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In the aftermath of disasters, humanitarian actors need to coordinate their activities based on accurate information about the disaster site, its surrounding environment, the victims and survivors and the supply of and demand for relief supplies. In this study, the authors examine the characteristics of radio frequency identification (RFID) technology and those of disaster relief operations to achieve information visibility and actor coordination for effective and efficient humanitarian relief operations.

Building on the contingent resource-based view (CRBV), the authors present a model of task-technology fit (TTF) that explains how the use of RFID can improve visibility and coordination. Survey data were collected from humanitarian practitioners in India, and partial least squares (PLS) analysis was used to analyze the model.

The characteristics of both RFID technology and disaster relief operations significantly influence TTF, and TTF predicts RFID usage in disaster relief operations, providing visibility and coordination. TTF is also a mediator between the characteristics of RFID technology and disaster relief operations and between visibility and coordination.

The many recent humanitarian disasters have demonstrated the critical importance of effective and efficient humanitarian supply chain and logistics strategies and operations in assisting disaster-affected populations. The active and appropriate use of technology, including RFID, can help make disaster response more effective and efficient.

Humanitarian actors value RFID technology because of its ability to improve the visibility and coordination of relief operations. This study brings a new perspective to the benefits of RFID technology and sheds light on its antecedents. The study thus expands the understanding of technology in humanitarian operations.

The purpose of this study is to test whether the prediction of firm performance can be enhanced by incorporating nonfinancial disclosures, such as narrative disclosure tone and corporate governance indicators, into financial predictive models.

Three predictive models are developed, each with a different set of predictors. This study utilises two machine learning techniques, random forest and stochastic gradient boosting, for prediction via the three models. The data are collected from a sample of 1,250 annual reports of 125 nonfinancial firms in Pakistan for the period 2011–2020.

Our results indicate that both narrative disclosure tone and corporate governance indicators significantly add to the accuracy of financial predictive models of firm performance.

Our results offer implications for the restoration of investor confidence in the highly uncertain Pakistani market by establishing nonfinancial disclosures as reliable predictors of future firm performance. Accordingly, they encourage investors to pay more attention to these disclosures while making investment decisions. In addition, they urge regulators to promote and strengthen the reporting of such nonfinancial information.

This study addresses the neglect of nonfinancial disclosures in the prediction of firm performance and the scarcity of corporate governance literature relevant to the use of machine learning techniques.

Given the dominance of family ownership in India, this paper aims to examine whether the impact of board gender diversity (BGD) on voluntary disclosure (VD) is moderated by family ownership.

Based on a panel data set of the top 100 listed Indian firms for five years, this study examines the impact of BGD on VD by segregating the sample between family-owned and nonfamily firms. For empirical analysis, we use appropriate panel data models. For robustness, we employ a three-stage least square (3SLS) model.

The findings reveal the significant positive impact of BGD in terms of its different measures on VD for family and nonfamily firms. However, the impact becomes insignificant for nonfamily-owned firms when female directors are not substantially represented on the board.

This study extends the ongoing debate about the outcomes of the mandatory gender quota on board by providing novel evidence on the difference between the impact of BGD on VD for family and nonfamily firms in the Indian context.

The purpose of this paper is to propose and validate a theoretical model to investigate the relationship between self-organisation, information integration, adaptability and supply chain agility in humanitarian organisations.

A theoretical model was developed from extant studies and assessed through a structured questionnaire survey of 86 humanitarian organisations operating in South Sudan. The data were analysed using partial least square structural equation modelling.

The study found that self-organisation has a discernible positive influence on supply chain agility not only directly but also indirectly through adaptability. Further, information integration does not significantly influence supply chain agility directly but is fully mediated by adaptability. Together, the antecedent variables account for 53.9% variance in supply chain agility.

This study contributes to providing an empirical understanding of a humanitarian supply chain as a complex adaptive system and hence the need to incorporate self-organising and adaptive dimensions in supply chain management practice. Furthermore, it confirms the centrality of the complex adaptive system feature of adaptability when building supply chain agility through self-organisation and information integration.

The findings provide a firm ground for managerial decisions on investment in self-organisation and information integration dimensions so as to enhance adaptability and improve supply chain agility in humanitarian organisations.

This study is distinctive in the sense that it uses the complex adaptive system variables to empirically validate the relationships between self-organisation, information integration, adaptability and supply chain agility in humanitarian organisations in the world’s youngest developing economy with a long history of conflict and humanitarian intervention. The mediating influence of adaptability examined in this study is also novel.

The main objective of this study is to investigate the factors that influence the adoption intention of cloud computing services among individual users using the extended theory of planned behavior.

A purposive sampling technique was used to collect a total of 339 data points, which were analyzed using SmartPLS to derive variance-based structural equation modeling and fuzzy-set qualitative comparative analysis (fsQCA).

The results obtained from PLS-SEM indicate that attitude towards cloud computing, subjective norms, perceived behavioral control, perceived security, cost-effectiveness, and performance expectancy all have a positive and significant impact on the adoption intention of cloud computing services among individual users. On the other hand, the findings from fsQCA provide a clear interpretation and deeper insights into the adoption intention of individual users of cloud computing services by revealing the complex relationships between multiple combinations of antecedents. This helps to understand the reasons for individual users' adoption intention in emerging countries.

This study offers valuable insights to cloud service providers and cyber entrepreneurs on how to promote cloud computing services to individual users in developing countries. It helps these organizations understand their priorities for encouraging cloud computing adoption among individual users from emerging countries. Additionally, policymakers can also understand their role in creating a comfortable and flexible cloud computing access environment for individual users.

This study has contributed to the increasingly growing empirical literature on cloud computing adoption and demonstrates the effectiveness of the proposed theoretical framework in identifying the potential reasons for the slow growth of cloud computing services adoption in the developing world.

The purpose of this study is to investigate the various challenges of humanitarian supply chains (HSC) and how these challenges can be addressed using artificial intelligence (AI).

This study employs exploratory analysis to identify various issues in HSC and the use cases of AI to address these issues through published literature. Subsequently, we collected tweets from Twitter and posts from LinkedIn using relevant keywords over four months. The collected data were cleaned, analyzed and interpreted to gain insights into users' perspectives on the various issues and use cases of AI in HSC.

The analysis reveals that various issues of HSC such as logistical challenges, security concerns, health and safety, access constraints, information gaps, coordination and collaboration, cultural sensitivity, funding constraints, climate and environmental factors and ethical dilemmas are predominantly discussed in published literature. Meanwhile, user-generated content reveals different levels of prioritization of these issues and AI attributes and offers AI-based solutions.

This study is subject to certain limitations, including a restricted data collection period of only four months and the use of just two social media platforms. These limitations could be addressed by conducting a more comprehensive and extended data collection across additional platforms to produce more conclusive findings. Another limitation is the lack of contextual information, which may have provided more specific insights.

To the best of the authors’ knowledge, this is possibly the first paper to explore both published literature and the collective intelligence of social media users to examine AI attributes, the various challenges of HSC and how AI can address these challenges.

International investment law governs matters related to transnational investments. The extensive reach of transnational corporations (TNCs) has granted them substantial economic, political and social influence, often intertwining them with public interest issues and implications in human rights violations. This paper aims to explore the profound influence exerted by TNCs in today’s globalized world and its implications for human rights and social responsibility within the framework of international investment law. Particularly, it acknowledges the vulnerability of economically weak South Asian states and cites past instances such as the Bhopal gas tragedy in India and the Rana Plaza disaster in Bangladesh as egregious violations of human rights. Focusing on South Asian bilateral investment treaties (BITs), this paper aims to examine the scope of investors’ social accountability.

This research engages with doctrinal and analytical methods in traversing through primary and secondary sources. It would parse the arbitral tribunals’ jurisprudence for their discussion on the inclusion of social accountability obligations within international investment agreements (IIAs). Further, it engages in a quantitative analysis related to the nature of the social accountability-related obligation of the corporation within South Asian BITs.

The findings reveal a glaring absence of the law on investors’ social accountability and the need for enhanced regulatory mechanisms to address the escalating influence of TNCs on human and social rights. The absence of a robust legal framework, coupled with the asymmetric nature of international investment law, granting investors greater rights and leverage compared to states, exacerbates this challenge. The phenomenon of “regulatory chill” inhibits states from effectively enforcing regulatory measures aimed at protecting human rights and the environment. Furthermore, the broad interpretation of clauses such as “fair and equitable treatment” by investment tribunals often undermines states’ ability to implement measures in the public interest. While international organizations such as the UNCTAD and the UNCITRAL Working Group III are actively discussing reforms to IIAs, the existing guidelines addressing investors’ social accountability are woefully lacking in the content as well as the method of their integration with international human rights law. The findings underscore the imperative for South Asian nations, the subject of this research’s empirical analysis, to adopt a comprehensive approach involving both domestic law reforms to promote corporate social accountability and active pursuit of negotiations for the inclusion of binding social obligations for investors within IIAs.

This research, drawing upon international law developments, offers suggestions for incorporation of social accountability provisions via relevant domestic law reform. The research could be viewed as a prelude for mapping the legal developments in the area of investors’ social accountability within investment agreements, as well as investment contracts, drawing guidance from international law instruments.

To the best of the authors’ knowledge, no other study analysed the scope of investors’ social accountability in South Asian BITs.

This paper studies the impact of societal trust on the conservative financing policy puzzle, aiming to cover a gap in the relationship between cultural values and the conservative financing policy.

We use a sample of 14,509 privately held medium-sized manufacturing firms from 26 European countries between 2015 and 2020 and rely on logistic regression methods controlling for firm-specific and macroeconomic factors.

We show that societal trust decreases the odds of being a zero-leverage or almost zero-leverage firm. Also, the probability of being a conservatively financed firm increases for older and more profitable firms and decreases with tangibility. In more trustworthy national environments, firms are less averse to debt as a source of financing. Our results are robust to the specific measure of trust, estimation methods, sampling procedures, and annual financial constraint status. Moreover, we show that the effect is noticed both in the long-term debt and the short-term debt with a lower economic impact in the latter situation and that increased societal trust attenuates (reinforces) the effect of being a financially constrained (unconstrained) firm on the odds of adopting a conservative financing policy.

Societal trust strategically impacts debt financing policy and could help foster firms’ growth, particularly for those facing heavier financial constraints.

Novel evidence on the impact of societal trust on the conservative financing policy, for privately held medium-sized European firms.

Manufacturing enterprises have started to offer the “outcome” derived from machines with the help of outcome-based contracts (OBCs). Offering OBCs leads to benefits such as increased revenues, stronger customer relationships and sustainability. However, implementing OBCs requires critical capabilities. Existing literature has focused on identifying these necessary capabilities, but the prioritization and interrelationships among them remain unexplored. This study aims to address this gap.

Our study employs a hybrid analytical hierarchy process and interpretative structural modeling approach to prioritize and map interrelationships among OBC-related capabilities within small and medium-sized enterprises (SMEs).

The findings highlight the importance of digitalization capabilities such as data privacy and security, remote monitoring, and data analytics; and organizational and governance capabilities, including quantifying, controlling, and monitoring risks, teamwork, and leadership, are highlighted.

We quantitatively prioritize OBC capabilities and establish their level-wise structural interrelationships, which will facilitate a more effective and efficient implementation of OBCs. Due to the emergent nature of OBCs, our study could identify just one SME case company meeting our selection criteria.

Existing OBC literature focusses on the design of OBCs in large companies. Similarly, earlier capability-related OBC literature is oriented toward identifying the OBC capabilities to perform specific functions. However, in the current study, we propose a systematic decision-making approach that comprehensively prioritizes and identifies the interrelationships among the capabilities necessary to provide OBCs, thus complementing the existing scientific literature on OBCs. In addition, we focus on SMEs, that have specific limitations and characteristics.

Product returns are a major challenge for e-businesses as they involve huge logistical and operational costs. Therefore, it becomes crucial to predict returns in advance. This study aims to evaluate different genres of classifiers for product return chance prediction, and further optimizes the best performing model.

An e-commerce data set having categorical type attributes has been used for this study. Feature selection based on chi-square provides a selective features-set which is used as inputs for model building. Predictive models are attempted using individual classifiers, ensemble models and deep neural networks. For performance evaluation, 75:25 train/test split and 10-fold cross-validation strategies are used. To improve the predictability of the best performing classifier, hyperparameter tuning is performed using different optimization methods such as, random search, grid search, Bayesian approach and evolutionary models (genetic algorithm, differential evolution and particle swarm optimization).

A comparison of F1-scores revealed that the Bayesian approach outperformed all other optimization approaches in terms of accuracy. The predictability of the Bayesian-optimized model is further compared with that of other classifiers using experimental analysis. The Bayesian-optimized XGBoost model possessed superior performance, with accuracies of 77.80% and 70.35% for holdout and 10-fold cross-validation methods, respectively.

Given the anonymized data, the effects of individual attributes on outcomes could not be investigated in detail. The Bayesian-optimized predictive model may be used in decision support systems, enabling real-time prediction of returns and the implementation of preventive measures.

There are very few reported studies on predicting the chance of order return in e-businesses. To the best of the authors’ knowledge, this study is the first to compare different optimization methods and classifiers, demonstrating the superiority of the Bayesian-optimized XGBoost classification model for returns prediction.