Purpose of this paper:

Additive Manufacturing (AM) is expected to have a transformative impact on operations and strategy (Eyers et al., 2022), with implications for the performance of individual operations and the Supply Chain (SC). However, its evolving technology makes its adoption over Conventional Manufacturing (CM) a complex decision (Sgarbossa et al., 2021), The literature reports on several cases of either standalone AM adoption over CM, or as a hybrid approach in which AM and CM are used interchangeably or in sequence. Occasionally, the two manufacturing technologies overlap, and in many use cases, they can both produce functional parts. While the literature acknowledges the success of AM in high-variety, low-volume applications, adoption of AM also poses challenges, which are associated with switching to a less mature and less proven technology with significant competitive implications for the production system, the overall investment cost, and skilled labour. The simple choice of the technology based on the output geometry, part material and overall economic benefits has become less obvious, and additional performance objectives need to be considered

Our research, which is work in progress, aims to shed light on how managers rate AM-related Relative Advantage (RA) factors (including competitive priorities and performance criteria) when considering the new manufacturing technology adoption for low-volume production and the aftermarket domain in the context of High Value Manufacturing (HVM). The Research Question guiding our research is: “What’s the difference between Additive Manufacturing and Conventional Manufacturing in terms of importance for a product with low-volume production and spares aftermarket?”

Design/methodology/approach:

Our paper extends the work published by Jimo et al. (2025). We focus on how AM compares to CM for making metal components for assembly or as aftermarket parts (spares). Since we’re working with low-volume production and a small aftermarket in HVM domain, we look at different operations and SC factors that would be important for both scenarios, depending on the product and the manufacturing process. We developed a multi-criteria decision analysis using the Analytic Hierarchy Process (AHP) (Saaty, 1977), with the aim to identify the operational and supply chain strategic aspects, and their potential effects on sustainability. Our research approaches AM not as a mere machine replacement of CM-dedicated equipment, but as a capability-building system: identifying cases for application, adopting the relative technology, and reconfiguring the manufacturing process and business case for competitive advantage. Through pairwise comparisons of RA factors and measures for operations and SCs, our survey identifies the key decision factors influencing the adoption of AM over CM, determining their relative importance. Our research was grounded on, and contributes to two underpinning theories, namely the Resource-Based View and Dynamic Capabilities.

Findings:

We present the theoretical grounding, research approach, and data collection approach with for the pairwise comparisons of RA factors and measures for operations and SCs. Our contribution, which is work in progress, presents Relative Advantage domains of industrial operations when deciding the manufacturing technology and the factors/measures to be considered with implications for operations and SCs. We elaborate on the bundles of factors that are visible and measurable, and constitute the RA sources.

Value:

Our research aims to add clarity and prioritisation on the decision-making influencing factors and measures, and their relative significance, when deciding on the adoption of AM and CM in terms of importance for a product with low-volume production and a small spares aftermarket. The outcomes of the advanced production processes in the focal industrial context of HVM are of high economic and strategic value, making HVM a relevant ground for the transition to AM in producing metal components. However, it remains a sector not sufficiently explored in terms of the systematic comparison of benefits when considering Additive over the Conventional Manufacturing technology, and past research has mainly been qualitative or case-based, not ascertaining the hierarchy of the relevant decision-making process.

Practical implications:

Our research aims to assist practitioners in their decision-making process in terms of the choice of adoption of AM by elaborating on the relative importance of RA factors, including competitive priorities and performance criteria.

References:

Eyers, D.R., Potter, A.T., Gosling, J. and Naim, M.M. (2022), “The impact of Additive Manufacturing on the product-process matrix”, Production Planning & Control, Vol. 33 No. 15, pp. 1432–1448, doi: 10.1080/09537287.2021.1876940.

Jimo, A., Braziotis, C. and Pawar, K. (2025) ‘Beyond Prototyping: Mapping the Relative Advantages of Adopting Additive Manufacturing for Industrial Production’. International Journal of Production Research: 1–30. https://doi.org/10.1080/00207543.2025.2504165.

Saaty, T.L. (1977), “A scaling method for priorities in hierarchical structures”, Journal of Mathematical Psychology, Vol. 15 No. 3, pp. 234–2

Sgarbossa, F., Peron, M., Lolli, F. and Balugani, E. (2021), “Conventional or additive manufacturing for spare parts management: An extensive comparison for Poisson demand”, International Journal of Production Economics, Vol. 233, p. 107993, doi: 10.1016/j.ijpe.2020.107993.81, doi: 10.1016/0022-2496(77)90033-5.

Purpose

The rapid growth of e-commerce has increased the demand for last-mile delivery in urban areas, intensifying traffic congestion, greenhouse gas emissions, and competition for limited roadside space. In response, crowdshipping is garnering attention as a flexible complement to existing logistics systems to enhance the sustainability of urban transport. Existing crowdshipping research has primarily focused on sociodemographic and attitudinal factors influencing delivery participation (Cebeci et al., 2023; Fessler et al., 2022), while some studies have pointed out operational issues and limitations (Mohri et al., 2023). However, the impact of urban infrastructure on delivery participation decisions remains insufficient. This research gap is particularly critical for cargo-bike-based delivery, which is attracting attention as a low-emission logistics alternative. This is because the feasibility of delivery depends heavily on infrastructure conditions, such as safety, route continuity, and traffic conditions. In this context, participation reflects a behavioral response to operational conditions rather than a mere attitudinal intention; therefore, an analysis that considers the operational environment influencing delivery participation decisions is necessary.

To address this gap, this study analyzes the impact of infrastructure conditions and job attributes on the decision to accept cargo-bike crowdshipping. In particular, by identifying operational conditions under which individuals are highly likely to accept crowdshipping, this study aims to expand a behavior-based understanding of crowdshipping labor supply and provide implications for building a sustainable urban logistics system.

Design/methodology/approach

This study utilizes a discrete choice experiment (DCE) to analyze the acceptance behavior of cargo-bike crowdshipping under various operational conditions, targeting adult respondents with potential for participation in flexible delivery tasks within a city. Respondents are presented with a hypothetical delivery scenario in which the mode of transport is fixed as a cargo-bike, and key job attributes change. Key attributes include delivery payment, delivery distance, and number of parcels, along with infrastructure conditions such as cycling infrastructure condition and stopping/loading space availability. These attributes reflect operational conditions that may influence participation in cargo-bike crowdshipping in an urban delivery environment.

This study analyzes individual responses to realistic delivery constraints by focusing on actual task acceptance behavior rather than simple participation intentions. Additionally, general characteristic information such as gender, age group, and cargo-bike usage frequency is collected to analyze differences in participation preferences based on individual traits. The experimental data are used to estimate task acceptance probabilities and compensation requirements using conditional logit models and mixed logit models based on the existing discrete choice model approach (Sarrias & Daziano, 2017). Through this, the study analyzes the marginal effects of changes in operational conditions on participation decisions and the heterogeneity of participation preferences.

Findings

This study analyzes the impact of infrastructure conditions and job characteristics on the decision to accept cargo-bike crowdshipping. By estimating the probability of job acceptance and the level of compensation required under various operational conditions, it provides empirical evidence regarding the behavioral analysis that shapes the labor supply in crowdshipping.

The study evaluates how infrastructure conditions, such as cycling infrastructure conditions and stopping/loading space availability, influence the decision to accept delivery work. Furthermore, by analyzing the impact of job characteristics, such as workload, compensation, and delivery time flexibility, on participation decisions, the study aims to understand the effects of operational constraints on delivery workforce availability in urban environments.

Additionally, by analyzing differences in participation preferences based on individual characteristics such as gender, age group, and cargo-bike usage frequency, this study provides insights into how labor supply responses may vary across diverse operational environments. Understanding these differences is crucial for evaluating how crowdshipping platforms can utilize flexible delivery labor in various urban settings.

This study provides an empirical foundation for evaluating the impact of infrastructure and delivery operation design on participation decisions, and contributes to evidence-based discussions for designing sustainable last-mile logistics systems and assessing workforce availability by quantifying behavioral responses to operating conditions.

Value

This study contributes to the urban logistics literature by shifting the focus of analysis from attitudinal intention to participatory behavior based on operational context. While existing research has primarily focused on the willingness to participate in crowdshipping, this study advances this understanding by identifying the operational conditions under which individuals accept delivery tasks.

By integrating a choice model-based approach with infrastructure attributes, this study presents an initial empirical approach to analyzing the impact of the environment on the labor supply of cargo-bike crowdshipping. The findings provide practical insights for policymakers, platform operators, and urban planners seeking to expand low-emission delivery solutions without compromising labor stability.

Furthermore, by conceptualizing participation as a decision to accept tasks, this study establishes an analytical framework applicable to analyzing the labor supply of new logistics models and supports relevant research on sustainable and resilient last-mile delivery systems.

References

Cebeci, M. S., Tapia, R. J., Kroesen, M., de Bok, M. A., & Tavasszy, L. (2023). The effect of trust on the choice for crowdshipping services: Evidence from a stated choice experiment. Transportation Research Part A: Policy and Practice, 170, 103622.

Fessler, A., Thorhauge, M., Mabit, S. L., & Haustein, S. (2022). A public transport-based crowdshipping concept as a sustainable last-mile solution: Assessing user preferences with a stated choice experiment. Transportation Research Part A: Policy and Practice, 158, 210–223.

Mohri, S. S., Ghaderi, H., Nassir, N., & Thompson, R. G. (2023). Crowdshipping for sustainable urban logistics: A systematic review. Transportation Research Part E: Logistics and Transportation Review, 176, 103145.

Sarrias, M., & Daziano, R. (2017). Multinomial logit models with continuous levels of alternatives. Transportation Research Part B: Methodological, 103, 42–59.

Purpose of the paper: Although digital transformation adoption has attracted increasing scholarly attention in recent years, empirical research focusing specifically on SMEs remains limited, particularly given their inherent resource constraints. SMEs often operate with restricted financial, technological, and managerial resources, which may hinder their ability to implement and leverage digital initiatives effectively. Despite the increasing attention to DT adoption, the mechanisms through which it enhances sustained performance in resource-constrained SMEs remain underexplored. Therefore, this study aims to empirically examine the relationship between digital transformation adoption and sustained logistics performance in Vietnamese SMEs. Furthermore, it investigates the mediating role of absorptive capacity in this relationship.

Design/methodology/approach: Grounded in the Resource-Based View (RBV) and Dynamic Capabilities (DC) theory, this study developed a conceptual model linking digital transformation adoption, absorptive capacity, and sustained logistics performance, from which hypotheses were derived. A structured survey questionnaire was designed and pilot-tested before being administered via the Qualtrics platform to senior managers of Vietnamese SMEs. By using CB-SEM approach, the proposed model was empirically tested with data collected from 214 managerial-level respondents. Exploratory factor analysis (EFA) was first conducted in SPSS to assess the internal consistency and factor structure of the measurement scales. Confirmatory factor analysis (CFA) was subsequently performed to evaluate convergent and discriminant validity. To examine the mediating role of absorptive capacity, bootstrapping with 5,000 resamples was employed to test the significance of indirect effects. Mediation was assessed by first evaluating the significance of the indirect paths, followed by examining the direct effect between the independent and dependent variables to determine the type of mediation.

Findings: The findings reveal a significant positive direct relationship between digital transformation adoption and sustained logistics performance. In addition, absorptive capacity was found to play a significant mediating role in this relationship, indicating that SMEs are better able to translate DT initiatives into sustained logistics performance when they possess strong capabilities to acquire, assimilate, and apply external knowledge. These results underscore the importance of dynamic learning capabilities in leveraging digital transformation for long-term performance outcomes in Vietnamese SMEs.

Originality/value: This study contributes to enhancing the existing body of knowledge in several ways. First, it represents one of the first attempts to extend the digital transformation literature by explicitly incorporating the sustained dimension of performance when examining the impact of DT adoption. By moving beyond short-term or contemporary performance indicators, the study highlights the importance of long-term stability and continuity in logistics outcomes. Furthermore, it advances theoretical understanding by clarifying the role of absorptive capacity as a key organisational learning capability that enables SMEs to convert DT initiatives into stable and consistent performance improvements over time.