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This study aims to improve the passenger accessibility of passenger demands in the end-of-operation period.

A mixed integer nonlinear programming model for last train timetable optimization of the metro was proposed considering the constraints such as the maximum headway, the minimum headway and the latest end-of-operation time. The objective of the model is to maximize the number of reachable passengers in the end-of-operation period. A solution method based on a preset train service is proposed, which significantly reduces the variables of deciding train services in the original model and reformulates it into a mixed integer linear programming model.

The results of the case study of Wuhan Metro show that the solution method can obtain high-quality solutions in a shorter time; and the shorter the time interval of passenger flow data, the more obvious the advantage of solution speed; after optimization, the number of passengers reaching the destination among the passengers who need to take the last train during the end-of-operation period can be increased by 10%.

Existing research results only consider the passengers who take the last train. Compared with previous research, considering the overall passenger demand during the end-of-operation period can make more passengers arrive at their destination. Appropriately delaying the end-of-operation time can increase the proportion of passengers who can reach the destination in the metro network, but due to the decrease in passenger demand, postponing the end-of-operation time has a bottleneck in increasing the proportion of passengers who can reach the destination.

The purposes of this research are to study the theory and method of multi-attribute index system design and establish a set of systematic, standardized, scientific index systems for the design optimization and inspection process. The research may form the basis for a rational, comprehensive evaluation and provide the most effective way of improving the quality of management decision-making. It is of practical significance to improve the rationality and reliability of the index system and provide standardized, scientific reference standards and theoretical guidance for the design and construction of the index system.

Using modern methods such as complex networks and machine learning, a system for the quality diagnosis of index data and the classification and stratification of index systems is designed. This guarantees the quality of the index data, realizes the scientific classification and stratification of the index system, reduces the subjectivity and randomness of the design of the index system, enhances its objectivity and rationality and lays a solid foundation for the optimal design of the index system.

Based on the ideas of statistics, system theory, machine learning and data mining, the focus in the present research is on “data quality diagnosis” and “index classification and stratification” and clarifying the classification standards and data quality characteristics of index data; a data-quality diagnosis system of “data review – data cleaning – data conversion – data inspection” is established. Using a decision tree, explanatory structural model, cluster analysis, K-means clustering and other methods, classification and hierarchical method system of indicators is designed to reduce the redundancy of indicator data and improve the quality of the data used. Finally, the scientific and standardized classification and hierarchical design of the index system can be realized.

The innovative contributions and research value of the paper are reflected in three aspects. First, a method system for index data quality diagnosis is designed, and multi-source data fusion technology is adopted to ensure the quality of multi-source, heterogeneous and mixed-frequency data of the index system. The second is to design a systematic quality-inspection process for missing data based on the systematic thinking of the whole and the individual. Aiming at the accuracy, reliability, and feasibility of the patched data, a quality-inspection method of patched data based on inversion thought and a unified representation method of data fusion based on a tensor model are proposed. The third is to use the modern method of unsupervised learning to classify and stratify the index system, which reduces the subjectivity and randomness of the design of the index system and enhances its objectivity and rationality.

The purpose of this paper is to analyze a variety of factors that can explain the differences in commercial bank efficiency among 17 countries in Latin America (LatAm).

In a first stage, data envelopment analysis (DEA) and conditional efficiency analysis techniques are used to assess the relative efficiency level of 409 banks for the 2014-2016 period. The conditional efficiency approach considers environmental variables (that are beyond the manager’s control), which could influence the shape and the level of the boundary of the attainable set. In the second stage, the resulting conditional efficiency scores are correlated with internal variables (those that are under the manager’s control), which might affect the distribution of the inefficiencies. For this purpose, an econometric approach developed by Simar and Wilson (2007) is used.

First stage scores reveal the heterogeneity of average efficiency within the region. Regarding the factors that may explain the differences in performance in the LatAm banking sector, the results allow us to state that certain internal variables such as bank size, the ratio of loans to total assets and the ratio of non-performing loans show the expected relationship to efficiency, in line with much of the previous literature.

This is the first time that conditional efficiency and Simar and Wilson (2007) approaches have been applied at the same time to analyse the LatAm banking industry.

Taiwan’s hotel industry was adversely impacted by the COVID-19 pandemic. This study aims to examine the effect of strategic choices by Taiwanese international tourist hotels before and during the pandemic environments.

A data envelopment analysis (DEA)-based Malmquist methodology is used in this study to provide a mechanism to assess Taiwanese hotel strategy performance. Changes in the productivity and performance of Taiwanese international tourist hotels were analyzed in the periods before and during the pandemic to uncover insights useful should a similar crisis occur in the future. Panel data were obtained from the annual report of international tourist hotels published by the Taiwan Tourism Bureau from 2017–2020. Two groups of hotels were analyzed in this study: city hotels and scenic hotels.

The findings of this study reveal that chain hotels tended to perform better than independent hotels in both city and scenic areas during the global pandemic. Specifically, the crisis caused a substantial decline in productivity and profitability for international tourist hotels in Taipei City during the COVID-19 period. Compared to city hotels, findings also indicate that most international tourist hotels in scenic areas were able to maintain better productivity, including larger-sized scenic hotels.

The DEA-based analysis provides unique and valuable insights for hotel firm leaders on how to better identify and make strategic choices when responding to future crises.

The purpose of this paper is to address the issue of knowledge visualization and its connection with performance measurement from an epistemological point of view, considering quantification and measurement not just as technical questions but showing their relevant implications on the management decision-making of knowledge-based organizations.

This study proposes a theoretical contribution that combines two lines of research for identifying the three main meta-choices problems that arise in the multidimensional benchmarking of knowledge-based organizations. The first is the meta-choice problem related to the choice of the algorithm used (Iazzolino et al., 2012; Laise et al., 2015; Daraio, 2017a). The second refers to the choice of the variables to be included in the model (Daraio, 2017a). The third concerns the choice of the data on which the analyses are carried out (Daraio, 2017a).

The authors show the interplay existing among the three meta-choices in multidimensional benchmarking, considering as key performance indicators intellectual capital, including Human Capital, Structural Capital and Relational Capital, and performances, evaluated in financial and non-financial terms. This study provides an empirical analysis on Italian Universities, comparing the ranking distributions obtained by several efficiency and multi-criteria methods.

This study demonstrates the difficulties of the “implementation problem” in performance measurement, related to the subjectivity of results of the evaluation process when there are many evaluation criteria, and proposes the adoption of the technologies of humility related to the awareness that we can only achieve “satisficing” results.

Research into the interpretability and explainability of data analytics and artificial intelligence (AI) systems is on the rise. However, most recent studies either solely promote the benefits of explainability or criticize it due to its counterproductive effects. This study addresses this polarized space and aims to identify opposing effects of the explainability of AI and the tensions between them and propose how to manage this tension to optimize AI system performance and trustworthiness.

The author systematically reviews the literature and synthesizes it using a contingency theory lens to develop a framework for managing the opposing effects of AI explainability.

The author finds five opposing effects of explainability: comprehensibility, conduct, confidentiality, completeness and confidence in AI (5Cs). The author also proposes six perspectives on managing the tensions between the 5Cs: pragmatism in explanation, contextualization of the explanation, cohabitation of human agency and AI agency, metrics and standardization, regulatory and ethical principles, and other emerging solutions (i.e. AI enveloping, blockchain and AI fuzzy systems).

As in other systematic literature review studies, the results are limited by the content of the selected papers.

The findings show how AI owners and developers can manage tensions between profitability, prediction accuracy and system performance via visibility, accountability and maintaining the “social goodness” of AI. The results guide practitioners in developing metrics and standards for AI explainability, with the context of AI operation as the focus.

This study addresses polarized beliefs amongst scholars and practitioners about the benefits of AI explainability versus its counterproductive effects. It poses that there is no single best way to maximize AI explainability. Instead, the co-existence of enabling and constraining effects must be managed.

The Indian pharmaceutical industry has contributed significantly to global healthcare by securing superior-quality, inexpensive and reachable medicines worldwide. However, supply chain management (SCM) has been challenging due to constantly shifting requirements for short lifecycles of products, the convergence of industry and changeable realities on the ground. This study aims to identify, assess and prioritize the strengths, weaknesses and opportunities of the pharmaceutical SCM environment in India.

The paper employs a Strength, Weakness, Opportunity, Threat (SWOT) analysis and recognizes strategies to utilize the advantages of the strengths and opportunities, rectify weaknesses and resolve threats.

A variety of strategies that could have a positive effect on the Indian pharmaceutical business are presented. Findings and suggested strategies can significantly advance knowledge, enhance understanding and contribute to the growth of a successful SCM for the Indian pharmaceutical sector.

This paper would act as a roadmap to greater comprehension of the market leaders and market leaders' operating climate. The findings from this study will offer academic scholars and business practitioners deeper insights into the environment of SCM.

Construction projects operate within a risky environment. It materialises as delays, which must be prevented or mitigated to avoid becoming amplified into late completion. But previous research has largely ignored how structural complexity of the underlying network schedules shapes their resilience.

This research hypothesizes that schedule structure plays a vital role in its ability to absorb or propagate delays. The impact of activity-level local risk factors is represented via activity duration distributions, i.e. probability density functions. The impact of project-level global risk factors is more challenging because they arise via interactions between multiple activities.

Modelling resilience to local and global risk factors can employ a matrix approach. Simulation shows that delay amplification depends on local structure, not global complexity.

Criticality had merely relied upon a single deterministic analysis of a network schedule to categorize activities as having zero or nonzero float from fixed relative duration a dependency structure. Repeated probabilistic analysis with sampled durations gives criticality indices of activities. This research limits itself to network schedules with point-wise relations between activities.

Managers can use this knowledge to develop schedules that protect their expected project duration with a suitable structural complexity.

Contributions to the body of knowledge are as follows: It converts the dependency structure into a reachability matrix and adds a correlation matrix to capture how the predecessor performance may impact its successors. It correlates criticality of activities with structural complexity indices. And it ranks activities objectively by their cruciality, i.e. potential delay propagation.

Motivated to know more about the internal means through which accountability for sustainability takes shape within organisations (in what ways and by whom), this paper aims to explore how accountability for sustainability is constructed within an organisation during a process of establishing a control system for sustainability.

This paper adopts a qualitative case study approach of a decentralised industrial group, operating mainly in Scandinavia, between 2017 and 2020. Both primary and secondary data are used (e.g. document analyses, semi-structured interviews, informal conversations and site visits) to inform the findings and analysis.

The findings reveal a multi-faceted path towards accountability for sustainability that involves several concerns and priorities at organisational and individual levels, resulting in a separate sustainability control systems within each subsidiary company. Although hierarchical structures for accountability exist, socialising accountability activities are needed to (further) mobilise sustainable accounts.

Successful sustainable control systems require employees making sense of formalised accountability instruments (e.g. policies and procedures) to establish their roles and responsibilities in organisations.

This paper proposes socialisation processes as important for driving forward sustainability solutions.

This study elaborates on the internal accountability dynamic for the construction of sustainable accounts. Its novelty is built upon the interaction of hierarchical and socialising accountability forms as necessary for establishing a control system for sustainability. It furthermore illustrates the relationship between the external and internal pathways of accountability.

This study aimed to assess whether sociodemographic variables explain significant differences in attitudes towards transforming academic conferences into more sustainable events.

An analytical model of participants' attitudes towards sustainable conferences based on literature review as well as the theories of reasoned action and planned behaviour was developed and applied to a sample of 532 surveyed individuals from 68 countries who regularly attended academic conferences in the last five years prior to 2020. The results were refined using statistical and computational techniques to achieve more empirically robust conclusions.

Results reveal that sociodemographic variables such as attendees' gender and age explain differences in attitudes. Women and older adults have stronger pro-environmental attitudes regarding event sustainability. On the other hand, attitudes towards more sustainable academic conferences are quite strong and positive overall. More sustainable events' venues, catering, conference materials and accommodations strongly influence attendees' attitudes towards more sustainable conferences. The strength of attitudes was weaker towards transportation.

First, the analyses focused on only aspects related to the attendees' attitudes. Assessing their real behaviour would complete this research. The geographical areas defined by the U.N. and used in this study have the limitation of combining highly developed countries and developing countries in the same geographical area, for example, the Americas and Asia and the Pacific.

Specific socio-demographic variables' effects on attitudes towards sustainable academic conferences can indicate how organisers can best promote these events according to attendees' characteristics and develop differentiated marketing campaigns. For women and older adults, event sustainability should be emphasised as a competitive strategy to promote events and attract these audiences. Marketing strategies for younger attendees (under 30 years old) could focus on technology, networking or attractive social programmes. Sustainable venues, catering, conference materials and accommodations are easier to promote. Event organisers should encourage participants to make more environmentally friendly decisions regarding more sustainable event transport.

A strategy based on promoting the event as contributing to sustainable development could educate attendees and put them on the path to developing stronger positive attitudes regarding sustainability and more sustainable behaviours. Sustainable academic conferences can educate students, organisers, service providers and delegates through their involvement in sustainable practices.

To our best knowledge, this research is the first to assess whether sociodemographic variables explain significant differences in attitudes towards the sustainable transformation of academic conferences.