Subtopic Deep Dive
Mobile Commerce Adoption Factors
Research Guide
What is Mobile Commerce Adoption Factors?
Mobile Commerce Adoption Factors refer to empirical studies applying TAM and UTAUT models to identify drivers such as perceived usefulness, ease of use, trust, and perceived risk influencing consumer uptake of m-commerce, particularly in emerging markets like Malaysia and Indonesia.
Research examines factors like perceived usefulness (PU), perceived ease-of-use (PEOU), and trust on m-commerce intention to use, with over 2,000 citations across key studies. Toh Tsu Wei et al. (2009) analyzed five factors in Malaysia, garnering 546 citations. Recent works extend to e-wallets and COVID-19 impacts, such as Aji et al. (2020) with 274 citations.
Why It Matters
These factors guide e-commerce platforms in optimizing user interfaces and building trust to boost adoption in high smartphone penetration markets like Indonesia and Malaysia. Toh Tsu Wei et al. (2009) empirical analysis informs Malaysian m-commerce strategies amid rising mobile usage. Aji et al. (2020) multigroup study between Indonesia and Malaysia highlights e-wallet policy responses to COVID-19, aiding contactless payment promotion. Tahar et al. (2020) link technology readiness to e-filing intention, supporting digital government transformations.
Key Research Challenges
Cross-Cultural Model Variations
TAM/UTAUT factors like PU and PEOU vary across Malaysia and Indonesia due to cultural differences in trust and risk perception. Toh Tsu Wei et al. (2009) focused on Malaysia, while Aji et al. (2020) required multigroup analysis for Indonesia-Malaysia comparisons. Standardizing models remains difficult.
Measuring Post-COVID Shifts
Pandemic accelerated e-wallet adoption, but longitudinal data on sustained intention is limited. Aji et al. (2020) captured initial COVID-19 effects on usage intention. Tracking long-term behavioral changes post-physical distancing poses data collection challenges.
Integrating Trust and Security
Perceived security and trust mediate ease-of-use effects, but quantification in mobile contexts is inconsistent. Tahar et al. (2020) showed security's role in e-filing via technology readiness. Abdul-Halim et al. (2021) examined e-wallet continuance, yet dynamic risk metrics need refinement.
Essential Papers
What drives Malaysian m‐commerce adoption? An empirical analysis
Toh Tsu Wei, Govindan Marthandan, Alain Yee‐Loong Chong et al. · 2009 · Industrial Management & Data Systems · 546 citations
Purpose This study aims to empirically examine the factors that affect the consumer intention to use (IU) mobile commerce (m‐commerce) in Malaysia. The five factors examined in this study are perce...
COVID-19 and e-wallet usage intention: A multigroup analysis between Indonesia and Malaysia
Hendy Mustiko Aji, Izra Berakon, Maizaitulaidawati Md Husin · 2020 · Cogent Business & Management · 274 citations
Physical distancing policy that is encouraged by the World Health Organization (WHO) has inspired consumers to do contactless activities, including payment transaction. Government authorities in a ...
Perceived Ease of Use, Perceived Usefulness, Perceived Security and Intention to Use E-Filing: The Role of Technology Readiness
Afrizal Tahar, Hosam Alden Riyadh, Hafiez Sofyani et al. · 2020 · Journal of Asian Finance Economics and Business · 256 citations
This study aimed to analyze evidence of the effect of perceived ease-of-use, perceived usefulness, and perceived security on the citizen's intention to use e-Filing with information technology read...
Repurchase intention of e-commerce customers in Indonesia: An overview of the effect of e-service quality, e-word of mouth, customer trust, and customer satisfaction mediation
Yanti Mayasari Ginting, Teddy Chandra, Ikas Miran et al. · 2022 · International Journal of Data and Network Science · 153 citations
The rapid development of e-commerce in Indonesia makes the competition in this business increasingly fierce. This study aims to determine and analyze the effect of e-service quality, e-word of mout...
Understanding the determinants of e-wallet continuance usage intention in Malaysia
Nurul-Ain Abdul-Halim, Ali Vafaei‐Zadeh, Haniruzila Hanifah et al. · 2021 · Quality & Quantity · 118 citations
PENGARUH TAMPILAN WEB DAN HARGA TERHADAP MINAT BELI DENGAN KEPERCAYAAN SEBAGAI INTERVENING VARIABLE PADA E-COMMERCE SHOPEE
Edwin Japarianto, Stephanie Adelia · 2020 · Jurnal Manajemen Pemasaran · 117 citations
E-commerce dipengaruhi oleh berbagai macam faktor, misalnya tampilan web dan harga, serta kepercayaan yang ikut andil dalam mempengaruhi pertumbuhan minat beli online.Dari penelitian ini penulis me...
Factors Affecting Customer Loyalty of Using Internet Banking in Malaysia
Beh Yee, Tengku Mohamed Faziharudean · 2010 · Journal of Electronic Banking Systems · 113 citations
Internet banking (IB) has become one of the widely used banking services among Malaysian retail banking customers in recent years.Despite its attractiveness, customer loyalty towards Internet banki...
Reading Guide
Foundational Papers
Start with Toh Tsu Wei et al. (2009, 546 citations) for core TAM factors in Malaysian m-commerce, then Beh Yee and Faziharudean (2010, 113 citations) for loyalty extension, and Alsajjan and Dennis (2006) for trust integration.
Recent Advances
Study Aji et al. (2020, 274 citations) for COVID-19 e-wallet shifts, Abdul-Halim et al. (2021, 118 citations) for continuance intention, and Ginting et al. (2022, 153 citations) for repurchase mediation.
Core Methods
TAM/UTAUT via PLS-SEM for path analysis (Toh Tsu Wei et al., 2009); technology readiness as moderator (Tahar et al., 2020); multigroup invariance tests (Aji et al., 2020).
How PapersFlow Helps You Research Mobile Commerce Adoption Factors
Discover & Search
Research Agent uses searchPapers with 'mobile commerce adoption TAM Malaysia' to retrieve Toh Tsu Wei et al. (2009, 546 citations), then citationGraph reveals forward citations like Aji et al. (2020), and findSimilarPapers uncovers Indonesia parallels such as Tahar et al. (2020). exaSearch scans for cross-cultural UTAUT extensions.
Analyze & Verify
Analysis Agent applies readPaperContent to extract TAM constructs from Toh Tsu Wei et al. (2009), runs verifyResponse (CoVe) for empirical claim accuracy, and uses runPythonAnalysis to meta-analyze PU-PEOU correlations across 10 papers with GRADE grading for evidence strength in adoption models.
Synthesize & Write
Synthesis Agent detects gaps in post-adoption loyalty via contradiction flagging between initial (Toh Tsu Wei et al., 2009) and continuance studies (Abdul-Halim et al., 2021), while Writing Agent employs latexEditText for model diagrams, latexSyncCitations for bibliographies, and latexCompile for publication-ready reviews; exportMermaid visualizes TAM extensions.
Use Cases
"Run statistical meta-analysis on PU and PEOU effects in Malaysian m-commerce papers."
Research Agent → searchPapers → Analysis Agent → runPythonAnalysis (pandas meta-regression on effect sizes from Toh Tsu Wei et al. 2009 and similars) → GRADE-graded summary table of beta coefficients.
"Draft a LaTeX review on trust factors in Indonesian e-wallet adoption."
Synthesis Agent → gap detection → Writing Agent → latexEditText (UTAUT-trust model) → latexSyncCitations (Aji et al. 2020, Tahar et al. 2020) → latexCompile → PDF with embedded UTAUT diagram.
"Find GitHub repos implementing TAM models from m-commerce papers."
Research Agent → paperExtractUrls (from Tahar et al. 2020) → Code Discovery → paperFindGithubRepo → githubRepoInspect → executable Python TAM simulator for adoption prediction.
Automated Workflows
Deep Research workflow conducts systematic review of 50+ TAM/UTAUT papers on m-commerce adoption: searchPapers → citationGraph → DeepScan 7-step analysis with CoVe checkpoints on cross-cultural validity. Theorizer generates theory on COVID-19 moderated trust effects from Aji et al. (2020) chains: readPaperContent → gap detection → hypothesis synthesis. DeepScan verifies empirical claims across Toh Tsu Wei et al. (2009) citations.
Frequently Asked Questions
What is the definition of Mobile Commerce Adoption Factors?
Studies applying TAM/UTAUT to drivers like perceived usefulness, ease-of-use, trust, and risk in m-commerce uptake, focused on emerging markets like Malaysia (Toh Tsu Wei et al., 2009).
What are the primary methods used?
Structural equation modeling tests TAM constructs (PU, PEOU) on intention to use; multigroup analysis compares cultures (Aji et al., 2020); surveys measure trust mediation (Tahar et al., 2020).
What are the key papers?
Foundational: Toh Tsu Wei et al. (2009, 546 citations) on Malaysian m-commerce; Beh Yee and Faziharudean (2010, 113 citations) on banking loyalty. Recent: Aji et al. (2020, 274 citations) on e-wallets.
What are the open problems?
Longitudinal post-COVID continuance models; AI integration in trust measurement; generalizable metrics beyond Malaysia-Indonesia (gaps in Abdul-Halim et al., 2021).
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