2.1. Organizational Context and Implementation of IOT

Opasvitayarux et al. (2022) suggest an analytical model rooted in the technological-organizational-environmental (TOE) framework, bolstered by a collaborative structure, to underscore the significance of the supply chain network. The study delves into organizational factors, encompassing firm size, adaptive capability, absorptive capability, innovative capability, and executive support, as pivotal determinants in the adoption of IoT technologies.

2.2. Internet of Things

IoT integration in manufacturing processes has revolutionized traditional practices, enabling enhanced automation, real-time monitoring, and predictive maintenance. According to Cohen et al. (2019), IoT-enabled smart factories facilitate agile production by connecting machines, sensors, and devices, thereby optimizing resource utilization and minimizing downtime. Moreover, IoT-driven data analytics empower manufacturers with valuable insights for improving efficiency and quality (Kusiak, 2018). In the realm of supply chain management, IoT technologies offer unprecedented visibility and transparency throughout the entire logistics network. Research by Ivanov et al. (2014) suggests that IoT-enabled supply chains enhance inventory management, reduce lead times, and mitigate risks through advanced tracking and tracing capabilities. According to Imran et al. (2018), Industry 4.0 achieves seamless operations across organizational boundaries in production and service sectors through integration with diverse applications and software arrangements, thereby establishing networked organizations. As literature mentioned above, this study proposes a hypothesis:

H6: Implementation of Internet of Things has a significant influence on production and services.

2.3. Production and Services

Production activities play a pivotal role in organizational performance across various sectors. Scholars such as Stevenson (2018) emphasize the importance of efficient production processes in enhancing operational effectiveness and ultimately improving overall performance. For instance, research by Adebanjo et al. (2020) highlights the significance of lean production practices in reducing waste and enhancing productivity, thereby positively impacting organizational performance metrics such as cost efficiency and quality. In parallel, the provision of services has emerged as a critical determinant of organizational success. Studies by Fitzsimmons and Fitzsimmons (2011) underscore the role of service quality in shaping customer satisfaction and loyalty, consequently influencing organizational performance outcomes. Furthermore, Grönroos (2007) emphasizes the pivotal role of service innovation in enhancing competitiveness and differentiation, ultimately contributing to improved performance in service-centric industries. Within the manufacturing, supply chain, and distribution sectors, the integration of production and services is paramount for achieving optimal performance. Scholars such as Christopher (2016) highlight the importance of aligning production processes with customer demand to minimize lead times and enhance responsiveness, thereby improving overall supply chain performance. Therefore, the interplay between production and services within organizational contexts has garnered significant attention due to its profound implications for organizational performance (Govorukha & Kuchkova, 2018; Imran et al., 2018), as shown in below hypothesis:

H7: Production and services have a significant influence on organizational performance.

2.4. Organizational Performance

Organizational performance is a multifaceted concept that encompasses various dimensions of success, including financial, operational, and strategic achievements (Opasvitayarux et al., 2022). In the context of manufacturing, supply chain, and distribution sectors, understanding and optimizing organizational performance are paramount for sustained competitiveness and growth (Imran et al., 2018). Numerous factors influence organizational performance in manufacturing, supply chain, and distribution contexts. Research by Marshoudi et al. (2023) emphasizes the role of strategic alignment between organizational goals and operational activities in driving performance outcomes. The adoption of technology and innovation plays a crucial role in driving organizational performance across manufacturing, supply chain, and distribution sectors. Scholars such as Porter and Heppelmann (2014) highlight the transformative impact of digital technologies, such as Internet of Things (IoT), big data analytics, and automation, in enhancing operational efficiency, visibility, and decision-making capabilities. Furthermore, research by Lee et al. (2004) emphasizesthe role of innovation in product design, process optimization, and business model innovation as key drivers of competitive advantage and performance improvement.

3.1. Research Framework and Hypotheses

The research framework is derived from theoretical models established in prior studies. Imran et al. (2018) studied that Industry 4.0 plays a pivotal role in advancing Pakistan's production and services sectors, significantly influencing their overall performance. This research stands as a pioneering investigation into the impact of Industry 4.0 on Pakistan's textile and logistics industries. Opasvitayarux et al. (2022) highlighted factors such as compatibility, trialability, adaptive capacity, innovative capability, executive support, pressure from value chain partners, presence of service providers, and information sharing had significant effects on attitudes toward adopting Quality Management (QM) Internet of Things (IoT). Thus, the subsequent framework and hypotheses, depicted in Figure 1, are suggested.

H1: Firm size has a significant influence on implementation of Internet of Things.

H2: Adaptive capability has a significant influence on implementation of Internet of Things.

H3: Absorptive capability has a significant influence on implementation of Internet of Things.

H4: Innovative capability has a significant influence on implementation of Internet of Things.

H5: Executive support has a significant influence on implementation of Internet of Things.

H6: Implementation of Internet of Things has a significant influence on production and services.

H7: Production and services have a significant influence on organizational performance.

3.2. Methodology

The research methodology employed is quantitative, utilizing questionnaire distribution. The questionnaire comprises three sections: screening questions, 30 items rated on a five-point Likert scale ranging from "strongly disagree" (1) to "strongly agree" (5), and demographic information encompassing gender, age, educational level, and years of work experience. 30 items are presented in Table 1. Prior to data collection, the Item–Objective Congruence (IOC) index was employed, involving evaluation by four experts holding Ph.D. and c-level positionsin manufacturing and distribution companies. All scale items received a score of 0.5 or higher. The reliability of the constructs was assessed using Cronbach’s Alpha coefficient, achieving values of 0.70 or above during the pilot test involving 50 participants, in line with Nunnally and Bernstein (1994). Subsequently, the survey was disseminated widely to 460 employees occupying managerial and strategic roles. These individuals possessed a minimum of one year of experience within 20 prominent manufacturing and distribution companies in Thailand, each with a workforce exceeding 250 employees. Statistical software was utilized for data analysis. Confirmatory Factor Analysis (CFA) was conducted to assess reliability, validity, and goodness-of-fit indices. Furthermore, Structural Equation Model (SEM) analysis was employed to evaluate the goodness-of-fit of the model and test hypotheses.

3.3. Population and Sample Size

The target population is participantsfrom managerial and strategic positions within 20 prominent manufacturing and distribution firms in Thailand, each with a workforce of 250 employees or more, offers several advantages. Employees who have been working in managerial and strategic roles for at least one year possess valuable experience and expertise. This ensures that they have a deep understanding of the organization's operations, challenges, and potential areas for improvement.

Focusing on large firms with substantial workforces ensures a robust sample size, enhancing the credibility and generalizability of the study findings. According to Kline (2011), a complex model typically requires a minimum sample size of at least 200. In this study, the survey was distributed to approximately 2,000 participants within a three-month period from August to October 2023. Ultimately, 460 responses were obtained per quota sampling, meeting the criteria for data analysis after passing the screening process.

3.4. Sampling Technique

Sampling techniques employed include judgmental, quota, and convenience sampling. Initially, judgmental sampling was utilized, whereby the researcher selected a group of 460 employees occupying managerial and strategic positions. These individuals possessed a minimum of one year of experience within 20 prominent manufacturing and distribution companies in Thailand, each with a workforce exceeding 250 employees. Second, quota sampling was implemented to determine the sample size proportionally, ensuring 23 participants per company to achieve a balanced representation. Due to consent considerations, the company names cannot be disclosed. Finally, convenience sampling was employed, distributing surveys both offline through human resources departments and online via email, social media platforms, and messaging applications throughout the period from August to October 2023.

4.1. Demographic Profile

According to Table 2, The demographic profile of 460 participants in this study provides key insights into the sample population. Gender distribution shows a slight predominance of males (56.1%) over females (43.9%). The age distribution is diverse, with the largest group aged 31-40 (31.7%), followed by 41-50 (25.9%), 30 and below (22.4%), and 51 and above (20.0%). Regarding education, most hold Bachelor's degrees (62.8%), followed by Master's (22.2%), Doctorate (9.3%), and Diploma/Below Bachelor’s Degree (5.7%). In terms of work experience, 6-10 years is the most common (28.0%), followed by 1-5 years (23.3%), 11-15 years (19.1%), 16-20 years (16.1%), and 21 years or more (13.5%). This diverse profile enrichesthe study'sfindings. In conclusion, the demographic profile of participants in the study reflects a diverse and representative sample population across various demographic factors. The balanced representation of gender, varied age distribution, diverse educational backgrounds, and broad range of work experience contribute to the richness and robustness of the study's findings and analyses.

4.2. Confirmatory Factor Analysis (CFA)

Confirmatory Factor Analysis (CFA) serves as a multivariate analysis, ensuring the validation of the measurement model. Table 3 presents the CFA results, assessed through various metrics including Cronbach’s Alpha, factor loadings, composite reliability (CR), and average variance extraction (AVE). The findings indicate that Cronbach’s Alpha coefficients are validated at 0.70 or higher, consistent with established standards (Nunnally & Bernstein, 1994). Factor loadings of 0.5 or above are deemed acceptable. Moreover, both composite reliability (CR) and average variance extraction (AVE) values surpass the recommended thresholds of 0.7 and 0.5, respectively (Fornell & Larcker, 1981). These outcomes collectively affirm the convergent and discriminant validities of the study, underscoring the significance of the CFA analysis results.

Note: CR = Composite Reliability, AVE = Average Variance Extracted

The examination of the square root of the average variance extracted reveals that all correlations exceed the respective correlation values for each variable, as indicated in Table 4 (Fornell & Larcker, 1981). According to Hair et al. (2006), the presence of multicollinearity is assessed through correlation coefficients, with the factor correlations remaining below 0.80. Consequently, this study does not encounter issues related to multicollinearity.

Note: The diagonally listed value is the AVE square roots of the variables

The measurement model underwent assessment to evaluate its goodness of fit within the Confirmatory Factor Analysis (CFA). This study employed various criteria including CMIN/DF, GFI, AGFI, NFI, CFI, TLI, IFI, and RMSEA, as detailed in Table 5. The findings revealed that all values indicated an acceptable fit after the adjustments. Consequently, the study's convergent and discriminant validities were validated.

Remark: CMIN/DF = The ratio of the chi-square value to degree of freedom, GFI = goodness-of-fit index, AGFI = adjusted goodness-of-fit index, NFI = normalized fit index, CFI = comparative fit index, TLI = Tucker-Lewis index, IFI = Incremental Fit Index, and RMSEA = root mean square error of approximation

4.3. Structural Equation Model (SEM)

The structural model was employed to ascertain the satisfactory fit of the Structural Equation Model (SEM), as outlined in Table 6. Initially, the model'sfit needed alignment with empirical data. Following adjustments to the model, the fit values became acceptable, with parameters including CMIN/DF = 2.681, GFI = 0.877, AGFI = 0.851, NFI = 0.912, CFI = 0.943, TLI = 0.935, IFI = 0.943, and RMSEA = 0.061 meeting the criteria.

Remark: CMIN/DF = The ratio of the chi-square value to degree of freedom, GFI = goodness-of-fit index, AGFI = adjusted goodness-of-fit index, NFI = normalized fit index, CFI = comparative fit index, TLI = Tucker-Lewis index, IFI = Incremental Fit Index, and RMSEA = root mean square error of approximation

4.4. Research Hypothesis Testing Result

The research hypothesis testing results are derived from the analysis of standardized coefficients (β) along with their corresponding t-values, asillustrated in Table 7 and Figure 2. Standardized coefficients indicate the strength and direction of the relationships between predictor variables (e.g., firm size, adaptive capability, absorptive capability) and the outcome variable (e.g., Internet of Things, production and services, organizational performance) in the structural equation model.

Note: *p<0.05

The t-values associated with the standardized coefficients are used to determine the statistical significance of these relationships. A t-value greater than 1.96 (for a two-tailed test) or the critical value corresponding to the desired significance level indicates that the relationship is statistically significant at the specified alpha level, which in this case is p = 0.05.

The analysis of standardized path coefficients reveals significant relationships between organizational factors and their influence on Internet of Things (IoT) adoption, as well as its impact on production, services, and organizational performance.

H1 posited that firm size has a significant influence on IoT adoption. The results indicate a positive standardized path coefficient of 0.186 (T-value = 2.973, p < 0.05), supporting the hypothesis. Thissuggeststhat larger firms are more likely to adopt IoT technologies compared to smaller ones. Hence, this finding establishes firm size as a pivotal factor influencing the dynamics of IoT implementation (Opasvitayarux et al., 2022).

H2 suggested that adaptive capability influences IoT adoption. The findings demonstrate a positive relationship with a standardized path coefficient of 0.146 (T-value = 2.623, p < 0.05), supporting the hypothesis. Thisimpliesthat organizations with higher adaptive capabilities are more inclined to embrace IoT technologies, as aligned with several studies (Gracias et al., 2023; Hong et al., 2011; Sievers et al., 2021).

H3 proposed that absorptive capability affects IoT adoption. The analysis reveals a significant positive relationship, with a standardized path coefficient of 0.148 (T-value = 3.035, p < 0.05), supporting the hypothesis (Junaid et al., 2022; Lin et al., 2016). This suggests that organizations with enhanced absorptive capabilities are more likely to adopt IoT technologies effectively.

H4 hypothesized that innovative capability influences IoT adoption. The results demonstrate a strong positive relationship, with a standardized path coefficient of 0.349 (T-value = 6.056, p < 0.05), supporting the hypothesis. This indicates that organizations with higher innovative capabilities are more inclined to adopt IoT technologies to drive innovation and competitive advantage (Lee, 2015; Opasvitayarux et al., 2022; Porter & Heppelmann, 2014).

H5 suggested that executive support influences IoT adoption. However, the analysis reveals a non-significant relationship, with a standardized path coefficient of 0.005 (T-value = 0.121, p > 0.05), indicating that executive support does not significantly impact IoT adoption in this context, which is consistent to the study of Hsu and Yeh (2017) and Vatanpour et al. (2013).

H6 posited that IoT adoption influences production and services. The resultsshow a significant positive relationship, with a standardized path coefficient of 0.489 (T-value = 9.932, p < 0.05), supporting the hypothesis. This suggests that IoT adoption positively impacts both production and service sectors (Imran et al., 2018).

H7 proposed that production and services influence organizational performance. The analysis reveals a significant positive relationship, with a standardized path coefficient of 0.426 (T-value = 8.377, p < 0.05), supporting the hypothesis. Imran et al. (2018) indicated that effective management of production and services leads to improved organizational performance.

Overall, these findings underscore the importance of various organizational capabilities, such as adaptive, absorptive, and innovative capabilities, in driving IoT adoption. Moreover, they highlight the significant impact of IoT adoption on both production and services sectors, ultimately enhancing organizational performance. However, the non-significant influence of executive support on IoT adoption suggests potential areas for further exploration and managerial intervention.

5.1. Conclusion and Discussion

The results of this study provide valuable insights into the factors influencing the implementation of Internet of Things (IoT) technologies and their impact on various aspects of organizational performance within the context of IoT implementation. The discussion will focus on the implications of these findings for organizations seeking to adopt and leverage IoT solutions effectively.

Firstly, the significant positive relationships observed between firm size, adaptive capability, absorptive capability, and innovative capability with IoT implementation underscore the importance of organizational readiness and capacity for technological innovation in the context of IoT adoption (Opasvitayarux et al., 2022). Larger organizations, as well as those with higher adaptive, absorptive, and innovative capabilities, are better positioned to embrace IoT technologies and navigate the complexities associated with their implementation (Rey et al., 2021). These findings highlight the need for organizations to invest in building the necessary capabilities and resources to support successful IoT implementation initiatives.

However, the non-significant influence of executive support on IoT implementation raises important considerations regarding the role of leadership in driving and supporting technological change within organizations. While executive support is often considered critical for facilitating organizational change, its limited impact on IoT implementation suggests potential challenges or barriers at the leadership level that warrant further investigation. Organizations must explore ways to engage and align executive leadership with IoT implementation initiatives, foster a culture of innovation, and provide the necessary resources and support to drive successful implementation efforts. Zhu and Kraemer (2005) examined post-adoption variations in the usage and value of e-business technologies in the retail industry and addressed the insignificant role of executive support in technology implementation and utilization.

Moreover, the significant positive relationship between IoT implementation and both production and services sectors highlight the transformative potential of IoT technologies in optimizing business processes and enhancing operational efficiency, as supported by Imran et al. (2018). By integrating IoT solutions into production processes, organizations can improve resource utilization, enable real-time monitoring and control, and streamline operations. Similarly, in the services sector, IoT implementation can enhance service delivery, improve customer experiences, and enable new business models and revenue streams. These findings underscore the need for organizations to leverage IoT technologies strategically to drive innovation and value creation across their operations.

Furthermore, the positive relationship between production/services and organizational performance emphasizes the critical role of effective management of core business functions in driving overall organizational success in the context of IoT implementation (Imran et al., 2018). Organizations that effectively manage their production and service operations are better positioned to achieve operational excellence, deliver value to customers, and maintain a competitive edge in the market. By focusing on enhancing production and service capabilities through IoT implementation, organizations can drive improvements in key performance metrics such as productivity, quality, customer satisfaction, and profitability.

In conclusion, the findings of this study underscore the importance of organizational capabilities, leadership support, and effective management practices in driving successful IoT implementation and realizing its potential benefits. Organizations must prioritize investment in building adaptive, absorptive, and innovative capabilities, engage executive leadership in supporting IoT initiatives, and leverage IoT technologies strategically to drive innovation and value creation across their operations. By doing so, organizations can position themselves for success in an increasingly digital and interconnected business environment, driving improvements in operational efficiency, customer satisfaction, and overall organizational performance through effective IoT implementation.

5.2. Recommendation

Based on the findings and analysis presented in this study, several recommendations can be made to organizations seeking to implement Internet of Things (IoT) technologies effectively to enhance production, services and organizational performance:

Invest in Organizational Capabilities: Organizations should prioritize investment in building adaptive, absorptive, and innovative capabilities to support successful IoT implementation. This includes providing training and development opportunities for employees to enhance their skills and knowledge related to IoT technologies and fostering a culture of innovation and continuous learning within the organization.

Engage Executive Leadership: Executive leadership plays a critical role in driving and supporting IoT implementation initiatives. Organizations should actively engage executive leaders in IoT strategy development, decision-making processes, and resource allocation to ensure alignment with organizational goals and priorities. Executive leaders should champion IoT initiatives, provide visible support and endorsement, and actively promote a culture of innovation and change throughout the organization.

Leverage IoT for Operational Optimization: Organizations should strategically leverage IoT technologies to optimize business processes, improve operational efficiency, and enhance productivity across various functions, including production, services, supply chain management, and customer service. By deploying IoT sensors, devices, and data analytics solutions, organizations can gain real-time insights into their operations, identify areas for improvement, and drive continuous process optimization and innovation.

Focus on Customer Value: Organizations should prioritize the use of IoT technologies to create value for customers by enhancing the quality of products and services, improving customer experiences, and delivering personalized and customized solutions. By leveraging IoT data and insights, organizations can better understand customer needs and preferences, anticipate future demand, and develop innovative products and services that meet or exceed customer expectations.

Ensure Data Security and Privacy: With the proliferation of IoT devices and sensors collecting vast amounts of data, organizations must prioritize data security and privacy to protect sensitive information and maintain customer trust. Organizations should implement robust cybersecurity measures, including encryption, authentication, access controls, and data encryption, to safeguard IoT data from unauthorized access, breaches, and cyberattacks.

Foster Collaboration and Partnerships: Given the complex and interconnected nature of IoT ecosystems, organizations should foster collaboration and partnerships with industry stakeholders, technology providers, research institutions, and government agencies to share knowledge, expertise, resources, and best practices. Collaborative initiatives can accelerate innovation, drive ecosystem growth, and address common challenges and barriers to IoT adoption and implementation.

Monitor and Evaluate Performance: Organizations should establish key performance indicators (KPIs) and metrics to monitor and evaluate the performance and impact of IoT implementation initiatives. By tracking KPIs such as operational efficiency, cost savings, revenue growth, customer satisfaction, and market share, organizations can assess the effectiveness of their IoT strategies, identify areas for improvement, and make data-driven decisions to optimize outcomes and achieve their strategic objectives.

Overall, successful IoT implementation requires a strategic and holistic approach that encompasses organizational capabilities, leadership support, customer focus, data security, collaboration, and performance monitoring. By following these recommendations, organizations can unlock the full potential of IoT technologies to drive innovation, create value, and achieve sustainable competitive advantage in today's digital economy.

5.3. Limitation and Further Study

While this study provides valuable insights into the factors influencing Internet of Things (IoT) adoption and its impact on organizational performance, there are several limitations that warrant consideration for future research. First, the findings of this study are based on a specific sample population and context, which may limit their generalizability to other industries, regions, or organizational contexts. Future research could explore the applicability of these findings in different settings to enhance the generalizability of the results. Second, the study did not account for potential external factors or contextual variables that may influence IoT adoption and organizational performance, such as industry trends, regulatory environment, technological advancements, or market dynamics. Future research could consider these external factors to provide a more comprehensive understanding of the phenomenon. Last, the study primarily relied on quantitative data analysis, which may overlook nuanced or contextual factors that influence IoT adoption and organizational performance. Future research could incorporate qualitative methods such as interviews or case studies to gain deeper insights into the experiences and perceptions of stakeholders involved in IoT initiative.