In the Vietnamese business market, the impact of the pandemic has further expedited and strengthened this process relationship between shared data architecture and the performance of the
INTRODUCTION
Contextual details derived from research and an issue description
In recent years, companies have accelerated digital transformation and automation to gain a competitive edge, particularly in the Vietnamese market, where the pandemic has further driven these initiatives, especially in manufacturing Rapid advancements in technology and data are pivotal to this transformation, creating new opportunities for operational optimization However, the true value of data for businesses lies not just in the volume collected but in effective strategies for leveraging this data to facilitate informed and timely decision-making during the digital transformation journey.
Data, the valuable resource of the 21st century, akin to black gold (Sestino et al.,
In 2023, data is recognized as a crucial driver of business value, particularly in the context of the global manufacturing industry's shift towards Industry 4.0, where smart sensors and secure communication networks generate vast amounts of time-series data throughout the product lifecycle To navigate this digital transformation effectively, organizations must enhance their understanding of the organizational logic shaped by big industrial data However, over 70% of digital transformation initiatives fail due to inadequate utilization of data potential Consequently, Data-driven digital transformation (DDDT) has emerged as a vital process for organizations, leveraging data and digital technologies to facilitate significant organizational change and improvement.
The structured data characteristics of a business significantly influence decision-making and digital transformation amidst rapid data changes In the face of supply chain disruptions, the importance of Shared Data Architecture (SDA) is increasingly recognized, yet research on this topic remains limited SDA enhances data management by ensuring uniformity and accuracy, enabling the integration of emerging technologies like artificial intelligence and machine learning to extract value from shared data A key benefit of data is its ability to optimize logistics processes; intelligent logistics systems leverage data analysis to adapt schedules and transportation methods in response to market trends and demand fluctuations As a result, traditional operational cycles are being replaced by intelligent models that incorporate advanced technologies, such as Intelligent Logistic Management (ILM).
This study aims to investigate the role of Smart Digital Automation (SDA) in Data-Driven Decision Technology (DDDT) for supply chain management, an area that has not been previously explored With the rapid advancements in robotics and technology, there are growing concerns regarding the interplay between technology, robots, and humans, a relationship that has often been overlooked in prior research To fill this gap, the research employs Vroom-Yetton decision theory to elucidate the decision-making process based on analyzed data Additionally, the integration of intellectual and human capital is used as a foundational framework, focusing on the interplay between human and technological capital Data was collected through quality interviews with manufacturing businesses that utilize Automated Guided Vehicles (AGVs), Autonomous Mobile Robots (AMRs), or drones, all interconnected with IoT technology.
The findings indicate that SDA and DDDT significantly influence Operation and Supply Chain Performance (OSCP), with CbHTR playing a crucial role in contemporary supply chain operations This study enhances the understanding of knowledge management and decision theory, introducing new dimensions to OSCP Additionally, it positively impacts data management during digital transformation and the integration of technology in improving supply chain performance.
This study investigates essential factors in Operational Supply Chain Performance (OSCP) and introduces a shared data architecture within the supply chain, enhancing the understanding of data and technology in the digital transformation era, particularly in manufacturing Utilizing a quantitative methodology, it reveals insights into intermediary mechanisms such as CbHTR, DDDT, and ILM, which aid research and empower managers to optimize OSCP Significantly, this is the first study to integrate Shared Data Architecture (SDA) for evaluating the effectiveness of data-driven digital transformation The findings suggest that businesses should create a data-sharing platform, implement tracking and delivery monitoring applications, adopt intelligent operating models, and harness data and its users to improve supply chain performance and fully realize the potential of data through technological advancements.
Objectives of the study
Effective operation of a value chain in an enterprise relies on the crucial interplay between tangible and intangible factors, especially in the fast-paced environment of OSCP where critical processes are ongoing This paper addresses unresolved issues and poses significant research questions to explore these dynamics further.
• How can data-driven digital transformation enhance operational and supply chain performance?
• What benefits does a shared data platform offer for manufacturing businesses in supply chain activities such as logistics and warehouse operations?
• What distinctive characteristics does the interaction among the three elements of human, technology, and robots have in comparison to just two elements in an intelligent manufacturing business?
Research subjects
Research subjects: Operation and Supply Chain Performance in Ho Chi Minh City.
Respondents: Managers and directors at enterprises in HCMC.
Research scope
Research space: Ho Chi Minh City area.
Period: From August 2023 to December 2023.
The importance of research 1 0 1.6 Research structure
This article underscores the significance of multidisciplinary approaches in supply chain research, particularly in the context of digital transformation It examines the interplay between technology, data, and human involvement, focusing on decision-making processes and business models Unlike previous studies that primarily addressed Big Data, this research highlights the critical role of Systematic Data Analysis (SDA) in organizing and managing data within supply chains It also identifies the essential function of data-sharing facilities in enhancing supply chain outcomes By addressing the evolving role of data and technology, this study emphasizes the importance of leveraging data-sharing platforms to facilitate digital transformation, optimize logistics, and integrate human and robotic technologies Ultimately, this research enriches the theoretical framework surrounding digital transformation, intelligent logistics, and successful production operations.
To enhance Operational Supply Chain Performance (OSCP), businesses should invest in a data-sharing platform equipped with tracking and delivery monitoring applications, which, despite initial costs, promotes efficient transportation and delivery control Deploying intelligent operating models that leverage data and technology is essential for driving production growth Leaders can empower their teams by gathering employee input and making informed decisions using the Vroom model, which aids in resource allocation and innovation While integrating technology and robotics automates processes and mitigates risks, human development remains crucial, as humans are capable of flexible and innovative decision-making The System for Data Access (SDA) facilitates data sharing between humans and robots, allowing for the design of intelligent production processes and the monitoring of robot performance to ensure quality This collaborative approach fosters interaction among humans, robots, and technology, thereby enhancing OSCP and ensuring smooth supply chain operations.
The study is divided into six chapters, which are as follows:
Chapter 1: Introduction - This chapter highlights the pressing nature of the topic, detailing both the general and specific objectives of the study It explores the research subject and its scope, underscoring its importance and structure.
Chapter 2: Literature Review and Hypothesis Development explores the theoretical framework underpinning the study while introducing the research model This section critically analyzes pertinent theoretical concepts and reviews existing studies and research models, thereby establishing a robust foundation for the development of our own research framework.
In Chapter 3: Research Method, we detail the research process, focusing on the survey sampling techniques utilized We discuss both qualitative and quantitative methods applied to analyze and measure key research concepts Furthermore, we outline the construction and evaluation of scales used throughout the study.
In Chapter 4: Data Analysis and Results, we present the findings of our research, detailing the survey sample and conducting tests on the research models This chapter also includes a comprehensive assessment of the research concepts, highlighting the key insights derived from the data analysis.
Chapter 5: Discussion and Implications delves into the analysis and interpretation of the collected results, providing a comprehensive overview of the findings This section emphasizes the significance derived from the research practice and explores the broader implications of these results.
In the concluding chapter, we summarize the key findings of our research, highlighting both the contributions and limitations within practical educational institutions Additionally, we suggest potential avenues for future research inspired by the insights derived from this study.
LITERATURE REVIEW AND HYPOTHESIS DEVELOPMENT
Underpinning theories
Within the supply chain context, the utilization of data and technology has engendered a substantial amount of vital and untapped information (Bayraktar et al.,
To maximize the value of data in 2023, leaders need to implement effective decision-making methodologies (Wang et al., 2021; Chen et al., 2021) The Vroom-Yetton-Jago theory plays a crucial role in this process, providing essential insights for enhancing decision-making strategies.
According to Vroom & Yetton (1974), effective leaders identify the best decision-making approach by collecting and analyzing data to understand the problem and gauge group involvement By assessing the availability of information, they can choose from five decision-making methods: Autocratic I (AI), Autocratic II (AH), and Consultative I (CI), ensuring that their decisions are well-informed and appropriate for the situation.
1), CI1 (Consultative II), or Gil (Group 11).
The Vroom-Yetton-Jago theory significantly benefits data-driven transformation by improving decision-making speed and accuracy in production and transportation processes It fosters greater consensus and commitment among supply chain members through group involvement and effective use of information Additionally, this theory provides a flexible framework that adapts to the dynamic and complex nature of the business environment within the supply chain.
The theory of augmented intelligence highlights the synergy between human cognitive abilities and technological innovations, aimed at improving decision-making and problem-solving skills In supply chain technology applications, the indispensable role of humans remains crucial and irreplaceable.
Technologies like IoT and robotics enable real-time data collection and transmission, enhancing logistics management systems These systems leverage data to monitor and optimize supply chain processes Nevertheless, effective data analysis, pattern recognition, and strategic decision-making still depend on human expertise and intelligence.
2021) Humans possess the ability to apply their expertise and experience to ensure that data is understood within the appropriate context and used intelligently and efficiently.
In the architecture of shared data, technology facilitates information exchange and collaboration among supply chain stakeholders (Rejeb et al., 2021) However, human intelligence is crucial for evaluating, authenticating, and applying information in decision-making processes (Trunk et al., 2020) The theory of augmented intelligence highlights the importance of integrating human capabilities with technology, enhancing technological functions while valuing human roles Ultimately, the effective management of the supply chain relies on this smart integration, making it a key factor for successful technology implementation.
Shared Data Architecture (SDA)
Shared data architecture (SDA) is an innovative model that leverages technological advancements to enhance digital transformation within organizations By synthesizing and storing knowledge from various systems and databases, SDA reduces information asymmetry, allowing businesses and stakeholders—such as clients and staff—to efficiently gather, exchange, and update accurate data Implementing SDA ensures data integrity and correctness, particularly in supply chain management, where it facilitates automated processes, minimizes waiting times, and accurately addresses customer demands while eliminating human error Additionally, SDA supports cost control and inventory management, ultimately improving the overall efficiency of supply chain operations.
Data-Driven Digital Transformation (DDDT)
Data-driven digital transformation (DDDT) refers to the reorganization of organizational operations through technology and data (Bag et al., 2023) In supply chains, DDDT emphasizes the use of big data analytics, enabling enterprises to better understand consumer needs and market trends by collecting and analyzing vast amounts of data This capability allows for quicker and more accurate decision-making (Richey et al., 2016) Additionally, integrating artificial intelligence with technology to automate production processes enhances product quality, minimizes errors, and optimizes inventory management, leading to reduced customer wait times (Kostakis & Kargas, 2021) Ultimately, DDDT seeks to boost supply chain operational efficiency amid rapid technological advancements.
Connection between Human - Technology - Robot (CbHTR)
The evolving interaction between humans, technology, and robots in modern supply chains is crucial, as robots increasingly take over dangerous and repetitive tasks, enhancing productivity while minimizing human error (Evjemo et al., 2020) The integration of emerging technologies such as IoT analytics, Big Data, and artificial intelligence (AI) facilitates real-time updates and better decision-making, automating various processes within the supply chain According to Shu et al (2018), robots require data for effective planning, production automation, and error management As robots and technology advance, ensuring human safety remains a priority, with a focus on enhancing human roles in controlling and managing robotic systems to boost supply chain efficiency (Frederico, 2021).
Intelligent Logistic Management (ILM)
Intelligent logistics management (ILM), or "smart logistics," leverages internet technology to enhance business logistics solutions (Li et al., 2012) Utilizing smart technology for data collection, ILM facilitates tracking and control within logistics operations (Uckelmann, 2008) It integrates the Internet of Things (IoT), big data analytics, and artificial intelligence to optimize planning, management, and control (Feng & Ye, 2021) ILM ensures efficient transfer of raw materials, information, and finances between suppliers, manufacturers, distributors, and consumers (Tiejun, 2012) By enabling continuous supply chain activities such as manufacturing, transportation, and warehousing with high accuracy and minimal errors, ILM positions companies to capitalize on future technological advancements in logistics management.
Operation and Supply Chain Performance (OSCP)
Operation and supply chain performance involves optimizing information flow and accelerating manufacturing and service processes to transform resources into finished products for consumers (Lee et al., 2022) By effectively managing order quantities, inventory, and lead times through a Smart Data Analytics (SDA) approach, organizations can achieve a more agile and efficient production process (Carvalho et al., 2012) The integration of humans, robots, technology, and smart logistics has further enhanced packaging, shipping, and real-time order status updates Superior supply chain and operational performance can give companies a competitive advantage by minimizing waste, increasing speed, enhancing product quality, and boosting customer satisfaction (Alzoubi & Yanamandra, 2020).
Hypothesis Development
The implementation of Supply Data Architecture (SDA) in warehouse and logistics operations offers significant advantages, including enhanced data integration that allows for seamless information sharing among supply chain stakeholders This unified data system improves communication, reduces errors, and fosters collaboration Furthermore, SDA enables comprehensive data analysis by consolidating information from diverse sources, allowing organizations to identify hidden insights, patterns, and trends As a result, supply chain entities gain a clearer understanding of their processes, performance, and associated risks, which supports intelligent decision-making Therefore, it is evident that SDA plays a crucial role in supporting Data-Driven Decision Theory (DDDT), leading to the proposal of the following hypothesis.
Hl: SDA has a positive effect on DDDT
SDA is essential for integrating technology into warehouse operations by enabling data sharing with systems like inventory management software and robotic automation This collaboration allows human operators to enhance processes and performance, as robots can use shared data for optimized movements and real-time decision-making, resulting in higher productivity and fewer errors (Qiao et al., 2021; Yang et al., 2022) Additionally, SDA fosters a culture of continuous improvement, helping organizations identify enhancement opportunities and implement proactive measures for operational excellence This iterative process leads to ongoing refinement and optimization, ultimately improving efficiency and effectiveness Based on the insights from SDA and CbHTR, a hypothesis can be proposed.
H2: SDA has a positive effect on CbHTR
In technology-driven manufacturing enterprises, Service Data Architecture (SDA) offers substantial advantages for Integrated Logistics Management (ILM) by enabling seamless data sharing that enhances real-time visibility and coordination throughout the supply chain This integration optimizes inventory levels, reduces lead times, and improves overall performance Additionally, SDA supports predictive analytics, facilitating informed decisions on demand forecasting, inventory optimization, and route planning, which leads to cost reduction, risk mitigation, and improved service levels Moreover, SDA boosts agility by allowing rapid responses to market fluctuations and disruptions, enabling dynamic adjustments in production, inventory, and delivery to ensure timely order fulfillment and heightened customer satisfaction.
H3: SDA has a positive effect on ILM
SDA enhances decision-making by integrating and evaluating data from multiple sources, enabling leaders to formulate informed strategies (Singh et al., 2019) According to Gong et al (2018), a shared database facilitates the seamless integration of supply chain operations, including purchasing, inventory management, production, and transportation By accelerating and simplifying automation, SDA allows organizations to significantly reduce costs, save time, and improve supply chain efficiency Consequently, implementing SDA in supply chain management increases responsiveness, decreases resource and time consumption, and boosts productivity, while also addressing process optimization and enhancing customer satisfaction, ultimately leading to improved supply chain efficiency This study proposes the following hypothesis:
H4: SDA has a positive effect on OSCP
In today's business landscape, the integration of technology and data in supply chain operations is increasingly prevalent, with DDDT (Data-Driven Decision Technology) gaining traction (Kache & Seuring, 2017) The adoption of modern technologies such as blockchain, IoT, AI, and big data plays a crucial role in automating processes, thereby enhancing the flexibility and efficiency of supply chains.
By utilizing digital technology, companies may quickly identify changes from customers and adapt their processes to match their expectations (Papanagnou et al.,
In 2022, DDDT aims to enhance decision-making and mitigate supply chain risks by issuing notifications regarding operational changes, including sales activities, inventory levels, manufacturing status, and product distribution This proactive approach is designed to ensure a more resilient supply chain.
H5: DDDT has a positive effect on OSCP
The interaction and collaboration between humans, robots, and technology are essential in optimizing processes within the OSCP CbHTR emphasizes the integration of human and robotic roles, where humans leverage technology to enhance performance, while robots utilize data for automated tasks Technology serves as a backbone, providing data and decision support systems that foster efficient collaboration This synergy creates an intelligent work environment, improving automation, minimizing errors, and increasing flexibility Based on these insights, several hypotheses are proposed for further investigation.
H6: CbHTR has a positive effect on OSCP
ILM, or Intelligent Logistics Management, leverages advanced technologies like AI, big data, and the Internet of Things (IoT) to enhance both warehouse and operational efficiency The primary objective is to optimize supply chain performance through increased digitization and automation, resulting in streamlined workflows across purchasing, production, storage, and distribution processes By utilizing collected data, smart logistics can enhance operational outcomes, often replacing human labor with robots for repetitive and hazardous tasks Additionally, the integration of tracking software minimizes operational errors Consequently, effective smart logistics management is essential for improving efficiency in operations and the overall supply chain.
H7: ILM has a positive effect on OSCP
Previous research has offered limited insights into the intermediary roles of various operational technologies, such as Intelligent Logistics Management (ILM), the collaboration between humans, robots, and technology (CbHTR), and Data-driven digital transformation (DDDT), in understanding the relationship between data-sharing platforms (SDA) and operational supply chain performance (OSCP) These technologies emphasize the importance of SDA in improving supply chain and operational performance This study posits that ILM, DDDT, and CbHTR function as intermediaries in the interaction between SDA activities and OSCP Specifically, DDDT utilizes SDA data for analysis and intelligent decision-making, while also automating manual processes to enhance accuracy and lower operational costs Consequently, SDA influences OSCP through the mediation of DDDT, leading to the formulation of the following hypothesis.
H8: DDDT mediates the relationship between SDA and OSCP
Supply chain data exchange, integration, and analysis are enhanced by Supply Data Analytics (SDA), which improves forecasting, streamlines workflows, and boosts responsiveness While humans play a crucial role in managing and operating robots and smart technologies, these systems can take over repetitive and hazardous tasks, operating continuously to enhance supply chain efficiency and accuracy Effective data utilization is essential for analysis, decision-making, process automation, and error management across human and robotic systems Based on these insights, this study proposes the following hypothesis.
H9: CbHTR mediates the relationship between SDA and OSCP
Identifying data sources is essential for effective logistics management, as it relies on various sensors, IoT devices, and machines that require input data Ensuring seamless data exchange between smart devices and machines is vital to minimize disruptions The secure and efficient implementation of smart logistics operations, facilitated by SDA, will enhance supply chain performance and operations Based on these discussions, this study proposes the following hypothesis:
H10: ILM mediates the relationship between SDA and OSCP
The proposed research model, illustrated in Figure 1, identifies five key factors influencing the study It features one independent variable, SDA, measured by a 5-item scale Additionally, three intermediate variables—DDDT with a 4-item scale, CbHTR with a 3-item scale, and ILM with a 5-item scale—impact the dependent variable, OSCP, which is assessed using a 10-item scale.
Figure 2.1 The Recommended Research Model
RESEARCH METHODOLOGY
Research process
—► and create a — ► research draft scale (n)
Processing data, performing analysis and concluding hypotheses
Research method
To assess the awareness of managers and directors in manufacturing enterprises about the importance of data and shared data platforms, a study was conducted in Ho Chi Minh City, known for its established businesses and diverse job opportunities The city has also become a key center for digital transformation in business operations and technology, making it an ideal location for the research This unique environment was expected to provide insights into the complex relationships among the factors being studied.
The authors utilized a norm sampling approach to obtain a comprehensive and representative sample, focusing on firms actively engaged in environmental initiatives This strategy aimed to include a diverse array of organizations that have proactively adopted environmentally conscious practices.
The significance of a larger sample size is underscored by the reduction of margin of error, which enhances the reliability of the findings In this research, the authors utilized factor analysis and linear structural model SEM for data analysis According to Hair (2019), an exploratory factor analysis (EFA) requires at least 50 participants, with a preference for even larger samples to ensure robust results.
For effective Structural Equation Modeling (SEM), it is crucial to maintain an observation ratio of at least 5:1 or ideally 10:1 for each variable, as recommended by Kline (2011) In this study, which examined seven variables, a total of 27 observed variables were utilized to fulfill the necessary sample size criteria Consequently, the research team determined that a minimum sample size of 270 participants was essential, employing a normative sampling method to achieve this requirement.
The study explores the impact of data-driven digital transformation and a shared data model on supply chain advancements, including technology, robotics, and intelligent logistics networks Engaging ten experienced managers from manufacturing firms in Ho Chi Minh City, the research team facilitated discussions through a carefully designed set of questions aimed at understanding the factors influencing long-term business performance Additionally, the author integrated existing research to identify and refine relevant observable factors, ensuring their alignment with the unique research context of Ho Chi Minh City.
The research team utilized established theoretical foundations to analyze data-driven approaches and shared data from a managerial perspective To ensure accuracy, they refined a measurement scale that included 27 observed variables Initially conducted overseas, the original research statements were in Vietnamese, prompting subsequent adjustments to align grammar, vocabulary, and cultural nuances with modern Vietnamese society This careful revision process led to the successful development of an official measurement scale.
The study utilized a quantitative research method to thoroughly examine and assess the applicability of the research model An online survey questionnaire was deployed, allowing for the efficient collection of high-quality data from a substantial sample size The survey was primarily conducted through the popular social media platform LinkedIn, which enabled the gathering of diverse demographic information, including participants aged 35 to over 55 years.
This study employed a detailed questionnaire featuring a 5-point Likert scale to evaluate the importance of factors identified through qualitative research The quantitative research sample was gathered using convenience sampling, a method that enables researchers to select easily accessible subjects for their study.
In one month, 620 surveys were distributed, yielding 466 responses After final filtering, 458 valid samples were included, achieving a utilization rate of 73.87%.
The research team utilized the partial least squares structural equation modeling (PLS-SEM) method to assess the proposed model, leveraging its effectiveness in estimating complex models with multiple latent and observable variables (Hair et al., 2019) For the evaluation of both the measurement and structural models in this study, the team employed SMARTPLS 3.3.2 software.
* Evaluation of the measurement model
• Assess the quality of the observed variable
The evaluation of the measurement model encompasses a pivotal preliminary stage that involves the assessment of observed variables' quality through factor loadings
According to Hair et al (2019), a factor loading coefficient of 0.708 or higher is crucial for determining the quality of an observed variable This threshold indicates that the latent variable explains more than 50% of the variation in the observed variable, demonstrating a bidirectional relationship between the two.
To evaluate the reliability of the scale, two key indices are utilized: Cronbach's Alpha and composite reliability (CR) According to Hair et al (2019), for the scale to be considered reliable, both coefficients should be equal to or greater than 0.7.
The Average Extracted Variance (AVE) of a latent variable is calculated by summing the squared mean load coefficients of its observable variables This coefficient serves as a key metric for assessing the convergence of observed variables An AVE value of 0.5 or higher indicates that the latent variable explains at least 50% of the variance in its observable variables, demonstrating an acceptable level of convergence for the measurement scale (Hair et al., 2019).
The Fornell-Larcker criteria, introduced in 1981, assess the discriminant validity of a scale through the Average Variance Extracted (AVE) Fornell and Larcker (1981) assert that for discriminant validity to be achieved, the square root of the AVE for each latent variable must be greater than the correlation coefficients between that variable and other factors in the model.
*Heterotrait-Monotrait Ratio of Correlations (HTMT)
The HTMT index offers a more thorough assessment of a scale's discriminant validity than the Fornell-Larcker criterion This index is calculated by taking the mean of inter-structure indicator correlations, known as heterotrait-heteromethod correlations, and dividing it by the mean of the measure correlations, as suggested by Henseler et al.
RESEARCH RESULTS
Descriptive statistics of the samples
A total of 620 surveys were sent, and 466 samples were received 45 8 valid samples were used (73.87%) once the final ready filtering results were obtained Table
Table 4.1 Descriptive statistics of the samples
Fast moving consumer goods (FMCG) 241 52.6
Measurement model analysis
To effectively evaluate the measurement model, it is essential to assess the scale's reliability and its convergent and discriminant validity Reliability can be measured using correlation coefficients, composite reliability (CR), and Cronbach's Alpha coefficient According to Hair et al (2019) and Nunally and Bernstein (1994), the scale utilized in this study demonstrates adequate reliability, ensuring the robustness of the findings.
Cronbach's Alpha coefficient exceeding 0.7, a correlation coefficient greater than 0.3, and a Composite Reliability (CR) above 0.7 indicate strong reliability in the data Table 3 presents the factor loadings, V1F, Cronbach's Alpha, CR, and Average Variance Extracted (AVE) The V1F coefficient serves as a tool to assess multicollinearity and common method bias in the analysis.
Table 4.2 Factor Loading, VIF, Cronbach’s Alpha, Composite Reliability (CR) and Average Variance Extract (AVE)
Convergent validity is confirmed through factor loading and average variance extracted (AVE) values, which exceed 0.7 and 0.5, respectively (Hair et al., 2014) Discriminant validity is assessed using the HTMT criterion and the Fornell and Larcker method, where it is established that the square root of the AVE must be greater than the correlation value Additionally, a HTMT value of less than 0.85 further validates discriminant validity, as indicated by Henseler et al.
(2016) Discriminant validity was acknowledged in this investigation, and tables 4 and
Table 4.3 Fornell and Larcker criterion
CbHTR DDDT ILM OSCP SDA
CbHTR DDDT ILM OSCP SDA
SRMR (Henseler et al., 2016), adjusted R2 (Heinzl & Miltlbock, 2002), and GoF
According to Tenenhaus et al (2004), several key indicators are essential for assessing the goodness of fit of a research model The findings reveal a GoF value of 0.501, which exceeds the "large" threshold established by Wetzels et al (2009) Additionally, the model demonstrates a strong alignment with real data, as indicated by an SRMR value of 0.045, which is below the 0.08 threshold set by Henseler et al (2016) These results are further supported by the criteria established by Falk and Miller.
(1992), the adjusted R2 values of CbHTR, DDDT, ILM, and OSCP were 0.308, 0.313,
0.334, and 0.544, respectively All of these values were higher than the acceptable limit of 0.1 The findings above demonstrate the excellent suitability of the proposed model.
Structure Model Analysis
Starting with assessing the model structure, the variance inflation factor (VIF) and
Harman's one test is essential for evaluating common method bias and multicollinearity issues As indicated by Hair et al (2014), the study's findings show no signs of multicollinearity, as evidenced by the VIF values ranging from 1.574 to 2.597, all of which are below the threshold of 3.3.
3 In addition, CMB is not seen when all the factors are examined Variance extracted within the same factor for observed variables has a value of 40.76% < 50% (Fuller et al., 2016).
The findings from the bootstrapping study assessing the structural model are detailed in Table 6, which includes key metrics such as path coefficients, t-statistics, p-values, and confidence ranges Additionally, Figure 2 illustrates the results of the SEM analysis following the application of the bootstrapping technique.
Table 4.5 structural model and hypothesis assessment
Hypot hesis Path Coefficic nt t- statisti cs p- value s
H3 SDA -> ILM 0.578 17.153 0.000 0.511 0.643 n/a Supported H4 SDA -> OSCP 0.147 2.723 0.007 0.039 0.248 n/a Supported
The results show that all hypotheses are supported Hypothesis Hl is supported with p = 0.5 60, t-statistics= 14.891 > 1.96, p-values = 0.000 < 0.05, similar to hypothesis
H2 with p = 0.5 5 5, t-statistics = 17.605 > 1.96, p-values = 0.000 < 0.05 and hypothesis
Recent findings indicate that SDA significantly influences DDDT, CbHTR, and ILM, with the most substantial effect observed on ILM The statistical analysis, including H3 with p = 0.5, l-statistics of 17.15 (greater than 1.96), and p-values of 0.000 (less than 0.05), confirms the positive relationship and supports the proposed hypotheses.
H4 (p = 0.147, t-statistics = 2.723 > 1.96, p-values = 0.007 < 0.05); H5 (P = 0.258, t- statistics = 6.620 > 1.96, p-values = 0.000 < 0.05); H6 (P = 0.267, t-statistics = 5.808 >
1.96, p-values = 0.000 < 0.05) and H7 (p = 0.257, t-statistics = 5.587 > 1.96, p-values 0.000 < 0.05) indicates that CbHTR has a strong positive impact stronger effect on
OSCP than DDDT then ILM and SDA.
The mediating relationship between SDA and OSCP through DDDT, CbHTR and ILM is also tested through hypotheses H8 (p = 0.144, t-statistics = 5.764 > 1.96, p-values
< 0.05, VAF = 50%) and H10 (p = 0.148, t-statistics = 5.250 > 1.96, p-values = 0.000 < 0.05, VAF = 50%) All hypotheses are accepted, demonstrating that there is an indirect relationship between SDA and OSCP through DDDT, CbHTR and ILM.
DISCUSSION AND IMPLICATIONS
Discussion
This article explores the impact of data-driven digital transformation on enhancing operational performance within the supply chain sector Our investigation reveals that manufacturing companies have successfully improved their operations through either partial or complete digital transformations A key finding highlights the importance of integrating a shared data platform with supply chain partners, which significantly boosts operational efficiency This analysis contributes valuable insights to the existing literature on digital transformation in supply chain management.
Firstly, our article simultaneously addresses the role of integrating data and digital transformation, an approach rarely examined in the operations management literature
We have adapted the concept of "data-driven digital transformation" from retail to the manufacturing sector, revealing that manufacturing companies effectively integrate data sources related to products and processes This integration fosters improvements in production processes, marking a significant advancement in operations management literature We suggest that maximizing data utilization for decision-making during this digital transformation is crucial for extracting valuable insights from both internal and external data, while also leveraging second-order knowledge to enhance machinery and operational procedures within the enterprise.
Our analysis strongly supports the architecture of shared data assets (SDA), highlighting that their operational benefits are rooted in effective communication with internal and supply chain partners The success of SDA implementation depends on a company's ability to manage and process data across its enterprise and supply chain Sharing information with supply chain partners enhances the timely flow of knowledge, boosting cost-effectiveness and fostering high-performance clusters for information sharing The integration of SDA is vital for companies to leverage knowledge within their production networks, as combining external knowledge assets with internal insights can significantly improve operational efficiency However, the effective exploitation of external knowledge requires the enterprise to proficiently manage and process that information, making the development of SDA essential for broadening the organization's knowledge base.
This study highlights the crucial intermediary role of intelligent logistics integration, data-driven digital transformation, and the HTR model in supply chains, emphasizing that human involvement is essential for strong interconnections It underscores the importance of combining human creativity with technological expertise for sustainable development, as seen in production processes where robots execute tasks, technology fosters connectivity, and humans refine operations based on data insights The findings reveal the significant relationship between humans, technology, and robots, exemplified by companies like Vinamilk and Vinfast, which achieve high operational performance through effective management of these relationships This integration not only enhances manufacturing efficiency but also offers substantial benefits to the healthcare sector, improving overall performance.
Implications
This article explores foundational theories in supply chain research, highlighting the need for a diverse theoretical approach as emphasized by Koot et al (2021) By adopting a multidisciplinary framework, it underscores the significance of analyzing data from various sources to facilitate transformative decision-making and optimize business processes Furthermore, it examines the influence of technology and data during the digital transformation era, expanding theoretical perspectives and emphasizing the critical role of human involvement in this evolving landscape.
We have leveraged theoretical frameworks to elucidate decision-making processes and develop business decision models, while also contributing to the theory of augmented intelligence, which highlights the synergy between human cognition and technological advancements to improve decision-making and problem-solving Although extensive research has been conducted on the role of data and digitization in supply chain processes, prior studies predominantly concentrated on Big Data, which pertains to the scale and diversity of data from various sources In contrast, Supply Chain Data Architecture (SDA) focuses on the organization and management of data within a system, emphasizing data sharing and management among network members This institutional perspective underscores the necessity for supply chains to prioritize the development of SDA to effectively engage top-level leadership in data initiatives.
This study highlights the critical intermediary role of data-sharing facilities in enhancing supply chain outcomes and emphasizes the importance of data analysis for effective digital transformation It reveals a lack of extensive research on the impact of these facilities in the context of evolving technologies, establishing a theoretical framework for understanding digital transformation, intelligent logistics, and the collaboration between humans, technology, and decision-making robots in production operations The findings underscore the necessity of utilizing data-sharing platforms to facilitate digital transformation, optimize logistics, and strengthen human-technology-robot interactions in manufacturing As global volatility increases, the research stresses the significance of developing data-sharing capabilities when engaging with various suppliers and stakeholders, thereby addressing a vital gap in the literature regarding the intersection of data, technology, and supply chain dynamics.
To enhance OSCP, businesses should establish a data-sharing platform integrated with tracking and delivery monitoring applications This initiative will enable efficient oversight of transportation and delivery processes, promoting transparency and collaboration among suppliers and customers Although the initial investment in a shared architecture may be high, it represents a strategic financial commitment that can yield significant long-term benefits Additionally, companies must adopt intelligent operating models, as the current emphasis on data and its users reflects the ongoing exploration of how to maximize data utilization through various technologies.
Leaders should embrace technological trends to enhance production processes and drive innovation It's essential to gather employee input to understand the current landscape before making significant decisions Utilizing the Vroom model for problem-solving will help leaders make informed choices based on data and resources, fostering a progressive digital transformation By empowering leaders with intelligent systems, companies can accelerate resource deployment and innovation, ultimately enhancing operational supply chain performance (OSCP) This approach not only promotes digital transformation but also improves intelligent logistics models, facilitating process improvements and ensuring seamless supply chain operations.
Lastly, in the relationship between robots, technology, and humans, humans play the role of the main brain in innovation and improvement of production processes.
Integrating technology and robots into business processes can effectively replace humans in strenuous, repetitive, and hazardous tasks, leading to enhanced efficiency and reduced risks However, to fully leverage the potential of these advancements, businesses must prioritize human development, as humans are the key creative and decision-making force in technology application and production management The use of Smart Data Analytics (SDA) is crucial for facilitating effective interaction among humans, robots, and technology, enabling data sharing and informed decision-making Humans utilize SDA to gather and analyze data, drawing on their knowledge and experience to make intelligent choices Additionally, humans are essential in designing intelligent production systems that robots can follow, as they possess the necessary understanding of business operations to define goals, set parameters, and monitor robotic performance, ensuring process quality and efficiency.
CONCLUSION, LIMITATIONS, AND FUTURE RESEARCH DIRECTIONS
Conclusion
This study evaluates the correlation between Shared Data Architecture (SDA) and Operational Supply Chain Performance (OSCP) in Vietnamese manufacturing businesses, focusing on three key aspects: Data-Driven Decision Making (DDDT), Information Lifecycle Management (ILM), and Collaborative Business High-Throughput Requirements (CbHTR) Amid digital transformation, companies are leveraging data and decision-making models to adapt to rapid changes, with SDA identified as an effective platform for enhancing DDDT The research indicates a positive impact of SDA on OSCP, revealing that DDDT, ILM, and CbHTR serve as intermediaries in the relationship between shared data architecture and supply chain performance This study significantly contributes to both theoretical and practical management insights for manufacturing businesses in Vietnam's supply chains, while expanding the literature on digital transformation and technology application in operations Furthermore, it explores the intricate relationships among humans, robots, and technology, emphasizing that humans are central to sustaining and innovating the entire process system The findings provide essential guidelines for manufacturing enterprises to identify and implement key technologies that drive growth within dynamic supply chains.
Limitations and the future research directions
This study presents significant scientific and practical contributions, yet it has several limitations that future research should address Primarily, the reliance on quantitative methods suggests the need for a broader range of research approaches to capture multidimensional insights on the topic Additionally, focusing on small and medium-sized enterprises in emerging economies, specifically Vietnam, highlights the necessity for further research across diverse economic contexts to better understand the implications of various policy options Moreover, conducting specialized studies could yield in-depth findings in specific areas Finally, future research should consider varying business sizes to facilitate comparisons between organizations.
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