Improving last-mile delivery in Amman: An exploratory study of challenges and solutions

3249 International Journal of Innovative Research and Scientific Studies, 8(6) 2025, pages: 3249-3264 ISSN: 2617-6548 URL: www.ijirss.com Improving last-mile delivery in Amman: An exploratory study of challenges and solutions Abdelrahim Alsoussi German Jordanian University, Business School, Department of Logistics, Jordan. (Email: [email protected]) Abstract Urban last-mile delivery (LMD) is a challenging and costly endeavor, particularly in developing cities. This study examines the challenges of LMD in Amman, Jordan, through a qualitative analysis of 20 semi-structured interviews with local logistics stakeholders. The respondents, including delivery drivers, fleet managers, coordinators, and others, provided insights into operational realities. The interviews were analyzed using thematic coding to identify key obstacles and potential enablers. Findings reveal that Amman’s last-mile sector faces critical cost pressures, driven by high fuel prices, vehicle maintenance, and labor expenses. Traffic congestion and infrastructure constraints (e.g., narrow streets, address issues) cause delivery delays and inefficiencies. Operational challenges peak during high-demand periods, as companies struggle with tight time windows and a lack of unloading zones. Intense price competition in the courier market is eroding profit margins, affecting service sustainability. Human resource issues also emerged, including difficulty retaining couriers and an aging workforce that struggles with physical and technological demands. Participants further highlighted environmental and sustainability pressures, noting limited adoption of green delivery modes due to infrastructure gaps. Policy and regulatory factors, such as parking restrictions, also hinder last-mile operations. The study contributes a contextual understanding of LMD challenges in a Middle Eastern city. It offers recommendations for local practitioners and policymakers, from investing in electric vehicles and route optimization systems to improving urban infrastructure and delivery regulations, and outlines recommendations for future research. Keywords: Amman, Developing countries, Jordan, Last-mile delivery, Logistics challenges, Qualitative study, Urban logistics. DOI: 10.53894/ijirss.v8i6.10322 Funding: This study received no specific financial support. History: Received: 09 July 2025 / Revised: 12 August 2025 / Accepted: 14 August 2025 / Published: 29 September 2025 Copyright: © 2025 by the author. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). Competing Interests: The author declares that there are no conflicts of interests regarding the publication of this paper. Transparency: The author confirms that the manuscript is an honest, accurate, and transparent account of the study; that no vital features of the study have been omitted; and that any discrepancies from the study as planned have been explained. This study followed all ethical practices during writing. Publisher: Innovative Research Publishing 1. Introduction The “last mile” of delivery, which involves moving the product from a distribution hub to the end user, is widely recognized as the most expensive and complex segment of the supply chain [1]. It can account for a large proportion of total logistics costs, in some cases up to half or more [2] while also generating negative externalities, such as congestion and emissions, in urban areas [3]. The growth of e-commerce and increased consumer expectations for fast and reliable International Journal of Innovative Research and Scientific Studies, 8(6) 2025, pages: 3249-3264 3250 shipping have only intensified last-mile challenges [4]. Companies worldwide are under pressure to offer rapid deliveries and real-time tracking; however, doing so profitably is challenging due to inherent inefficiencies in urban delivery networks [5]. Last-mile logistics often involves multiple stops with low drop sizes, resulting in higher unit transportation costs and productivity losses compared to line-haul transportation [4]. Furthermore, last-mile operations contribute significantly to traffic congestion, air pollution, and noise in cities [6]. These impacts heighten the need for sustainable urban delivery solutions. Developing countries face additional constraints in LMD. Prior research indicates that cities in emerging markets often face limited logistics service coverage and infrastructure deficiencies, such as poorly planned road networks and a lack of formal addresses [7]. For example, a lack of proper addressing systems is a common barrier, impeding reliable home deliveries in many developing contexts [7]. Urban growth in such cities tends to outpace infrastructure development, resulting in traffic bottlenecks and inadequate loading and unloading spaces for delivery vehicles [8]. The systematic review conducted by Arvianto, et al. [8] also found that the literature on city logistics in developing economies emphasizes challenges such as fleet capacity shortfalls and a lack of dedicated delivery zones. In contrast, developed countries focus more on regulatory issues and advanced business models. Developing cities may also lag in technological adoption; for instance, smaller delivery firms might still rely on manual routing and communication methods due to cost or resource constraints [5]. These contextual differences underline the importance of examining last-mile issues in specific emerging urban settings. Despite a growing global literature on urban logistics, there is a scarcity of academic studies focusing on LMD in the Middle East, including Jordan. Amman, Jordan’s capital and largest city, presents a compelling case with its rapid urbanization and booming e-commerce activity. Amman’s metropolitan area has expanded rapidly, resulting in urban sprawl and strains on infrastructure [9]. Recent research on city logistics in Jordan from an urban planning perspective highlighted regulatory inefficiencies, inadequate information systems, and coordination gaps among stakeholders [3]. However, little is known about the micro-level, day-to-day challenges faced by LMD operators in Amman. This study addresses that gap by investigating the LMD challenges in Amman through primary qualitative data. By capturing the perspectives of a range of logistics actors – from delivery drivers and fleet managers to logistics coordinators – it provides an empirical account of the operational hurdles and contextual factors shaping urban deliveries in a developing city environment. This paper aims to achieve three main objectives. First, this research identifies and articulates the key challenges faced in LMD within the urban context of Amman, as reported by practitioners in the field. Second, it connects these findings to the existing literature on urban LMD, highlighting both similarities and discrepancies, and derives theoretical and contextual implications. Lastly, it offers practical recommendations for managers and policymakers to enhance last-mile efficiency and sustainability in Amman and similar cities. To achieve these objectives, 20 in-depth interviews were conducted, and a thematic analysis was performed to identify the dominant themes. 2. Literature Review 2.1. Urban LMD Challenges Urban LMD is often regarded as the most inefficient and costly phase of the supply chain. This segment, typically comprising the journey from a local distribution center to the end customer, can account for an estimated 13% to 75% of total logistics costs, depending on the context [10]. A commonly cited figure is that last-mile activities make up around 41% of overall supply chain costs on average [4]. The reasons for this disproportionate cost are well-documented. LMD involves many stops with low drop densities, resulting in poor economies of scale, unlike long-haul trucks that transport thousands of items to a single destination [4]. Additionally, failed delivery attempts and return logistics add to last-mile inefficiency [5]. These factors drive up fuel consumption and labor hours per package delivered. Beyond cost, LMD poses significant environmental and social challenges in cities. The proliferation of delivery vehicles contributes to urban congestion, increased emissions, and noise pollution. Studies in large cities have shown that the growth of e-commerce has led to a surge in light goods vehicle traffic in urban centers [11]. In London, for example, van traffic associated with online retail rose markedly, exacerbating peak-hour congestion [11]. The externalities of LMD include not only traffic delays but also higher greenhouse gas output and local air quality impacts from delivery trucks and motorbikes [12] found that traditional diesel van deliveries produce substantial emissions, motivating a search for cleaner last-mile modes. Moreover, the social externalities include safety concerns, such as increased traffic on narrow streets and interactions with pedestrians, as well as a reduced quality of life in neighborhoods due to noise and visual intrusion from frequent delivery stops [1]. Another major challenge in LMD is meeting the rising expectations of customers. Modern consumers, conditioned by e-commerce giants, demand faster and more flexible delivery options, such as same-day delivery, along with end-to-end visibility of their shipments [13]. Such expectations put pressure on logistics providers to invest in advanced tracking systems and to tighten delivery windows, which can further reduce efficiency. Indeed, offering fast deliveries often requires additional resources, such as drivers and micro-fulfillment centers, that raise operational costs [4]. Tight delivery time windows also tend to increase failed attempts and suboptimal routing, as vehicles must crisscross cities to meet specific customer time demands [5]. In summary, urban LMD is a complex balancing act between efficiency (cost and time) and service quality; achieving both simultaneously remains challenging. Figure 1 illustrates the main challenges facing LMD, as identified by Boysen, et al. [14]. In a review article, Bosona [15] categorized last-mile logistics challenges into four main categories: technological, infrastructural, managerial, and cost-related challenges. International Journal of Innovative Research and Scientific Studies, 8(6) 2025, pages: 3249-3264 3251 Figure 1. Last-mile delivery challenges. 2.2. Last-Mile Constraints in Developing Countries Developing urban centers often lacks well-organized street networks, sufficient road capacity, or appropriate loading zones, which complicates delivery operations [8]. A systematic review comparing city logistics in developed vs. developing economies found that “inadequate loading/unloading spaces” and the need for fleet expansion are predominantly highlighted in developing country contexts [1]. Another critical issue is the lack of formal addressing systems and reliable maps. The absence of a proper postal addressing system makes LMD particularly challenging, as noted by Rodriguez [7]. Resource limitations for local delivery service providers also shape LMD in developing contexts. A recent study found that in some developing markets, only the major carriers have begun integrating advanced IT systems, while smaller companies lag behind [5]. Labor constraints are another factor – while labor costs may be lower than in developed countries, courier jobs often experience high turnover due to challenging working conditions (long hours in traffic, relatively low pay), as well as limited career advancement opportunities. In certain cases, an aging workforce in the logistics sector further complicates matters, as older drivers might struggle with physically intensive tasks and adapting to new technologies [14]. Cultural and regulatory environments contribute to last-mile challenges as well. Some developing cities do not yet incorporate freight considerations into urban planning, resulting in a lack of integration between city authorities and logistics needs Alnsour, et al. [3]. Arvianto, et al. [8] noted that public policy issues were underrepresented in the logistics literature of developing countries, implying that less policy attention has been given to city logistics in these contexts compared to Europe or North America. 2.3. LMD in Amman With a population exceeding 4.5 million people, an approximate area of 800 km², and an estimated five million transportation trips per day [16]. Amman is Jordan’s economic hub and has experienced a surge in e-commerce in recent years. Amman’s rapid growth (partly fueled by regional migration and refugee influx) has led to urban sprawl and informal housing areas where roads may be narrow or unpaved [17]. Delivering to such areas can be time-consuming; drivers frequently must rely on local knowledge or phone guidance. Albatayneh, et al. [18] argue that Amman’s accelerated metropolitan expansion imposes distinctive pressures on its urban transport network, pressures shaped by the capital’s complex topography, sociocultural environment, and existing infrastructural constraints. Traffic congestion is another prominent issue in Amman [16]. The city’s vehicle ownership rates have increased, and public transportation remains underdeveloped [18] leading to a heavy reliance on cars. Recent studies and reports specific to Jordan underscore similar points. Alnsour, et al. [3] in a survey-based study of city logistics management in Jordan, found that urban authorities recognized a need for improvement in the cost efficiency and sustainability of logistics, pointing to factors such as regulatory inefficiencies and suboptimal human resource performance as impediments. They recommended measures such as amending outdated regulations and strengthening Last-mile delivery challenges Volume increase due to urbanization and ecommerce Highe cost due to multibe stops and low drop densities Time pressure imposed by customers Sustainability issues due to increased number of vehicles Aging workforce working on a physically demanding operations International Journal of Innovative Research and Scientific Studies, 8(6) 2025, pages: 3249-3264 3252 infrastructure for information systems to support logistics. One prior study relevant to Jordan’s last-mile is Al-nawayseh, et al. [19] which explored online grocery delivery in Jordan. Although somewhat dated, the research concluded that setting up pickup point networks for online grocery retail might be more cost-effective than direct home delivery in Jordan’s context [19]. This insight aligns with a broader trend in literature suggesting alternative delivery models (like pickup lockers or parcel shops) can mitigate last-mile inefficiencies in areas with infrastructure limitations [20]. 2.4. Technology Enablers and Innovative Solutions for Last-Mile In response to LMD challenges, a range of technological and operational innovations have been investigated in the literature. One major category of solutions is route optimization and IT-enabled delivery management. Advanced algorithms, often driven by Artificial Intelligence (AI) and real-time data, can significantly improve routing efficiency for delivery fleets [21]. Studies have documented substantial savings from such systems. However, the adoption of these technologies varies; larger firms and those in developed markets lead the way, whereas smaller couriers in developing cities may still be transitioning from manual planning [5]. Another set of innovations involves alternative delivery methods and modes. To alleviate congestion and mitigate emissions, cities and companies are exploring alternative modes, such as electric vehicles (EVs), cargo bikes, and on-foot deliveries, for the LMD. Siragusa, et al. [22] conducted an economic and environmental assessment of using electric vehicles for last-mile B2C deliveries, finding that EVs can substantially reduce CO₂ emissions and even achieve cost parity with diesel vans under favorable conditions (given lower energy and maintenance costs). Many European cities have pilot programs for cargo e-bikes, which can navigate dense urban areas faster and park more easily than trucks. For example, research in Italy’s Pro-E-Bike project demonstrated that e-bikes could handle a significant portion of small parcel deliveries with higher speed in congested zones and a positive environmental effect [23]. Autonomous delivery vehicles, including drones (unmanned aerial vehicles) and ground delivery robots, represent more radical innovations on the horizon. Research suggests drones could be beneficial for delivering lightweight parcels, especially to hard-to-reach rural or peri-urban areas, or for time-sensitive deliveries [24]. They offer speed and point-topoint routing, but face serious regulatory, safety, and payload limitations. Autonomous delivery robots could reduce labor costs and operate 24/7, but they require supportive infrastructure and clear legal frameworks. According to Mohammad, et al. [25] drones and autonomous ground robots are still considered “near-future” concepts rather than current mainstream solutions. A more immediately viable innovation is leveraging the “sharing economy” or crowdsourcing for deliveries, often referred to as crowdshipping. In crowdshipping, individuals (not professional couriers) carry packages on their regular commutes or trips, coordinated via digital platforms Carbone, et al. [26]. Huang and Ardiansyah [27] developed a decision model for integrating crowdsourcing into LMD planning, finding that it can be cost-effective under specific demand and incentive conditions, e.g., when there’s a dense network of available crowd carriers and the delivery time requirements are flexible. Finally, logistics network innovations, such as parcel lockers, pickup points, and micro-distribution centers, are proving effective in various contexts. Parcel lockers, automated locker banks placed in accessible locations, enable customers to collect packages at their convenience, which significantly reduces failed deliveries and consolidates multiple deliveries into a single stop [20]. For example, Poland’s national implementation of parcel lockers demonstrated improved efficiency, as couriers could drop off multiple packages at a single locker location instead of individual homes [20]. Additionally, micro-fulfilment centers can help logistics and supply chain professionals mitigate the challenges of urban LMD [28]. This is particularly useful for groceries and fast-moving goods, enabling very quick delivery promises. Mohammad, et al. [25] conducted a literature review on innovative solutions for LMD challenges, discussing cargo bikes, self-service techniques, drone parcel delivery, and robot-assisted delivery. 3. Methodology 3.1. Research Design and Context This study adopted a qualitative research design to deeply explore the phenomena of LMD challenges and solutions in Amman. A qualitative approach was appropriate, given the exploratory nature of the research, allowing for rich and detailed insights into stakeholder experiences [29]. Semi-structured interviews were employed as the primary data collection method, involving key stakeholders in LMD operations. Interviews are a well-established qualitative tool for obtaining in-depth information and were chosen to allow participants the flexibility to share detailed experiences while still covering specific topics [29, 30]. To standardize data collection, a semi-structured interview guide was designed, grounded in themes extracted from the literature, while intentionally leaving scope for participants to introduce additional insights beyond those published in research. Table 1 summarizes the guide’s principal categories, their sub-categories, and corresponding analytical objectives. International Journal of Innovative Research and Scientific Studies, 8(6) 2025, pages: 3249-3264 3253 Table 1. Interview guide categories and analytical aims. Main Category Sub-categories explored Analytical aim of the questions Respondent profile Position, core duties, service area, product types, and years of experience. Establish contextual background for interpreting responses. LMD challenges Cost-related challenges such as fuel, labour, and routing. Identify the primary barriers and inefficiencies that shape LMD performance. Environmental and sustainability issues, such as vehicle emissions and packaging. Time pressure and operational issues, such as tight delivery windows, parking constraints, and peak demand surges. Demand and competition factors, such as price wars and customer expectations. Workforce and human resource issues, such as driver turnover and an aging workforce. Technology and infrastructure challenges, such as GPS reliability issues, EV charging problems, loading bays, and other infrastructure concerns. Existing mitigation practices Route optimization tools. Document existing solutions and assess their perceived effectiveness. Flexible delivery scheduling. Peak-period planning. Workforce incentives/training. Potential innovations and adoption barriers Electric vehicles. Evaluate stakeholder perceptions of advanced technologies, expected benefits, and implementation constraints. Cargo bikes and e-bikes. Crowdshipping. Parcel lockers and micro-fulfilment hubs. Drones. Autonomous delivery robots. Policy and regulatory environment Impact of existing regulations. Understand how governance frameworks hinder or facilitate efficient, sustainable LMD. Desired policy changes. Future outlook Emerging trends. Capture strategic visions for the evolution of LMD in Amman. Organizational preparedness. Open-ended questions were also included for participants to share any other challenges or suggestions they had. This guided format ensured that each interview addressed core research questions while still allowing interviewees to elaborate on issues they considered important. 3.2. Sampling and Participants The LMD sector in Amman includes local courier companies, international parcel firms, e-commerce retailers with inhouse delivery fleets, and crowdsourced delivery platforms. This study sought participants from various organizations to capture diverse perspectives. A purposive sampling strategy was employed to select interview participants, complemented by a snowball sampling technique to recruit additional participants. Initially, convenience sampling facilitated the recruitment of accessible participants [31]. Subsequently, some interviewees referred us to other knowledgeable individuals in the field, which broadened the sample to include a variety of perspectives. The final sample comprised 20 participants, which was sufficient to achieve data saturation, i.e., no new themes emerged in the last interviews [32]. In addition, the sample size was also aligned with the recommendations of Guest, et al. [33] for qualitative research, where 12–20 interviews are often sufficient to capture broad thematic saturation. Participants were selected based on their roles and experience in LMD, ensuring a mix of operational and managerial viewpoints. Table 2 presents an overview of the interviewees’ positions, years of experience, and the types of products they handle. All participants were based in Amman and had direct involvement in LMD activities. International Journal of Innovative Research and Scientific Studies, 8(6) 2025, pages: 3249-3264 3254 Table 2. Profile of interview participants. Interviewee Number Position Years of Experience Primary Product Categories Handled I1 Operation manager 15 years Electronics I2 Courier 4 years Groceries and other consumer goods I3 Senior delivery operation officer 6 years Pharmaceuticals and health care supplies I4 Logistics department manager 10 years Wide range of products I5 Manager of LMDs 7 years Electronics and fitness equipment I6 Route optimization analyst 5 years Groceries, clothing, and electronics I7 Delivery driver 5 years Wide range of products, from small parcels and documents to electronics and household items I8 Warehouse manager 8 years Clothing, groceries, household items, and seasonal items I9 Fleet manager 5.5 years Baby products I10 Dispatch Coordinator 5 years Medical supplies and office equipment I11 Logistics Coordinator 3 years Clothing and household items I12 Delivery tracker 3 years Groceries I13 Operation manager 5 years Food and groceries I14 Delivery manager 2 years Clothing and fashion items I15 Logistics Coordinator 10 years Electronics and small home appliances I16 Logistics Coordinator 5 years Automotive parts I17 Delivery operations manager 7 years Furniture and household items I18 Delivery operations manager 6 years Groceries and household items I19 Return & reverse logistics manager 12 years Electronics and clothing I20 LMD micro-fulfillment manager 10 years Office supplies, books, and media products Ethical considerations were addressed by obtaining informed consent from all interviewees. Before each interview, the researcher explained the study's purpose and assured participants of confidentiality. Interviewees were asked for permission to record the conversation for transcription purposes. Participants were assigned codes, I1 through I20, to ensure anonymity. In quotations presented in the results, the interview codes were paired with a generic role descriptor, e.g., “I7, Delivery Driver”, to provide context. 3.3. Data Collection Interviews were conducted face-to-face, each lasting approximately 30–50 minutes. All sessions were held in an informal, conversational manner to help respondents feel comfortable discussing operational challenges [30]. With participants’ consent, detailed notes were taken during each interview, rather than audio recordings, as several interviewees preferred not to be recorded. The note-taking process captured key points and verbatim quotes where possible. All participants were assured of confidentiality to encourage open and honest discussion. A preliminary pilot interview was undertaken with an experienced logistics manager to evaluate the comprehensibility, sequencing, and face validity of the semi-structured interview guide. Consistent with methodological guidance that recommends piloting to refine qualitative instruments [34] the transcript was reviewed and minor revisions—such as simplifying specialist jargon and adding illustrative questions to elicit richer responses—were incorporated. As the pilot data satisfied the study’s inclusion criteria, it was retained in the final corpus and coded as Interviewee I4. 3.4. Data Analysis A hybrid thematic analysis was conducted following Braun and Clarke [35] procedure. First, a deductive coding frame—drawn from the interview guide categories in Table 1 and informed by the literature review—steered the initial coding of transcripts toward recognized LMD issues. Second, an inductive layer of coding captured additional themes that surfaced when participants responded to open-ended questions (e.g., “Are there any other last-mile challenges we have not discussed?”), ensuring that context-specific insights beyond the extant literature were incorporated. International Journal of Innovative Research and Scientific Studies, 8(6) 2025, pages: 3249-3264 3255 This research adopted Braun and Clarke [35] six-phase framework for thematic analysis and operationalized it using a hybrid deductive–inductive coding strategy, as recommended by Fereday and Muir-Cochrane [36]. Figure 2 details each analytic phase and illustrates how the hybrid approach was implemented. Figure 2. Data analysis steps and their hybrid application. 4. Results 4.1. LMD Challenges in Amman The interviews revealed a multifaceted set of challenges affecting LMD in Amman. Participants discussed obstacles ranging from high operational costs and traffic congestion to workforce issues and competition. Table 3 presents an overview of the significant challenges identified. The challenges listed on the table below align with the categories presented in the interview guide questions. Table 3. Key last-mile delivery challenges in Amman. Challenge Challenge sources Clarification Support from the data Cost of LMD operations Fuel Expenses Fuel prices are a primary cost driver for LMD, exacerbated by traffic congestion that increases fuel consumption. “Fuel costs, vehicle maintenance, and labor costs… are our primary financial challenges.” (I4) “Our biggest financial concerns are rising fuel prices and auto maintaining.” (I5) “Vehicle maintenance (wear and tear on vehicles, oil changes, accident repairs) [is a major cost].” (I8) Vehicle Maintenance Heavy deterioration in delivery vehicles results in significant maintenance and repair costs, further adding to the financial burden. Labor and Wages Labor costs (courier wages and related benefits) constitute a significant portion of LMD expenses, pressuring companies, especially when margins are low. “Labor costs are among the main costrelated challenges” (I7) Inefficient Routing Suboptimal or inefficient routing results in longer distances and increased time, thereby inflating fuel and labor costs. Companies recognize that better route planning is needed to control expenses. “Inefficient routes can lead to increased fuel consumption and wear and tear on our vehicles.” (I11) “We use route optimization software to plan the most efficient routes. This helps reduce fuel consumption and delivery times.” (I7) Failed Deliveries & Returns Unsuccessful delivery attempts and product returns add extra transportation and handling costs, further straining LMD's profitability. “Vehicle maintenance and delivery attempts and returns [are key challenges].” (I12) “The high cost of handling returns – including transportation, restocking, and potential loss of value – is a major issue.” (I19) Environmenta l and Sustainability Vehicle Emissions & Pollution The proliferation of delivery vehicles (especially older diesel vans) is causing air pollution and emissions in the city. “Air pollution from delivery vehicles [is a key environmental challenge]” (I1) “Fuel consumption and smoke from delivery Familiarization Read all transcripts to grasp overall content and write initial impressions. Initial Coding Deductive pass: Apply pre-set codes aligned with Table 1 categories. Inductive pass: Code any segment that does not fit those labels with new, descriptive data-driven codes. Theme Generation Merge deductive and inductive codes into candidate themes. Some themes will mirror Table 1 (e.g., Cost pressures), others may be entirely emergent (e.g., Cultural expectations around cash-ondelivery). Theme review Check coherence within themes and distinctiveness between them; collapse or split as needed. Validate against raw data and research aims. Theme definition & naming Produce clear definitions capturing each theme’s “essence” and its relationship to the research questions. Reporting Present themes with illustrative quotations, linking back to theory and extant literature. Explicitly note which insights were deductively anticipated and which emerged inductively. International Journal of Innovative Research and Scientific Studies, 8(6) 2025, pages: 3249-3264 3256 Issues Participants noted that LMD contributes to poor air quality and noise. vans, especially old model vans that run on diesel” (I2) “The primary environmental challenges include air pollution from vehicle emissions and noise pollution.” (I7) Traffic Congestion Impact Severe traffic congestion in Amman not only delays deliveries but also worsens environmental impacts through increased vehicle idling and emissions during peak hours. “Traffic congestion [is a major environmental issue].” (I13) and (I14) “Heavier traffic during peak times slows down deliveries.” (I18) Packaging Waste The rise in e-commerce is generating significant packaging waste (e.g., cardboard, plastics), and inadequate disposal or recycling of delivery packaging is viewed as an emerging environmental concern. “Reducing carbon emissions and managing waste [are key challenges].” (I4) “Bad disposal of packaging materials leads to increased waste.” (I8) “The increase in online shopping and home deliveries leads to a significant amount of packaging waste.” (I12) Limited Green Vehicle Adoption Adoption of eco-friendly delivery modes (like electric vehicles or cargo bikes) remains limited due to infrastructural and operational hurdles, meaning most deliveries still rely on conventional polluting vehicles. “Emissions from our diesel vehicles and the lack of available charging infrastructure for electric alternatives [are challenges].” (I9) “We’re starting to introduce electric vehicles into our fleet, which is helping, but the charging infrastructure is still a bit of a challenge.” (I11) “No waste control and a lot of old delivery vehicles that produce bad emissions” (I17) Time Pressure and Operational Issues Peak Delivery Volume Surges Sudden increases in parcel volume during peak periods (holidays, sales) strain delivery capacity, leading to operational overload and delays. Couriers must handle significantly higher workloads, sometimes necessitating the use of extra staff or overtime. “Keeping up with the increasing volume of deliveries and adhering to tight delivery deadlines present significant challenges during peak delivery periods.” (I5) “The surge in parcel volumes can strain our resources and lead to delays. It also increases the workload on staff.” (I7) “When parcel volumes spike, we have to work much harder and sometimes bring in extra help.” (I13) Tight Delivery Deadlines Customers’ expectations for fast and timely deliveries impose tight delivery windows. Meeting same-day or nextday delivery promises is challenging, especially during high demand, and often requires significant additional resources or prioritization of urgent orders. “Customer expectations – the demand for faster delivery times, often within the same day – requires more resources.” (I17) “It can be overwhelming during peak periods; we sometimes struggle to meet delivery windows.” (I3) “Managing high volumes of deliveries, meeting tight deadlines… are major challenges during peak periods.” (I7) Traffic and Urban Delays Heavy urban traffic and unpredictable road conditions significantly slow down delivery routes. Congested streets and road bottlenecks make it challenging to maintain schedules, especially during rush hours, directly impacting delivery speed and reliability. “Uncertain traffic conditions [are an operational challenge].” (I4) “Traffic congestion… during peak times slows down deliveries.” (I18) “Dealing with traffic congestion [is one of our] major challenges during peak periods.” (I7) Parking & Unloading Difficulties A lack of available parking spots and unloading zones in dense or busy areas forces drivers to circle or park illegally, wasting time and complicating deliveries. Difficulty finding a suitable place to stop near the customer can cause inefficiency and occasionally result in delivery failures. “Lack of parking spots.” (I1) “Difficulty finding a parking space in large crowds.” (I4) “Limited unloading and loading zones [in busy areas].” (I8) Demand and Competition Price Competition & Intense price-based competition among delivery companies is driving down “Price competition forces us to find ways to cut costs without compromising service International Journal of Innovative Research and Scientific Studies, 8(6) 2025, pages: 3249-3264 3257 Factors Profit Margins delivery fees, which in turn erodes profit margins. Firms feel pressure to cut costs to match their competitors’ low prices, which threatens their financial sustainability. quality.” (I1) “Price competition can lead to reduced profit margins, which affects our ability to invest in better service.” (I7) “When companies lower prices to compete, they might not make enough profit….” (I14) Pressure to Cut Costs & Service Quality To remain competitive in a crowded market, companies are compelled to reduce operational costs, but this can come at the expense of service quality. Some participants noted that under pricing pressures, there’s a risk of reducing service levels. “Price competition often pushes companies to cut costs to stay competitive.” (I13) “To maintain profitability under price pressure, companies might cut corners in service.” (I17) “To remain competitive, companies might cut costs, potentially impacting service quality.” (I18) Workforce Impact of Competition Aggressive competition and costcutting measures can hurt employees, potentially leading to lower wages or even layoffs. The struggle to offer the lowest delivery prices sometimes translates into constrained labor budgets and reduced staff morale or stability. “Workers are forced to either accept lowerpaying jobs or move to find a different job.” (I16) “In some cases, we have to do budget cuts and lay off employees.” (I19) Workforce and Human Resource Issues High Turnover & Retention Difficulties Delivery firms face challenges in retaining drivers and recruiting new ones. High turnover rates are attributed to harsh working conditions (long hours, traffic stress, relatively low pay), making it hard to maintain a stable, experienced workforce. “An aging workforce and hiring challenges are significant issues.” (I6) “We have difficulties in hiring and retaining delivery personnel.” (summary of multiple interviewee comments; e.g., I6, I19) “Reducing driver turnover is important because hiring and training new employees is costly.” (I1) Aging Workforce Limitations In cases where the delivery workforce is older, employees may struggle with the physical demands of the job and have difficulty adapting to new technologies. Older couriers often experience fatigue and may be less inclined or able to use new delivery apps or GPS tools, affecting efficiency. “The aging workforce may face physical challenges and may be less familiar with new technologies.” (I7) “It’s challenging to keep up with the speed required for efficient deliveries and sometimes [older couriers] can’t work long hours, which may slow us down.” (I8) Workforce Skills and Training Gaps (Emergent in some responses) There is a need for continuous training and upskilling of delivery staff. As technology becomes increasingly integral to LMD (routing software, tracking systems), ensuring that all employees, regardless of age, are proficient is an ongoing challenge. “Older employees [have a] hard time with skill development.” (I3) “Our delivery personnel are trained to handle packages carefully and to communicate effectively… [but adapting to new systems is ongoing].” (I11) “We provide training and a supportive work environment for our delivery staff.” (I14) Technology and Infrastructure Challenges Inadequate Urban Infrastructure Poor physical infrastructure in parts of Amman, including narrow streets, informal neighborhoods with no road access, and incomplete addressing systems, makes deliveries timeconsuming and inefficient. Drivers often must take detours or spend extra time locating recipients. “In areas with poor or no street access, delivering packages can be very challenging and impacts our delivery times and efficiency.” (I5) “In some parts of Amman, addressing systems may be incomplete or outdated, making it difficult to locate specific delivery addresses… Navigating poorly accessible areas requires more time for route planning and execution.” (I11) “Poor infrastructure can lead to longer delivery times and increased operational costs.” (I7) Limited EV Charging Infrastructure The infrastructure to support electric delivery vehicles is lacking. There are not enough public charging stations in the city, and charging is time- “Not enough charging stations and lack of infrastructure.” (I1) “The limited number of charging stations and the time required to charge are the main International Journal of Innovative Research and Scientific Studies, 8(6) 2025, pages: 3249-3264 3264 [39] J. Muñuzuri, J. Larrañeta, L. Onieva, and P. Cortés, "Solutions applicable by local administrations for urban logistics improvement," Cities, vol. 22, no. 1, pp. 15-28, 2005. https://doi.org/10.1016/j.cities.2004.10.003 [40] R. Gevaers, E. Van de Voorde, and T. Vanelslander, "Cost modelling and simulation of last-mile characteristics in an innovative B2C supply chain environment with implications on urban areas and cities," Procedia-Social and Behavioral Sciences, vol. 125, pp. 398-411, 2014. https://doi.org/10.1016/j.sbspro.2014.01.1483 [41] A. H. Hübner, H. Kuhn, and J. Wollenburg, "Last mile fulfilment and distribution in omni-channel grocery retailing: a strategic planning framework," International Journal of Retail & Distribution Management, vol. 44, no. 3, pp. 228–247, 2016. https://doi.org/10.1108/IJRDM-11-2014-0154 [42] M. Christopher, "The agile supply chain: Competing in volatile markets," Industrial Marketing Management, vol. 29, no. 1, pp. 37-44, 2000. https://doi.org/10.1016/S0019-8501(99)00110-8 [43] J. Olsson, D. Hellström, and H. Pålsson, "Framework of last mile logistics research: A systematic review of the literature," Sustainability, vol. 11, no. 24, p. 7131, 2019. https://doi.org/10.3390/su11247131 [44] E. M. Rogers, "Diffusion of innovations," 5th ed. New York: Free Press, 2003. [45] F. Basso, N. Dovichi, and F. Mazzarella, "Horizontal collaboration in logistics: A review of the literature and outlook," Sustainability, vol. 11, no. 21, p. 6102, 2019.