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This paper aims to evaluate the role of depositor-specific features in a bank resolution. As the resolution framework in the EU is rather new, there are no empirical studies referring to the efficiency of this mechanism in protecting financial stability. Thus, the authors have checked the role of societal awareness of deposit guarantee schemes and the resolution, as well as the trust in public institutions, in avoiding bank runs in the case of resolution scenarios.

The study is based on telephone interviews conducted with 1,000 Poles, including bank customers whose banks have undergone resolution in recent years, and basic statistics of the resolved banks. The authors then apply two classes of models: binary probit regression and ordered probit regression.

The findings have indicated that the trust in public institutions and the experience gained with age play a key role in overall depositor behaviour. However, for resolutions, declared trust is replaced by case-specific trust based on the obtained information.

The survey is based on a sample of Polish citizens. In the future, international surveys may help diagnose cross-country differences among depositors. Moreover, studies on communication approaches may also support finding highly effective ways to reach various cohorts of depositors.

The existing literature on depositor behaviour in bank failure scenarios has relied on an experimental approach to test various research hypotheses. The research sample is not based on an experiment but on the responses of customers whose banks have actually undergone resolution.

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.

The purpose of this paper is to advance the understanding of paradoxes, underlying tensions and potential management strategies when integrating digital technologies into existing lean-based production systems (LPSs), with the aim of achieving synergies and fostering the development of production systems.

This study adopts a collaborative management research (CMR) approach to identify patterns of organisational tensions and paradoxes and explore management strategies to overcome them. The data were collected through interviews and focus group interviews with experts on lean and/or digital technologies from the companies, from documents and from workshops with the in-case researchers.

The findings of this paper provide insights into the salient organisational paradoxes embraced in the integration of digital technologies in LPS by identifying different aspects of the performing, organising, learning and belonging paradoxes. Furthermore, the findings demonstrate the intricacies and relatedness between different paradoxes and their resolutions, and more specifically, how a resolution strategy adopted to manage one paradox might unintentionally generate new tensions. This, in turn, calls for either re-contextualising actions to counteract the drift or the adoption of new resolution strategies.

This paper adds perspective to operations management (OM) research through the use of paradox theory, and we (1) provide a fine-grained perspective on why integration sometimes “fails” and label the forces of internal drift as mechanisms of imbalances and (2) provide detailed insights into how different management and resolution strategies are adopted, especially by identifying re-contextualising actions as a key to rebalancing organisational paradoxes in favour of the integration of digital technologies in LPSs.

Family constitutions are relatively new to the law of family companies, although there might have been forerunners in the history of entrepreneur families. The practical importance and the proliferation of family constitutions in German family companies are increasing, along with the discussion of family constitutions in legal literature. This new instrument of family governance is not law driven but business driven, it has been designed by business advisors. Its analysis and classification are still at the very beginning in academic research and practice. Even though family constitutions are generally deemed to be without any legal effect and not legally binding, from a legal point of view, this assumption is at least highly questionable.

This paper provides practical advice for archaeologists and heritage specialists wishing to use ML approaches to identify archaeological features in high-resolution satellite imagery (or other remotely sensed data sources). We seek to balance the disproportionately optimistic literature related to the application of ML to archaeological prospection through a discussion of limitations, challenges and other difficulties. We further seek to raise awareness among researchers of the time, effort, expertise and resources necessary to implement ML successfully, so that they can make an informed choice between ML and manual inspection approaches.

Automated object detection has been the holy grail of archaeological remote sensing for the last two decades. Machine learning (ML) models have proven able to detect uniform features across a consistent background, but more variegated imagery remains a challenge. We set out to detect burial mounds in satellite imagery from a diverse landscape in Central Bulgaria using a pre-trained Convolutional Neural Network (CNN) plus additional but low-touch training to improve performance. Training was accomplished using MOUND/NOT MOUND cutouts, and the model assessed arbitrary tiles of the same size from the image. Results were assessed using field data.

Validation of results against field data showed that self-reported success rates were misleadingly high, and that the model was misidentifying most features. Setting an identification threshold at 60% probability, and noting that we used an approach where the CNN assessed tiles of a fixed size, tile-based false negative rates were 95–96%, false positive rates were 87–95% of tagged tiles, while true positives were only 5–13%. Counterintuitively, the model provided with training data selected for highly visible mounds (rather than all mounds) performed worse. Development of the model, meanwhile, required approximately 135 person-hours of work.

Our attempt to deploy a pre-trained CNN demonstrates the limitations of this approach when it is used to detect varied features of different sizes within a heterogeneous landscape that contains confounding natural and modern features, such as roads, forests and field boundaries. The model has detected incidental features rather than the mounds themselves, making external validation with field data an essential part of CNN workflows. Correcting the model would require refining the training data as well as adopting different approaches to model choice and execution, raising the computational requirements beyond the level of most cultural heritage practitioners.

Improving the pre-trained model’s performance would require considerable time and resources, on top of the time already invested. The degree of manual intervention required – particularly around the subsetting and annotation of training data – is so significant that it raises the question of whether it would be more efficient to identify all of the mounds manually, either through brute-force inspection by experts or by crowdsourcing the analysis to trained – or even untrained – volunteers. Researchers and heritage specialists seeking efficient methods for extracting features from remotely sensed data should weigh the costs and benefits of ML versus manual approaches carefully.

Our literature review indicates that use of artificial intelligence (AI) and ML approaches to archaeological prospection have grown exponentially in the past decade, approaching adoption levels associated with “crossing the chasm” from innovators and early adopters to the majority of researchers. The literature itself, however, is overwhelmingly positive, reflecting some combination of publication bias and a rhetoric of unconditional success. This paper presents the failure of a good-faith attempt to utilise these approaches as a counterbalance and cautionary tale to potential adopters of the technology. Early-majority adopters may find ML difficult to implement effectively in real-life scenarios.

Unlike many high-profile reports from well-funded projects, our paper represents a serious but modestly resourced attempt to apply an ML approach to archaeological remote sensing, using techniques like transfer learning that are promoted as solutions to time and cost problems associated with, e.g. annotating and manipulating training data. While the majority of articles uncritically promote ML, or only discuss how challenges were overcome, our paper investigates how – despite reasonable self-reported scores – the model failed to locate the target features when compared to field data. We also present time, expertise and resourcing requirements, a rarity in ML-for-archaeology publications.

This paper aims to identify and evaluate resolutions to key learning and teaching challenges in very large courses that involve practical mathematics, such as foundational finance.

A design-based research approach is used across three semesters to iteratively identify practical problems within the course and then develop and evaluate resolutions to these problems. Data are collected from both students and teachers and analysed using a mixed-method approach.

The results indicate that key learning and teaching challenges in large foundational finance courses can be mitigated through appropriate consistency of learning materials; check-your-understanding interactive online content targeting foundational concepts in the early weeks; connection points between students and the coordinator to increase teacher presence; a sustained focus on supporting student achievement within assessments; and signposting relevance of content for the broader program and professional settings. Multiple design iterations using a co-design approach were beneficial to incrementally improve the course and consider multiple perspectives within the design process.

This paper develops a set of design principles to provide guidance to other practitioners who seek to improve their own courses.

The use of design-based research and mixed-method approaches that consider both student and teacher perspectives to examine the design of very large, foundational finance courses is novel.

This paper aims to examine the extent of price clustering in a selection of Islamic stocks listed in Indonesia, Malaysia and Pakistan and also investigates the determinants of the phenomenon at the firm level.

The author test the uniformity of price distribution in the selected securities. Then, the determinants of price clustering were investigated through multivariate analysis based on a binary logistic regression model. Following the arguments of Narayan et al. (2011), who emphasize the importance of considering firm heterogeneity when studying the phenomenon, the author conducts the empirical study at the firm level.

The evidence indicates that Islamic stocks show a mild level of price clustering. Only half of the stocks under analysis rejected the uniformity test in the distribution of prices. In these cases, investors exhibited a preference for prices ending at zero and five. The evidence does not confirm the cultural clustering theories. Price clustering is found to be positively associated with price level and relative bid-ask spread. Overall, the negotiation hypothesis, which predicts that investors prefer round prices to minimize the costs associated with negotiations, best explains most of our results.

The existence of price clustering is difficult to reconcile with the prediction of the efficient market hypothesis that prices should follow a random walk. Moreover, the evidence indicates that Muslim investors share a preference for round prices in some settings, under the assumption that Islamic stocks are mostly traded by Muslim investors.

To the author’s best knowledge, this is the first study to address the subject of price clustering in Islamic stocks.

Annual general meetings have been variously described as dull rituals for accountability versus entertaining theatre at the expense of accountability. The research analyses director and shareholder participation and dialogic interactions at annual and extraordinary general meetings of Cecil Rhodes' British South Africa Company (BSAC). The BSAC was incorporated under a royal charter in 1889 in return for power to exploit a huge territory, Rhodesia/now Zimbabwe. The BSAC's administration ceased in 1924/25. Thus, the BSAC had a dual mandate as a private for-profit listed company and to occupy and develop the territories on behalf of the British government.

The article analyses 29 BSAC general meeting minutes, comprising 25 full sets of verbatim minutes between 1895 and 1925. The study adopts manual content analysis. First, the research adopts conversational analysis to analyse director and shareholder turn-taking and moves by approving and dissenting shareholders. Second, the study identifies and analyses incidents of shareholder sentiment from the shareholder turns/moves. Finally, the article assesses how shareholder sentiment changed throughout the period and whether the BSAC's share price reflected the shareholder sentiment.

The BSAC's general meetings were associated with the greater colonial project of building the British Empire. The authors find almost 1,500 incidents of shareholder sentiment. Directors and shareholders take roughly an equal number of turns (excluding shareholder sentiment). Ritual and ceremony dominate director and shareholder turns and moves, while accountability to shareholders was minimal. The BSAC share price spiked in the early years of the project, waning after that. Shareholder sentiment, both positive and negative, reflect the share price behaviour.

A unique database of verbatim general meeting minutes records shareholders' reactions to what they heard in the form of sounding off through cheering, “hear, hears,” laughter and applause (i.e. shareholder sentiment).

Trade relations between China and the USA have been marked by conflict, especially since China’s membership in the World Trade Organization (WTO). These conflicts have been analyzed from a variety of perspectives, including the loss of jobs in the USA due to Chinese imports, competition in high technology sectors and the balance of trade. Conceptual frameworks have employed models of domestic differences as well as models of international power distribution. Among domestic differences examined are the existence of state-owned enterprises in China compared to the domination of the USA economy by private firms, the large role of the Communist Party in China and the influence of labor and environmental and labor groups in the USA. Power distribution theories focus on the systemic effects of the distribution of power on trade openness and on the pattern of intra-bloc versus between-bloc trade. This paper aims to examine the role of macroeconomic policy factors in China and the USA, in particular, the role of national patterns of savings, investment and consumption (both private and government). The paper concludes that insofar as the balance of trade is an important component of the trade conflict, domestic macroeconomic factors continue to be important. The resolution of the conflict will have to take into account the respective macroeconomic policies of China and the USA.

The design is an analytic case study of US–China trade relations with a particular focus on the balance of trade. The conceptual framework employed involves an analysis of macroeconomic policy categories, especially the overall pattern of savings (household, firm and government), investment and consumption. Process tracing over time since China's membership in the WTO is carried out with an eye toward the relationship between the balance of trade and macroeconomic policy.

The main findings are that there is a strong relation between the respective macroeconomic policies of the USA and China and their trade relations. The domestic political economy of the USA encourages consumption and a low rate of savings. The opposite is true of China where household income is low by design and national savings are high. China depends on the USA to consume what is not consumed domestically. The USA depends on Chinese imports for additional consumption encouraged by its low rate of savings. The two economies are locked in a mutual dependence.

Key research implications are that there should be more focus on domestic macroeconomic policies since these are the root causes of the trade imbalance. This is not to say that trade frictions centering on jobs, subsidies and competition in high technology are unimportant. However, without the resolution of differences in the management of macroeconomic policies, trade conflicts between the USA and China will continue.

Practical implications are huge, in some ways much more important than the academic implications. Macroeconomic policy differences in savings, investment, government spending, taxation and infrastructure are important. Furthermore, there are available tools in both China and the USA to manage the macroeconomy, particularly, monetary and fiscal policy.

One implication of this paper is that satisfaction or dissatisfaction of workers is dependent on income distribution which in turn affects trade. Treatment of people in different socioeconomic categories, such as the elderly, the young, and those at working age are a function of macroeconomic policies.

Many people have written about macroeconomics. It is a conventional subfield of economics. The originality of this paper lies in its advocacy of a shift of focus and attention and in the argument that traditional macroeconomics is related to trade. Despite its importance, macroeconomics has not been the center of attention for most political scientists, though economists have made it more central.

With the rapid development of railway-intelligent video technology, scene understanding is becoming more and more important. Semantic segmentation is a major part of scene understanding. There is an urgent need for an algorithm with high accuracy and real-time to meet the current railway requirements for railway identification. In response to this demand, this paper aims to explore a variety of models, accurately locate and segment important railway signs based on the improved SegNeXt algorithm, supplement the railway safety protection system and improve the intelligent level of railway safety protection.

This paper studies the performance of existing models on RailSem19 and explores the defects of each model through performance so as to further explore an algorithm model dedicated to railway semantic segmentation. In this paper, the authors explore the optimal solution of SegNeXt model for railway scenes and achieve the purpose of this paper by improving the encoder and decoder structure.

This paper proposes an improved SegNeXt algorithm: first, it explores the performance of various models on railways, studies the problems of semantic segmentation on railways and then analyzes the specific problems. On the basis of retaining the original excellent MSCAN encoder of SegNeXt, multiscale information fusion is used to further extract detailed features such as multihead attention and mask, solving the problem of inaccurate segmentation of current objects by the original SegNeXt algorithm. The improved algorithm is of great significance for the segmentation and recognition of railway signs.

The model constructed in this paper has advantages in the feature segmentation of distant small objects, but it still has the problem of segmentation fracture for the railway, which is not completely segmented. In addition, in the throat area, due to the complexity of the railway, the segmentation results are not accurate.

The identification and segmentation of railway signs based on the improved SegNeXt algorithm in this paper is of great significance for the understanding of existing railway scenes, which can greatly improve the classification and recognition ability of railway small object features and can greatly improve the degree of railway security.

This article introduces an enhanced version of the SegNeXt algorithm, which aims to improve the accuracy of semantic segmentation on railways. The study begins by investigating the performance of different models in railway scenarios and identifying the challenges associated with semantic segmentation on this particular domain. To address these challenges, the proposed approach builds upon the strong foundation of the original SegNeXt algorithm, leveraging techniques such as multi-scale information fusion, multi-head attention, and masking to extract finer details and enhance feature representation. By doing so, the improved algorithm effectively resolves the issue of inaccurate object segmentation encountered in the original SegNeXt algorithm. This advancement holds significant importance for the accurate recognition and segmentation of railway signage.