Search results

This article analyzes the relationships between different conceptions of time, socioeconomic development and cultural values.

We focus on three major aspects of time, namely, 1) duration, 2) orientation and 3) tempo. Furthermore, we draw on modernization theory to distinguish between agrarian/traditional and industrial/modern societies and their respective cultural values.

Analyses indicate that agrarian/traditional societies with cultural values such as collectivism, survival, religiosity and hierarchical structures are marked by subjective/cyclical/inaccurate, past-oriented and slow-paced conceptions of time. In contrast, industrial/modern societies with cultural values such as individualism, self-expression, secularism and egalitarianism are marked by objective/linear/accurate, future-oriented and accelerated conceptions of time.

This paper introduces an original conceptualization of the three dimensions of time – duration, orientation and tempo – previously overlooked in the literature. Additionally, it provides an in-depth and comprehensive analysis of the relationships between time, culture and socioeconomic development.

The purpose of this paper is to analyze numerically the blowup in finite time of the solutions to a one-dimensional, bidirectional, nonlinear wave model equation for the propagation of small-amplitude waves in shallow water, as a function of the relaxation time, linear and nonlinear drift, power of the nonlinear advection flux, viscosity coefficient, viscous attenuation, and amplitude, smoothness and width of three types of initial conditions.

An implicit, first-order accurate in time, finite difference method valid for semipositive relaxation times has been used to solve the equation in a truncated domain for three different initial conditions, a first-order time derivative initially equal to zero and several constant wave speeds.

The numerical experiments show a very rapid transient from the initial conditions to the formation of a leading propagating wave, whose duration depends strongly on the shape, amplitude and width of the initial data as well as on the coefficients of the bidirectional equation. The blowup times for the triangular conditions have been found to be larger than those for the Gaussian ones, and the latter are larger than those for rectangular conditions, thus indicating that the blowup time decreases as the smoothness of the initial conditions decreases. The blowup time has also been found to decrease as the relaxation time, degree of nonlinearity, linear drift coefficient and amplitude of the initial conditions are increased, and as the width of the initial condition is decreased, but it increases as the viscosity coefficient is increased. No blowup has been observed for relaxation times smaller than one-hundredth, viscosity coefficients larger than ten-thousandths, quadratic and cubic nonlinearities, and initial Gaussian, triangular and rectangular conditions of unity amplitude.

The blowup of a one-dimensional, bidirectional equation that is a model for the propagation of waves in shallow water, longitudinal displacement in homogeneous viscoelastic bars, nerve conduction, nonlinear acoustics and heat transfer in very small devices and/or at very high transfer rates has been determined numerically as a function of the linear and nonlinear drift coefficients, power of the nonlinear drift, viscosity coefficient, viscous attenuation, and amplitude, smoothness and width of the initial conditions for nonzero relaxation times.

This survey explores the application of real options theory to the field of health economics. The integration of options theory offers a valuable framework to address these challenges, providing insights into healthcare investments, policy analysis and patient care pathways.

This research employs the real options theory, a financial concept, to delve into health economics challenges. Through a systematic approach, three distinct models rooted in this theory are crafted and analyzed. Firstly, the study examines the value of investing in emerging health technology, factoring in future advantages, associated costs and unpredictability. The second model is patient-centric, evaluating the choice between immediate treatment switch and waiting for more clarity, while also weighing the associated risks. Lastly, the research assesses pandemic-related government policies, emphasizing the importance of delaying decisions in the face of uncertainties, thereby promoting data-driven policymaking.

Three different real options models are presented in this study to illustrate their applicability and value in aiding decision-makers. (1) The first evaluates investments in new technology, analyzing future benefits, discount rates and benefit volatility to determine investment value. (2) In the second model, a patient has the option of switching treatments now or waiting for more information before optimally switching treatments. However, waiting has its risks, such as disease progression. By modeling the potential benefits and risks of both options, and factoring in the time value, this model aids doctors and patients in making informed decisions based on a quantified assessment of potential outcomes. (3) The third model concerns pandemic policy: governments can end or prolong lockdowns. While awaiting more data on the virus might lead to economic and societal strain, the model emphasizes the economic value of deferring decisions under uncertainty.

This research provides a quantified perspective on various decisions in healthcare, from investments in new technology to treatment choices for patients to government decisions regarding pandemics. By applying real options theory, stakeholders can make more evidence-driven decisions.

Decisions about patient care pathways and pandemic policies have direct societal implications. For instance, choices regarding the prolongation or ending of lockdowns can lead to economic and societal strain.

The originality of this study lies in its application of real options theory, a concept from finance, to the realm of health economics, offering novel insights and analytical tools for decision-makers in the healthcare sector.

Untimely responses to emergency situations in urban areas contribute to a rising mortality rate and impact society's primary capital. The efficient dispatch and relocation of ambulances pose operational and momentary challenges, necessitating an optimal policy based on the system's real-time status. While previous studies have addressed these concerns, limited attention has been given to the optimal allocation of technicians to respond to emergency situation and minimize overall system costs.

In this paper, a bi-objective mathematical model is proposed to maximize system coverage and enable flexible movement across bases for location, dispatch and relocation of ambulances. Ambulances relocation involves two key decisions: (1) allocating ambulances to bases after completing services and (2) deciding to change the current ambulance location among existing bases to potentially improve response times to future emergencies. The model also considers the varying capabilities of technicians for proper allocation in emergency situations.

The Augmented Epsilon-Constrained (AEC) method is employed to solve the proposed model for small-sized problem. Due to the NP-Hardness of the model, the NSGA-II and MOPSO metaheuristic algorithms are utilized to obtain efficient solutions for large-sized problems. The findings demonstrate the superiority of the MOPSO algorithm.

This study can be useful for emergency medical centers and healthcare companies in providing more effective responses to emergency situations by sending technicians and ambulances.

In this study, a two-objective mathematical model is developed for ambulance location and dispatch and solved by using the AEC method as well as the NSGA-II and MOPSO metaheuristic algorithms. The mathematical model encompasses three primary types of decision-making: (1) Allocating ambulances to bases after completing their service, (2) deciding to relocate the current ambulance among existing bases to potentially enhance response times to future emergencies and (3) considering the diverse abilities of technicians for accurate allocation to emergency situations.

This paper aims to better understand how business model innovation (BMI) occurs in the context of sustainable entrepreneurship, emphasizing the dialectical nature of entrepreneurial relationships. To do so, key interdependencies and reciprocal influences between internal/firm-specific and external/environmental factors underlying BMI for sustainability are analysed through co-evolutionary lenses.

A co-evolutionary framework is developed and applied to a longitudinal business model (BM) analysis of 15 Italian widespread hotels, which creatively use historic villages at risk of abandonment to establish their hotels.

Largely influenced by the interplay between internal and external factors, BMI of widespread hotels occurs through multilevel co-adaptations, which are recognised as virtuous by all stakeholders involved. Effective variations of the BM value elements are selected resulting in circular economy practices, which are retained for successful BMI, radical (first) and incremental (thereafter). Knowledge of specific local and multi-local conditions, time awareness and a future-oriented temporal perspective, by both entrepreneurs and policymakers, favour this dynamic.

Developing targeted policies and practices based on increased organisational knowledge supported by indicators can help in selecting and retaining successful variations of BMs appropriately in/with time with positive effects on firms' performance and sustainable development.

This study provides a novel co-evolutionary framework that explicitly links sustainable entrepreneurship and BM concepts in the accommodation sector. It further proposes a dynamic and holistic explanation of BMI for sustainability from which the crucial roles of the time-knowledge binomial and circular practices emerge.

The purpose of this study is to test the impact of time and price sensitivity on consumer satisfaction and purchase intention on online-to-offline (O2O) takeout platforms and explore the moderating effect of purchase preference on time sensitivity and satisfaction, as well as price sensitivity and satisfaction, in order to guide market pricing.

A structural equation model (SEM) of customer purchase intention was constructed, and the relationships between the variables (time sensitivity, price sensitivity, satisfaction and purchase intention) were examined. The completed questionnaires of 349 respondents were collected from the Questionnaire Star platform in China. The research model and hypotheses were then tested. Analytic hierarchy procedure was used to determine the moderating effect of purchase preference. Finally, the study proposes a pricing strategy for customer-active selective services.

Satisfaction positively influences purchase intention, and price sensitivity significantly increases satisfaction and further increases purchase intention; however, time sensitivity negatively affects satisfaction. Specifically, purchase preference has strongly moderated the relationship between time, price sensitivity and satisfaction. In addition, the findings show that when purchase preference is high, the effect of price sensitivity on satisfaction is stronger, suggesting the importance of purchase preference in strengthening purchase intentions. The research work recommends a pricing strategy involving value-added pricing primarily for time-sensitive customers, which can help build a high-end brand image and reduce price competition. Reduced pricing is mainly for price-sensitive customers, which is conducive to stimulating consumption within a specific time. This pricing strategy is important for adjusting market sensitivity and flexibility.

This research provides new ideas for related disciplines and guidance for the differentiated pricing and promotion of takeout platforms, as well as a theoretical basis for the diversified development of takeout platforms, improvement of personalized service quality and enhancement of customer stickiness. This study fills gaps in the existing literature on the moderating effect of purchase preference on time sensitivity and satisfaction and price sensitivity and satisfaction.

This article aims to focus on implementing Lean Six Sigma (LSS) in steel manufacturing to enhance productivity and quality in the galvanizing process line. In recent trends, manufacturing organizations have expressed strong interest in the LSS since they attempt to enhance its overall operations without imposing significant financial burdens.

This article used lean tools and Six Sigma's DMAIC (Define, Measure, Analyze, Improve and Control) with Yin's case study approach. This study tried to implement the LSS for the steel galvanizing process in order to reduce the number of defects using various LSS tools, including 5S, Value stream map (VSM), Pareto chart, cause and effect diagram, Design of experiments (DoE).

Results revealed a significant reduction in nonvalue-added time in the process, which led to improved productivity and Process cycle efficiency (PCE) attributed to applying lean-Kaizen techniques. By deploying the LSS, the overall PCE improved from 22% to 62%, and lead time was reduced from 1,347 min to 501 min. DoE results showed that the optimum process parameter levels decreased defects per unit steel sheet.

This research demonstrated how successful LSS implementation eliminates waste, improves process performance and accomplishes operational distinction in steel manufacturing.

Since low-cost/high-effect improvement initiatives have not been adequately presented, further research studies on adopting LSS in manufacturing sectors are needed. The cost-effective method of process improvement can be considered as an innovation.

This paper aims to study the value of informal care (IC) time from the perspective of caregivers using two alternative contingent valuation tools – willingness to pay (WTP) and willingness to accept (WTA) – and to identify the variables that affect the stated values.

The authors used data from a multi-centre study of 610 adult caregivers conducted in two Spanish regions in 2013. The existence of “protest zeros” and “economic zeros” because of the severe budgetary constraints of the households was also considered. Two-part multivariate models were used to analyse the main factors that explained the declared values of WTA and WTP.

The average WTP and WTA were €3.12 and €5.98 per hour of care, respectively (€3.2 and €6.3 when estimated values for “protest zeros” and “economic zeros” were considered). Some explanatory variables of WTA and WTP are coincident (place of residence and intensity of care time), whereas other variables only help to explain WTP values (household and negative coping with caregiving) or WTA values (age and burden of care). Some nuances are also identified when comparing the results obtained without protest and economic zeros with the estimated values of these special zeros.

Studies analysing the determinants of WTP and WTA in IC settings are very scarce. This paper seeks to provide information to fill this gap. The results indicate that the variables that explain the value of IC from one perspective may differ from the variables that explain it from an alternative perspective. Given the relevance of contextual factors, studies on the topic should be expanded, and care should be taken with the extrapolation of results across countries and settings.

This paper aims to propose a stepwise method to improve the sustainability of manufacturing processes.

The proposed approach is based on an extensive literature review and research around the environmental, economic and social pillars of sustainability in manufacturing firms. Considering the lean approach, the manufacturing processes are mapped in a value stream and analyzed through the extensive identified sustainability criteria.

The findings reveal the consumption and waste of natural and nonrenewable resources, through going beyond the existing boundaries and focusing on relevant derived production pieces and tracing to their origins. The findings also present the effect of the time value of money on sustainability by using the cost–time profile as a sustainability criterion. This research finds out the employees’ impacts on sustainability improvement through an effective focus on technical, cultural and personal aspects.

The research outcomes provide operations managers and decision-makers in the field of sustainability with a practical platform to comprehend and assess the factors contributing to the manufacturing process sustainability and to plan relevant corrective actions accordingly.

The extended view of sustainability criteria in this research as well as its visual-analytical approach will help practitioners to assess and improve sustainability in their operations in a more holistic way.

This study aims to quantify and prioritize the main causes of lean wastes and to apply reduction methods by employing better waste cause identification methodologies.

We employed fuzzy techniques for order preference by similarity to the ideal solution (FTOPSIS), fuzzy analytical hierarchy process (FAHP), and failure mode effect analysis (FMEA) to determine the causes of defects. To determine the current defect cause identification procedures, time studies, checklists, and process flow charts were employed. The study focuses on the sewing department of a clothing industry in Addis Ababa, Ethiopia.

These techniques outperform conventional techniques and offer a better solution for challenging decision-making situations. Each lean waste’s FMEA criteria, such as severity, occurrence, and detectability, were examined. A pairwise comparison revealed that defect has a larger effect than other lean wastes. Defects were mostly caused by inadequate operator training. To minimize lean waste, prioritizing their causes is crucial.

The research focuses on a case company and the result could not be generalized for the whole industry.

The study used quantitative approaches to quantify and prioritize the causes of lean waste in the garment industry and provides insight for industrialists to focus on the waste causes to improve their quality performance.

The methodology of integrating FMEA with FAHP and FTOPSIS was the new contribution to have a better solution to decision variables by considering the severity, occurrence, and detectability of the causes of wastes. The data collection approach was based on experts’ focus group discussion to rate the main causes of defects which could provide optimal values of defect cause prioritization.