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It is well known that micromanagement — excessive control of employees — is detrimental to the employees' morale and thus, decreases their productivity. But what if the managers keep people happy — will there still be negative consequences of micromanagement? This is the problem analyzed in this paper.

To analyze our problem, we use general — but simplified — mathematical models of how productivity depends on the working rate.

We show that even in the absence of psychological discomfort, micromanagement is still detrimental to productivity. Interestingly, the negative effect of micromanagement increases as the population becomes more diverse.

This is the first paper in which the purely economic consequences of micromanagement — separate from its psychological consequences — are studied in precise mathematical terms, and is the first paper that analyzes the relation between these consequences and diversity of the population.

This paper applies the decomposition method proposed by Wang et al. (2013), together with the multi-national input-output tables from World Input-Output Database (WIOD) to estimate the value-chain transition in East Asian production network. Specifically, we calculate and examine the domestic value-added absorbed abroad, foreign value-added embodied in country’s gross exports, and vertical specialization measures to explore the relative positions of major East Asian countries in the global production chain over the period of 1995-2011. The analyses are at country-aggregate, country-sector, bilateral-aggregate and bilateral-sector levels. Based on our results, we answer the important question of whether Taiwan and South Korea have used China’s production chains as an intermediary to re-export their products to other countries in the world. Furthermore, we answer the question that over the 1995-2011 periods, have Taiwan and South Korea exploited cheap labor from China to add value to their products before re-exported them to the rest of the world?

In light of the recently experienced systemic shocks (the COVID-19 pandemic and the war in Ukraine), we investigate supply chain robustness. We aim to understand the potential consequences of uncertain events or adversary’s action on critical supplies in the Alliance.

We leverage a parsimonious supply chain model and investigate the relationship between upstream supplier concentration/diversification and the supply chain’s robustness (survival probability) in the presence of uncertain systemic shocks. In several scenarios of shock events, we simulate alternative input sourcing strategies in the presence of uncertainty.

A firm-level cost-focused optimisation may lead all upstream suppliers to concentrate in one location, which – when subsequently hit by a shock – would result in a disruption of the entire supply chain. A chain-level forward-looking optimisation diversifies the upstream supplier location and sourcing decisions. As a result, the supply chain’s survival probability is maximised, and critical supplies will continue even under the most demanding circumstances.

Our findings encourage political and military decision makers to enhance upstream supply chain robustness in critical and strategic sectors, such as the diversification of nitrocellulose supplies currently sourced almost exclusively from China by European gunpowder manufacturers.

Our findings have direct recommendations to supply chain downstream decision makers and to the government’s policy choices. Since global supply chain (GSC) disruptions in critical sectors may have catastrophic impacts on social welfare and the probability of shocks such as COVID-19 and Russia’s war may not be known even approximately, robust decision rules seem to be the appropriate tools for policymaking in critical and strategic sectors such as energy supplies, food and water, communication and defence. A robust supply chain is one in which the survival probability is maximised, which we show in a central planner strategy’s simulations.

The paper shows formally why a market-based global input sourcing strategy may be efficient from an individual firm’s perspective but may be suboptimal from a societal resilience perspective.

This study conducts a comparative analysis of the impact of educational mismatch on Spanish wages. This paper aims to focus on the industrial, construction and service sectors at three levels of disaggregation: sector, occupation and gender.

The over-education, required education and under-education (ORU model), was applied to data from the 2018 Spanish Wages Structure Survey conducted by the Spanish National Statistics Institute.

The industrial sector is the one that best manages over-education by offering the highest returns to each year of over-education. It is also the sector that most values the education of women, particularly those in highly qualified positions.

This study compares the wage effects of educational mismatch in the service, industry and construction sectors. Previous literature has ignored the latter sectors in this field of study, but the results of the present study show that the industrial sectors significantly value and remunerates worker education. Therefore, it may be worthy to focus certain economic and social policies on this sector, to contribute to reducing gender wage gaps and gender employment discrimination in the economy.

Comprehensive studies examining how Korean e-commerce trade works are currently limited. This study seeks to explore whether Korea’s e-commerce trade is more applicable to traditional trade theory or to modern theories. According to our analysis, the share of intra-industry trade (IIT) in modern trade theory is less than that of general trade for Korean e-commerce. Therefore, trade based on comparative advantage can be more valid in explaining e-commerce trade. From results in analyzing the gravity model, it was found that Korea’s e-commerce exports are higher as IIT with its FTA partners. In contrast, it is found that the lower the proportion of e-commerce trade, the higher chance for the import growth. Lastly, this study looked at what kind of comparative advantage is realized through imports. While Korea has been mostly exporting goods of high quality, its major trading partners import products based on price and selection of goods. In order for Korea’s e-commerce to grow, a more strategic approach is necessary. A strategy of high price based on superior quality is not effective, and as e-commerce has radically reduced sales and marketing costs, so a price reduction needs to be reflected in the price of goods for consumers.

For aiding computer security experts in their study, log files are a crucial piece of information. Especially the time domain is very important for us because in most cases, timestamps are the only linking points between events caused by attackers, faulty systems or simple errors and their corresponding entries in log files. With the idea of storing and analyzing this log information in graph databases, we need a suitable model to store and connect timestamps and their events. This paper aims to find and evaluate different approaches how to store timestamps in graph databases and their individual benefits and drawbacks.

We analyse three different approaches, how timestamp information can be represented and stored in graph databases. For checking the models, we set up four typical questions that are important for log file analysis and tested them for each of the models. During the evaluation, we used the performance and other properties as metrics, how suitable each of the models is for representing the log files’ timestamp information. In the last part, we try to improve one promising looking model.

We come to the conclusion, that the simplest model with the least graph database-specific concepts in use is also the one yielding the simplest and fastest queries.

Limitations to this research are that only one graph database was studied and also improvements to the query engine might change future results.

In the study, we addressed the issue of storing timestamps in graph databases in a meaningful, practical and efficient way. The results can be used as a pattern for similar scenarios and applications.

This study explores whether a new machine learning method can more accurately predict the movement of stock prices.

This study presents a novel hybrid deep learning model, Residual-CNN-Seq2Seq (RCSNet), to predict the trend of stock price movement. RCSNet integrates the autoregressive integrated moving average (ARIMA) model, convolutional neural network (CNN) and the sequence-to-sequence (Seq2Seq) long–short-term memory (LSTM) model.

The hybrid model is able to forecast both linear and non-linear time-series component of stock dataset. CNN and Seq2Seq LSTMs can be effectively combined for dynamic modeling of short- and long-term-dependent patterns in non-linear time series forecast. Experimental results show that the proposed model outperforms baseline models on S&P 500 index stock dataset from January 2000 to August 2016.

This study develops the RCSNet hybrid model to tackle the challenge by combining both linear and non-linear models. New evidence has been obtained in predicting the movement of stock market prices.

Public health strategies and activities are intrinsically complex. According to the literature, this “wickedness” depends on the different interests and expectations of the stakeholders and the community, the fragmented governance of the related services and the challenges in measuring and assessing public health outcomes. Existent performance measures and management systems for public health are not designed to cope with wickedness since they are mainly focused on inputs and outputs, neglecting broader outcomes because of their long-term impact and the poor accountability of results. This research aims to tackle this shortfall by adopting a dynamic performance management (DPM) approach.

This research explores the case of the vaccination campaign of a Regional Health System. Through the analysis of an illustrative case study, the research discusses both opportunities and limits of the proposed approach.

This research highlights that DPM supports performance management (PM) in wicked contexts, thanks to the adoption of a system-wide perspective and the possibility of using simulation to experiment with alternative strategies and benchmarking performance results with simulated trends.

This article tackles a gap related to the management of wicked problems both from a theory and a practical perspective. In particular, this research suggests the adoption of DPM as an approach that may support policymakers in tackling social pluralism, institutional complexity and scientific uncertainty all at once.

Due to population ageing, the European Union (EU) has adopted active ageing as a guiding principle in labour and retirement policies. Among the strategies for active ageing, age-friendly workplaces play a crucial role. This study compares age-friendly human resource (HR) practices in the Baltic and Nordic countries. The latter are pioneers in active ageing, and as the employment rate of older employees in the Baltics is like that in the Nordic countries, we may assume equally age-friendly workplaces in both regions.

We used the latest CRANET survey data (2021–2022) from 1,452 large firms in seven countries and constructed the fuzzy logic model on age-friendliness at the workplace.

Despite a high employment rate of older individuals in the Baltics, HR practices in these countries fall short of being age-friendly compared to their Nordic counterparts. Larger firms in the Nordic countries excel in every studied aspect, but deficiencies in the Baltics are primarily attributed to the absence of employer-provided health and pension schemes. The usage of early retirement is more frequent in the Nordic countries; however, its conceptualisation as an age-friendly HR practice deserves closer examination. Our findings suggest that the success of active ageing in employment has translated into age-friendly HR practices in larger organisations in the Nordics, but not in the Baltics. It is likely that high employment of older individuals in Estonia, Latvia and Lithuania is a result of the relative income poverty rate.

Our model represents one of the few attempts to utilise fuzzy logic methodology for studying human resource practices and their quantitative evaluation, especially concerning age-friendly workplaces.