2.1 Performance Expectancy

Performance expectancy refers to the belief that using a particular technology will enhance task performance or achieve desired outcomes (Abbad 2021). It encompasses perceptions of usefulness, efficiency, and productivity improvements (Kabra et al. 2017). According to Brown et al. (2010), often the strongest predictor of technology adoption, performance expectancy highlights the expected benefits individuals or organizations associate with adopting new technologies, such as increased effectiveness, time savings, or better results in their specific context.

2.2 Effort Expectancy

Effort expectancy refers to the perceived ease of use of a technology. It reflects the extent to which individuals believe that adopting and using a new system will require minimal effort (Alfalah 2023). Technologies that are intuitive, user-friendly, and require less learning time are more likely to be adopted. Effort Expectancy is particularly important in influencing early adoption, as users are more inclined to embrace technologies that do not pose significant usability challenges (Barnard et al. 2013).

2.3 Social Influence

Social influence refers to the degree to which individuals believe that important others—such as peers, supervisors, or society—expect or encourage them to adopt a particular technology (Joa and Magsamen-Conrad 2022). As highlighted by Leow et al. (2021), social influence encompasses external pressure, recommendations, and social norms that shape adoption decisions. Faqih and Jaradat (2021) further emphasize that when influential figures or organizations endorse a technology, individuals are more likely to adopt it, particularly in environments where conformity and professional validation play a crucial role.

2.4 Facilitating Conditions

Facilitating conditions refer to the degree to which individuals believe that the necessary resources, infrastructure, and support are available to enable the effective use of a technology (Bhatiasevi 2016). This includes access to technical assistance, training, organizational support, and compatibility with existing systems. Strong facilitating conditions reduce barriers to adoption, making it easier for individuals and organizations to integrate new technologies into their workflows (Kim et al. 2015).

2.5 Perceived Motivation

Perceived motivation extends the UTAUT by incorporating the intrinsic and extrinsic factors that drive individuals to adopt and use technology. Perceived motivation reflects the degree to which users feel motivated—whether through personal interest, rewards, environmental benefits, or organizational incentives—to engage with a technology (Camilleri and Falzon 2021). By integrating perceived motivation into UTAUT, the model gains a deeper understanding of how enthusiasm, goal alignment, and perceived benefits influence technology adoption, particularly in contexts like sustainability and green innovation.

2.6 Perceived Environmental Knowledge

Perceived environmental knowledge represents an organization's self-assessed ability to comprehend and implement environmental issues, sustainability principles, and eco-friendly technologies (Farrukh et al. 2022). It highlights the organization's awareness of its environmental impact and its perceived capability to adopt measures for environmental improvement (Sahoo et al. 2023). When effectively applied, this knowledge enables organizations to implement stronger environmental policies, encourage sustainable resource utilization, and embrace innovative green technologies. As a result, perceived environmental knowledge can be integrated into the UTAUT framework as an additional factor, supporting organizations in achieving their sustainability objectives.

This study extends the UTAUT model by positioning digital social networking as a key mediating factor in the relationship between GIoT adoption and environmental performance. Digital networks enable organizations to overcome adoption barriers, exchange insights on best practices, and enhance technological diffusion (Volberda et al. 2021). The role of peer influence, industry collaboration, and knowledge-sharing within these networks helps accelerate the adoption process (Eslami et al. 2023), ensuring that SMEs can effectively leverage GIoT for sustainable business practices.

By incorporating perceived motivation and perceived environmental knowledge into the UTAUT framework, this study provides a comprehensive perspective on GIoT adoption in manufacturing and service SMEs. The model underscores the interconnectedness of technology adoption, digital networking, and sustainability, offering valuable contributions to both academic research and policy discussions. Additionally, it highlights the need for customized adoption strategies that account for sector-specific requirements, motivational factors, and the perceived attributes of GIoT solutions. This theoretical expansion not only strengthens the explanatory power of UTAUT but also lays the foundation for future research on green innovation, SME digital transformation, and sustainable entrepreneurship.

3.1 Performance Expectancy and GIoT

The UTAUT posits that performance expectancy—the degree to which an individual believes that using a technology will help achieve gains in job performance—is a critical determinant of technology adoption (Camilleri 2024). n organizational contexts, performance expectancy is especially crucial in driving the adoption of digital technologies, particularly in their pursuit of enhanced environmental performance (Huong and Thanh 2022).

In the case of SMEs, performance expectancy is closely tied to the perceived benefits of IoT (Kisanjara 2023), such as improved operational efficiency, reduced energy consumption, and compliance with environmental regulations. GIoT technologies offer real-time data monitoring, predictive analytics, and optimized resource utilization (Albreem et al. 2021), which can significantly enhance SMEs' environmental sustainability practices. These capabilities resonate strongly with SMEs' strategic goals to reduce their carbon footprint, optimize resource usage, and enhance their competitiveness in a sustainability-driven market (Alalwan et al. 2024).

Empirical evidence from prior studies (such as Faqih and Jaradat 2021; Venkatesh 2022; AL-Nuaimi et al. 2024) supports the positive influence of performance expectancy on the adoption of innovative technologies, particularly in organizational contexts. Pappas et al. (2021) emphasize that in SMEs—where resources and expertise are often limited—the expectation of substantial performance improvements serves as a key motivator for adopting GIoT. This alignment of perceived benefits with organizational objectives reinforces the central role of performance expectancy in shaping GIoT adoption decisions. Drawing on UTAUT theory and the unique benefits of GIoT for environmental performance, we hypothesize:

3.2 Effort Expectancy and GIoT

Effort expectancy, as conceptualized within the UTAUT, refers to the degree of ease associated with the use of a technology (Prassida and Asfari 2022). Effort expectancy is a critical predictor of technology adoption, particularly in organizational contexts where users are influenced by the perceived complexity or simplicity of new systems (Emon et al. 2024). In the case of the IoT, effort expectancy plays a significant role in shaping SMEs' adoption decisions (Shah et al. 2024).

According to Albreem et al. (2021), the adoption of GIoT entails the integration of advanced technologies, such as smart sensors, cloud computing, and real-time monitoring systems, into existing business processes. For SMEs, which often operate with limited technical expertise and resources (Hervas-Oliver et al. 2021), the perceived ease of deploying, managing, and using GIoT solutions is a critical consideration. Alalwan et al. (2024) identified that high effort expectancy—where users believe that GIoT is straightforward to implement and operate—can reduce resistance to adoption and foster a positive attitude toward embracing these technologies.

Prior research (such as Andrews et al. 2021; Srivastava et al. 2024; Norzelan et al. 2024) highlights that effort expectancy is particularly influential in the early stages of technology adoption, as organizations assess whether the benefits of a new system outweigh the effort required to adopt and implement it. GIoT solutions are perceived as requiring minimal training (Varjovi and Babaie 2020), offering user-friendly features (Patel et al. 2024), and integrating seamlessly with existing systems (Allioui and Mourdi 2023), SMEs are more likely to adopt these technologies. By highlighting the relationship between effort expectancy and GIoT adoption, this research contributes to a deeper understanding of how perceived ease of use drives green technology integration, ultimately supporting SMEs in their environmental performance goals, we hypothesize:

3.3 Social Influence and GIoT

Social influence refers to the extent to which persons perceive that significant individuals—such as peers, colleagues, industry leaders, or regulators—believe they should use a particular technology (Khan et al. 2022). In the context of sustainable technology adoption, social influence plays a pivotal role, particularly for SMEs, where external and internal networks often guide technological decision-making (Qalati et al. 2021).

Research (such as Lutfi 2022; Kwarteng et al. 2024) indicates that SMEs are particularly susceptible to social influence because they often rely on external networks and stakeholder guidance to mitigate their limited internal expertise and resources. According to Alraja et al. (2022), SMEs are more likely to view green technology adoption as a valuable and essential investment when influential stakeholders promote it as a strategy for enhancing environmental performance. Furthermore, SMEs that experience social endorsement of GIoT adoption may also benefit from reputational advantages, such as being recognized as leaders in sustainability (Rane et al. 2023; Kineber 2024; Xu et al. 2025).

Drawing from the UTAUT framework, the role of social influence in driving technology adoption is well established. As organizations navigate the decision-making process, the opinions of influential stakeholders, the norms of the business ecosystem, and the visibility of successful GIoT use cases can significantly impact their willingness to adopt the technology (Thomas et al. 2023). Based on these considerations, we hypothesize:

3.4 Facilitating Conditions and GIoT

According to Abbad (2021), facilitating conditions refer to the extent to which individuals believe that the necessary organizational and technical infrastructure is in place to support technology use. In the context of SMEs, the adoption of GIoT technologies is heavily influenced by the availability of resources, infrastructure, and support systems, which simplify both implementation and operation (Shahadat et al. 2023; Al-Emran and Griffy-Brown 2023).

GIoT adoption involves integrating interconnected devices, data analytics platforms, and advanced sensors to achieve real-time monitoring and sustainability goals (Varjovi and Babaie 2020). Facilitating conditions such as access to technical expertise, financial resources, training programs, and supportive regulatory frameworks are critical enablers in this process (Xu et al. 2022). SMEs, often constrained by limited resources, rely heavily on these conditions to bridge the gap between intention and adoption.

Existing research (such as Chung et al. 2022; Salimon et al. 2023; Nazir and Khan 2024) emphasizes that a lack of facilitating conditions can act as a significant barrier to technology adoption, particularly for resource-constrained SMEs. Conversely, when SMEs perceive strong facilitating conditions—such as reliable networks, accessible training, and financial incentives—they are more likely to adopt GIoT technologies (Albreem et al. 2023). These conditions not only reduce the perceived complexity of GIoT systems but also enhance confidence in their long-term viability and benefits. Therefore, we hypothesize:

3.5 Perceived Motivation and GIoT

This study extends the UTAUT framework by incorporating perceived motivation, offering a more comprehensive perspective on the adoption of green digital technologies and their impact on the environmental sustainability performance of organizations. Perceived motivation plays a pivotal role in influencing SMEs' adoption of GIoT technologies, as both intrinsic and extrinsic motivational factors shape their willingness to embrace sustainable innovations aligned with their strategic objectives. Intrinsic motivation, such as a strong commitment to environmental sustainability and corporate social responsibility (CSR) (Dodds et al. 2022), drives SMEs to implement GIoT solutions to reduce their carbon footprint and optimize resource efficiency. On the other hand, extrinsic motivation, including regulatory compliance, customer demand for eco-friendly products, and financial incentives (Patwary et al. 2024), reinforces the urgency for SMEs to integrate GIoT technologies into their operations. When organizations perceive strong motivational drivers, they are more inclined to invest in GIoT technologies, ultimately enhancing their environmental performance and ensuring long-term business sustainability (Alalwan et al. 2024).

Prior studies have consistently highlighted the role of motivation in technology adoption, particularly in the context of green and digital transformation. For instance, research on sustainable technology adoption has shown that perceived motivation—driven by environmental regulations, market competitiveness, and organizational sustainability goals—significantly enhances the likelihood of implementing eco-friendly innovations (Shahzad et al. 2022; Liao et al. 2022). Additionally, studies on digital transformation in SMEs (such as Tűrkeș et al. 2020; Suciu et al. 2021) suggest that businesses with higher motivational alignment toward innovation are more proactive in integrating digital solutions, including IoT-based smart technologies, to optimize energy use and reduce emissions. These findings provide empirical support for the argument that perceived motivation acts as a strong determinant of GIoT adoption, influencing SMEs to prioritize sustainability-driven technological investments. Given the importance of perceived motivation in influencing technology adoption decisions, this study proposes the following hypothesis:

3.6 Perceived Environmental Knowledge and GIoT

Perceived environmental knowledge significantly influences organizations' willingness and capability to adopt green technologies by shaping their understanding of sustainability challenges and solutions (Koo and Chung 2014). In the context of SMEs, a strong knowledge base on environmental issues increases their awareness of the urgency of green technology adoption as a strategy to enhance environmental performance and ensure compliance with sustainability regulations (Thomas et al. 2022). Awareness of environmental issues, such as carbon emissions, energy waste, and resource depletion, enhances SMEs' motivation to leverage GIoT solutions (Rane et al. 2023), which offer real-time monitoring, smart resource management, and data-driven decision-making for sustainable operations. Therefore, SMEs with higher levels of environmental knowledge are better positioned to integrate GIoT into their business strategies, leading to more effective implementation and utilization of these technologies.

Additionally, perceived environmental knowledge plays a key role in helping SMEs overcome adoption barriers by enhancing their understanding of green technology benefits, technical functionalities, and financial justifications (Arfi et al. 2018). Bai (2021) found that a lack of knowledge about green digital technologies often leads to resistance to change, skepticism about their effectiveness, and uncertainty about their long-term impact. However, SMEs that are well-informed about the role of IoT in sustainability are more likely to view green technology solutions as valuable investments rather than operational burdens (Shah et al. 2024). Additionally, environmental knowledge strengthens strategic decision-making by enabling SMEs to identify the most relevant and cost-effective green technology applications tailored to their specific industry (Krara et al. 2025). This enhanced understanding fosters greater confidence in adopting and integrating GIoT solutions into existing business models, ultimately accelerating sustainable transformation. Based on discussion, the study proposes the following hypothesis:

3.7 GIoT and Environmental Performance of SMEs

According to Alalwan et al. (2024), the GIoT represents a transformative approach to achieving sustainability through the integration of IoT technologies designed specifically to enhance environmental outcomes. GIoT encompasses a range of applications, such as smart sensors for energy optimization, real-time monitoring systems for waste reduction, and cloud-based platforms for efficient resource management (Albreem et al. 2021). For SMEs, adopting GIoT technologies offers a powerful mechanism to improve their environmental performance, a critical factor in remaining competitive in increasingly eco-conscious markets (Bag et al. 2023; Alshahrani et al. 2024).

Environmental performance, defined as the ability of organizations to minimize their environmental impact through sustainable practices (Rehman et al. 2021; Khan et al. 2023), has become a key strategic goal for SMEs. Unlike larger enterprises, SMEs often face resource constraints, limiting their ability to implement large-scale sustainability initiatives (Journeault et al. 2021). According to Turskis and Šniokienė (2024), GIoT provides SMEs with scalable, cost-effective solutions that enable precise monitoring and management of environmental metrics such as energy consumption, emissions, and waste.

Prior research (such as Pérez-Pons et al. 2020; Wen et al. 2021; Chiarini 2021) suggests that IoT technologies have a significant positive impact on environmental performance by improving operational efficiency and reducing ecological footprints. The adoption of GIoT technologies extends these benefits by explicitly focusing on green outcomes (Tang et al. 2018), enabling SMEs to align with global sustainability standards and enhance their reputation as environmentally responsible organizations. Based on this understanding, we hypothesize:

3.8 GIoT and Digital Social Networking

The GIoT integrates IoT technologies with environmentally sustainable practices, enabling organizations to monitor, manage, and optimize their environmental performance (Hu et al. 2022). A critical enabler of green technologies adoption is digital social networking, which facilitates information sharing, collaboration, and innovation among individuals and organizations (Siba Borah et al. 2024; Nazish et al. 2024). Digital social networking platforms serve as a conduit for disseminating knowledge about green technologies (Li et al. 2024), fostering peer-to-peer interactions, and enabling SMEs to engage with broader networks of stakeholders.

According to Syed et al. (2024), digital social networking platforms such as LinkedIn, Twitter, and industry-specific forums have emerged as vital tools for businesses to share knowledge about innovative technologies, including GIoT. For SMEs, leveraging these platforms can create opportunities to learn from industry leaders, collaborate with technology providers (Borah et al. 2022), and share success stories related to GIoT adoption. Through social networking, SMEs can access valuable insights on implementation strategies (Crammond et al. 2018), financial incentives, and best practices (Tiwasing 2021), reducing the uncertainty and perceived risks associated with adopting new technologies (Scuotto et al. 2017). Moreover, digital social networking establishes an ecosystem for SMEs where peer influence, expert guidance, and shared experiences drive decision-making (Zhao et al. 2022). This dynamic interaction creates a supportive environment that encourages the adoption of GIoT technologies. By facilitating collaboration, knowledge exchange, and co-creation, digital social networking plays a crucial role in enhancing SMEs' willingness to embrace sustainable innovations. Based on this, we hypothesize:

3.9 Digital Social Networking and Environmental Performance of SMEs

Digital social networking has become a transformative tool for collaboration, knowledge sharing, and innovation among organizations (Chierici et al. 2021), playing a crucial role in enhancing environmental performance (Qalati et al. 2022). Environmental performance reflects an organization's ability to reduce its environmental footprint through optimized resource utilization, lower emissions, and adherence to sustainability standards (Khan et al. 2023). However, SMEs, often constrained by limited resources, face significant challenges in achieving these sustainability goals.

The interactive nature of digital social networking allows SMEs to actively engage with stakeholders such as policymakers, customers, and technology providers (Bruce et al. 2023). This engagement fosters a culture of accountability and innovation, driving SMEs to integrate environmentally conscious practices into their operations. Furthermore, by leveraging digital platforms to showcase their sustainability efforts, SMEs can enhance their reputation, attract eco-conscious customers, and secure investment from sustainability-driven stakeholders (Yildiz et al. 2024). Based on these insights, we hypothesize:

3.10 Mediating Role of Digital Social Networking

The adoption of GIoT technologies plays a vital role in enhancing the environmental performance of SMEs by enabling real-time monitoring, data-driven decision-making, and optimized resource management (Alalwan et al. 2024). However, the effectiveness of GIoT adoption is significantly influenced by the social and digital networks in which SMEs operate. Digital social networking provides SMEs with access to knowledge, expertise, and industry best practices, which can facilitate the adoption and integration of green technologies into their business operations (Scuotto et al. 2017). Through digital platforms, SMEs can exchange insights, share sustainability strategies, and collaborate on green initiatives, thereby accelerating the adoption of GIoT solutions and their contribution to environmental performance.

The role of digital social networking extends beyond knowledge sharing to enhancing SMEs' visibility and credibility in sustainability efforts (Yildiz et al. 2024). Through actively participating in online sustainability discussions and showcasing their GIoT-driven green initiatives, SMEs can attract environmentally conscious consumers, investors, and business partners. This digital engagement not only strengthens SMEs' commitment to sustainability but also enhances the impact of GIoT adoption on their environmental performance. Thus, digital social networking acts as a crucial mediator in the relationship between GIoT adoption and SMEs' sustainability outcomes. Therefore, we hypothesize:

3.11 GIoT and Environmental Performance Between Manufacturing and Service SMEs

In Malaysia, SMEs are officially classified by SME Corp Malaysia based on two primary criteria: annual sales turnover and number of full-time employees. These criteria vary across the manufacturing and service sectors to reflect their distinct operational and economic characteristics. Table 1 presents the SME definitions applicable to this study.

These classifications serve as foundational benchmarks for government policy, support programs, and research on SME development. The distinctions between the manufacturing and service sectors reflect their differing economic roles, operational scales, and resource requirements, which are essential considerations in studies focusing on SME development and sustainability, such as the adoption of GIoT technologies.

The adoption of GIoT technologies and the environmental performance outcomes they generate vary notably between the manufacturing and service sectors. These differences are rooted in the operational intensity, resource utilization, and regulatory exposure that characterize each sector.

Manufacturing SMEs typically operate within resource- and energy-intensive environments, making them more directly accountable for carbon emissions, waste generation, and other environmental externalities. As a result, they face greater pressure from both regulatory authorities and supply chain partners to implement sustainable practices (Sony et al. 2021). GIoT technologies—such as sensor-based monitoring, automated energy control systems, and smart logistics—are highly applicable in these contexts, offering manufacturers the ability to track emissions, reduce energy use, and ensure compliance with environmental standards (Pylaeva et al. 2022). Additionally, manufacturing SMEs often exhibit a stronger technological orientation, which further facilitates their ability to adopt and integrate GIoT into daily operations.

On the other hand, service SMEs are generally less energy- or material-intensive in nature and may perceive fewer immediate incentives for adopting environmentally focused technologies (Thomas et al. 2022). However, GIoT technologies can still add significant value in areas like energy-efficient building management, smart customer service systems, sustainable logistics coordination, and digital waste reduction solutions. The key barrier lies in the perceived applicability and return on investment of GIoT in service-based contexts, which may not be as readily apparent as in manufacturing.

Moreover, prior studies have shown that technology adoption in service firms tends to lag behind due to lower levels of digital infrastructure, skill gaps, and a weaker culture of innovation (Opazo-Basáez et al. 2022). This sectoral divide influences not just the likelihood of adopting GIoT but also the degree to which such adoption translates into measurable environmental performance. These variations in GIoT adoption and its impact on environmental performance highlight the need for sector-specific strategies. Such differences influence the effectiveness with which SMEs integrate GIoT technologies into their operations to achieve sustainability objectives. Based on these observations, we propose the following hypotheses:

3.12 Conceptual Framework

The proposed conceptual framework (refer to Figure 1) for this study examines the relationship between GIoT adoption and environmental performance of SMEs, with a specific focus on the mediating role of digital social networking. The framework is built on the extended UTAUT, which provides a robust theoretical foundation for understanding the factors influencing technology adoption and its subsequent impacts.

This conceptual framework offers a comprehensive approach to understanding the interplay of GIoT adoption, digital social networking, and environmental performance across different SME sectors. It underscores the critical pathways and sector-specific dynamics that contribute to achieving sustainability objectives.

4.1 Questionnaire Design

To effectively capture the data required for addressing the research objectives, a structured questionnaire comprising two distinct sections was meticulously developed. Section A was designed to gather essential demographic and background information from the respondents. This included key variables such as the nature of the SME—categorized as either manufacturing or service sector—and their level of familiarity or experience with GIoT technologies. These demographic insights were crucial for contextualizing the data and enabling subgroup comparisons, particularly in examining sector-specific adoption patterns.

Section B of the questionnaire was dedicated to assessing the core constructs underpinning the study's conceptual framework. This section included a series of items measured on a 7-point Likert scale, ranging from “strongly disagree” to “strongly agree.” It was structured to evaluate respondents' perceptions of GIoT adoption. Furthermore, this section also explored how digital social networking acts as a mediating mechanism in the relationship between GIoT adoption and the environmental performance of SMEs. Carefully adapted from validated scales in existing literature, all questionnaire items were refined to ensure clarity, reliability, and contextual relevance to the SME environment.

This two-part design facilitated the collection of both descriptive and inferential data, enabling a comprehensive analysis of how GIoT adoption—mediated by digital social networking—contributes to sustainable business practices within SMEs.

4.2 Pre-Testing and Validation

Prior to the full-scale data collection, the questionnaire instrument underwent a comprehensive pre-testing and validation process to ensure its effectiveness in capturing reliable and meaningful data (Abied et al. 2022). This critical step was undertaken to assess the clarity, content validity, and contextual appropriateness of the measurement items (Liu et al. 2024).

To achieve this, the draft questionnaire was reviewed by a panel of 15 experts with substantial experience in technology management and SME operations. The panel comprised a balanced mix of academic scholars and industry practitioners, allowing for diverse perspectives that strengthened the instrument's overall validity. These experts were invited to provide constructive feedback on a range of elements, including the phrasing and structure of items, the logical flow of sections, the comprehensiveness of constructs, and the alignment of items with the study's conceptual framework.

Based on expert evaluations, several revisions were made to improve the questionnaire's clarity, eliminate ambiguity, and remove redundant items. From the original 56 measurement items, 51 were retained, reflecting those considered most relevant, clear, and theoretically sound. Five items were removed due to conceptual overlap or limited applicability within the context of SMEs and GIoT technologies. Specifically, one item from Performance Expectancy (“Adopting GIoT improves decision-making in my SME”) was removed. From Effort Expectancy, the item “It is easy to become skilful at using GIoT technology” was excluded. In Facilitating Conditions, the item “There is adequate technical support for using GIoT” was dropped. Additionally, the Perceived Motivation item “I find the idea of using GIoT rewarding and inspiring” and the Environmental Performance item “We actively monitor and manage environmental impact” were also removed. These adjustments ensure the final questionnaire is more streamlined, focused, and contextually appropriate for studying GIoT adoption among SMEs.

4.3 Measurement Scale

To capture respondents' perceptions accurately and support rigorous statistical analysis, a seven-point Likert scale was utilized for all items in Part 2 of the questionnaire, which measured the latent constructs related to GIoT adoption, digital social networking, and environmental performance. The scale ranged from 1 = “Strongly Disagree” to 7 = “Strongly Agree,” allowing participants to express varying degrees of agreement or disagreement with each statement.

The use of a seven-point scale was strategically chosen based on methodological recommendations from prior research in the fields of social science (such as Yusoff and Mohd Janor 2014) and behavioral studies (such as D'Souza et al. 2021), especially those incorporating structural equation modelling (SEM). Compared to narrower scales, such as five-point Likert scales, the seven-point format offers greater granularity and sensitivity, enabling more precise measurement of attitudes, beliefs, and behavioral intentions (Chandrasekaran et al. 2012).

4.4 Questionnaire Translation

Recognizing the linguistic diversity of the target respondents, a comprehensive translation protocol was implemented to ensure the questionnaire's accessibility and conceptual clarity across language boundaries (Hall et al. 2018). To this end, the original English version of the survey instrument was subjected to a systematic translation and back-translation procedure, following widely accepted practices in cross-cultural research (such as Squires et al. 2013).

The initial translation was conducted by a bilingual professional proficient in both English and the local language (Ibáñez et al. 2010), and well-versed in the contextual nuances of green technology adoption and SME operations. This ensured that technical terms and research-specific phrases were appropriately rendered for local comprehension. To verify the accuracy and semantic consistency of the translation, a second independent bilingual translator, who had no prior exposure to the original text, retranslated the localized version back into English (Wood et al. 2018).

The back-translated version was then carefully compared with the original English instrument to detect any variations in meaning, tone, or emphasis. Differences that emerged were thoroughly reviewed and addressed through collaborative deliberations among the translators and the research team (Colina et al. 2017). This iterative reconciliation process aimed to uphold the integrity of the constructs and preserve the intentional alignment of the measurement items.

Through this rigorous approach, the translated version of the questionnaire maintained linguistic and conceptual equivalence, ensuring that the instrument was both culturally sensitive and semantically accurate. This enhanced the validity and reliability of the data collected from respondents with varying levels of English proficiency, ultimately contributing to the robustness of the study's empirical findings across a diverse SME population.

4.5 Ethics Statement

This study was conducted in accordance with established ethical standards to ensure the rights, dignity, and confidentiality of all participants were protected. Ethical clearance for this research was obtained from the Universiti Malaysia Sarawak (UNIMAS) Ethics Committee. The study protocol, including the research design, participant recruitment procedures, and data handling practices, was reviewed and approved under the approval number UNIMAS-EC-11/2024/R06, dated 15 November 2024. All participants were informed about the purpose of the study, and informed consent was obtained prior to their involvement. Participation was voluntary, and respondents were assured of the confidentiality and anonymity of their responses.

4.6 Sample and Procedure

To examine the proposed research model, this study employed a cross-sectional survey design, enabling data collection from a broad range of participants at a single point in time. A quota sampling approach was used to ensure balanced representation of SMEs from both the manufacturing and service sectors. The sample was strategically drawn from multiple regions across Malaysia, including East Malaysia (Sarawak and Sabah) and West Malaysia (Perlis, Kedah, Penang, Perak, Selangor, Johor, and Kuala Lumpur). The intentional inclusion of SMEs from both sides of the country enhances the study's contextual relevance and provides a more comprehensive understanding of sectoral dynamics. This geographic diversification is particularly important given the distinct socio-cultural dynamics, economic development levels, infrastructural disparities, and policy enforcement variations that exist between the two regions. These contextual differences have the potential to shape how businesses perceive, adopt, and implement technological innovations such as the GIoT. For instance, SMEs in West Malaysia may benefit from greater access to digital infrastructure, technical expertise, and government support programs, whereas those in East Malaysia might operate under different regulatory conditions or resource constraints.

The data collection instrument—a structured questionnaire—was disseminated electronically through various channels, including email invitations and social media platforms such as Twitter, LinkedIn, and Facebook. This digital approach was adopted to enhance reach and engagement, particularly in light of the wide geographic dispersion of SMEs. All participants were clearly informed about the confidentiality and anonymity of their responses, in line with ethical standards for research involving human participants.

According to SME Corp Malaysia, the SME sector in Malaysia is predominantly composed of service sector, which comprises 89.2% (809,126 firms) of the total SME population, while 5.3% (47,698 firms) operate within the manufacturing sector. These national statistics informed the sampling framework, prompting the need to set quotas for both groups to ensure balanced insights. Guided by Krejcie and Morgan's (1970) widely referenced sample size table, the study aimed to collect responses from 381 manufacturing SME representatives and 384 service SME representatives, resulting in a total targeted sample of 765 respondents.

Data collection was carried out during December 2024, culminating in a noteworthy response rate of 80.3%. From the 765 distributed surveys, 614 fully completed and valid responses were returned. Of these, 291 originated from manufacturing SMEs, while 323 were from service SMEs. This high response rate is consistent with the industry research benchmarks proposed by Baruch and Holtom (2008), reinforcing the reliability and effectiveness of the data collection process.

4.7 Measures

4.8 Statistical Analysis

To rigorously test the proposed hypotheses and assess the interrelationships among the latent constructs, this study adopted a two-step SEM approach. This methodology, grounded in the recommendations of Mia et al. (2019), is well-recognized for its capacity to evaluate complex theoretical models involving multiple dependent and independent variables simultaneously. The analysis was executed using AMOS version 28.0, with maximum likelihood estimation (MLE) employed as the primary estimation technique due to its robustness and suitability for handling multivariate data in SEM contexts.

The first phase of the SEM process involved the assessment of the measurement model through confirmatory factor analysis (CFA), in accordance with the guidelines outlined by Hair et al. (2014). CFA was conducted to verify the reliability and validity of the constructs by examining individual factor loadings, composite reliability (CR), and average variance extracted (AVE). This ensured that each item accurately represented its corresponding latent construct, thereby establishing convergent validity. To assess discriminant validity, the AVE values were compared against the squared inter-construct correlations. These measures collectively confirmed that the constructs were both conceptually distinct and statistically sound.

Following validation of the measurement model, the structural model was evaluated to explore the hypothesized causal pathways between GIoT adoption, digital social networking, and environmental performance. Special emphasis was placed on investigating the mediating effect of digital social networking. For this purpose, a bootstrapping procedure—a non-parametric resampling method advocated by Hair et al. (2021)—was employed. This technique involved generating 1000 bootstrap samples and calculating bias-corrected confidence intervals to assess the significance of indirect effects. The bootstrapping analysis provided robust evidence supporting the mediation hypothesis and offered deeper insights into the indirect mechanisms driving environmental performance improvements.

Moreover, Multi-Group Analysis (MGA) was employed in this study, a method widely used in group comparison studies within SEM, particularly for examining whether structural relationships differ across predefined categories (Ramadani et al. 2022). In the present context, MGA was not utilized to assess mediation effects, but rather to compare the strength of relationships—such as the link between GIoT adoption and environmental performance—across two distinct groups: manufacturing and service SMEs. This application aligns with conceptual frameworks that treat industry type as a grouping variable, allowing for the exploration of whether key structural paths vary significantly between sectors. Accordingly, the use of MGA is both methodologically appropriate and theoretically justified for testing hypothesis H11.

5.1 Demographic Profile of Participants

Demographic information plays a vital role in understanding the representativeness of a sample group for generalization purposes (Cash et al. 2022). In this study, a total of 614 owners/managers from manufacturing and service SMEs were surveyed. These participants were selected based on their knowledge of green technologies or IoT applications, ensuring relevance to the research context.

The demographic characteristics of the participants, as summarized in Table 2, provide insights into their backgrounds, including key variables such as gender, age, educational qualifications, SME sector involvement, and so on. This information not only aids in assessing the diversity of the sample but also provides a foundation for interpreting the study's findings within the broader SME context. Such a comprehensive demographic analysis ensuring that the participants represent a suitable cross-section of the population relevant to green IoT adoption and environmental performance of SMEs.

5.2 Normality Test for SEM-AMOS Analysis

In the context of SEM using AMOS, assessing the normality of data is a vital prerequisite to ensure the accuracy and reliability of the analysis. SEM techniques generally assume that the data follow a multivariate normal distribution, as this underpins the validity of parameter estimates, standard errors, and model fit indices (AL-Fadhali 2024). Any significant departure from normality—such as skewed or overly peaked (leptokurtic) distributions—can introduce bias into parameter estimates, distort standard errors, and lead to misleading conclusions (Tavakkoli et al. 2024). Therefore, a thorough examination of normality is essential prior to conducting SEM procedures.

Normality assessment typically begins with evaluating univariate skewness and kurtosis for each observed variable. Skewness measures the asymmetry of a distribution (Nasrollahi et al. 2021), while kurtosis evaluates the “peakedness” or flatness of the distribution relative to a normal curve (Mohammed et al. 2022). According to guidelines by Lai (2018), skewness and kurtosis values within the range of −3 to +3 are generally acceptable when applying MLE in AMOS.

In this study, each construct was assessed for univariate normality. The results indicate that all variables fall within the acceptable skewness and kurtosis thresholds, suggesting the absence of significant univariate non-normality. Furthermore, multivariate normality was assessed to strengthen the robustness of the analysis. Multivariate kurtosis values and critical ratios were examined for all cases. As reported in Table 3, multivariate kurtosis values were within the recommended range of −3 to +3, and critical ratio values were below the threshold of 1.96 (Corrêa Ferraz et al. 2022). These results confirm that the assumption of multivariate normality is adequately met.

5.3 Assessment of Discriminant Validity

Discriminant validity is a critical criterion in SEM that ensures constructs are distinct and measure separate theoretical concepts. Rönkkö and Cho (2022), emphasized the importance of assessing discriminant validity by evaluating correlations adjusted for measurement errors. One widely adopted method for this evaluation is the heterotrait-monotrait (HTMT) ratio of correlations.

Roemer et al. (2021) proposed a threshold value of 0.85 for the HTMT ratio, indicating that constructs with HTMT values below this threshold exhibit adequate discriminant validity. This approach has gained significant empirical support, with Cheung et al. (2024) further validating its reliability and effectiveness as a diagnostic tool in SEM analyses.

In this study, the HTMT was applied to assess discriminant validity. As shown in Table 4, all HTMT values fall below the 0.85 threshold, confirming that the constructs in the proposed model are sufficiently distinct and not excessively correlated. This confirmation strengthens the robustness of the model and enhances the validity of the theoretical relationships examined in the research.

5.4 Assessment of Confirmatory Factor Analysis (CFA)

During the CFA, several items were removed from the initial measurement model due to low factor loadings, ensuring the robustness and precision of the model: 1 item from performance expectancy, 2 items from effort expectancy, 1 item from social influence, 1 item from facilitating conditions, 2 items from perceived motivation, 1 item from perceived environmental knowledge, 1 item from digital social networking, and 1 item from environmental performance. After refining the model, all remaining items demonstrated satisfactory factor loadings, exceeding the threshold of 0.70, indicating strong item-to-construct relationships. Figure 2 presents the refined measurement model, showcasing the relationships between the constructs and their associated indicators.

The model demonstrated an acceptable level of fit, as indicated by the following indices: RMSEA = 0.051, chi-square = 553.671, df = 613, GFI = 0.919, AGFI = 0.942, CFI = 0.951, and CMIN/df = 3.419. These values fall within the recommended thresholds for a good model fit, consistent with the guidelines provided by Dash and Paul (2021). Overall, the results confirm that the revised measurement model effectively captures the underlying data structure, supporting the validity and reliability of the constructs employed in this study.

In the CFA, construct validity was evaluated using two key metrics: AVE and CR. AVE assesses the degree to which a construct captures the variance of its indicators relative to the variance attributed to measurement error, with a threshold of 0.50 or higher indicating adequate convergent validity (Nomran and Haron 2022). CR measures the internal consistency of the indicators associated with each construct, with values exceeding 0.70 reflecting acceptable reliability (Al Masud et al. 2024). Together, these indicators confirm that the constructs reliably and validly represent the intended theoretical dimensions.

As presented in Table 5, all constructs achieved AVE values above the recommended minimum of 0.50 and CR values exceeding the 0.70 benchmark. These results indicate that the measurement model possesses satisfactory convergent validity and strong internal consistency. Accordingly, the constructs are considered both reliable and valid, confirming their appropriateness for subsequent analysis within the SEM framework.

5.5 Assessment of Structural Model

In this study, the structural model was assessed to examine the hypothesized relationships between the exogenous (independent) and endogenous (dependent) latent variables following the successful validation of the measurement model through CFA. According to Hair and Alamer (2022), the structural model evaluation is a critical step in SEM using AMOS, as it tests the strength, direction, and statistical significance of the proposed paths among the constructs based on the theoretical framework developed.

Additionally, the Goodness-of-Fit Indices from the SEM analysis were assessed to evaluate the overall fit of the structural model. The indices—Chi-square = 3.631, GFI = 0.954, AGFI = 0.942, CFI = 0.961, TLI = 0.965, and RMSEA = 0.047—indicate an excellent model fit, consistent with the recommended thresholds outlined by Hair et al. (2014). As depicted in Figure 3, the structural model accurately captures the relationships among the latent constructs. These results affirm the model's appropriateness for hypothesis testing and reinforce the credibility of the study's findings.

The model was evaluated by analyzing the standardized path coefficients (β values) and their corresponding p values, which indicate the magnitude and significance of the relationships between constructs. A path coefficient is considered significant if the p value is less than 0.05, indicating that the relationship between the constructs is statistically meaningful. In addition, the critical ratio (CR), which functions like a z-value, was also considered, with values above 1.96 confirming statistical significance at the 5% level.

The research model tested eleven hypotheses, nine of which involved direct relationships, as detailed in Table 6. The analysis revealed that performance expectancy (PE: β = 0.32, z = 4.379, p < 0.001), effort expectancy (EE: β = 0.39, z = 5.285, p < 0.001), social influence (SI: β = 0.36, z = 4.971, p < 0.001), facilitating conditions (FC: β = 0.41, z = 6.243, p < 0.001), perceived motivation (PM: β = 0.37, z = 5.118, p < 0.001), and perceived environmental knowledge (PEK: β = 0.43, z = 6.493, p < 0.001) all had significant and positive effects on GIoT adoption. Furthermore, GIoT adoption was found to significantly influence environmental performance (EP: β = 0.29, z = 4.017, p < 0.001) and digital social networking (DSN: β = 0.54, z = 7.352, p < 0.001). In addition, digital social networking positively impacted environmental performance (β = 0.57, z = 7.896, p < 0.001). Based on these results, hypotheses H1–H9 were supported. The structural model exhibited an acceptable fit to the data, reinforcing the validity of the proposed theoretical framework and confirming the hypothesized relationships among GIoT adoption, digital social networking, and environmental performance.

5.6 Assessment of Mediating Effect

The mediating role of digital social networking in the relationship between GIoT adoption and the environmental performance of SMEs was rigorously examined in this study. The results provided strong empirical evidence supporting Hypothesis (H10), which proposed that digital social networking serves as a significant mediator in this relationship. This mediation was evaluated using the SEM approach in AMOS, incorporating both direct and indirect effects analysis.

Consistent with the complementary mediation criteria articulated by Zhao et al. (2010), the findings revealed that the direct effect of GIoT adoption on environmental performance remained significant even after including digital social networking as a mediating variable. Simultaneously, the indirect effect of GIoT on environmental performance through digital social networking was also statistically significant. These results collectively suggest that while GIoT adoption directly contributes to improved environmental performance, its impact is substantially amplified through the engagement of SMEs in digital social networking activities.

Moreover, the standardized path coefficients for both the direct (GIoT → Environmental Performance) and indirect (GIoT → Digital Social Networking → Environmental Performance) paths were positive and significant, confirming the strength and directionality of the hypothesized relationships. The presence of both significant direct and indirect effects satisfies the conditions for partial mediation, indicating that digital social networking does not replace but rather enhances the positive influence of GIoT technologies on SMEs' sustainability outcomes.

To rigorously validate the mediating role of digital social networking in the relationship between GIoT adoption and the environmental performance of SMEs, this study implemented an advanced statistical approach grounded in bootstrapping methodology. Following the recommendation of Rasoolimanesh et al. (2021), the analysis employed the Maximum Likelihood Bootstrapping technique, which offers a robust mechanism for estimating indirect effects in structural models, particularly when data may exhibit non-normal distributions. A bootstrap resampling procedure was conducted with 1000 iterations, using a bias-corrected confidence interval of 95% to enhance the precision and credibility of the estimates (Dhaene and Rosseel 2022). The outcomes, presented in Table 7, provided strong statistical support for the hypothesized complementary mediation effect. Specifically, both the direct path from GIoT adoption to environmental performance and the indirect path through digital social networking were found to be positive and statistically significant, confirming the mediating role of digital social networking as proposed.

5.7 Assessment of Multi-Group Analysis

These findings highlight the nuanced differences in how GIoT adoption influences environmental performance within manufacturing and service SMEs while maintaining consistent factor correlations. The detailed results of the invariance analysis are presented in Table 8, offering insights into sector-specific dynamics and reinforcing the importance of tailoring green technology initiatives to the unique characteristics of each sector.

To conduct a comprehensive comparison between the two structural models illustrated in Figures 4 and 5, the study adopted a multi-step approach rooted in established methodological guidelines. Following the recommendation of Costa et al. (2017), we employed critical ratio (CR) analysis to examine the equivalence of individual path coefficients across the two models. This method allows for pairwise comparison of parameters to determine whether their values significantly differ between samples or subgroups.

As detailed in Table 9, the results of the critical ratio analysis demonstrate that all Z-values for the examined path differences were greater than the threshold of 1.96. This empirical evidence confirms that there are statistically significant disparities in the factor loadings and path relationships between the two models under consideration. The findings suggest that the constructs behave differently across the two groups, highlighting the importance of contextual or sample-specific dynamics in shaping the structural relationships within the GIoT adoption framework.

The analysis revealed a notable variation in the strength of the relationship between GIoT adoption and environmental performance across different SME sectors. Specifically, the results indicate that this relationship is significantly stronger in manufacturing SMEs compared to their counterparts in the service sector. This finding underscores the sector-specific relevance and applicability of GIoT technologies, particularly in manufacturing settings where operational processes often have a more direct and measurable environmental impact.

These empirical results lend robust support to H11, affirming that manufacturing SMEs demonstrate a higher inclination to adopt GIoT technologies as a strategic initiative for improving their environmental performance, compared to service SMEs. The differential impact further highlights the importance of tailoring GIoT implementation strategies according to sectoral characteristics and needs, thereby maximizing sustainability outcomes across diverse SME ecosystems.

5.8 Addressing Research Gaps

The analytical findings of the study directly respond to the identified research gaps in the literature on GIoT adoption by SMEs, particularly its environmental implications and the underexplored mediating role of digital social networking. Addressing RQ1, the results empirically confirm that GIoT adoption positively influences environmental performance, thereby validating the technology's strategic value in supporting sustainable business practices. This finding contributes to the growing literature on green digital transformation by positioning GIoT as a viable mechanism for achieving environmental objectives in SME contexts.

In response to RQ2, the study provides robust evidence for the mediating role of digital social networking in the GIoT–environmental performance relationship. By demonstrating that digital social networking facilitates knowledge exchange, collaboration, and diffusion of best practices, the study advances theoretical understanding of how digital platforms can amplify the environmental benefits of technological adoption—a dimension that remains underexplored in existing models.

Finally, RQ3 is addressed through multi-group analysis, which reveals that manufacturing SMEs benefit more substantially from GIoT adoption than service SMEs. This sectoral insight adds depth to the literature by highlighting the differentiated impact of GIoT across SME categories, further reinforcing the need for context-specific policy and implementation strategies.

Collectively, these findings expand the UTAUT framework by incorporating perceived motivation and environmental knowledge as critical antecedents and by integrating environmental sustainability and digital mediation as key outcomes. This comprehensive approach offers a meaningful theoretical advancement in the fields of green technology adoption and digital innovation in SMEs.

8.1 Theoretical Implications

This study contributes significantly to the existing body of knowledge by advancing the theoretical understanding of technology adoption within the domain of green digital transformation among SMEs. By extending the UTAUT, this research introduces two novel constructs—perceived motivation and perceived environmental knowledge—which enhance the framework's explanatory power in the context of sustainable technology uptake.

Perceived motivation, an internal psychological driver, interacts synergistically with traditional UTAUT constructs such as performance expectancy and effort expectancy. For instance, SME owners with high motivation are more inclined to view GIoT as instrumental in achieving long-term sustainability goals, which in turn strengthens the influence of performance expectancy on their adoption decisions. Similarly, motivation can facilitate the relationship with effort expectancy, where motivated individuals are more willing to invest effort into learning and using GIoT, even when initial usability challenges exist. This interplay highlights motivation not merely as a parallel predictor but as an intrinsic enhancer of core adoption determinants.

Perceived environmental knowledge also plays a critical role in reinforcing UTAUT components. Individuals with a strong understanding of environmental issues are more likely to respond positively to social influence, especially when sustainability norms are promoted within their professional networks or industry associations. This cognitive factor supports more informed assessments of facilitating conditions, such as infrastructure or government support, because knowledgeable users can better evaluate what resources or capabilities are truly required to implement GIoT effectively. In this sense, environmental knowledge serves as a cognitive filter that shapes how traditional UTAUT constructs are interpreted and acted upon within a green innovation context.

This study also broadens the scope of UTAUT by applying it to GIoT adoption, a niche yet increasingly critical area in the realm of environmental sustainability. While UTAUT has traditionally been used to explain general-purpose technology adoption, this research applies the model specifically to technologies aimed at achieving sustainability outcomes. By doing so, it not only confirms the relevance of constructs like performance expectancy, effort expectancy, social influence, and facilitating conditions but also tailors them to the specific features and challenges of environmentally focused IoT systems. For instance, performance expectancy in the context of GIoT refers not only to productivity or efficiency gains but also to environmental improvements, such as energy reduction, emissions monitoring, or waste management—outcomes that are often undervalued in standard UTAUT applications.

Furthermore, the study integrates environmental performance as a key outcome of GIoT adoption, thereby bridging technology adoption theory with the sustainability literature. By empirically demonstrating that GIoT positively influences environmental performance among SMEs, this research positions sustainability as a valid and measurable result of digital innovation, enriching the theoretical intersection between green technology frameworks and behavioural models of adoption. This offers a new lens through which UTAUT can be used—not only to explain adoption behavior but also to evaluate its environmental consequences.

Finally, the study introduces and validates the mediating effect of digital social networking between GIoT adoption and environmental performance, offering a novel contribution to the theoretical landscape. Digital social networking is theorized as a digital enabler that facilitates knowledge sharing, peer learning, and collaborative innovation among SMEs. This insight contributes to a deeper understanding of the mechanisms through which adoption translates into tangible sustainability outcomes. Specifically, digital social networking enhances the diffusion of best practices, encourages problem-solving through peer support, and creates social reinforcement that further accelerates the adoption and effective use of GIoT technologies. As such, digital social networking not only mediates but amplifies the impact of adoption by embedding it within a dynamic digital ecosystem.

8.2 Practical Implications

The adoption of a Green Internet of Things (GIoT) framework presents valuable practical implications for SMEs striving to enhance their environmental performance while aligning with global sustainability goals. This study confirms that GIoT technologies allow SMEs to monitor, analyze, and optimize resource consumption, such as energy usage, emissions, and waste, which contributes to both operational efficiency and environmental stewardship. SME owners and managers are strongly encouraged to integrate GIoT into their operations, not only to improve their environmental footprint but also to strengthen their market competitiveness, meet regulatory expectations, and appeal to increasingly eco-conscious consumers and business partners.

To facilitate this, SMEs should begin by assessing their readiness for digital transformation, identifying areas where GIoT can deliver quick and measurable benefits. Implementing pilot projects, such as smart energy monitoring or automated waste reduction systems, can serve as practical starting points before scaling up adoption. In parallel, SMEs should invest in digital skills development to ensure that their workforce can effectively manage and operate GIoT systems. Appointing sustainability champions or forming green teams within the organization can further support GIoT implementation and promote a culture of environmental responsibility.

Digital social networking platforms also play a vital role in the successful adoption of GIoT by enabling SMEs to share best practices, exchange technical know-how, and collaborate with other businesses and stakeholders. These platforms offer access to essential resources such as training modules, implementation guides, and sector-specific case studies, thereby reducing information barriers and promoting peer learning. SMEs are encouraged to actively participate in online sustainability forums and industry-focused digital networks to stay informed and inspired by the experiences of others.

Policymakers have a critical role to play in creating an enabling environment for GIoT adoption. One practical step is to develop national or regional GIoT adoption roadmaps that outline step-by-step implementation strategies tailored to different sectors. These roadmaps should be developed in collaboration with industry stakeholders to ensure relevance and applicability. In addition, governments can support SMEs financially by offering tax incentives, grants, or green technology subsidies that specifically target GIoT investments. Priority should be given to industries with high environmental impact such as manufacturing, logistics, and construction.

Infrastructure development is equally essential. Policymakers should consider establishing regional GIoT support centers or digital innovation hubs that provide SMEs with technical guidance, training, and access to digital tools. These hubs can be linked to national platforms that connect SMEs with GIoT vendors, consultants, and peer networks. Sector-specific strategies should also be implemented—manufacturing SMEs can benefit from templates and technical blueprints to integrate GIoT into production lines, while service SMEs may need practical demonstrations and training to understand how GIoT applies to their specific operations, such as energy management or customer-facing sustainability tools.

Awareness and capacity building are foundational to any successful green technology policy. Governments and industry associations should launch targeted awareness campaigns to promote understanding of GIoT and its benefits. Simultaneously, practical training programs such as workshops, bootcamps, and webinars should be organized at local, regional, and national levels to upskill SME leaders and staff. These programs can demystify GIoT, provide actionable knowledge, and encourage a mindset of continuous learning and innovation.