Information technology systemInformation system success model Information system use-related activityInformation technology Individual information technology adaptation modelKaiser-Meyer
Relevant Previous Empirical Studies -< sôô< sô<<s se 65
ATSI-based individual adaptation for IT use
Drawn on ATS (DeSanctis & Poole, 1994), Schmitz et al (2016) developed a framework ASTI for the level of individuals The authors proposed an ATSI-
66 based research model and conducted a survey study of part-time business graduate students to investigate adaptation to malleable IT (mobile application used at a workplace) The research model is shown in Figure 2.6 The study results disclosed the impacts of a single process of a structuration episode, including four distinct adaptation behaviors.
The proposed model was experimentally confirmed; however, there were some issues that Schmitz et al.’s (2016) model left disputed First, the model only focuses on a few individual characteristics (i.e., innovativeness and experience); unfortunately, two main substantial AST of input structures (i.e., technology and social characteristics) were not a research target Second, the study was limited to investigating task and technology adaptation, and was not directly associated with the “domain of the user” (Schmitz et al., 2016, p 683).
Third, the research has yet to mention several essential adaptation behaviors, such as self-adaptation (Fadel, 2012), intention to explore IT functions
(Nambisan et al., 1999), and trying to innovate with IT (Ahuja & Thatcher, 2005).
Fourth, malleable IT (smartphones) and respondents (part-time students) were chosen as research contexts to explore adaptive behaviors that appear to be relevant, yet not sufficiently convincing to generalize for progressively diverse IT- enabled services (e.g., e-commerce) In addition, using the research context of smartphones may need to be reinforced in explaining user behaviors from complicated perspectives (i.e., voluntary or mandatory, personal, or organizational use).
Furthermore, BhattacherJee and Harris (2009) explored factors related to user adaptation to information technology (IT) This study proposed a research model of individual-level IT adaptation and validated the model empirically.
Bhattacherjee and Harris’s (2009) model integrated and operationalized adaptation constructs which were drawn on TAM (Davis et al., 1989) as the dominant theory perspective, and AST (DeSanctis & Poole, 1994) for the individual level The study collected data from respondents (students) for a school application called “MyYahoo web portal usage” to investigate user IT adaptation process The proposed model was empirically validated with the moderation role of work adaptation to IT adaptation.
Two pending issues require further investigation First, the controversial research design impacted the generalizability of the study in terms of the respondent subjects and measurements (Bhattacherjee & Harris, 2009, p 43).
Second, the study oversimplifies essential concepts (e.g., IT adaptation) by describing it as changing the layout and interface when explaining the process of users performing IT adaptive behavior, which the author compares with the ‘IT assimilation gap’ (Fichman & Kemerer, 1999).
Information system use-related aCtIVItY ô+- 67
Barki et al (2007) investigated the construct of IS use by conceptualizing a new construct labeled IS use-related activity (ISURA) that accounts for insights into individual user behavior of IT use in the workplace The ISURA is conceptualized as a behavioral construct involving technology interaction, task-
68 technology adaptation, and self-adaptation Based on the conceptualization of interaction behaviors (Doll & Torkzadeh, 1998) and the TTF framework
(Goodhue & Thompson, 1995), Barki et al (2007) have validated the ISURA model with the analysis of data collected from both individuals and organizations.
Despite the significant findings, the study was controversial in several issues.
First, there needs to be a satisfactory explanation for the theoretical link between task adaptation and technology adaptation of the dual concept of task-technology adaptation (Schmitz et al., 2016) Second, there needs to be another clarification of the peer hierarchy among the three main structures in the ISURA aggregate concept (i.e., IT interaction, task-technology adaptation, and individual adaptation) (Bhattacherjee & Harris, 2009) Third, due to the fact that the ISURA model was quite simplified, it does not consider “intermediate state” variables such as satisfaction and behavioral intention, thus leading to the outcome variables of the net benefits.
Influence of HRM factors on technology adaptation
Rubel et al (2020) investigated the effects of five types of organizational human resource management (HRM) practices, including employees’ information sharing and participation, firm’s management support and training, and organizational reward and recognition, such as high involvement work practice
(HIWP) on information technology adaptation In the context of the banking industry, the five HRM factors drive employees to perform their jobs, leading to adaptation to technology, which, in this case, is the information technology system
69 that the bank management expects its employees to utilize The researchers concluded that, when staffs have a chance to take part in decision-making and share information and knowledge, enjoy management support and acknowledgment, and receive sufficient training, this initiates employees’ obligation to fulfill their job, adopt the IT implemented by the organization, and then adapt to that IT.
Although the research findings confirmed that management must ensure IT users adapt to technology by providing supportive measures via training, empowerment, rewards, and recognition, there are several limitations that need further investigation First, the study investigated only the technology-use aspect of users’ adaptive behavior, yet did not consider the task adaptation and self- adaptation Second, the technical attributes of the IT and user personal characteristics and their influences on the technological adaptation were not recognized in the study Future research should explore these variables and their potential impact on IT adoption and adaptation Future studies should consider other appropriate methodologies and measurements of outcome variables (e.g., longitudinal or experimental designs) Further works should explore other context settings and sharing economy industry to verify the results and strengthen cross- sector validity globally.
Research Gap Identification G5 S6” 69
The aforementioned subsections have intensively reviewed the essential literature pertinent to the substantive concepts and foundational theories as well
70 as previous empirical studies relevant to user adaptation and continuance intention in m-app based services and specifically, a typical type of ride-hailing service As mentioned in the above literature review, while IT acceptance has been studied intensively, the literature has ignored user adaptation, its causes and effects in IT implementation leading to user continuance, and more specifically, IT-enabled service settings Furthermore, empirical work has yet to explain the linkage between IT adaptation and IT continuance explicitly In the context of platform- based ride-hailing services, the need for an in-depth study of the relationship between behavioral adaptation and continuance intention remains a research gap that must be filled Examining the variables mentioned above is expected to fill the gap in IT adaptation literature and answer our research questions in the context of ride-hailing services.
Based on the knowledge of this review and the research gap, the next sections propose a theoretical framework of individual IT adaptation- continuance intention for mobile- applications and a research model for an empirical study of the influence of adaptation behavior on continuance intention in the context of ride-hailing service.
Drawing on the literature review in previous sections, the theoretical root for the development of the theoretical framework of this research are (1) the adaptive structuration theory for individuals (ATSĐD (Schmitz et al., 2016), which is based on structuration theory (Giddens, 1984) and adaptive structuration theory
(DeSanctis & Poole, 1994), (2) the framework for the decomposed theory of planned behavior (Ajzen, 1991; Taylor & Todd, 1995), and (3) the expectation- confirmation model (Bhattacherjee, 2001).
The new framework applies the ideas of individual adaptation
(structuration) to m-app-based business settings This framework identifies the following three structure inputs: (1) technological characteristics of the m-app, (2) social (e.g., organizational, workplace) characteristics, and (3) individual characteristics of users As such, user adaptation behaviors and continuance intention are investigated by this framework with the process approach comprising three stages: input, process, and output (see Figure 2.4).
Figure 2.4 Theoretical framework for m-application adaptation
Inputs Process Outcomes ¢ Technical features ¢ Functional affordances
Decision Performance e Task-Technology ôỔ Workplace ¢ Self-adaptation e Task practices ô Environmental conditions
Source: Adapted from Schmitz et al (2016), Bhattacherjee and Harris (2009), and Taylor and Todd (1995)
For the “process” block, this research employs individual behavioral adaptation to represent the adaptation process, in which users adjust technology attributes, modify work procedures, and change themselves to harmonize with the m-app For the output stage, the outcomes of the adaptation may vary as individuals progressively adapt to the mobile application, leading them to make decisions and perform better The other outcomes of the adaptation process are emergent and temporarily new structures (i.e., adapted platform) that are inherent from current structuration episodes that serve as new input for upcoming use and adaptation in the model.
It is significant and necessary to clarify the difference between a theoretical framework and a conceptual framework or empirical research model (Kivunja,
2018; Osanloo & Grant, 2016; Sekaran & Bougie, 2016) As explained in the previous section, a theoretical framework is the application of a theory, or a set of thoughts drawn from one or two relevant theories, to explain an event, phenomenon or research problem (Lysaght, 2011; Sekaran & Bougie, 2016) On the other hand, a research problem is solved based on more than one or a few theories or a limited number of concepts contained in those theories (Lederman &
Lederman, 2015; Osanloo & Grant, 2016; Varpio et al., 2020) As such, the researcher may have to “synthesize” theories, empirical findings, and other perspectives through a literature review and contextual variables relevant to a
73 research problem (Imenda, 2014, p 189; Osanloo & Grant, 2016, p 17) The synthesis can be called a conceptual framework or a synthesized theoretical research model, and a research model, for short (Ibrahim et al., 2016; Imenda,
2014; Varpio et al., 2020) It is worth noting that theoretical frameworks are occasionally referred to as a research model (1.e., a conceptual framework); however, all too often, these words are used interchangeably or without a clear understanding of the differences, but they are neither interchangeable nor synonymous These terms can be ambiguous and cause misperceptions for researchers conducting research projects (Kivunja, 2018; Osanloo & Grant, 2016;
While a theoretical framework is constructed on a prevailing theory (or theories) that has already been tested, validated, and is considered a widely accepted theory in the scholarly literature (Osanloo & Grant, 2016), a research model offers important factors, constructs, or variables, and presumes associations between them (Imenda, 2014; Luse et al., 2012) In other words, a research model is derived from concepts and variables, as a theoretical framework is rooted from an acceptable theory or theories The research model offers a rational structure of connected variables that helps to provide a depiction or visual schema of how factors and variables relate to each other within the theoretical framework.
Drawn on Figure 2.4 of the theoretical framework and Figure 2.5, explaining how the research model is established, the research model is proposed
74 to investigate the influence of user adaptation on continuance intention towards the ride-hailing application empirically The proposed research model illustrates a connected set of hypotheses that based on the underlying theories and model of
(1) IT adaptation and the adaptive structuration theory for the individual (Schmitz et al., 2016) (2) the theory of decomposed planned behaviors (DTPB) (Taylor &
Todd, 1995) and (3) the empirically validated models of IT continuance, expectation-confirmation model (ECM) (Bhattacherjee, 2001; Bhattacherjee &
Lin, 2015) On the one hand, IT adaptation literature provides solid justification for interpreting user adaptation and its relationships Thus, it helps to explain appropriately the IT user’s adaptation process, its antecedents, and more importantly, its likely consequences such as changes in user’s decisions, performance or behaviors (Bhattacherjee & Harris, 2009; Nguyen & Ha, 2022).
On the other hand, TPB and its extension DTPB have determined how users’ behavioral intentions and usage are affected by their belief structures (i.e., their perception, environment factors and behavioral controls factors) After the initial adoption, the user adapts to IT and may generate the intention to continue using the IT The IT continuance intention is the outcome variable of ECM, based on which this study expects to investigate the user continuance intention to use app- based ride-hailing services According to these three theoretical frameworks and inspired from previous empirical research, we propose a set of hypothesized relationships and the research model The hypothesized relationships among
ECM’s, ASTI’s, and DTPB’s antecedents, including perceived usefulness,
75 satisfaction, self-efficacy, and management support, with behavioral adaptation, trust, and continuance intention will be discussed in the following paragraphs The research model for the empirical study is proposed in Figure 2.6 in this section.
2.6.2.1 ECM-based constructs and relationships
The concept of CI in an IT-enabled service setting such as ride-hailing 1s similar to a customer’s retention and repurchase from marketing and customer behavior literature (Tam et al., 2020; Venkatesh et al., 2011) Continuance usage is studied in mobile app literature as post-acceptance and post-adoptive stages, in which users continuously use the m-app within various services (Faber & de
Reuver, 2019; Poromatikul et al., 2019; Tang, 2019) According to Oliver’s (1980)
ECT, satisfaction (SA) is determined by prior experience with the product and, in turn, influences their intentions to reuse that product (Dholakia & Zhao, 2010; Yan et al., 2021) ECT argues that consumers decide to repurchase a product or to continue using a service based on their satisfaction of prior use of that product or service In the IS context, satisfaction is conceptualized as the “affective attitude towards a computer application” (Doll & Torkzadeh, 1988, p 261), and as a result of users’ interactions with that IS In general m-commerce and mobile food- delivery settings, user satisfaction is the “customer’s reaction or feeling concerning the experience” with m-commerce applications (Molla & Licker,
2001; Y.-S Wang et al., 2019, p 3) Only satisfied users will continue to use the existing service while those who are dissatisfied will likely discontinue in preference for a new service (Deng et al., 2010; Susanto et al., 2016) The direct
76 influence of satisfaction on continuance intention has been studied in a multitude of contexts (Franque, Oliveira, Tam, & de Oliveira Santini, 2020; Nabavi et al.,
2016; Sabah, 2020), such as in online tourism (Liu et al., 2020), mobile commerce
(Gao et al., 2015), e-learning (Joo et al., 2017), health applications (Alsyouf &
Proposed Research Model s- <5 5< s55 9952 72
Development of research model . -ô -ôô++ss<++ss++ 72
It is significant and necessary to clarify the difference between a theoretical framework and a conceptual framework or empirical research model (Kivunja,
2018; Osanloo & Grant, 2016; Sekaran & Bougie, 2016) As explained in the previous section, a theoretical framework is the application of a theory, or a set of thoughts drawn from one or two relevant theories, to explain an event, phenomenon or research problem (Lysaght, 2011; Sekaran & Bougie, 2016) On the other hand, a research problem is solved based on more than one or a few theories or a limited number of concepts contained in those theories (Lederman &
Lederman, 2015; Osanloo & Grant, 2016; Varpio et al., 2020) As such, the researcher may have to “synthesize” theories, empirical findings, and other perspectives through a literature review and contextual variables relevant to a
73 research problem (Imenda, 2014, p 189; Osanloo & Grant, 2016, p 17) The synthesis can be called a conceptual framework or a synthesized theoretical research model, and a research model, for short (Ibrahim et al., 2016; Imenda,
2014; Varpio et al., 2020) It is worth noting that theoretical frameworks are occasionally referred to as a research model (1.e., a conceptual framework); however, all too often, these words are used interchangeably or without a clear understanding of the differences, but they are neither interchangeable nor synonymous These terms can be ambiguous and cause misperceptions for researchers conducting research projects (Kivunja, 2018; Osanloo & Grant, 2016;
While a theoretical framework is constructed on a prevailing theory (or theories) that has already been tested, validated, and is considered a widely accepted theory in the scholarly literature (Osanloo & Grant, 2016), a research model offers important factors, constructs, or variables, and presumes associations between them (Imenda, 2014; Luse et al., 2012) In other words, a research model is derived from concepts and variables, as a theoretical framework is rooted from an acceptable theory or theories The research model offers a rational structure of connected variables that helps to provide a depiction or visual schema of how factors and variables relate to each other within the theoretical framework.
Drawn on Figure 2.4 of the theoretical framework and Figure 2.5, explaining how the research model is established, the research model is proposed
74 to investigate the influence of user adaptation on continuance intention towards the ride-hailing application empirically The proposed research model illustrates a connected set of hypotheses that based on the underlying theories and model of
(1) IT adaptation and the adaptive structuration theory for the individual (Schmitz et al., 2016) (2) the theory of decomposed planned behaviors (DTPB) (Taylor &
Todd, 1995) and (3) the empirically validated models of IT continuance, expectation-confirmation model (ECM) (Bhattacherjee, 2001; Bhattacherjee &
Lin, 2015) On the one hand, IT adaptation literature provides solid justification for interpreting user adaptation and its relationships Thus, it helps to explain appropriately the IT user’s adaptation process, its antecedents, and more importantly, its likely consequences such as changes in user’s decisions, performance or behaviors (Bhattacherjee & Harris, 2009; Nguyen & Ha, 2022).
On the other hand, TPB and its extension DTPB have determined how users’ behavioral intentions and usage are affected by their belief structures (i.e., their perception, environment factors and behavioral controls factors) After the initial adoption, the user adapts to IT and may generate the intention to continue using the IT The IT continuance intention is the outcome variable of ECM, based on which this study expects to investigate the user continuance intention to use app- based ride-hailing services According to these three theoretical frameworks and inspired from previous empirical research, we propose a set of hypothesized relationships and the research model The hypothesized relationships among
ECM’s, ASTI’s, and DTPB’s antecedents, including perceived usefulness,
75 satisfaction, self-efficacy, and management support, with behavioral adaptation, trust, and continuance intention will be discussed in the following paragraphs The research model for the empirical study is proposed in Figure 2.6 in this section.
2.6.2.1 ECM-based constructs and relationships
The concept of CI in an IT-enabled service setting such as ride-hailing 1s similar to a customer’s retention and repurchase from marketing and customer behavior literature (Tam et al., 2020; Venkatesh et al., 2011) Continuance usage is studied in mobile app literature as post-acceptance and post-adoptive stages, in which users continuously use the m-app within various services (Faber & de
Reuver, 2019; Poromatikul et al., 2019; Tang, 2019) According to Oliver’s (1980)
ECT, satisfaction (SA) is determined by prior experience with the product and, in turn, influences their intentions to reuse that product (Dholakia & Zhao, 2010; Yan et al., 2021) ECT argues that consumers decide to repurchase a product or to continue using a service based on their satisfaction of prior use of that product or service In the IS context, satisfaction is conceptualized as the “affective attitude towards a computer application” (Doll & Torkzadeh, 1988, p 261), and as a result of users’ interactions with that IS In general m-commerce and mobile food- delivery settings, user satisfaction is the “customer’s reaction or feeling concerning the experience” with m-commerce applications (Molla & Licker,
2001; Y.-S Wang et al., 2019, p 3) Only satisfied users will continue to use the existing service while those who are dissatisfied will likely discontinue in preference for a new service (Deng et al., 2010; Susanto et al., 2016) The direct
76 influence of satisfaction on continuance intention has been studied in a multitude of contexts (Franque, Oliveira, Tam, & de Oliveira Santini, 2020; Nabavi et al.,
2016; Sabah, 2020), such as in online tourism (Liu et al., 2020), mobile commerce
(Gao et al., 2015), e-learning (Joo et al., 2017), health applications (Alsyouf &
Ishak, 2018), and e-government (Veeramootoo et al., 2018) In the context of m- app sharing commerce, the significant relationship between satisfaction and continuance intention was also examined by scholars such as Cheng et al (2019) in bike-sharing, Alalwan (2020) in food-delivering and Poromatikul et al (2019) in mobile banking, to name a few Hence, based on Bhattacherjee’s 2001 original
ECM theoretical model, and referring to the abovementioned existing empirical works in different research settings (see e.g Franque et al., 2021; Shaikh &
Karjaluoto, 2015; Yan et al., 2021), we propose the following hypotheses:
HI: User satisfaction is positively associated with continuance intention to use ride- hailing service;
H2: User perceived usefulness is positively associated with continuance intention to use ride-hailing service;
H3: User confirmation is positively associated with continuance intention with ride- hailing service;
H4: User confirmation is positively associated with perceived usefulness of ride- hailing service;
HS: User perceived usefulness is positively associated with satisfaction with ride- hailing service.
2.6.2.2 Relationships of behavioral adaptation with continuance intention and satisfaction
According to the IS success model, the interrelationship between users’ usage and their satisfaction is reasonably established, and a positive experience with the adaptation/usage of IS is likely to result in more satisfaction with that IS
(Bala & Venkatesh, 2016; DeLone & McLean, 2003) While user adaptation has been considered as a post-adoptive behavior promoting extended use, continued use, and satisfaction (Bala & Venkatesh, 2016; Bhattacherjee & Barfar, 2011), little research has been conducted to date on the relationship between usage- satisfaction (e.g., Aldholay et al., 2018; Isaac et al., 2017; Isaac et al., 2019; Sabah,
2020) Furthermore, although the relationships between behavioral adaptation, user satisfaction, and continuance intention have also been proposed (Barki et al.,
2007; Bhattacherjee & Barfar, 2011), prior studies on digital platform service systems have rarely investigated the interrelationships between adaptation, CI, and satisfaction (Franque, Oliveira, Tam, & de Oliveira Santini, 2020; Nguyen & Ha,
2021), and these links between the three constructs are yet to be confirmed in the ride-hailing service settings For a ride-hailing service, the more users that perform the adaptation process (1.e., learning how to use platform services or adjusting the platform features accordingly on a smartphone, etc.), the more likely they are to have already adapted to the platform, and to make the platform a better fit for themselves, meaning that they will be satisfied with the platform and will continue to use it (Bala & Venkatesh, 2016; Barki et al., 2007; Beaudry & Pinsonneault,
2005; Y.-S Wang et al., 2019) Drawing on the previous literature, we expect the positive links between SA with CI and between BA with SA and CI Thus, we propose the following hypotheses:
H6: Users’ behavioral adaptation is positively associated with continuance intention to use ride-hailing service.
H10: Users’ behavioral adaptation is positively associated with satisfaction with ride- hailing service.
H14: User perceived usefulness is positively associated with behavioral adaptation with ride-hailing service.
H18: The relationship of perceived usefulness and continuance intention is mediated by behavioral adaptation.
2.6.2.3 Relationships of behavioral with continuance intention and self-efficacy
Research mOel - - + s++s xxx ng ng rệt 83
This current proposed model is an extension of the continuance model which was drawn from the original expectation confirmation model
(Bhattacherjee, 2001), adaptive structuration theory for individual (DeSanctis &
Poole, 1994; Schmitz et al., 2016), and trust perspective As revealed earlier, while
IT acceptance has been studied intensively, the literature has completely ignored user adaptation mechanism, and its causes and effects in IT implementation So far, very few works have explicitly explained the linkage between IT adaptation and IT continuance For the ride-hailing service setting, once the app is downloaded and installed on the users’ smartphones, registered users can begin the initial usage process, and gradually adapt to the platform through activities.
These adaptation behaviors may take place via modifications to the technological functions of ride-hailing service (e.g., location sharing, smartphone interface).
Furthermore, users also choose to adapt their working procedure (e.g., driving using trip planner, online payment) in their routine work Moreover, users also learn how to exploit ride-hailing service features (e.g., rating riders and be rated by riders, helpdesk support), which means that they spend effort adapting themselves to harsh interactive (IT-enabled) transportation services settings Thus, drawing on two seminal theoretical frameworks of ATSI and ECM (Bhattacherjee, 2001;
Bhattacherjee & Lin, 2015) , and a review of influential empirical works on IS adoption (Bala & Venkatesh, 2016; Barki et al., 2007; Bhattacherjee & Harris,
2009; Nguyen & Ha, 2022; Rubel et al., 2020), the research model is proposed explaining the adaptation mechanism.
ECM Na aaa ¡ ‘characterteth ics
Continuance intention to use ride-hailing service i Brivaronimenfal ©" chracterictic:
Source: Developed by the author for this research
The adaptation process involves input factors comprised of technological characteristics (RHA usefulness), individual characteristics (user confirmation on initial use and user self-efficacy toward RHA), and an environmental factor
This chapter discussed concepts and relevant theories within the original and associated literature for the focal topic under investigation The chapter critically reviewed the key publications related to continuance intention, user adaptation, trust, and management concepts A theoretical framework for the study and proposed research model is constructed from germane literature It was established that there is a dire need for researchers and practitioners into the effects of user adaptation on continuance usage This view is shared by (Bala &
Venkatesh, 2016; Bhattacherjee & Barfar, 2011; Faber & de Reuver, 2019; Jiang
& Lau, 2021) when presenting their views of critical issues on user adaptation and continuance research Hypotheses were developed for a user adaptation process with an outcome variable of continuance intention toward a ride-hailing application.
METHODOLOGY 5-5 <1 1010, 86
Research Approach and Philosophy .-s=-==<<ôse=e 87
Research approaches address plans and the procedures that researchers step up from general philosophical assumptions to research designs, detailed data collection methods, analysis, and interpretation of the study findings (Creswell,
2014) The selection of a research approach is grounded on the nature of the research problem and questions being addressed, the researchers’ personal experiences, and the targeted audience for the study The research approach, a plan or proposal, encompasses a combination of three relative components: research philosophy or philosophical worldviews, research design, and research methods.
A research paradigm is an approach for researchers to carry out research.
Research paradigm first used by Kuhn (1962) to denote a conceptual framework shared by a community of scientists for examining problems and finding solutions.
The term paradigm refers to a research culture with a set of beliefs, values, and assumptions that a community of researchers has in common regarding the nature and conduct of research (Kuhn, 1977) Johnson et al (2007) claimed that “research paradigm” would be termed “methodological paradigm” in the way that “a paradigm can develop around what it means to conduct research and how it is undertaken” (p.130) Guba (1990) and Creswell (2014) assumed the research paradigm as a researcher’s worldview, which means the beliefs that navigate the researcher conducting a study The nature of the beliefs of individual researchers
88 indeed leads to employing a qualitative, quantitative, or mixed methods approach in their work However, the choice of either method has been of research interest.
The choice of a specific approach is subject to the research problem, questions, and objectives to collect and analyze data (Lapadat & Lindsay, 1999; Reilly &
Guba and Lincoln (1994) stated that a research paradigm is intrinsically associated with ontology, epistemology, and methodology concepts In simple terms, the methodology is the method used in conducting the investigation, and while ontology explains how the investigator defines the truth and reality, epistemology denotes the process by which the investigator comes to know the truth and reality (Antwi & Hamza, 2015; Guba & Lincoln, 1994; Hiller, 2016).
Ontology refers to “the nature of our beliefs about reality” (Richards, 2003, p 33) Ontology refers to a segment of philosophy regarding enunciating the nature and structure of the real world (Wand & Weber, 1993) The term postulates the form and nature of reality and what we can know about it There are two distinct positions, namely objectivism and constructionism For the objectivism paradigm, the nature of social reality for positivists (Hiller, 2016) and empirical facts exist apart from personal ideas or thoughts; they are governed by laws of cause and effect; patterns of social reality are stable and knowledge of them is additive (Antwi & Hamza, 2015; Crotty, 1998; Neuman, 2014) This worldview is sometimes called the scientific method, or positivist research and empirical science, or post-positivism (Creswell, 2014) Thus, the approach holds true more
89 for quantitative research than for qualitative research (Creswell, 2014; Rehman &
On the other hand, for a constructionism paradigm, constructivist or interpretivist researchers often address the interaction and specific contexts in which people live and work in order to understand the phenomenon (Cohen et al.,
2017; Creswell, 2014) The constructivist-interpretivist paradigm “can be perceived as an alternative to the “received view” or positivist paradigm”
(Ponterotto, 2005, p 129) The meaning of a phenomenon is not simply sealed on individual subjects, but are formed through social interaction (i.e., social constructivist) of individuals Rather than initiating a theory (as in post- positivism), researchers generate or inductively develop a theory or pattern of meaning It is typically seen as an approach to qualitative research.
Epistemology is regarded as the nature of knowledge, and most meaningful is how it can be attained and circulated to others (Cohen et al., 2017) In other words, the epistemology concept refers to a branch of philosophy that explores the nature of knowledge and the process by which knowledge is learned and perceived
(Rehman & Alharthi, 2016) Epistemology sets out the question about the link between the researcher and acquired knowledge and leads researchers to debate about the objectivity, subjectivity, causality, validity, and generalizability of the research (Antwi & Hamza, 2015; Patton, 2014; Rehman & Alharthi, 2016).
Methodology is defined as “an articulated, theoretically informed approach to the production of data” (Ellen, 1984, p 9) Methodology refers to the process
90 and procedures of the research The researcher’s perception or assumptions of philosophical dimensions is to unearth and elucidate relationships among variables that will eventually lead to universal laws “that form the foundation for prediction and control of phenomena” (Ponterotto, 2005, p 132) Methodology is concerned with how researchers practically conduct research to find out or explain any phenomenon they believe can be knowledgeable In other words, methodology is a research strategy, and a plan of action and procedures that informs ontological and epistemological perspectives into an introduction that guides how a research is to be carried out (Antwi & Hamza, 2015; Sarantakos,
Research philosophy refers to a system of beliefs and assumptions about the development of knowledge (Antwi & Hamza, 2015) Although this sounds rather profound, research philosophy is precisely what you are doing when embarking on research: developing knowledge in a particular field Others have named philosophy as epistemologies and ontologies (Crotty, 1998; Rehman &
Alharthi, 2016) and philosophical worldview (Creswell, 2014) Although philosophical ideas remain largely hidden in research, they play a vital role in conducting the research and must be established (Slife et al., 1995) In addition, research worldview refers to a “general philosophical orientation about the world and the nature of research that a researcher brings to a study” (Creswell, 2014, p.
35) It is suggested that researchers developing a proposal or research plan
91 properly articulate the philosophical ideas they espouse The notions will explain why they take qualitative, quantitative, or mixed methods approaches for their research and there are four philosophical worldviews commonly discoursed in the literature: post-positivism, constructivism, transformative, and pragmatism
Thus, positivism research is to determine and assess the causal effect of a phenomenon of interest Positivists are referred as reductionists, in that they study a phenomenon by breaking it into its simpler elements or items (Creswell, 2014;
Easterbrook et al., 2008) Furthermore, the causal linkages of a phenomenon are empirically validated because positivists believe that “scientific knowledge is built up incrementally from verifiable observations, and inferences based on them”
Evaluation of Mediating Effect of Behavioral Adaptation
Kline (2015) named the indirect effects as an “intervening” by one or more
‘variables presumed to “transmit” some of the causal effects of prior variables onto subsequent variables’ (p.68) Researchers have equated intervening variables in indirect effects with mediator variables or a mediator (Hayes, 2018; Kline,
2015) While the direct effects are named paths, and their statistical estimates are path coefficients, the indirect effects or mediating effects are also path coefficients with their statistical estimates as the product of involved direct effects (Kline,
Source: Result output from AMOS The evaluation of the ADP mediating effect was carried out by employing a bootstrapping method to apprehend the cause-effect relationship fully between the variables of interest (Hayes & Preacher, 2014; Mathieu & Taylor, 2006) The level of bias-corrected confidence interval was established at 95%, and the analysis was conducted with 5,000 bootstrap samples Our findings show that (1) the association between TR and CI was not mediated by ADP, (2) there is a partial mediation of ADP in the positive association of the perceived usefulness and CI, and (3) there is a full mediation of ADP on the path of self-efficacy and CI Table
7 displays the mediation analysis results.
Results of the mediation test using bootstrap analysis
Relationship Mediation path Estimate interval Probability Conclusion
TR-ADP-CI 0.047 0.019 0.086 0.018 wasNo mediation
SE-ADP-CI 0.031 0.009 0.081 0.015 - Full mediation
Note: *** p < 0.001; ** p < 0.01; (ns) non-significant at p < 0.05 Bootstrap sample 5,000 with replacement.
Source: Result output from AMOS.
DISCUSSIONS 5G G55 Ăn TH c0 0000000910086 06 117
This research aims to examine the relationships between continuance intention with user behavioral adaptation and its determinants in the ride-hailing service context in Vietnam While the research model preserves the originality of the Bhattacherjee’s (2001) ECM, it also synthesizes two input factors of the adaptation process, management support and self-efficacy, with a trust perspective to unveil how the adaptation process of the RHA influences the user decision to continue using the service At the time of writing, little attention has been paid to the systematic analysis of how the adaptation process impacts its outcome for making an improved decision (i.e., continuance intention) (Schmitz et al., 2016;
Y Wang et al., 2016) Surprisingly, there have been no publications examining
118 the relationships between the input factors of the adaptation process, which are technological (i.e., perceived usefulness), environmental (i.e., management support) and individual characteristics (i.e., self-efficacy), and trust, with continuance intention as the adaptation process outcome (Bhattacherjee & Harris,
2009; Schmitz et al., 2016) To fill this research gap, our study proposes a model delineating how different input structures and trust enable ADP, which then influences CI By demonstrating that ADP mediates the relationships between PU,
SE, MS, and TRT with CI, the empirical findings largely support our hypothesized model, except for the link SE-CI (H8) Simultaneously, our findings underline the importance of ADP as both a primary benefit of input structures and a major driving force of continuance intention to use the ride-hailing service Having integrated the ASTI-based factors highlighted in Schmitz et al (2016), including user adaptation (process structure), self-efficacy and management support (input structures), the current model, which is statistically well validated, is worthy of being considered as a unified information technology adaptation-continuance model.
The original finding of this study is that ADP statistically impacts on CI It is noteworthy that, for the first time in IT continuance research, the results of an investigation uncover a causal relationship between user ADP and CI towards the ride-hailing service This finding is in line with the argument of DeLone and
McLean (2016, p 57), who asserted that “intention to reuse” is contextually aligned to the purpose of the IS usage and the measure of the IS success outcome.
The unveiled significant link ADP-CI (H6) is also endorsed by the research of D.
Wang et al (2016), who found that the technology adaptation (i.e., daily use of smartphone by travelers) leads to users changing their behaviors in terms of “kept using these mobile applications” and “exploring and learning about technology”
(D Wang et al., 2016, pp 58, 61) In our ride-hailing context, this means that, as drivers spend more time and effort adapting (to) the RHA (e.g., learning to use, suggesting a modification for the app) they have more intention to continue using the app, thus leading to appropriateness between users and the RHA, and the success of the adapted RHA.
The study results affirm all five essential associations of Bhattacherjee’s
ECM (2001), namely H1 to H5, of the existing model As observed in previous studies on CI (Bhattacherjee, 2001; Malik & Rao, 2019; Weng et al., 2017), this is confirmation as a post-adoptive judgment influencing positively on user perceptual experience such as perceived usefulness and satisfaction In other words, assessment of a user’s initial use affects one’s likeliness to continue with the IT-enabled services (in this case, ride-hailing), which is attributed to cumulative satisfaction (Malik & Rao, 2019) Our results reveal that confirmation is a strong predictor of user satisfaction in the ride-hailing context and users realize their satisfaction and expected benefits through actually using the specific RHA.
The findings also confirm that perceived usefulness and satisfaction are two factors that motivate users’ intentions to continue using RHAs, in line with reported investigations in a comparable contexts, such as the ride-hailing service
120 in Iran (Akbari et al., 2021), taxis’ m-app service in Malaysia (Weng et al., 2017), the ride-sharing market in China (Jiang & Lau, 2021; Si et al., 2022), and the
Additionally, the study findings show that the association between self- efficacy and ADP (H9) is supported This firmly agrees with the findings of
Aldholay et al (2018), and Hatlevik and Hatlevik (2018) for the context of applying ICT in education, Wang et al (2013) in an IT-enabled retailing service, and in the ride-hailing service with Nguyen and Ha (2022) Unexpectedly, and contrary to predictions, our work finds an insignificant relationship between user
SE and CI (H8), meaning that in this context, drivers who have high confidence in their own capability in using the RHA, did not have high intention to continue their usage of the ride-hailing app in the future Prior research ardently shows that self-efficacy has a positive impact on individuals’ intentions to go on using the IT
(e.g Compeau & Higgins, 1995; Thakur, 2018b) Nevertheless, the weak linkages of self-efficacy and intention to continue and engage using IT-enabled services were reported in several contexts such as online education (San-Martin et al.,
2020; Sun & Rueda, 2012), e-healthcare (Imlawi & Gregg, 2020), knowledge management (Lin & Hwang, 2014), and e-banking (Koksal, 2016) In this study context, one’s self-efficacy does not sway individuals to decide to use the app further There are three possible explanations for this insignificance First, as drivers feel overconfident in their ability to use an RHA, they are likely to look down on it; in other words, rather than deciding to move on with the app, they may
121 intend to think of using a more challenging app or service Second, when drivers believe in their ability to succeed in using a ride-hailing app, they are unlikely to think about deciding merely to stay with an existing ride-hailing app, and they think about more attractive applications offered by other businesses in the marketplace instead Third, the weak link may be due to the lack of comprehensive consideration of other factors involved in influencing CI In this study, SE indirectly affects CI through the mediation role of user behavioral adaptation.
Moreover, the study findings also indicate that trust is driven by PU of the
RHA While previous studies on the link of trust and perceived usefulness have reported varied and mixed results (e.g Gefen et al., 2003; Y Wang et al., 2016;
Wuet al., 2011), our findings show the significant effect of perceived usefulness on users’ trust (H15), which is in agreement with most of the studies on mobile commerce (Afshan & Sharif, 2016; Sarkar et al., 2020; Susanto et al., 2016) and more particular, ride-hailing service (Akbari et al., 2020) Also, as predicted, our survey data has confirmed a direct association between trust and the intention to continue using RHA (H10) This significant association is in line with many prior studies in m-commerce, ride-hailing and across service contexts, (e.g., Ejdys,
2018; Humaidi & Balakrishnan, 2018; Mou et al., 2017; Najjar & Dahabiyeh,
CONCLUSION AND IMPLICA TIONS
Limitations and Future ResearCH .- s55 5<ôôsss ôse 134 REFERENCES 0 G5 G0 họ cọ cm 00000006 137
As with most research, this study is bound in some ways, and its limitations should be recognized and explored in future studies.
First, our research was conducted in Vietnam, an emerging economy, and so is limited to cross-cultural generalizability Future studies should apply a cross-
135 cultural approach to collect samples from different level economies (e.g., developed and developing countries) and different culture (e.g., Western and
Eastern cultures) to investigate users’ behaviors in the context of the relatively new IT-enabled services, such as ride-hailing services Additionally, the instrument is self-administered; therefore, only drivers who were selected to participate in the interview will have a chance to answer about their perceptions of mobile ride-hailing applications.
Second, the fact that the data for this research was collected from only a single point in time may hinder monitoring possible changes in user behaviors during the usage process The cross-sectional design prevents us from observing the changes that CI and its determinants may undergo Future research should consider a longitudinal design that allows the researchers to capture any change in
CI and its determinants over time It could be helpful for managers to plan appropriate strategies designed to improve BA, which would lead to the formation of CI.
Third, our study model only focuses on management support organizational characteristic) and self-efficacy (individual characteristic) as vital determinants of behavioral adaptation, together with the original ECM’s variables, to investigate the continuance of the RHA while precluding others Therefore, future studies should consider potential factors and their cross effects to gain better insights into
CI Several promising variables are suggested, such as user innovativeness toward
IT-enabled and “smart” services and the trade-off between technology usefulness
136 and user information privacy Additional variables in the technical domain, such as digital artifacts, are also worth examining.
Abbasi, G A., Sandran, T., Ganesan, Y., & Iranmanesh, M (2022) Go cashless! Determinants of continuance intention to use E-wallet apps: A hybrid approach using PLS-SEM and fsQCA Technology in Society, 68(1), 101937. https://doi.org/10.1016/j.techsoc.2022.101937 Abdul-Halim, N.-A., Vafaei-Zadeh, A., Hanifah, H., Teoh, A P., & Nawaser, K (2021).
Understanding the determinants of e-wallet continuance usage intention in Malaysia.
Quality & Quantity https://doi.org/10.1007/s11135-021-01276-7 Acquier, A., Daudigeos, T., & Pinkse, J (2017) Promises and paradoxes of the sharing economy: An organizing framework Technological Forecasting and Social Change,
125, 1-10 https://doi.org/10.1016/j.techfore.2017.07.006 Adaji, I., & Vassileva, J (2017) Perceived Effectiveness, Credibility and Continuance
Intention in E-commerce: A Study of Amazon Persuasive Technology: Development and Implementation of Personalized Technologies to Change Attitudes and Behaviors, Cham.
Adams, D A., Nelson, R R., & Todd, P A (1992) Perceived usefulness, ease of use, and usage of information technology: A replication MIS quarterly, 227-247.
Afshan, S., & Sharif, A (2016) Acceptance of mobile banking framework in Pakistan.
Telematics and Informatics, 33(2), 370-387 https://doi.org/10.1016/j.tele.2015.09.005 Agarwal, R., & Prasad, J (1997) The role of innovation characteristics and perceived voluntariness in the acceptance of information technologies Decision sciences, 28(3), 557-582 https://doi.org/10.1111/).1540-5915.1997.tb01322.x
Ahmed, R., Mohamad, N A B., & Ahmad, M S (2016) Effect of multidimensional top management support on project success: an empirical investigation Quality &
Quantity, 50(1), 151-176 https://doi.org/10.1007/s11135-014-0142-4 Ahuja, M K., & Thatcher, J B (2005) Moving beyond Intentions and toward the Theory of
Trying: Effects of Work Environment and Gender on Post-Adoption Information Technology Use MIS Quarterly, 29(3), 427-459 https://doi.org/10.2307/25 148691 Ajzen, I (1991) The theory of planned behavior Organizational behavior and human decision processes, 50(2), 179-211 https://doi.org/10.1016/0749-5978(91)90020-T
Akbari, M., Amiri, N S., Zỳủiga, M A., Padash, H., & Shakiba, H (2020) Evidence for
Acceptance of Ride-Hailing Services in Iran 2674(11), 289-303. https://doi.org/10.1177/0361198120942224
Akbari, M., Moradi, A., SeyyedAmiri, N., Zỳủiga, M A., Rahmani, Z., & Padash, H (2021).
Consumers’ intentions to use ridesharing services in Iran Research in Transportation Business & Management, 41, 100616 https://doi.org/10.1016/j.rtbm.2020 100616
Akel, G., & Armagan, E (2021) Hedonic and utilitarian benefits as determinants of the application continuance intention in location-based applications: the mediating role of satisfaction Multimedia Tools and Applications, 80(5), 7103-7124. https://doi.org/10 1007/s1 1042-020-10094-2
AI Amin,M., Arefin, M S., Sultana, N., Islam, M R., Jahan, I., & Akhtar, A (2020).
Evaluating the customers' dining attitudes, e-satisfaction and continuance intention toward mobile food ordering apps (MFOAs): evidence from Bangladesh European Journal of Management and Business Economics, 30(2), 211-229. https://doi.org/10.1108/EJMBE-04-2020-0066 Alalwan, A A (2020) Mobile food ordering apps: An empirical study of the factors affecting customer e-satisfaction and continued intention to reuse International Journal of Information Management, 50, 28-44 https://doi.org/10.1016/j.ijinfomgt.2019.04.008 Albaum, G (1997) The Likert Scale Revisited Market Research Society, 39(2), 1-21. https://doi.org/10.1177/147078539703900202
Aldholay, A., Isaac, O., Abdullah, Z., Abdulsalam, R., & Al-Shibami, A H (2018) An extension of Delone and McLean IS success model with self-efficacy The International Journal of Information and Learning Technology, 35(4), 285-304. https://doi.org/10.1108/IJILT-11-2017-0116 Alemi, F., Circella, G., Handy, S., & Mokhtarian, P (2018) What influences travelers to use
Uber? Exploring the factors affecting the adoption of on-demand ride services in California Travel Behaviour and Society, 13, 88-104. https://doi.org/10.1016/j.tbs.2018.06.002 Alhassan, M D., & Adam, I O (2021) The Effects of Gratification, Trust, and Platform
Quality on the Continuance Use of Ride-Sharing Services in a Developing Country:
Evidence from Ghana International Journal of Information Communication Technologies Human Development, 13(2), 21-41. https://doi.org/10.4018/IJICTHD.2021040102 Alsaad, A., Mohamad, R., & Ismail, N A (2019) The contingent role of dependency in predicting the intention to adopt B2B e-commerce Information Technology for Development, 25(4), 686-714 https://doi.org/10.1080/0268 1 102.2018.1476830 Alsyouf, A., & Ishak, A K (2018) Understanding EHRs continuance intention to use from the perspectives of UTAUT: practice environment moderating effect and top management support as predictor variables International Journal of Electronic Healthcare, 10(1-2), 24-59 https://doi.org/10.1504/IJEH.2018.092175
Amoroso, D., & Lim, R (2017) The mediating effects of habit on continuance intention.
International Journal of Information Management, 37(6), 693-702. https://doi.org/10.1016/j.1jinfomgt.2017.05.003 Amoroso, D L., Ackaradejruangsri, P., & Lim, R A (2018) The impact of inertia as mediator and antecedent on consumer loyalty and continuance intention In Mobile Commerce:
Concepts, Methodologies, Tools, and Applications (pp 960-981) IGI Global. https://doi.org/DOI: 10.4018/IJCRMM.2017040101 Anderson, J C., & Gerbing, D W (1988) Structural equation modeling in practice: A review and recommended two-step approach Psychological bulletin, 103(3), 411. https://doi.org/10.1037/0033-2909.103.3.411 Anshuman, D., & Aradhana, A (2021) From Harvard to Nasdaq listing: Grab CEO’s ride to world’s biggest SPAC deal Reuters https://www.reuters.com/business/harvard- nasdaq-listing-grab-ceos-ride-worlds-biggest-spac-deal-2021-04-14/ Accessed April 2021
Antwi, S K., & Hamza, K (2015) Qualitative and quantitative research paradigms in business research: A philosophical reflection European journal of business management, 7(3), 217-225.
Arbuckle, J (2006) AmosTM 6.0 User’s Guide Smallwaters Corporation.
Arbuckle, J (2012) IBM® SPSS® AmosTM 2] User’s Guide IBM Corp.
Arcidiacono, D., Gandini, A., & Pais, I (2018) Sharing what? The ‘sharing economy’ in the sociological debate The Sociological Review, 66(2), 275-288. https://doi.org/10.1177/00380261 18758529 Arteaga-Sánchez, R., Belda-Ruiz, M., Ros-Galvez, A., & Rosa-Garcia, A (2020) Why continue sharing: Determinants of behavior in ridesharing services 62(6), 725-742. https://doi.org/10.1177/14707853 18805300 Asgari, A., Silong, A D., Ahmad, A., & Samah, B A (2008) The relationship between transformational leadership behaviors, organizational justice, leader-member exchange, perceived organizational support, trust in management and organizational citizenship behaviors European Journal of Scientific Research, 23(2), 227-242.
Ashford, S J (1986) Feedback-seeking in individual adaptation: A resource perspective.
Academy of Management journal, 29(3), 465-487 https://doi.org/10.5465/256219
Ashraf, M., Ahmad, J., Sharif, W., Raza, A A., Salman Shabbir, M., Abbas, M., & Thurasamy,
R (2020) The role of continuous trust in usage of online product recommendations.
Online Information Review, 44(4), 745-766 https://doi.org/10.1108/OIR-05-2018-0156 Aversa, P., Huyghe, A., & Bonadio, G (2021) First impressions stick: Market entry strategies and category priming in the digital domain Journal of Management Studies, 58(7), 1721-1760 https://doi.org/10.1111/joms.12712
Aw, E C.-X., Basha, N K., Ng, S I., & Sambasivan, M (2019) To grab or not to grab? The role of trust and perceived value in on-demand ridesharing services Asia Pacific Journal of Marketing and Logistics, 31(5), 1442-1465 https://doi.org/10.1108/APJML- 09-2018-0368
Bagayogo, F F., Lapointe, L., & Bassellier, G (2014) Enhanced use of IT: A new perspective on post-adoption Journal of the Association for Information Systems, 15(7), 3. https://doi.org/10.17705/1jais.00367 Bagozzi, R P (1984) A prospectus for theory construction in marketing Journal of
Marketing, 48(1), 11-29 https://doi.org/10.1177/002224298404800102 Bagozzi, R P., & Yi, Y (1988) On the evaluation of structural equation models Journal of the academy of marketing science, 16(1), 74-94 https://doi.org/10.1007/BF02723327 Bala, H., & Venkatesh, V (2016) Adaptation to information technology: A holistic nomological network from implementation to job outcomes Management science, 62(1), 156-179 https://doi.org/10.1287/mnsc.2014.2111
Bandura, A (1986) Social foundations of thought and action (Vol 1986) Prentice-Hall.