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6 June 2025

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

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Industrial and Systems Engineering Graduate Program, Pontifical Catholic University of Parana (PUCPR), Curitiba 80215-901, Brazil
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Sustainability2025, 17(12), 5260;https://doi.org/10.3390/su17125260
This article belongs to the Special Issue Impact of Digital Transformation of Industries on Economic Sustainability
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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 provides a comprehensive overview of this literature review, highlighting the themes discussed and the real contributions of each article.
Table 1. Current literature on IIMS, PLM, I4.0, and MP.
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.
Figure 1. 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.
Figure 2. Conceptual structure for Hypothesis 3.
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.
Figure 3. Conceptual structure for Hypothesis 4.
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 presents the questions and factor loadings.
Table 2. Measurement validation.

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 shows the values for reliability and convergent validity.
Table 3. Reliability and composite reliability.
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 shows the HTMT Ratios.
Table 4. Discriminant validity: Heterotrait–Monotrait Ratio.
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.
Figure 4. Path coefficient results.
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.
Table 5. Total effects matrix.
Table 6 shows the f2 effects matrix.
Table 6. f2 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.

Author Contributions

Conceptualisation, C.E.M.S. and J.L.S.; methodology, C.E.M.S., P.T.d.J.C.F. and J.L.S.; software, C.E.M.S. and P.T.d.J.C.F.; validation, J.L.S.; formal analysis, C.E.M.S. and J.L.S.; investigation, C.E.M.S.; resources, O.C.J.; data curation, C.E.M.S. and J.L.S.; writing—original draft preparation, C.E.M.S., P.T.d.J.C.F. and J.L.S.; writing—review and editing, J.L.S. and O.C.J.; visualisation, C.E.M.S. and J.L.S.; supervision, J.L.S. and O.C.J.; project administration, O.C.J.; funding acquisition, O.C.J. All authors have read and agreed to the published version of the manuscript.

Funding

This research was funded by Conselho Nacional de Desenvolvimento Científico e Tecnológico—CNPq—MAI-DAI Program.

Institutional Review Board Statement

Ethical review and approval were waived for this study by Comitê de Ética em Pesquisa due to legal regulations RESOLUÇÃO Nº 510, DE 07 DE ABRIL DE 2016.

Data Availability Statement

Data will be made available on request.

Acknowledgments

The authors thank the Industrial and Systems Engineering Graduate Program (PPGEPS) from Pontifical Catholic University of Parana (PUCPR).

Conflicts of Interest

The authors declare no conflicts of interest.

References

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