The Role of Integrated Information Management Systems in the Relationship Between Product Lifecycle Management and Industry 4.0 Technologies and Market Performance

Abstract

This research explores the relationship between Product Lifecycle Management (PLM) and Industry 4.0 (I4.0) technologies with Integrated Information Management Systems (IIMS) and the impact on the Market Performance (MP) of organisations. A survey was conducted with 106 company managers with experience ranging from the strategic to the operational level of IIMS practices. The data were analysed quantitatively through Exploratory Factorial Analysis (EFA), Confirmatory Factorial Analysis (CFA), and Structural Equation Modelling (SEM). The results indicated that integrating IIMS, PLM, and I4.0 is crucial to improving the effectiveness of organisational processes. However, its direct impacts on MP are more moderate. This shows the need for companies to fully integrate IIMS with PLM and I4.0 technologies, taking advantage of the synergies observed between IoT, Automation, and AI to improve operational efficiency and information security. As for practical and sustainability implications, the research discusses the importance of data optimisation and process management, mediating impacts and investment strategies, training and organisational culture, strategic planning, and the efficient and responsible use of resources. The originality of this work is highlighted by its approach, considering the research context broadly and uniquely. SEM made this approach possible, where the structural model is evaluated entirely, resulting in how the constructs behave based on how they are modelled. In addition, the research contributes to expanding theoretical knowledge and studying the practical applications of the results in business policies.

1. Introduction

In the constant search for improving their Market Performance (MP), companies need high flexibility and responsiveness to the dynamics of the ecosystem in which they operate [1]. Since they work together, these business ecosystems are orchestrated by integrating companies from different areas and scopes with a common goal. Thus, implementing systemically integrated approaches to operations management provides companies with a strategic and global vision of the ecosystem in which they operate [2]. In this sense, Integrated Information Management Systems (IIMSs) play a fundamental role in improving MP. IIMS are information processing tools in organisations that ensure authentication, confidentiality, privacy, origin, and integrity in information systems [3]. IIMS associated with MP objectives contribute to improvements in productivity, competitiveness, and responsiveness [4].

Different theories and approaches illustrate how companies can optimise information management in a rapidly changing digital environment [5]. The adoption of Industry 4.0 (I4.0) technologies can improve product development strategies and Product Lifecycle Management (PLM) [6]. In addition, integrating PLM with IIMSs, combined with I4.0 technologies such as Big Data, has proven essential to drive smart and sustainable production [7,8].

PLM plays an important role in the development of innovation in interdepartmental collaboration, being a catalyst for information traceability [6]. Sharing information between the stakeholders involved in processes and projects improves interaction, accelerates the flow of information, and facilitates error detection [9]. Furthermore, integrating IIMS with PLM from the initial project and design stages has been shown to reduce manufacturing errors and generate significant cost savings [10]. In this sense, PLM complements IIMS so that portfolio management is carried out in the face of the challenges of each stage of the product lifecycle, promoting greater integration between these systems.

PLM and implementing I4.0 technologies can significantly change an organisation’s information flow. In addition, companies are also concerned about constantly becoming more resilient and capable of facing the challenges of a dynamic and flexible market. With these changes, and in this scenario, it is suggested that data and information management play an important role in demonstrating the relevance of IIMSs. Thus, it becomes necessary to understand the dynamics of this context from the perspective of data and information management, PLM, and the implementation of I4.0 technologies, with a view to the organisations’ MP.

In this sense, this research aims to explore the relationships between PLM and I4.0 technologies and the IIMS and their impact on organisations’ MPs.

This research seeks to contribute to the clarity in understanding and selecting the information technologies used, allowing organisations to improve their operational efficiency and achieve success in the market. As a theoretical contribution, this research explores and confirms constructs and compares existing theories, enabling an in-depth analysis of these. As a practical contribution, this research will enable companies to adopt new information management practices and technological innovation towards sustainable development. In addition, the research contributes from an environmental point of view, as it points to adopting strategies to reduce resource use.

2. Current Literature on Integrated Information Management Systems, Product Lifecycle Management, Industry 4.0, and Market Performance

A literature review was conducted using the Scopus and Web of Science databases to highlight the state of the art in IIMSs, PLM, I4.0, and MP. To seek an updated and conclusive perception on the topics, highlighting the post-pandemic situation, only articles were considered, and the period considered was from 2021 to the present.

To search the databases on the topic IIMSs, the terms used were “integrated AND information AND system” and “integrated AND information AND model”. To search the databases on the topic PLM, the terms used were “product AND life AND cycle AND management”, “plm”, and “life AND cycle AND management”. To Search the databases on the topic I4.0, the terms used were “industry 4.0”, “smart AND manufacturing”, and “smart AND factory”. And for the MP theme, the terms used were “Market AND Performance” and “performance AND assessment”.

Initially, searches were performed in the databases with the four terms together using the AND operator, and no articles were found. Afterwards, searches were performed with combinations referring to three themes, using the AND operator, totalling four searches with different combinations.

For the combination of IIMS, PLM, and I4.0, 21 articles were found in the sum of the databases. For the combination of IIMS, PLM, and MP, 35 articles were found in the sum of the databases. For the combination IIMS, I4.0, and MP, 26 articles were found in the sum of the databases. Finally, for this literature review, we searched for articles that address the theme IIMS together with at least two others; therefore, the combination PLM, I4.0, and MP was not performed because it does not cover the theme IIMS.

The 82 articles were read to analyse and verify adherence to the theme. The inclusion criterion used was to address the IIMS theme, establish direct relationships with at least two of the other themes, discuss these relationships, and provide relevant information for analysis and construction of hypotheses.

Table 1. Current literature on IIMS, PLM, I4.0, and MP.

Authors	IIMS	PLM	I4.0	MP	Contribution
[11]	X	X		X	Provides insights into the use of modern technologies for PLM and IIMS, aligning with the principles of I4.0.
[12]	X	X		X	Compares conventional and organic farming methods, highlighting more sustainable options and their environmental impacts.
[13]	X	X	X		The Systems of Systems Lifecycle Management concept complements the management of complex and interoperable systems, aligning with I4.0 and IoT to promote more efficient and sustainable strategies.
[14]	X	X	X		Addresses the application of the Digital Twin to improve PLM in the AECO-FM sector, reinforcing the importance of an integrated and holistic approach to the management of information and technologies in I4.0.
[15]	X		X	X	Highlights the relevance of integrating business processes with ERP systems, essential for the development of an IIMS framework in I4.0 and shows how technology can improve organisational performance.
[16]	X	X		X	Introduces spatial variability in the assessment of costs and environmental effectiveness, offering insights into how local conditions affect mitigation measures. This aligns with the development of an IIMS framework, providing a valuable perspective to optimise investment decisions and improve the sustainability of production systems.
[17]	X		X	X	Provides a technological solution for PHM education in I4.0, using XRepo 2.0 to process sensor data. Facilitates the development of technical skills and the integration of advanced technologies in information management and educational processes.
[18]	X		X	X	Demonstrates the use of advanced simulations to assess and mitigate the impacts of catastrophic events, providing a useful model for the resilience of systems and structures in I4.0 and IIMS.
[19]	X		X	X	Provides a quantitative approach to assess smart technologies in urban contexts, offering insights into the integration and assessment of emerging technologies in its framework and the economic viability of implementation.
[20]	X	X		X	Highlights how digitalisation and the use of Digital Twin platforms can optimise processes and reduce costs in the photovoltaic sector, illustrating the application of advanced technologies for efficient and sustainable management, aligning with I4.0 trends.
[21]	X	X		X	Explores the application of LCA to assess the sustainability of integrated sanitation systems, providing insights into how improvements can reduce the environmental footprint. Highlights the importance of systematic methodologies and databases for environmental management in infrastructure, complementing the IOPT perspective by demonstrating how LCA can inform decisions about complex systems and sustainable practices.
[22]	X	X	X		Demonstrates the application of Lifecycle Assessment (LCA) to promote sustainability in the management of complex systems, aligning with the objective of integrating I4.0 technologies for efficient and sustainable management of the product lifecycle.
[23]	X	X	X		Illustrates the practical application of I4.0 concepts to improve business processes and highlights data integration and evidence-based decision-making, which are fundamental to IIMS in PLM.
[24]	X	X		X	Highlights how sustainable technologies and practices can improve economic and environmental performance in production systems, aligning with the development of an integrated framework that uses I4.0 to optimise PLM.
[25]	X		X	X	Provides a practical model based on KPIs and a fuzzy approach for risk assessment in the implementation of I4.0 technologies, offering insights into risk management and prioritisation during the transition to I4.0. The analysis of the main risks and decision-making techniques contribute to understanding the challenges in adopting new technologies and practices, helping to develop frameworks for IIMS and system optimisation.
[26]	X		X	X	Presents a solution for interoperability and data integration in IoT systems, using OPC UA and REST-based middleware to promote efficient communication and remote process supervision.
[27]	X		X	X	Provides insights into thermal management in e-textiles, using thermography and mathematical modelling. Essential for optimising the reliability and sustainability of integrated technologies, improving the design and performance of intelligent systems.
[28]	X	X		X	Identifies barriers to the adoption of sustainable and integrated systems, offering insights into challenges and strategies to overcome obstacles in the implementation of sustainable practices, relevant to promoting the adoption of efficient systems in manufacturing.
[29]	X	X		X	Provides a detailed analysis of the environmental impacts of aquaculture, highlighting the importance of environmental assessments and offering practical recommendations to improve sustainable performance, which can contribute to the development of more efficient and sustainable practices in emerging industries.
[30]	X		X	X	Highlights the use of digital technologies in monitoring psychological conditions and their contribution to organisational efficiency and well-being. Relevant for sustainable models when integrating health and performance analysis.
[31]	X		X	X	Shows how technological innovations improve operational efficiency and communication in health, supporting decision-making and information management, aligning with data integration and complexity management in OIPT.
[32]	X	X	X		Highlights the importance of Digital Twins in the automation of connected vehicles, promoting model reuse and data integration. Essential to understand how the evolution of digital models can transform information management, aligning with IIMS and I4.0.
[33]	X	X		X	Offers insights into the integration of innovations in the construction process and how these innovations can be operationalised throughout the project lifecycle, aligning with the development of an IIMS framework in I4.0.
[34]	X		X	X	Explores the application of I4.0 technologies in the manufacturing of nanocomposites for water treatment, offering insights into the integration of emerging technologies for efficiency and sustainability.
[35]	X	X	X		Shows how the integration of BIM with sensor systems improves the management and operational efficiency of facilities. Offers insights into the integration of I4.0 technologies to optimise management throughout PLM, relevant to the IIMS framework and its impact on MP.
[36]	X		X	X	Provides insights into how information models and quantitative assessment can be applied to manage the complexity and flexibility of PLM, informing the development of its integrated framework.
[4]	X		X	X	Proposes a framework for improving innovative behaviour in SMEs through the integration of BI and knowledge sharing. Offers insights to optimise decision-making and organisational performance, essential for IIMS throughout PLM.
[37]	X		X	X	Shows how to integrate strategic flexibility into information management and PLM in contexts of rapid change and uncertainty, relevant to organisational performance and I4.0 technologies.
[38]	X	X		X	Illustrates the integration of information systems with Lifecycle Assessments to optimise performance and sustainability, aligning with the use of advanced technologies and integrated systems in product and information management.
[39]	X	X	X		The integration of detailed feedback and sentiment analysis enhances the personalisation and efficiency of recommender systems, promoting a collaborative and user-centric environment, aligned with the evolution towards Industry 5.0.
[40]	X	X		X	Highlights the application of LCA and information modelling in sustainable building renovation management, aligning with the integration of technologies throughout PLM in I4.0.
[41]	X	X		X	Provides insights into cost management throughout the lifecycle of complex products, such as aircraft, aligning with IIMS and the application of emerging technologies to increase efficiency.
[42]	X	X	X		The knowledge graph-based configuration optimises processes in information management systems and product personalisation, aligning with the integration of I4.0 technologies to improve efficiency and adaptability.
[43]	X	X		X	Shows how the integration of BIM and LCA can optimise the selection of heating systems, improving environmental impact assessment and technical performance, which are essential for sustainable design and reducing the environmental footprint of buildings.
[44]	X	X		X	Provides insights into how the integration of I4.0 technologies can improve environmental and energy efficiency in anaerobic digestion processes, aligning with the development of frameworks for PLM and the holistic approach of OIPT.
[45]	X		X	X	Explores the integration of human capabilities into digital systems, highlighting how the combination of human and technological resources can improve efficiency and operations management.
[46]	X	X		X	Offers a critical view on weighting methods in LCA, enriching the framework with a more comprehensive approach to sustainability and improving environmental assessment in PLM.
[47]	X		X	X	Illustrates how digital supplier selection can optimise supply chain management and business performance, using advanced multi-criteria decision methods, aligning with integration and automation in I4.0.
[48]	X	X		X	Highlights how the centralised mode of bioenergy can improve environmental sustainability and economic viability in waste management in pig farming. Provides valuable insights for optimising sustainable agricultural processes and practices, aligning with the integration of information management and advanced technologies in PLM.
[49]	X	X		X	Provides a robust framework for evaluating advanced materials, combining environmental and economic analysis. The methodology is useful for assessing the impact of emerging technologies and new materials on PLM, essential for the development of an IIMS framework. The use of graphical tools for integrated comparisons can also improve data visualisation and communication in I4.0.
[50]	X	X		X	Offers a practical approach to integrating advanced digital technologies into asset management, providing insights into optimisation, collaboration, and cost and risk reduction.

provides a comprehensive overview of this literature review, highlighting the themes discussed and the real contributions of each article.

Data and information management must face the challenges posed by the complexity and dynamism of modern business environments. Adapting to rapid changes and integrating new systems and technologies are critical to the success of IIMS [42]. The ability to deal with these complexities and implement effective data management solutions is essential to ensuring organisations’ competitiveness and resilience [13].

The existing literature provides a solid foundation for understanding IIMS and its interactions with PLM and I4.0. However, it is possible to observe an important research gap, where the themes are studied in an integrated approach, considering them together in a context aiming at continuous improvement in MP. In this sense, this research seeks to fill this gap using a qualitative-quantitative approach and statistically testing through the Structural Modelling Equation (SEM) the integration of the themes with a structural model based on the pre-existing theory. With this, it is possible to demonstrate the behaviour of PLM, I4.0, IIMS, and MP from a holistic view in a real market scenario.

3. Hypotheses Development

Following the context presented, our first analysis places IIMS as a central pillar and variable of PLM and I4.0, placing these as independent variables. Thus, we seek to analyse the behaviour of IIMS and MP with this scenario.

3.1. Industry 4.0, Integrated Information Management Systems, and Market Performance

The literature review identified that I4.0, by integrating technologies such as IoT and AI, has strengthened IIMS, increasing efficiency and facilitating real-time decision-making, contributing to more robust MP [26,42]. This integration of smart technologies has significantly enhanced the capacity of IIMS, providing more accurate data and agile processes essential for informed decision-making [25]. It was also possible to identify that I4.0 has increased organisational accuracy and competitiveness by improving IIMS operations [4]. Building upon this context, we formulate the following hypothesis:

H1: 

There are positive effects of I4.0 technologies on IIMS and MP.

3.2. Product Lifecycle Management, Integrated Information Management Systems, and Market Performance

Another interesting perspective identified by the literature review was that PLM increased the efficiency of IIMS by enabling better integration and sharing of information throughout the product lifecycle [14]. This is because PLM strengthened coordination and agility in IIMS by facilitating continuous data management, providing a competitive advantage and superior MP [23]. PLM also effectively integrated product information, strengthening the structure and effectiveness of IIMS [36]. Based on these considerations, we formulate the following hypothesis:

H2: 

PLM has positive effects on IIMS and MP.

Figure 1 shows the conceptual structure for Hypotheses 1 and 2.

3.3. Mediating or Moderating Effect of I4.0 on the Relation Between PLM and IIMS on MP

The literature indicates that I4.0 has amplified the benefits of PLM by increasing its integration with IIMS [22]. In the context of I4.0, technologies such as IoT and AI have improved the efficiency of PLM, creating a synergy between PLM and IIMS, resulting in better operational results [35]. In addition, I4.0 has enhanced the role of PLM by intensifying data integration, which has resulted in more effective decisions and superior MP [42]. Thus, for hypothesis H3, we elaborate the conceptual framework in Figure 2.

Based on these considerations, we formulate the following hypothesis:

H3: 

I4.0 positively mediates or moderates the relation between PLM and IIMS to achieve MP.

3.4. Mediating or Moderating Effect of PLM on the Relation Between I4.0 and IIMS on MP

PLM facilitates the effective application of I4.0 technologies within IIMS, promoting more integrated and complete information management and a more robust MP [23]. In addition, it was also possible to identify that PLM can act as a mediator between I4.0 and IIMS, enhancing the benefits of this technological integration [22]. Thus, for Hypothesis H4, we elaborated the conceptual framework of Figure 3.

Consequently, we propose the following hypothesis:

H4: 

PLM positively mediates or moderates the relation between I4.0 and IIMS to achieve MP.

4. Research Development

The development of this research was based on a qualitative and quantitative approach that guided the collection, analysis, and interpretation of the results. Data was collected through a survey due to the need for quantitative analysis to rigorously evaluate our hypotheses based on a target population formed by managers of companies responsible for IIMS. Within this scope, we applied Exploratory Factorial Analysis (EFA) and Confirmatory Factorial Analysis (CFA) to identify and reduce the dimensions of variables. Afterwards, SEM was applied to perform the associations and analyse the hypotheses. Analytically, we performed two evaluation stages in SEM, the first to examine the interrelations of the variables [51] and the second to examine the effects of mediation and moderation, deepening the research analysis [52].

4.1. Sampling

For this research, a survey was conducted with directors, managers, supervisors, and consultants with experience covering strategic, tactical, and operational levels, providing a complete overview of the challenges and practices in IIMS. The research was conducted anonymously, and respondents did not need to provide any personal information. The first question answered was a statement of informed consent for participation, where participants declared their consent to participate in the research. Data was collected from small to large companies of different sizes. Data collection took place in October 2024, obtaining 106 responses.

The research instrument was divided into four groups of questions: 25 questions for IIMS, 20 questions for PLM, 20 questions for I4.0, and 18 questions for MP.

In the IIMS questions, the degree of use of IIMS in each topic was measured considering the scale from 0—Does not use to 4—Fully uses. The level of interference of IIMS with PLM was measured for PLM, considering the scale from 0—No interference to 4—Constant interference of IIMS with PLM. I4.0: the level of IIMS interference in applying I4.0 technologies was measured, considering the scale from 0—No interference to 4—Constant interference. For MP, it was assessed whether there is IIMS interference on MP (Yes—1 or No—0). The respondents’ characteristics were also collected and used as control variables in binary evaluations, such as position in the company, education, length of experience, and company size. The responses were examined to eliminate outliers, that is, those that did not adequately and comprehensively address all the issues. [51].

4.2. Survey Instrument

The questionnaire was developed using consolidated constructs: IIMS, PLM, I4.0, and MP. The IIMS construct was subdivided into authentication, confidentiality, privacy, provenance, and integrity, aspects aligned with the analysis of competitiveness and business performance, as discussed by Schaefer et al. [53]. The PLM construct was subdivided into introduction, growth, maturity, and decline, as addressed in research on evaluating the success of management systems, such as the model proposed by Liu and Chen [54]. The I4.0 construct was subdivided into IoT, Automation, Artificial Intelligence, and Cloud Computing, corroborating studies on digital transformation and its impact on the environmental performance of supply chains, as suggested by Lerman et al. [55]. Finally, the MP construct was subdivided into Productivity, Responsiveness, and Competitiveness.

Table 2. Measurement validation.

Construct Element	Variable	Variable	EFA—Factor Loadings	CFA—Factor Loadings	CFA—Error Variance	CFA Measures
IIMS—Integrity	VAR01	Verifying the identity of the user or system attempting to access a resource	0.699	0.7869645	0.3806868	RMSEA—0.089 CFI—0.987 TLI—0.979 AVE—0.750 Alpha—0.9468 CR—0.9473
	VAR02	Validating and protecting the identity of users in information systems	0.619	Removed	Removed
	VAR21	Implementation of mechanisms to guarantee the information non-repudiation and the traceability of the actions	0.777	0.8750153	0.2343483
	VAR22	Maintenance of a system of registration or log of all activities performed	0.805	0.8920751	0.204202
	VAR23	Policies or procedures to preserve digital evidence in the event of legal disputes	0.726	0.9125343	0.1672811
	VAR24	Treatment of records and digital signatures	0.654	0.8361371	0.3008748
	VAR25	Adoption of controls and protocols to prevent the unauthorised manipulation or deletion of records or logs	0.678	0.8890158	0.2096509
IIMS—Provenance	VAR03	Adopting robust authentication methods, such as two-factor authentication	0.501	0.7488319	0.4392508	RMSEA—0.095 CFI—0.986 TLI—0.977 AVE—0.908 Alpha—0.948 CR—0.9555
	VAR16	Permission to verify where data and resources originated	0.720	0.8509173	0.2759397
	VAR17	Permission to track how data and resources have been modified or processed over time	0.848	0.8991795	0.1914763
	VAR18	Current ownership of data and resources	0.833	0.9122964	0.1677153
	VAR19	Ensuring verification of who has had access to data and resources	0.752	0.9021847	0.1860627
	VAR20	Permission to track how data and resources have been used in different parts of the network	0.771	0.8941641	0.2004706
IIMS—Authentication	VAR04	Granting appropriate permissions for users to access a specific resource	0.594	Removed	Removed	RMSEA—0.075 CFI—0.993 TLI—0.986 AVE—0.733 Alpha—0.9317 CR—0.93206
	VAR05	Establishing authorisations and access restrictions	0.615	Removed	Removed
	VAR06	Classification of confidential information or data	0.584	0.8152095	0.3354335
	VAR07	Permission to access confidential information	0.612	Removed	Removed
	VAR08	Establishment of protocols that prevent confidential information from being manipulated or intercepted by third parties	0.638	0.878864	0.2275981
	VAR09	Secure disposal of confidential information	0.645	Removed	Removed
	VAR10	Control and monitoring of information over time	0.605	0.8744643	0.2353122
	VAR11	Informing the user which personal data is being collected and stored	0.749	0.8322043	0.3074359
	VAR12	Allowing the user to know who has access to the information collected	0.739	Removed	Removed
	VAR13	Guaranteeing transparency to the user regarding the purposes for which the information collected is used	0.784	Removed	Removed
	VAR14	Allowing the user to control the sharing of their information	0.712	Removed	Removed
	VAR15	Informing the user of the policies and regulations on how their rights are protected	0.802	0.8781872	0.2287872
IIMS interference on PLM Introduction	VAR26	Sharing relevant information and data when introducing the product	0.748	0.8830942	0.2201446	RMSEA—0.075 CFI—0.993 TLI—0.988 AVE—0.771 Alpha—0.9317 CR—0.9525
	VAR27	Conducting market studies before producing your products	0.812	0.9261034	0.1423325
	VAR28	Conducting preliminary research on the products you want to manufacture	0.842	0.9317065	0.131923
	VAR29	Developing product and process diversification projects	0.819	0.9201888	0.1532525
	VAR30	Conducting environmental impact studies related to your production	0.741	0.8464296	0.283557
	VAR31	Development of growth studies for your products during the manufacturing process	0.727	Removed	Removed
	VAR32	Implementation of monitoring plans integrated with the SGII	0.635	Removed	Removed
	VAR33	Investment in technology aligned with the SGII to improve the growth of your products	0.814	Removed	Removed
	VAR35	Development of practices to improve and accept your products in the market during growth	0.718	0.7452543	0.4445961
IIMS interference on PLM Maturity	VAR34	Implementation of material reuse projects during the growth phase	0.644	0.8036853	0.3540899	RMSEA—0.084 CFI—0.988 TLI—0.982 AVE—0.789 Alpha—0.9621 CR—0.96306
	VAR36	Search for partnerships in the market to keep your products in the maturity phase	0.736	0.9139678	0.1646629
	VAR37	Development of innovation practices seeking to guarantee the maturity of your product in the market	0.712	Removed	Removed
	VAR38	Conducting productivity studies of your products in the maturity phase	0.779	0.9509515	0.0956912
	VAR39	Conducting studies to improve products and processes during the maturity phase	0.748	0.937634	0.1208425
	VAR40	Establishing partnerships that aim to guarantee the availability of qualified labour during the maturity phase	0.808	0.925719	0.1430443
	VAR41	Implementation of practices for withdrawing products from the market	0.804	0.8073125	0.3482465
	VAR42	Analysis of the lifecycle of your products after the end of their cycle	0.853	Removed	Removed
	VAR43	Development of techniques to prepare for the next generation of products when there is low demand in the market	0.803	Removed	Removed
	VAR44	Preparation of the production environment for the launch of new products	0.784	0.8659285	0.2501678
	VAR45	Adoption of different production techniques during the decline phase of your products	0.790	Removed	Removed
IIMS interference on I4.0—IoT	VAR46	Application of sensors and connected devices to collect real-time data on the production line	0.841	0.9682911	0.0624123	RMSEA—0.086 CFI—0.996 TLI—0.989 AVE—0.823 Alpha—0.9482 CR—0.9489
	VAR47	Use of the Internet of Things to remotely monitor and control industrial equipment and processes	0.769	0.8669021	0.2484807
	VAR48	Use of RFID (Radio Frequency Identification) technology to track and manage assets in the production chain	0.801	0.9020688	0.1862719
	VAR49	Integration of inventory and production control systems with IoT sensors to optimise resource management	0.761	Removed	Removed
	VAR50	Applying efforts to explore IoT application opportunities to improve efficiency and safety in operations	0.746	0.8886428	0.210314
IIMS interference on I4.0—Automation	VAR51	Use of automation systems to control and optimise production processes	0.665	0.8848284	0.2170786	RMSEA—0.094 CFI—0.985 TLI—0.975 AVE—0.731 Alpha—0.9413 CR—0.9420
	VAR52	Use of robots and automated machines to perform repetitive and low-value-added tasks	0.751	0.872101	0.2394398
	VAR53	Adoption of automated control and monitoring systems to ensure product quality	0.712	0.9212256	0.1513434
	VAR54	Application of automation in your logistics and storage processes	0.605	0.8952624	0.1985052
	VAR55	Search for automation opportunities in new areas of your business	0.632	Removed	Removed
	VAR56	Using machine learning algorithms for demand analysis and forecasting	0.777	0.7585294	0.4246332
	VAR57	Implementing chatbots or virtual assistants to interact with customers	0.716	Removed	Removed
	VAR58	Applying AI to optimise scheduling and resource allocation in your production	0.823	Removed	Removed
	VAR59	Using computer vision systems for automated inspection and quality control	0.762	0.787117	0.3804469
	VAR60	Using AI in advanced data analysis to identify insights and opportunities for improvement	0.818	Removed	Removed
IIMS interference on I4.0—Cloud	VAR61	Using cloud storage services to securely share data	0.886	0.8932597	0.2020871	RMSEA—0.235 CFI—0.978 TLI—0.935 AVE—0.865 Alpha—0.9614 CR—0.96237
	VAR62	Hosting applications and systems in the cloud to ensure availability and scalability	0.881	0.9804021	0.0388117
	VAR63	Using Cloud Computing services for high-performance data processing	0.832	0.9470244	0.1031448
	VAR64	Adopting cloud-based data backup and recovery solutions	0.886	Removed	Removed
	VAR65	Using cloud-based collaboration platforms to improve internal communication and collaboration	0.847	0.8965861	0.1961333
IIMS interference on MP—Productivity	VAR66	Effect on company productivity	0.812	0.8543626	0.2700646	RMSEA—0.086 CFI—0.986 TLI—0.972 AVE—0.615 Alpha—0.9413 CR—0.88798
	VAR67	Significant contribution to production efficiency	0.630	Removed	Removed
	VAR68	Change in the productivity of its processes	0.741	0.7950155	0.3679503
	VAR69	Direct impact on the optimisation of the company’s production resources	0.754	0.6888882	0.5254331
	VAR70	Avoids failures or errors in the production system	0.667	0.7392386	0.4535263
	VAR71	Existence of a correlation between the adoption of SGII and the change in productivity	0.712	0.8307619	0.3098347
IIMS interference on MP—Responsiveness	VAR72	Contribution to agility and responsiveness to market demands	0.757	0.849474	0.2783939	RMSEA—0.076 CFI—0.987 TLI—0.974 AVE—0.731 Alpha—0.8466 CR—0.9420
	VAR73	Allows the company to be responsive to market changes	0.759	Removed	Removed
	VAR74	Demonstration of the ability to adapt and respond to customer needs	0.711	0.8924634	0.2035091
	VAR75	Provides an efficient flow of information	0.600	0.7012303	0.5082761
	VAR80	Contributes to the control and development of human resources	0.545	0.5108994	0.7389818
	VAR82	Allows the identification of market opportunities and the development of competitive and effective strategies	0.737	0.6664024	0.5559079
	VAR83	Plays a fundamental role in improving the company’s competitive position	0.793	Removed	Removed
IIMS interference on MP—Competitiveness	VAR76	Allows for agile and accurate decision-making	0.558	0.7142274	0.4898792	RMSEA—0 CFI—1.0 TLI—1.019 AVE—0.535 Alpha—0.9614 CR—0.85045
	VAR77	Changes competitive responsiveness	0.601	0.7441114	0.4462982
	VAR78	Has an impact on the company’s productive competitiveness	0.641	0.8538926	0.2708674
	VAR79	Results in financial advantage	0.689	0.5941861	0.6469428
	VAR81	Has an impact on environmental controls	0.777	0.7284681	0.4693342

presents the questions and factor loadings.

4.3. Exploratory Factorial Analysis

EFA was performed with the aid of IBM^® SPSS Statistics software version 30.0, which is widely used to identify the underlying structure of the data and verify the validity of the initial construct [56]. This analysis aimed to identify the latent variables underlying the constructs investigated in the IIMS context.

The IIMS construct was theoretically suggested with the cluster’s authentication, confidentiality, privacy, provenance, and integrity. However, with the EFA, it was possible to observe the grouping of the variables into only three clusters: integrity, provenance, and authentication. These findings highlight the relevance of secure and private data management in organisational performance. The PLM construct was initially subdivided into introduction, growth, maturity, and decline. With the application of the EFA, there was grouping into only two clusters, introduction and maturity, demonstrating that the impact of the growth and decline phases was distributed in the other two clusters due to external factors, such as the adoption of new technologies or changes in the market. The I4.0 construct was initially proposed with subdivisions into IoT, Automation, Artificial Intelligence, and Cloud Computing, and with the EFA, there was grouping into only three clusters: IoT, Automation, and Cloud Computing. Finally, for the MP, the three proposed clusters, Productivity, Responsiveness, and Competitiveness, were maintained after the EFA. Table 2 shows all these clusters, the variables, and EFA factor loadings.

4.4. Confirmatory Factorial Analysis

A CFA was conducted to assess unidimensionality. The model demonstrated a good fit, as the reference values for the Comparative Fit Index (CFI), Root Mean Square Error of Approximation (RMSEA), Average Variance Extracted (AVE), Composite Reliability (CR), and Cronbach’s Alpha were within acceptable limits [51], as presented in Table 2. Ensuring consistency in CFA requires careful consideration of the following metrics: RMSEA, CFI, Tucker–Lewis Index (TLI), AVE, Cronbach’s Alpha, CR, and Factor Loading. Thus, the CFA was performed considering the results of the EFA and the clusters formed from it. Thus, the clusters of the IIMS, PLM, I4.0, and MP constructs were tested.

We began the analysis with the IIMS construct, where in the integrity cluster, VAR02 was removed because it negatively affected the RMSEA value, compromising the adequacy of the model. The quality metrics for this cluster suggest a good fit of the model to the data. In the provenance cluster, all variables were maintained, since they presented a strong load on the latent factor, which was a good indicator for the construct. For the authentication construct, the variables VAR04, VAR05, VAR07, VAR09, VAR12, VAR13, and VAR14 were removed from the model, since they negatively influenced the RMSEA value, compromising the fit of the model. With the configuration, the variables are explained by the latent factor, with an acceptable margin of error.

Regarding the PLM construct, VAR31, VAR32, and VAR33 were removed from the model in the introduction cluster. Thus, the RMSEA reached a value below 0.08, and the CFI and TLI were high, demonstrating that the model fits the data. The results validate the structure of the construct and confirm the relationship between the observed variables. In the maturity cluster, the variables VAR37, VAR42, VAR43, and VAR45 were removed due to the negative impact on the RMSEA. Thus, the model can be adjusted with consistent quality metrics.

Considering the I4.0 construct, the variable VAR49 was removed from the model in the IoT cluster. Thus, the results demonstrated the validity of the cluster, reflecting its strong connection to the IoT concept in the context of I4.0. Regarding the automation cluster, the variables VAR55, VAR57, VAR58, and VAR60 were removed from the model. VAR56 had a variance error of 0.4246 in this cluster, with a larger unexplained variance. The RMSEA of 0.094 was slightly above 0.08, and the AVE of 0.731 indicates that the latent factor automation explains a significant portion of the observed variances. In the Cloud Computing cluster, only VAR64 was removed from the model, and the variance errors were below 0.2021, demonstrating that the variables effectively explain the variance of the latent factor. The RMSEA of 0.235 indicates that the model needs improvements. Still, since the other indices achieved good results, it can be suggested that the cluster has the potential to represent the latent construct based on additional improvements.

Regarding the MP construct, VAR67 was removed from the model in the productivity cluster. It is observed that VAR69 has a lower load, contributing less to the latent factor, and, together with the error of 0.5254, this variable does not explain the cluster as the others do. However, the other results suggest that the cluster has adequate representation in the productivity construct, although further adjustments to the model are possible. VAR73 and VAR83 were removed from the responsiveness cluster due to their negative effect on the RMSEA. VAR80 had a lower load, generating less contribution to explaining the latent construct. The results suggest a good overall fit, but the variability in the factor loadings and error variances indicates room for adjustments in the cluster. Finally, the competitiveness cluster revealed an RMSEA of 0, an unusual fact that suggests that the model is not fully adjusted to the data. This value would represent a perfect fit, but it may also represent a model that is not correctly capturing the relationships between the variables. The CFI and TLI also reinforce this indication of overfitting or incorrect specifications. In addition, the low AVE also suggests that it is impossible to categorise this cluster’s validity in the construct.

4.5. PLS-SEM Results

SmartPLS 4 was chosen for hypothesis analysis because it specialises in Partial Least Squares-Structural Equation Modelling (PLS-SEM), which is ideal for moderated samples and complex models [57]. It stands out for its ease in working with latent variables, allowing the estimation of direct and indirect relationships, and mediating or moderating effects between constructs. In addition, its graphical interface simplifies the interpretation and presentation of results, and the PLS-SEM method is robust to non-normal data and small sample sizes.

First, analysing the measurement model, the composite reliability ranged from 0.940 to 0.979, results considered undesirable to problematic, suggesting the need for adjustments to the model. The AVE of the constructs can be considered satisfactory, since values above 0.5 suggest that the indicators explain a significant amount of variance in the latent variable, indicating good convergent validity.

Table 3. Reliability and composite reliability.

	Cronbach Alpha	Composite Reliability (rho_a)	Composite Reliability (rho_c)	Average Variance Extracted (AVE)
MP	0.932	0.944	0.940	0.500
I4.0	0.961	0.963	0.965	0.666
PLM	0.977	0.978	0.979	0.746
IIMS	0.971	0.972	0.974	0.685

shows the values for reliability and convergent validity.

Regarding the Heterotrait–Monotrait Ratio (HTMT), the values obtained were satisfactory as they were below 0.90, as Henseler et al. [58] indicated for constructs that may have some theoretical similarity.

Table 4. Discriminant validity: Heterotrait–Monotrait Ratio.

	MP	I4.0	PLM
I4.0	0338	-	-
PLM	0.353	0.885	-
IIMS	0.371	0.695	0.718

shows the HTMT Ratios.

Regarding the path coefficients of the structural model (Figure 4), it is possible to observe that the path between I4.0 and MP presents a relationship of 0.039. In contrast, the path in which the IIMS is the medium between I4.0 and MP presents a relationship of 0.277 added to 0.100, resulting in 0.377. The direct relationship between PLM and MP is 0.042, while the path in which the IIMS is the medium between PLM and MP presents a relationship of 0.442 added to 0.100, resulting in 0.542. These results demonstrate the important role of the IIMS as a connecting link between I4.0 and PLM with MP.

The results for the R² value for the endogenous constructs of the structural model were 0.152 for MP and 0.527 for IIMS, while the adjusted R2 was 0.127 for MP and 0.513 for IIMS. Therefore, MP has a weak explanation, while IIMS has a moderate explanation, following the proposal of Hair et al. [51].

The R² of IIMS indicates that the model moderately explains the behaviour of IIMS. This value reflects the relative maturity of organisational practices related to IIMS. The literature, such as Tatiparti et al. [59], suggests that IIMS is already widely adopted in industries that recognise the strategic value of systems and data integration. The R² of MP reveals a more limited explanation, indicating that MP depends on external factors, such as innovation, strategic positioning, and industry dynamics. As noted by Wu et al. [49], the impact of management technologies on organisational performance is often indirect, mediated by organisational practices, operational efficiency, and market response. This indirect reflection also occurs concerning sustainable practices, since the efficient and responsible use of resources and waste reduction, especially from management practices, occur continuously as IIMS are implemented in organisations.

The total effects matrix, resulting from the sum of path coefficients and indirect effects, is presented in

Table 5. Total effects matrix.

	MP	IIMS
I4.0	0.066	0.277
PLM	0.087	0.442
IIMS	0.100	-
I4.0 > PLM-IIMS	0.008	0.011
PLM > I4.0-IIMS	0.044	0.075

.

Table 6. f² matrix.

	MP	IIMS
I4.0	0.002	0.042
PLM	0.003	0.101
IIMS	0.031	-
I4.0 > PLM	-	0.011
PLM > I4.0	-	0.011

shows the f² effects matrix.

From Table 6, the influence of the I4.0 and PLM constructs on MP and the effects of I4.0 > PLM and PLM > I4.0 on IIMS can be considered null, given the values below 0.02, as indicated by Hair et al. [51]. The influence of IIMS on MP and the influences of I4.0 and PLM can be considered small, given the values below 0.15, as indicated by Hair et al. [51].

5. Hypothesis Outcomes

H1: 

There are positive effects of I4.0 technologies on IIMS and the MP.

The path coefficient of 0.277 between I4.0 and IIMS, added to that of 0.100 between IIMS and MP, results in 0.377, a value higher than the direct relationship between I4.0 and MP at 0.039. Thus, H1 was validated, indicating the positive influence of companies’ adoption of IIMS. These findings corroborate the literature, recognising digitalisation as facilitating process integration and optimisation [42,47].

H2: 

PLM has positive effects on IIMS and MP.

The path coefficient 0.442 between PLM and IIMS, added to that of 0.100 between IIMS and MP, results in 0.542, a higher value than the direct relationship between PLM and MP at 0.042. Thus, H2 was validated, indicating the positive influence of companies’ adoption of IIMS. This finding is consistent with the literature that points to efficient data management throughout PLM as essential [49,50].

H3: 

I4.0 positively mediates or moderates the relation between PLM and IIMS to achieve MP.

The indirect effect of I4.0 on the relationship between PLM and IIMS is modest, at 0.011, partially confirming the hypothesis. Although modest, this result suggests that I4.0 technologies can enhance the relationship between PLM and IIMS, acting as catalysts in improving IIMS. This analysis of I4.0 as an integrating element strengthens the data and process management practices provided by PLM [49,50].

H4: 

PLM positively mediates or moderates the relation between I4.0 and IIMS to achieve MP.

The indirect effect of PLM on the relationship between I4.0 and IIMS is equally modest, at 0.075, suggesting that PLM contributes to integrating I4.0 technologies and information management systems, but to a lesser extent. This finding is consistent with the literature, which points to a relevant but not predominant role of PLM [60,61].

These results demonstrate the substantial role of IIMS as a means for firms to achieve MP using I4.0 and PLM technologies. Furthermore, I4.0 and PLM have a modest mediating role in the relationships between PLM and IIMS and I4.0 and IIMS, respectively.

6. Discussion and Implications

The results confirm that I4.0, PLM, IIMS, and MP are interdependent and essential pillars for modernising processes and promoting organisational competitiveness [62,63]. Furthermore, this research differs from others due to its approach, which considers the research context broadly and uniquely. SEM made this approach possible, where the structural model is evaluated entirely, resulting in how the constructs behave based on how they are modelled. Thus, based on these results, this research has practical and sustainable development implications.

6.1. Practical Implications

From a practical point of view, managers can apply the following guidelines:

• Optimisation of Data and Process Management

The insights obtained through SEM indicate that integrating PLM with IIMS is a critical factor for effective PLM. This interaction allows for greater control and traceability, optimising new product development, quality monitoring, and waste reduction [6,64]. For managers, this implies adopting integrated systems that connect data from different departments, ensuring decision-making coherence and eliminating informational redundancies.

In addition, the results indicate that adopting I4.0 technologies, such as IoT, Cloud Computing, and AI, can amplify the benefits of IIMS, providing predictive analysis and real-time insights to support strategic decisions [65,66]. These technologies enable, for example, the implementation of predictive maintenance systems, which reduce operating costs and increase the efficiency of industrial assets. The literature also suggests that advanced digitalisation, as in cases analysed by [44,45], is more effective in organisations with high technological maturity.

Thus, based on the research, it is possible to state that the implementation of IIMS contributes strongly to the effectiveness of PLM and I4.0 actions. This is accomplished through the connection of data systems and the use of predictive analyses to support decisions that are relevant to companies strategically.

• Mediating Impacts and Investment Strategies

The research showed that the effects of I4.0 technologies on IIMS are mediating and complex. This suggests that strategic investments in digital technologies should be accompanied by efforts to restructure internal processes and align organisational strategies. Companies should prioritise the creation of collaborative platforms that integrate production, engineering, and market data, facilitating a holistic view of organisational performance [63,67].

For example, using cloud technologies to host IIMS can reduce infrastructure costs and enable scalability, while AI-based platforms can improve the personalisation of products and services. These advances require adapting digital governance models and defining clear metrics to measure the returns on technology investments. According to studies such as those by [47,50], the positive impact of IIMS is amplified when aligned with innovation strategies and IIMS.

In this sense, strategic investments are suggested to create collaborative platforms that integrate production, engineering, and company data, facilitate and personalise data and information management, and generate a digital governance model.

• Training and Organisational Culture

The results also highlight the importance of continuous training and development to maximise the benefits of the technologies adopted. This corroborates Pilloni [62], reinforcing that organisations that successfully adopt digital technologies invest in training aligned with the new tools’ specific needs, promoting a cultural change that facilitates adaptation to new technological requirements.

Trained teams can more efficiently explore the functionalities offered by integrated systems, reducing operational errors and increasing productivity. In addition, creating an organisational culture oriented towards innovation and data use should be a strategic priority.

• Strategic Planning and Causal Relationships

Finally, managers must incorporate the analysis of causal relationships and indirect effects into strategic planning, considering that technologies’ impacts may be more evident in the medium and long term. This includes mapping mediating variables, such as innovation practices, and assessing how these practices influence organisational results [59,61,66]. Recent studies [47] indicate that companies that adopt an integrated strategic planning approach obtain a greater competitive advantage in dynamic and globalised markets. In addition, the findings of Meindl et al.’s findings [65] indicate that success in digital transformation requires a balance between technology, processes, and people, with a focus on sustainable results in the long term.

Thus, strategic management also involves implementing IIMS, which catalyses organisational information, provides more assertive decision-making, and directly influences the organisation’s practices and results.

Therefore, this research’s practical implications reinforce the need for a holistic approach that combines technology, processes, and people to achieve the goals of digital transformation and organisational innovation.

6.2. Implications for Sustainable Development

From a sustainable development perspective, this research aligns directly with SDG 9, Industry, Innovation and Infrastructure, as it explores how integrating emerging I4.0 technologies, such as IoT, Big Data, and Artificial Intelligence, can transform information management and optimise organisational performance [50]. The research demonstrates how these technologies help organisations become more efficient, innovative, and resilient, key factors for developing a more sustainable and inclusive industry, as highlighted by Carvalho et al. [68,69] and Schaefer et al. [70]. IIMS and PLM in conjunction with I4.0 technologies contribute to improving innovation capacity, reducing costs, and promoting company efficiency, all within a sustainable and adaptive industry paradigm [7,8]. From a practical and theoretical perspective, this focus on technological advancement directly supports promoting innovation in the industrial sector, as stipulated in SDG 9 [71]. This research also aligns with SDG 12, Responsible Consumption and Production, as integrating IIMS and PLM can reduce resource waste, improve PLM, and promote energy efficiency, directly aligning with responsible production principles [6,72]. I4.0 also plays a crucial role, with predictive maintenance and process optimisation technologies that can minimise waste and improve resource utilisation [73]. By focusing on the impact of I4.0 and IIMS technologies, the research seeks to promote practices that favour environmental sustainability within organisations, helping companies manage their resources more efficiently and responsibly, reducing environmental impact [74]. In this sense, the study proposes practical solutions for implementing systems that minimise waste and promote a more sustainable product lifecycle, as observed by Lenz et al. [7].

7. Conclusions

This research investigated the relationship between IIMS, I4.0 technologies, PLM, and MP of organisations. Quantitative approaches were adopted, such as bibliometric analysis, literature review, and application of statistical models (EFA, CFA, SEM), and primary data was collected through a questionnaire with 106 professionals from different sectors. The study allowed an in-depth analysis of the interaction between the constructs and their implications for business practices.

The research highlighted the relevance of IIMS as a means of facilitating activities involving PLM and I4.0 technologies, contributing to the literature on information management and industrial digitalisation. The results provide practical guidelines for integrating these technologies to improve operational efficiency and organisational performance. Furthermore, the results also demonstrate that the adoption of IIMS by organisations has repercussions on sustainability, as it contributes to the efficient and responsible use of resources and the reduction of waste arising from management practices. The research also suggests that companies prioritise an integrated approach, investing in technological infrastructure and team training to maximise the benefits of digital transformation.

The research has some limitations. The sample of 106 professionals limits the generalisation of the results to a broader context. As the sample is made up of Brazilian companies, there may also be limitations regarding the reflection of the diversity of industry practices worldwide. In addition, the quantitative approach did not explore qualitative aspects of companies’ experiences with the technologies investigated, which could provide a more holistic understanding of these technologies’ impacts. For future research, it is recommended to expand the sample to include companies of different sizes and sectors and adopt a qualitative approach to better understand the practical experiences of implementing the technologies. In addition, longitudinal studies or international comparisons are suggested to capture information that allows for a broader scope of the approach taken. Future research could also investigate the impact of external factors and the effects of emerging technologies, such as Blockchain, in industrial environments.