Inclusiveness and Sustainability of Road Transport and Logistics Services in Ethiopia

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Using the SERVQUAL framework, quantitative data from 379 households were analyzed through ordinal logistic regression, complemented by qualitative interviews with transport stakeholders to capture systemic and spatial dimensions of service delivery. The findings reveal persistently low levels of user satisfaction across all service quality dimensions, with reliability, assurance, and tangibility emerging as the most influential predictors. Significant disparities are observed across income groups, gender, education levels, and spatial location, particularly disadvantaging rural and peri-urban kebeles. These inequities indicate that current RTLS provision risks reinforcing social and territorial inequalities rather than promoting inclusive mobility. From a sustainability perspective, the results demonstrate that infrastructure expansion alone is insufficient to achieve equitable transport outcomes without parallel improvements in service reliability, institutional trust, and responsiveness. The study contributes empirical evidence linking transport service quality to SDG 9 (resilient infrastructure), SDG 10 (reduced inequalities), and SDG 11 (inclusive and sustainable communities), and underscores the need for place-based, people-centered transport planning to advance inclusive and sustainable development in emerging regions. Road Transport Service Quality SERVQUAL Spatial Inequality User Satisfaction Ordinal Logistic Regression Ethiopia Figures Figure 1 1. Introduction Road transport plays a critical role in supporting community development and access to essential services in Ethiopia’s emerging regions. Despite investments in rural roads and public service expansion, gaps remain in the quality and accessibility of Road Transport and Logistics Services (RTLS). The Sidama National Regional State, with a mix of rural and peri-urban settlements, presents a useful case for analyzing community satisfaction with transport services. Using the SERVQUAL model, this study explores user perceptions across five key dimensions: tangibility, reliability, responsiveness, assurance, and empathy, while also investigating the role of spatial and socio-demographic factors. Sustainable and inclusive road transport and logistics services (RTLS) are vital for community development, particularly in emerging economies. They facilitate mobility, improve access to essential services, and drive socio-economic progress. Yet, community satisfaction with these services remains under-researched, especially in developing regions where infrastructure gaps, inequities, and service inefficiencies persist. While previous research has contributed meaningfully to the understanding of customer satisfaction in transport and logistics services, the existing literature is regionally concentrated. Studies by Sobaih and AlSaif (2023) and Alotaibi and Mafimisebi (2021) have emphasized service quality in Middle Eastern contexts, often within highly regulated or urbanized environments. In Southeast Asia, works such as Gultom et al. (2022) and Frinaldi et al. (2020) examine customer satisfaction in comparatively well-connected transport corridors. Similarly, Yemer and Abayneh (2023) and Mekonnen (2019) provide insights from East African cities, focusing on more formalized transport systems. While the study by Siyum, (2024), suggested public sector services and urban land management in Ethiopia are consistently failing to meet customer expectations, resulting in widespread dissatisfaction and the need for significant improvements. However, this regional clustering of studies poses limitations for broader application. It restricts the external validity of SERVQUAL-based models, as transport infrastructure, regulatory frameworks, and community expectations vary significantly across regions. In peri-urban and rural areas of Sub-Saharan Africa, for example, road transport systems are often informal, underregulated, and infrastructure-constrained (Tabi & Adams, 2020; Enimola et al., 2021). Consequently, it remains uncertain whether service quality predictors validated in urban or highly structured systems generalized to such resource-limited and geographically dispersed contexts. From a theoretical perspective, this study applies the SERVQUAL model (Parasuraman et al., 1988) to assess service quality across five dimensions, such as tangibility, reliability, responsiveness, assurance, and empathy within the context of Road Transport and Logistics Services (RTLS) in Sidama, Ethiopia. Drawing from customer satisfaction theory (Oliver, 1980), the study examines how perceived gaps between expected and actual service influence user satisfaction. Additionally, the study guided by the principles of the Sustainable Development Goals (SDGs), particularly SDG 9 (Industry, Innovation, and Infrastructure) and SDG 11 (Sustainable Cities and Communities), which call for inclusive, safe, and sustainable transport systems. Satisfaction with RTLS serves not only as a measure of service performance but also as an indicator of how well transport systems respond to the needs of disadvantaged and marginalized communities. By applying the SERVQUAL framework within Ethiopia’s Sidama National Regional State, this study bridges a critical knowledge gap by offering context-sensitive insights into how service quality dimensions influence community satisfaction in underserved environments. Central to this analysis is the concept of inclusive transport , defined here as the degree to which road transport and logistics services ensure equitable, affordable, reliable, and safe access for all, with a specific focus on the needs of low-income households, women, informal workers, and residents in rural or peri-urban areas. 1.2 Empirical Review: Service Quality and Satisfaction in Transport and Related Sectors Satisfaction with Road Transport and Logistics Services (RTLS) is increasingly recognized as a vital component of community well-being, mobility equity, and economic participation. The SERVQUAL model, which evaluates service quality through five dimensions such as tangibility, reliability, responsiveness, assurance, and empathy has applied to assess user satisfaction across various service sectors. A growing body of transport-specific research has refined the SERVQUAL perspective on service quality and user satisfaction. In public transport planning, leadership (Wetzel & Hofmann, 2020) highlights quality discrepancies in the existing service offerings of logistics service that needs as a core metric for network design. Large-sample analyses in Europe show that critical service incidents decisively shape both satisfaction and loyalty (Allen et al., 2020). Studies across bus systems confirm that tangible elements such as vehicle comfort, cleanliness, and stop facilities remain dominant drivers, although desired quality levels vary by user type (Grisé, & El-Geneidy, 2017). Cross-national work by de Oña et al., (2024) demonstrates that satisfaction consistently mediates the link between perceived service quality and behavioral intentions, even among habitual car users. Similar patterns emerge in rail networks: perceived reliability and responsiveness strongly predict customer satisfaction and mode choice (Ibrahim et al., 2025). Finally, structural‐equation modelling in emerging-market cities indicates that overall service quality positively affects satisfaction and, in turn, continued public-transport use (Gizaw, et al., (2021). Collectively, these studies underscore the persistent importance of tangibility and reliability while revealing context-specific nuances in responsiveness and assurance, all highly relevant to the Sidama, Ethiopia: RTLS setting examined in the present study. 1.2.1 SERVQUAL Applications in Transport and Logistics While prior research on RTLS in Ethiopia has focused narrowly on SERVQUAL-based user perceptions (e.g., Yemer & Abayneh, 2023), this study integrates quantitative service quality assessments with qualitative analysis of systemic barriers (e.g., infrastructure gaps, policy enforcement). This dual lens addresses gaps in both user-centric and system-level literature, offering a comprehensive view of RTLS challenges in Sidama. In Saudi Arabia, reliability and empathy identified as primary predictors of satisfaction with parcel delivery services (Sobaih & AlSaif, 2023). Studies on public transport in Indonesia and Nigeria emphasized punctuality, driver behavior, and frequency, attributes tied to reliability, assurance, and responsiveness (Frinaldi et al., 2020; Enimola et al., 2021). Southeast Asian research highlighted concerns related to safety and logistics responsiveness in the context of e-commerce-driven intercity transport (Muangpan, 2022; Gultom et al., 2022). African urban studies further showed dissatisfaction rooted in overcrowding, irregular schedules, and infrastructure deficits, reflecting issues with tangibility, reliability, and responsiveness (Tabi & Adams, 2020; Mekonnen, 2019). 1.2.2 Theoretical Background Beyond traditional models like SERVQUAL (Amponsah & Adams, 2016) and Oliver's satisfaction theory(Sobaih & AlSaif, 2023), this text integrates Accessibility and Mobility Theories to emphasize the importance of infrastructure in driving sustainable growth (Alçura, 2024). It notably references Infrastructure-led Development Theory to argue that infrastructure acts as a key driver for long-term development (Papanikos, 2024). Additionally, Sustainability Frameworks, such as the Sustainable Development Goals 9 and 11, are mentioned as essential for connecting service quality to wider developmental outcomes (Brussel et al., 2019). In this context, sustainability is conceptualized not merely as the expansion of physical road infrastructure, but as the long-term capacity of transport institutions to deliver reliable, responsive, and accountable services that equitably serve diverse users across space and socio-economic groups. 1.2.3 Conceptual Framework and Model Orientation This study adopts a conceptual framework grounded primarily in Accessibility and Mobility Theory and Place-Based Development Theory. It explores how perceived service quality and socio-demographic factors directly influence satisfaction with Road Transport and Logistics Services (RTLS), particularly in rural and peri-urban contexts. Satisfaction is modeled as an ordinal dependent variable, shaped by two main predictors: (1) service quality attributes, based on the SERVQUAL model: tangibility, reliability, responsiveness, assurance, and empathy; and (2) socio-demographic characteristics, including age, education, income, and location. These variables capture differentiated user experiences and expectations within the local transport system. Table 1 : Theoretical Constructs and Indicators Construct Indicators Description Service Quality Tangibility, Reliability, Responsiveness, Assurance, Empathy Measured using SERVQUAL-based items Socio-demographic Factors Gende, Age, Education Level, Income Status, Residential Location Reflect user diversity and expectation patterns Source: Survey data analysis (2025) 2. Research Question How do SERVQUAL dimensions predict RTLS satisfaction in Sidama Region? 2. 1 Research Objectives Thestudyaims to assess community satisfaction with Road Transport and Logistics Services (RTLS) in the Sidama National Regional State (SNRS), Ethiopia. 2.1.1 Specific Objective 1. To determine the overall satisfaction level of the community with RTLS based on the SERVQUAL dimensions. 2. To examine the influence of socio-demographic and geographic factors on satisfaction with RTLS. 3. To identify systemic service and infrastructure-related factors shaping user perceptions of RTLS. 4. To apply mixed-method findings to inform equitable and inclusive transport planning strategies in SNRS 3. Hypotheses of the Study Hypothesis (H 1 ): The five SERVQUAL dimensions (reliability, responsiveness, assurance, empathy, and tangibility) have a significant and positive influence on community satisfaction with Road Transport and Logistics Services (RTLS) in the Sidama National Regional State. Hypothesis (H 2 ) : Community satisfaction with RTLS significantly varies across socio-demographic and geographic factors, including location, age, education level, occupation, and household size. Hypothesis (H 3 ) : Lower perceived service quality across the SERVQUAL dimensions is associated with lower levels of satisfaction with RTLS. 4. Methodology 4.1 Study Area Description The study was conducted in Sidama National Regional State (SNRS), southeastern Ethiopia, home to over 4.8 million people across 991,099 households, the majority of whom live in rural areas and depend on subsistence farming and small-scale trade (Regional Development and Plan Bureau, 2024). Administratively, the region includes 30 districts, six city administrations, and 658 kebeles, with Hawassa as the capital. Its varied terrain and average elevation of 2,068 meters affect road access and infrastructure. Given the region’s reliance on road-based transport amid rising population demands, the study purposively sampled both central and remote kebeles to capture diverse spatial and socio-economic experiences with RTLS. 4.2 Research Design and Approach This study employed a cross-sectional convergent mixed-methods design to assess user satisfaction with Road Transport and Logistics Services (RTLS). The quantitative strand involved a structured household survey measuring five SERVQUAL dimensions: tangibility, reliability, responsiveness, assurance, and empathy, using a 5-point Likert scale. Overall satisfaction served as the outcome variable and was analyzed through ordinal logistic regression. The qualitative component included key informant interviews with transport officials, service providers, and community leaders. Thematic analysis was conducted to identify recurrent barriers and contextualize the quantitative results. 4.3 Data Sources and Collection Tools Primary data were collected using a structured questionnaire administered through KoboToolbox across ten randomly selected kebeles. The questionnaire assessed perceptions of RTLS across SERVQUAL dimensions and captured socio-demographic variables including age, gender, education, income, and residential location. 4.4 Target Population and Sampling Techniques The study targeted households in urban and peri-urban kebeles directly served by RTLS infrastructure. A multistage stratified sampling technique applied. First, SNRS selected for its relevance to emerging transport priorities. Next, from the five zones in region (including Hawassa city administration) one district per zone is systematically chosen based on the highest number of kebeles. Two kebeles randomly selected from each district (10 total). 4.5 Sample Size Determination Finally, a probability-based simple random sampling technique applied, yielding 379 households from a population of 24,551, based on proportional allocation (Regional Development and Plan Bureau, 2024). Sample size calculated using Cochran’s formula (Cochran, 1977 ) with finite population correction, assuming a 95% confidence level and 5% margin of error: $$\:n=\frac{N{Z}^{2}pq}{{e}^{2}\left(N-1\right)+{Z}^{2}pq}$$ Where: N = 24,551; Z = 1.96; p = 0.5; e = 0.05; resulting in n ≈ 379. Given the design included proportional stratification and random sampling within each cluster, a design effect (Deff) ≈ 1.0 assumed, consistent with minimal intra-cluster correlation. This justified retaining the calculated sample size without adjustment. 4.6 Data Analysis Methods Descriptive statistics (means, frequencies, and standard deviations) summarized community perceptions across SERVQUAL dimensions to identify performance gaps in service delivery. Basically, Ordinal Logistic Regression was employed to model satisfaction with RTLS as an ordinal dependent variable, measured on a 5-point Likert scale. This method retained the rank-order nature of satisfaction responses and allowed for nuanced interpretation across ordered categories. The model follows the cumulative logit form: Log \(\:(\frac{1-\text{P}\left(\text{Y}\le\:\text{j}\right)}{\text{P}\left(\text{Y}\le\:\text{j}\right)}\) ) = β 0 +β 1 X 1 +β 2 X 2 +⋯+βnXn Where: 1. Y is satisfaction with RTLS 2. j represents Likert categories (1 = Very Dissatisfied to 5 = Very Satisfied) 3. X₁...Xₙ represent predictor variables (SERVQUAL dimensions + socio-demographic factors) 4. β₁...βₙ are coefficients estimating log-odds changes This approach allows for estimating the directional influence of each predictor while maintaining the proportional odds assumption. A positive coefficient indicates that an increase in a predictor (e.g., service reliability) raises the likelihood of reporting higher satisfaction. 5. Data Analysis And Result 5.1 The Background of the Respondents A total of 379 valid responses were obtained, with no missing data. The sample was drawn from ten kebeles in the Sidama National Regional State, ensuring broad geographic representation across rural and peri-urban areas. The distribution of respondents across kebeles was relatively balanced, with a near-normal spread (M = 5.20, SD = 2.95), and minimal skewness (0.08) and kurtosis (− 1.16), indicating no substantial concentration of responses in any single kebele. The highest proportions of respondents were from Fara (17.4%), Hoganewaco (16.4%), and Sho’e (11.1%), while Shaicha contributed the smallest share (4.2%). The remaining kebeles each accounted for between 7.1% and 10.3% of the total sample. Key socio-demographic characteristics were also examined. The sample was predominantly male (71%), indicating a gender imbalance that reflects the higher participation of males in transport and logistics-related activities in the study area. The mean age of respondents was 39.3 years (SD = 12.85), suggesting that most participants were within the economically active and experienced working-age population. To assess the stability and representativeness of the sample estimates, bootstrap resampling with 1,000 iterations was performed. The bootstrap results confirmed the consistency of the geographic distribution and key demographic characteristics, supporting the reliability and generalizability of the findings to similar rural and peri-urban contexts within the Sidama National Regional State. A detailed summary of kebele-level distribution and associated descriptive statistics is presented in Table 2 . Table 2 Representative of the Kebele (Village) Kebele Code Frequency (n) Percent (%) 95% Confidence Interval (%) Hoganewaco 1 62 16.4 12.9–20.1 Soyama 2 33 8.7 6.1–11.6 Dilacange 3 27 7.1 4.5–9.5 Taramessa 4 32 8.4 5.8–11.6 Sho’e 5 42 11.1 7.9–14.2 Fara 6 66 17.4 14.0–21.4 Shaicha 7 16 4.2 2.4–6.3 Bultuma 8 31 8.2 5.5–11.1 Bansaware 9 31 8.2 5.5–11.3 Shantawenne 10 39 10.3 7.4–13.5 Total — 379 100.0 — Source : Survey Data Analysis (2025) 5.2 Model Fitting and Predictive Performance Across SERVQUAL Dimensions Ordinal logistic regression models were estimated separately for each SERVQUAL dimension (Tangibility, Reliability, Responsiveness, Assurance, and Empathy) to examine how socio-demographic and service-related factors differentiate ordered perceptions of transport service quality across kebeles in the Sidama Region. Model adequacy was assessed using likelihood-ratio chi-square tests comparing final models against intercept-only specifications. As summarized in Table 4 , all five dimension-specific models demonstrated statistically significant improvements over their respective null models (p < .001). This indicates that the included predictors jointly contribute to explaining variation in perceived service quality within each SERVQUAL dimension. The magnitude of the likelihood-ratio chi-square statistics varied across dimensions, suggesting differential sensitivity of service quality domains to user and contextual characteristics. Among the dimensions, Reliability and Assurance exhibited the largest chi-square values , indicating that perceptions related to punctuality, service consistency, trust, and professionalism are particularly structured by socio-demographic and spatial factors . Tangibility also showed strong model improvement, reflecting pronounced disparities in physical transport infrastructure across kebeles. Responsiveness displayed meaningful but more uneven explanatory strength, suggesting inconsistency in problem resolution and service feedback mechanisms. Empathy , while statistically significant, demonstrated comparatively lower explanatory capacity, reflecting the more subjective and interpersonal nature of this dimension. Importantly, these results should be interpreted as dimension-specific differentiation rather than comprehensive explanations of overall satisfaction . Each model captures how predictors relate to a single SERVQUAL domain in isolation, rather than the full service quality construct. Table 4 Model Fitting Summary for SERVQUAL Dimensions Dimension –2 Log Likelihood (Null) –2 Log Likelihood (Final) Chi-Square df Sig. Reliability 1988.057 1471.167 516.89 187 < .001 Assurance 1696.608 1178.562 518.05 187 < .001 Tangibles 1676.855 1213.324 463.53 187 < .001 Empathy 1688.270 1333.528 354.74 105 < .001 Responsiveness 1676.855 1213.324 463.53 187 < .001 All models use the logit link function; Source: Survey Data Analysis (2025). 5.2.1 Goodness-of-Fit Analysis Model goodness-of-fit was evaluated using Pearson and Deviance statistics (Table 5 ). Across all models, the Deviance tests were non-significant (p = 1.000), indicating no statistically detectable discrepancy between observed and model-predicted values. This suggests that the ordinal logistic specifications adequately capture the underlying structure of the data. In contrast, the Pearson chi-square tests were statistically significant (p < .001). In ordinal logistic regression , particularly with multiple predictors and sparse response patterns , the Pearson statistic is known to be highly sensitive to data sparsity and over-dispersion , often flagging apparent misfit even when the model is correctly specified. Consequently, the Deviance statistic is considered the more reliable indicator in this context. Taken together, the goodness-of-fit diagnostics indicate that the models are statistically appropriate and stable , supporting their use for interpreting dimension-specific predictor effects. Table 5 Goodness-of-Fit Statistics Test Chi-Square df Sig. Pearson 4034.711 3162 .000 Deviance 1331.802 3162 1.000 Source: Survey Data Analysis (2025). 5.2.2 Pseudo R-Square Analysis To evaluate relative model performance, pseudo R-square measures were examined (Table 6 ). For the SERVQUAL dimension models, Nagelkerke R² values were modest, consistent with expectations for ordinal logistic regression applied to perception-based outcomes influenced by complex spatial and institutional factors. The Nagelkerke R² value of 0.615 and Cox & Snell R² of 0.608 should be interpreted cautiously. Unlike OLS R², pseudo R-square statistics do not represent the proportion of variance explained in a strict sense but rather indicate relative improvement over intercept-only model s . Similarly, the McFadden R² value of 0.210, while numerically smaller, falls within the range typically regarded as indicative of good model performance in logistic regression. Overall, these measures suggest that the models provide meaningful predictive differentiation, while also underscoring that a substantial portion of variation in service quality perceptions remains attributable to unobserved contextual, institutional, and governance-related factors. Table 6 Pseudo R-Square Measures Measure Value Cox & Snell 0.608 Nagelkerke 0.615 McFadden 0.210 Source: Survey Data Analysis (2025). 5.3 Predictors of Transport Service Quality: Parameter Estimate Analysis A set of dimension-specific ordinal logistic regression models was estimated to identify the key predictors shaping perceived transport service quality across the five SERVQUAL domains. Full parameter estimates are presented in Appendix B; key patterns are summarized below. 5.3.1 Kebele-Level Variation Geographic location emerged as a consistently strong predictor across SERVQUAL dimensions. Relative to the reference kebele (Kebele 10), respondents from several kebeles reported significantly lower perceived service quality, particularly for Reliability and Assurance : Kebele 1: β = − 7.943, p < .00, Kebele 2: β = − 7.100, p < .001, Kebele 3: β = − 6.524, p < .001, and Kebele 6: β = − 3.933, p = .022. These results provide robust quantitative evidence of spatial inequality in transport service experience , with rural and peri-urban kebeles systematically disadvantaged. 5.3.2 Socio-Demographic Predictors Several socio-demographic variables significantly shaped service quality perceptions. Age showed a small but positive association (β = 0.026, p = .011), indicating that older respondents tend to report higher satisfaction . Occupation was negatively associated with perceived quality (β = − 0.135, p = .019), with informal workers expressing lower satisfaction levels. Annual income exhibited a small but statistically significant negative effect (β = − 1.744e–6, p < .001), suggesting higher expectations among wealthier users. Education also showed a marginally negative association (β = − 0.150, p = .055), indicating more critical assessments among educated respondents. These findings highlight the importance of expectation heterogeneity and reinforce the need for equity-sensitive transport planning. 5.4 SERVQUAL Dimension Scores Overview 5.4.1 Key Trends in Service Quality Perceptions As detailed in Table 3 , the analysis of SERVQUAL dimensions such as Reliability, Assurance, Tangibles, Empathy, and Responsiveness reveals consistently low user satisfaction across all service quality aspects in the Sidama Region’s transport services. Accordingly, all dimensions scored below the neutral midpoint of 3.0, indicating overall dissatisfaction. The most frequent response across all dimensions was “2.00”, corresponding to a "Poor" rating. Reliability emerged as the most problematic dimension (mean = 2.48), with 41.4% of respondents rating it poor, confirming persistent delays and low service predictability. Assurance (mean = 2.57) similarly shows concern, with 41.7% of users expressing low confidence in staff competence and safety. Tangibles (mean = 2.63) exhibited a bimodal pattern, pointing to infrastructure inequality across kebeles. Further, Empathy (2.77) and Responsiveness (2.79) scored marginally higher but still below acceptable levels, indicating inconsistent personal attention and complaint resolution. These findings point to systemic service underperformance, with user dissatisfaction dominating across all domains. 5.4.2 Critical Service Gaps The analysis of SERVQUAL scores (Table 3 ) and response frequencies reveals critical service gaps undermining Road Transport and Logistics Services (RTLS) in the Sidama Region. These gaps fall into three major categories. First, as shown in Table 3 , tangibility received a mean score of 2.63, with a bimodal distribution, 16.6% of respondents rated it 1.00 (Poor) and another 16.6% rated it 4.00 (Good). This stark contrast indicates sharp disparities in infrastructure quality and access. Urban kebeles such as Fara and Hoganewacho benefited from better roads and vehicle fleets, while rural kebeles like Shaicha and Dilacange reported service deficiencies. These disparities reflect uneven investment and planning, pointing to the urgent need for spatially targeted infrastructure upgrades to close the rural-urban service gap. Second, staff training and professionalism (Assurance & Empathy) scored below average (means of 2.57 and 2.77, respectively), with over one-third of respondents rating them at 2.00 (Poor). These results reflect weak user perceptions of staff safety, competence, and interpersonal engagement. The data suggest a systemic lack of customer service orientation, likely due to insufficient staff training in communication, safety, and complaint handling. This gap is particularly troubling for marginalized groups and women, who reported lower empathy scores (Appendix A), emphasizing the need for inclusive training programs across RTLS personnel. Further, however, Responsiveness had the highest mean among the five dimensions (2.79), a substantial 29.3% of respondents rated it Poor (2.00), and 22.4% rated it Good (4.00). This variation reveals significant inconsistency in issue resolution and feedback mechanisms across kebeles. In areas with active local transport authorities or private operators, users reported better responsiveness. In contrast, other kebeles experienced neglect and service delays. This fragmentation undermines system-wide trust and indicates the absence of standardized response protocols or complaint systems. Table 3 Descriptive result of the SERVQUAL Dimension Dimension Mean Median Mode Key Concern Reliability 2.48 2.00 2.00 41.4% rated “Poor”; low predictability, delays Assurance 2.57 2.00 2.00 41.7% rated “Poor”; safety and staff competence lacking Tangibles 2.63 2.29 2.00 Bimodal: high inequality in infrastructure access Empathy 2.77 2.40 2.00 36.4% rated “Poor”; limited personalized service Responsiveness 2.79 2.50 2.00 29.3% rated “Poor”; uneven issue resolution Source : Survey Data Analysis (2025) 5.5 SERVQUAL Dimension Effects Among the SERVQUAL dimensions, Reliability emerged as the most consistently influential, with strong positive associations across multiple response levels (e.g., β = 6.147, p < .001), underscoring the central role of punctuality and service consistency in shaping perceptions. Assurance coefficients were largely negative and significant across lower levels, reflecting the detrimental effects of low trust and perceived lack of professionalism. Responsiveness also showed several significant and negative coefficients, indicating persistent dissatisfaction with complaint handling and problem resolution. Empathy displayed fewer significant effects, though selected levels suggested notable gaps in user-centered service delivery. Tangibility variables, while prominent in descriptive analysis, were less consistently significant in the regression models, indicating that physical infrastructure alone does not guarantee positive service perceptions without corresponding operational reliability and institutional support . Overall, the results confirm that perceived transport service quality is multifactorial , shaped by spatial context, socio-economic characteristics, and service performance dimensions. Reliability, Assurance, and Responsiveness are the most influential domains, while kebele-level location and income function as critical moderators of user experience. 5.6 Systemic Implications and Alignment with SDGs The RTLS system in the Sidama Region presents as fragmented, inequitable, and lacking standardization. Patterns of user dissatisfaction, particularly in tangibility, assurance, and responsiveness are strongly shaped by spatial and social inequalities. These systemic shortcomings directly undermine Ethiopia’s progress toward several Sustainable Development Goals. For instance, SDG 9.1 (Industry, Innovation, and Infrastructure) is compromised by unequal investment that has resulted in regional disparities in road quality and service access. Similarly, SDG 11.2 (Sustainable Cities and Communities) is affected by inconsistent and often poor user experiences, which limit inclusivity and reduce access to essential transport services. Furthermore, SDGs 5 (Gender Equality) and 10.2 (Reduced Inequalities) are challenged by persistent gaps in empathy and service quality, particularly among women and marginalized communities who consistently report lower satisfaction. To address these challenges, the evidence underscores an urgent need for targeted infrastructure investment in underperforming kebeles, comprehensive and standardized training for transport personnel on safety, responsiveness, and customer care, and the institutionalization of feedback and accountability mechanisms to ensure consistent service delivery and inclusive participation. These reforms are essential for guiding the RTLS system toward the broader goals of sustainability, accessibility, and transport equity. 5.6.1 Implications of SERVQUAL-Based Findings for Sustainable Development Goals (SDGs) From a sustainability perspective, these findings reveal that the primary weaknesses of the RTLS system stem less from a lack of physical infrastructure and more from persistent failures in service reliability, institutional assurance, and responsiveness. These qualitative gaps undermine the long-term effectiveness and equity of transport provision. Furthermore, the dimension-specific ordinal regression results illustrate how transport service quality serves as a critical lever for advancing Sustainable Development Goals (SDGs) 9, 10, and 11, offering a roadmap for progress within low-income and spatially heterogeneous contexts like the Sidama Region. Such performance-driven deficiencies suggest that transport sustainability in the study area is constrained by governance and operational limitations, rather than by infrastructure coverage alone. From a sustainability perspective, these findings indicate that the principal weaknesses of the RTLS system lie not in the physical absence of infrastructure, but in persistent failures of service reliability, institutional assurance, and responsiveness, undermining the long-term effectiveness and equity of transport provision. The dimension-specific ordinal regression results provide important insights into how transport service quality contributes to progress toward Sustainable Development Goals 9, 10, and 11, particularly in low-income and spatially heterogeneous contexts such as the Sidama Region. SDG 9: Industry, Innovation, and Infrastructure The strong and consistent effects observed for the Reliability and Assurance dimensions underscore that infrastructure effectiveness extends beyond physical road provision to include operational dependability, schedule adherence, and institutional professionalism. While Tangibility captured disparities in physical infrastructure, its weaker regression effects suggest that infrastructure alone is insufficient to deliver development outcomes without reliable service operations. This finding reinforces SDG 9’s emphasis on resilient and efficient infrastructure systems, highlighting the need to integrate “software” elements, such as service management, monitoring, and accountability, alongside physical investments. SDG 10: Reduced Inequalities Kebele-level location emerged as a dominant and consistent predictor across SERVQUAL dimensions, providing robust evidence of spatial inequality in transport service quality. Respondents from rural and peri-urban kebeles systematically reported lower perceived reliability, assurance, and responsiveness, even after controlling for socio-demographic characteristics. These findings indicate that transport services may unintentionally reproduce or deepen territorial inequalities, contradicting the equity objectives of SDG 10. Addressing such disparities requires place-based transport strategies that explicitly target underserved kebeles rather than uniform, region-wide interventions. SDG 11: Sustainable Cities and Communities While Reliability remains the primary driver of satisfaction, the significant—albeit weaker—roles of Responsiveness and Empathy highlight critical deficiencies in user-centered service delivery. Within the framework of SDG 11, which mandates inclusive, safe, and accessible transport, these results suggest that current RTLS provision fails to accommodate diverse user needs, particularly for informal workers and residents of peripheral areas. Enhancing complaint resolution and customer engagement is, therefore, a prerequisite for advancing inclusive mobility and community-level sustainability. Viewed through a sustainability lens, these findings confirm that the core weaknesses of the RTLS system are institutional and operational rather than merely infrastructural. Persistent unreliability and low levels of assurance reflect governance failures that undermine the equity and resilience of transport provision, even where physical road networks exist. Ultimately, progress toward sustainable transport depends on moving beyond "ribbon-cutting" for new roads; it requires improving service reliability and institutional trust. The modest explanatory power of individual SERVQUAL dimensions further underscores that regulatory enforcement and local administrative capacity serve as the critical mediators for long-term success. 6. Hypothesis Testing and Theoretical Implications Findings on Road Transport and Logistics Services (RTLS) satisfaction in the Sidama Region indicate that core service quality attributes from the SERVQUAL framework (reliability, tangibility, responsiveness, and assurance) are critical to user perceptions. Higher satisfaction correlates with dependable services and well-maintained infrastructure, while dissatisfaction stems from insufficient oversight and slow maintenance. Key factors affecting satisfaction include vehicle condition, road quality, and timeliness, alongside socioeconomic variables like income and education. Notably, wealthier respondents experienced lower satisfaction due to an "income–expectation paradox." Despite infrastructure investments, maintenance delays and contractor performance issues pose obstacles to RTLS reliability. In the reliability dimension, informal workers reported significantly lower satisfaction (β = −4.95, p = .032), and the model explained 68.8% of the variance. Neither age nor gender significantly influenced reliability perceptions. This dissatisfaction stems from operational failures rather than service absence, including irregular scheduling, inconsistent enforcement of standards, delayed maintenance, and poor coordination among institutions. Such unreliability disproportionately impacts informal workers who depend on consistent transport for market access and daily needs, aligning with the quantitative findings regarding occupation and satisfaction levels. Tangibility scored low among respondents, with nearly half below 2.00. Significant positive drivers included vehicle comfort (β = 0.74, p = .002) and road maintenance (β = 0.65, p = .012). Qualitative findings indicate that aging vehicle fleets, limited modernization, and inconsistent road upkeep contribute to this perception. Despite policies for infrastructure upgrade, reliance on outdated minibuses and poorly maintained roads leads to partial satisfaction, as tangible improvements are ineffective without corresponding modernization and maintenance. Assurance received the lowest overall ratings, with 41.7% of respondents scoring it at 2.00. Dissatisfaction clustered in Kebeles 2, 3, 6, 8, and 9. Secondary education and smaller household size were positive predictors. Institutional trust deficits correlate with findings of weak regulatory enforcement, inadequate driver training, limited accountability, and insufficient safety oversight, contributing to low user confidence and assurance scores. Over half of respondents rated service responsiveness below average, with higher-income groups expressing greater dissatisfaction due to a lack of formal feedback mechanisms and complaint systems. Empathy was rated low by about one-third of respondents, with positive perceptions associated with male respondents, higher education, and Kebele 2 residency. Qualitative evidence indicates empathy deficits arise from inadequate frontline service training and informal norms, especially affecting low-income and informal workers. Overall, the study reveals systemic spatial disparities, greater influence of socioeconomic status on service expectations, and the significance of operational factors over mere infrastructure presence. Hypothesis Testing (Mixed-Methods Interpretation) The summary in Table 7 demonstrates the alignment of theoretical assumptions with empirical observations in the Sidama Region. Qualitative findings support hypotheses about reliability, assurance, and responsiveness by explaining institutional governance gaps, enforcement weaknesses, and capacity constraints. Conversely, the lack of significance for age and gender is supported by qualitative evidence indicating that service deficiencies impact users broadly, irrespective of demographic factors, once spatial and economic conditions are controlled. Table 7 Summary of Hypothesis Test Hypotheses General Hypothesis Statement Result H1: Tangibility positively influences satisfaction. The SERVQUAL dimensions (including tangibility) have a significant influence on satisfaction; lower quality is associated with lower satisfaction. Supported H2: Reliability differs across locations. Satisfaction (and its dimensions) varies across geographic factors (location). Supported H3: Responsiveness influenced by income and location. Satisfaction varies across socio-demographic (income) and geographic (location) factors. Supported H4: Age, gender, and education predict satisfaction. Satisfaction varies across socio-demographic factors like age and education. Not supported H5: Socioeconomic status influences reliability perceptions. Socio-demographic factors (socioeconomic status) influence satisfaction dimensions. Supported H6: Assurance varies across kebeles. Satisfaction significantly varies across geographic factors (kebeles/location). Supported H7: Higher education increases assurance. Socio-demographic factors (education level) significantly vary with satisfaction indicators. Partially supported H8: Household size positively influences assurance. Satisfaction varies across socio-demographic factors (household size). Supported Source : Survey Data Analysis (2025) 6.1 Theoretical Implications (Quantitative–Qualitative Synthesis) The results substantiate Place-Based Development Theory , as satisfaction levels varied significantly by kebele. Underperforming areas such as Kebeles 1 and 6 consistently recorded lower satisfaction across SERVQUAL dimensions. Qualitative findings reinforce this by documenting uneven infrastructure execution, delayed projects, and weaker institutional oversight in peripheral kebeles. These findings validate the theory’s assumption that geographic context critically shapes public service experiences, necessitating localized and targeted interventions. Similarly, Infrastructure and Economic Development Theory is strongly supported by the salience of road maintenance and vehicle condition. However, qualitative evidence nuances this theory by demonstrating that infrastructure investment alone is insufficient. Without institutional capacity, workforce skills, and effective governance, infrastructure fails to translate into reliable and trusted services. This extends the theory by emphasizing implementation quality as a mediating factor. Accessibility and Mobility Theories are also confirmed. Quantitative results show that satisfaction depends on reliable and responsive access, while qualitative findings reveal how maintenance delays, outdated fleets, and weak ITS adoption constrain mobility in practice, particularly for rural and low-income users. 6.2 Discussion and Implications (Enhanced with Qualitative Explanations) Section 6 offers qualitative insights into systemic issues affecting RTLS quality in Sidama, as revealed by SERVQUAL-based ordinal logistic regression analysis. Key findings indicate that reliability failures stem from institutional fragmentation and maintenance delays; tangibility issues are linked to fleet modernization and contractor performance problems; assurance deficits arise from weak enforcement and data gaps; responsiveness is hindered by absent feedback systems; and empathy gaps reflect inadequate workforce training and informal service expectations. These insights affirm that dissatisfaction with RTLS quality is rooted in broader systemic factors rather than isolated incidents, highlighting the importance of place-sensitive differentiation in understanding service delivery. 1. Salience of Reliability in RTLS Satisfaction Among all SERVQUAL dimensions, Reliability emerged as the most decisive factor differentiating user satisfaction across spatial and socio-economic contexts. Indicators related to punctuality, service consistency, and predictability exerted strong and statistically significant effects, with the model explaining a substantial share of variation in perceptions. This finding underscores that dissatisfaction with RTLS in Sidama is driven less by the absence of services and more by their operational dependability. This result is particularly salient in rural and peri-urban kebeles, where households depend on transport services for time-sensitive livelihoods, market access, and social obligations. From an SDG 9 perspective, the findings reinforce the principle that infrastructure effectiveness depends on functional performance, not merely physical presence. Investments that improve service reliability (scheduling discipline, fleet maintenance, and operational oversight) are therefore likely to yield greater welfare gains than road expansion alone. 2. Tangibility and the Limits of Infrastructure-Only Approaches Tangibility was consistently rated low across the sample, with nearly half of respondents scoring this dimension below average. Vehicle comfort and road maintenance emerged as significant positive predictors, confirming that visible and experiential infrastructure elements remain central to user evaluations. However, despite their importance, tangibility effects were less consistent than those of reliability and assurance, indicating diminishing perceptual returns when physical investments are not complemented by effective service management. This finding carries important implications for SDG 9 and SDG 11. While expanding road networks and upgrading vehicles are necessary conditions for improved mobility, the results suggest they are insufficient on their own to ensure positive user experiences. Users appear more sensitive to how services function on a daily basis regularity, safety, and professionalism than to infrastructure visibility per se. This challenges infrastructure-centric development strategies and supports a shift toward service-oriented transport planning . 3. Assurance, Responsiveness, and Institutional Trust Deficits Assurance and Responsiveness exhibited substantial spatial and socio-demographic variation, reflecting deeper institutional and relational challenges within RTLS provision. Low assurance scores linked to limited trust, weak professionalism, and perceived lack of accountability were concentrated in several kebeles, particularly those characterized by poor infrastructure and irregular service provision. Similarly, dissatisfaction within the responsiveness dimension highlights shortcomings in complaint handling, information dissemination, and problem resolution. These patterns indicate that RTLS dissatisfaction is not purely technical but also institutional in nature. From an SDG 11 standpoint, such deficits undermine the objective of inclusive and people-centered mobility systems. Even where access exists, low assurance and weak responsiveness erode user confidence, reducing the broader social and economic value of transport services. 4. Empathy and Social Inclusion Gaps Although Empathy showed fewer statistically significant effects than other dimensions, its distribution reveals important equity concerns. Lower ratings among low-income, low-education, and informal workers suggest that transport services are often experienced as functionally available but socially unaccommodating. This points to subtle yet consequential forms of exclusion, where service design and staff conduct fail to recognize diverse user needs. These findings resonate with SDG 10, highlighting how uneven service experiences can reinforce social and economic marginalization. Addressing empathy-related gaps requires attention to frontline service behavior, inclusive communication practices, and mechanisms that amplify the voices of vulnerable users within transport governance structures. 5. Socio-Demographic Patterns and Expectation Effects The ordinal regression results demonstrate that satisfaction is shaped not only by service attributes but also by user expectations. Older respondents tended to report more favorable perceptions, while higher-income and more educated users were consistently more critical. This pattern reflects an expectation effect, where rising socio-economic status increases sensitivity to service shortcomings. The observed “income–expectation paradox,” particularly evident in the responsiveness dimension, suggests that improved access does not automatically translate into higher satisfaction. Instead, unmet expectations among wealthier users generate dissatisfaction, even when objective conditions improve. This finding underscores the importance of managing expectations through transparent communication, performance reporting, and participatory engagement. 6. Spatial Inequality and Place-Based Disparities Kebele-level location emerged as a robust predictor across all SERVQUAL dimensions, even after controlling for income, education, age, and occupation. Persistent dissatisfaction in specific kebeles reflects structural, place-based disadvantages rooted in remoteness, infrastructure deficits, and weak service oversight. These spatial inequalities directly challenge the equity ambitions of SDG 10. Without targeted, location-specific interventions, transport systems risk reproducing territorial disparities rather than alleviating them. The findings therefore call for place-sensitive planning frameworks that prioritize underserved kebeles and tailor interventions to local conditions. 7. Integrated Interpretation and Policy Implications Taken together, the SERVQUAL-based ordinal regression analysis reveals that RTLS satisfaction in the Sidama Region is uneven, context-dependent, and shaped by intersecting spatial and socio-economic factors. Reliability, Assurance, and Responsiveness are the most consequential dimensions differentiating user experiences, while Tangibility alone is insufficient to ensure positive perceptions. Advancing SDGs 9, 10, and 11 through transport development thus requires a strategic reorientation from infrastructure-dominated investments toward service quality, institutional capacity, and equity-focused interventions. Enhancing operational reliability, rebuilding institutional trust, and strengthening responsiveness, particularly in disadvantaged kebeles are essential for translating transport investments into inclusive and sustainable mobility outcomes. Conclusion This study confirms the value of using ordinal logistic regression and mixed methods to assess transport service quality in resource-constrained settings. By focusing on user experience and demographic context, the research provides evidence-based recommendations to guide equitable transport development in Ethiopia and similar regions in rural and peri-urban contexts of Sub-Saharan Africa, an area often overlooked in transport scholarship. The study extends the applicability of service quality models to low-capacity, informally structured road transport and logistics services (RTLS). Beyond measuring satisfaction, the research reveals that infrastructure investment alone does not guarantee improved outcomes. Instead, it must be complemented by stronger institutional coordination, workforce capacity, and community engagement to ensure transport systems are inclusive, reliable, and sustainable. The result shows that user satisfaction is influenced not only by tangible service features like reliability and timeliness but also by deeper systemic issues, including spatial inequality, income disparities, and governance gaps. These insights offer practical guidance for policymakers, particularly in identifying where investments and reforms should be prioritized to enhance both service delivery and public trust. By foregrounding transport equity and systemic constraints, the study contributes meaningfully to emerging debates on transport justice, infrastructure inclusivity, and sustainable mobility in developing regions. Recommendations To advance inclusive mobility and align with national priorities and Sustainable Development Goals (SDGs), this study recommends the following targeted interventions for Road Transport and Logistics Services (RTLS) in Sidama National Regional State: Prioritize infrastructure upgrades, route planning, and service reliability improvements in low-performing kebeles (1, 2, 3, 5, 6, 8, and 9). Introduce fare discounts or subsidy programs for low-income and informal workers to improve affordability and access. Launch education campaigns to help low-literacy communities understand transport rights, routes, and feedback systems. Enhance public engagement, facilitate two-way communication platforms, and publish service performance reports to manage expectations and build trust, especially among higher-income users. Scale-up successful practices (use Kebele 10 as a benchmark) to replicate effective service, staffing, and infrastructure models across the region. Establish local monitoring systems, create kebele-based user committees or mobile feedback tools to track satisfaction in real time, and improve accountability. Produce monitoring and evaluation, conduct surveys to capture subtle differences in satisfaction, particularly in responsiveness and high-scoring areas. Strength the initiative aims to tackle socioeconomic and spatial disparities in transport services through a data-driven, localized approach, focusing on SDG 9.1, SDG 11.2, and SDG 10.2. Contributions of the Study This study emphasizes the need for equitable infrastructure planning at the community level, highlighting that enhancements in road conditions, station cleanliness, and service staff capacity can increase user satisfaction and improve access to employment, education, and healthcare, especially for women and underserved residents. Academically, it validates the SERVQUAL model's tangibility dimension in a rural African context and showcases the use of ordinal logistic regression for analyzing service satisfaction outcomes in social science. Globally, the findings align with international development goals, particularly SDG 11 and SDG 9, advocating for infrastructure investments that meet community needs and promote socially equitable mobility systems. Overall, the research supports a people-centered planning approach with wider applicability. Declarations Acknowledgment The authors would like to express their sincere gratitude Hawassa University for their invaluable support in facilitating the study. Special thanks are extended to the respondents who generously shared their time and insights during the survey, interviews, and focus group discussions. Appreciation is also extended to the field data collectors and supervisors for their commitment to ensuring high-quality data collection. Data Availability Statement The dataset used to analyze this study is available with the corresponding author and can be accessed through a request for rational reasons. Ethical Approval and Accordance The study received ethical approval from the Hawassa University College of Business and Economics Research Ethics Committee (Protocol Version No. 1; Approval Number: CBE_RTT-87/2024; issued on December 11, 2024).All research procedures were performed in accordance with the ethical guidelines and regulations of Hawassa University and the principles outlined in the Declaration of Helsinki. Consent to Participate Informed consent to participate in the study was obtained from all participants prior to data collection. Participants were informed about the purpose of the study, their voluntary participation, their right to withdraw at any time without consequence, and the confidentiality of their responses. Oral consent was obtained before focus group discussions, key informant interviews, and individual interviews. For the Kobo-Collect survey, participants were required to provide agreement before proceeding with the questionnaire, and verbal consent was recorded in Kobo Toolbox. Minors were not included in the study. Consent to publish declaration All research participants provided consent for the publication of anonymized data. No identifiable personal information is included in this manuscript. Clinical trial number: Not applicable. Conflict of Interest Statement The authors declare that there are no conflicts of interest regarding the publication of this manuscript. All authors have approved the manuscript and agree to its submission to SAGE Open . The manuscript is original, has not been published previously, and is not under consideration for publication elsewhere. The authors grant the publisher the right to publish this manuscript in its present form and confirm that all authors have approved its submission for publication. References Alçura, G. (2024). 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Research in Transportation Business & Management, 59 , 101289. https://doi.org/10.1016/j.rtbm.2025.101289 Salleh, S. S., Ishak, S. Z., Wahab, N. A. A., & Abdul Hadi, A. (2024). Exploring journey experiences of disabled passengers in using public transport. International Journal of Religion, 5 (3). https://doi.org/10.61707/xv2n3n21 Sidama Region Plan and Development Bureau. (2024). Regional statistical abstract for socio-economic growth and household population report . Regional Planning Department. Sobaih, A. E. E., & AlSaif, S. (2023). Effects of parcel delivery service on customer satisfaction in the Saudi Arabian logistics industry: Does the national culture make a difference? Logistics, 7 (4), 94. https://doi.org/10.3390/logistics7040094 Sogbe, E., Susilawati, S., & Pin, T. C. (2025). Scaling up public transport usage: A systematic literature review of service quality, satisfaction and attitude towards bus transport systems in developing countries. Public Transport, 17 (1), 1–44. https://doi.org/10.1007/s12469-024-00367-6 Tabi, J., & Adams, S. (2020). Customer satisfaction in public road transport in sub-Saharan Africa: A case study of Ghana. African Journal of Economic and Management Studies, 11 (3), 419–437. https://doi.org/10.1108/AJEMS-04-2019-0152 Ubaidillah, N. Z., Sa’ad, N. H., Nordin, N., Baharuddin, N., Ismail, F., & Hassan, M. K. H. (2022). The impact of public bus service quality on the users’ satisfaction: Evidence from a developing Asian city. Review of Applied Socio-Economic Research, 23 (1). https://doi.org/10.54609/reaser.v23i1.185 Yasin, M., & Abayneh, A. (2023). The effect of service quality on customer satisfaction in railway transport service in Ethio-Djibouti. Global Journal of Business, Economics and Management: Current Issues, 13 (2), 145–158. https://doi.org/10.18844/gjbem.v13i2.8096 Additional Declarations No competing interests reported. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-8701435","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":599937512,"identity":"1d902b8a-15bf-458a-9d8e-1f53bbf89b53","order_by":0,"name":"Siquarie Shudda Dangisso","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAzklEQVRIiWNgGAWjYDACZjApwcAPohIKSNEi2QDSYkCKbQYHwCQRKuXbmbdJ/NxjIWd8fnXihwcGDPL8YgcIGH6YrUyy55mEsdmNt5slgA4znDk7gYAWZh4zCZ4DEonbbpzdANKSYHCbgBb5Zh4zyT8HJOo3zzi7+QdRWhgO85hJA21JMODv3UacLUC/FFvLHJAwnHGDd5tFgoEEYb/I9x/eePPNgTp5/v6zm2/+qLCR55cm5DB4XEiAVUoQVI6khf8AUapHwSgYBaNgBAIAbYA/XffjshoAAAAASUVORK5CYII=","orcid":"","institution":"Hawassa University","correspondingAuthor":true,"prefix":"","firstName":"Siquarie","middleName":"Shudda","lastName":"Dangisso","suffix":""},{"id":599937513,"identity":"06fcdb70-6890-4686-873e-e4daa3d3568e","order_by":1,"name":"Dayanandan R.","email":"","orcid":"","institution":"Hawassa University","correspondingAuthor":false,"prefix":"","firstName":"Dayanandan","middleName":"","lastName":"R.","suffix":""},{"id":599937514,"identity":"0990996a-4f3c-4683-815b-3ba1680cfbce","order_by":2,"name":"Wogene Dumo","email":"","orcid":"","institution":"Hawassa University","correspondingAuthor":false,"prefix":"","firstName":"Wogene","middleName":"","lastName":"Dumo","suffix":""}],"badges":[],"createdAt":"2026-01-26 14:39:50","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8701435/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8701435/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":103933277,"identity":"c289f942-d44c-42b8-8635-f0cce18ff81c","added_by":"auto","created_at":"2026-03-04 17:04:37","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":253916,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eMap of the Study Area\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSource:\u003c/strong\u003e \u003cem\u003eSidama Region, Planning and Development Bureau (2023).\u003c/em\u003e\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-8701435/v1/4f16f4040687c42e8943504c.png"},{"id":104401964,"identity":"913444d9-647f-43c9-940c-efa8f423e772","added_by":"auto","created_at":"2026-03-11 12:13:59","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1897850,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8701435/v1/1a0747d3-6f75-461b-9d5d-abac381e95f7.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Inclusiveness and Sustainability of Road Transport and Logistics Services in Ethiopia","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eRoad transport plays a critical role in supporting community development and access to essential services in Ethiopia\u0026rsquo;s emerging regions. Despite investments in rural roads and public service expansion, gaps remain in the quality and accessibility of Road Transport and Logistics Services (RTLS). The Sidama National Regional State, with a mix of rural and peri-urban settlements, presents a useful case for analyzing community satisfaction with transport services. Using the SERVQUAL model, this study explores user perceptions across five key dimensions: tangibility, reliability, responsiveness, assurance, and empathy, while also investigating the role of spatial and socio-demographic factors.\u003c/p\u003e\n\u003cp\u003eSustainable and inclusive road transport and logistics services (RTLS) are vital for community development, particularly in emerging economies. They facilitate mobility, improve access to essential services, and drive socio-economic progress. Yet, community satisfaction with these services remains under-researched, especially in developing regions where infrastructure gaps, inequities, and service inefficiencies persist.\u003c/p\u003e\n\u003cp\u003eWhile previous research has contributed meaningfully to the understanding of customer satisfaction in transport and logistics services, the existing literature is regionally concentrated. Studies by Sobaih and AlSaif (2023) and Alotaibi and Mafimisebi (2021) have emphasized service quality in Middle Eastern contexts, often within highly regulated or urbanized environments. In Southeast Asia, works such as Gultom et al. (2022) and Frinaldi et al. (2020) examine customer satisfaction in comparatively well-connected transport corridors. Similarly, Yemer and Abayneh (2023) and Mekonnen (2019) provide insights from East African cities, focusing on more formalized transport systems. While the study \u0026nbsp;by \u0026nbsp; Siyum, \u0026nbsp;(2024), suggested public sector services and urban land management in Ethiopia are consistently failing to meet customer expectations, \u0026nbsp;resulting in widespread dissatisfaction and the need for significant improvements.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eHowever, this regional clustering of studies poses limitations for broader application. It restricts the external validity of SERVQUAL-based models, as transport infrastructure, regulatory frameworks, and community expectations vary significantly across regions. In peri-urban and rural areas of Sub-Saharan Africa, for example, road transport systems are often informal, underregulated, and infrastructure-constrained (Tabi \u0026amp; Adams, 2020; Enimola et al., 2021). Consequently, it remains uncertain whether service quality predictors validated in urban or highly structured systems \u0026nbsp;generalized to such resource-limited and geographically dispersed contexts.\u003c/p\u003e\n\u003cp\u003eFrom a theoretical perspective, this study applies the SERVQUAL model (Parasuraman et al., 1988) to assess service quality across five dimensions, such as tangibility, reliability, responsiveness, assurance, and empathy\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003ewithin the context of Road Transport and Logistics Services (RTLS) in Sidama, Ethiopia. Drawing from customer satisfaction theory (Oliver, 1980), the study examines how perceived gaps between expected and actual service influence user satisfaction.\u003c/p\u003e\n\u003cp\u003eAdditionally, the study guided by the principles of the Sustainable Development Goals (SDGs), particularly SDG 9 (Industry, Innovation, and Infrastructure) and SDG 11 (Sustainable Cities and Communities), which call for inclusive, safe, and sustainable transport systems. Satisfaction with RTLS serves not only as a measure of service performance but also as an indicator of how well transport systems respond to the needs of disadvantaged and marginalized communities.\u003c/p\u003e\n\u003cp\u003eBy applying the SERVQUAL framework within Ethiopia\u0026rsquo;s Sidama National Regional State, this study bridges a critical knowledge gap by offering context-sensitive insights into how service quality dimensions influence community satisfaction in underserved environments. Central to this analysis is the concept of inclusive transport\u003cem\u003e,\u003c/em\u003e defined here as the degree to which road transport and logistics services ensure equitable, affordable, reliable, and safe access for all, with a specific focus on the needs of low-income households, women, informal workers, and residents in rural or peri-urban areas.\u003c/p\u003e\n\u003cp id=\"_Toc200894023\"\u003e\u003cstrong\u003e1.2 Empirical Review: Service Quality and Satisfaction in Transport and Related Sectors\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSatisfaction with Road Transport and Logistics Services (RTLS) is increasingly recognized as a vital component of community well-being, mobility equity, and economic participation. The SERVQUAL model, which evaluates service quality through five dimensions such as tangibility, reliability, responsiveness, assurance, and empathy has applied to assess user satisfaction across various service sectors.\u003c/p\u003e\n\u003cp\u003eA growing body of transport-specific research has refined the SERVQUAL perspective on service quality and user satisfaction. In public transport planning, leadership (Wetzel \u0026amp; Hofmann, 2020) highlights quality discrepancies in the existing service offerings of logistics service that needs as a core metric for network design. Large-sample analyses in Europe show that critical service incidents decisively shape both satisfaction and loyalty (Allen et al., 2020). Studies across bus systems confirm that tangible elements such as vehicle comfort, cleanliness, and stop facilities remain dominant drivers, although desired quality levels vary by user type (Gris\u0026eacute;, \u0026amp; El-Geneidy, 2017). Cross-national work by de O\u0026ntilde;a et al., (2024) demonstrates that satisfaction consistently mediates the link between perceived service quality and behavioral intentions, even among habitual car users. Similar patterns emerge in rail networks: perceived reliability and responsiveness strongly predict customer satisfaction and mode choice (Ibrahim et al., 2025). Finally, structural‐equation modelling in emerging-market cities indicates that overall service quality positively affects satisfaction and, in turn, continued public-transport use (Gizaw, et al., (2021). Collectively, these studies underscore the persistent importance of tangibility and reliability while revealing context-specific nuances in responsiveness and assurance, all highly relevant to the Sidama, Ethiopia: RTLS setting examined in the present study.\u003c/p\u003e\n\u003cp id=\"_Toc200894024\"\u003e\u003cstrong\u003e1.2.1 SERVQUAL Applications in Transport and Logistics\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWhile prior research on RTLS in Ethiopia has focused narrowly on SERVQUAL-based user perceptions (e.g., Yemer \u0026amp; Abayneh, 2023), this study integrates quantitative service quality assessments with qualitative analysis of systemic barriers (e.g., infrastructure gaps, policy enforcement). This dual lens addresses gaps in both \u003cem\u003euser-centric\u003c/em\u003e and \u003cem\u003esystem-level\u003c/em\u003e literature, offering a comprehensive view of RTLS challenges in Sidama. In Saudi Arabia, reliability and empathy identified as primary predictors of satisfaction with parcel delivery services (Sobaih \u0026amp; AlSaif, 2023). Studies on public transport in Indonesia and Nigeria emphasized punctuality, driver behavior, and frequency, attributes tied to reliability, assurance, and responsiveness (Frinaldi et al., 2020; Enimola et al., 2021). Southeast Asian research highlighted concerns related to safety and logistics responsiveness in the context of e-commerce-driven intercity transport (Muangpan, 2022; Gultom et al., 2022). African urban studies further showed dissatisfaction rooted in overcrowding, irregular schedules, and infrastructure deficits, reflecting issues with tangibility, reliability, and responsiveness (Tabi \u0026amp; Adams, 2020; Mekonnen, 2019).\u003c/p\u003e\n\u003cp id=\"_Toc200894025\"\u003e\u003cstrong\u003e1.2.2 \u0026nbsp;Theoretical Background\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eBeyond traditional models like SERVQUAL (Amponsah \u0026amp; Adams, 2016) and Oliver\u0026apos;s satisfaction theory(Sobaih \u0026amp; AlSaif, 2023), this text integrates Accessibility and Mobility Theories to emphasize the importance of infrastructure in driving sustainable growth (Al\u0026ccedil;ura, 2024). It notably references Infrastructure-led Development Theory to argue that infrastructure acts as a key driver for long-term development (Papanikos, 2024). Additionally, Sustainability Frameworks, such as the Sustainable Development Goals 9 and 11, are mentioned as essential for connecting service quality to wider developmental outcomes (Brussel et al., 2019). In this context, sustainability is conceptualized not merely as the expansion of physical road infrastructure, but as the long-term capacity of transport institutions to deliver reliable, responsive, and accountable services that equitably serve diverse users across space and socio-economic groups.\u003c/p\u003e\n\u003cp id=\"_Toc200894026\"\u003e\u003cstrong\u003e1.2.3 Conceptual Framework and Model Orientation\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study adopts a conceptual framework grounded primarily in Accessibility and Mobility Theory and Place-Based Development Theory. It explores how perceived service quality and socio-demographic factors directly influence satisfaction with Road Transport and Logistics Services (RTLS), particularly in rural and peri-urban contexts.\u003c/p\u003e\n\u003cp\u003eSatisfaction is modeled as an ordinal dependent variable, shaped by two main predictors: (1) service quality attributes, based on the SERVQUAL model: tangibility, reliability, responsiveness, assurance, and empathy; and (2) socio-demographic characteristics, including age, education, income, and location. These variables capture differentiated user experiences and expectations within the local transport system.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003e1\u003c/strong\u003e\u003cstrong\u003e: Theoretical Constructs and Indicators\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"3\" cellpadding=\"0\" width=\"558\"\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 135px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eConstruct\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 178px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eIndicators\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 237px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eDescription\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 135px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eService Quality\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 178px;\"\u003e\n \u003cp\u003eTangibility, Reliability, Responsiveness, Assurance, Empathy\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 237px;\"\u003e\n \u003cp\u003eMeasured using SERVQUAL-based items\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 135px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSocio-demographic Factors\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 178px;\"\u003e\n \u003cp\u003eGende, Age, Education Level, Income Status, \u0026nbsp; \u0026nbsp; Residential Location\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 237px;\"\u003e\n \u003cp\u003eReflect user diversity and expectation patterns\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003eSource:\u003c/strong\u003e Survey data analysis (2025)\u003c/p\u003e"},{"header":"2. Research Question","content":"\u003cp\u003eHow do SERVQUAL dimensions predict RTLS satisfaction in Sidama Region?\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2. 1 Research Objectives\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThestudyaims to assess community satisfaction with Road Transport and Logistics Services (RTLS) in the Sidama National Regional State (SNRS), Ethiopia.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.1.1 Specific Objective\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e1. To determine the overall satisfaction level of the community with RTLS based on the SERVQUAL dimensions.\u003c/p\u003e\n\u003cp\u003e2. To examine the influence of socio-demographic and geographic factors on satisfaction with RTLS.\u003c/p\u003e\n\u003cp\u003e3. To identify systemic service and infrastructure-related factors shaping user perceptions of RTLS.\u003c/p\u003e\n\u003cp\u003e4. To apply mixed-method findings to inform equitable and inclusive transport planning strategies in SNRS\u003c/p\u003e"},{"header":"3.\tHypotheses of the Study","content":"\u003cp\u003e\u003cstrong\u003eHypothesis (H\u003csub\u003e1\u003c/sub\u003e):\u0026nbsp;\u003c/strong\u003eThe five SERVQUAL dimensions (reliability, responsiveness, assurance, empathy, and tangibility) have a significant and positive influence on community satisfaction with Road Transport and Logistics Services (RTLS) in the Sidama National Regional State.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eHypothesis (H\u003csub\u003e2\u003c/sub\u003e)\u003c/strong\u003e: Community satisfaction with RTLS significantly varies across socio-demographic and geographic factors, including location, age, education level, occupation, and household size.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eHypothesis (H\u003csub\u003e3\u003c/sub\u003e)\u003c/strong\u003e: Lower perceived service quality across the SERVQUAL dimensions is associated with lower levels of satisfaction with RTLS.\u003c/p\u003e"},{"header":"4. Methodology","content":"\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e\n \u003ch2\u003e4.1 Study Area Description\u003c/h2\u003e\n \u003cp\u003eThe study was conducted in Sidama National Regional State (SNRS), southeastern Ethiopia, home to over 4.8 million people across 991,099 households, the majority of whom live in rural areas and depend on subsistence farming and small-scale trade (Regional Development and Plan Bureau, 2024). Administratively, the region includes 30 districts, six city administrations, and 658 kebeles, with Hawassa as the capital. Its varied terrain and average elevation of 2,068 meters affect road access and infrastructure. Given the region\u0026rsquo;s reliance on road-based transport amid rising population demands, the study purposively sampled both central and remote kebeles to capture diverse spatial and socio-economic experiences with RTLS.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\n \u003ch2\u003e4.2 Research Design and Approach\u003c/h2\u003e\n \u003cp\u003eThis study employed a cross-sectional convergent mixed-methods design to assess user satisfaction with Road Transport and Logistics Services (RTLS). The quantitative strand involved a structured household survey measuring five SERVQUAL dimensions: tangibility, reliability, responsiveness, assurance, and empathy, using a 5-point Likert scale. Overall satisfaction served as the outcome variable and was analyzed through ordinal logistic regression. The qualitative component included key informant interviews with transport officials, service providers, and community leaders. Thematic analysis was conducted to identify recurrent barriers and contextualize the quantitative results.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\n \u003ch2\u003e4.3 Data Sources and Collection Tools\u003c/h2\u003e\n \u003cp\u003ePrimary data were collected using a structured questionnaire administered through KoboToolbox across ten randomly selected kebeles. The questionnaire assessed perceptions of RTLS across SERVQUAL dimensions and captured socio-demographic variables including age, gender, education, income, and residential location.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e\n \u003ch2\u003e4.4 Target Population and Sampling Techniques\u003c/h2\u003e\n \u003cp\u003eThe study targeted households in urban and peri-urban kebeles directly served by RTLS infrastructure. A multistage stratified sampling technique applied. First, SNRS selected for its relevance to emerging transport priorities. Next, from the five zones in region (including Hawassa city administration) one district per zone is systematically chosen based on the highest number of kebeles. Two kebeles randomly selected from each district (10 total).\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e\n \u003ch2\u003e4.5 Sample Size Determination\u003c/h2\u003e\n \u003cp\u003eFinally, a probability-based simple random sampling technique applied, yielding 379 households from a population of 24,551, based on proportional allocation (Regional Development and Plan Bureau, 2024). Sample size calculated using Cochran\u0026rsquo;s formula (Cochran, \u003cspan class=\"CitationRef\"\u003e1977\u003c/span\u003e) with finite population correction, assuming a 95% confidence level and 5% margin of error:\u003c/p\u003e\n \u003cdiv id=\"Equa\" class=\"Equation\"\u003e\n \u003cdiv class=\"mathdisplay\" id=\"FileID_Equa\" name=\"EquationSource\"\u003e$$\\:n=\\frac{N{Z}^{2}pq}{{e}^{2}\\left(N-1\\right)+{Z}^{2}pq}$$\u003c/div\u003e\u003c/div\u003e\u003cp\u003eWhere: \u003cem\u003eN\u003c/em\u003e\u0026thinsp;=\u0026thinsp;24,551; \u003cem\u003eZ\u003c/em\u003e\u0026thinsp;=\u0026thinsp;1.96; \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.5; \u003cem\u003ee\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.05; resulting in n\u0026thinsp;\u0026asymp;\u0026thinsp;379.\u003c/p\u003e\u003cp\u003eGiven the design included proportional stratification and random sampling within each cluster, a design effect (Deff)\u0026thinsp;\u0026asymp;\u0026thinsp;1.0 assumed, consistent with minimal intra-cluster correlation. This justified retaining the calculated sample size without adjustment.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec15\" class=\"Section2\"\u003e\u003ch2\u003e4.6 Data Analysis Methods\u003c/h2\u003e\u003cp\u003eDescriptive statistics (means, frequencies, and standard deviations) summarized community perceptions across SERVQUAL dimensions to identify performance gaps in service delivery. Basically, Ordinal Logistic Regression was employed to model satisfaction with RTLS as an ordinal dependent variable, measured on a 5-point Likert scale. This method retained the rank-order nature of satisfaction responses and allowed for nuanced interpretation across ordered categories. The model follows the cumulative logit form:\u003c/p\u003e\u003cp\u003eLog \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:(\\frac{1-\\text{P}\\left(\\text{Y}\\le\\:\\text{j}\\right)}{\\text{P}\\left(\\text{Y}\\le\\:\\text{j}\\right)}\\)\u003c/span\u003e\u003c/span\u003e) = \u0026beta;\u003csub\u003e0\u003c/sub\u003e+\u0026beta;\u003csub\u003e1\u003c/sub\u003eX\u003csub\u003e1\u003c/sub\u003e+\u0026beta;\u003csub\u003e2\u003c/sub\u003eX\u003csub\u003e2\u003c/sub\u003e+⋯+\u0026beta;nXn\u003c/p\u003e\u003cp\u003eWhere:\u003c/p\u003e\u003cspan\u003e\u003cp\u003e\u003cem\u003e1. Y\u003c/em\u003e is satisfaction with RTLS\u003c/p\u003e\u003c/span\u003e \u003cspan\u003e\u003cp\u003e\u003cem\u003e2. j\u003c/em\u003e represents Likert categories (1\u0026thinsp;=\u0026thinsp;Very Dissatisfied to 5\u0026thinsp;=\u0026thinsp;Very Satisfied)\u003c/p\u003e\u003c/span\u003e \u003cspan\u003e\u003cp\u003e\u003cem\u003e3. X₁...Xₙ\u003c/em\u003e represent predictor variables (SERVQUAL dimensions\u0026thinsp;+\u0026thinsp;socio-demographic factors)\u003c/p\u003e\u003c/span\u003e \u003cspan\u003e\u003cp\u003e\u003cem\u003e4. \u0026beta;₁...\u0026beta;ₙ\u003c/em\u003e are coefficients estimating log-odds changes\u003c/p\u003e\u003c/span\u003e\u003cp\u003e\u003cbr\u003e\u003c/p\u003e\u003cp\u003eThis approach allows for estimating the directional influence of each predictor while maintaining the proportional odds assumption. A positive coefficient indicates that an increase in a predictor (e.g., service reliability) raises the likelihood of reporting higher satisfaction.\u003c/p\u003e\u003c/div\u003e"},{"header":"5. Data Analysis And Result","content":"\u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003e5.1 The Background of the Respondents\u003c/h2\u003e \u003cp\u003eA total of 379 valid responses were obtained, with no missing data. The sample was drawn from ten kebeles in the Sidama National Regional State, ensuring broad geographic representation across rural and peri-urban areas. The distribution of respondents across kebeles was relatively balanced, with a near-normal spread (M\u0026thinsp;=\u0026thinsp;5.20, SD\u0026thinsp;=\u0026thinsp;2.95), and minimal skewness (0.08) and kurtosis (\u0026minus;\u0026thinsp;1.16), indicating no substantial concentration of responses in any single kebele. The highest proportions of respondents were from Fara (17.4%), Hoganewaco (16.4%), and Sho\u0026rsquo;e (11.1%), while Shaicha contributed the smallest share (4.2%). The remaining kebeles each accounted for between 7.1% and 10.3% of the total sample.\u003c/p\u003e \u003cp\u003eKey socio-demographic characteristics were also examined. The sample was predominantly male (71%), indicating a gender imbalance that reflects the higher participation of males in transport and logistics-related activities in the study area. The mean age of respondents was 39.3 years (SD\u0026thinsp;=\u0026thinsp;12.85), suggesting that most participants were within the economically active and experienced working-age population.\u003c/p\u003e \u003cp\u003eTo assess the stability and representativeness of the sample estimates, bootstrap resampling with 1,000 iterations was performed. The bootstrap results confirmed the consistency of the geographic distribution and key demographic characteristics, supporting the reliability and generalizability of the findings to similar rural and peri-urban contexts within the Sidama National Regional State. A detailed summary of kebele-level distribution and associated descriptive statistics is presented in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e.\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\u003eRepresentative of the Kebele (Village)\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=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" 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\u003eKebele\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCode\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eFrequency (n)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003ePercent (%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e95% Confidence Interval (%)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHoganewaco\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e62\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e16.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e12.9\u0026ndash;20.1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSoyama\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e8.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e6.1\u0026ndash;11.6\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDilacange\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e7.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4.5\u0026ndash;9.5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTaramessa\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e8.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e5.8\u0026ndash;11.6\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSho\u0026rsquo;e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e11.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e7.9\u0026ndash;14.2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFara\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e66\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e17.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e14.0\u0026ndash;21.4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eShaicha\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.4\u0026ndash;6.3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBultuma\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e8.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e5.5\u0026ndash;11.1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBansaware\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e8.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e5.5\u0026ndash;11.3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eShantawenne\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e10.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e7.4\u0026ndash;13.5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eTotal\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e379\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e100.0\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003e\u003cb\u003eSource\u003c/b\u003e: Survey Data Analysis (2025)\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003e5.2 Model Fitting and Predictive Performance Across SERVQUAL Dimensions\u003c/h2\u003e \u003cp\u003eOrdinal logistic regression models were estimated separately for each SERVQUAL dimension (Tangibility, Reliability, Responsiveness, Assurance, and Empathy) to examine how socio-demographic and service-related factors differentiate ordered perceptions of transport service quality across kebeles in the Sidama Region. Model adequacy was assessed using likelihood-ratio chi-square tests comparing final models against intercept-only specifications.\u003c/p\u003e \u003cp\u003eAs summarized in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e4\u003c/span\u003e, all five dimension-specific models demonstrated statistically significant improvements over their respective null models (p \u0026lt; .001). This indicates that the included predictors jointly contribute to explaining variation in perceived service quality within each SERVQUAL dimension. The magnitude of the likelihood-ratio chi-square statistics varied across dimensions, suggesting differential sensitivity of service quality domains to user and contextual characteristics.\u003c/p\u003e \u003cp\u003eAmong the dimensions, \u003cem\u003eReliability and Assurance exhibited the largest chi-square values\u003c/em\u003e, indicating that perceptions related to punctuality, service consistency, trust, and professionalism are particularly structured by \u003cem\u003esocio-demographic\u003c/em\u003e and \u003cem\u003espatial factors\u003c/em\u003e. \u003cem\u003eTangibility\u003c/em\u003e also showed strong model improvement, reflecting pronounced disparities in physical transport infrastructure across kebeles. \u003cem\u003eResponsiveness\u003c/em\u003e displayed meaningful but more uneven explanatory strength, suggesting inconsistency in problem resolution and service feedback mechanisms. \u003cem\u003eEmpathy\u003c/em\u003e, while statistically significant, demonstrated comparatively lower explanatory capacity, reflecting the more subjective and interpersonal nature of this dimension.\u003c/p\u003e \u003cp\u003eImportantly, these results should be interpreted as \u003cem\u003edimension-specific differentiation\u003c/em\u003e rather than \u003cem\u003ecomprehensive explanations of overall satisfaction\u003c/em\u003e. Each model captures how predictors relate to a single SERVQUAL domain in isolation, rather than the full service quality construct.\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 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eModel Fitting Summary for SERVQUAL Dimensions\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=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" 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 \u003cp\u003eDimension\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026ndash;2 Log Likelihood (Null)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026ndash;2 Log Likelihood (Final)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eChi-Square\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003edf\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eSig.\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eReliability\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1988.057\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1471.167\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e516.89\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e187\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAssurance\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1696.608\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1178.562\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e518.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e187\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTangibles\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1676.855\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1213.324\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e463.53\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e187\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEmpathy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1688.270\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1333.528\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e354.74\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e105\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eResponsiveness\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1676.855\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1213.324\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e463.53\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e187\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cem\u003eAll models use the logit link function; Source: Survey Data Analysis (2025).\u003c/em\u003e \u003c/p\u003e \u003cdiv id=\"Sec19\" class=\"Section3\"\u003e \u003ch2\u003e5.2.1 Goodness-of-Fit Analysis\u003c/h2\u003e \u003cp\u003eModel goodness-of-fit was evaluated using \u003cem\u003ePearson\u003c/em\u003e and \u003cem\u003eDeviance statistics\u003c/em\u003e (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e5\u003c/span\u003e). Across all models, the \u003cem\u003eDeviance tests\u003c/em\u003e were \u003cem\u003enon-significant\u003c/em\u003e (p\u0026thinsp;=\u0026thinsp;1.000), indicating no statistically detectable discrepancy between observed and model-predicted values. This suggests that the ordinal logistic specifications adequately capture the underlying structure of the data.\u003c/p\u003e \u003cp\u003eIn contrast, the \u003cem\u003ePearson chi-square tests\u003c/em\u003e were statistically \u003cem\u003esignificant\u003c/em\u003e (p \u0026lt; .001). In \u003cem\u003eordinal logistic regression\u003c/em\u003e, particularly with \u003cem\u003emultiple predictors\u003c/em\u003e and \u003cem\u003esparse response patterns\u003c/em\u003e, the \u003cem\u003ePearson statistic\u003c/em\u003e is known to be \u003cem\u003ehighly sensitive to data sparsity\u003c/em\u003e and \u003cem\u003eover-dispersion\u003c/em\u003e, often flagging apparent misfit even when the model is correctly specified. Consequently, the \u003cem\u003eDeviance statistic\u003c/em\u003e is considered the more reliable indicator in this context. Taken together, the \u003cem\u003egoodness-of-fit diagnostics\u003c/em\u003e indicate that the models are \u003cem\u003estatistically appropriate\u003c/em\u003e and \u003cem\u003estable\u003c/em\u003e, supporting their use for interpreting dimension-specific predictor effects.\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 5\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eGoodness-of-Fit Statistics\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=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" 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\u003eTest\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eChi-Square\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003edf\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSig.\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePearson\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e4034.711\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3162\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDeviance\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1331.802\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3162\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cem\u003eSource: Survey Data Analysis (2025).\u003c/em\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec20\" class=\"Section3\"\u003e \u003ch2\u003e5.2.2 Pseudo R-Square Analysis\u003c/h2\u003e \u003cp\u003eTo evaluate relative model performance, \u003cem\u003epseudo R-square\u003c/em\u003e measures were examined (Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e6\u003c/span\u003e). For the SERVQUAL dimension models, \u003cem\u003eNagelkerke R\u0026sup2;\u003c/em\u003e values were modest, consistent with expectations for ordinal logistic regression applied to perception-based outcomes influenced by complex spatial and institutional factors.\u003c/p\u003e \u003cp\u003eThe \u003cem\u003eNagelkerke R\u0026sup2;\u003c/em\u003e value of 0.615 and \u003cem\u003eCox \u0026amp; Snell R\u0026sup2;\u003c/em\u003e of 0.608 should be interpreted cautiously. Unlike \u003cem\u003eOLS R\u0026sup2;, pseudo R-square\u003c/em\u003e statistics do not represent the proportion of variance explained in a strict sense but rather indicate relative improvement over intercept-only model\u003cb\u003es\u003c/b\u003e. Similarly, the \u003cem\u003eMcFadden R\u0026sup2;\u003c/em\u003e value of 0.210, while numerically smaller, falls within the range typically regarded as indicative of good model performance in logistic regression. Overall, these measures suggest that the models provide meaningful predictive differentiation, while also underscoring that a substantial portion of variation in service quality perceptions remains attributable to unobserved contextual, institutional, and governance-related factors.\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 6\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003ePseudo R-Square Measures\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"2\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMeasure\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eValue\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCox \u0026amp; Snell\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.608\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNagelkerke\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.615\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMcFadden\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.210\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cem\u003eSource: Survey Data Analysis (2025).\u003c/em\u003e \u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec21\" class=\"Section2\"\u003e \u003ch2\u003e5.3 Predictors of Transport Service Quality: Parameter Estimate Analysis\u003c/h2\u003e \u003cp\u003eA set of dimension-specific ordinal logistic regression models was estimated to identify the key predictors shaping perceived transport service quality across the five SERVQUAL domains. Full parameter estimates are presented in \u003cem\u003eAppendix B;\u003c/em\u003e key patterns are summarized below.\u003c/p\u003e \u003cdiv id=\"Sec22\" class=\"Section3\"\u003e \u003ch2\u003e5.3.1 Kebele-Level Variation\u003c/h2\u003e \u003cp\u003eGeographic location emerged as a \u003cem\u003econsistently strong predictor\u003c/em\u003e across SERVQUAL dimensions. Relative to the reference kebele (Kebele 10), respondents from several kebeles reported significantly lower perceived service quality, particularly for \u003cem\u003eReliability\u003c/em\u003e and \u003cem\u003eAssurance\u003c/em\u003e: Kebele 1: β = \u0026minus;\u0026thinsp;7.943, p \u0026lt; .00, Kebele 2: β = \u0026minus;\u0026thinsp;7.100, p \u0026lt; .001, Kebele 3: β = \u0026minus;\u0026thinsp;6.524, p \u0026lt; .001, and Kebele 6: β = \u0026minus;\u0026thinsp;3.933, p = .022. These results provide robust quantitative evidence of \u003cem\u003espatial inequality in transport service experience\u003c/em\u003e, with rural and peri-urban kebeles systematically disadvantaged.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec23\" class=\"Section3\"\u003e \u003ch2\u003e5.3.2 Socio-Demographic Predictors\u003c/h2\u003e \u003cp\u003eSeveral socio-demographic variables significantly shaped service quality perceptions. \u003cem\u003eAge\u003c/em\u003e showed a small but \u003cem\u003epositive association\u003c/em\u003e (β\u0026thinsp;=\u0026thinsp;0.026, p = .011), indicating that older respondents tend to report \u003cem\u003ehigher satisfaction\u003c/em\u003e. \u003cem\u003eOccupation\u003c/em\u003e was negatively associated with perceived quality (β = \u0026minus;\u0026thinsp;0.135, p = .019), with \u003cem\u003einformal workers\u003c/em\u003e expressing \u003cem\u003elower satisfaction\u003c/em\u003e levels. \u003cem\u003eAnnual income\u003c/em\u003e exhibited a small but statistically \u003cem\u003esignificant negative effect\u003c/em\u003e (β = \u0026minus;\u0026thinsp;1.744e\u0026ndash;6, p \u0026lt; .001), suggesting higher expectations among wealthier users. \u003cem\u003eEducation\u003c/em\u003e also showed a marginally \u003cem\u003enegative association\u003c/em\u003e (β = \u0026minus;\u0026thinsp;0.150, p = .055), indicating more critical assessments among educated respondents. These findings highlight the importance of \u003cem\u003eexpectation heterogeneity\u003c/em\u003e and reinforce the need for equity-sensitive transport planning.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec24\" class=\"Section2\"\u003e \u003ch2\u003e5.4 SERVQUAL Dimension Scores Overview\u003c/h2\u003e \u003cdiv id=\"Sec25\" class=\"Section3\"\u003e \u003ch2\u003e5.4.1 Key Trends in Service Quality Perceptions\u003c/h2\u003e \u003cp\u003eAs detailed in Table\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e3\u003c/span\u003e, the analysis of SERVQUAL dimensions such as Reliability, Assurance, Tangibles, Empathy, and Responsiveness reveals consistently low user satisfaction across all service quality aspects in the Sidama Region\u0026rsquo;s transport services. Accordingly, all dimensions scored below the neutral midpoint of 3.0, indicating overall dissatisfaction. The most frequent response across all dimensions was \u0026ldquo;2.00\u0026rdquo;, corresponding to a \"Poor\" rating. Reliability emerged as the most problematic dimension (mean\u0026thinsp;=\u0026thinsp;2.48), with 41.4% of respondents rating it poor, confirming persistent delays and low service predictability. Assurance (mean\u0026thinsp;=\u0026thinsp;2.57) similarly shows concern, with 41.7% of users expressing low confidence in staff competence and safety. Tangibles (mean\u0026thinsp;=\u0026thinsp;2.63) exhibited a bimodal pattern, pointing to infrastructure inequality across kebeles. Further, Empathy (2.77) and Responsiveness (2.79) scored marginally higher but still below acceptable levels, indicating inconsistent personal attention and complaint resolution. These findings point to systemic service underperformance, with user dissatisfaction dominating across all domains.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec26\" class=\"Section3\"\u003e \u003ch2\u003e5.4.2 Critical Service Gaps\u003c/h2\u003e \u003cp\u003eThe analysis of SERVQUAL scores (Table\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e3\u003c/span\u003e) and response frequencies reveals critical service gaps undermining Road Transport and Logistics Services (RTLS) in the Sidama Region. These gaps fall into three major categories. First, as shown in Table\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e3\u003c/span\u003e, tangibility received a mean score of 2.63, with a bimodal distribution, 16.6% of respondents rated it 1.00 (Poor) and another 16.6% rated it 4.00 (Good). This stark contrast indicates sharp disparities in infrastructure quality and access. Urban kebeles such as \u003cem\u003eFara\u003c/em\u003e and \u003cem\u003eHoganewacho\u003c/em\u003e benefited from better roads and vehicle fleets, while rural kebeles like \u003cem\u003eShaicha\u003c/em\u003e and \u003cem\u003eDilacange\u003c/em\u003e reported service deficiencies. These disparities reflect uneven investment and planning, pointing to the urgent need for spatially targeted infrastructure upgrades to close the rural-urban service gap.\u003c/p\u003e \u003cp\u003eSecond, staff training and professionalism (Assurance \u0026amp; Empathy) scored below average (means of 2.57 and 2.77, respectively), with over one-third of respondents rating them at 2.00 (Poor). These results reflect weak user perceptions of staff safety, competence, and interpersonal engagement. The data suggest a systemic lack of customer service orientation, likely due to insufficient staff training in communication, safety, and complaint handling. This gap is particularly troubling for marginalized groups and women, who reported lower empathy scores (Appendix A), emphasizing the need for inclusive training programs across RTLS personnel.\u003c/p\u003e \u003cp\u003eFurther, however, Responsiveness had the highest mean among the five dimensions (2.79), a substantial 29.3% of respondents rated it Poor (2.00), and 22.4% rated it Good (4.00). This variation reveals significant inconsistency in issue resolution and feedback mechanisms across kebeles. In areas with active local transport authorities or private operators, users reported better responsiveness. In contrast, other kebeles experienced neglect and service delays. This fragmentation undermines system-wide trust and indicates the absence of standardized response protocols or complaint systems.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab6\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eDescriptive result of the SERVQUAL Dimension\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=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" 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\u003eDimension\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\u003eMedian\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMode\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eKey Concern\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eReliability\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2.48\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e41.4% rated \u0026ldquo;Poor\u0026rdquo;; low predictability, delays\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAssurance\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2.57\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e41.7% rated \u0026ldquo;Poor\u0026rdquo;; safety and staff competence lacking\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eTangibles\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2.63\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2.29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eBimodal: high inequality in infrastructure access\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eEmpathy\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2.77\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2.40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e36.4% rated \u0026ldquo;Poor\u0026rdquo;; limited personalized service\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eResponsiveness\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2.79\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2.50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e29.3% rated \u0026ldquo;Poor\u0026rdquo;; uneven issue resolution\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003e\u003cb\u003eSource\u003c/b\u003e: Survey Data Analysis (2025)\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec27\" class=\"Section2\"\u003e \u003ch2\u003e5.5 SERVQUAL Dimension Effects\u003c/h2\u003e \u003cp\u003eAmong the SERVQUAL dimensions, \u003cem\u003eReliability\u003c/em\u003e emerged as the most consistently influential, with strong positive associations across multiple response levels (e.g., β\u0026thinsp;=\u0026thinsp;6.147, p \u0026lt; .001), underscoring the central role of punctuality and service consistency in shaping perceptions. \u003cem\u003eAssurance\u003c/em\u003e coefficients were largely \u003cem\u003enegative\u003c/em\u003e and \u003cem\u003esignificant\u003c/em\u003e across lower levels, reflecting the detrimental effects of low trust and perceived lack of professionalism. \u003cem\u003eResponsiveness\u003c/em\u003e also showed several \u003cem\u003esignificant\u003c/em\u003e and \u003cem\u003enegative\u003c/em\u003e coefficients, indicating persistent dissatisfaction with complaint handling and problem resolution.\u003c/p\u003e \u003cp\u003e \u003cem\u003eEmpathy\u003c/em\u003e displayed fewer significant effects, though selected levels suggested notable gaps in user-centered service delivery. \u003cem\u003eTangibility\u003c/em\u003e variables, while prominent in descriptive analysis, were less consistently significant in the regression models, indicating that physical infrastructure alone does not guarantee positive service perceptions without corresponding \u003cem\u003eoperational reliability\u003c/em\u003e and \u003cem\u003einstitutional support\u003c/em\u003e.\u003c/p\u003e \u003cp\u003eOverall, the results confirm that perceived transport service quality is \u003cem\u003emultifactorial\u003c/em\u003e, shaped by spatial context, socio-economic characteristics, and service performance dimensions. Reliability, Assurance, and Responsiveness are the most influential domains, while kebele-level location and income function as critical moderators of user experience.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec28\" class=\"Section2\"\u003e \u003ch2\u003e5.6 Systemic Implications and Alignment with SDGs\u003c/h2\u003e \u003cp\u003eThe RTLS system in the Sidama Region presents as fragmented, inequitable, and lacking standardization. Patterns of user dissatisfaction, particularly in tangibility, assurance, and responsiveness are strongly shaped by spatial and social inequalities.\u003c/p\u003e \u003cp\u003eThese systemic shortcomings directly undermine Ethiopia\u0026rsquo;s progress toward several Sustainable Development Goals. For instance, SDG 9.1 (Industry, Innovation, and Infrastructure) is compromised by unequal investment that has resulted in regional disparities in road quality and service access. Similarly, SDG 11.2 (Sustainable Cities and Communities) is affected by inconsistent and often poor user experiences, which limit inclusivity and reduce access to essential transport services. Furthermore, SDGs 5 (Gender Equality) and 10.2 (Reduced Inequalities) are challenged by persistent gaps in empathy and service quality, particularly among women and marginalized communities who consistently report lower satisfaction.\u003c/p\u003e \u003cp\u003eTo address these challenges, the evidence underscores an urgent need for targeted infrastructure investment in underperforming kebeles, comprehensive and standardized training for transport personnel on safety, responsiveness, and customer care, and the institutionalization of feedback and accountability mechanisms to ensure consistent service delivery and inclusive participation. These reforms are essential for guiding the RTLS system toward the broader goals of sustainability, accessibility, and transport equity.\u003c/p\u003e \u003cdiv id=\"Sec29\" class=\"Section3\"\u003e \u003ch2\u003e5.6.1 Implications of SERVQUAL-Based Findings for Sustainable Development Goals (SDGs)\u003c/h2\u003e \u003cp\u003eFrom a sustainability perspective, these findings reveal that the primary weaknesses of the RTLS system stem less from a lack of physical infrastructure and more from persistent failures in service reliability, institutional assurance, and responsiveness. These qualitative gaps undermine the long-term effectiveness and equity of transport provision. Furthermore, the dimension-specific ordinal regression results illustrate how transport service quality serves as a critical lever for advancing Sustainable Development Goals (SDGs) 9, 10, and 11, offering a roadmap for progress within low-income and spatially heterogeneous contexts like the Sidama Region. Such performance-driven deficiencies suggest that transport sustainability in the study area is constrained by governance and operational limitations, rather than by infrastructure coverage alone.\u003c/p\u003e \u003cp\u003eFrom a sustainability perspective, these findings indicate that the principal weaknesses of the RTLS system lie not in the physical absence of infrastructure, but in persistent failures of service reliability, institutional assurance, and responsiveness, undermining the long-term effectiveness and equity of transport provision. The dimension-specific ordinal regression results provide important insights into how transport service quality contributes to progress toward Sustainable Development Goals 9, 10, and 11, particularly in low-income and spatially heterogeneous contexts such as the Sidama Region.\u003c/p\u003e \u003cp\u003e \u003cb\u003eSDG 9: Industry, Innovation, and Infrastructure\u003c/b\u003e \u003c/p\u003e \u003cp\u003eThe strong and consistent effects observed for the \u003cem\u003eReliability\u003c/em\u003e and \u003cem\u003eAssurance dimensions\u003c/em\u003e underscore that infrastructure effectiveness extends beyond physical road provision to include operational dependability, schedule adherence, and institutional professionalism. While \u003cem\u003eTangibility\u003c/em\u003e captured disparities in physical infrastructure, its weaker regression effects suggest that infrastructure alone is insufficient to deliver development outcomes without reliable service operations. This finding reinforces SDG 9\u0026rsquo;s emphasis on resilient and efficient infrastructure systems, highlighting the need to integrate \u0026ldquo;software\u0026rdquo; elements, such as service management, monitoring, and accountability, alongside physical investments.\u003c/p\u003e \u003cp\u003e \u003cb\u003eSDG 10: Reduced Inequalities\u003c/b\u003e \u003c/p\u003e \u003cp\u003eKebele-level location emerged as a dominant and consistent predictor across SERVQUAL dimensions, providing robust evidence of spatial inequality in transport service quality. Respondents from rural and peri-urban kebeles systematically reported lower perceived reliability, assurance, and responsiveness, even after controlling for socio-demographic characteristics. These findings indicate that transport services may unintentionally reproduce or deepen territorial inequalities, contradicting the equity objectives of SDG 10. Addressing such disparities requires place-based transport strategies that explicitly target underserved kebeles rather than uniform, region-wide interventions.\u003c/p\u003e \u003cp\u003e \u003cb\u003eSDG 11: Sustainable Cities and Communities\u003c/b\u003e \u003c/p\u003e \u003cp\u003eWhile Reliability remains the primary driver of satisfaction, the significant\u0026mdash;albeit weaker\u0026mdash;roles of Responsiveness and Empathy highlight critical deficiencies in user-centered service delivery. Within the framework of SDG 11, which mandates inclusive, safe, and accessible transport, these results suggest that current RTLS provision fails to accommodate diverse user needs, particularly for informal workers and residents of peripheral areas. Enhancing complaint resolution and customer engagement is, therefore, a prerequisite for advancing inclusive mobility and community-level sustainability.\u003c/p\u003e \u003cp\u003eViewed through a sustainability lens, these findings confirm that the core weaknesses of the RTLS system are institutional and operational rather than merely infrastructural. Persistent unreliability and low levels of assurance reflect governance failures that undermine the equity and resilience of transport provision, even where physical road networks exist. Ultimately, progress toward sustainable transport depends on moving beyond \"ribbon-cutting\" for new roads; it requires improving service reliability and institutional trust. The modest explanatory power of individual SERVQUAL dimensions further underscores that regulatory enforcement and local administrative capacity serve as the critical mediators for long-term success.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"6. Hypothesis Testing and Theoretical Implications","content":"\u003cp\u003eFindings on Road Transport and Logistics Services (RTLS) satisfaction in the Sidama Region indicate that core service quality attributes from the SERVQUAL framework (reliability, tangibility, responsiveness, and assurance) are critical to user perceptions. Higher satisfaction correlates with dependable services and well-maintained infrastructure, while dissatisfaction stems from insufficient oversight and slow maintenance. Key factors affecting satisfaction include vehicle condition, road quality, and timeliness, alongside socioeconomic variables like income and education. Notably, wealthier respondents experienced lower satisfaction due to an \"income\u0026ndash;expectation paradox.\" Despite infrastructure investments, maintenance delays and contractor performance issues pose obstacles to RTLS reliability.\u003c/p\u003e \u003cp\u003eIn the reliability dimension, informal workers reported significantly lower satisfaction (β = \u0026minus;4.95, p = .032), and the model explained 68.8% of the variance. Neither age nor gender significantly influenced reliability perceptions. This dissatisfaction stems from operational failures rather than service absence, including irregular scheduling, inconsistent enforcement of standards, delayed maintenance, and poor coordination among institutions. Such unreliability disproportionately impacts informal workers who depend on consistent transport for market access and daily needs, aligning with the quantitative findings regarding occupation and satisfaction levels.\u003c/p\u003e \u003cp\u003eTangibility scored low among respondents, with nearly half below 2.00. Significant positive drivers included vehicle comfort (β\u0026thinsp;=\u0026thinsp;0.74, p = .002) and road maintenance (β\u0026thinsp;=\u0026thinsp;0.65, p = .012). Qualitative findings indicate that aging vehicle fleets, limited modernization, and inconsistent road upkeep contribute to this perception. Despite policies for infrastructure upgrade, reliance on outdated minibuses and poorly maintained roads leads to partial satisfaction, as tangible improvements are ineffective without corresponding modernization and maintenance.\u003c/p\u003e \u003cp\u003eAssurance received the lowest overall ratings, with 41.7% of respondents scoring it at 2.00. Dissatisfaction clustered in Kebeles 2, 3, 6, 8, and 9. Secondary education and smaller household size were positive predictors.\u003c/p\u003e \u003cp\u003eInstitutional trust deficits correlate with findings of weak regulatory enforcement, inadequate driver training, limited accountability, and insufficient safety oversight, contributing to low user confidence and assurance scores. Over half of respondents rated service responsiveness below average, with higher-income groups expressing greater dissatisfaction due to a lack of formal feedback mechanisms and complaint systems. Empathy was rated low by about one-third of respondents, with positive perceptions associated with male respondents, higher education, and Kebele 2 residency. Qualitative evidence indicates empathy deficits arise from inadequate frontline service training and informal norms, especially affecting low-income and informal workers. Overall, the study reveals systemic spatial disparities, greater influence of socioeconomic status on service expectations, and the significance of operational factors over mere infrastructure presence.\u003c/p\u003e \u003cp\u003eHypothesis Testing (Mixed-Methods Interpretation)\u003c/p\u003e \u003cp\u003eThe summary in Table\u0026nbsp;\u003cspan refid=\"Tab7\" class=\"InternalRef\"\u003e7\u003c/span\u003e demonstrates the alignment of theoretical assumptions with empirical observations in the Sidama Region. Qualitative findings support hypotheses about reliability, assurance, and responsiveness by explaining institutional governance gaps, enforcement weaknesses, and capacity constraints. Conversely, the lack of significance for age and gender is supported by qualitative evidence indicating that service deficiencies impact users broadly, irrespective of demographic factors, once spatial and economic conditions are controlled.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab7\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 7\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eSummary of Hypothesis Test\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\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 \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHypotheses\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGeneral Hypothesis Statement\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eResult\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eH1: Tangibility positively influences satisfaction.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eThe SERVQUAL dimensions (including tangibility) have a significant influence on satisfaction; lower quality is associated with lower satisfaction.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSupported\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eH2: Reliability differs across locations.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSatisfaction (and its dimensions) varies across geographic factors (location).\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSupported\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eH3: Responsiveness influenced by income and location.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSatisfaction varies across socio-demographic (income) and geographic (location) factors.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSupported\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eH4: Age, gender, and education predict satisfaction.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSatisfaction varies across socio-demographic factors like age and education.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNot supported\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eH5: Socioeconomic status influences reliability perceptions.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSocio-demographic factors (socioeconomic status) influence satisfaction dimensions.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSupported\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eH6: Assurance varies across kebeles.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSatisfaction significantly varies across geographic factors (kebeles/location).\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSupported\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eH7: Higher education increases assurance.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSocio-demographic factors (education level) significantly vary with satisfaction indicators.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePartially supported\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eH8: Household size positively influences assurance.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSatisfaction varies across socio-demographic factors (household size).\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSupported\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"3\"\u003e\u003cb\u003eSource\u003c/b\u003e: Survey Data Analysis (2025)\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cdiv id=\"Sec31\" class=\"Section2\"\u003e \u003ch2\u003e6.1 Theoretical Implications (Quantitative\u0026ndash;Qualitative Synthesis)\u003c/h2\u003e \u003cp\u003eThe results substantiate \u003cem\u003ePlace-Based Development Theory\u003c/em\u003e, as satisfaction levels varied significantly by kebele. Underperforming areas such as Kebeles 1 and 6 consistently recorded lower satisfaction across SERVQUAL dimensions.\u003c/p\u003e \u003cp\u003eQualitative findings reinforce this by documenting uneven infrastructure execution, delayed projects, and weaker institutional oversight in peripheral kebeles. These findings validate the theory\u0026rsquo;s assumption that geographic context critically shapes public service experiences, necessitating localized and targeted interventions.\u003c/p\u003e \u003cp\u003eSimilarly, \u003cem\u003eInfrastructure and Economic Development Theory\u003c/em\u003e is strongly supported by the salience of road maintenance and vehicle condition.\u003c/p\u003e \u003cp\u003e \u003cem\u003eHowever, qualitative evidence nuances this theory by demonstrating that infrastructure investment alone is insufficient.\u003c/em\u003e Without institutional capacity, workforce skills, and effective governance, infrastructure fails to translate into reliable and trusted services. This extends the theory by emphasizing implementation quality as a mediating factor.\u003c/p\u003e \u003cp\u003e \u003cem\u003eAccessibility and Mobility Theories\u003c/em\u003e are also confirmed. Quantitative results show that satisfaction depends on reliable and responsive access, while qualitative findings reveal how maintenance delays, outdated fleets, and weak ITS adoption constrain mobility in practice, particularly for rural and low-income users.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec32\" class=\"Section2\"\u003e \u003ch2\u003e6.2 Discussion and Implications (Enhanced with Qualitative Explanations)\u003c/h2\u003e \u003cp\u003eSection 6 offers qualitative insights into systemic issues affecting RTLS quality in Sidama, as revealed by SERVQUAL-based ordinal logistic regression analysis. Key findings indicate that reliability failures stem from institutional fragmentation and maintenance delays; tangibility issues are linked to fleet modernization and contractor performance problems; assurance deficits arise from weak enforcement and data gaps; responsiveness is hindered by absent feedback systems; and empathy gaps reflect inadequate workforce training and informal service expectations. These insights affirm that dissatisfaction with RTLS quality is rooted in broader systemic factors rather than isolated incidents, highlighting the importance of place-sensitive differentiation in understanding service delivery.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003e1. Salience of Reliability in RTLS Satisfaction\u003c/h3\u003e\n\u003cp\u003eAmong all SERVQUAL dimensions, \u003cem\u003eReliability\u003c/em\u003e emerged as the most decisive factor differentiating user satisfaction across spatial and socio-economic contexts. Indicators related to punctuality, service consistency, and predictability exerted strong and statistically significant effects, with the model explaining a substantial share of variation in perceptions. This finding underscores that dissatisfaction with RTLS in Sidama is driven less by the absence of services and more by their operational dependability.\u003c/p\u003e \u003cp\u003eThis result is particularly salient in rural and peri-urban kebeles, where households depend on transport services for time-sensitive livelihoods, market access, and social obligations. From an SDG 9 perspective, the findings reinforce the principle that infrastructure effectiveness depends on functional performance, not merely physical presence. Investments that improve service reliability (scheduling discipline, fleet maintenance, and operational oversight) are therefore likely to yield greater welfare gains than road expansion alone.\u003c/p\u003e\n\u003ch3\u003e2. Tangibility and the Limits of Infrastructure-Only Approaches\u003c/h3\u003e\n\u003cp\u003eTangibility was consistently rated low across the sample, with nearly half of respondents scoring this dimension below average. Vehicle comfort and road maintenance emerged as significant positive predictors, confirming that visible and experiential infrastructure elements remain central to user evaluations. However, despite their importance, tangibility effects were less consistent than those of reliability and assurance, indicating diminishing perceptual returns when physical investments are not complemented by effective service management.\u003c/p\u003e \u003cp\u003eThis finding carries important implications for SDG 9 and SDG 11. While expanding road networks and upgrading vehicles are necessary conditions for improved mobility, the results suggest they are \u003cem\u003einsufficient on their own\u003c/em\u003e to ensure positive user experiences. Users appear more sensitive to how services function on a daily basis regularity, safety, and professionalism than to infrastructure visibility per se. This challenges infrastructure-centric development strategies and supports a shift toward \u003cem\u003eservice-oriented transport planning\u003c/em\u003e.\u003c/p\u003e\n\u003ch3\u003e3. Assurance, Responsiveness, and Institutional Trust Deficits\u003c/h3\u003e\n\u003cp\u003eAssurance and Responsiveness exhibited substantial spatial and socio-demographic variation, reflecting deeper institutional and relational challenges within RTLS provision. Low assurance scores linked to limited trust, weak professionalism, and perceived lack of accountability were concentrated in several kebeles, particularly those characterized by poor infrastructure and irregular service provision. Similarly, dissatisfaction within the responsiveness dimension highlights shortcomings in complaint handling, information dissemination, and problem resolution.\u003c/p\u003e \u003cp\u003eThese patterns indicate that RTLS dissatisfaction is not purely technical but also \u003cem\u003einstitutional in nature.\u003c/em\u003e From an SDG 11 standpoint, such deficits undermine the objective of inclusive and people-centered mobility systems. Even where access exists, low assurance and weak responsiveness erode user confidence, reducing the broader social and economic value of transport services.\u003c/p\u003e\n\u003ch3\u003e4. Empathy and Social Inclusion Gaps\u003c/h3\u003e\n\u003cp\u003eAlthough Empathy showed fewer statistically significant effects than other dimensions, its distribution reveals important equity concerns. Lower ratings among low-income, low-education, and informal workers suggest that transport services are often experienced as functionally available but socially unaccommodating. This points to subtle yet consequential forms of exclusion, where service design and staff conduct fail to recognize diverse user needs.\u003c/p\u003e \u003cp\u003eThese findings resonate with SDG 10, highlighting how uneven service experiences can reinforce social and economic marginalization. Addressing empathy-related gaps requires attention to frontline service behavior, inclusive communication practices, and mechanisms that amplify the voices of vulnerable users within transport governance structures.\u003c/p\u003e\n\u003ch3\u003e5. Socio-Demographic Patterns and Expectation Effects\u003c/h3\u003e\n\u003cp\u003eThe ordinal regression results demonstrate that satisfaction is shaped not only by service attributes but also by user expectations. Older respondents tended to report more favorable perceptions, while higher-income and more educated users were consistently more critical. This pattern reflects an expectation effect, where rising socio-economic status increases sensitivity to service shortcomings.\u003c/p\u003e \u003cp\u003eThe observed \u0026ldquo;income\u0026ndash;expectation paradox,\u0026rdquo; particularly evident in the responsiveness dimension, suggests that improved access does not automatically translate into higher satisfaction. Instead, unmet expectations among wealthier users generate dissatisfaction, even when objective conditions improve. This finding underscores the importance of managing expectations through transparent communication, performance reporting, and participatory engagement.\u003c/p\u003e\n\u003ch3\u003e6. Spatial Inequality and Place-Based Disparities\u003c/h3\u003e\n\u003cp\u003eKebele-level location emerged as a robust predictor across all SERVQUAL dimensions, even after controlling for income, education, age, and occupation. Persistent dissatisfaction in specific kebeles reflects structural, place-based disadvantages rooted in remoteness, infrastructure deficits, and weak service oversight.\u003c/p\u003e \u003cp\u003eThese spatial inequalities directly challenge the equity ambitions of SDG 10. Without targeted, location-specific interventions, transport systems risk reproducing territorial disparities rather than alleviating them. The findings therefore call for place-sensitive planning frameworks that prioritize underserved kebeles and tailor interventions to local conditions.\u003c/p\u003e\n\u003ch3\u003e7. Integrated Interpretation and Policy Implications\u003c/h3\u003e\n\u003cp\u003eTaken together, the SERVQUAL-based ordinal regression analysis reveals that RTLS satisfaction in the Sidama Region is uneven, context-dependent, and shaped by intersecting spatial and socio-economic factors. Reliability, Assurance, and Responsiveness are the most consequential dimensions differentiating user experiences, while Tangibility alone is insufficient to ensure positive perceptions.\u003c/p\u003e \u003cp\u003eAdvancing SDGs 9, 10, and 11 through transport development thus requires a strategic reorientation from infrastructure-dominated investments toward service quality, institutional capacity, and equity-focused interventions. Enhancing operational reliability, rebuilding institutional trust, and strengthening responsiveness, particularly in disadvantaged kebeles are essential for translating transport investments into inclusive and sustainable mobility outcomes.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThis study confirms the value of using ordinal logistic regression and mixed methods to assess transport service quality in resource-constrained settings. By focusing on user experience and demographic context, the research provides evidence-based recommendations to guide equitable transport development in Ethiopia and similar regions in rural and peri-urban contexts of Sub-Saharan Africa, an area often overlooked in transport scholarship. The study extends the applicability of service quality models to low-capacity, informally structured road transport and logistics services (RTLS).\u003c/p\u003e \u003cp\u003eBeyond measuring satisfaction, the research reveals that infrastructure investment alone does not guarantee improved outcomes. Instead, it must be complemented by stronger institutional coordination, workforce capacity, and community engagement to ensure transport systems are inclusive, reliable, and sustainable. The result shows that user satisfaction is influenced not only by tangible service features like reliability and timeliness but also by deeper systemic issues, including spatial inequality, income disparities, and governance gaps.\u003c/p\u003e \u003cp\u003eThese insights offer practical guidance for policymakers, particularly in identifying where investments and reforms should be prioritized to enhance both service delivery and public trust. By foregrounding transport equity and systemic constraints, the study contributes meaningfully to emerging debates on transport justice, infrastructure inclusivity, and sustainable mobility in developing regions.\u003c/p\u003e"},{"header":"Recommendations","content":"\u003cp\u003eTo advance inclusive mobility and align with national priorities and Sustainable Development Goals (SDGs), this study recommends the following targeted interventions for Road Transport and Logistics Services (RTLS) in Sidama National Regional State:\u003c/p\u003e \u003cp\u003e \u003col\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003ePrioritize infrastructure upgrades, route planning, and service reliability improvements in low-performing kebeles (1, 2, 3, 5, 6, 8, and 9).\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eIntroduce fare discounts or subsidy programs for low-income and informal workers to improve affordability and access.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eLaunch education campaigns to help low-literacy communities understand transport rights, routes, and feedback systems.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eEnhance public engagement, facilitate two-way communication platforms, and publish service performance reports to manage expectations and build trust, especially among higher-income users.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eScale-up successful practices (use Kebele 10 as a benchmark) to replicate effective service, staffing, and infrastructure models across the region.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eEstablish local monitoring systems, create kebele-based user committees or mobile feedback tools to track satisfaction in real time, and improve accountability.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eProduce monitoring and evaluation, conduct surveys to capture subtle differences in satisfaction, particularly in responsiveness and high-scoring areas.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eStrength the initiative aims to tackle socioeconomic and spatial disparities in transport services through a data-driven, localized approach, focusing on SDG 9.1, SDG 11.2, and SDG 10.2.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003c/ol\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003e \u003cb\u003eContributions of the Study\u003c/b\u003e \u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003cp\u003eThis study emphasizes the need for equitable infrastructure planning at the community level, highlighting that enhancements in road conditions, station cleanliness, and service staff capacity can increase user satisfaction and improve access to employment, education, and healthcare, especially for women and underserved residents. Academically, it validates the SERVQUAL model's tangibility dimension in a rural African context and showcases the use of ordinal logistic regression for analyzing service satisfaction outcomes in social science. Globally, the findings align with international development goals, particularly SDG 11 and SDG 9, advocating for infrastructure investments that meet community needs and promote socially equitable mobility systems. Overall, the research supports a people-centered planning approach with wider applicability.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgment\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors would like to express their sincere gratitude Hawassa \u0026nbsp;University for their invaluable support in facilitating the study. Special thanks are extended to the respondents who generously shared their time and insights during the survey, interviews, and focus group discussions. Appreciation is also extended to the field data collectors and supervisors for their commitment to ensuring high-quality data collection.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData Availability Statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe \u0026nbsp;dataset used to analyze this study is available with the corresponding author and can be accessed through a request for rational reasons.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthical Approval and Accordance\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe study received ethical approval from the Hawassa University College of Business and Economics Research Ethics Committee (Protocol Version No. 1; Approval Number: CBE_RTT-87/2024; issued on December 11, 2024).All research procedures were performed in accordance with the ethical guidelines and regulations of Hawassa University and the principles outlined in the Declaration of Helsinki.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent to Participate\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eInformed consent to participate in the study was obtained from all participants prior to data collection. Participants were informed about the purpose of the study, their voluntary participation, their right to withdraw at any time without consequence, and the confidentiality of their responses.\u003c/p\u003e\n\u003cp\u003eOral consent was obtained before focus group discussions, key informant interviews, and individual interviews. For the Kobo-Collect survey, participants were required to provide agreement before proceeding with the questionnaire, and verbal consent was recorded in Kobo Toolbox. Minors were not included in the study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent to publish declaration\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll research participants provided consent for the publication of anonymized data. No identifiable personal information is included in this manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eClinical trial number:\u003c/strong\u003e Not applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflict of Interest Statement\u003c/strong\u003e\u003cbr\u003e\u0026nbsp;The authors declare that there are no conflicts of interest regarding the publication of this manuscript.\u003c/p\u003e\n\u003cp\u003eAll authors have approved the manuscript and agree to its submission to \u003cem\u003eSAGE Open\u003c/em\u003e. The manuscript is original, has not been published previously, and is not under consideration for publication elsewhere.\u003c/p\u003e\n\u003cp\u003eThe authors grant the publisher the right to publish this manuscript in its present form and confirm that all authors have approved its submission for publication.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eAl\u0026ccedil;ura, G. (2024). 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Customer satisfaction in public road transport in sub-Saharan Africa: A case study of Ghana. \u003cem\u003eAfrican Journal of Economic and Management Studies, 11\u003c/em\u003e(3), 419\u0026ndash;437. https://doi.org/10.1108/AJEMS-04-2019-0152\u003c/li\u003e\n\u003cli\u003eUbaidillah, N. Z., Sa\u0026rsquo;ad, N. H., Nordin, N., Baharuddin, N., Ismail, F., \u0026amp; Hassan, M. K. H. (2022). The impact of public bus service quality on the users\u0026rsquo; satisfaction: Evidence from a developing Asian city. \u003cem\u003eReview of Applied Socio-Economic Research, 23\u003c/em\u003e(1). https://doi.org/10.54609/reaser.v23i1.185\u003c/li\u003e\n\u003cli\u003eYasin, M., \u0026amp; Abayneh, A. (2023). The effect of service quality on customer satisfaction in railway transport service in Ethio-Djibouti. \u003cem\u003eGlobal Journal of Business, Economics and Management: Current Issues, 13\u003c/em\u003e(2), 145\u0026ndash;158. https://doi.org/10.18844/gjbem.v13i2.8096\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":"Road Transport, Service Quality, SERVQUAL, Spatial Inequality, User Satisfaction, Ordinal Logistic Regression, Ethiopia","lastPublishedDoi":"10.21203/rs.3.rs-8701435/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8701435/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThis study employs a convergent mixed-methods approach to evaluate the quality, equity, and inclusiveness of Road Transport and Logistics Services (RTLS) in southern Ethiopia, with explicit alignment to the Sustainable Development Goals (SDGs). Using the SERVQUAL framework, quantitative data from 379 households were analyzed through ordinal logistic regression, complemented by qualitative interviews with transport stakeholders to capture systemic and spatial dimensions of service delivery. The findings reveal persistently low levels of user satisfaction across all service quality dimensions, with reliability, assurance, and tangibility emerging as the most influential predictors. Significant disparities are observed across income groups, gender, education levels, and spatial location, particularly disadvantaging rural and peri-urban kebeles. These inequities indicate that current RTLS provision risks reinforcing social and territorial inequalities rather than promoting inclusive mobility. From a sustainability perspective, the results demonstrate that infrastructure expansion alone is insufficient to achieve equitable transport outcomes without parallel improvements in service reliability, institutional trust, and responsiveness. The study contributes empirical evidence linking transport service quality to SDG 9 (resilient infrastructure), SDG 10 (reduced inequalities), and SDG 11 (inclusive and sustainable communities), and underscores the need for place-based, people-centered transport planning to advance inclusive and sustainable development in emerging regions.\u003c/p\u003e","manuscriptTitle":"Inclusiveness and Sustainability of Road Transport and Logistics Services in Ethiopia","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-03-04 17:04:32","doi":"10.21203/rs.3.rs-8701435/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2026-04-08T05:33:38+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-03-19T23:15:11+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-03-11T12:14:46+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-03-10T05:57:17+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-03-07T11:22:10+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"19343076762068141198916727123246608153","date":"2026-03-07T10:57:57+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"201817657737783052196652566958112759104","date":"2026-03-07T07:00:32+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"143873841149259214706888283454824833168","date":"2026-03-05T14:47:13+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"285515153527184543131416966972220635230","date":"2026-03-05T13:57:10+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"168257876000019226944145386459100448608","date":"2026-03-03T11:15:44+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"247101119937153542722183296463974552648","date":"2026-03-03T06:33:55+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-02-27T06:36:15+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-02-14T08:02:50+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-02-14T06:40:08+00:00","index":"","fulltext":""},{"type":"submitted","content":"Discover Sustainability","date":"2026-02-14T06:35:26+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":"96bedffe-cb9d-498e-b370-5c15cf28067c","owner":[],"postedDate":"March 4th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-04-21T07:54:01+00:00","versionOfRecord":[],"versionCreatedAt":"2026-03-04 17:04:32","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8701435","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8701435","identity":"rs-8701435","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}