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Article

Leveraging IoT Micro-Factories for Equitable Trade: Enhancing Semi-Finished Orange Juice Value Chain in a Citriculture Society

by
Joseph Andrew Chakumba
1,*,
Jiafei Jin
1 and
Dalton Hebert Kisanga
2
1
Business School, Harbin Institute of Technology, Harbin 150001, China
2
Computer Studies, Dar es Salaam Institute of Technology, Dar es Salaam 11103, Tanzania
*
Author to whom correspondence should be addressed.
Systems 2025, 13(5), 384; https://doi.org/10.3390/systems13050384
Submission received: 25 March 2025 / Revised: 8 May 2025 / Accepted: 12 May 2025 / Published: 16 May 2025
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Abstract

:
Sustainable development initiatives are essential for enhancing the social economy and environmental preservation in marginalised rural areas of Tanzania. This study examines the impact of an IoT micro-factory on sustainable development, addressing issues such as inadequate production techniques, agribusiness monopolisation practices, the shortage of small-scale factories, and the failure to leverage global market comparative advantages. It explores the mediating role of architectural innovation and the moderating role of industrial symbiosis. The study surveyed 196 participants, including 100 orange farmers, 96 industrial engineers in the beverage sector, and conducted interviews with 3 industrial managers and 3 industrial consultants. SmartPLS 4 was used to evaluate the relationships between constructs. The results indicate that both IoT micro-factories and global production networks (GPNs) have a direct influence on sustainable social-economic development. Architectural innovation mediates these relationships, while industrial symbiotic moderates the interaction between IoT micro-factories and architectural innovation. The findings emphasise the importance of IoT micro-factories for sustainable development, with industrial symbiotic relationships addressing gaps in knowledge, skills, and equitable trade. The industrial stakeholders should prioritise IoT micro-factories as small-scale factories to promote sustainable development in rural communities of developing countries.

1. Introduction

1.1. Background

The IoT micro-factory is a critical enabler for sustainable development in developing countries. The influx of second-hand goods from developed countries floods the markets, saturating them and impeding industrial growth, particularly in Tanzania [1,2]. In developing countries, demand and supply are well balanced, fostering industrial growth. However, products and services from developed countries, characterised by modern design, often outperform those from developing countries in both domestic and global markets due to significant disparities in product quality [3]. Socio-economic discrimination between developed and developing countries has become widespread, and limited efforts to address socio-economic equality have had little impact on narrowing the gap between impoverished and prosperous societies, leading to substandard living conditions [4]. Developing countries lack the financial resources to invest in cyber–physical systems, which undermines the development of their industries, such as Mwanza Textile (MWATEX), Musoma Textile (MUTEX), and Urafiki Textile Company (URAFIK) in Tanzania. As a result, the global market continues to present an inequitable comparative advantage for developing countries [5].
There is an increasing need for novel strategies to foster industrial development that align with specific contextual factors, particularly for rural communities, by addressing challenges related to financial, knowledge, and technological issues [6]. The interplay between human development and industrialisation encourages developing countries to pursue industrialisation through innovation that aligns with Industry 4.0, aiming to mitigate challenges associated with the higher cost of modern manufacturing systems.
The absence of small-scale factories (SSFs) for raw materials value addition creates an inequitable business environment, enabling merchants to dominate agribusiness markets. The existing traditional conventional industries contribute to environmental degradation, and resource-based production exacerbates the situation. The proposed framework seeks to address several global issues, including climate change, ecosystem degradation, waste accumulation, and food security, which contribute to poverty, affecting many communities in Tanzania [3,4,7]. The lack of an appropriate contextual framework with proper theoretical simplification hinders the ability of these communities to perceive reality, understand, and engage in sustainable production practices that could address their socio-economic and environmental challenges [8].
This study focuses on IoT micro-factories as small-scale automated manufacturing units that utilise the Internet of Things for real-time data collection, analysis, and control. These units rely on integrated connected sensors, actuators, microprocessors, or microcontrollers to facilitate quick decision making, flexibility, and scalability. The innovative design and configuration of IoT micro-factories involve the integration of both physical and digital infrastructures, combining IoT devices, data network, cloud computing, and automated devices. The architectural innovation extends beyond the physical layout to the connectivity of various manufacturing units through a software framework, data analysis, and automation. The outcome includes increased efficiency, performance, flexibility, scalability, and responsiveness. The study applies multi-level perspective theory as a theoretical framework to explore the diffusion of IoT micro-factories, integrating technical operations and business aspects across large-scale and small-scale industries through industrial symbiotic relationships [6].
This study explores the significant roles of IoT micro-factories in addressing the challenges related to orange fruit production in Muheza, Tanga. These challenges include the spoilage of oranges, price volatility, market monopolies imposed by merchants, and the limited shelf life of oranges as perishable products, all of which contribute to impoverished farmers [4,9]. An IoT micro-factory is designed to extend the shelf-life of orange products via the production of semi-finished orange juice (SFOJ) in the form of frozen concentrated orange juice (FCOJ), classified as HS200919 by UN Comtrade [9,10]. Fresh orange juice is dehydrated to extend its shelf life, occupying less volume and being nearly weightless, thus facilitating easier packaging, storage, and transportation from geographically scattered locations; one tonne of HS200919 can produce 10 tonnes of orange juice [11].
Another crucial role of the IoT micro-factory, as SSFs, is to foster collaboration with LSIs, referred to as inter-industrial symbiotic relationships. This partnership enhances technical, operational, managerial, and business support [12]. It increases productivity, creates new production opportunities, and promotes fair trade by eliminating merchants, streamlining the lean supply chain (LSC) of products between SSFs and LSIs [12]. The internal relationships among the IoT micro-factory modules enable production interactions that optimise resource utilisation and minimise waste through reuse and recycling. This form of interaction is termed intra-industrial symbiosis [12]. It contributes to the circular economy by transforming the Shamba economy into an industrial economy. Intra-industrial symbiosis involves interaction between different industries, particularly between SSFs and LSIs, to achieve objectives, including resources and infrastructure sharing, collaborative innovation, and technical and marketing support [13]. This process enhances access of marginalised citriculture communities in Tanzania to the global production network [12].
The multi-level perspective (MLP) theories promote spatial integration in industrial symbiotic interaction between SSFs and LSIs, as well as within global production networks across geographically dispersed regions [14]. This approach strengthens the interconnections of emerging production networks and socio-economic roles, serving as transitional mechanisms toward sustainable development. Furthermore, it explains that the technological innovation, based on a multi-tiered and inclusive approach, involves the industrial stakeholders and rural communities [14].
This study aims to provide a comprehensive analysis of the contribution of IoT micro-factories through industrial symbiosis, architectural innovation, and global production networks in promoting sustainable development in developing countries, particularly in Tanzania. While many studies have explored the roles of industries in developing countries, with a focus on socio-economic factors, there remains a lack of comprehensive research on techniques to enhance SSFs for achieving equitable comparative advantage in the global economy. The proposed framework enhances knowledge and offers practical guidance for the IoT micro-factories to optimise resource management and business strategies that align equitable trade growth with ecological and social welfare. This is achieved through an innovation ecosystem and a collaborative innovation network that strengthens the resource-based view, contributing to the United Nations Sustainable Development Goals (SDGs), including No Poverty (SDG 1), Decent Work and Economic Growth (SDG 8), Industry, Innovation and Infrastructure (SDG 9), and Responsible Consumption and Production (SDG 12).

1.2. Theoretical Framework

The multi-level perspective theory (MLP) is a pivotal framework for exploring the diffusion of IoT micro-factories in addressing vulnerabilities within the supply chain of perishable orange fruits, production localisation, on-demand manufacturing, and the potential for new production opportunities. Initially conceived by Arie Rip and Rene’ Kemp, it was later refined by Frank Geels to better understand the societal transition towards sustainable development. It enables interaction among various stakeholders within the socio-technical system, thereby influencing economic trends, cultural norms, and social values.
The MLP reshapes management structures both horizontally and vertically by promoting technological operations and communication across geographically dispersed industries. It fosters multiple collaboration efforts across different organisations, enabling social cooperation, responsibility, and comparative advantages in the digital economy [15]. Industrial collaboration networks enable SSFs in a rural area to receive technical and operational support from large-scale industries (LSIs), while ensuring the ability to receive just-in-time personnel support remotely. The framework emphasises on-demand production and the bargaining power between the SSFs and LSIs without merchants’ intervention [16]. The theory strengthens relationships across local, regional, and global enterprises, thereby supporting socio-economic transition towards sustainable development. The global operation framework of MLP allows the IoT micro-factories regime to respond to exogenous landscape pressure from global benchmarking organisations (ISO, WTO, GTT) by meeting standard and quality criteria required for entry into the global market [17].
MLP provides the framework for socio-economic transformation and the neo-institutionalisation of technological innovation, recognising the role of cultural norms, consumption patterns, and infrastructure design in localising production [18]. Theory highlights innovation through various strategies, such as learning, practices, and the transfer of niche innovations from the global to the regional level [18]. Nevertheless, it responds to the existing landscape and prompts the regime to undermine the merchant’s authority and promote just-in-time customer-centric production [18].
MLP promotes innovation in SSFs through the involvement of various industrial stakeholders, including consumers, manufacturers, consultancy agencies, socio-activists, social networks, policymakers, and investors across sectors such as transportation, communication, production, water, renewable energy, and agri-business [19]. The implementation of MLP varies by country due to specific contextual factors, such as the fact that local social movements exert pressure on the indigenous landscape to develop niche innovations that address internal demand before addressing the exogenous landscape pressure for GPN and entering the global market [19].
The theoretical framework facilitates the promotion of sustainable development by technological adopters through niche, regime, and landscape knowledge, as well as technology pathways. It also incorporates institutional frameworks and social networks to drive innovation within the context. This process involves retrofitting existing systems, enhancing cost efficiency, and improving high performance. R&D agencies, along with universities, provide a supportive environment to nurture and develop IoT micro-factory innovations through laboratory experiments before market introduction. The MLP adapts to external landscape pressures, aligning with the region’s economy and fostering collaboration with various institutional and organisational stakeholders [18,19,20].

1.3. Development of Hypothesis

1.3.1. Sustainable Development Through IoT Micro-Factory Diffusion

This study addresses sustainable development by extending the shelf life of oranges through SFOJ production, and by optimising the utilisation of raw materials to generate other valuable semi-finished products from by-products, all within the interconnected niche subsystems of IoT micro-factory modules [21]. These modules are integrated to form a collaborative network, ensuring the smooth flow of materials and their associated analytical capabilities [21,22]. In this system, the by-products from SFOJ production serve as inputs for further production processes, including the production of albedo tissue, oxidant compounds, fragrance, and compost manure [21]. The recycling of SFOJ by-products opens new business opportunities for both economic revitalisation and ecological sustainability. The symbiotic relationship between modules fosters a closed-loop system and a circular economy in which by-products are recovered and reused to create new products, thus minimising waste generation [21,22,23].
The interdependence of interconnected modules also has implications for natural ecosystems [22,24]. By integrating modules, the efficient utilisation and optimisation of raw materials through recycling and reuse are promoted, thereby enhancing the circular economy (CE) [24,25]. This creates a self-sustaining system within subsystems that incorporates multiple processes to reduce inefficiencies and inconsistencies in production units [25]. The relationship between subsystems is categorised as intra-symbiosis, while the collaboration between the SSFs and LSIs is considered as inter-symbiotic, as it enables on-demand production for socio-economic development [22].
The IoT micro-factory simplifies the absorptive capacity (ACAP) theory by utilising connectivity and automation to facilitate assimilation for technologies and knowledge from both internal and external sources, subsequently integrating them into its innovation strategies through open architecture methods [26]. This model contributes to the improvement in the socio-economic environment in various countries, as exemplified by industries in Mexico [27].
Hypothesis 1.
IoT micro-factories have a significant direct impact on sustainable development.

1.3.2. Global Production Network

The IoT micro-factory enables citriculture societies to access the global market through the global production network (GPN) and to leverage comparative advantages in global markets [14]. The GPN facilitates a sustainable transition in production and influences societal consumption patterns by introducing new products in a specific context [28]. The global commodities chain (GCC) consists of various industries in a production network, representing a collaborative effort among original equipment manufacturers (OEMs). Thus, different manufacturers produce distinct components for the same product. The global consultancy of international benchmark organisations has established regulations to govern standards that ensure the quality of products and services within GVC, such as ISO, WTO, and UN Comtrade. Both GCC and GVC promote the advancement of citriculture through the development of IoT micro-factories, which meet product standards and facilitate access to global markets [28].
The GNP serves as the central element of a complex, interconnected manufacturing system designed for the exchange of economic resources, information, and fiscal liabilities. It aims to enhance the value of products and services, satisfy customer demands, and promote both socio-well-being and economic prosperity [29]. Through obligations set by MLP, SSFs can access the global market via the GPN [30]. The GPN employs various strategies within SSFs such as production design, network development, and managerial operations [29]. It promotes the sharing of comparative advantages in the global market and supports the shift towards socioeconomic and environmental sustainability [31]. Recently, large companies such as Toyota, Tesla, and electric car manufacturing companies from Silicon Valley have shared their production patents with emerging industries [32]. MLP integrates GPV in an IoT micro-factory for SFOJ production by disseminating production patents and technical expertise across diverse geographical regions, improving comparative advantages over conventional industrial models through digital innovation [31,33].
The MLP and GPN contribute to enhancing the cloud collaboration of IoT micro-factories (CCIMs) by providing manufacturing systems with features such as reliability, availability, quality, and safety through real-time operation [29,31]. The GPN promotes open interactive innovation and facilitates the transition from linear to circular production within a complex network of interconnected manufacturing systems that add value [34]. China represents approximately 15% of global value-added product exports, driven by niche innovations that adhere to the international standards established by the GPN. This compliance enables China to penetrate the global market and contribute to the global economy [33,35,36].
Hypothesis 2.
Global production network has a significant direct impact on sustainable development.

1.3.3. The Roles of Architectural Innovation in IoT Micro-Factory Era

The IoT micro-factory subsystems are designed in a modular fashion to facilitate efficient machinery changeover [25,37]. The enhancement of the existing manufacturing system improves the interoperability of each subsystem, increasing machinery flexibility and enabling a high level of production customisation or personalisation, without disrupting the overall manufacturing facility. The interactive machinery units establish an open collaborative system architecture that supports the reconfiguration of various machinery components. This approach accommodates production variability in responses to both local and global demands, while also facilitating just-in-time products and service delivery [25].
These subsystems are designed to integrate with existing LSIs [38]. The archetype must comply with legal agreements to purchase and support semi-finished production, including the products services system (PSS), which offers both tangible and intangible services to meet customer demands [25,38]. These relationships foster the market for SFOJ and other semi-finished products derived from its by-products. In addition to being the primary purchaser of semi-finished products, the LSI also provides technical support to mitigate the impacts of low-skilled labour in SSIs through systems integration and knowledge transfer [22].
Architectural innovation provides a means to address socio-economic and environmental challenges more cost-effectively, efficiently, and with minimal retrofitting when integrated with LSI as an affiliated company [39,40]. This invention involves the dissemination of IoT micro-factories, along with remote control and monitoring capabilities across various LSI frameworks, as well as promoting an equitable business model in regional and global markets. The production infrastructures are integrated via gateways to ensure the interoperability in a streamlined engineering process [39,41].
Hypothesis 3.
Architectural innovation has a positive influence on IoT micro-factories.
Acquiring quality standards aligned with the global production network (GPN) through the global value chain (GVC) and global commodities chain (GCC) enables the modernisation of existing manufacturing systems, leading to the production of high-quality products. It also strengthens joint-ownership through equitable trade, establishing the foundation for sharing the market’s comparative advantages [42]. As a result, the societies benefit from comparative advantages in the global market [21,25,37,43].
Comparative analysis and innovations ensure that the products meet the quality standards established by the GCC. The IoT micro-factory is anticipated to become the original manufacturer of semi-finished products. This transformation allows developing countries, the latecomers in the global market, to redefine business models, raise awareness of social responsibilities and citizenship, and boost the national GDP through taxation derived from transparent transactions [42,43].
It enables modifications to facilitate equitable trade in online business-to-business transactions across regional and global industries, supporting closed-loop entrepreneurship, supplier management, and partnerships [25,43]. The system provides a lean upstream supply chain where semi-finished products are supplied based on required quality and quantity. It promotes holistic business approaches by creating new products services systems (PSSs), equitable trade, and fostering robust partnerships with both local and international stakeholders, including ISO, WTO, and the GTT board of consultants, as well as benchmarking [44].
Hypothesis 4.
The architectural innovation positively influences the global production network.
The focus is the retrofitting of existing manufacturing systems to generate and capture new value at minimal cost, time, and effort, in alignment with the GPN condition for sustainable development [45,46,47]. Architectural innovation is closely related to incremental innovation, as it does not create new technologies, whereas radical innovation is characterised by the development of new technology [48,49]. It represents a continuous innovation, enhancing the existing system to facilitate the new product invention [48,49]. Digital retrofitting enables flexible modularity through high interoperability, allowing for the swift adaptation to dynamic market fluctuations and reducing the time required for product invention [50]. Furthermore, it also offers production agility in response to uncertainty, enabling the system to counteract system changes. The issue of price inflation for new technologies is avoided, as no new technologies are necessary [50].
Sustainable development through CE accelerates resource utilisation via closed-loop production. IoT micro-factories are designed to align with the resource-based view (RBV), producing a variety of semi-finished products from SFOJ by-products, such as medicinal compounds like flavonoids, citric acid, ethanol, and hesperidin, as well as pectin for the production of jams, jellies, low-calorie foods, foams, plasticisers, and acid dyes. Additionally, they are used to produce essential oils, perfumes, and solvents for other food products [51,52]. The circular economy goes beyond green innovation by optimising raw material utilisation for socio-economic development [40,53,54]. The European Union adopted CE in 2015, and it is strongly supported by the UN Sustainable Development Goals [54]. The IoT micro-factory facilitates the attainment of the UN’s Sustainable Development Goals in societies.
Hypothesis 5.
Architectural innovation is positively related to sustainable development.

1.3.4. Mediating Role of Architectural Innovation

Architectural innovation functions as a mediating construct, acting as the conduit through which IoT micro-factories influence sustainable development. It leads to modification of the layout of the existing system with IoT capability, which, in turn, increases production performance, fosters equitable trade that embodies integrative cooperative industries, and promotes open interactive innovation [55]. IoT capabilities enable connectivity with large-scale industries (LSIs), facilitate sharing of inter-firm infrastructures based on their production metric [56]. This industrial symbiosis extends from production tasks and the supply chain to the affiliation of inter-trading industries, fostering internalisation of manufacturing and transition into associate or affiliated companies [55,57].
The internationalisation of IoT micro-factories, guided by open and interactive innovation theory, mitigates adverse shocks associated with technological and market fluctuations in the global arena [37,43,56,57]. However, operation under remote access diminishes equitable sharing of infrastructure and establishes an asymmetrical affiliation to reduce conflict [57,58]. The optimal utilisation of raw materials for SFOJ production by-products under the supervision of LSIs supports sustainable development [58].
The incorporation of value-added initiatives through the exchanges of knowledge, technology, and skills among the countries within the global partnership serves to sustainable development [59]. This mediation promotes standards and quality by adopting production criteria from international standards and benchmarking organisations [60]. It establishes operational frameworks, collaboration, and organising within the manufacturing systems [60]. Innovation drives the creation and capture of value alongside GCC and GVC to foster sustainable development by leveraging comparative advantages in the global market [61]. It is regarded as an international trade innovation aimed at achieving the United Nations Sustainable Development Goals (SDGs) [60,61].
Hypothesis 6.
Architectural innovation mediates (a) the IoT micro-factory, (b) the global production network, and (c) Industrial symbiosis, in their relationships with sustainable development.

1.3.5. Moderating Role of Industrial Symbiotic Relationship

The integration of SSFs and large-scale industries (LSIs) is facilitated by a symbiotic relationship that enables mutual reliance for technical operational support, knowledge exchange, and information sharing. The technical and operational support provided by LSIs mitigates the impacts of unskilled labourers in rural communities. Partnerships between LSIs and SSFs foster equitable trade by removing the need for merchants’ intervention and establishing a streamlined, lean upstream supply chain [61]. This industrial symbiotic relationship promotes sustainable development by enhancing socio-economic growth, environmental conservation, and food security within the framework of the circular economy (CE), as conceptualised through an innovation ecosystem theory [12]. It also strengthens financial stability by sharing regional and global comparative advantages, thereby boosting international innovation capacity [62].
The innovation ecosystem introduces IoT micro-factories as a solution to landscape pressures, retrofitting existing systems through collaboration with interconnected industries and other stakeholders to meet demand [45,63,64]. Manufacturing frameworks undergo slight modifications to enhance production efficacy and re-establish a competitive environment through innovations and industrial partnerships [45]. Sustainable development is unachievable without innovation and support from other technical experts, as shifts in lifestyles, including customer preferences, consumption patterns, geopolitical dynamics, environmental factors, and significant changes in knowledge and skills continue to unfold [48].
The collaborative network of diverse stakeholders forms a closed loop cycle for the exchange of resources, including raw material, by-products, and waste [64]. This industrial symbiotic fosters collaborative innovation networks and establishes structural roles for the exchange of materials [65]. Recently, industrial symbiotic relationships have become a pivotal force for sustainable development. Under the Belt and Road Initiative (BRI), the Chinese government has been enhancing the international innovation capacity of developing countries [66]. The initiative is implemented through technical cooperation, international scientific alliance, trade liberalisation, and bilateral investments. The nations that participate in the BRI include Tanzania, Ethiopia, Uganda, Zambia, Myanmar, and Laos. Achieving sustainable development is nearly impossible without collaboration with other international industrial stakeholders [66].
Hypothesis 7.
Industrial symbiotic relationships moderate the IoT and architectural innovation.
All the study constructs are systematically presented in the conceptual model as shown in Figure 1. Additionally, the overall interactions among these constructs and their separate contributions in context are broadly illustrated in Figure 2.

2. Materials and Methods

2.1. Data Collection

The survey for this study was divided into two sections. The first part of the questionnaire aimed to identify the landscape pressure associated with orange fruit production, particularly fruit spoilage, market monopolistic practices by merchants, and limited access to global markets. It sought to explore the underlying causes for fruit’s spoilage, the extent of loss, inequitable trade practices, and barriers to access to global markets. The second part of the questionnaire focused on research constructs, including IoT-enabled micro-factories, industrial collaboration, and architectural innovation, to test the hypothesis and address the challenges. To assess the technological capacity, the questionnaire was administered in reflection to large-scale industries, such as AZAM, SAYONA, MO, and TIRDO, as an R&D agency. During data collection, challenges were encountered due to the diverse roles of engineers across industries, therefore, online methods were used for data collection, although the majority of farmers expressed a preference for not using smartphones to complete the questionnaire, necessitating the use of a physical survey instead. The questionnaires were initially designed in English and then translated into Swahili, the native language of Tanzania, to facilitate local administrations. The responses were later translated back into English for subsequent data analysis [67]. A total of 215 data samples were collected, after excluding outliers and missing data, and 196 samples were retained for analysis. The final sample was estimated to be equivalent to 195 samples based on a 2% defective sample rate and a Z-score of 2.005, corresponding to a 95.5% confidence level from a population of 100,000 industrial stakeholders [68].
n = N   p   q   Z 2 e 2 N − 1 + Z 2 p   q  
N = population estimated;
e = the estimated error (defective) sample size;
z = per table area under the standard curve for the given confidence level;
p = estimate defective sample;
q = (1 − P);
n = sample size to be estimated.
Secondary data concerning the establishment of medium-sized factories for beverage processing were collected from the Tanzania National Bureau of Statistics (NBS) website to assess the diffusion of IoT micro-factories and SFOJ [68,69]. Forty-eight new themes emerged from the first six interviews; however, the seventh and eighth interviews contributed only two new themes, falling 0.41% short of the 0.5% interview saturation threshold. Consequently, six interviews were deemed sufficient for this study [70].

Data Analysis

The study employed symmetric data analysis to explore the relationships between the independent variables, namely IoT micro-factories and global production, and sustainable development as the dependent variables. Additionally, the mediating role of architectural innovation and the moderating effect of industrial symbiotic relationships. The analysis methods are based on a bivariate correlation of variables through Charles Spearman’s coefficient and Karl Pearson’s correlation to assess direct and indirect associations, alongside multivariate techniques for partial correlation and cross-tabulation to evaluate interdependencies within the construct framework of mediated and moderated effects [71]. Various coding techniques were employed during qualitative data analysis, such as initial (open) coding, focused coding, axial coding, theoretical (selective) coding, template coding, and data integration for explorative analysis [72].

2.2. Diffusion of IoT Micro-Factories

The Bass diffusion model includes both internal and external adapters. External adopters are regarded as the initial innovation adopters influenced by advertisements or researchers, whereas the internal adopters, influenced by word of mouth, are referred to as imitators [73].
The model initialises the error reduction via optimising and calibrating various parameters during installation. It allows flexibility from the outset and maintains external influence throughout the diffusion process [73].
f ( t ) 1 − F ( t ) = P + q F ( t )
F ( t i ) = 1 − e − p + q t i 1 + q p e − ( p + q ) t i
F(ti) = cumulative fraction of installed IoT micro-factory at generation i.
f(t) = change of installed IoT micro-factory at time t.
p = coefficient of innovation.
q = coefficient of imitation.
The Bass diffusion model initialises error reduction by optimising and calibrating various parameters during the installation process. This flexibility is maintained from early stages and continues to be influenced by external factors throughout the diffusion process.

2.3. Diffusion of SFOJ (HS200919)

The adoption and repurchase of the SFOJ of HS200919 are crucial for sustaining the functionality of SSFs in citriculture societies [74,75]. Initially, the Bass diffusion model was applied solely to repeat purchases of durable products, however, it has recently been extended to capture repeat purchases of perishable products as well [76]. The Frank Bass model was employed to forecast the diffusion rate of HS200919 sales and IoT micro-factories [76]. A comparison of similar products is often used to forecast the sales of new products and services within the same market context and potential [76]. For SFOJ, orange juice is utilised to evaluate the forecast sales. Conducting predictive studies of SFOJ before implementation is essential for making informed long-term investments [77].
From the Bass mode, for the adoption of SFOJ (HS200919) by
f ( t ) = m 1 − e − p + q t 1 + q p e − p + q t
F(t) = cumulative fraction of SFOJ (HS200919).
M = ultimate market potential.
f(t) = likelihood of adoption.
p = coefficient of innovation.
q = coefficient of imitation.

3. Results and Discussion

3.1. Descriptive Data Analysis

The sample consists of 108 males (53.4%) and 94 females (46.60%). In terms of age distribution, 9.9% of respondents are under 25 years old, 16.7% are between 26 and 30 years old, 14.6% are aged 31 to 35 years, 15.6% fall within 36 to 40 years, and 43.2% are over 41 years of age. Regarding education qualification, non-diploma holders constituted 55.7% of the population, while 14.9% possessed diplomas, 11.9% were undergraduates, 12.9% held master’s degrees, and 4.9% were PhD holders.

3.1.1. Measurement Model Assessment

The reliability and validity of the collected data were assessed through convergent and discriminant validity. Internal consistency reliability was evaluated using Cronbach’s alpha and composite reliability. Additionally, the variance inflation factor (VIF) was employed to detect multicollinearity [78].
Multicollinearity: A VIF value of 1 indicates the absence of multicollinearity, whereas a VIF of 10 or greater signifies severe multicollinearity for the outer model. A VIF inner model above 3.3 indicates the presence of abnormal collinearity and common method biases in PLS-SEM algorithms [79]. The computed outer model VIF suggests the absence of common method variance biases and any issues related to the sample survey, as detailed in Table 1. Also, in Table 2, the inner model VIF is below 3.3, signifying no multicollinearity [78,80].
Convergent validity of the measurement was assessed using the average variance extracted (AVE) and factor loading (FL). An AVE greater than 0.5 and a factor loading (FL) above 0.7 indicate strong convergent validity [81,82]. The AVE and FL values in this study reflect high convergent validity, as presented in Table 1.
Internal consistency reliability was examined using Cronbach’s alpha (CA) and composite reliability (CR). CA measures the intercorrelation among constructs. While CR assesses the extent of consistency of latent constructs [82,83,84]. The values of CA and CR range between 0.7 and 0.95, and these are considered acceptable for reliability analysis [20,84,85]. The study demonstrates high internal consistency reliability, with CA and CR values ranging between 0.7 and 0.91, as shown in Table 1.
To evaluate the discriminant validity test, a liberal HTMT threshold of 0.9 was adopted, considering the inherent correlation between industrialisation and development constructs under prevailing landscape pressure in developing countries [82,86]. The HTMT value exceeding 0.9 indicates inadequate discriminant validity [86,87]. Accordingly, the measurement model demonstrates acceptable discriminant validity, as shown in Table 3.

3.1.2. Structural Model

The statistical significance of study hypotheses is illustrated within a structural model, and the corresponding bootstrapping results are shown in Figure 3.
The findings presented in Table 4 support the proposed model for promoting industrialisation in marginalised societies in Tanzania as a means for achieving sustainable development. The relationship between IoT micro-factories and sustainable development H1 is statistically significant (β = 0.136, t = 2.077, p = 0.038), suggesting that sustainable development in these communities is influenced by SSFs [88]. The coding process identifies a range of concise patterns reflecting the essential roles of IoT micro-factories in advancing industrialisation in developing regions, particularly within marginalised communities. Expressions such as need, want of industries, very important, good, and apply were frequently observed in participant responses. The participants highlighted the lack of industrial infrastructure in rural areas and underscored the need for localised manufacturing units. The IoT micro-factory is well-positioned to support job creation, empower rural communities, and foster sustainable development. It is perceived as a transitional mechanism contributing to improving welfare, living standards, economy, production, and the reduction of reliance on merchants. Respondents recognise technology as a driver of economic retention, capable of transforming adverse working environments into decent ones, and curbing exploitative practices in a marginalised context [88].
The relationship between the global production network and sustainable development H2 is statistically significant (β = 0.542, t = 8.22, p = 0.000), underscoring the importance of developing economies being able to access the global market to leverage the global market comparative advantage for sustainable development [89]. The association between the IoT micro-factory and architectural innovation H3 is also significant (β = 0.184, t = 2.684, p = 0.007), suggesting that micro-factories are highly linked with the structural adoption driven by innovations within the framework of sustainable development. The integration of global production networks through architectural innovation is signified by H4 (β = 0.0261, t = 3.919, p = 0.000), suggesting that such innovation enables SSFs to meet quality standards set by ISO, WTO, GTT, and other international bodies [90]. The global production network is characterised by themes, such as international market, entrepreneurship, global market, and trade liberalisation. The respondents emphasise priority for the export-oriented production through GPN and cross-border economic activity. GPN facilitates localised production and promotes seamless international operations by aligning production with trade to reduce market volatility. It supports SSFs in evaluating and maintaining production quality through GVC, while also promoting trade liberalisation by reducing tariffs, trade barriers, inequitable regulation, polices, and quotas [91].
A significant relationship between architectural innovation and sustainable development is confirmed by H5 (β = 0.241, t = 3.163, p = 0.002). The assimilation and adoption process of IoT micro-factories are strongly associated with sustainable development initiatives [92,93,94]. Architectural innovation serves as a significant mediator between IoT micro-factories and sustainable development, as indicated by H6 (a) (β = 0.044, t = 2.162, p = 0.031), enabling SSFs to adopt a resource management perspective, lower production cost, and improve the responsiveness to market volatility [95]. It also mediates the relationship between global production and sustainable development by H6 (b) (β = 0.241, t = 3.163, p = 0.002), thereby enhancing market accessibility, industrial partnerships, equitable trade, and corporate social responsibility [96]. Strengthening the collaboration with other industrial agents, fostering innovation from technical colleges, and leveraging the role of the Tanzania Industrial Research and Development Organisation (TIRDO) are critical for advancing industrialisation and sustainable development [92,93,94].
The moderating roles of the industrial symbiotic relationship between the IoT micro-factory and architectural innovation are statistically significant, as evidenced by H7 (β = −0.117, t = 2.289, p = 0.022). The negative coefficient suggests that the increase in technical and operational support from LSIs corresponds with a reduced interaction between the IoT micro-factory and innovation initiatives [97]. Variations in SSFs are addressed by LSIs within the framework of industrial symbiosis [98]. Furthermore, a decrease in support from LSIs prompts enhanced innovation activity between the IoT micro-factory and R&D institutions, such as TIRDO [98].
The modulator’s negative path coefficient indicates an increased independence of the dependent variable, thereby weakening the inter-variable relationships. The moderated mediation effects of industrial symbiotic relationships on the link between architectural innovation and sustainable development are significant, as evidenced by H6 (c) (β = 0.075, t = 3.163, p = 0.002), suggesting that enhanced innovation and industrial partnerships contribute positively to sustainable development. Nonetheless, patterns such as collaboration, help, support, cooperation, and commitment underscore the significance of mutual learning, shared objectives, resource sharing, and active participation in a collaboration initiative to strengthen capacity building [99]. Addressing the limitation in technological competence, operational workflow, and online business-to-business interaction both domestically and globally is crucial to foster socio-economic development. Preliminary technical and business assistance from large industries remains essential in supporting marginalised communities and enhancing productivity across both SSIs and LSIs [100].
The internal path effects within the structural model were assessed using R2, which signifies that 62% of the variance in SD is effectively explained by IoT micro-factories and GPN [100,101]. The Q2 value reflects the predictive relevance of the constructs model, indicating that 59% of the variance in SD is attributable to IoT micro-factories and GPN [102], as shown in Table 5.

3.2. Diffusion Estimation of IoT Micro-Factory and SFOJ Production

The data utilised to forecast SFOJ production and the diffusion of the IoT micro-factory were obtained from the National Bureau of Statistics (NBS) via the 2020 National Census. In addition, the data from the industrial census conducted jointly by NBS and UNIDO between 2015 and 2020 were analysed using the Bass model to project the SFOJ production in Tanzania [103]. The Bass model estimated the market size to be approximately 95,409,069 based on the innovation coefficient of p = 0.01 and an imitation coefficient of q = 0.23. with statistical significance (p-value = 0.000), underscoring the importance of entering the global market, as shown in Table 6 and Figure 4. The forecasted diffusion of the IoT micro-factory is 1754 at an innovation coefficient of p 0.01 and q = 0.25, also statistically significant (p-value = 0.000), and is consistent with the 2030 UN SDGs framework, as illustrated in Table 6 and Figure 5.

4. Conclusions

These findings offer critical insights into production archetypes shaped by corporate orientations. The study anticipates the diffusion of new products, including SFOJ of H200919 and IoT micro-factories, as interventions to alleviate extreme poverty in developing countries, particularly within citric marginalised communities. The proposed model addresses contextual pressure faced by least developed countries through a synergy of IoT micro-factory implementation, architectural innovation, industrial symbiosis, and integration into global production networks, thereby bridging sustainable development gaps. Simulation results indicate that innovation plays a pivotal role in tackling fiscal challenges, especially regarding the importation of advanced technologies. Architectural innovation, led by technical colleges and the Tanzania Industrial Research and Development Organisation (TIRDO), underpins the development of IoT micro-factories tailored to local contexts.
The study further illustrates how industrial symbiosis can supply essential technical, managerial, and operational capabilities to farmer-operated IoT micro-factories, closing skill gaps and enabling scalable, sustainable outcomes. To implement these findings, governments and industrial stakeholders must: (1) establish digital skills training programmes and support industrial R&D enterprises such as TIRDO; (2) optimise lean supply chain strategies that link smallholders directly with LSIs and international markets, bypassing exploitative intermediaries; and (3) provide incentives for the adoption of IoT micro-factories to empower marginalised producers. The framework ultimately aims to enhance livelihoods, reduce environmental waste, and increase productivity by promoting local innovation integrated with IoT functionalities, sustained R&D efforts, and collaborative engagement. These strategies may serve as systemic catalysts for achieving the UN SDGs in marginalised communities. Future initiatives should prioritise skill development, continuous innovation, and the evaluation of broader industrial impacts in developing countries.

Author Contributions

All authors participated and contributed equally in every section of this study. All authors have read and agreed to the published version of the manuscript.

Funding

This research received no external funding.

Institution Review Board Statement

The study research procedures involved human participation reflecting ethical criteria set by the Institutional Review Board (IRB) in the School of Management of Harbin Institute of Technology, approval number 2025-5. Informed consent was obtained for both in-person and online surveys.

Informed Consent Statement

Informed consent was acquired from all participants in this study.

Data Availability Statement

No data are available for sharing for this article due to privacy protection laws for respondents. The confidential commitments made between the authors and respondents are well observed. However, some of the data are available on the web of the Tanzania National Bureau of Statistics (NBS).

Acknowledgments

The author expresses their heartfelt appreciation to all industrial stakeholders in Dar es Salaam and Tanga who participated in this study. We appreciate your dedication and encouragement.

Conflicts of Interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

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Figure 1. Conceptual model of the study, sourced by authors (2024).
Figure 1. Conceptual model of the study, sourced by authors (2024).
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Figure 2. Overview of the conceptual framework for the diffusion of IoT micro-factory and SFOJ products.
Figure 2. Overview of the conceptual framework for the diffusion of IoT micro-factory and SFOJ products.
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Figure 3. Graphical output structural model * p < 0.05, ** p < 0.01, *** p < 0.001.
Figure 3. Graphical output structural model * p < 0.05, ** p < 0.01, *** p < 0.001.
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Figure 4. Estimation of the SFOJ production using the Bass model by numberanalytics.com.
Figure 4. Estimation of the SFOJ production using the Bass model by numberanalytics.com.
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Figure 5. Diffusion of IoT micro-factory using Bass Model by using numberanalytics.com.
Figure 5. Diffusion of IoT micro-factory using Bass Model by using numberanalytics.com.
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Table 1. Summary of multicollinearity (VIF) and convergent validity.
Table 1. Summary of multicollinearity (VIF) and convergent validity.
PItemsFactor LoadingsCronbach’s Alpha (CA)CRAVEVIF
IoT micro-factoryIoT C0.850.7620.7670.6791.897
D. IoT0.858 1.824
IoT I0.761 1.319
ISRTS0.8430.7890.7940.7031.794
OS0.843 1.877
C & M0.828 1.48
AIAI0.8770.8570.8590.7772.024
TC0.876 2.192
R&D0.892 2.227
GPNGVC0.8780.8350.8350.7512.035
GCC0.866 1.981
GCA0.857 1.828
SDECONOMIC0.8620.8200.8250.7341.786
SOCIAL0.839 1.746
ENVIRONM0.869 2.099
IoT = IoT micro-factory; AI = architectural innovation; SD = sustainable development; ISR = industrial symbiotic relationship; GPN = global production network; IoT D = diffusion of IoT; IoT C = IoT capacity; IoT I = IoT infrastructures; TS = technical support; OS = operations support; C &M = commercialisation and marketing; TC = technical capacity; R&D = Research and Development; GVC = global value chain; GCC = global commodities chain; GCA = global comparative advantage.
Table 2. Collinearity statistics (VIF inner model).
Table 2. Collinearity statistics (VIF inner model).
Relationship VIF
AI → SD1.691
GPN → AI1.455
GPN → SD1.532
ISR → AI2.089
IoT micro-factory → AI1.655
IoT micro-factory → SD1.502
ISR × IoT micro-factory → AI1.337
Table 3. Discriminant validation.
Table 3. Discriminant validation.
ConstructsAIGPNISRIoT Micro-FactorySD
AI
GPN0.654
ISR0.7650.643
IoT micro-factory0.6680.5840.780
SD0.7310.8840.7350.655
ISR × IoT micro-factory0.4740.3140.5620.3580.432
Table 4. Variable relationship (path coefficient, statistic T value, p value, and decision).
Table 4. Variable relationship (path coefficient, statistic T value, p value, and decision).
HypothesisRelationshipsβtpResults
Direct effects
H1IoT micro-factory → SD0.1362.0770.038Supported
H2GPN → SD0.5428.220.000Supported
H3IoT micro-factory → AI0.1842.6840.007Supported
H4GPN → AI0.2613.9190.000Supported
H5AI → SD0.2413.1630.002Supported
Mediating effects
H6 (a)IoT micro-factory → AI → SD0.0442.1620.031Supported
H6 (b)GPN → AI → SD0.0632.3620.018Supported
H6 (c)ISR → AI → SD0.0752.4510.014Supported
Modulating effects
H7ISR × IoT micro-factory → AI−0.1172.2890.022Supported
Note: β, beta coefficient; t, t-statistics; p, p-value; Q2, Q-square value; R2, R-square value.
Table 5. Predictive relevant.
Table 5. Predictive relevant.
ConstructsQ2R2
AI0.4700.507
SD0.5920.620
Table 6. Summary for forecasted SFOJ production and IoT micro-factory.
Table 6. Summary for forecasted SFOJ production and IoT micro-factory.
Diffusion VariablesP
(Est)		P
(t-Value)		P Sig:
(p-Value)		Ϥ
(Est)		ϥ
(t-Value)		ϥ Sig:
(p-Value)		Total Forecasted
IoT micro-factory0.0166.848560.0000.2547.71670.0001754
SFOJ (HS200919)0.01410.3020.0000.23311.990.00095,409,069
Note: P = innovation parameter; ϥ = imitator parameter; P (Est) = estimated innovation scale; ϥ (Est) = estimated imitation scale; P:t-value = innovation t-value; ϥ:t-value = imitation t-value; P Sig(p-value) = innovation significant value; ϥ Sig(p-value) = imitation significant value.
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MDPI and ACS Style

Chakumba, J.A.; Jin, J.; Kisanga, D.H. Leveraging IoT Micro-Factories for Equitable Trade: Enhancing Semi-Finished Orange Juice Value Chain in a Citriculture Society. Systems 2025, 13, 384. https://doi.org/10.3390/systems13050384

AMA Style

Chakumba JA, Jin J, Kisanga DH. Leveraging IoT Micro-Factories for Equitable Trade: Enhancing Semi-Finished Orange Juice Value Chain in a Citriculture Society. Systems. 2025; 13(5):384. https://doi.org/10.3390/systems13050384

Chicago/Turabian Style

Chakumba, Joseph Andrew, Jiafei Jin, and Dalton Hebert Kisanga. 2025. "Leveraging IoT Micro-Factories for Equitable Trade: Enhancing Semi-Finished Orange Juice Value Chain in a Citriculture Society" Systems 13, no. 5: 384. https://doi.org/10.3390/systems13050384

APA Style

Chakumba, J. A., Jin, J., & Kisanga, D. H. (2025). Leveraging IoT Micro-Factories for Equitable Trade: Enhancing Semi-Finished Orange Juice Value Chain in a Citriculture Society. Systems, 13(5), 384. https://doi.org/10.3390/systems13050384

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