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This paper aims to investigate whether professional data analysts’ trust of black-box systems is increased by explainability artifacts.

The study was developed in two phases. First a black-box prediction model was estimated using artificial neural networks, and local explainability artifacts were estimated using local interpretable model-agnostic explanations (LIME) algorithms. In the second phase, the model and explainability outcomes were presented to a sample of data analysts from the financial market and their trust of the models was measured. Finally, interviews were conducted in order to understand their perceptions regarding black-box models.

The data suggest that users’ trust of black-box systems is high and explainability artifacts do not influence this behavior. The interviews reveal that the nature and complexity of the problem a black-box model addresses influences the users’ perceptions, trust being reduced in situations that represent a threat (e.g. autonomous cars). Concerns about the models’ ethics were also mentioned by the interviewees.

The study considered a small sample of professional analysts from the financial market, which traditionally employs data analysis techniques for credit and risk analysis. Research with personnel in other sectors might reveal different perceptions.

Other studies regarding trust in black-box models and explainability artifacts have focused on ordinary users, with little or no knowledge of data analysis. The present research focuses on expert users, which provides a different perspective and shows that, for them, trust is related to the quality of data and the nature of the problem being solved, as well as the practical consequences. Explanation of the algorithm mechanics itself is not significantly relevant.

The paper provides a detailed historical account of Douglass C. North's early intellectual contributions and analytical developments in pursuing a Grand Theory for why some countries are rich and others poor.

The author approaches the discussion using a theoretical and historical reconstruction based on published and unpublished materials.

The systematic, continuous and profound attempt to answer the Smithian social coordination problem shaped North's journey from being a young serious Marxist to becoming one of the founders of New Institutional Economics. In the process, he was converted in the early 1950s into a rigid neoclassical economist, being one of the leaders in promoting New Economic History. The success of the cliometric revolution exposed the frailties of the movement itself, namely, the limitations of neoclassical economic theory to explain economic growth and social change. Incorporating transaction costs, the institutional framework in which property rights and contracts are measured, defined and enforced assumes a prominent role in explaining economic performance.

In the early 1970s, North adopted a naive theory of institutions and property rights still grounded in neoclassical assumptions. Institutional and organizational analysis is modeled as a social maximizing efficient equilibrium outcome. However, the increasing tension between the neoclassical theoretical apparatus and its failure to account for contrasting political and institutional structures, diverging economic paths and social change propelled the modification of its assumptions and progressive conceptual innovation. In the later 1970s and early 1980s, North abandoned the efficiency view and gradually became more critical of the objective rationality postulate. In this intellectual movement, North's avant-garde research program contributed significantly to the creation of New Institutional Economics.

In this conceptual paper the authors aim to discuss the implications of a second-order cybernetic approach and eigenbehavior in ecosystem management. The authors argue that traditional management practices rely on the agreement of stakeholders, but this approach cannot satisfy multiple-objective realities, making radical constructivism necessary.

The authors examined this conjecture using a multi-agent simulation exercise. Agents display individual behavior consistent with the organizing principles proposed by von Foerster (eigenform and eigenbehavior), Hoffman (perception and fitness) and Maturana (domains of realization). This exercise is complemented with an ecosystem management classification exercise to show how convergence is achieved when a team of trained professionals describes entities of low versus high complexity in a spatially explicit domain.

The authors showed that eigenforms can diverge significantly depending on the complexity of the problem. The capacity of observers to stabilize an eigenform and create a common ground for understanding depends on the complexity of the problem at hand and their cognitive diversity. The authors highlighted the difficulties that arose when observation and modification through the intervention of the environment cannot be detached. The authors argue that this situation deeply permeates ecosystem resource management due to the unpredictable outcomes displayed by entities embedded into open system dynamics.

Finally, the authors propose that the association between indication and transformation can be operationally interconnected as Eigenperception, which corresponds to a novel dynamic state bridging the gap between noise and eigenform. This concept of Eigenperception encapsulates entities that arise from eigenbehavior, encompassing observations, meanings, human transformative actions and ecological processes, all in a simultaneous manner.

This article examines the influence of market opportunity, risk, and distance on the choice of destination country for Mexican franchises.

The research hypotheses are developed under the theoretical approaches of institutional theory, agency theory, and transaction costs theory and were contrasted on the data obtained from 52 Mexican international franchisors operating in 37 countries as of 2016. This study uses linear regression with ordinary minimums using the STATA 13.1 software.

The results reveal that a larger market size, a greater level of economic freedom, and a smaller geographic distance are determining factors in the choice of destination country. No statistical significance was found in the variables GDP per capita, level of democracy and cultural distance.

This research contributes to the theoretical and practical field. On the theoretical side, this study integrates institutional theory, agency theory, and transaction cost theory to evaluate the factors of the destination country that influence the internationalization process of the franchise. Another contribution of this study is to apply theories and models of developed economies to the process of internationalization of franchises in an emerging economy. Additionally, this study is based on a model that considers the distance, opportunities and risks that are considered by Mexican franchisors in the selection of the international markets in which they maintain operations. This study contains important practical implications that can serve as relevant information for decision-making in the franchise sector and its internationalization. This data is valuable for new models of Mexican franchises that decide to start their internationalization process.

The main purpose of this study is to examine the impact of imported inputs on firms' productivity in selected South Asian economies, namely Pakistan, India and Bangladesh. Furthermore, this study explores the complementarity between firms' capabilities and imported inputs in an augmented productivity framework.

A dataset comprising 7117 manufacturing firms of selected South Asian economies was taken from the World Bank for 2013 and 2014. The empirical analysis was based on stochastic frontier models, the ordinary least square method and instrumental variable estimation techniques.

The empirical results show that imported inputs have positive and significant effects on the firms' productivity in the selected countries. Moreover, the study findings demonstrate that firms' capabilities play a complementary role in expanding the firms' production frontier.

The study outcomes suggest that reducing tariffs on imported inputs will enhance the firms' productivity in the selected emerging economies. However, the study further finds that the potential gain of imported inputs is conditional on the firm's capabilities. It implies that firms operating in these countries can improve their performance by allocating more resources to capabilities, such as workers’ training, management and internal R&D effort.

The existing literature on the subject is sceptical about the positive impact of imported inputs on firms' productivity in the case of developing countries. In this regard, the shortage of skilled labour and firms' capabilities are compelling rationales that need to be explored. Thus, the potential contribution of the study lies in explaining the moderating role of firm's capabilities operating in the selected emerging economies in the nexus of imported inputs and productivity.

The purpose of this study is to examine the impact of carbon performance on carbon disclosure among nonfinancial French-listed firms, while also considering the corporate board’s characteristics as a secondary objective.

This study uses a sample of Société des Bourses Françaises 120 Index (SBF-120) French-listed firms to investigate the effect of multiple carbon performance proxies on carbon disclosure based on random effects models for the period 2010–2021. Generalized method of moments regressions are used to encounter endogeneity problems.

Drawing on stakeholder theory, this paper finds that greater carbon performance leads to greater carbon disclosure. Given the growing societal awareness about climate-change issues, carbon-responsible firms are likely to disseminate relevant carbon-related information through disclosures to respond to the information demands of a varied stakeholder group. Coherent with signaling theory, large firms that undertake carbon-reduction initiatives tend to disclose more information about their enhanced carbon performance to equity participants to distinguish themselves and highlight their decarbonization efforts.

This study offers significant insights given that SBF-120 firms are involved in climate-change activities as a response to the growing institutional and societal pressure to perform better and disclose reliable environmental information in their sustainability reports.

Supply chain finance (SCF) is an advanced financing solution for medium and small organizations. However, due to its nascent stage, SCF is still struggling with its application in the supply chain. The blockchain application in SCF can bring lots of opportunities and smart techniques to deal with financing to organizations. However, some factors restrict the proper adoption of blockchain in SCF. Therefore, this study aims to identify and analyze the factors obstructing blockchain adoption in SCF.

The present study applied a hybrid fuzzy DELPHI-AHP-DEMATEL to validate and analyze the factors. The fuzzy Delphi techniques were adopted to validate the identified factors. Thereafter, the analytic hierarchy process (AHP) has been used to prioritize the validated factors. Further, the decision-making trial and evaluation laboratory (DEMATEL) method has opted to classify the factors into cause-and-effect groups.

The result of the study revealed that lack of technological competence and confidentiality leaking are the most prioritized factors and act as the prime factors for the obstacles in the blockchain application in the SCF. The application of the DEMATEL method revealed that the highest causing factor is authenticity, and the highest effecting factor is high cost.

The current study enhances the knowledge and information of managers and policymakers about the challenges associated with the blockchain application in SCF as well as provides better insight to deal with various factors obstructing blockchain adoption in SCF.

The current study is the first of its kind, where factors obstructing blockchain adoption in SCF are identified and validated with the help of experts and practitioners by incorporating the fuzzy Delphi method. Thereafter, the validated factors are prioritized by using fuzzy AHP and fuzzy DEMATEL techniques helps to identify the cause-and-effect group, which opens the door for future research work.

This study presents a systematic literature review (SLR) of the interdisciplinary literature on drones in last-mile delivery (LMD) to extrapolate pertinent insights from and into the logistics management field.

Rooting their analytical categories in the LMD literature, the authors performed a deductive, theory refinement SLR on 307 interdisciplinary journal articles published during 2015–2022 to integrate this emergent phenomenon into the field.

The authors derived the potentials, challenges and solutions of drone deliveries in relation to 12 LMD criteria dispersed across four stakeholder groups: senders, receivers, regulators and societies. Relationships between these criteria were also identified.

This review contributes to logistics management by offering a current, nuanced and multifaceted discussion of drones' potential to improve the LMD process together with the challenges and solutions involved.

The authors provide logistics managers with a holistic roadmap to help them make informed decisions about adopting drones in their delivery systems. Regulators and society members also gain insights into the prospects, requirements and repercussions of drone deliveries.

This is one of the first SLRs on drone applications in LMD from a logistics management perspective.

The financial world of today is evolving at a rate that can be challenging to keep up with and comprehend due to developments in information and communication technology. When compared to a conventional disclosure, the eXtensible Business Reporting Language (XBRL), which was named one of the top ten accounting technologies, has a clear advantage in reducing information asymmetry by providing interactive data disclosure. This study aims to examine whether forcing companies to adopt XBRL would cause them to prefer misclassifying income statement items as an alternative to more risky earnings management methods.

The study sample includes nonfinancial UAE companies listed on Dubai Financial Market and Abu Dhabi Securities Exchange from 2012 to 2019. Fixed effect and system General Method of Moments regressions were used to analyze the study data.

The study found that XBRL reporting resulted in lowering the quality of financial reporting as companies have a higher tendency to misclassify income statement items as earnings management mechanism.

The findings of this research can be used by stakeholders and practitioners in the UAE to better understand whether the use of XBRL is linked to the engagement of financial reporting manipulative practices. The findings of this study also inform policymakers and regulators about the consequences of companies formally adopting digital disclosure language in an effort to improve the quality of their reporting. Besides, the results offer guidance to regulators considering imposing XBRL usage regulations.

Limited number of studies have tested the association between XBRL mandatory adoption and misclassification of income statement items as an earnings management tool in the Gulf Cooperation Council region.

Neo-institutional theories and their constructs have so far only received limited attention in supply chain management literature. As recent supply chain disruptions and their ripple effects affect actors on a broader institutional level, supply chains are confronted with multiple new and emerging, often conflicting, institutional demands. This study aims to unpack the notion of institutional complexity behind supply chain disruptions and present a novel institutional framework to lower supply chain susceptibility and increase supply chain resilience.

The authors identify the patterns of complexity that shape the supply chain susceptibility, namely, distance, diversity and ambiguity, and present three institutional responses to susceptibility to increase supply chain resilience, namely, institutional entrepreneurship, institutional alignment and institutional layering.

This paper analyses the current situational relevance to better understand the various and patterned ways how logics influence both supply chain susceptibility and the supply chain resilience. The authors derive six propositions on how complexity can be reduced for supply chain susceptibility and can be increased for supply chain resilience.

By expanding and extending research on institutional complexity to supply chains, the authors broaden how researchers in supply chain management view supply chain susceptibility, thereby providing managers with theory to think differently about supply chains and its resilience.