinese translation. The questionnaire was designed and managed in English. Then equivalently translated into the Chinese language by language experts to avoid the biases of language and culture because the target population of the survey was smartphone users in China. Afterwards, according to the translation-back translation method, it was reversely translated into the English language. In order to minimise the nonResponse rate, a short introduction and respondent-friendly survey questionnaire techniques were applied in the survey (Lynn, 2008). 7.4.2. Data collection The online questionnaire was designed via Wenjuan.com (a Chinese online survey platform). According to the formulae from Westland (2010) and the numbers of 34 observed indicators and eight latent variables in the proposed model, the recommended minimum sample size for the model structure was 91. The questionnaires were distributed via online and WeChat (Chinese mobile social media application) on 9 August 2020 for data collection. After four weeks of data collection, 400 empirical data were collected on 6 September 2020, of which 138 were derived from online responses and 262 via WeChat. After filtering out the responses