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Drawing on information processing theory, the linkage between buffering and bridging and the ability on the part of procurement to resolve demand–supply imbalances is investigated, as well as contexts in which these strategies may be particularly useful or detrimental. Buffering may be achieved through demand change or redundancy, while bridging may be achieved by the means of collaboration or monitoring.

This study employs a hierarchical regression analysis of a survey of 150 Finnish and Swedish procurement and sales and operations planning professionals, each responding from the perspective of their own area of supply responsibility.

Both the demand change and redundancy varieties of buffering are associated with procurement's ability to resolve demand–supply imbalances without delivery disruptions, but not with cost-efficient resolution. Bridging is associated with the cost-efficient resolution of imbalances: while collaboration offers benefits, monitoring seems to make things worse. Dynamism diminishes, while the co-management of procurement in S&OP improves procurement's ability to resolve demand–supply imbalances. The most potent strategy for tackling problematic contexts appears to be buffering via demand change.

The results highlight the importance of procurement in the S&OP process and suggest tactical measures that can be taken to resolve and reduce the effects of supply and demand imbalances.

The results contribute to the procurement and S&OP literature by increasing knowledge regarding the role and integration of procurement to the crucial process of balancing demand and supply operations.

Marx suggested that it is infeasible and wrong to arrange the economic categories according to the order by which they have worked in history. Their order is determined by their interrelationship in the modern bourgeois society, which is in contrast to their natural sequence or that which is in accordance with the course of history. Sometimes, a logical sequence is precisely opposite to the historical sequence. There are many efforts to be done in the study of China’s economic and social issues with Marxist logical and historical methods. The paper aims to discuss these issues.

When reading Das Kapital, we can clearly see the historical materialism methods. Another method of Marxist political economics is the scientific abstract method.

This is based on the new development idea to carry out scientific and technological innovation and change the focus of development from quantity to quality. With regard to the supply side structural reform as the main focus, people’s ever-growing demand for a better life can be satisfied and the higher level dynamic supply–demand balance can be kept.

In fact, measures to remedy unbalanced and inadequate development of the social principal contradiction have been plainly indicated in the report delivered at the 19th National Congress of the Communist Party of China. This is based on the new development idea to carry out scientific and technological innovation and change the focus of development from quantity to quality.

The disability datasets are the datasets that contain the information of disabled populations. By analyzing these datasets, professionals who work with disabled populations can have a better understanding of the inherent characteristics of the disabled populations, so that working plans and policies, which can effectively help the disabled populations, can be made accordingly.

In this paper, the authors proposed a big data management and analytic approach for disability datasets.

By using a set of data mining algorithms, the proposed approach can provide the following services. The data management scheme in the approach can improve the quality of disability data by estimating miss attribute values and detecting anomaly and low-quality data instances. The data mining scheme in the approach can explore useful patterns which reflect the correlation, association and interactional between the disability data attributes. Experiments based on real-world dataset are conducted at the end to prove the effectiveness of the approach.

The proposed approach can enable data-driven decision-making for professionals who work with disabled populations.

Considering the increased financial responsibility of local government (LG), the impact of global crises and the growing adoption of accrual accounting and common standards such as IPSAS, this work focuses on financial indicators for LGs. It explores whether the literature on financial indicators has grown, investigates whether there is any consensus on which indicators to use for assessing LG's financial condition, develops a critical reading of the literature and offers suggestions for future research and policy agendas.

A structured literature review was carried out for publications in English about LG financial indicators.

Results reveal that the number of publications dealing with financial indicators has increased over the past ten years. However, rather than focusing on a set of common indicators, the literature reports a plethora of different ones used for four main purposes: transparency and accountability compliance, performance monitoring and benchmarking, assessing LG's financial health and helping deal with exogenous crises. There is no evidence of convergence towards a common set of indicators, even though liquidity and solvency are the most popular dimensions explored by scholars.

Findings highlight the challenges in converging on financial indicators, yet no claim can be made beyond the reviewed material.

Results provide legislators, public managers, investors and rating agencies with insights about trends in financial indicators, their benefits and limitations.

The article focuses on a less popular aspect of recent financial management reforms for local administration, that is the growing fragmentation in LG indicators, accentuated by the need for common assessment tools during unprecedented widespread crises across countries and sectors.

Recommendation systems are a fundamental tool for hotels to adopt a differentiating competitive strategy. The main purpose of this work is to use machine learning techniques to treat imbalanced data sets, not applied until now in the tourism field. These techniques have allowed the authors to analyse the influence of imbalance data on hotel recommendation models and how this phenomenon affects client dissatisfaction.

An opinion survey was conducted among hotel customers of different categories in 120 different countries. A total of 135.102 surveys were collected over eleven quarters. A longitudinal design was conducted during this period. A binary logistic model was applied using the function generalized lineal model (GLM).

Through the analysis of a representative amount of data, the authors empirically demonstrate that the imbalance phenomenon is systematically present in hotel recommendation surveys. In addition, the authors show that the imbalance exists independently of the period in which the survey is done, which means that it is intrinsic to recommendation surveys on this topic. The authors demonstrate the improvement of recommendation systems highlighting the presence of imbalance data and consequences for marketing strategies.

The main contribution of the current work is to apply to the tourism sector the framework for imbalanced data, typically used in the machine learning, improving predictive models.

The purpose of this study is to reveal the magnitude of empty container movements (ECM) arising from cargo seasonality by means of long-term datasets of Turkish terminals. Trade imbalance is one of the well-known major reasons of ECM. Cargo seasonality apart from some other operational drivers and market effect, i.e. commercial decisions of the ship operators, is the major operational driver in Turkish terminals effecting ECM. Furthermore, this study highlights the significance of market effect, leading to take measures for more effective empty container operations in terms of decision makers leading the ship operators.

Time series analysis of full container datasets was performed through X-13ARIMA-SEATS methodology, implementing seasonal adjustment.

The results indicate that 17 of 112 time series in hand, based on a terminal/hinterland, container type and “in and out” foreign trade, exhibit cargo seasonality. Roughly, the amount of ECM originating from cargo seasonality in Turkish terminals represents 10 per cent of total ECM except trade imbalance in those terminals where seasonality is present. This reveals that ECM arising from market effect should not be underestimated.

Reefer container traffic could not be sorted from the datasets.

This paper focuses on one of the major reasons of ECM, cargo seasonality. It brings a novel point of view and interpretations which were not suggested previously about ECM, motivating to overcome inefficiency in container operations.

Traditional Chinese medicine (TCM) prescriptions have always relied on the experience of TCM doctors, and machine learning(ML) provides a technical means for learning these experiences and intelligently assists in prescribing. However, in TCM prescription, there are the main (Jun) herb and the auxiliary (Chen, Zuo and Shi) herb collocations. In a prescription, the types of auxiliary herbs are often more than the main herb and the auxiliary herbs often appear in other prescriptions. This leads to different frequencies of different herbs in prescriptions, namely, imbalanced labels (herbs). As a result, the existing ML algorithms are biased, and it is difficult to predict the main herb with less frequency in the actual prediction and poor performance. In order to solve the impact of this problem, this paper proposes a framework for multi-label traditional Chinese medicine (ML-TCM) based on multi-label resampling.

In this work, a multi-label learning framework is proposed that adopts and compares the multi-label random resampling (MLROS), multi-label synthesized resampling (MLSMOTE) and multi-label synthesized resampling based on local label imbalance (MLSOL), three multi-label oversampling techniques to rebalance the TCM data.

The experimental results show that after resampling, the less frequent but important herbs can be predicted more accurately. The MLSOL method is shown to be the best with over 10% improvements on average because it balances the data by considering both features and labels when resampling.

The authors first systematically analyzed the label imbalance problem of different sampling methods in the field of TCM and provide a solution. And through the experimental results analysis, the authors proved the feasibility of this method, which can improve the performance by 10%−30% compared with the state-of-the-art methods.

This paper aims to provide empirical evidence on formal stakeholder participation (or “lobbying”) in the early phase of the International Sustainability Standards Board’s (ISSB’s) standard-setting.

Drawing on a rational-choice framework, this paper conducts a content analysis of comment letters (CLs) submitted to the ISSB in response to its first two exposure drafts (published in 2022) to investigate stakeholder participation across different groups and jurisdictional origins. The analyses examine participation in terms of frequency (measured using the number of participating stakeholders) and intensity (measured using the length of CLs).

Preparers and users of sustainability reports emerge as the largest participating stakeholder groups, while the accounting/sustainability profession participates with high average intensity. Surprisingly, preparers do not outweigh users in terms of participation frequency and intensity; and large preparers outweigh smaller ones in terms of participation intensity but not participation frequency. Internationally, stakeholders from countries with a private financial accounting standard-setting system participate more frequently and intensively than others. In addition, country-level economic wealth and sustainability performance are positively associated with more participating stakeholders.

This study is of interest for organizations and stakeholders involved in or affected by standard-setting in the field of sustainability reporting. The finding of limited participation by investors and from developing countries suggests the ISSB take actions to enhance the voice of those stakeholders.

The imbalances in stakeholder participation that were found pose potential threats to an important aspect of the input legitimacy of the ISSB’s standard-setting process.

To the best of the authors’ knowledge, this paper is the first to explore stakeholder participation by means of CLs with the ISSB in terms of frequency and intensity.

Additive manufacturing, that is, layer-based manufacturing technologies, is thought to change supply chain operations from global to local, while also affecting design processes and product structures. As this transformation happens, a power struggle among various actors relating themselves to additive manufacturing has emerged. The purpose of this paper is to discuss and explain the development of additive manufacturing from a power dependence point of view.

The paper is based on data collected from a number of seminars hosting a total of 620 industry experts representing 102 companies in the area, and reflecting every step of the supply chain.

The paper points out how measures to deal and create power imbalances occur also related to indirect parties, and how the disruptive character of the supply chain leads to exercised power.

The power struggle provides new insights into how an emerging technology is realised and the effect of protectionism on such attempts. Specifically related to additive manufacturing, the paper illustrates the business side from various actors’ point of view, which adds to technological perspectives on additive manufacturing, as well as studies viewing the supply chain from a bird’s-eye perspective.