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Leveraging Data Analytics Across Digital Product Development Stages: A Systematic Review and Conceptual Framework
(2026-06-01)
Alamsyah, Noor
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Thanasopon, Bundit
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Jamsri, Pornsuree
Background: Data analytics (DA) is a field that has expanded greatly and is an important tool for digital product development, and has captured researcher and practitioner interest. Nevertheless, from an Information Systems and Business Intelligence (IS/BI) view, there is still a lack of knowledge regarding the role that data analytics plays in the digital product development lifecycle for decision making. The volume and the complexity of digital product innovation and analytics continues to increase, thereby further enhancing the need for existing knowledge to be consolidated in this area. Objective: This research systematically reviewed the latest academic research in the field of data analytics in digital product development and explain the specific uses of data analytics in the different stages of digital product development for supporting decision making and innovation activities. Methods: This study followed the systematic literature review method through ScienceDirect, IEEE Xplore and Emerald databases. Upon initial search, 1,554 articles were found; 33 relevant articles were identified after a structured screening and eligibility assessment of the articles in line with Kitchenham's protocol. Results: The results reveal the differentiated use of data analytics in the various stages, namely opportunity identification through text mining, feasibility assessment through predictive modelling, prototyping through digital twins and generative design and market responsiveness through predictive analytics and recommender systems. Even with analytical processes and concepts in place, companies often face challenges due to data integration issues, analytical skill, and organizational preparedness. The data quality issues, algorithmic bias, ethical considerations, or lack of algorithm transparency all contribute to these limitations, hindering the full potential of data analytics in digital product development. Conclusion: To realize more effective and aligned outcomes of innovation, it is important to understand how data analytics can assist in decision making throughout the digital product development lifecycle. This research is valuable for researchers and practitioners as it provides a structured conceptual framework for association of analytics initiatives with digital product development goals. There is potential for this work to be extended in future studies, involving the creation and validation of scalable analytics frameworks in various organisational contexts.
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The adoption of online food delivery in facing COVID-19 among the Indonesian food MSMEs
(2025-06-01)
Yasirandi, Rahmat
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Thanasopon, Bundit
This study investigates the factors influencing the Indonesian food micro, small, and medium enterprises (MSMEs) in adopting online food delivery (OFD) during the corona virus disease-2019 (COVID-19) pandemic, by employing the technology-organization-environment (TOE) framework. Through a quantitative approach involving 378 respondents, this research explores the multi-dimensional factors affecting OFD service adoption, there are innovation compatibility, innovation complexity (IC), innovation cost, owner’s self-efficacy, owner’s commitment, customer pressure (CSP), competitive pressure (CMP), government support (GS), and health protocol guarantee. Employing covariance-based structural equation modeling (CBSEM), the study reveals interesting relationships among the proposed factors. The findings underscore the role of GS and health protocol guarantees in enhancing owner's self-efficacy and commitment towards OFD adoption. Moreover, it challenges the presumed barriers of IC, suggesting a nuanced understanding of adoption process amid a crisis. This study not only enriches the theoretical discourse on technology adoption in the context of a pandemic but also provides practical implications for stakeholders in navigating the post-pandemic business landscape. Future research directions are proposed to explore the continuous intention of food MSMEs towards OFD services postpandemic, highlighting the evolving nature of the global business environment and the enduring impact of the COVID-19 pandemic on food industries.
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Understanding Continuance Intention of Merchants as End User in Online Food Delivery After COVID-19
(2025-03-01)
Yasirandi, Rahmat
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Thanasopon, Bundit
This study explores the continuance intention of merchants in using Online Food Delivery (OFD) services post-pandemic, employing an extended Expectation-Confirmation Model (ECM). While existing research on OFD predominantly examines food consumers, this study focuses on the merchant side and investigates how confirmation, perceived usefulness, satisfaction, perceived risk, and perceived critical mass influence their continuance intention. Using Structural Equation Modeling (SEM) on data collected from 378 Indonesian merchants, the findings highlight several critical factors. Perceived critical mass fosters platform adoption by creating a sense of widespread utility, while perceived risk indirectly affects continuance intention through its impact on satisfaction, emphasizing the need to address merchants’ concerns. This research enhances the understanding of merchant behavior in the OFD ecosystem by incorporating context-specific factors that influence their decisions. The findings offer practical recommendations for OFD providers to improve system reliability, mitigate perceived risks, and foster a robust user community to ensure sustained engagement. Future studies could build on these insights by investigating similar dynamics in different regions or by including other stakeholders, such as delivery drivers, to provide a broader understanding of the OFD ecosystem.
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Integrating Machine Learning for Automated Root Cause Analysis of Critical-to-Quality (CTQ) in Hard Disk Drive Manufacturing
(2025-01-01)
Inpang, Mullika
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Thanasopon, Bundit
This research was studying the process of analyzing key factors that significantly impact the quality of Head Stack Assembly (HSA) in the production of Hard Disk Drives (HDDs). Various factors are considered, such as production machine, testing equipment, lot numbers of raw materials, production time and testing times etc. The assembly of the Head Stack is a critical step in Hard Disk Drives manufacturing. As the number of heads increases, the process requires greater precision and careful consideration of multiple factors. The use of artificial Intelligence technology in research is to be able to analyze the causal factors that affect the assembly of Head Stack more quickly and accurately in order to help reduce damage that will affect the production process and the quality of the Hard Disk Drives. It also increases the reliability of the product and raises the production standards, including quality control for the Hard Disk Drives manufacturing industry.
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Automated Assembly Machine Stopping System Using Machine Learning for Electrical Failure Prediction in Hard Disk Drive Manufacturing
(2025-01-01)
Panjasamanwong, Tanakrit
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Thanasopon, Bundit
The assembly of diode lasers and sliders is a critical process in hard disk drive (HDD) manufacturing, directly affecting product electrical performance. Delays in detecting problematic assembly machines increase scrap rates, reduce yield, and raise production costs. This research presents the development of supervised machine learning models to predict electrical testing results using key parameters from assembly machines, for example, diode laser alignment position after assembly and laser intensity measurement during assembly. Dataset was preprocessed through outlier removal and feature selection using correlation analysis and permutation importance before applying SMOTE technique to balance the minority class in the training set, followed by normalization before modeling and evaluating performance. Multiple models were developed and evaluated to select the best performing model. Evaluation results showed that the AdaBoost model combined with Random Forest achieved the highest performance, with an AUC of 92.36 %. The selected model was integrated into monitoring, alerting and machine auto stopping system when the predicted failure rate exceeded a predefined threshold. The implemented system reduces the detection time for problematic assembly machines, minimizes defective parts, and improves overall manufacturing efficiency.
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Predicting Faulty Production Lines Causing Head Gimbal Assembly Damage from Electrostatic Discharge Using Machine Learning
(2025-01-01)
Bilgasun, Nuttanon
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Thanasopon, Bundit
This research was conducted to analyze abnormal production lines leading to Head Gimbal Assembly (HGA) damage, mainly caused by electrostatic discharge. HGAs are critical component in hard disk. The HGA was constantly developed to produce higher storage capacity products to delight customer demands. Newer product design HGAs are highly sensitive to electrostatic discharge. This results in more damage and higher scrap costs. The main goal of this research is to leverage artificial intelligence technology to predict abnormal production lines that cause electrostatic damage in the assembly process of HGA. The research aims to develop timely solutions to reduce damage during production. This proactive approach is essential in lowering production costs and ensuring the HGAs have a longer lifespan and increased reliability.
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Institutionalization and adoption of digital technology in Thai maritime industry
(2025-01-01)
Janmethakulwat, Atcharaporn
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Thanasopon, Bundit
Purpose This study explores the complex process of digital technology adoption and institutionalization within the Thai maritime industry. It aims to discern the pivotal factors that influence the integration of digital technologies into organizational practices, focusing on members of the Thai Shipowners Association (TSA). Design/methodology/approach This study integrates the technology organization and environment (TOE) framework, technology acceptance model (TAM) and institutional theory to investigate digital technology adoption in the Thai maritime industry. A survey was distributed to all shipowners in the TSA, achieving a 22% response rate with 240 valid responses from an estimated 1,100 employees. Data were analyzed using partial least squares-structural equation modeling (PLS-SEM). Findings Key findings of the research include the identification of IT skills and IT support as strong predictors of perceived ease of use, while organizational culture and top management endorsement significantly influence perceived benefits. The study also highlights the less definitive impact of organizational rules and legal requirements on adoption intentions. A notable insight is the role of social pressure in driving institutional changes, emphasizing the importance of external influences in technology integration. Research limitations/implications This study’s primary limitation is its context-specific focus on Thai maritime companies, which may not generalize to other industries or cultural contexts. Findings could vary under different organizational conditions or regulatory frameworks. Additionally, some results, such as the non-significant impacts of reliable IT infrastructure and legal frameworks, suggest complexities in technology adoption and institutionalization not fully captured by the study. Future research should expand these findings across diverse industries and explore how evolving digital technologies influence organizational strategies. Originality/value This research contributes uniquely by deploying an integrative theoretical model to examine digital technology adoption in an industry that is traditionally slow to innovate. The findings offer substantial insights into the processes of adoption and institutionalization, demonstrating how these contribute to long-term operational improvements in maritime businesses. This dual focus is instrumental for stakeholders aiming to navigate the complexities of digital transformation effectively.
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Data Analytics Maturity Model for Digital Product Innovation in Firm: An Overview
(2025-01-01)
Alamsyah, Noor
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Thanasopon, Bundit
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Jamsri, Pornsuree
This paper aims to report the results of the review, map the characteristics, and conduct a comparative analysis of the selected data analytics maturity models that are commonly implemented in firms engaged in digital product innovation. The maturity models examined include TDWI, Hortonworks, Gartner, and DELTA Plus, which represent some of the most recognized framework guiding organizations in assessing and improving their data analytics capabilities. Characteristics and comparative analysis were developed based on investigative literature reviews, referring to official reports from institutions that developed the model and publications that discussed the model. Each maturity model is explained concisely and comprehensively, highlighting the structure, dimensions, and progression criteria that define each maturity level and stage. The comparison indicates that although these models outline well-defined conceptual stages of analytics maturity, most provide limited methodological guidance for assessing firms or determining their placement within a spesific maturity level. In the future, it is expected there will be an increase in the availability of maturity models that are more adaptive and personalized for various business sectors. This paper contributes to present a fairly intensive comparative analysis and characterization of data analytics maturity models that are widely recognized and frequently applied in supporting digital product development within firms. In addition to that, this paper provides a cost model. The model is evaluated using Genetic Algorithm (GA) and Simulated Annealing (SA).
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Digital technology adoption and institutionalization in Thai maritime industry: An exploratory study of the Thai shipowners
(2024-09-01)
Janmethakulwat, Atcharaporn
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Thanasopon, Bundit
This exploratory study delves into the factors influencing digital technology adoption and the dynamics of its institutionalization within the Thai maritime industry, a key sector underpinning Thailand's economic expansion. Data was collected through semi-structured interviews with executive leaders from seven Thai shipowners, utilizing a snowball sampling method. The study reveals that digital technology adoption is driven by a complex interplay of factors including reliable IT infrastructure, perceived technological benefits, organizational culture, top management support, IT skills and support, legal, regulatory, and policy requirements, social pressure influence, and varying degrees of government support. Additionally, the institutionalization of such technologies within the maritime sector is heavily reliant on well-defined organizational rules and the establishment of trust in technological advancements. These findings not only enrich the theoretical landscape regarding digital adoption but also offer practical insights for industry stakeholders, paving the way for more nuanced interventions and policy formulations aimed at enhancing digital integration in this traditionally conservative sector.
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A Comparison of Restaurant-Based and E-Commerce Food Delivery: Customer Evaluation Based on Expectations and Satisfaction
(2024-01-01)
Yasirandi, Rahmat
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Thanasopon, Bundit
This study examines the impact of the COVID-19 pandemic on consumer preferences between restaurant-based and e-commerce food delivery services. Utilizing the E-Service Quality (E-ServQual) model, which includes dimensions such as Efficiency, Fulfillment, Reliability, Privacy, Responsiveness, Compensation, and Contact, the research assesses which delivery method satisfactions align more closely with user expectations through customer evaluations. Methods include a comparative analysis using data collected from surveys. To obtain a robust dataset, the study surveyed 200 consumers who have used both restaurant-based and e-commerce services during the pandemic. This sample size provides sufficient statistical power to discern significant differences between the two service types. Findings indicate that e-commerce platforms generally excel in efficiency, responsiveness, and compensation, whereas restaurant-based services perform better in terms of fulfillment, reliability, privacy, and contact. The results also identify key dimensions from both types of online food delivery services. This analysis provides valuable insights into specific areas where service providers can focus their improvement efforts to meet and exceed consumer expectations. The study contributes to the understanding of shifting consumer behaviors in response to global disruptions like the pandemic.

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