Subtopic Deep Dive
Machine Learning in Digital Transformation
Research Guide
What is Machine Learning in Digital Transformation?
Machine Learning in Digital Transformation applies ML techniques to frameworks assessing technology adoption and organizational performance in digital shifts within information retrieval and data mining.
This subtopic examines ML integration in models like TAM and UTAUT for e-learning, e-commerce, and digital services. Key studies use SEM to analyze readiness during COVID-19 (Almaiah et al., 2022, 82 citations; Almaiah et al., 2022, 78 citations). Over 10 papers from 2020-2024 highlight ML in EdTech and customer service.
Why It Matters
ML models predict user acceptance of digital tools, enabling enterprises to optimize e-learning platforms and e-commerce usability (Almaiah et al., 2022; Kumar et al., 2023). In MSMEs, ChatGPT integration improves service quality via data-driven personalization (Subagja et al., 2023). Governments leverage ML for AI policy challenges in Industry 4.0, boosting efficiency (Febiandini and Sony, 2023). These applications drive competitiveness through informed digital strategies.
Key Research Challenges
Technology Acceptance Modeling
Adapting TAM for ML-driven platforms faces issues in measuring readiness amid disruptions like COVID-19. Almaiah et al. (2022) use SEM to quantify factors but note data scarcity in diverse contexts. Extensions for live streaming e-commerce reveal intention gaps (Chen et al., 2024).
Usability Security Evaluation
Static ML methods struggle with dynamic e-commerce threats post-COVID. Kumar et al. (2023) propose evaluation but highlight sequential data tracing limitations. Balancing usability and security requires scalable metrics.
Public Sector AI Integration
Governments face adoption barriers in AI for Industry 4.0. Febiandini and Sony (2023) identify policy and infrastructure challenges. Measuring organizational impact lacks standardized ML frameworks.
Essential Papers
Employing the TAM Model to Investigate the Readiness of M-Learning System Usage Using SEM Technique
Mohammed Amin Almaiah, Shaha Al‐Otaibi, Abdalwali Lutfi et al. · 2022 · Electronics · 82 citations
During COVID-19, universities started to use mobile learning applications as one of the solutions to support distance learning. The readiness of universities to apply new systems, such as mobile le...
Explaining the Factors Affecting Students’ Attitudes to Using Online Learning (Madrasati Platform) during COVID-19
Mohammed Amin Almaiah, Fahima Hajjej, Abdalwali Lutfi et al. · 2022 · Electronics · 78 citations
This study aims to investigate students’ perceptions about the Madrasati platform as well as to identify the crucial factors that could influence the adoption of the Madrasati platform. Online quan...
A Static Machine Learning Based Evaluation Method for Usability and Security Analysis in E-Commerce Website
Biresh Kumar, Sharmistha Roy, Kamred Udham Singh et al. · 2023 · IEEE Access · 46 citations
Measurement of e-commerce usability based on static quantities variable is state-of-the-art because of the adoption of sequential tracing of the next phase in the categorical data. The global COVID...
Improving Customer Service Quality in MSMEs through the Use of ChatGPT
Agus Dedi Subagja, Abu Muna Almaududi Ausat, Ade Risna Sari et al. · 2023 · Jurnal Minfo Polgan · 45 citations
In the current era of digitalisation, technological developments are accelerating and changing the way humans communicate and interact, including in MSME businesses. This study aims to evaluate the...
EdTech in Indonesia : Ready for Take-off?
Riaz Bhardwaj, Noah Yarrow, Massimiliano Calì · 2020 · The World Bank Open Knowledge Repository (World Bank) · 24 citations
This EdTech landscape survey provides an \n overview of the Indonesian startup ecosystem in EdTech, \n drawing upon three main sources of information: publicly \n available data, inform...
TAM-Based Study of Farmers’ Live Streaming E-Commerce Adoption Intentions
Xinqiang Chen, Xiue Zhang, Jiangjie Chen · 2024 · Agriculture · 23 citations
Amidst the digital economy surge, live streaming e-commerce of agricultural products has significantly boosted agricultural prosperity. Investigating farmers’ behavioral intentions toward adopting ...
A Review of Emerging Technologies and Their Acceptance in Higher Education
Santiago Criollo-C, Mario González, Andrea Guerrero-Arias et al. · 2023 · Education Sciences · 21 citations
The pandemic caused by COVID-19 impacted the entire world, but technological progress led to the appearance of new and innovative emerging technologies (ETs). These technologies proved to have a wi...
Reading Guide
Foundational Papers
Start with Nugroho (2013) for open data policies enabling ML transformations in Indonesia, as it sets context for digital readiness pre-ML boom.
Recent Advances
Study Almaiah et al. (2022, 82 citations) for TAM-SEM in m-learning, Kumar et al. (2023) for e-commerce ML, and Chen et al. (2024) for live streaming adoption.
Core Methods
Core techniques include SEM for acceptance modeling (Almaiah et al., 2022), static ML filtering for usability (Kumar et al., 2023), and surveys for platform attitudes.
How PapersFlow Helps You Research Machine Learning in Digital Transformation
Discover & Search
Research Agent uses searchPapers and exaSearch to find Almaiah et al. (2022) on TAM for m-learning, then citationGraph reveals 78-cited follow-up on Madrasati platform, and findSimilarPapers uncovers Chen et al. (2024) on e-commerce adoption.
Analyze & Verify
Analysis Agent applies readPaperContent to extract SEM results from Almaiah et al. (2022), verifies claims with CoVe against raw data, and runs PythonAnalysis with pandas to replicate usability metrics from Kumar et al. (2023), graded via GRADE for evidence strength.
Synthesize & Write
Synthesis Agent detects gaps in TAM extensions across e-learning papers, flags contradictions in COVID impacts, while Writing Agent uses latexEditText and latexSyncCitations to draft frameworks, latexCompile for reports, and exportMermaid for adoption model diagrams.
Use Cases
"Run statistical analysis on TAM factors from Almaiah 2022 m-learning paper."
Research Agent → searchPapers(Almaiah 2022) → Analysis Agent → readPaperContent → runPythonAnalysis(pandas SEM replication) → matplotlib plot of readiness factors.
"Write LaTeX review on ML in EdTech adoption with citations."
Synthesis Agent → gap detection(EdTech papers) → Writing Agent → latexEditText(intro) → latexSyncCitations(Almaiah, Criollo-C) → latexCompile(PDF output).
"Find GitHub repos for e-commerce ML usability code from recent papers."
Research Agent → searchPapers(Kumar 2023) → Code Discovery → paperExtractUrls → paperFindGithubRepo → githubRepoInspect(evaluation scripts).
Automated Workflows
Deep Research workflow scans 50+ adoption papers via searchPapers → citationGraph → structured TAM report with GRADE scores. DeepScan applies 7-step CoVe to verify ML impacts in Febiandini (2023), checkpointing policy claims. Theorizer generates hypotheses on UTAUT extensions from Almaiah datasets.
Frequently Asked Questions
What defines Machine Learning in Digital Transformation?
It applies ML to adoption frameworks like TAM/UTAUT for digital shifts in e-learning and e-commerce, assessing organizational performance.
What methods are used?
SEM techniques analyze readiness (Almaiah et al., 2022), static ML evaluates e-commerce usability (Kumar et al., 2023), and surveys measure ChatGPT impacts (Subagja et al., 2023).
What are key papers?
Top cited: Almaiah et al. (2022, 82 citations) on m-learning TAM; Almaiah et al. (2022, 78 citations) on Madrasati; Kumar et al. (2023, 46 citations) on e-commerce ML.
What open problems exist?
Scalable ML for public AI policies (Febiandini and Sony, 2023), dynamic usability beyond static metrics (Kumar et al., 2023), and generalizing TAM across sectors.
Research Information Retrieval and Data Mining with AI
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