Search results

Outpatient care delivery is one of the key revenue sources of a hospital which plays a salient role in timely care delivery. The key purpose of the study is to propose a multi-objective simulation-based decision support model that considers the cost of care delivery and patient dissatisfaction as its two key conflicting objectives. Patient dissatisfaction considers service fairness. Patient idiosyncrasies such as no-show, unpunctuality and balking have been considered in the model involving multiple classes of patients.

A model has been designed using data collected from field investigations. In the first stage, queuing theory based discrete event simulation model has been developed. Genetic algorithm has been used to solve the scalarized problem and obtain actionable insights. In the second stage, non-dominated sorting genetic algorithm II (NSGA-II) has been involved to achieve the Pareto optimal fronts considering equal priority of the two objectives.

The computational results considering various parameter settings can help in efficient resource planning while ensuring better care delivery. The model proposed in the study provides structural insights on the business strategy of healthcare service providers on optimizing the dual goals of care delivery cost and service fairness.

The study is one of the early works that helps to improve the care delivery process by taking into consideration the environmental factors as well as service fairness. The study demonstrates the usage of simulation-based multi-objective optimization to provide a more sustainable patient centric care delivery.

Little is known about what emotionally un(intelligent) CEOs really say to their close collaborators within the boardroom. Would the rhetoric content differ between an emotionally intelligent and an emotionally unintelligent CEO, especially during a crisis? This chapter aims to answer this question.

40 CEOs of large corporations were asked to deliver a verbal address to their board members in reaction to a vignette describing a critical situation for the company. Participants were provided with the Schutte self-report emotional intelligence (EI) test. The verbal content of CEOs' closed-door discourses was analyzed using Cognitive-Discursive Analysis (CDA) and, subsequently, Geometric Data Analysis (GDA).

The results revealed that CEOs with low EI tend to evoke unpleasant emotions, talk about competition, and often blame some – or all – of the board members for their (poor) actions in comparison to CEOs with high or medium EI. In contrast, CEOs with high EI tend to use terms in relation to decision or realization and appear to be more cooperative than those with lower EI and were also ready to make decisions on behalf of team.

Previous research has mainly focused on CEOs' public speeches. But the content of CEOs' speeches within the boardroom might noticeably differ from what they would say in a public address. The results of our exploratory study can serve CEOs as a basis toward improving their closed-door rhetoric during a crisis.

It would be interesting to enlarge the size of our population in order to strengthen our statistical analyses as well as explore other cultural and linguistic environments and other channels through which emotions can be expressed (e.g., human face, gesture, vocal tone).

The climate in Mendoza is significantly different from the climate in most global wine-making regions. This paper aims to explore the relationship between the quality of the Malbec wine from the Mendoza region and its weather.

This study uses a multivariate regression model with fixed effects to assess how weather variations relate to wine quality. The Wine Spectator ratings are used as a measure of wine quality and to build a longitudinal data set of Malbec wine ratings from 1995 to 2020. The weather is described with several variables based on temperature, rainfall, humidity and cloudiness data from the Mendoza region. The model controls for wineries which are treated as fixed effects.

The results of this study indicate that the weather has a modest explanatory power when it comes to the quality of Mendoza’s Malbec. Additionally, the analyses show that the wineries are more important than the weather to explain quality differences in the wines. These findings are in agreement with previous studies carried out in regions with stable weather such as California and Australia.

The quality of Mendoza’s Malbec depends more on the winery of origin than the year-to-year weather variations. Therefore, consumers should focus more on the winery and less on the vintage when making purchasing decisions. Additionally, given the relevance of the winery in relation to quality, the findings of this study indicate that future research efforts should focus on directly linking the wine ratings to quality-drivers behind the winery effects.

To the best of the authors’ knowledge, this is the first study that explores the relationship between wine quality assessed through wine ratings and the weather in the Mendoza (Argentina) region. Most such studies have been done in connection with northern hemisphere wines.

The purpose of this study is to investigate the errors arising from the numerical treatment of model processes, paying particular attention to the impact of key system features including widely variable dispersion coefficients, spatiotemporal velocities of algal cells, and the aggregation of algae from single cells to large colonies. An advection–dispersion model has been presented to describe the vertical transport of colonial and motile harmful algae in a lake environment.

Model performance is examined for two different numerical treatments of the advective term: first-order upwind and quadratic upwind with a stability-preserving flux limiter (SMART). To determine how these schemes impact predictions, comparisons are made across a sequence of models with increasing complexity.

Using first-order upwinding for advection–dispersion calculations with a time oscillating velocity field leads to oscillatory numerical dispersion. Subjecting an initially uniform distribution of large-sized algal colonies to a spatiotemporal velocity creates a concentration pulse, which reaches a steady-state width at high-grid Peclet numbers when using the SMART scheme; the pulse exhibits contraction–expansion behavior throughout a velocity cycle at all Peclet numbers when using first-order upwinding. When aggregation dynamics are included with advection-dominated spatiotemporal transport, results indicate the SMART scheme predicts larger peak concentration values than those predicted by first-order upwind, but peak location and the time to large colony appearance remain largely unchanged between the two advective schemes.

To the best of the authors’ knowledge, this study is the first numerical investigation of a novel advection–dispersion model of vertical algal transport. In addition, a generalized expression for the effective dispersion coefficient of temporally variable flow fields is presented.

We investigate how firing notification procedures influence wage growth. Using a sample of 33 countries over the period 2006–2015, we show that administrative requirements in cases of dismissal have a positive and significant effect on wage growth. The result is robust even after controlling for the endogeneity of the firing notification restrictions, the involvement of third parties in the wage bargaining process, the minimum wage, the firms' training policy, and the composition of employment. These findings suggest that firing notification procedures foster the growth of wages by increasing the bargaining power of incumbent workers.

This research investigates Airbnb’s financial implications in emerging economies and their potential to influence stock market profitability.

Employing a multifaceted approach, the study combines parametric and nonparametric tests, robustness checks, and regression analysis to assess the impact of Airbnb’s announcements on emerging economy stock markets.

Airbnb’s announcements affect emerging economies' stock markets with a distinct pattern of cumulative abnormal returns (CAR): negative before the announcement and positive afterward. Informed investors strategically leverage this opportunity through short selling before the announcement and acquiring positions following it. Regression analysis validates these trends, revealing that stock index returns and inbound tourism affect CAR before announcements, while GDP growth influences CAR afterward. Announcements pertaining to emerging economies exert a more pronounced impact on stock indices compared to city-specific announcements, with COVID-19 period announcements demonstrating greater significance in abnormal returns than non-COVID-19 period announcements.

This study advances existing literature through a comprehensive range of statistical tests, differentiation between emerging countries and cities, introduction of five macroeconomic variables, and reliance on credible primary Airbnb data. It highlights the potential for investors to leverage Airbnb announcements in emerging markets for stock market profits, emphasizing the need for adaptive investment strategies considering broader macroeconomic factors.

Perishable inventory management for the grocery sector has become more challenging with extended omnichannel activities and emerging consumer expectations. This paper aims to identify and formalize key performance measures of omnichannel perishable inventory management (OCPI) and explore the influence of operational and market-related factors on these measures.

The inductive approach of this research synthesizes three performance measures (product waste, lost sales and freshness) and four influencing factors (channel effect, demand variability, product perishability and shelf life visibility) for OCPI, through industry investigation, expert interviews and a systematic literature review. Treating OCPI as a complex adaptive system and considering its transaction costs, this paper formalizes the OCPI performance measures and their influencing factors in two statements and four propositions, which are then tested through numerical analysis with simulation.

Product waste, lost sales and freshness are identified as distinctive OCPI performance measures, which are influenced by product perishability, shelf life visibility, demand variability and channel effects. The OCPI sensitivity to those influencing factors is diverse, whereas those factors are found to moderate each other's effects.

To manage perishables more effectively, with less waste and lost sales for the business and fresher products for the consumer, omnichannel firms need to consider store and online channel requirements and strive to reduce demand variability, extend product shelf life and facilitate item-level shelf life visibility. While flexible logistics capacity and dynamic pricing can mitigate demand variability, the product shelf life extension needs modifications in product design, production, or storage conditions. OCPI executives can also increase the product shelf life visibility through advanced stock monitoring/tracking technologies (e.g. smart tags or more comprehensive barcodes), particularly for the online channel which demands fresher products.

This paper provides a novel theoretical view on perishables in omnichannel systems. It specifies the OCPI performance, beyond typical inventory policies for cost minimization, while discussing its sensitivity to operations and market factors.

The purpose of this paper is to show how data mining techniques can improve the performance management of the judiciary, helping judges in steering position with specific and timely measures. It explores different approaches to analyse the length of trials, based on the case of an Italian judicial office.

The paper presents a temporal analysis to compare the timeliness of trials, using data and process mining approaches with the support of a specific software to represent graphically the results. Data were gathered directly from the office data base, improving precision and the opportunity to monitor specific phases of the trials.

The results highlight the progress that can be reached using data mining approaches to develop performance analyses helping courts to correct inefficiencies and to manage the personnel distribution, overcoming the critical comments arisen against traditional KPI (Raine, 2000). The work proposes a methodology to analyse cases deriving from different juridical matters useful to set up a performance monitoring system that could be diffused to different courts.

The limitations of the research regard the analysis of a selected, limited number of cases in terms of judicial matters.

Data mining techniques can improve the performance management processes in providing more accurate feedback to the judicial offices leaders and increasing the organisational learning.

The performance of the judiciary is one of the relevant issues that emerged in the recent decade in the field of public sector reforms. Several reasons explain this interest, which has gone beyond the specific legal disciplines to involve public policy, management, economics and ICT studies.

Considering the literature on the judiciary (Visser et al., 2019; Di Martino et al., 2021; Troisi and Alfano, 2023) the contribution differs as both the methodological approach and the predictive analysis considers the intrinsic differences that define cases belonging to different juridical matters performing a cross-sectional analysis, with a specific focus of process variants.

The relationships between integrated reporting quality (IRQ) and corporate governance characteristics have been studied extensively, but the results are still inconclusive and, sometimes, contradictory. The purpose of this paper is to systematize the results of previously published studies on the relationship between corporate governance and IRQ.

This paper uses several complementary theoretical perspectives (agency, stakeholder and signaling theory). The relevant aspects of the corporate governance system are the attributes and composition of the board, the existence of a social responsibility committee, the quality of the audit committee, integrated report assurance and ownership structures. The sample consisted of 61 papers published in top journals between 2015 and 2021. Meta-analytic procedures were applied on bivariate and partial correlations between IRQ and the identified corporate governance characteristics.

The results confirm that director independence, the existence of a social responsibility committee, institutional ownership and the hiring of a Big 4 auditor are significantly correlated with IRQ. On the other hand, board gender diversity, audit committee independence and dedicated assurance have a positive but nonsignificant impact on IRQ. Chairperson-chief executive officer duality does not seem to impact report quality, while ownership concentration has a negative but nonsignificant impact on IRQ.

Future research can improve the measurement of focal indicators by using a common set of variables for comparability, favoring disaggregate measures of corporate governance and updating the measurement of some indicators. Future research could also propose new indicators in the area of corporate governance and expand the theoretical domain of IRQ research.

The findings emphasize the need to explicitly consider the role of corporate governance structures and arrangements in improving IRQ. Through meta-analysis, the paper aims to provide a comprehensive and generalizable set of findings, suggesting that corporate governance indicators cannot be overlooked as predictors of integrated reporting.

To assess the introduction and performance of light electric freight vehicles (LEFVs), more specifically cargo cycles in major 3PL organizations in at least two Nordic countries.

Case studies. Interviews. Company data on performance before as well as after the introduction. Study of differing business models as well as operational setups.

The results from the studied cases show that LEFVs can compete with conventional vans in last mile delivery operations of e-commerce parcels. We account for when this might be the case, during which circumstances and why.

Inherent limitations of the case study approach, specifically on generalization. Future research to include more public–private partnership and multi-actor approach for scalability.

Adding to knowledge on the public sector facilitation necessary to succeed with implementation and identifying cases in which LEFVs might offer efficiency gains over more traditional delivery vehicles.

One novelty is the access to detailed data from before the implementation of new vehicles and the data after the implementation. A fair comparison is made possible by the operational structure, area of delivery, number of customers, customer density, type of packages, and to some extent, the number of packages being quite similar. Additionally, we provide data showing how city hubs can allow cargo cycles to work synergistically with delivery vans. This is valuable information for organizations thinking of trying LEFVs in operations as well as municipalities/local authorities that are interested.