Luận án tiến sĩ Quản trị kinh doanh The influence of adaptation behaviors on continuance intention to use digital platforms the case of ridehailing service in Vietnam

GLOSSARY OF ACRONYMSACM Association for computing machinery ADP Adaptation behavior ASEAN Association of Southeast Asian nations AST Adaptive structuration theory ASTI Adaptive structura

VIETNAM NATIONAL UNIVERSITY HO CHI MINH CITY

INTERNATIONAL UNIVERSITY

THE INFLUENCE OF ADAPTATION BEHAVIORS ON

CONTINUANCE INTENTION TO USE DIGITAL

PLATFORMS: THE CASE OF RIDE-HAILING

NGUYEN GIANG DO

THE INFLUENCE OF ADAPTATION BEHAVIORS ON

CONTINUANCE INTENTION TO USE DIGITAL

PLATFORMS: THE CASE OF RIDE-HAILING SERVICE

STATEMENT OF ORIGINALITY

I certify that, except where due acknowledgement has been made, the thesis

is the work of the author alone, and the work has not been submitted previously,

in whole or in part, to qualify for any other academic award; the content of this

thesis is the result of work which has been carried out since the official

commencement date of the approved research program Any editorial work, paid

or unpaid, carried out by a third party is acknowledged

Nguyen Giang Do

Ho Chi Minh City

January, 2023

REFEREED JOURNAL ARTICLES AND CONFERENCE PAPERS

Nguyen, D G., & Ha, M T (2022) What makes users continue to want to use the

digital platform? Evidence from the ride-hailing service platform in

Vietnam SAGE Open, 12(1), DOI:10.1177/2158244021 106914 (ISI/Scopus

- Q2).

Ha, M T., Nguyen, G D., Nguyen, M L., & Tran, A C (2022) Understanding

the influence of user adaptation on the continuance intention towards

ride-hailing services: the perspective of management support Journal for Global

Business Advancement, 15(1), 39-62, DOI: 10.1504/JGBA.2022.127208

(Scopus — Q3).

Nguyen, G D., & Ha, M T (2021) The role of user adaptation and trust in

understanding continuance intention towards mobile shopping: An extended

expectation-confirmation model Cogent Business & Management, 8(\),

DOI: 10.1080/23311975.2021.1980248 (Scopus - Q2)

Giang-Do Nguyen, Minh-Tri Ha, and Bao-Son Doan, (2023) Service value,

service quality, customer satisfaction, and customer loyalty: The mediating

role of switching costs Humanities and Social Sciences Communications

DOI: 10.1057/s41599-023-01797-6 (ISI/Scopus-Q1)

Vuong-Bach, N., Giang-Do, N., Thu-Hien, D T., & Cong-Trinh, H N (2023)

The Dual Role of Online Trust: A Study about Generation Z through Online

Purchase intentions: A Vietnamese perspective Journal of InternationalBusiness and Entrepreneurship Development, DOI:10.1504/JIBED.2023.10056252 (Scopus - Q2)

Giang-Do, N., Minh-Tri, H., Nguyen, M L., & Tran, A C (2022) Understanding

the influence of user adaptation on continuance intention towards ride-hailing

services: the perspectives of management support and online

experience Presentation at the 18th Annual World Congress of the Academy

for Global Business Advancement [AGBA, Istanbul, Turkey, July 2- 4, 2022]

(Best Paper Award)

Other research and conference publications

Thanh-Huong, N.T., Giang-Do, N., Minh-Tri, Ha (2022) Content-Based

Attributes of Online Reviews and Its Role in Travel Decision: A PRISMA

Method of Studies from 2009 to 2021 Vietnam AGBA’s 2022 Conference

Proceedings (Advances in Global Business Research) Vol 18, No 1, ISSN:

1549-9332, Istanbul, Turkey [AGBA (USA), Millikin University (USA),

Indian Institute of Management Rohtak (India), Antalya Bilim University

(Turkyei), and KIMEP University] (Scopus-indexed) (Best Paper Award)

Nguyen, G D & Ha, M T., (2020) Understanding behavioral adaptation on

continuance intention for ride-hailing service in Ho Chi Minh city, Vietnam

Presentation at the 2nd International Conference on Economics, Business

and Tourism (ICEBT-2020) [International University, VNU-HCM, Ho Chi

Minh, Vietnam, January 12, 2020]

Do, N G., Hien, D T T., & Huong, N T T (2021) International accreditation

impacts teaching and learning: Case of Vietnam non-public

universities Ho Chi Minh City Open University Journal of Science-Social

Sciences, 11(2),60-74 DOI:

10.46223/HCMCOUJS.soci.en.11.2.1921.2021 (ACI-indexed)

Ha, M T., Huong, N T T., Nguyen, G D., & Hoai, T.T (2023) Strengthening

Information-Seeking Behavior Toward International Destinations Among

Young Travelers in Vietnam During the Pandemic Recovery Ho Chi Minh

City Open University Journal of Science-Social Sciences, 13 (2), 1-26

DOI: 10.46223/HCMCOUJS.soci.en.13.2.2744.2023 (ACI-indexed)

GLOSSARY OF ACRONYMS

ACM Association for computing machinery

ADP Adaptation behavior

ASEAN Association of Southeast Asian nations

AST Adaptive structuration theory

ASTI Adaptive structuration theory for individuals

AVE Average variance extracted

BI Behavioral intention

CMB Common method bias

Cl Continuance intention

CFA Confirmatory factor analysis

CFI Comparative fit

CMIN Chi-square statistics

CMUA Coping model of user adaptation

COG Cognitive model

CR Composite reliability

CS/C Consumer satisfaction/confirmation

CTT Commitment-trust theory

DP Digital platform

DTPB Decomposed theory of planned behaviors

GDP Gross domestic product

GFI Goodness-of-fit index

GPS Global position system

GWP Global warming potential

EBSCO Elton Bryson Stephens Sr Information serviceECM Expectation-confirmation model

ECT Expectation-confirmation theory

ERP Enterprise resource planning

ES-QUAL Electronic Service quality model

HIWP High involvement work practice

HRM Human resource management

IC International conferences

ICT Information and communications technology

TEEE Institute of Electrical and Electronics Engineers

DIT Diffusion of innovation theory

IS Information technology system

ISSM Information system success model

ISURA Information system use-related activity

IT Information technology

ITA Individual information technology adaptation model

KMO Kaiser-Meyer-Olkin test

M-app Mobile application

M-commerce Mobile commerce

M-health Mobile health service

MEC Means-end chain theory

MM Mixed method

MS Management support

NFI Normed fit index

OB Organizational behavior

OIT Organismic integration theory

OST Organizational support theory

PBC Perceived behavioral control

PEOU Perceived ease of use

PIB Perceived individual benefits

POB Perceived organizational benefits

PU Perceived usefulness

QUAL Qualitative method

QUAN Quantitative method

Regulatory focus theory

Root mean square error of approximation

Standardized factor loading

Structural equation modeling

Service quality measurement model

Social exchange theory

Small and medium enterprise

Stimulus-Organism-Response

Standardized root mean square residual

Social support theory

Socio-technical systems theory

Technology acceptance model

Technology affordances and constraints theory

Technology continuance theory

Theory of consumption value

U&G Uses and gratification theory

UTAUT The unified theory of acceptance and use of technology

UTAUT2 The extended unified theory of acceptance and use of

technology

TABLE OF CONTENTS

STATEMENT OF ORIGINALITY << se ESsSSSEesee2 iii

REFEREED JOURNAL ARTICLES AND CONFERENCE PAPERS

1.3 Research Objectives and Questions s 5-5 << se sesse 9

1.4 Scope of the Research G5 < G9 0009608 10

1.4.1 Unit of analySIS - SG Sàn rệt 10

1.4.2 The application-based fOCUS 5-55 <+s<<<sssserske 10

1.5 Key Findings and Contributions of the Research 10

1.6 Structure of the DiSS€F{iafÏOIA 5- 5< 55s se erse 11

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2.1 V€TVÏCW Go cọc HT 0000 12

2.1.1 Literature Review Method si seexke 12

2.1.2 Ride-Hailing Service Con(€X( - 5 5S s+sseeske 12

2.2 Review Theoretical Con€€JDfS o5 <5 5 19508956 14

2.2.1 Continuance Inf€n(1OT - 5 + + k*skskskrevke 14

2.2.2 Information technology adapfatIOn - - «+ x+xssx+ 15

2.2.3 TTUS( Q TQ QQQQQQnnnSSnSS SH SH net 17

2.2.4 Management SUDpOIẨ 5 5 3 E**vEEeseseserereereere 18

2.3 Relevant Previous Empirical Studies Error! Bookmark not

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2.3.1 ATSI-based individual adaptation for IT use 18

2.3.2 Information system use-related aCfIVIEY « «<2 18

2.3.3 Influence of HRM factors on technology adaptation 19

2.4 Research Gap Identification 5< s5 1 2e 19

2.5 Development of Theoretical FramewOrFFK -<ss«<< s««es se 19

2.6 Proposed Research Model ssccsssssssccscssssesesscseessccesesseeee 20

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3.2.4 Justification for quantitative approach « ««- 27

3.3 Research Design Process o <6 S900 8056 840 0 66 28

3.4 Measurement Items and SCaÌ€§ -<-<< 5< «=e<s=esssesse 30

3.5 Questionnaire DeveÏ0PIN€TIE 5 5 <5 55 se 9S 5694 591 32

3.6 Data Collection Procedure s- 55-55 555555 5 555 556955e 32

3.6.1 Interview bias DreV€TIOH 5 S5 SE +svseeereeerreee 32

3.6.2 Data collection procedure 5 55c S+£+s+ssserseerrs 33

CHAPTER 4 RESEARCH EFINDINGS c- se °seessee 34

4.1 OVErVIEW 2 34

4.2 Demographic Profile of RespontdenfS << s «<< «se< se 34

4.3 Data AnaÌySÌS c5 G0 ng c0 00088808860080 805.6 35

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4.3.2 Reliability and validity f€S( Ă se 35

4.4 Measurement Model Evaluation s =< 55555 «<<5se=es 354.5 Structural Model Evaluation -<<5c<<s°Ăs°ĂSSe°Seesseesse 38

4.6 Evaluation of Mediating Effect of Behavioral Adaptation 40

4.7 DÏSCUSSỈOTNS 0G G5 cọ n T000 00000008 41

CHAPTER 5 CONCLUSION AND IMPLICA TIONS 43

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5.2 Timp lication o5 5 << G 5< 9 9 00.0 0000008900 43

5.2.1 Theoretical ImpÏICafIOTS s55 s se sssesesereeeereeee 43

5.2.2 Managerial ImpÌiCafOIS 5 «xe ssssesrskrrke 45

5.3 Limitations and Future ResearCH s-< «=5 «=5 seesssssse 45

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LIST OF TABLES

Table 1 Research methods 0nn 27

Table 2 Measurements items ANd SOHTC€S àà.cẶẶ 5S ssksseesses 30

Table 3 Descriptive statistics Of reSPONAENES àeSĂẶSSSSSs++ssks+ss 34

Table 4 CFA analysis with reliability and vaÏidify «« <<<<<++<5 37

Table 5 Correlation and discriminant ValHdÏfy e«sscs<css+sscssss 38

Table 6 Results of hypothesis f€SfÏH ào SH key 39

Table 7 Results of the mediation test using bootstrap analysis 41

LIST OF FIGURESFigure 2.1 Business Model of Ride-Hailing SerViC€ -.«<- 13

Figure 2.2 Adaptive structuration theory cccceccceccesccessseceesceesseeseeeeeseees 16

Figure 2.3 Adaptive structuration theory for individual 16

Figure 2.4 Theoretical framework for m-application adaptation 20

Figure 2.5 Proposed research IOd6ÏL 5 «<< + + k+seeEsseeeesexs 24

Figure 3.1 Research DFOC€SS Gv 28

Figure 4.1 SEM testing T€SHÏHS Ăn kg kt 40

The development of the mobile application-based businesses such as

ride-hailing services are becoming popular However, continuance intention to re-use

the hailing service has yet to be adequately explored for users, both

ride-hailing drivers and riders Furthermore, few studies have analyzed the continuance

intention from the perspective of ride-hailing platform drivers Relying on (i) the

adaptive structuration theory for individuals, (1) an expectation-confirmation

model, and (iii) the decomposed theory of planned behavior, our study proposes a

theoretical framework and a research model to examine how the users’ adaptation

process influences the intention to continue using ride-hailing applications Our

research employs questionnaire-based and face-to-face interviews to collect data

from over 500 drivers of the largest application-based ride-hailing firms across

Vietnam A structural equation modeling method was used to analyze the data and

validate the hypothetical relationships Our empirical findings broaden the

comprehensive insights into the effect of users’ adaptation on their continuance

intention to use the mobile application In addition, the study also provides a fresh

outlook to understanding how input factors of the adaptation process, including

perceived usefulness, management support, self-efficacy, and trust, influence

continuance intention to use the ride-hailing application in an emerging country

setting, such as Vietnam in this case

Keywords: adaptation behavior; continuance intention; management

support; self-efficacy; trust; mobile application; ride-hailing service; Vietnam

CHAPTER 1 INTRODUCTION

1.1 Overview

Many of the world’s largest companies are currently doing their business

with mobile commerce (m-commerce) via mobile applications (m-apps), digital

platforms (DP), or an information technology system (IS) Firms such as

Facebook, Uber, Airbnb, Didi, Amazon, Alibaba, Lyft, Grab, and many others are

application-based and have each been given valuations of billions of dollars

(Cusumano et al., 2020) McKinsey has estimated that more than one third of all

global economic transactions, over US$60 trillion, will be carried out through DP

and m-apps by 2030 (Basu, 2021; Chauhan, 2020) M-commerce has advanced

sufficiently to allow users to conduct a convenient information search for a

product or service, purchase, and payment anywhere and anytime (GroB, 2016;

Ko et al., 2009; Tam et al., 2020) M-app was originally seen as a web-based

software, application, or platform that “allows stakeholders to interact and share

experiences,” and a core of m-commerce (Ramaswamy & Gouillart, 2010, p 5).

Tang (2019) considered m-apps as “mobile application software designed to

support the functions of performing tasks on smartphones, tablet computers, and

other personal mobile devices” (p 3).

The ride-hailing service application (RHA) represents a type of

m-commerce that facilitates rider-driver interactions for IT-enabled transportation

services via smartphones (Alemi et al, 2018; Joia & Altieri, 2018;

Kourouthanassis & Giaglis, 2012; Turban et al., 2018) In other words,

ride-hailing is a mobile application-based business model that connects two main type

of ride-hailing users, including drivers (service providers) and riders (customers,

end-users), and other users (product providers) in the transportation service sector

(Chalermpong et al., 2022; Gielens & Steenkamp, 2019)

According to Google, Temasek, and Bain, the ride-hailing service in

Vietnam has the leading growth rate of Southeast Asia, which is at 15.9%

annually, projected to reach $3.77 billion in 2022 and $4.6 billion by 2026, with a

total of 21.2 million users (Davis & Neves, 2021; Statista, 2022) While the

hailing service has the potential for growth, coupled with the competitive

challenges from many international (e.g., Grab; Gojek, Lalamove) and domestic

(e.g., AhaMove; Be) players, it is still in its nascent stages (Brail, 2022; Chauhan,

2021)

While extant literature on IT implementation (Jasperson et al., 2005; Kwon

& Zmud, 1987; Saga & Zmud, 1993), IT adaptation (Raisch et al., 2009; Schmitz

et al., 2016), and innovative technology diffusion (Cooper & Zmud, 1990; Rogers

et al., 2014) have claimed that the importance of determining input factors of

adaptation process such as technology characteristics (e.g., the usefulness of

mobile app), individual characteristics (e.g., user IT self-efficacy), and

environmental characteristics (e.g., management support), affect decision and

performance outcomes (1.e., behavioral intention and usage) (Bhattacherjee & Lin,

2015; Joia & Altieri, 2018; Karahanna et al., 2006; Schmitz et al., 2016; Taylor &

Todd, 1995), there is currently a scarcity of studies investigating the influences of

these potential factors on user adaptation and the outcome of continuance usage

in an integrated model So far, no empirical work has clarified the linkage between

user adaptation and their continuance intention in IT implementation 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 These are the rationale that explains why the

research matters and should be undertaken Therefore, this study sets out to

connect this gap by examining the effect of users’ adaptation to their continuing

usage in the context of the m-app ride-hailing service

1.2 Problem Statement

The major challenge for application-based firms is how to retain users in a

highly competitive marketplace RHA firms are facing rivalry over a shared pool

of drivers who could easily switch to other competitors The central problem to be

studied by the current research is the process by which ride-hailing users adapt to

the mobile application and then make the decision to continue using it

The first major issue is the question regarding how users’ adaptation of the

application will influence their continued use the m-app (Bala & Venkatesh, 2016;

Bhattacherjee & Harris, 2009; Li et al., 2017; Orlikowski, 2000; Song et al., 2018)

The second issue identified as the basis for this research study is the

adaptation process Recent research findings into IT adaptation and post-adoptive

behavior have been inconclusive and contradictory (see e.g Barki et al., 2007;

Bhattacherjee & Barfar, 2011; Schmitz et al., 2016)

Finally, the most important issue is the understanding of the factors that

influence the adaptation and lead to the continuance intention of platform users

These factors are technology and social characteristics, personality traits, and

others and personal characteristics (Schmitz et al., 2016)

1.3 Research Objectives and Questions

According to the research gaps and the business context in Vietnam, this

research aims to investigate the influence of user adaptation behavior and its

determining factors on continuance intention to use a mobile application-based

service This research was based on foundational theoretical frameworks and

referred to previous empirical works, broadening the understanding of the user

adaptation process and its outcome of continuance intention More specifically,

the three objectives of this research are:

1 To determine the antecedents of user adaptation and continuance

intention to use the ride-hailing service in the Vietnamese context

2 To investigate the influences of user adaptation and its antecedents on

the continuance intention to use the ride-hailing service

3 To investigate the mediating mechanism of adaptation behaviors playing

on the influence between input factors and continuance intention

Guided by these objectives, the research is structured and carried out to

seek explanations of the three research questions:

1 What factors may affect the behavioral adaptation and continuance

intention of mobile application users in ride-hailing services across Vietnam?

2 How do these factors affect the behavioral adaptation and continuance

intention of mobile application users in ride-hailing services across Vietnam?

3 Which mediating mechanism does the adaptation process play in the

relationships between its input factors and the process outcome?

1.4 Scope of the Research

The scope of this study focuses on measuring and validating the

relationships between the input factors of the IT adaptation process and their

outcome of continuance intention in the Vietnamese ride-hailing context, which

are likely to be generalizable to other countries To answer the research questions,

the proposed research model is based on the underlying theories and model of (1)

the adaptive structuration theory for the individual (ASTI) (Schmitz et al., 2016);

(2) the expectation-confirmation model (ECM) (Bhattacherjee, 2001); and (3) the

decomposed theory of planned behavior (DTPB) (Taylor & Todd, 1995)

1.4.1 Unit of analysis

The unit of analysis of this study is the user behavioral adaptation with the

m-commerce app in ride-hailing service sector

1.4.2 The application-based focus

The scope of this research focuses on ride-hailing services in Vietnam The

study targets the five most used ride-hailing applications for travel

(transportation), food, and goods delivery service (logistics) in Vietnam (Davis &

Neves, 2021; Statista, 2019)

1.5 Key Findings and Contributions of the Research

This study makes a variety of remarkable contributions to the

understanding of user IT acceptance and adaptation behaviors, in marketing and

information management fields

The first is a theoretical contribution Most research on IT-enabled service

systems (e.g., mobile commerce, ride-hailing) has been primarily carried out from

either a variance or a process approach (De Guinea & Webster, 2017; Rivard,

2014) By utilizing the hybrid of distinctive approaches, this study helps to explain

the phenomena of “IT adaptation” and “IT use” which are intertwined with both

process and variance properties (Langley, 1999; Whelan et al., 2016)

The second theoretical contribution is that this study proposes a novel

theoretical framework for forthcoming investigations into the individual

adaptation-continuance relationship of mobile application users in comparable

service contexts The proposed theoretical framework elucidates the entire process

formatting of continuance usage from the input factors (input structures),

behavioral adaptation (core process), and outcomes (output structures)

Third, the study proposes a research model ingrained in the new individual

adaptation-continuance theoretical framework to examine continuance intention

toward a ride-hailing service This study is among the first to enlighten the

interrelationships of continuance intention, behavioral adaptation, trust and their

antecedents

The final theoretical contribution is that the study unveils the mediating

role of user behavioral adaptation for continuance intention At the time of writing,

no research has been found investigating the mediating role of user adaptation for

the linkages between essential factors (i.e., self-efficacy, management support,

trust) to CI in the ride-hailing context By conceptualizing behavioral adaptation

as a mediator, our study enhances the insight into how these input structures affect

user post-adoption behaviors from the driver-user perspective

In addition, this paper reports more than a few issues of potential

applicability to managers in app-based businesses and those who make every

effort to foster users’ continuance behaviors with their core business ap

CHAPTER 2 LITERATURE REVIEW AND RESEARCH MODEL

2.1 Overview

This chapter reviews the literature on application-based ride-hailing

services, and the related theoretical basis of the adaptation behavior and continued

use of service users

2.1.1 Literature Review Method

2.1.1.1 Concept-centric approach

This research adopts the concept-centric approach (Webster & Watson,

2002) for conducting a literature review on the continuance intention in the

ride-hailing setting The four main concepts of this study are “ride-ride-hailing application,”

99 66

“adaptation behavior,” “continuance intention,” and “trust,” so these keywords

and their several synonymous terms are targets of the search process Since the

literature on user adaptation, mobile applications, and related concepts seems to

be scattered and interdisciplinary, we count on many disciplinary areas, including

consumer behavior, marketing, service, mobile commerce, hospitality, tourism

management, information management, and education

2.1.1.2 Determining scientific databases

The literature review employs a structured procedure (Templier & Paré,

2015; Webster & Watson, 2002) to identify the scientific data source

2.1.2 Ride-Hailing Service Context

2.1.2.1 Sharing economy and mobile applications

The growth of an economic model so-called sharing economy, or SECON,

has changed routine users’ behaviors, facilitating numerous successful disruptive

businesses, that offer an IT-enabled transportation and hospitality business model

through ride-sharing or ride-hailing service (Pepi¢, 2018) The term “sharing

economy” is comparatively new and is often considered as an “umbrella

construct” (Acquier et al., 2017; Arteaga-Sanchez et al., 2020; Cheng et al., 2021),

which is a “broad concept or idea used loosely to encompass and account for a set

of diverse phenomena” (Hirsch & Levin, 1999, p 200) Researchers have linked

mobile commerce with the sharing economy and its business models (Cheng et

al., 2018; Marinkovic et al., 2016) M-application service systems are shaping

modern mobile commerce and how users search for and place an order, and then

decide on products and services, such as in retail or the transportation service

industry

2.1.2.2 Ride-hailing services

Ride-hailing has reformed the way in which users enjoy commuting and

transportation services in crowded cities Definitions of various forms of

ride-hailing in the digitized transport industry are mixed and controversial because

terms are often used relatively loosely in academic and practical literature, the

speedy development of the industry, and the conceptual overlaps between the

types of transportation While we acknowledge conceptual restriction of

terminologies, we use the acceptable term ride-hailing to communicate the content

of our study and form a link with both academic and practical audiences Figure

2.1 below illustrates the business model of the ride-hailing service with the

relationships between RHA firms, end-users (riders, passengers), drivers (RHA

drivers) The RHA, with its essential functions, and the service streams are

depicted

Figure 2.1

Business Model of Ride-Hailing Service

T T Transportation infrastructure (offline) &>

app « RHA firm guarantees a RHA firm fulfills Pid

« standard ride to riders agreements to drivers „ Z

Ride-hailing app -User-rider base; User-driver base -Matching algorithm

-Global position system -Online payment

-Other functions

Source: Adapted from Kong et al (2020) and Wang and Yang (2019)

2.2 Review Theoretical Concepts

2.2.1 Continuance intention

2.2.1.1 IT continuance intention

IT continuance intention refers to a user’s commitment to the continued use

of IT-enabled systems (IS) and services by individual users over a period of time

(Bhattacherjee & Lin, 2015; Franque, Oliveira, Tam, & Santini, 2020; Gao et al.,

2015) Bhattacherjee (2001) defined IS continuance intention as “an individual’s

intention to continue using an information system” (p.359) Researchers’ views

on IT continuance are diverse Our work regards continuance intention as a

post-adoption behavioral decision to continue using a ride-hailing app, which is an

IT-enabled service system

2.2.1.2 Underpinning IT continuance intention theories

Reviews on the CI of Nabavi et al (2016) and Shaikh and Karjaluoto

(2015) show the most predominantly applied theories including

confirmation model (ECM) (Bhattacherjee, 2001), Olivier’s (1980)

expectation-confirmation theory (ECT), technology acceptance model (TAM) (Davis et al.,

1989), theory of reasoned action (TRA) (Fishbein & Ajzen, 1975), theory of

planned behavior (TPB) (Ajzen, 1991), unified theory of acceptance and use of

technology (UTAUT) (Venkatesh et al., 2003), technology continuance theory

(TCT) (Liao et al., 2009), IS success model (DeLone & McLean, 2003), and flow

theory (Csikszentmihalyi & Csikzentmihaly, 1990) In addition, a family of

theories, such as social cognitive theory (SCT) (Bandura, 1986), diffusion of

innovation theory (DIT) (Rogers et al, 2014), task- technology fit (TTF)

(Goodhue & Thompson, 1995), decomposed theory of planned behavior (DTPB)

(Taylor & Todd, 1995), uses and gratifications theory (U&G), service-dominant

logic (SDL) (Vargo & Lusch, 2016), have also been applied in the field Besides,

a number of perspectives have been used to explain the continuance intention such

as habit, trust, satisfaction, value, quality, service quality, network externalities,

individual characteristics, innovation, social interaction, and cognitive absorption

(CA), to name a few

2.2.1.3 IT continuance research streams

The studies on IT continuance intention can be classified into three main

streams of research on these post-adoption behaviors (Bhattacherjee & Lin, 2015;

De Guinea & Markus, 2009; Ha et al., 2022) The first stream has considered CI

as purposeful or planned behaviors This stream is represented primarily by the

TAM, original ECM (Bhattacherjee, 2001) and TCT (Liao et al., 2009) The

second research stream has argued that IT continuance is influenced additionally

by a habitual factor, a repeated behavior, and automatic behavior performed by

users (De Guinea & Markus, 2009; Liao et al., 2006; Limayem et al., 2007)

The third, an novel stream, has claimed that IT continuance is undoubtedly

to be impacted by the adaptation factor, and the user adaptation process is

indispensable to continuance intention and usag (Bala & Venkatesh, 2016;

Beaudry & Pinsonneault, 2005; Ha et al., 2022; Nguyen & Ha, 2022)

2.2.2 Information technology adaptation

2.2.2.1 Adaptation and IT adaptation

Kwon and Zmud (1987) and Purvis et al (2001) identified IT adaptation as

a stage of a consecutive six-stage process where an organization introduces an IT

system This study defines it as the extent to which individual users adjust, modify

the RHA features, work procedures, and themselves to accommodate the platform

and to fit their needs and situations (Bala & Venkatesh, 2016; Barki et al., 2007;

Bhattacherjee & Harris, 2009)

Individual IT adaptation

Based on the original ECM and by encompassing the theoretical framework

of ASTI to the level of individuals, this study enlightens the influence of individual

adaptation with an IT-enabled service (i.e., ride-hailing) by non-technical users

(ie., ordinary RHA drivers) In so doing, our research responds to calls for IT

adaptation research at the individual level to observe technology, user,

environment, and task in various contexts collectively (Barki et al., 2007;

Bhattacherjee & Harris, 2009; DesAutels, 2011; Hong et al., 2011; Schmitz et al.,

2016)

2.2.2.2 Theories underpinning IT adaptation

16

Adaptive structuration theory and adaptive structuration theory forindividual

Adaptive structuration theory or AST (DeSanctis & Poole, 1994) and

adaptive structuration theory for individual or ASTI (Schmitz et al., 2016) have

been considered to be strong theoretical lenses for investigating the adaptation process

Figure 2.2 Adaptive structuration theory

Other structures Social Interaction

Source: Adapted from DeSanctis and Poole (1994) and Schmitz et al (2016)

While AST is often applied to study adaptation at group and organizational

levels, ASTI is applied at the individual level The theories view the adaptation as

a “structuration” process, which is the mutual interaction of IT (i.e., IT functions)

and the users’ working environment (i.e., work procedures)

Source: Adapted from Schmitz et al (2016)

Coping model of user adaptation

The coping model of user adaptation, or CMUA (Beaudry & Pinsonneault,

2005) defined adaptation behaviors as “efforts exerted by users to manage specific

consequences associated with a significant IT event that occurs in their work

environment” (p.496) Thus, the CMUA relates IT adaptation to how individual

user behaviors perform on the IT, the work, and the users themselves

Diffusion innovation theory

Rogers (1983) defined diffusion as “the process by which an innovation is

communicated through specific channels over time among the members of a social

system” (Rogers, 1983, p 5) DIT portrayed the five attributes of innovations: (1)

Relative advantage (the extent to which an innovative technology is perceived as

being more beneficial than its predecessor).; (2) Compatibility (the extent to which

the innovative technology fits with the potential user’s current principles, values,

and situation); (3) Complexity, a perception of how innovation is challenging to

comprehend, learn, or use; (4) Trialability, a perception of how innovative

technology is experimented with on a limited basis; and (5) Observability, (the

extent to which the outcomes of innovative technology usage are evident to others)

(Rogers, 1983, pp 213, 222-223)

2.2.3 Trust

As the mobile business takes shape and expands worldwide, many users

may feel that conducting commerce with m-apps is unsafe and unprotected (GroB,

2016; Kim & Peterson, 2017; Li & Tsai, 2022) Researchers reasoned that the

absence of trust is one of the causes behind a user’s hesitancy to participate in

online business (Gao et al., 2015; Liébana-Cabanillas et al., 2017) and the lack of

online trust averts users from purchasing online (Gefen et al., 2003; Qureshi et al.,

2009; Shao et al., 2019; Wu et al., 2011) An extensive literature review was

conducted of authoritative journals published from 2015 to 2021 of empirical

studies on online trust in the general context of e-commerce and ride-hailing This

study considers trust to be the belief which enables users to participate in a

hailing service willingly, while taking into account its exposed interactive

characteristics

2.2.4 Management support

IT implementation literature described MS as being critical for enhancing

the deployment of IT-enabled service systems in a firm and with its users (Gavidia

et al., 2021; Purvis et al., 2001; Sharma & Yetton, 2003) Orlikowski et al (1995)

regarded management support as “technology-use mediation,” which is a set of

purposeful and enduring management support measures, or “meta-structuring,”

that helps users to adapt the functions of the service system into the workplace

setting, so they can each adjust their own practice to be appropriate to the system

(p 424) Top managers’ support for an innovative IT-enabled service system

warrants the long-run vision, assurance and reasonable resources, formation of a

productive working environment, assistance in overcoming complications, and

resistance to innovation (Clohessy et al., 2019; Gangwar et al., 2015) Based on

previous works and according to this research context, this study considers

management support as the extent to which a firm’s managers encourage and

provide the necessary resource for RHA users to adapt to the ride-hailing app

2.2.5 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-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 study results disclosed the impacts of a single process of a

structuration episode, including four distinct adaptation behaviors

2.2.6 Information system use—related activity

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-technology adaptation, and self-adaptation

2.2.7 Influence of HRM factors on technology adaptation

Rubel et al (2020) investigated the effects of five types of organizational

human resource management (HRM) practices on information technology

adaptation 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 Future research should explore these

variables and their potential impact on IT adoption and adaptation

2.3 Research Gap Identification

The aforementioned subsections have intensively reviewed the essential

literature pertinent to the substantive concepts and foundational theories as well

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 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

2.4 Development of Theoretical Framework

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 (ATSI) (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

Source: Adapted from Schmitz et al (2016), Bhattacherjee and Harris (2009),

and Taylor and Todd (1995)

2.5 Proposed Research Model

2.5.1 Development of research model

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

2.5.2 Hypotheses development

The proposed research model illustrates a connected set of hypotheses that

based on the underlying theories and model of (1) ATSI (Schmitz et al., 2016) (2)

DTPB (Taylor & Todd, 1995) and (3) ECM (Bhattacherjee, 2001; Bhattacherjee

& Lin, 2015) We propose a set of hypothesized relationships and the research

model as described in the next section

2.5.2.1 ECM-based constructs and relationships

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)

The set of direct and indirect of satisfaction, perceived usefulness, confirmation

on continuance intention has been studied in multiple contexts (Franque, Oliveira,

Tam, & de Oliveira Santini, 2020; Nabavi et al., 2016; Sabah, 2020), 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:

H1: User satisfaction is positively associated with continuance intention to use hailing service;

ride-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

2.5.2.2 Relationships of behavioral adaptation with continuance

intention and satisfaction

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) For a ride-hailing service, the more users

that perform the adaptation process (i.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; Wang et al., 2019) 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

Ajzen (1991) argued that self-efficacy, which was decomposed from a

structure of the control belief of a user, will affect both user’s behavioral intention

and actual behavior in adoption stages Scholars defined self-efficacy as an

individual characteristic that plays a vital role in determining a user’s behavioral

intention and usage (Mahdavian & Mostajeran, 2013; Prior et al., 2016; Strong et

al., 2006; Yu, 2012) Prior empirical research by Bhattacherjee et al (2008),

Compeau and Higgins (1995) and Sharif and Raza (2017) included the studying

of IT self-efficacy, and investigated its positive relationship with behavioral

intention In line with these findings, we hypothesize the following:

H7: Self-efficacy is positively associated with continuance intention to use

2.5.2.4 Relationships of behavioral adaptation with continuance

intention and trust

Researchers postulated that trust is a prerequisite and facilitating factor for

online interactions for m-app businesses such as m-shopping and ride-hailing

(Ashraf et al., 2020; Chadwick, 2001; Chen et al., 2022; Ma et al., 2019;

Mittendorf, 2016) Previous research has also looked at trust as a variable that

influences both behavioral intention and actual use in both the pre-adoptive and

post-adoptive phases of the adoption process in an online environment (Nguyen

& Ha, 2021; Venkatesh et al., 2011) Given such advantages delivered by

m-commerce, users turn to develop trust, and we believe that users’ assessments of

usefulness significantly influence trust, which in turn impacts the adoption (ie.,

the initial use) and CI to use mobile shopping (Marinkovic et al., 2020; Susanto et

al., 2016; Venkatesh et al., 2011) Thus, we propose the following hypotheses:

H10: Trust is positively associated with continuance intention to use ride-hailing

service

H11: Trust is positively associated with behavioral adaptation with ride-hailing service

H15: Perceived usefulness is positively associated with trust

HI6: The relationship of trust and continuance intention is mediated by behavioral

adaptation

2.5.2.5 Relationships of behavioral adaptation with management support

and trust

Management support (MS) has been considered as an enabler of a

successful IT-enabled service system implementation (Ahmed et al., 2016; Alsaad

et al., 2019) While the significant link between MS and technology adaptation

has been claimed by Rubel et al (2020) in the banking service, and the

relationships between MS and CI have been found in healthcare (Alsyouf & Ishak,

2018), software service (Martins et al., 2019) and e-learning (McGill et al., 2014),

there is little knowledge of its role of IT adaptation process, or the extent to which

RHA drivers are willing to remain partnered with the firm in the ride-hailing

context Putting these arguments together, it is projected that management support

has a potential impact on user behavioral adaptation and continuance intention As

such, we propose that:

H12: Management support has a positive impact on trust

H13: Management support has a positive impact on behavioral adaptation

2.5.3 Research model

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 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

Figure 2.5

Proposed research model

Source: Developed by the author for this research