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This study aims to advance a behavioural approach towards understanding how managerial perception impacts the enactment of responses to risk management during the implementation of digital technologies in industrial operations and supply chains. The purpose is to investigate the influence of (digital) technology and task uncertainty on the risk perception of managers and how this impacts risk responses adopted by managers.

Following an exploratory theory elaboration approach, the authors collected more than 80 h of interview material from 53 expert interviews. These interviews were conducted with representatives of 46 German companies that have adopted digital technologies for different industrial applications within manufacturing, assembly and logistics processes.

The findings provide nuanced insights on how individual and combined sources of uncertainty (technology and task uncertainty) impact the perception of decision makers and the resulting managerial responses adopted. The authors uncover the important role played by the interaction between digital technology and human being in the context of industrial operations. The exploratory study shows that the joint collaboration between humans and technologies has negative implications for managerial risk responses regardless of positive or negative perception, and therefore, requires significant attention in future studies.

The empirical base for this study is limited to German companies (mainly small and medium size). Moreover, German culture can be characterised by a high uncertainty avoidance and this may also limit the generalizability of the findings.

Managers should critically revise their perception of different types of digital technologies and be aware of the impact of human-machine interaction. Thereby, they should investigate more systematic approaches of risk identification and assessment.

This paper focuses on the managerial risk responses in the context of digitalisation projects with practical insights of 53 expert interviews.

The COVID-19 outbreak reached a critical stage when it became imperative for public health systems to act decisively and design potential behavioral operational strategies aimed at containing the pandemic. Isolation through social distancing played a key role in achieving this objective. This research study examines the factors affecting the intention of individuals toward social distancing in India.

A correlation study was conducted on residents from across Indian states (N = 499). Online questionnaires were floated, consisting of health belief model and theory of planned behavior model, with respect to social distancing behavior initially. Finally, structural equation modeling was used to test the hypotheses.

The results show that perceived susceptibility (PS), facilitating conditions (FC) and subjective norms are the major predictors of attitude toward social distancing, with the effect size of 0.277, 0.132 and 0.551, respectively. The result also confirms that the attitude toward social distancing, perceived usefulness of social distancing and subjective norms significantly predict the Intention of individuals to use social distancing with the effect size of 0.355, 0.197 and 0.385, respectively. The nonsignificant association of PS with social distancing intention (IN) (H1b) is rendering the fact that attitude (AT) mediates the relationship between PS and IN; similarly, the nonsignificant association of FC with IN (H5) renders the fact that AT mediates the relationship between FC and IN.

The results of the study are helpful to policymakers to handle operations management of nudges like social distancing.

The research is one of its kind that explores the behavioral aspects of handling social nudges through FC.

Smart Manufacturing (SM) lies at the core of Industry 4.0. Operations management research has investigated the determinants of SM advancement but there is still limited understanding of the linkages between SM and organizational factors and about whether both the technological and organizational subsystems for SM are guided by firms’ competitive priorities. To close these gaps, building on operations strategy theory, this paper aims to empirically test a model positing that competitive priorities drive SM advancement. The relation between competitive priorities and SM advancement is assumed to be mediated by organizational microfoundations.

Using data from a single respondent survey with 234 firms in the automotive component industry, structural equation modeling is adopted to test the model hypotheses. Relevant constructs are measured with reference to the lead plant for SM.

Findings highlight that SM advancement is driven by the need to simultaneously compete in terms of cost, quality and delivery, thus suggesting that manufacturers view SM as a mean to develop multiple manufacturing capabilities. Organizational microfoundations fully mediate the relation between competitive priorities and SM advancement.

Results have implications for SM research, as they provide an understanding of the strategic priorities of firms engaging in SM. Findings also bear relevance for manufacturing executives engaged in the SM transformation, as they provide quantitative evidence that shaping an adequate organizational environment is a prerequisite for SM advancement.

This study aims to empirically test the effects of key characteristics of performance-based contracting (PBC) (i.e. reward/payment scheme, increased supplier autonomy and transfer of responsibilities) on supplier goal commitment.

This study developed a conceptual model applying goal-setting theory (GST), expectancy theory (ET) and job characteristics theory (JCT). Survey data were collected and analyzed using structural equation modeling (SEM) to establish a validated measurement instrument for testing the hypotheses.

The findings revealed that PBC positively affects supplier goal commitment due to its unique characteristics, which translates into improved supplier performance. Furthermore, this study validated the mediating role of goal alignment and felt accountability operating between PBC characteristics and supplier goal commitment.

This study explored the buyer–supplier relationship from the supplier's standpoint. Using a more inclusive data set, future research may involve a dyadic analysis and focus on the effects of the following factors on the supplier goal commitment: relational aspects (e.g. trust and collaboration), the risk transfer from the buyer to the supplier, different incentive schemes and successful PBC implementation factors.

This study presents new, validated insights for contract selection, design and management. It underlines the importance of choosing the proper contract, having the appropriate contract design based on the desired outcomes and effective contract management by exhibiting the psychological/behavioral effect of fundamental PBC characteristics.

PBC represents an active research stream, but its psychological/behavioral implications are understudied. Therefore, this research puts forth a conceptual framework with multiple testable hypotheses illustrating the relationship between PBC and supplier goal commitment.

Despite the escalating significance and intricate nature of supply chains, there has been limited scholarly attention devoted to exploring the cognitive processes that underlie supply chain management. Drawing on cognitive-behavioral theory, the authors propose a moderated-mediation model to investigate how paradoxical leadership impacts manufacturing supply chain resilience.

By conducting a two-wave study encompassing 164 supply chain managers from Chinese manufacturing firms, the authors employ partial least squares structural equation modeling (PLS-SEM) to empirically examine and validate the proposed hypotheses.

The findings indicate that managers' paradoxical cognition significantly affects supply chain resilience, with supply chain ambidexterity acting as a mediating mechanism. Surprisingly, the study findings suggest that big data analytics negatively moderate the effect of paradoxical cognition on supply chain ambidexterity and supply chain resilience, while positively moderating the effect of supply chain ambidexterity on supply chain resilience.

These findings shed light on the importance of considering cognitive factors and the potential role of big data analytics in enhancing manufacturing supply chain resilience, which enriches the study of behavioral operations.

The results offer managerial guidance for leaders to use paradoxical cognition frames and big data analytics properly, offering theoretical insight for future research in manufacturing supply chain resilience.

This is the first empirical research examining the impact of paradoxical leadership on supply chain resilience by considering the role of big data analytics and supply chain ambidexterity.

This paper aims to examine and compare extant literature on the application of multi-criteria decision-making (MCDM) techniques in humanitarian operations (HOs) and humanitarian supply chains (HSCs). It identifies the status of existing research in the field and suggests a roadmap for academicians to undertake further research in HOs and HSCs using MCDM techniques.

The paper systematically reviews the research on MCDM applications in HO and HSC domains from 2011 to 2022, as the field gained traction post-2004 Indian Ocean Tsunami phenomena. In the first step, an exhaustive search for journal articles is conducted using 48 keyword searches. To ensure quality, only those articles published in journals featuring in the first quartile of the Scimago Journal Ranking were selected. A total of 103 peer-reviewed articles were selected for the review and then segregated into different categories for analysis.

The paper highlights insufficient high-quality research in HOs that utilizes MCDM methods. It proposes a roadmap for scholars to enhance the research outcomes by advocating adopting mixed methods. The analysis of various studies revealed a notable absence of contextual reference. A contextual mind map specific to HOs has been developed to assist future research endeavors. This resource can guide researchers in determining the appropriate contextual framework for their studies.

This paper will help practitioners understand the research carried out in the field. The aspiring researchers will identify the gap in the extant research and work on future research directions.

To the best of the authors’ knowledge, this is the first literature review on applying MCDM in HOs and HSCs. It summarises the current status and proposes future research directions.

The purpose of our study is to investigate how a manufacturing plant’s internal operations along with its network of connections (upstream and downstream) can have an impact on its recovery time from a disruption. The authors also examine the inverse-U impact of complexity. Finally, the authors test the moderating role that business continuity management plans (BCP) at the plant level have on recovery time.

To test our hypotheses, the authors partnered with Resilinc Corporation, a Silicon Valley-based provider of supply chain risk management solutions to identify focal firms’ suppliers, customers and plant-level data including information on parts, manufacturing activities, bill of materials, alternate sites and formal business continuity plans. The authors employed censored data regression technique (Tobit).

Several important findings reveal that the plant’s internal operations and network connections impact recovery time. Specifically, the number of parts manufactured at the plant as well as the number of internal plant processes significantly increase disruption recovery time. In addition, the number of supply chains (upstream and downstream) involving the plant as well as the echelon distance of the plant from its original equipment manufacturer significantly increase recovery time. The authors also find that there exists an inverted-U relationship between complexity and recovery time. Finally, the authors find partial support that BCP will have a negative moderating effect between complexity and recovery time.

This research highlights gaps in the literature related to supply chain disruption and recovery. There is a need for more accurate methods to measure recovery time, more research on recovery at the supply chain site level and further analysis of the impact of supply chain complexity on recovery time.

The complexity and uncertainty of healthcare operations increasingly require agility to safeguard a high quality of care. Using a microfoundations of dynamic capabilities perspective, this study investigates the effects of nurses' implicit voice theories (IVTs) on the behaviors that influence their individual agility.

This research uses quantitative survey data collected from 2,552 Canadian nurses during the fourth wave of the Covid-19 pandemic in the fall of 2021. Structural equation modeling is used to test a conceptual model that hypothesizes the effects of three different IVTs on nurses' creativity, spontaneity, agility and the quality of care they deliver to patients.

The results reveal that voice-inhibiting cognitions (like “suggestions are criticisms for higher-ups”, “I first need a solution or solid data”, and “speaking up has negative repercussions”) negatively impact nurses' creativity and spontaneity in crafting solutions to problems they face daily. In turn, this affects nurses' individual agility as they attempt to adapt to changing circumstances and, ultimately, the quality of care they provide to their patients.

Even if organizations have little control over employees' pre-held beliefs regarding voice, they can still reverse them by developing and nurturing a voice-welcoming culture to boost their workers' agility.

This study combines two theoretical frameworks, voice theory and dynamic capabilities theory, to study how individual-level factors (cognitions and behaviors) contribute to nurses' individual agility and the quality of care they provide to their patients. It answers the recent calls of scholars to study the mechanisms through which healthcare operations can develop and sustain dynamic capabilities, such as agility, and better face the “new normal”.

The purpose of this study is to assess the validity and predictability of insights from the investment model (IM) in the context of strategic manufacturer–industrial supplier relationships. IM is a theoretical model in social psychology pertaining to interpersonal relationship discontinuity. This formal empirical test of IM in a different context supports vertical theory borrowing and minimizes the risk of committing atomistic fallacy.

Data collected from 256 sourcing professionals participating in a scenario-based role-playing experiment were analyzed via structural equation modeling. The authors also performed bootstrapping to assess indirect effects.

The IM is generally applicable to the context of interfirm relationship dissolution. Relative to the original context of interpersonal relationship dissolution, three nuances are detected: investment size as an antecedent has lowered prominence in influencing commitment; satisfaction level, quality of alternatives and investment size have non-orthogonal effects on commitment; and satisfaction level influences relationship continuity through and beyond commitment.

The empirical findings broaden boundary conditions for IM insights. Beyond interpersonal relationship dissolution, the IM appears to also describe, explain and predict interfirm relationship dissolution.

Keeping the manufacturer satisfied is critical. Moreover, suppliers should be cautious when entering joint product development agreements.

This study appears to be among the first to formally validate the applicability of IM insights as they pertain to the dissolution of strategic manufacturer–industrial supplier relationships.