Journal of Business Logistics最新文献

This study aims to investigate the sources of resilience for logistics operations as it relates to food supply chains and their responses during natural disasters and other disruptions. The literature tends to treat inherent and adaptive resilience as somewhat separate constructs. Thus, this study also aims to provide some insights into the relatively underexamined area of how inherent resilience synergistically works with adaptive resilience to maintain flows for food supply chains in a developing economy context. To address the research aim, two supply chain wide case studies were used to investigate the contribution of logistic activities toward the resilience of foods supply chains in two different South Asian regions. This context was selected as these regions are prone to frequent natural disruptions and other disruptions, and these food supply chains also play a crucial role in economy and providing relief supplies to their communities. Logistics operations play a key role in achieving overall supply chain resilience. The sources of inherent resilience are the network structure, buffer capacity, and strong social connections among the actors. Adaptive resilience is derived from transportation flows, storage, information sharing, and demand management processes. Surprisingly, resilience in this context is achieved despite the relatively low level of integration, an unsophisticated technological landscape and compromised logistics infrastructure. Still, resilience can be enhanced further. This study contributes to the sparse literature that examines logistics activities that influence the resilience of food supply chains in the context of natural disasters and other disruptions. Furthermore, uncovering the substructure of logistics resilience activities, and surprisingly, the synergies between inherent and adaptive resilience, adds to the emerging body of literature.

The dawn of generative artificial intelligence (AI) has the potential to transform logistics and supply chain management radically. However, this promising innovation is met with a scholarly discourse grappling with an interplay between the promising capabilities and potential drawbacks. This conversation frequently includes dystopian forecasts of mass unemployment and detrimental repercussions concerning academic research integrity. Despite the current hype, existing research exploring the intersection between AI and the logistics and supply chain management (L&SCM) sector remains limited. Therefore, this editorial seeks to fill this void, synthesizing the potential applications of AI within the L&SCM domain alongside an analysis of the implementation challenges. In doing so, we propose a robust research framework as a primer and roadmap for future research. This will give researchers and organizations comprehensive insights and strategies to navigate the complex yet promising landscape of AI integration within the L&SCM domain.

Researchers and practitioners insist on equipping supply chains with the adaptive capability to return to a more desirable state after being disrupted. Various resilience capabilities have been emphasized in the literature; however, limited research has linked them to resilience phases and cultural value orientations. Moreover, preparedness and adaption phases have hardly been studied. To address these gaps, we adopted middle-range theory to investigate agri-food supply chain (AFSC) resilience to the COVID-19 crisis in a cross-country setting. Data were collected from interviews with AFSC practitioners from China and Spain, followed by thematic and comparative analyses. The results indicate that frequently discussed resilience capabilities, such as collaboration, redundancy, flexibility, leadership, and innovation, were implemented across the preparedness, response and recovery, and adaption phases; however, successful AFSC recovery also depends on each country's cultural value orientations. A hierarchical cultural orientation generates sense-making and collectiveness and further leads to synergy across all AFSC stakeholders, thereby contributing to AFSC response and recovery. Under an egalitarianism cultural orientation that places self-interest ahead of group goals, organizations are encouraged to make decisions based on their own situational understanding, which contributes to their response and recovery. This study also provides theoretical contributions and managerial and policy implications.

This paper investigates how current megatrends (i.e., aging population, growing urbanization, shifts in consumer demands, geopolitical shifts, depletion of natural resources, climate change) are changing the supply chain landscape and the role of Industry 4.0 (I4.0) technologies to support alignment with these changes. Building on contingency theory, the study employs focus-group interviews with various experts to generate new insights into fitting supply chain capabilities and enabling technologies. Data collected in the focus groups helped us to identify five supply chain capabilities as prevalent and mostly fitting the external contingencies, i.e., customer-driven, urban-centered, resource-efficient, fast reactive, and human-centered supply chain. Moreover, this study highlights and compares the potential of I4.0 technologies and their applications in supporting specific supply chain capabilities. The findings of this study can inform supply chain managers in the definition of capabilities to be enhanced at the supply chain level and contribute toward understanding the extent of I4.0 technologies in empowering supply chains to face turbulent and changing conditions.

The possibility of product substitution due to a stockout provides retailers with a buffer against lost sales but can also complicate the management of inventory because both substitute and primary demand affect inventory levels. Since most retailers make their inventory decisions around a desired service level (e.g., fill rate), it is important to understand how customer substitution behavior can influence different measures of customer service. In this study, an extensive theoretical framework is presented and used to develop a decision tree approach for predicting realized item and category fill rates. We find that item fill rate is primarily a function of the target service level of the focal item and the willingness to switch from an alternate item to the focal item. Category fill rate is influenced by the target service level of both items, with willingness to switch amplifying their effect. The decision tree approach is found to be an accurate predictor in most cases, however, it tends to overestimate item fill rate when the willingness to substitute from the alternate item increases. The approach also accurately predicts category fill rates, outside of scenarios with asymmetric substitutability.

A welcomed addition to the logistics and supply chain management (L&SCM) research landscape has been growth in the use of archival data, defined as data collected by an entity outside the research team (Miller et al., 2021). Expansion in the use of archival data is stimulated by discussions concerning the generally accepted limitations of primary data research and related debate about when and how the use of primary data is appropriate (e.g., Montabon et al., 2018; Schoenherr et al., 2015). Concurrently, there has been recognition that research utilizing archival data opens new avenues of emphasis for L&SCM research in answering a wide array of questions. When archival data is generated from industry and operations (e.g., DeHoratius et al., 2022), direct application may be enhanced. Additionally, archival data is highly accessible, which aids in both replication and extension (Pagell, 2021).

As with every research design, archival data poses multiple limitations, and there are unique challenges as researchers employing the data are detached from the original collection process. Recent articles have tackled issues concerning how to establish strong validity claims for measures derived from archival sources (Miller et al., 2021) and how to formulate statistical models that ensure theoretical hypotheses map to estimated parameters (Ketokivi et al., 2021). We refer to this as statistical identification, which focuses on the confidence that an estimated statistical parameter (e.g., regression coefficient) is reasonably unbiased and not overly sensitive to changes in the structure of the statistical model. Possibly the greatest threat to statistical identification is the existence of one or more unmeasured variables that reside theoretically upstream or parallel to the independent variables. If included as predictors in a statistical model, these variables could be significantly related to the outcome variable. This is highly problematic if the estimates of the independent variables could shift substantially (Miller & Kulpa, 2022). We see a significant amount of emphasis on this aspect of the research, and the general remedy involves utilizing a combination of control variables and performing robustness tests (sometimes in excessive numbers) to rule out alternative explanations.

One fundamental issue that has not been adequately addressed in L&SCM research is related to theorizing. Theorizing involves devising hypotheses that will be tested with this archival data. It is not enough to develop statistical models where there is a reasonable degree of confidence that focal parameters are statistically identified. It is just as important to provide evidence of theoretical identification, defined here as the existence of strong and convincing rationale(s) that the theorized mechanisms bring to the reported resu

While greater competitive activity is generally associated with competitive advantage, certain competitive actions by a supplier may have spillover effects that adversely impact buying firms, leading them to reduce future purchases from the supplier. We study the effects of competitive actions in the context of vertical buyer–supplier relationships. Specifically, leveraging insights from screening theory, we examine how a supplier firm's value-diminishing competitive actions—moves that may negatively impact buyer firms—lead to subsequent reductions in the buyer's procurement allocations and how contextual factors moderate such adverse effects. We test the associated hypotheses using a panel dataset comprising 12,690 dyadic buyer–supplier observations. A series of econometric analyses provide consistent evidence that a supplier's value-diminishing actions are associated with decreases in the buyer's purchases from the supplier, thus highlighting the “dark side” of competitive actions. Furthermore, we find that the supplier's downstream vertical relatedness and the degree to which rival suppliers pursue value-diminishing actions moderate this effect. Our findings, thus, add to our understanding of factors that shape the success and continuity of supply chain relationships and help supplier firms evaluate the economic viability of their competitive actions.

Global supply chains operate in a volatile environment characterized by risks like the 2008–2009 financial crisis, trade disputes, and the COVID-19 pandemic. Maritime shipping firms, the backbone of global supply chains, are particularly affected by this volatility. In response, these firms have pursued two strategies. First, they have acquired tangible assets to increase their capacity. These assets, however, are neither easily re-deployable to other uses nor quickly adjustable, preventing firms from flexibly matching supply and demand. Second, these firms have pursued global supply chain integration by acquiring similar firms or firms in their upstream or downstream supply chain to diversify into other supply chain activities. Anecdotal evidence suggests that some firms have successfully pursued these strategies, but others have failed, eventually exiting the market. We posit that one explanation for this difference may be how effectively these firms manage their assets relative to their supply chain integration activities. We test this proposition by drawing from resource-based theory and transaction cost economics and using longitudinal data for 148 maritime shipping firms. We also test post hoc whether typically acquired supply chain activities are beneficial. Our findings offer insights into asset management and global supply chain integration and offer advice to practitioners.

Our research reveals the continued and evolving role of the human factor in decision making in digitalized retail supply chains. We compare managerial roles in a pre- and post-COVID era through conducting in-depth interviews of 25 executives spanning the retail supply chain ecosystem. We use grounded theory to develop four main contributions. First, we find that the involvement of managerial judgment is found to be progressively greater moving up the retail supply chain, away from the customer and the demand signal. Second, integration of analytics and judgment is now the primary method of decision making, and we identify elements needed for success. Third, we develop an essential framework for a successful integration process. Fourth, we isolate the necessary components of a successful process for analytics/artificial intelligence (AI) implementation. Our paper offers important insights into how analytics and AI are—and should be used—in judgment and decision making and opportunities for researchers to understand the changing role of the human factor in digitalized retail supply chains.

With the growth of e-commerce and associated home deliveries, understanding the role of drivers in shaping the customer experience in last-mile delivery is now more crucial than ever. Delivery drivers increasingly act as retailers' frontline employees and are thus instrumental in developing pseudorelationships between customers and retailers. Industry surveys, however, reveal that drivers admit to engaging in unprofessional behaviors with customers and often refuse to address customers' requests beyond package delivery. Following a middle-range theorizing approach and leveraging Cognitive Appraisal Theory, we investigate how two negative driver behaviors, inappropriate behavior and inflexibility, impact customer satisfaction and repurchase intentions. We also examine the moderating effect of driver affiliation, private versus outsourced, in altering the magnitude of customer responses. Results from a scenario-based experiment indicate that while the negative effects of driver inappropriate behavior on customer outcomes are mediated by anger, the effects of driver inflexibility are mediated by sadness. Moreover, the negative effect of driver inflexibility on customer outcomes is weaker for outsourced logistics than for private fleet drivers. In turn, driver inappropriate behavior exhibits similar negative effects on customer outcomes for both driver affiliations. These findings offer important insights for last-mile delivery strategy and operations research and practice.