The moderating effect of technological adoption on route optimization and sustainable environmental performance of truck operators in Tanzania

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Rissasi, Barnabas Maagi This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8561334/v1 This work is licensed under a CC BY 4.0 License Status: Under Revision Version 1 posted 14 You are reading this latest preprint version Abstract Route optimization is regarded as a strategic emission reduction practice in freight transport, but its contribution to sustainable environmental performance in developing economies and the accelerating impact of digital technology are understudied. This study fills that gap empirically with Tanzanian truck operators based on the Natural Resource-Based View (NRBV) theory. Across 170 trucking operating firms, a cross-sectional survey was carried out in SmartPLS 4 with covariance-based structural equation modelling (CB-SEM). In this way, it tested the direct effect of route optimization on sustainable environmental performance as well as the moderating effect of technology adoption (GPS, IoT). Statistically, the results show that route optimization improves sustainable environmental performance (β = 0.229, p = 0.007). In addition to this effect (β = 0.621, p = 0.017), technology adoption confirms that the fuel savings as well as emission reductions of optimized routing are augmented by digital tools. Theoretically, the study builds on NRBV theory and green logistics practices, as it shows how pollution prevention capability via route optimization in combination with digital resources leads to sustainability outcomes. In practice, it shapes environmental policy and sustainable transport in Tanzania by pushing policymakers & industry to adopt technology for cleaner and more efficient freight operations. Route Optimization Sustainable Environmental Performance Technology Adoption Truck Operators Green Logistics Figures Figure 1 Figure 2 Figure 3 1. Introduction Road freight transport plays a leading role in moving goods across Sub-Saharan Africa, serving as the main link for trade within and between countries. In Tanzania, this sector is especially important because the nation is a transportation hub connecting landlocked destinations like Rwanda, Burundi, Uganda, Zambia and the Democratic Republic of the Congo with world markets via the Port of Dar es Salaam, in particular (Kunaka et al., 2016 ). Almost three-quarters of Tanzania's freight tonnage is carried by road, which is an essential means of trade, agriculture and industrial development (Chacha et al., 2024 ). National Development Vision 2050 further identifies transport as a key driver for achieving sustainable infrastructure and environmental protection in the country (URT, 2025 ). Environmental pollution has drawn global attention in recent years to sustainable transport and logistics, which are at the heart of economic growth (Yadav & Sharma, 2021 ). Freight transport is a major source of pollution, resource waste, and climate change (García-Olivares et al., 2020 ). Road freight accounts for about one-third of all carbon dioxide (CO2) emissions worldwide, and transport in general accounts for more than one-quarter of all emissions (Statista, 2025 ). In the European Union (EU), for example, road transport in 2022 released 760 million tonnes of CO2 24% more CO2 than since 1990, largely due to freight trucks (Zeyen et al., 2025 ). Freight trucks emitted about 126g of CO2 per ton-kilometre in Germany in 2020, while in the United States, 12% of CO2 and 10% of nitrogen oxides produced caused the country to incur an annual environmental and health cost of about USD 30 billion (Allekotte et al., 2021 ; Lathwal et al., 2022 ). Respiratory diseases and early death among people are also linked to these diesel and carbon emissions from the trucks and overall road freight operations (Long & Carlsten, 2022 ). In Africa, the transport sector accounts for almost a third of all emissions, estimated at around 346 million tonnes CO2 per year (Bongard et al. 2023). Road freight alone accounts for 90% of transport emissions and 14% of total greenhouse gases in South Africa (Du Plessis et al., 2022 ). Figures like these show how greener transport is needed. Efforts like the African Continental Free Trade Area Agenda 2063 for sustainable logistics integration (AfCFTA, 2022) and the African Development Bank (AfDB, 2025) promote investment in resilient transport infrastructures. In Tanzania, the transport and logistics sector expanded by about 7.4% in 2024, which was a good indicator towards the growth of the national economy (NBS, 2025 ). Nevertheless, inefficiency, high fuel costs and low use of modern technology remain as environmental pressures and challenges for the sector (Arvis et al., 2023 ). National frameworks like the National Transport Policy as well as the National Environmental Policy recommend greener Transport practices, but few companies actually implement them (Issa, 2023 ). In this aspect, Interventions which look promising are renewable energy use (Multiconsult, 2022 ), route optimization and better resource management (Jarašūnienė & Bazaras, 2023 ). Route optimization is one of the impactful interventions in terms of reducing emissions and fuel costs. Transport operators can cut travel distances with data, computer-based planning, use their fleets more effectively, and avoid unnecessary trips (Wu, 2024 ). In fact, Route optimization, if well implemented, may save up to 20% fuel consumption per year (Hussain, 2025 ). In this way, achieving international sustainability Goals and initiatives, as emphasized by the Paris Agreement and the Sustainable Development goals, are supported (UNFCCC, 2015 ; Ouni & Abdallah, 2024 ). Technology adoption makes route optimization even more effective. GPS tracks vehicle movements, avoids congestion, and changes routes. IoT sensors track fuel use, tire pressure, and engine condition for better maintenance and fleet efficiency. (Chacha et al., 2024 ). All these technologies save time, reduce emissions and enhance safety. Nevertheless, such technologies are underutilized in developing countries, specifically in Tanzania, because of high costs, low awareness, and weak infrastructure. (Moses et al., 2024 ; Issa, 2023 ). In developed countries, research suggests that digital technologies improve logistics performance (Tan et al., 2020 ). However, in contrast, in many developing countries, technology adoption progress is lagging because of scarcity of resources, low skills, and weak institutional support (Miyoba et al., 2024 ; Leonard & Macha, 2021 ). This, therefore, reveals a research gap on how technology adoption affects the environmental benefits of green logistics practices, such as route optimization in Tanzania, where the trucking sector is still the main transport mode of goods across the country and beyond. To fill this gap, the study focuses on two specific objectives: To examine how route optimization practices affect the sustainable environmental performance of truck operators in Tanzania. To explore how technology adoption moderates the relationship between route optimization practices and sustainable environmental performance among truck operators in Tanzania. The study is thus important for policy and practice. It shows that digital systems support route efficiency and emissions for Tanzania's Vision 2050 and National Environmental Policy (2021). The findings can help policymakers, industry regulators and logistics firms improve low-carbon transport plans. The work also extends green logistics practice by examining how technology adoption moderates sustainability in developing economies. 2. Literature Review 2.1 The Natural Resource-Based View The present study is grounded in the Natural Resource-Based View (NRBV) developed by Hart ( 1995 ), which goes beyond the Resource-Based View (RBV) and incorporates environmental considerations in firm strategy. The NRBV argues that firms gain sustainable competitive advantage by developing capabilities that reduce their ecological footprint while increasing operational efficiency (Hart & Dowell, 2010 ). Rather than focusing on economic and internal efficiency as the main goal of the RBV, the NRBV looks at pollution prevention, product stewardship, and sustainable development as means through which firms can simultaneously meet environmental and economic goals (Yunus & Michalisin, 2016 ; McDougall et al., 2021 ). In logistics, route optimization is an example of pollution prevention, process improvement, reduced waste and emissions due to better fuel use and less idle time. That corresponds to Hart's (1995) argument that operational efficiency initiatives are environmental strategies if they reduce negative ecological externalities. Through GPS and IoT integration, technology adoption itself is a dynamic capability that enhances the sensitivity of pollution prevention routines (McDougall et al., 2021 ; Jansson 2022 ). With these complementarities, firms can turn environmental strategies into performance gains, reflecting the core proposition of NRBV. In addition, NRBV offers a coherent theoretical account of how capabilities are embedded in logistics operations translate to environmental performance. Advanced digitally oriented firms can better coordinate transport activities, optimize routes, and monitor emissions directly in line with NRBV's sustainable development goal (Li et al., 2025 ). All this dynamic interaction supports Hart's later proposition that environmental capabilities improve competitiveness and ecological outcomes through innovation and learning (Hart & Dowell, 2010 ; McDougall et al., 2021 ). Thus, this study extends the concept of NRBV to the Tanzanian trucking industry, where logistics inefficiencies and technological gaps are major sustainability barriers. By defining route optimization as a pollution prevention capability and technology adoption as a complementary dynamic capability, this research shows how digital integration increases the environmental value of core logistics routines. Thus, NRBV is the best theoretical account of direct and moderating relations between route optimization, technology adoption, and sustainable environmental performance. 2.2 Route optimization and sustainable environment performance The logistics sector is necessary for trade and national development, but it is also a major cause of environmental degradation due to carbon emissions, air pollution and high fuel consumption (Lin, 2024 ). In response, route optimization is now seen as a core activity in green logistics, reducing travel distance, idle time and fuel consumption, all of which contribute to environmental performance (Kamanga, 2019 ; Miyoba et al., 2024 ). It further contributed to streamlining delivery routes so that operational goals are in line with sustainability goals. Route optimization has been shown to improve logistics sustainability. Studies in different contexts show that optimized routing reduces fuel consumption, operation costs and greenhouse gas emissions (Psaraftis, 2019 ; Wu, 2024 ). For example, route planning can cut delivery time by up to 30% and CO2 emissions by almost 20% in African transport systems (Miyoba et al., 2024 ). Similarly, Ingrao et al. ( 2020 ) point out that efficient routing reduces energy use as well as supports eco-efficiency by integrating environmental and operational performance objectives. These results indicate that route optimization contributes directly to the environmental aspect of logistics sustainability by reducing waste and improving resource efficiency. Nevertheless, there are two distinct literature streams. The first is for developed economies and involves advanced optimization models and sustainable freight planning in structured road systems (Psaraftis, 2019 ; Ingrao et al., 2020 ). The second, still emerging, looks at developing economies where routing is constrained by infrastructural deficits, long travel distances and inefficient scheduling (Kamanga, 2019 ; Lin, 2024 ). Though sustainable routing in freight and cargo systems has been studied in Zambia and South Africa (Miyoba et al., 2024 ), there is little empirical data from Tanzania. Some of the studies in Tanzania, like Kamanga ( 2019 ), deal only with public transport optimization and urban mobility, while long-haul truck operations and their environmental impacts are of little interest and narrowly studied. Moreover, since road freight transport in Tanzania accounts for more than 80% of goods movement and is a major source of CO2 emissions (Richard, 2020 ), route optimization is of prime interest for environmental performance. In addition to that, despite the increasing awareness of green logistics, there are still few local studies linking route optimization to sustainability outcomes in the trucking industry. Accordingly, this study fills that gap by investigating how route optimization practices affect the sustainable environmental performance of truck operators in Tanzania. It is therefore worth hypothesizing that: H1: Route optimization significantly contributes to the sustainable environmental performance of the truck operators in Tanzania. 2.3 The moderating role of technology adoption For logistic companies that operate in transport logistics, technologies such as the Global Positioning System and Internet of Things sensors are becoming more important for safe and sustainable delivery processes. In this respect, truck operators need to make sure such technologies are applied in routing operations to increase the environmental benefits of route optimization. In the current research, we postulate that route optimization leads to better sustainable environmental performance when technology adoption is done right. But high costs, limited infrastructure and inadequate technical capacity may prevent operators from realizing these benefits (Issa, 2023 ). GPS and IoT technologies in logistics are increasingly used for route management and for reducing environmental impacts (Ding et al., 2023 ). These tools give real-time information about vehicle location, fuel consumption and emissions for better routing decisions and environmental outcomes (Mathauer & Hofmann, 2019 ; Saqib & Qin, 2024 ). However, most studies dealt with the direct impact of technology on logistics rather than with its effect on the strength of the relationship between route optimization and environmental performance. Technology adoption may therefore moderate this relationship, with the assumption that higher GPS and IoT usage leads to greater environmental benefits of route optimization. Conversely, where technology is used less, the benefits may be weak. To this end, it is worth hypothesizing that: H2: Technology adoption positively moderates the relationship between route optimization and the sustainable environmental performance of truck operators in Tanzania. Based on the reviewed literature and hypotheses, a conceptual model of NRBV Theory is proposed. The model shown in Fig. 1 shows that route optimization contributes to the sustainable environmental performance of truck operators in Tanzania. It also proposes that this relationship is influenced by a moderating variable, namely technology adoption (i.e. use of GPS and IoT sensors). All proposed relationships among study variables are used to derive H1 and H2 hypotheses, as shown in Fig. 1 . 3. Methodology 3.1 Study Area and Research Design This study was conducted in the Dar es Salaam region of Tanzania, the country’s key logistics hub and home to the Dar es Salaam Port, which handles nearly 95% of Tanzania’s international freight traffic (Magomba, 2025 ). The area was selected because it contains many truck operators and logistics firms registered under TATOA ( 2025 ) and thus is an ideal place to study route optimization effects on sustainable environmental performance. Similar studies (Mlimbila & Mbamba, 2018 ; Peter & Yang, 2019 ) identified that emissions and congestion are major environmental concerns in Dar es Salaam city. This study used a cross-sectional design to collect quantitative data from truck operators at one point in time. Such a design is good for analyzing variable relationships without time effects (Bell et al., 2022 ). Previous literature in East Africa (Mutua et al., 2020 ; Kisinga et al., 2024 ) successfully used cross-sectional surveys to assess logistics and overall supply chain performances. This design was therefore appropriate for a snapshot of green logistics practices, technology use and environmental performance of Tanzanian truck operators in this study. 3.2. Population, Sample and Data Collection The study focused on 552 Truck operating firms as of 2025 in the Dar es Salaam corridor registered under the Tanzania Truck Owners Association (TATOA). All firms had one representative in this study, either a senior Executive, a Chief Executive Officer, operations Manager or Fleet Manager who is recognized as an essential individual in charge of logistics Operations, technology adoption and environmental decision issues. A representative sample of 232 firms was drawn from the population using the Taro Yamane ( 1967 ) formula at a 95% confidence level with 5% margin of error. Sampling procedures ensured that all firms were included equally according to simple random sampling principles (Taherdoost, 2017 ). The sampling frame was taken from the official TATOA directory, where contacts of registered members were verified. The association helped members become involved in the study by formally introducing it to them. Surveys were sent out by email, with additional hard copies delivered to selected firms with limited online access. Out of 232 questionnaires distributed, 170 were returned complete and valid, which equates to a 73.3% response rate. This response rate is good enough for survey research and for improvement of data reliability, as recommended by Mugenda & Mugenda ( 2019 ). With this approach, the data collected reflected truck operators in Tanzania and allowed relationship analysis between route optimization, technology adoption and sustainable environmental performance in logistics. 3.3. Variables, Measurements, Reliability and Validity The study used validated measurement scales from prior peer-reviewed research. In accordance with similar logistics studies, such as that of Miyoba et al. ( 2024 ), all constructs were scored on a five-point Likert scale from 1 (Strongly Disagree) to 5 (Strongly Agree). Three items adopted from Zrigui ( 2023 ) and Aloupogianni ( 2024 ) were used to measure route optimization, including route planning efficiency and emission reduction. Likewise, three items from Keivanpour ( 2021 ) and Swamidas and Ullas (2024) were adopted in measuring GPS/IoT systems as Technological equipment used in logistics operations and decision making. Sustainable environmental performance was measured using three items adopted from Zagurskiy et al. (2022), Rześny-Cieplińska and Szmelter-Jarosz ( 2020 ), as related to emission control, environmental monitoring and operational eco-efficiency. Measurement quality was confirmed by reliability and validity assessments. Cronbach's alpha & Composite Reliability (CR) for all constructs were above the 0.7 threshold for internal consistency (Hair et al., 2021 ). Average variance extracted (AVE) values above 0.5 justify that there was sufficient shared Variance between items for met convergent validity (Fornell & Larcker, 1981 ). Discriminant validity was established using the Fornell-Larcker criterion and HTMT ratios (Henseler et al., 2015 ) and thus affirmed the distinctiveness between the study's main constructs. These results showed that route optimization, technology adoption and sustainable environmental performance were measured reliably and differently empirically, demonstrating a good basis for structural model testing. 3.4. Data Analysis Data were analysed using SmartPLS 4 for Confirmatory Factor Analysis and Covariance-Based Structural Equation Modelling (CB-SEM). The CFA tested the measurement model for model fit indices, reliability and convergent and discriminant validity of constructs (Hair et al., 2021 ). Results showed that indicators met statistical thresholds required for a robust measurement model. The study hypotheses were then tested by CB-SEM using SmartPLS 4. The direct effect of route optimization on sustainable environmental performance and the moderating effect of technology adoption were analysed. Such a dual application approach is in line with the methodological recommendations for SmartPLS 4 for simultaneous CFA and CB-SEM procedures in research (Chua, 2024 ). The combined analysis gave valid and reliable results for structural relationships between study variables. 3.5. Common Method Variance Given that data were collected from single respondents across all trucking companies, common method bias could exist. Thus, Harman's single-factor test with Principal Component Analysis for bias check was applied (Podsakoff et al., 2003 ). The results showed that the first factor explained 31.65% of the variance. Because this value is below the 50% threshold, common method bias was not considered a major issue in this study, and thus, the collected data were not significantly influenced by measurement errors due to a single data collection source. 4. Results and Discussion 4.1 Respondents' Characteristics and Company Profile Table 1 shows the socio-economic characteristics of the respondents. Males accounted for 92.9 percent of the respondents, while females accounted for only 7.1 percent, reflecting the gender divide in the transport sector in Tanzania. This cadre is made up of mid-career professionals, with 72 percent aged 31 to 40 years and 32.4 percent in the 41 to 50 age group. The length of service experience confirms that the informants have sufficient knowledge about the sector. Respondents with 7 to 10 years of experience constitute 27.6 percent, followed by 25.9 percent with 1 to 3 years of experience and 23.5 percent with 4 to 6 years. These figures indicate a market dominated by small and medium-sized enterprises. Firms with 1 to 10 employees account for 39.4 percent of these reports, while those with a workforce of 11 to 50 people contribute 30.6 percent. On part of fleets type, this study notices most firms are still largely based on diesel trucks at 90 percent. Hybrid trucks account for 7.1 percent, while the use of compressed natural gas (CNG) or biodiesel trucks is the lowest at 2.9 percent. The size of company fleet varies, with 44.1 percent of firms owning 11 to 50 trucks and 35.2 percent owning between 51 and 100. Lastly, on operational years, most firms are satisfactory, with 34.1 percent of businesses having been in existence for 6 to 10 years and 28.8 percent for 1 to 5 years. All in all, the respondents represented an experienced, SME-based and diesel-dependent trucking industry that can be used to evaluate green logistics practices in Tanzania. Table 1 Respondents Characteristics and Company Profile Category Response Frequency Percentage (%) Gender Male 158 92.9 Female 12 7.1 Age 18–30 years 27 15.9 31–40 years 72 42.4 41–50 years 55 32.4 51 + years 16 9.4 Experience Less than 1 year 7 4.1 1–3 years 44 25.9 4–6 years 40 23.5 7–10 years 47 27.6 More than 10 years 32 18.8 Company size (No. of employees) 1–10 employees 67 39.4 11–50 employees 52 30.6 51–100 employees 24 14.1 101–200 employees 14 8.2 200 + employees 13 7.6 Main truck type Diesel engine trucks 153 90 Hybrid trucks 12 7.1 CNG/Biodiesel trucks 5 2.9 Company fleet size 1–10 trucks 22 12.3 11–50 trucks 79 44.1 51–100 trucks 63 35.2 Over 100 trucks 7 3.9 Operational years Less than 1 year 5 2.9 1–5 years 49 28.8 6–10 years 58 34.1 11–20 years 35 20.6 Over 20 years 23 13.5 Total 170 100 4.2. Descriptive Statistics and Correlations As shown in Table 2 below, the mean value of Route optimization (RO) was 3.790 with a standard deviation of 0.544, which means that on average, respondents moderately agreed with statements regarding route planning and fuel-saving practices, suggesting fair implementation among truck operators. Technology adoption (TA) had a mean of 3.845, and a 0.619 standard deviation, demonstrating that respondents generally agreed that technological tools like GPS and IoT are used in their operations. The mean and standard deviation for sustainable environmental performance (SEP) were 3.771 and 0.582, respectively, which indicate moderate environmental sustainability in logistics activities. Skewness was − 0.971 to -0.607, and kurtosis was 1.323 to 2.747, both within + -3 acceptable thresholds, indicating normal data distribution (Tabachnick & Fidell, 2019 ). Further, correlation results show a positive, statistically significant relationship between route optimization and sustainable environmental performance (r = 0.155, p < 0.05) and technology adoption with sustainable environmental performance (r = 0.198, p 0.05); still, all correlation coefficients were under 0.7, confirming that multicollinearity was nonexistent (Pallant, 2020 ). These results suggest that study variables are related enough to warrant further regression analysis to confirm the hypothesized relationships. Table 2 Descriptive Statistics and Correlations Variables Mean Std. Deviation Skewness Kurtosis RO TA SEP RO 3.790 0.544 -0.971 2.747 1 TA 3.845 0.619 -0.607 1.323 0.006 1 SEP 3.771 0.582 -0.705 1.805 0.155* 0.198** 1 *p < 0.05, ** p < 0.01 4.3. CFA Results As described under methodology, Confirmatory Factor Analysis was performed to validate the measurement model reliability and validity. In Table 3 , the model fit indices showed a good fit to the data. In particular, χ²/df = 1.084, CFI = 0.997, GFI = 0.968, NFI = 0.959, TLI = 0.995, SRMR = 0.039 and RMSEA = 0.022 are within the recommended thresholds for good model fit (Hair et al., 2021 ). According to these criteria, values of χ²/df 0.90, and RMSEA and SRMR < 0.05 indicate a well-fitting model (Kline, 2023 ). Thus, the CFA results confirm that the measurement model fits the observed data well. Based on the reliability/validity results in Table 3 , all constructs met the minimum requirements for internal consistency and convergent validity. Cronbach's alpha was 0.820 to 0.843, and Composite Reliability was 0.823 to 0.846, both above the 0.70 cutoff (Hair et al., 2021 ). AVE values were 0.605 to 0.643, sufficiently above the 0.50 benchmark, indicating convergent validity (Fornell & Larcker, 1981 ). Table 3 Measurements, factor loadings, AVE, reliability, and validity Construct/ Items Loadings α CR AVE Route Optimization (RO) 0.820 0.823 0.605 RO1 0.841 RO2 0.739 RO3 0.749 Technology Adoption (TA) 0.842 0.845 0.643 TA1 0.781 TA2 0.759 TA3 0.861 Sustainable Environmental Performance (SEP) 0.843 0.846 0.643 SEP1 0.826 SEP2 0.832 SEP3 0.744 Model fit indices: X 2 /df = 1.084, CFI = 0.997, SRMR = 0.039, RMSEA = 0.022, GFI = 0.968, NFI = 0.959, and TLI = 0.995 Moreover, as shown in Table 4 , the discriminant validity was tested with the Fornell-Larcker criterion and with the Heterotrait-Monotrait (HTMT) ratio. Square roots of AVE were larger than the inter-construct correlations, and all HTMT values were below the recommended thresholds of 0.85 (Henseler et al., 2015 ). Results indicate that Route Optimization (RO), Technology Adoption (TA), and Sustainable Environmental Performance (SEP) are distinct constructs with identified measurement properties that are suitable for structural model estimation. Table 4 Discriminant Validity Fornell-Larcker criterion Construct CR AVE MSV MaxR(H) RO TA SEP RO 0.820 0.605 0.043 0.830 0.778 TA 0.842 0.643 0.057 0.853 -0.003 0.802 SEP 0.843 0.643 0.057 0.849 0.208 0.238 0.802 Heterotrait-Monotrait Ratio (HTMT) Construct RO TA SEP RO 1 TA 0.042 1 SEP 0.180 0.233 1 4.4. Testing of Hypotheses For testing the study hypotheses and for assessing direct and moderating effects, we used SmartPLS 4 (CB-SEM). With moderate explanatory power, the model in Table 5 and Fig. 2 explained 14.3% of the variance in Sustainable Environmental Performance (R 2 = 0.143). The first hypothesis (H1) was that route optimization affects the sustainable environmental performance of truck operators in Tanzania. Results supported this relationship as it portrayed a significant and positive effect (β = 0.229, p = 0.007). This means that Truck operators who plan routes well, avoid congestion and idle less often achieve better environmental results. In essence, Route management effectively reduces fuel use, vehicle wear and emissions. According to the second hypothesis (H2), it posits that technology adoption moderates the relationship between route optimization and sustainable environmental performance. Findings from this study confirm this effect with a significant positive interaction term (β = 0.621, p = 0.017). Therefore, with this moderating result, it means that firms adopting advanced technologies benefit from route optimization more than firms adopting limited digital systems. Table 5 Structural model estimates for the direct and moderated effect Estimates S.E. T values P values R Square RO ---> SEP 0.229 0.084 2.715 0.007 TA 0.192 0.092 2.084 0.039 0.143 TA x RO ---> SEP 0.621 0.257 2.420 0.017 Figure 3 shows this interaction. Its slope increases sharply for high technology adoption (+ 1 SD), suggesting that sustainable environmental performance is best when technology use is high. The slope for average technology adoption shows moderate improvement, while the slope for low technology adoption (-1 SD) remains nearly flat, showing little environmental progress even when routes are optimized. Therefore, the result implies that technology adoption enhances the operational and environmental benefits of route optimization. GPS and IoT let truck operators monitor routes in real time, react to traffic and record fuel efficiency. In this way, emissions and fuel waste are reduced. Firms with low technology adoption miss these improvements because they lack data-driven decision support. More technology adoption is needed in Tanzania's trucking sector for environmental and operational gains. In this study, route optimization positively influenced the sustainable environmental performance of truck operators in Tanzania. These findings are in agreement with Srinivas ( 2024 ), who, through eco-optimized route planning, showed that efficient routing reduces costs, fuel consumption and carbon emissions in logistics operations. Similarly, Chen ( 2024 ), in a study on data-driven and sustainable transportation route optimization, found that optimizing vehicle routes reduces transportation costs and CO2 emissions. These findings are in line with those of Ouhader and Kyal ( 2020 ), who argued that route collaboration and optimization in urban freight delivery contribute to economic as well as ecological performance. Also, it is further consistent with the study of Vyakarnam et al. ( 2023 ), who developed and reported on an eco-efficient vehicle routing model, that environmentally focused routing decisions have lower operation costs as well as carbon footprints. These previous studies therefore confirm that route optimization is an important part of the sustainable environmental performance of logistics operations. In the present study, we extend this understanding with empirical data from the Tanzanian trucking industry, where optimization of delivery routes directly contributes to environmental sustainability despite infrastructural challenges. Moreover, technology adoption was found to positively moderate the effect of route optimization on sustainable environmental performance. It shows that the more truck operators with higher technology, specifically GPS and IoT-based systems, the greater sustainability benefits they get from route optimization. This concurs in line with the work of Rodrigues and Fiorini ( 2023 ), who analysed Logistics 4.0 applications and found that real-time tracking/automation and system integration support sustainable transport. Similarly, Ding et al. ( 2023 ) argued that, in logistics, IoT adoption leads to better real-time management, emissions and decision making. Also, Hussain ( 2025 ) highlighted that AI-and IoT-based route optimization systems significantly cut delivery time, fuel consumption and emissions, enabling logistics sustainability. Vudugula ( 2025 ) also pointed out that IoT, AI and renewable energy sources make logistics sustainable in terms of energy efficiency and carbon footprints. Such findings are especially relevant in the Tanzanian context, where logistics are structurally inefficient, fleet systems are old, and technology is scarce (Mlimbila & Mbamba, 2018 ). In spite of these constraints, the study shows that even partial technology adoption can improve environmental performance when coupled with route planning. This work, therefore, adds to the growing literature on green logistics by showing that digital tools can bring tangible environmental and operational benefits even in developing economies. 5. Conclusion, study’s implications and limitations 5.1 Conclusion This study examined how route optimization influences sustainable environmental performance among Tanzanian truck operators and how technology adoption moderates this relationship. In the Natural Resource-Based View (NRBV), route optimization is shown to be a pollution prevention capability that enables good environmental performance in terms of fuel consumption and emissions. And the positive moderating effect of technology adoption, notably via GPS and IoT systems, shows that digital tools make these capabilities more environmentally valuable. Route optimization is better for sustainability outcomes when technology use is high than when technology use is low. These results provide empirical evidence that digital technology in logistics operations translates environmental efficiency into competitive/ecological advantage. In other words, NRBV claims that environmental capabilities are sources of sustained advantage (Hart, 1995 ). Trucking firms in Tanzania should therefore embrace digital integration for cleaner, more sustainable freight systems. 5.2 Theoretical implication Based on the Natural Resource-Based View (NRBV), this research goes beyond the theory of how firms construct and combine environmental/digital capabilities for sustainable performance. They show that route optimization is pollution prevention, and technology adoption is complementary dynamic capability. Their interaction supports NRBV's assertion that environmental benefit is attained by a synergistic arrangement of resources rather than by isolated practices. In this study, the theoretical application of NRBV is extended to a developing economy logistics context of firms working with limited resources/infrastructure. In doing so, it demonstrates in green logistics that environmental performance improvement is possible even in emerging markets when operational as well as technological capabilities are in sync. It also builds on recent empirical results indicating that digital transformation opens up green capabilities in logistics (Rodrigues & Fiorini, 2023 ). So, the NRBV is a model of adaptive/changing integration of pollution prevention routines and sustainability management with a capability-based competitive advantage. 5.3 Practical implications The empirical insights derived from this study hold significant implications for managers, logistics practitioners, and policy-makers. From a managerial point of view, firms should consider route optimization as a strategic environmental capability supporting long-term operational efficiency and competitive advantage. GPS/IoT integration in daily fleet operations allows firms to monitor routes in real time, to reduce idle mileage and to predict maintenance needs, all of which saves emissions and fuel costs and improves delivery reliability. Managers are encouraged to invest in training drivers in eco-driving, data-driven dispatch systems and predictive analytics for adaptive route planning. At the policy level, government bodies like the Land Transport Regulatory Authority (LATRA) and the Truck Owners Association (TATOA) should come up with tax incentives, financing schemes, and better digital infrastructure for pollution prevention practices. All these actions would help small and medium-sized trucking firms get cheap digital tools and green logistics practices in place in the sector. Moreover, linking these initiatives with Tanzania's national Environmental Policy (2021) and Vision 2050 will also help the nation to achieve sustainable transport targets. Route optimization and digital integration of road freight operations are thus directly connected to the Sustainable Development Goals for environmentally responsible, flexible, and resilient logistics systems. 5.4 Limitations and directions for future research Although this study provides valuable empirical evidence linking operational and digital capabilities to environmental performance, several limitations open avenues for further inquiry. Causal interpretation is limited by the cross-sectional design as adopted in this study; therefore, future longitudinal or panel studies might be useful to explore how pollution prevention, technological capabilities, and their interactions change over time. Moreover, the application of NRBV to truck operators in Dar es Salaam is not generalizable; comparative studies among different Tanzanian regions or other African economies would help to understand contextual variability in NRBV application. Using self-reported survey data in this study may also induce response bias; therefore, further research might use objective indicators such as real fuel consumption logs, telematics data, or emissions records for better measurement accuracy. Lastly, technology adoption was the only variable tested; Other potential moderators/mediators, for example, firm size, regulatory compliance, environmental culture, or financial capacity, can be further studied to refine the mechanisms by which complementary resources build on NRBV capabilities and contribute to long-term environmental performance. In fact, longitudinal evidence of how these capabilities mature would expand the NRBV's explanatory power in dynamic, resource-constrained logistics environments. Declarations Ethical Approval and accordance: The study protocol was approved by the St. John's University of Tanzania Research and Ethics Committee, and administrative clearance was granted by the Tanzania Truck Owners Association (TATOA). All research procedures were carried out in accordance with the institutional guidelines and regulations for studies involving human participants. Informed Consent: Informed consent was obtained from all individual participants included in the study. Consent to publish statement: Not applicable as no identifiable data or images are present within the manuscript. Data Availability: The datasets collected and analyzed during this study are not publicly available due to the privacy and confidentiality of the participating truck operators. However, the data are available from the corresponding author on reasonable request. Funding: The authors received no financial support for the research, authorship, and/or publication of this article. Competing Interests: The authors declare no competing interests. Author Contribution The main idea for this study and the creation of the survey tools were done by Mfaume H. Rissasi, who also performed the data analysis. Barnabas Maagi contributed by adding important details to the discussion section and helping to interpret what the results mean for the industry. When it was time to understand the numbers, both authors worked together on the results and wrote the text for the manuscript. At the end, both authors read the full paper and agreed it was ready for submission. References African Development Bank. (2025). Annual Report 2024 (pp. 1–136). Abidjan: African Development Bank Group. https://www.afdb.org/en/documents/annual-report-2024 African Union Development Agency. (2022). Second Continental Report on the Implementation of Agenda 2063 (pp. 1–154). 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Introduction","content":"\u003cp\u003eRoad freight transport plays a leading role in moving goods across Sub-Saharan Africa, serving as the main link for trade within and between countries. In Tanzania, this sector is especially important because the nation is a transportation hub connecting landlocked destinations like Rwanda, Burundi, Uganda, Zambia and the Democratic Republic of the Congo with world markets via the Port of Dar es Salaam, in particular (Kunaka et al., \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). Almost three-quarters of Tanzania's freight tonnage is carried by road, which is an essential means of trade, agriculture and industrial development (Chacha et al., \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). National Development Vision 2050 further identifies transport as a key driver for achieving sustainable infrastructure and environmental protection in the country (URT, \u003cspan citationid=\"CR75\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). Environmental pollution has drawn global attention in recent years to sustainable transport and logistics, which are at the heart of economic growth (Yadav \u0026amp; Sharma, \u003cspan citationid=\"CR80\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Freight transport is a major source of pollution, resource waste, and climate change (Garc\u0026iacute;a-Olivares et al., \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Road freight accounts for about one-third of all carbon dioxide (CO2) emissions worldwide, and transport in general accounts for more than one-quarter of all emissions (Statista, \u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). In the European Union (EU), for example, road transport in 2022 released 760\u0026nbsp;million tonnes of CO2 24% more CO2 than since 1990, largely due to freight trucks (Zeyen et al., \u003cspan citationid=\"CR84\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). Freight trucks emitted about 126g of CO2 per ton-kilometre in Germany in 2020, while in the United States, 12% of CO2 and 10% of nitrogen oxides produced caused the country to incur an annual environmental and health cost of about USD 30\u0026nbsp;billion (Allekotte et al., \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Lathwal et al., \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Respiratory diseases and early death among people are also linked to these diesel and carbon emissions from the trucks and overall road freight operations (Long \u0026amp; Carlsten, \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2022\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eIn Africa, the transport sector accounts for almost a third of all emissions, estimated at around 346\u0026nbsp;million tonnes CO2 per year (Bongard et al. 2023). Road freight alone accounts for 90% of transport emissions and 14% of total greenhouse gases in South Africa (Du Plessis et al., \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Figures like these show how greener transport is needed. Efforts like the African Continental Free Trade Area Agenda 2063 for sustainable logistics integration (AfCFTA, 2022) and the African Development Bank (AfDB, 2025) promote investment in resilient transport infrastructures.\u003c/p\u003e \u003cp\u003eIn Tanzania, the transport and logistics sector expanded by about 7.4% in 2024, which was a good indicator towards the growth of the national economy (NBS, \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). Nevertheless, inefficiency, high fuel costs and low use of modern technology remain as environmental pressures and challenges for the sector (Arvis et al., \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). National frameworks like the National Transport Policy as well as the National Environmental Policy recommend greener Transport practices, but few companies actually implement them (Issa, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). In this aspect, Interventions which look promising are renewable energy use (Multiconsult, \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), route optimization and better resource management (Jarašūnienė \u0026amp; Bazaras, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2023\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eRoute optimization is one of the impactful interventions in terms of reducing emissions and fuel costs. Transport operators can cut travel distances with data, computer-based planning, use their fleets more effectively, and avoid unnecessary trips (Wu, \u003cspan citationid=\"CR78\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). In fact, Route optimization, if well implemented, may save up to 20% fuel consumption per year (Hussain, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). In this way, achieving international sustainability Goals and initiatives, as emphasized by the Paris Agreement and the Sustainable Development goals, are supported (UNFCCC, \u003cspan citationid=\"CR73\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Ouni \u0026amp; Abdallah, \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e2024\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eTechnology adoption makes route optimization even more effective. GPS tracks vehicle movements, avoids congestion, and changes routes. IoT sensors track fuel use, tire pressure, and engine condition for better maintenance and fleet efficiency. (Chacha et al., \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). All these technologies save time, reduce emissions and enhance safety. Nevertheless, such technologies are underutilized in developing countries, specifically in Tanzania, because of high costs, low awareness, and weak infrastructure. (Moses et al., \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Issa, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). In developed countries, research suggests that digital technologies improve logistics performance (Tan et al., \u003cspan citationid=\"CR71\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). However, in contrast, in many developing countries, technology adoption progress is lagging because of scarcity of resources, low skills, and weak institutional support (Miyoba et al., \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Leonard \u0026amp; Macha, \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). This, therefore, reveals a research gap on how technology adoption affects the environmental benefits of green logistics practices, such as route optimization in Tanzania, where the trucking sector is still the main transport mode of goods across the country and beyond.\u003c/p\u003e \u003cp\u003eTo fill this gap, the study focuses on two specific objectives:\u003c/p\u003e \u003cp\u003e \u003cul\u003e \u003cli\u003e \u003cp\u003eTo examine how route optimization practices affect the sustainable environmental performance of truck operators in Tanzania.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eTo explore how technology adoption moderates the relationship between route optimization practices and sustainable environmental performance among truck operators in Tanzania.\u003c/p\u003e \u003c/li\u003e \u003c/ul\u003e \u003c/p\u003e \u003cp\u003eThe study is thus important for policy and practice. It shows that digital systems support route efficiency and emissions for Tanzania's Vision 2050 and National Environmental Policy (2021). The findings can help policymakers, industry regulators and logistics firms improve low-carbon transport plans. The work also extends green logistics practice by examining how technology adoption moderates sustainability in developing economies.\u003c/p\u003e"},{"header":"2. Literature Review","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1 The Natural Resource-Based View\u003c/h2\u003e \u003cp\u003eThe present study is grounded in the Natural Resource-Based View (NRBV) developed by Hart (\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e1995\u003c/span\u003e), which goes beyond the Resource-Based View (RBV) and incorporates environmental considerations in firm strategy. The NRBV argues that firms gain sustainable competitive advantage by developing capabilities that reduce their ecological footprint while increasing operational efficiency (Hart \u0026amp; Dowell, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2010\u003c/span\u003e). Rather than focusing on economic and internal efficiency as the main goal of the RBV, the NRBV looks at pollution prevention, product stewardship, and sustainable development as means through which firms can simultaneously meet environmental and economic goals (Yunus \u0026amp; Michalisin, \u003cspan citationid=\"CR82\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; McDougall et al., \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2021\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eIn logistics, route optimization is an example of pollution prevention, process improvement, reduced waste and emissions due to better fuel use and less idle time. That corresponds to Hart's (1995) argument that operational efficiency initiatives are environmental strategies if they reduce negative ecological externalities. Through GPS and IoT integration, technology adoption itself is a dynamic capability that enhances the sensitivity of pollution prevention routines (McDougall et al., \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Jansson \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). With these complementarities, firms can turn environmental strategies into performance gains, reflecting the core proposition of NRBV.\u003c/p\u003e \u003cp\u003eIn addition, NRBV offers a coherent theoretical account of how capabilities are embedded in logistics operations translate to environmental performance. Advanced digitally oriented firms can better coordinate transport activities, optimize routes, and monitor emissions directly in line with NRBV's sustainable development goal (Li et al., \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). All this dynamic interaction supports Hart's later proposition that environmental capabilities improve competitiveness and ecological outcomes through innovation and learning (Hart \u0026amp; Dowell, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2010\u003c/span\u003e; McDougall et al., \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2021\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThus, this study extends the concept of NRBV to the Tanzanian trucking industry, where logistics inefficiencies and technological gaps are major sustainability barriers. By defining route optimization as a pollution prevention capability and technology adoption as a complementary dynamic capability, this research shows how digital integration increases the environmental value of core logistics routines. Thus, NRBV is the best theoretical account of direct and moderating relations between route optimization, technology adoption, and sustainable environmental performance.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2 Route optimization and sustainable environment performance\u003c/h2\u003e \u003cp\u003eThe logistics sector is necessary for trade and national development, but it is also a major cause of environmental degradation due to carbon emissions, air pollution and high fuel consumption (Lin, \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). In response, route optimization is now seen as a core activity in green logistics, reducing travel distance, idle time and fuel consumption, all of which contribute to environmental performance (Kamanga, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Miyoba et al., \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). It further contributed to streamlining delivery routes so that operational goals are in line with sustainability goals.\u003c/p\u003e \u003cp\u003eRoute optimization has been shown to improve logistics sustainability. Studies in different contexts show that optimized routing reduces fuel consumption, operation costs and greenhouse gas emissions (Psaraftis, \u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Wu, \u003cspan citationid=\"CR78\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). For example, route planning can cut delivery time by up to 30% and CO2 emissions by almost 20% in African transport systems (Miyoba et al., \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Similarly, Ingrao et al. (\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) point out that efficient routing reduces energy use as well as supports eco-efficiency by integrating environmental and operational performance objectives. These results indicate that route optimization contributes directly to the environmental aspect of logistics sustainability by reducing waste and improving resource efficiency.\u003c/p\u003e \u003cp\u003eNevertheless, there are two distinct literature streams. The first is for developed economies and involves advanced optimization models and sustainable freight planning in structured road systems (Psaraftis, \u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Ingrao et al., \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). The second, still emerging, looks at developing economies where routing is constrained by infrastructural deficits, long travel distances and inefficient scheduling (Kamanga, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Lin, \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Though sustainable routing in freight and cargo systems has been studied in Zambia and South Africa (Miyoba et al., \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2024\u003c/span\u003e), there is little empirical data from Tanzania. Some of the studies in Tanzania, like Kamanga (\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2019\u003c/span\u003e), deal only with public transport optimization and urban mobility, while long-haul truck operations and their environmental impacts are of little interest and narrowly studied.\u003c/p\u003e \u003cp\u003eMoreover, since road freight transport in Tanzania accounts for more than 80% of goods movement and is a major source of CO2 emissions (Richard, \u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e2020\u003c/span\u003e), route optimization is of prime interest for environmental performance. In addition to that, despite the increasing awareness of green logistics, there are still few local studies linking route optimization to sustainability outcomes in the trucking industry. Accordingly, this study fills that gap by investigating how route optimization practices affect the sustainable environmental performance of truck operators in Tanzania. It is therefore worth hypothesizing that:\u003c/p\u003e \u003cp\u003e \u003cem\u003eH1: Route optimization significantly contributes to the sustainable environmental performance of the truck operators in Tanzania.\u003c/em\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3 The moderating role of technology adoption\u003c/h2\u003e \u003cp\u003eFor logistic companies that operate in transport logistics, technologies such as the Global Positioning System and Internet of Things sensors are becoming more important for safe and sustainable delivery processes. In this respect, truck operators need to make sure such technologies are applied in routing operations to increase the environmental benefits of route optimization. In the current research, we postulate that route optimization leads to better sustainable environmental performance when technology adoption is done right. But high costs, limited infrastructure and inadequate technical capacity may prevent operators from realizing these benefits (Issa, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2023\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eGPS and IoT technologies in logistics are increasingly used for route management and for reducing environmental impacts (Ding et al., \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). These tools give real-time information about vehicle location, fuel consumption and emissions for better routing decisions and environmental outcomes (Mathauer \u0026amp; Hofmann, \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Saqib \u0026amp; Qin, \u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). However, most studies dealt with the direct impact of technology on logistics rather than with its effect on the strength of the relationship between route optimization and environmental performance. Technology adoption may therefore moderate this relationship, with the assumption that higher GPS and IoT usage leads to greater environmental benefits of route optimization. Conversely, where technology is used less, the benefits may be weak. To this end, it is worth hypothesizing that:\u003c/p\u003e \u003cp\u003e \u003cem\u003eH2: Technology adoption positively moderates the relationship between route optimization and the sustainable environmental performance of truck operators in Tanzania.\u003c/em\u003e \u003c/p\u003e \u003cp\u003eBased on the reviewed literature and hypotheses, a conceptual model of NRBV Theory is proposed. The model shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e shows that route optimization contributes to the sustainable environmental performance of truck operators in Tanzania. It also proposes that this relationship is influenced by a moderating variable, namely technology adoption (i.e. use of GPS and IoT sensors). All proposed relationships among study variables are used to derive H1 and H2 hypotheses, as shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"3. Methodology","content":"\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e3.1 Study Area and Research Design\u003c/h2\u003e \u003cp\u003eThis study was conducted in the Dar es Salaam region of Tanzania, the country\u0026rsquo;s key logistics hub and home to the Dar es Salaam Port, which handles nearly 95% of Tanzania\u0026rsquo;s international freight traffic (Magomba, \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). The area was selected because it contains many truck operators and logistics firms registered under TATOA (\u003cspan citationid=\"CR72\" class=\"CitationRef\"\u003e2025\u003c/span\u003e) and thus is an ideal place to study route optimization effects on sustainable environmental performance. Similar studies (Mlimbila \u0026amp; Mbamba, \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Peter \u0026amp; Yang, \u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e2019\u003c/span\u003e) identified that emissions and congestion are major environmental concerns in Dar es Salaam city. This study used a cross-sectional design to collect quantitative data from truck operators at one point in time. Such a design is good for analyzing variable relationships without time effects (Bell et al., \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Previous literature in East Africa (Mutua et al., \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Kisinga et al., \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2024\u003c/span\u003e) successfully used cross-sectional surveys to assess logistics and overall supply chain performances. This design was therefore appropriate for a snapshot of green logistics practices, technology use and environmental performance of Tanzanian truck operators in this study.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e3.2. Population, Sample and Data Collection\u003c/h2\u003e \u003cp\u003eThe study focused on 552 Truck operating firms as of 2025 in the Dar es Salaam corridor registered under the Tanzania Truck Owners Association (TATOA). All firms had one representative in this study, either a senior Executive, a Chief Executive Officer, operations Manager or Fleet Manager who is recognized as an essential individual in charge of logistics Operations, technology adoption and environmental decision issues. A representative sample of 232 firms was drawn from the population using the Taro Yamane (\u003cspan citationid=\"CR81\" class=\"CitationRef\"\u003e1967\u003c/span\u003e) formula at a 95% confidence level with 5% margin of error. Sampling procedures ensured that all firms were included equally according to simple random sampling principles (Taherdoost, \u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). The sampling frame was taken from the official TATOA directory, where contacts of registered members were verified. The association helped members become involved in the study by formally introducing it to them. Surveys were sent out by email, with additional hard copies delivered to selected firms with limited online access. Out of 232 questionnaires distributed, 170 were returned complete and valid, which equates to a 73.3% response rate. This response rate is good enough for survey research and for improvement of data reliability, as recommended by Mugenda \u0026amp; Mugenda (\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). With this approach, the data collected reflected truck operators in Tanzania and allowed relationship analysis between route optimization, technology adoption and sustainable environmental performance in logistics.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e3.3. Variables, Measurements, Reliability and Validity\u003c/h2\u003e \u003cp\u003eThe study used validated measurement scales from prior peer-reviewed research. In accordance with similar logistics studies, such as that of Miyoba et al. (\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2024\u003c/span\u003e), all constructs were scored on a five-point Likert scale from 1 (Strongly Disagree) to 5 (Strongly Agree). Three items adopted from Zrigui (\u003cspan citationid=\"CR85\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) and Aloupogianni (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2024\u003c/span\u003e) were used to measure route optimization, including route planning efficiency and emission reduction. Likewise, three items from Keivanpour (\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) and Swamidas and Ullas (2024) were adopted in measuring GPS/IoT systems as Technological equipment used in logistics operations and decision making. Sustainable environmental performance was measured using three items adopted from Zagurskiy et al. (2022), Rześny-Cieplińska and Szmelter-Jarosz (\u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e2020\u003c/span\u003e), as related to emission control, environmental monitoring and operational eco-efficiency.\u003c/p\u003e \u003cp\u003eMeasurement quality was confirmed by reliability and validity assessments. Cronbach's alpha \u0026amp; Composite Reliability (CR) for all constructs were above the 0.7 threshold for internal consistency (Hair et al., \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Average variance extracted (AVE) values above 0.5 justify that there was sufficient shared Variance between items for met convergent validity (Fornell \u0026amp; Larcker, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e1981\u003c/span\u003e). Discriminant validity was established using the Fornell-Larcker criterion and HTMT ratios (Henseler et al., \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2015\u003c/span\u003e) and thus affirmed the distinctiveness between the study's main constructs. These results showed that route optimization, technology adoption and sustainable environmental performance were measured reliably and differently empirically, demonstrating a good basis for structural model testing.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003e3.4. Data Analysis\u003c/h2\u003e \u003cp\u003eData were analysed using SmartPLS 4 for Confirmatory Factor Analysis and Covariance-Based Structural Equation Modelling (CB-SEM). The CFA tested the measurement model for model fit indices, reliability and convergent and discriminant validity of constructs (Hair et al., \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Results showed that indicators met statistical thresholds required for a robust measurement model. The study hypotheses were then tested by CB-SEM using SmartPLS 4. The direct effect of route optimization on sustainable environmental performance and the moderating effect of technology adoption were analysed. Such a dual application approach is in line with the methodological recommendations for SmartPLS 4 for simultaneous CFA and CB-SEM procedures in research\u003c/p\u003e \u003cp\u003e(Chua, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). The combined analysis gave valid and reliable results for structural relationships between study variables.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003e3.5. Common Method Variance\u003c/h2\u003e \u003cp\u003eGiven that data were collected from single respondents across all trucking companies, common method bias could exist. Thus, Harman's single-factor test with Principal Component Analysis for bias check was applied (Podsakoff et al., \u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e2003\u003c/span\u003e). The results showed that the first factor explained 31.65% of the variance. Because this value is below the 50% threshold, common method bias was not considered a major issue in this study, and thus, the collected data were not significantly influenced by measurement errors due to a single data collection source.\u003c/p\u003e \u003c/div\u003e"},{"header":"4. Results and Discussion","content":"\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003e4.1 Respondents' Characteristics and Company Profile\u003c/h2\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e shows the socio-economic characteristics of the respondents. Males accounted for 92.9 percent of the respondents, while females accounted for only 7.1 percent, reflecting the gender divide in the transport sector in Tanzania. This cadre is made up of mid-career professionals, with 72 percent aged 31 to 40 years and 32.4 percent in the 41 to 50 age group. The length of service experience confirms that the informants have sufficient knowledge about the sector. Respondents with 7 to 10 years of experience constitute 27.6 percent, followed by 25.9 percent with 1 to 3 years of experience and 23.5 percent with 4 to 6 years. These figures indicate a market dominated by small and medium-sized enterprises. Firms with 1 to 10 employees account for 39.4 percent of these reports, while those with a workforce of 11 to 50 people contribute 30.6 percent. On part of fleets type, this study notices most firms are still largely based on diesel trucks at 90 percent. Hybrid trucks account for 7.1 percent, while the use of compressed natural gas (CNG) or biodiesel trucks is the lowest at 2.9 percent. The size of company fleet varies, with 44.1 percent of firms owning 11 to 50 trucks and 35.2 percent owning between 51 and 100. Lastly, on operational years, most firms are satisfactory, with 34.1 percent of businesses having been in existence for 6 to 10 years and 28.8 percent for 1 to 5 years. All in all, the respondents represented an experienced, SME-based and diesel-dependent trucking industry that can be used to evaluate green logistics practices in Tanzania.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eRespondents Characteristics and Company Profile\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCategory\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eResponse\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eFrequency\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003ePercentage (%)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGender\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e158\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e92.9\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e7.1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e18\u0026ndash;30 years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e15.9\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e31\u0026ndash;40 years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e72\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e42.4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e41\u0026ndash;50 years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e32.4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e51\u0026thinsp;+\u0026thinsp;years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e9.4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eExperience\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLess than 1 year\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4.1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1\u0026ndash;3 years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e25.9\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4\u0026ndash;6 years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e23.5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7\u0026ndash;10 years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e27.6\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMore than 10 years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e18.8\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCompany size (No. of employees)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1\u0026ndash;10 employees\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e39.4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e11\u0026ndash;50 employees\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e52\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e30.6\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e51\u0026ndash;100 employees\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e14.1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e101\u0026ndash;200 employees\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e8.2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e200\u0026thinsp;+\u0026thinsp;employees\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e7.6\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMain truck type\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDiesel engine trucks\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e153\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e90\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHybrid trucks\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e7.1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCNG/Biodiesel trucks\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.9\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCompany fleet size\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1\u0026ndash;10 trucks\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e12.3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e11\u0026ndash;50 trucks\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e79\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e44.1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e51\u0026ndash;100 trucks\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e63\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e35.2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOver 100 trucks\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.9\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOperational years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLess than 1 year\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.9\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1\u0026ndash;5 years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e28.8\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6\u0026ndash;10 years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e58\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e34.1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e11\u0026ndash;20 years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e20.6\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOver 20 years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e13.5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eTotal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e170\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e100\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003e4.2. Descriptive Statistics and Correlations\u003c/h2\u003e \u003cp\u003eAs shown in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e below, the mean value of Route optimization (RO) was 3.790 with a standard deviation of 0.544, which means that on average, respondents moderately agreed with statements regarding route planning and fuel-saving practices, suggesting fair implementation among truck operators. Technology adoption (TA) had a mean of 3.845, and a 0.619 standard deviation, demonstrating that respondents generally agreed that technological tools like GPS and IoT are used in their operations. The mean and standard deviation for sustainable environmental performance (SEP) were 3.771 and 0.582, respectively, which indicate moderate environmental sustainability in logistics activities. Skewness was \u0026minus;\u0026thinsp;0.971 to -0.607, and kurtosis was 1.323 to 2.747, both within + -3 acceptable thresholds, indicating normal data distribution (Tabachnick \u0026amp; Fidell, \u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Further, correlation results show a positive, statistically significant relationship between route optimization and sustainable environmental performance (r\u0026thinsp;=\u0026thinsp;0.155, p\u0026thinsp;\u0026lt;\u0026thinsp;0.05) and technology adoption with sustainable environmental performance (r\u0026thinsp;=\u0026thinsp;0.198, p\u0026thinsp;\u0026lt;\u0026thinsp;0.01). Despite that, route optimization and technology adoption were not significantly correlated (r\u0026thinsp;=\u0026thinsp;0.006, p\u0026thinsp;\u0026gt;\u0026thinsp;0.05); still, all correlation coefficients were under 0.7, confirming that multicollinearity was nonexistent (Pallant, \u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). These results suggest that study variables are related enough to warrant further regression analysis to confirm the hypothesized relationships.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eDescriptive Statistics and Correlations\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"8\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariables\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMean\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eStd.\u003c/p\u003e \u003cp\u003eDeviation\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSkewness\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eKurtosis\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eRO\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eTA\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eSEP\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRO\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.790\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.544\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.971\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.747\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.845\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.619\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.607\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.323\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.006\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSEP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.771\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.582\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.705\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.805\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.155*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.198**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"8\"\u003e*p\u0026thinsp;\u0026lt;\u0026thinsp;0.05, ** p\u0026thinsp;\u0026lt;\u0026thinsp;0.01\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003e4.3. CFA Results\u003c/h2\u003e \u003cp\u003eAs described under methodology, Confirmatory Factor Analysis was performed to validate the measurement model reliability and validity. In Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e, the model fit indices showed a good fit to the data. In particular, χ\u0026sup2;/df\u0026thinsp;=\u0026thinsp;1.084, CFI\u0026thinsp;=\u0026thinsp;0.997, GFI\u0026thinsp;=\u0026thinsp;0.968, NFI\u0026thinsp;=\u0026thinsp;0.959, TLI\u0026thinsp;=\u0026thinsp;0.995, SRMR\u0026thinsp;=\u0026thinsp;0.039 and RMSEA\u0026thinsp;=\u0026thinsp;0.022 are within the recommended thresholds for good model fit (Hair et al., \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). According to these criteria, values of χ\u0026sup2;/df\u0026thinsp;\u0026lt;\u0026thinsp;3, CFI, GFI, NFI, and TLI\u0026thinsp;\u0026gt;\u0026thinsp;0.90, and RMSEA and SRMR\u0026thinsp;\u0026lt;\u0026thinsp;0.05 indicate a well-fitting model (Kline, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Thus, the CFA results confirm that the measurement model fits the observed data well. Based on the reliability/validity results in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e, all constructs met the minimum requirements for internal consistency and convergent validity. Cronbach's alpha was 0.820 to 0.843, and Composite Reliability was 0.823 to 0.846, both above the 0.70 cutoff (Hair et al., \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). AVE values were 0.605 to 0.643, sufficiently above the 0.50 benchmark, indicating convergent validity (Fornell \u0026amp; Larcker, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e1981\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eMeasurements, factor loadings, AVE, reliability, and validity\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eConstruct/ Items\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLoadings\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eα\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eCR\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eAVE\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRoute Optimization (RO)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.820\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.823\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.605\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRO1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.841\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRO2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.739\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRO3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.749\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTechnology Adoption (TA)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.842\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.845\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.643\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTA1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.781\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTA2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.759\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTA3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.861\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSustainable Environmental Performance (SEP)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.843\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.846\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.643\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSEP1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e0.826\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSEP2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e0.832\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSEP3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e0.744\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003eModel fit indices: \u003cem\u003eX\u003c/em\u003e\u003csup\u003e2\u003c/sup\u003e/df\u0026thinsp;=\u0026thinsp;1.084, CFI\u0026thinsp;=\u0026thinsp;0.997, SRMR\u0026thinsp;=\u0026thinsp;0.039, RMSEA\u0026thinsp;=\u0026thinsp;0.022, GFI\u0026thinsp;=\u0026thinsp;0.968, NFI\u0026thinsp;=\u0026thinsp;0.959, and TLI\u0026thinsp;=\u0026thinsp;0.995\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eMoreover, as shown in Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e, the discriminant validity was tested with the Fornell-Larcker criterion and with the Heterotrait-Monotrait (HTMT) ratio. Square roots of AVE were larger than the inter-construct correlations, and all HTMT values were below the recommended thresholds of 0.85 (Henseler et al., \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). Results indicate that Route Optimization (RO), Technology Adoption (TA), and Sustainable Environmental Performance (SEP) are distinct constructs with identified measurement properties that are suitable for structural model estimation.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eDiscriminant Validity\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"8\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"8\" nameend=\"c8\" namest=\"c1\"\u003e \u003cp\u003eFornell-Larcker criterion\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eConstruct\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCR\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAVE\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMSV\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eMaxR(H)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eRO\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eTA\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eSEP\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRO\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.820\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.605\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.043\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.830\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e0.778\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.842\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.643\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.057\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.853\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.003\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e0.802\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSEP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.843\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.643\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.057\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.849\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.208\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.238\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003e0.802\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eHeterotrait-Monotrait Ratio (HTMT)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eConstruct\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003eRO\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003eTA\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003eSEP\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRO\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.042\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSEP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.180\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.233\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003e4.4. Testing of Hypotheses\u003c/h2\u003e \u003cp\u003eFor testing the study hypotheses and for assessing direct and moderating effects, we used SmartPLS 4 (CB-SEM). With moderate explanatory power, the model in Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e and Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e explained 14.3% of the variance in Sustainable Environmental Performance (R\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0.143).\u003c/p\u003e \u003cp\u003eThe first hypothesis (H1) was that route optimization affects the sustainable environmental performance of truck operators in Tanzania. Results supported this relationship as it portrayed a significant and positive effect (β\u0026thinsp;=\u0026thinsp;0.229, p\u0026thinsp;=\u0026thinsp;0.007). This means that Truck operators who plan routes well, avoid congestion and idle less often achieve better environmental results. In essence, Route management effectively reduces fuel use, vehicle wear and emissions. According to the second hypothesis (H2), it posits that technology adoption moderates the relationship between route optimization and sustainable environmental performance. Findings from this study confirm this effect with a significant positive interaction term (β\u0026thinsp;=\u0026thinsp;0.621, p\u0026thinsp;=\u0026thinsp;0.017). Therefore, with this moderating result, it means that firms adopting advanced technologies benefit from route optimization more than firms adopting limited digital systems.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eStructural model estimates for the direct and moderated effect\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEstimates\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eS.E.\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eT values\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eP values\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eR Square\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRO ---\u0026gt; SEP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.229\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.084\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.715\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.007\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.192\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.092\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.084\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.039\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.143\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTA x RO ---\u0026gt; SEP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.621\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.257\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.420\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.017\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eFigure \u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e shows this interaction. Its slope increases sharply for high technology adoption (+\u0026thinsp;1 SD), suggesting that sustainable environmental performance is best when technology use is high. The slope for average technology adoption shows moderate improvement, while the slope for low technology adoption (-1 SD) remains nearly flat, showing little environmental progress even when routes are optimized. Therefore, the result implies that technology adoption enhances the operational and environmental benefits of route optimization. GPS and IoT let truck operators monitor routes in real time, react to traffic and record fuel efficiency. In this way, emissions and fuel waste are reduced. Firms with low technology adoption miss these improvements because they lack data-driven decision support. More technology adoption is needed in Tanzania's trucking sector for environmental and operational gains.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eIn this study, route optimization positively influenced the sustainable environmental performance of truck operators in Tanzania. These findings are in agreement with Srinivas (\u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e2024\u003c/span\u003e), who, through eco-optimized route planning, showed that efficient routing reduces costs, fuel consumption and carbon emissions in logistics operations. Similarly, Chen (\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2024\u003c/span\u003e), in a study on data-driven and sustainable transportation route optimization, found that optimizing vehicle routes reduces transportation costs and CO2 emissions. These findings are in line with those of Ouhader and Kyal (\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e2020\u003c/span\u003e), who argued that route collaboration and optimization in urban freight delivery contribute to economic as well as ecological performance. Also, it is further consistent with the study of Vyakarnam et al. (\u003cspan citationid=\"CR77\" class=\"CitationRef\"\u003e2023\u003c/span\u003e), who developed and reported on an eco-efficient vehicle routing model, that environmentally focused routing decisions have lower operation costs as well as carbon footprints. These previous studies therefore confirm that route optimization is an important part of the sustainable environmental performance of logistics operations. In the present study, we extend this understanding with empirical data from the Tanzanian trucking industry, where optimization of delivery routes directly contributes to environmental sustainability despite infrastructural challenges.\u003c/p\u003e \u003cp\u003eMoreover, technology adoption was found to positively moderate the effect of route optimization on sustainable environmental performance. It shows that the more truck operators with higher technology, specifically GPS and IoT-based systems, the greater sustainability benefits they get from route optimization. This concurs in line with the work of Rodrigues and Fiorini (\u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e2023\u003c/span\u003e), who analysed Logistics 4.0 applications and found that real-time tracking/automation and system integration support sustainable transport. Similarly, Ding et al. (\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) argued that, in logistics, IoT adoption leads to better real-time management, emissions and decision making. Also, Hussain (\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2025\u003c/span\u003e) highlighted that AI-and IoT-based route optimization systems significantly cut delivery time, fuel consumption and emissions, enabling logistics sustainability. Vudugula (\u003cspan citationid=\"CR76\" class=\"CitationRef\"\u003e2025\u003c/span\u003e) also pointed out that IoT, AI and renewable energy sources make logistics sustainable in terms of energy efficiency and carbon footprints.\u003c/p\u003e \u003cp\u003eSuch findings are especially relevant in the Tanzanian context, where logistics are structurally inefficient, fleet systems are old, and technology is scarce (Mlimbila \u0026amp; Mbamba, \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). In spite of these constraints, the study shows that even partial technology adoption can improve environmental performance when coupled with route planning. This work, therefore, adds to the growing literature on green logistics by showing that digital tools can bring tangible environmental and operational benefits even in developing economies.\u003c/p\u003e \u003c/div\u003e"},{"header":"5. Conclusion, study’s implications and limitations","content":"\u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003e5.1 Conclusion\u003c/h2\u003e \u003cp\u003eThis study examined how route optimization influences sustainable environmental performance among Tanzanian truck operators and how technology adoption moderates this relationship. In the Natural Resource-Based View (NRBV), route optimization is shown to be a pollution prevention capability that enables good environmental performance in terms of fuel consumption and emissions. And the positive moderating effect of technology adoption, notably via GPS and IoT systems, shows that digital tools make these capabilities more environmentally valuable. Route optimization is better for sustainability outcomes when technology use is high than when technology use is low. These results provide empirical evidence that digital technology in logistics operations translates environmental efficiency into competitive/ecological advantage. In other words, NRBV claims that environmental capabilities are sources of sustained advantage (Hart, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e1995\u003c/span\u003e). Trucking firms in Tanzania should therefore embrace digital integration for cleaner, more sustainable freight systems.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec19\" class=\"Section2\"\u003e \u003ch2\u003e5.2 Theoretical implication\u003c/h2\u003e \u003cp\u003eBased on the Natural Resource-Based View (NRBV), this research goes beyond the theory of how firms construct and combine environmental/digital capabilities for sustainable performance. They show that route optimization is pollution prevention, and technology adoption is complementary dynamic capability. Their interaction supports NRBV's assertion that environmental benefit is attained by a synergistic arrangement of resources rather than by isolated practices. In this study, the theoretical application of NRBV is extended to a developing economy logistics context of firms working with limited resources/infrastructure. In doing so, it demonstrates in green logistics that environmental performance improvement is possible even in emerging markets when operational as well as technological capabilities are in sync. It also builds on recent empirical results indicating that digital transformation opens up green capabilities in logistics (Rodrigues \u0026amp; Fiorini, \u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). So, the NRBV is a model of adaptive/changing integration of pollution prevention routines and sustainability management with a capability-based competitive advantage.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec20\" class=\"Section2\"\u003e \u003ch2\u003e5.3 Practical implications\u003c/h2\u003e \u003cp\u003eThe empirical insights derived from this study hold significant implications for managers, logistics practitioners, and policy-makers. From a managerial point of view, firms should consider route optimization as a strategic environmental capability supporting long-term operational efficiency and competitive advantage. GPS/IoT integration in daily fleet operations allows firms to monitor routes in real time, to reduce idle mileage and to predict maintenance needs, all of which saves emissions and fuel costs and improves delivery reliability. Managers are encouraged to invest in training drivers in eco-driving, data-driven dispatch systems and predictive analytics for adaptive route planning.\u003c/p\u003e \u003cp\u003eAt the policy level, government bodies like the Land Transport Regulatory Authority (LATRA) and the Truck Owners Association (TATOA) should come up with tax incentives, financing schemes, and better digital infrastructure for pollution prevention practices. All these actions would help small and medium-sized trucking firms get cheap digital tools and green logistics practices in place in the sector. Moreover, linking these initiatives with Tanzania's national Environmental Policy (2021) and Vision 2050 will also help the nation to achieve sustainable transport targets. Route optimization and digital integration of road freight operations are thus directly connected to the Sustainable Development Goals for environmentally responsible, flexible, and resilient logistics systems.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec21\" class=\"Section2\"\u003e \u003ch2\u003e5.4 Limitations and directions for future research\u003c/h2\u003e \u003cp\u003eAlthough this study provides valuable empirical evidence linking operational and digital capabilities to environmental performance, several limitations open avenues for further inquiry. Causal interpretation is limited by the cross-sectional design as adopted in this study; therefore, future longitudinal or panel studies might be useful to explore how pollution prevention, technological capabilities, and their interactions change over time. Moreover, the application of NRBV to truck operators in Dar es Salaam is not generalizable; comparative studies among different Tanzanian regions or other African economies would help to understand contextual variability in NRBV application. Using self-reported survey data in this study may also induce response bias; therefore, further research might use objective indicators such as real fuel consumption logs, telematics data, or emissions records for better measurement accuracy. Lastly, technology adoption was the only variable tested; Other potential moderators/mediators, for example, firm size, regulatory compliance, environmental culture, or financial capacity, can be further studied to refine the mechanisms by which complementary resources build on NRBV capabilities and contribute to long-term environmental performance. In fact, longitudinal evidence of how these capabilities mature would expand the NRBV's explanatory power in dynamic, resource-constrained logistics environments.\u003c/p\u003e \u003c/div\u003e"},{"header":"Declarations","content":"\u003cul\u003e\n \u003cli\u003e\u003cstrong\u003eEthical Approval and accordance:\u0026nbsp;\u003c/strong\u003eThe study protocol was approved by the St. John\u0026apos;s University of Tanzania Research and Ethics Committee, and administrative clearance was granted by the Tanzania Truck Owners Association (TATOA). All research procedures were carried out in accordance with the institutional guidelines and regulations for studies involving human participants.\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003eInformed Consent:\u0026nbsp;\u003c/strong\u003eInformed consent was obtained from all individual participants included in the study.\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003eConsent to publish statement:\u0026nbsp;\u003c/strong\u003eNot applicable as no identifiable data or images are present within the manuscript.\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003eData Availability:\u0026nbsp;\u003c/strong\u003eThe datasets collected and analyzed during this study are not publicly available due to the privacy and confidentiality of the participating truck operators. However, the data are available from the corresponding author on reasonable request.\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003eFunding:\u0026nbsp;\u003c/strong\u003eThe authors received no financial support for the research, authorship, and/or publication of this article.\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003eCompeting Interests:\u0026nbsp;\u003c/strong\u003eThe authors declare no competing interests.\u003c/li\u003e\n\u003c/ul\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eThe main idea for this study and the creation of the survey tools were done by Mfaume H. Rissasi, who also performed the data analysis. Barnabas Maagi contributed by adding important details to the discussion section and helping to interpret what the results mean for the industry. When it was time to understand the numbers, both authors worked together on the results and wrote the text for the manuscript. At the end, both authors read the full paper and agreed it was ready for submission.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eAfrican Development Bank. (2025). Annual Report 2024 (pp. 1\u0026ndash;136). Abidjan: African Development Bank Group. https://www.afdb.org/en/documents/annual-report-2024\u003c/li\u003e\n\u003cli\u003eAfrican Union Development Agency. (2022). Second Continental Report on the Implementation of Agenda 2063 (pp. 1\u0026ndash;154). African Union Commission \u0026amp; African Union Development Agency - NEPAD. https://au.int/en/documents/20220210/second-continental-report- implementation-agenda-2063\u003c/li\u003e\n\u003cli\u003eAllekotte, M., Althaus, H., Bergk, F., Biemann, K., Kn\u0026ouml;rr, W., Sutter, D., \u0026amp; Friedl, C. (2021). Environmentally friendly mobility: An ecological comparison of transport modes for passenger and freight transport in Germany (pp. 1\u0026ndash;43). 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(2023). \u003cem\u003eReducing carbon footprint with real-time transport planning and big data analytics. \u003c/em\u003e\u003cem\u003eE3s Web of Conferences, 412, 01082. \u003c/em\u003ehttps://doi.org/10.1051/e3sconf/202341201082\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"discover-sustainability","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"disu","sideBox":"Learn more about [Discover Sustainability](https://www.springer.com/43621)","snPcode":"","submissionUrl":"","title":"Discover Sustainability","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Discover Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Route Optimization, Sustainable Environmental Performance, Technology Adoption, Truck Operators, Green Logistics","lastPublishedDoi":"10.21203/rs.3.rs-8561334/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8561334/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eRoute optimization is regarded as a strategic emission reduction practice in freight transport, but its contribution to sustainable environmental performance in developing economies and the accelerating impact of digital technology are understudied. This study fills that gap empirically with Tanzanian truck operators based on the Natural Resource-Based View (NRBV) theory. Across 170 trucking operating firms, a cross-sectional survey was carried out in SmartPLS 4 with covariance-based structural equation modelling (CB-SEM). In this way, it tested the direct effect of route optimization on sustainable environmental performance as well as the moderating effect of technology adoption (GPS, IoT). Statistically, the results show that route optimization improves sustainable environmental performance (β\u0026thinsp;=\u0026thinsp;0.229, p\u0026thinsp;=\u0026thinsp;0.007). In addition to this effect (β\u0026thinsp;=\u0026thinsp;0.621, p\u0026thinsp;=\u0026thinsp;0.017), technology adoption confirms that the fuel savings as well as emission reductions of optimized routing are augmented by digital tools. Theoretically, the study builds on NRBV theory and green logistics practices, as it shows how pollution prevention capability via route optimization in combination with digital resources leads to sustainability outcomes. In practice, it shapes environmental policy and sustainable transport in Tanzania by pushing policymakers \u0026amp; industry to adopt technology for cleaner and more efficient freight operations.\u003c/p\u003e","manuscriptTitle":"The moderating effect of technological adoption on route optimization and sustainable environmental performance of truck operators in Tanzania","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-02-13 12:54:47","doi":"10.21203/rs.3.rs-8561334/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2026-03-27T12:25:55+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-03-23T09:27:25+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-03-16T16:58:22+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"72346032884870128574886302162606414414","date":"2026-03-16T16:22:38+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"103044138053457192505887135197075763847","date":"2026-03-15T20:33:46+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-02-18T05:29:26+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"137681989137128378005991795163783991508","date":"2026-02-15T17:25:46+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"251592973249718633502558920646550591075","date":"2026-02-14T13:45:19+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"167228545809975010384203525655945258870","date":"2026-02-13T15:27:50+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-02-10T08:46:12+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2026-01-21T22:25:12+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-01-21T12:27:13+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-01-20T16:18:53+00:00","index":"","fulltext":""},{"type":"submitted","content":"Discover Sustainability","date":"2026-01-20T16:03:20+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"discover-sustainability","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"disu","sideBox":"Learn more about [Discover Sustainability](https://www.springer.com/43621)","snPcode":"","submissionUrl":"","title":"Discover Sustainability","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Discover Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"90be7683-0b9d-44d7-a96a-9f58dca51ffa","owner":[],"postedDate":"February 13th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"in-revision","subjectAreas":[],"tags":[],"updatedAt":"2026-03-27T13:17:44+00:00","versionOfRecord":[],"versionCreatedAt":"2026-02-13 12:54:47","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8561334","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8561334","identity":"rs-8561334","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}