Supply chain resilience in mindful humanitarian aid organizations: the role of big data analytics

Under “my way or the highway”! The weaker partner's synergy on collaborative performance in humanitarian relief when experiencing power tactics

Purpose

My way or the highway: A stronger partner in humanitarian relief collaboration often seeks control over its weaker partners by leveraging diverse power tactics that may be non-coercive or coercive in nature. In this backdrop, this research accentuates the perspective of weaker partners to understand how a weaker partner drives collaborative synergy under power tactics. Weaker partner in the collaborative dyad resembles the humanitarian organization (HO) who is less capable in terms of access to resources. This partner is further dependent on the stronger HO's directives to participate in the relief work.

Design/methodology/approach

Based on social exchange theory (SET), a collaborative dyad in humanitarian relief work is visualized from the perspectives of power and power disparity. In terms of power, mediated power tactics such as legal, coercive and reward powers are considered. Disparity recognizes the presence of a stronger and a weaker partner. Set also magnifies the insight on conflict and trust that can respectively hamper and improve the synergy between partners. In total, 295 executives representing self-reported weaker HOs are surveyed.

Findings

Legal power, as experienced by the weaker partner, bears a positive impact over collaborative performance to improve collaborative synergy. Coercive power shows a negative impact. Reward power again bears a positive impact. Coercion can complicate collaborative synergy by establishing conflicts. Yet, the trust of weaker partner on stronger HO positively moderates the negative impact of conflict over collaborative performance.

Originality/value

This research invokes the view of power disparity to explain synergy in inter-organizational collaboration from the perspective of weaker partners.

Data, analytical techniques and collaboration between researchers and practitioners in humanitarian health supply chains: a challenging but necessary way forward

Purpose

This paper aims to provide a discussion on the interface and interactions between data, analytical techniques and impactful research in humanitarian health supply chains. New techniques for data capturing, processing and analytics, such as big data, blockchain technology and artificial intelligence, are increasingly put forward as potential “game changers” in the humanitarian field. Yet while they have potential to improve data analytics in the future, larger data sets and quantification per se are no “silver bullet” for complex and wicked problems in humanitarian health settings. Humanitarian health supply chains provide health care and medical aid to the most vulnerable in development and disaster relief settings alike. Unlike commercial supply chains, they often lack resources and long-term collaborations to enable learning from the past and to improve further.

Design/methodology/approach

Based on a combination of the authors’ research experience, interactions with practitioners throughout projects and academic literature, the authors consider the interface between data and analytical techniques and highlight some of the challenges inherent to humanitarian health settings. The authors apply a systems approach to represent the multiple factors and interactions between data, analytical techniques and collaboration in impactful research.

Findings

Based on this representation, the authors discuss relevant debates and suggest directions for future research to increase the impact of data analytics and collaborations in fostering sustainable solutions.

Originality/value

This study distinguishes itself and contributes by bringing the interface and interactions between data, analytical techniques and impactful research together in a systems approach, emphasizing the interconnectedness.

How Does Misinformation and Capricious Opinions Impact the Supply Chain - A Study on the Impacts During the Pandemic

Misinformation or fake news has had multifaceted ramifications with the onset of the Covid-19 pandemic, creating widespread panic amongst people. This study investigates the impact of misinformation/ fake news (on internet platforms) on consumer buying behavior, impact of fear (created by fake news) on hoarding of essential products and consumer spending and finally impact of misinformation-induced panic buying on supply chain disruptions. It draws upon the consumer decision theory and the cognitive load theory for explaining the psychological and behavioral responses of consumers. The study follows an inductive approach towards theory building using a multi-method approach. Initially, a qualitative research method based on interviews followed by text-mining has been used followed by analysis using python for topic modelling using Latent Dirichlet Allocation (LDA). The findings revealed several prominent themes like consumer shift to online buying, two contrasting spending intentions namely financial security and compensatory consumptions, irrational panic buying, uncertainty/ambiguity of government protocol and norms, social media fraudulent practices and misinformation dissemination, personalized buying experience, reduced trust on news and marketers, logistics and transportation bottlenecks, labor shortage due to migration and plant closures, and bullwhip effect in supply chains.

Service supply chain resilience: a social-ecological perspective on last-mile delivery operations

Purpose

Considering the last-mile delivery service supply chain as a social-ecological system rather than just a firm-based service system, this research exploit the COVID-19 pandemic disruption to investigate how the supply chain develops resilience from a viewpoint that integrates a social-ecological perspective with the traditional engineering one.

Design/methodology/approach

This research adopt a multi-case study approach using qualitative data collected via semi-structured interviews with executive-level managers from nine leading UK last-mile delivery companies. Data analysis is guided by a research framework which is developed by combining the social-ecological perspective with the structure–conduct–performance paradigm. This framework aids the investigation of the impacts of external challenges on companies' resilience strategies and practices, as well as performance, in response to disruptions.

Findings

The research identifies three distinct pathways to resilience development: stabilization, focussing on bouncing back to the original normal; adaptation, involving evolutionary changes to a new normal; transformation, involving revolutionary changes in pursuit of a new normal-plus. Three strategic orientations are identified as operating across these pathways: people orientation, digital orientation, and learning orientation.

Originality/value

In contrast to the manufacturing supply chain focus of most current research, this research concentrates on the service supply chain, investigating its resilience with a social-ecological perspective alongside the traditional engineering one.

Artificial intelligence and big data analytics for supply chain resilience: a systematic literature review

Artificial Intelligence (AI) and Big Data Analytics (BDA) have the potential to significantly improve resilience of supply chains and to facilitate more effective management of supply chain resources. Despite such potential benefits and the increase in popularity of AI and BDA in the context of supply chains, research to date is dispersed into research streams that is largely based on the publication outlet. We curate and synthesise this dispersed knowledge by conducting a systematic literature review of AI and BDA research in supply chain resilience that have been published in the Chartered Association of Business School (CABS) ranked journals between 2011 and 2021. The search strategy resulted in 522 studies, of which 23 were identified as primary papers relevant to this research. The findings advance knowledge by (i) assessing the current state of AI and BDA in supply chain literature, (ii) identifying the phases of supply chain resilience (readiness, response, recovery, adaptability) that AI and BDA have been reported to improve, and (iii) synthesising the reported benefits of AI and BDA in the context of supply chain resilience.

Modelling the relationships between the barriers to implementing machine learning for accident analysis: the Indian petroleum industry

Purpose

This paper aims to identify, prioritise and explore the relationships between the various barriers that are hindering the machine learning (ML) adaptation for analysing accident data information in the Indian petroleum industry.

Design/methodology/approach

The preferred reporting items for systematic reviews and meta-analysis (PRISMA) is initially used to identify key barriers as reported in extant literature. The decision-making trial and evaluation laboratory (DEMATEL) technique is then used to discover the interrelationships between the barriers, which are then prioritised, based on three criteria (time, cost and relative importance) using complex proportional assessment (COPRAS) and multi-objective optimisation method by ratio analysis (MOORA). The Delphi method is used to obtain and analyse data from 10 petroleum experts who work at various petroleum facilities in India.

Findings

The findings provide practical insights for management and accident data analysts to use ML techniques when analysing large amounts of data. The analysis of barriers will help organisations focus resources on the most significant obstacles to overcome barriers to adopt ML as the primary tool for accident data analysis, which can save time, money and enable the exploration of valuable insights from the data.

Originality/value

This is the first study to use a hybrid three-phase methodology and consult with domain experts in the petroleum industry to rank and analyse the relationship between these barriers.

Capabilities for enhancing supply chain resilience and responsiveness in the COVID-19 pandemic: exploring the role of improvisation, anticipation, and data analytics capabilities

Purpose

This study aims to identify critical capabilities to address unforeseen and novel disruptions, such as those instigated by COVID-19, and explore their role as essential enablers of supply chain resilience and responsiveness, leading to improved performance.

Design/methodology/approach

The structural equation modeling technique was employed for analyzing the proposed associations using survey data from 206 manufacturers operating during the COVID-19 pandemic in a developing country, Pakistan.

Findings

Key findings show how improvisation and anticipation act distinctly yet jointly to facilitate supply chain resilience and responsiveness during the COVID-19 pandemic. Also, data analytics capability positively affects anticipation and improvisation, which mediate the effect of data analytics on supply chain resilience and responsiveness.

Research limitations/implications

The findings contribute to the theoretical and empirical understanding of the existing literature, suggesting that a combination of improvisation, anticipation and data analytics capabilities is highly imperative for enhancing supply chain resilience and responsiveness in novel and unexpected disruptions.

Originality/value

This is the first study to examine the impact of data analytics on improvisation and anticipation and the latter as complementary capabilities to enhance supply chain resilience and responsiveness. The empirical investigation explores the interplay among data analytics, improvisation, and anticipation capabilities for enhancing supply chain resilience, responsiveness, and performance during the unforeseen and novel disruptions, such as brought to bear by the COVID-19 pandemic.

Can intercluster coordination mediate the relationship between supply chain flexibility and humanitarian supply chain performance?

Purpose

The occurrence of disaster and crisis is increasing. They are complex as well as challenging for humanitarian organizations (HOs) and societies involved in disaster relief operations. This study examined the nexus between supply chain flexibility (SCF) and humanitarian supply chain performance (HSCP) among HOs with empirical evidence from HOs in Ghana.

Design/methodology/approach

The study employed the quantitative method to explore the interdependencies among the variables. In congruence with this, the study employed the purposive and convenience sampling technique to obtain information from 168 respondents. The analysis was done using SPSS version 23 and Smart PLS version 3.

Findings

The outcome indicates that intercluster coordination (ICC) plays a significant mediating role between SCF and HSCP.

Practical implications

The outcome of the study indicates that a closer and stronger relationship ensures proper channel use among the HOs. This will improve the performance of the supply chain of HOs and their ability to deal with supply chain uncertainties.

Originality/value

The discovery of this study provides empirical support to the resource-based view theory. Thus, practitioners in the humanitarian setting give priority to factors that could enhance flexibility in their supply chain as well as implement coordination strategies to achieve a responsive humanitarian supply chain (HSC) system in the quest to minimize the outcome of disasters.

Big Data Analytics in Supply Chain Management: A Systematic Literature Review and Research Directions

Big data analytics has been successfully used for various business functions, such as accounting, marketing, supply chain, and operations. Currently, along with the recent development in machine learning and computing infrastructure, big data analytics in the supply chain are surging in importance. In light of the great interest and evolving nature of big data analytics in supply chains, this study conducts a systematic review of existing studies in big data analytics. This study presents a framework of a systematic literature review from interdisciplinary perspectives. From the organizational perspective, this study examines the theoretical foundations and research models that explain the sustainability and performances achieved through the use of big data analytics. Then, from the technical perspective, this study analyzes types of big data analytics, techniques, algorithms, and features developed for enhanced supply chain functions. Finally, this study identifies the research gap and suggests future research directions.

Mitigating interorganizational conflicts in humanitarian logistics collaboration: the roles of contractual agreements, trust and post-disaster environmental uncertainty phases

Purpose

Governance is the key to establishing effective collaboration among humanitarian logistics partners addressing an ongoing relief work. With a focus on humanitarian interorganizational collaboration, this research draws on governance theories to investigate how conflicts can be mitigated in this challenging setting.

Design/methodology/approach

The focus on governance extends attention to the frontiers of contractual agreement, trust and environmental uncertainty to be applied in the humanitarian setting. To develop perspectives, an online survey of 289 field executives working in humanitarian organizations across the globe is conducted. The findings are based on hierarchical regressions.

Findings

Environmental uncertainty, in humanitarian logistics, is not straightforward, but wields distinctive challenges in the response phase (immediate to the disaster) as well as the recovery phase (beginning of build back) – to loom prospects of conflict between partners. Findings outline that contractual agreement can increase conflict during the response phase (high environmental uncertainty), but mitigate it during the recovery phase (low environmental uncertainty). Furthermore, contractual agreement interactively strengthens the ability of trust to reduce conflict. Yet, trust acting alone shows best outcome to mitigate conflict.

Research limitations/implications

Contrary to the established understanding in traditional logistics suggesting the vitality of contracts to easily mitigate challenges posed by environmental uncertainty, the humanitarian setting extends a unique outset for interorganizational governance based on the temporality of response and recovery phases.

Originality/value

This research pioneers to quantitatively examine the setting of humanitarian logistics based on survey. Given the difficulty of data acquisition, the extant research has largely relied on qualitative investigations when considering the agenda of governance.