Barbara B. Flynn

We examine the impact of human errors by front‐line supply chain employees on delivery delays. We build on normal accident theory (NAT), a multilevel theory describing the relationship between a firm's latent conditions (systemic managerial, technology, and social factors) and human errors. Latent conditions can have the unintended consequence of intensifying the impact of a human error, thus, we hypothesize a moderating effect of latent conditions on the relationship between errors and delivery delays. Hypotheses are tested using archival shipment data provided by a Fortune 500 company and archival carrier violations data. A multilevel design, with 299,399 shipments (level 1) nested within 97 carriers (level 2), was tested using mixed effects regression modeling. The results indicated that both dispatcher and driver errors were related to the probability of a late delivery, and that dispatcher errors were associated with longer delays. The moderating effects of several carrier latent conditions were significant and positive, indicating that both types of errors were more strongly associated with the likelihood of late delivery and that dispatcher errors were associated with longer delays when moderated by carrier latent conditions. The results are discussed from the perspective of NAT and technology management, developing prescriptions, and suggesting opportunities for future research.

This chapter addresses the relationship between a supplier and the manufacturers that it serves, from the perspective of transaction cost economics theory (TCE). TCE deals with relationships between organizations, such as customers, manufacturers and suppliers. It states that investment in transaction-specific assets opens the door for opportunistic behavior by an organization’s partners. Interpreted from the perspective of a supplier, the supplier’s investments in transaction-specific assets, such as dedicated plant and equipment, workforce with transaction-specific skills and the development of transaction-specific relationships, will lead to opportunistic behavior on the part of the manufacturers that they serve, providing an environment where behaviors intentionally designed to take advantage of the supplier will flourish. Because the supplier will not be able to redeploy those investments to a different manufacturer if the relationship is discontinued, manufacturers will be motivated to capitalize on this vulnerability by employing behaviors such as seeking unfair price concessions, sharing proprietary information with competitors or other unethical behaviors. This study advances the application of TCE to the context of supply chain management by breaking investments in transaction-specific assets into investments in transaction-specific tangible assets, such as plant and equipment, and transaction-specific intangible assets, such as relationships and the development of human resources, and examining their impact on opportunistic behavior separately. These relationships are examined using a survey of 230 suppliers in the household appliances industry in China. Hierarchical regression analysis revealed that there was a positive relationship between suppliers’ investment in transaction-specific tangible assets and opportunistic behavior by the manufacturers that they serve, but that there was a negative relationship between suppliers’ investments in transaction specific intangible assets and opportunistic behavior by their manufacturers. The moderating role of contracts and relational norms was also examined.

We develop a theoretical definition of operational capabilities, based on the strategic management and operations management literature, and differentiate this construct from the related constructs of resources and operational practices, drawing upon the resource‐based view of the firm as our foundation. We illustrate the key features of operational capabilities using the illustration of a restaurant kitchen. Because the traits of operational capabilities are distinct, they create a barrier to imitation, making them a potential source of competitive advantage. However, operational capabilities are particularly challenging to measure, because they emerge gradually and are tacit, embedded, and manifested differently across firms. In solving this measurement conundrum, we draw upon similar situations experienced by Schein (2004) and Eisenhardt and Martin (2000) in operationalizing organizational culture and dynamic capabilities. A taxonomy of six emergent operational capabilities is developed: operational improvement, operational innovation, operational customization, operational cooperation, operational responsiveness, and operational reconfiguration. A set of measurement scales is developed, in order to measure each of the operational capabilities, and validated using two different datasets. This allows replication of the psychometric properties of the multi‐item scales and helps to ensure the validity of the resulting measures.