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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.

Online ride-hailing platforms match drivers with passengers by receiving ride requests from passengers and forwarding them to the nearest driver. In this context, the low acceptance rate of offers by drivers leads to friction in the process of driver and passenger matching. What policies by the platform may increase the acceptance rate and by how much? What factors influence drivers' decisions to accept or reject offers and how much? Are drivers more likely to turn down a ride offer because they know that by rejecting it, they can quickly receive another offer, or do they reject offers due to the availability of outside options? This paper aims to answer such questions using a novel dataset from Tapsi, a ride-hailing platform located in Iran.

The authors specify a structural discrete dynamic programming model to evaluate how drivers decide whether to accept or reject a ride offer. Using this model, the authors quantitatively measure the effect of different policies that increase the acceptance rate. In this model, drivers compare the value of each ride offer with the value of outside options and the value of waiting for better offers before making a decision. The authors use the simulated method of moments (SMM) method to match the dynamic model with the data from Tapsi and estimate the model's parameters.

The authors find that the low driver acceptance rate is mainly due to the availability of a variety of outside options. Therefore, even hiding information from or imposing fines on drivers who reject ride offers cannot motivate drivers to accept more offers and does not affect drivers' welfare by a large amount. The results show that by hiding the information, the average acceptance rate increases by about 1.81 percentage point; while, it is 4.5 percentage points if there were no outside options. Moreover, results show that the imposition of a 10-min delay penalty increases acceptance rate by only 0.07 percentage points.

To answer the questions of the paper, the authors use a novel and new dataset from a ride-hailing company, Tapsi, located in a Middle East country, Iran and specify a structural discrete dynamic programming model to evaluate how drivers decide whether to accept or reject a ride offer. Using this model, the authors quantitatively measure the effect of different policies that could potentially increase the acceptance rate.

This study aims to investigate the mediating role of lean management on the relationship between workforce management and value-added time in private hospitals. This study also investigates the direct influences of workforce management and lean management on the value-added time of the hospitals.

This study applied a quantitative approach to obtain data from the private hospitals’ staff in Peninsular Malaysia. A self-administered survey questionnaire was used to collect data from 287 hospital staff using a stratified random sampling method. The partial least squares structural equation modeling (PLS-SEM) approach was used to determine the internal consistency, reliability, validity of the constructs. The PLS-SEM method was also used to test the hypothesised research model via SmartPLS 3.3.4 version.

The findings of the study indicate that lean management has a direct and significant effect on the value-added time of private hospitals. The findings also revealed that lean management significantly mediates the relationship between workforce management and value-added time in private hospitals. The analysis of the results indicates that both workforce and lean management have a significant impact on the value-added time of the hospitals.

This study provides empirical contributions to enhance the quality of workforce management, lean management and value-added time. The findings of this study provide valuable insights into how effectively managing the workforce and providing guidelines to augment the lean management practices can ensure value-added time in Malaysian hospitals and the overall health-care industry. The lean management framework provides useful insights for the policymakers to understand the significance of workforce management, lean management on ensuring value-added time through reducing waiting times, unnecessary delays, generating a higher degree of patient safety, satisfaction and loyalty.

The research findings provide some essential indications for the health-care service providers to understand how the lean management approach can be implemented to enhance value-added time and how lean management can play a mediating role in creating a link between workforce management and value-added time in hospitals. This study also contributes to the theoretical and practical perspectives. The present study contributes to a better understanding of workforce management and lean management in health-care sectors from theoretical and practical perspectives.

The evaluation of time-based performance is a valued approach in the lean management thinking which is based on delivering value from customer's perspective. This approach contributes for long-term competitiveness and success in today's business environment. The focus of this study is to analyse the cycle time and manufacturing lead time with value stream mapping (VSM) in the preparatory stage of the textile fabric manufacturing process and to identify and improve the non-value adding activities in the value chain (VC). The study presents an insight on the translation of performance improvement across functions and how upstream supply chain (SC) segments can be linked in the performance improvement program. It also covers how the application of VSM improves visibility and planning flexibility in textile fabric manufacturing process.

The time-based performance was evaluated using VSM and recording of the activity times in the existing process. The impact of the quality of supplier's raw material was also measured contributing to identify the strategy for procurement and the means to establish a feedback system to the upstream segments of the SC. The methodology of VSM, observation of the practice and the expertise of the individuals involved with the process were utilised to develop the value stream maps and to identify value adding activities, non-value adding activities, existing gaps and plans for improvement.

The means for improving the time-based performance were identified and their impact was measured. The factors responsible for improvement are related to the production system and with the procurement strategy. The improvement was achieved in terms of available capacity utilisation, balancing the work flow in the preparatory stages, visibility of the process by measuring its capability and flexibility for the planning function. The study revealed that the effectiveness and enhancement of VSM and related tools should be adopted to address the issue of limiting success rates of long term and repeating application of such tools. Continuous improvement, innovations and the systematic embedding of VSM in the process life cycle provide the ways for achieving long-term success.

The paper presents a real and in-depth study on the application of VSM in the textile manufacturing process. The scope of the study is broad; it covers activities across functions with actual estimates of activity times in the manufacturing process for the focused value streams. It offers researchers the opportunity to analyse the translation of productivity improvement across functions and how upstream SC segments can be linked in a performance improvement program.

The study offers useful insight for the managers in textile manufacturing and other sectors for improving the time-based performance and achieving higher utilisation of capacity. It identified the production factors and their impact on warping and sizing cycle time in selected value streams and those which share common activities. It also identified the directions for future research when repeating the application of VSM in the continuous improvement cycle. Furthermore, since the industries need to progress towards advanced systems including Industry 4.0 standards, adoption of advanced VSM tool with relevant technology can align their production systems to develop the required capability. This will also bring a sustainable competitive advantage in the system.

The focused sector is stagnant in terms of productivity and innovation. The adoption of the advanced tools can facilitate the implementation of continuous improvement and innovation strategies.

The main focus of this study is to analyse and improve the cycle time in the preparatory stage of the fabric manufacturing process. This has impact on other important and tangible measures including capacity utilisation and work flow and intangible measures including production planning flexibility and process visibility. The improvement impact is across departments.

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.

The advent of digitalization and the trend of social distancing coincide with the individualized lifestyle that is emerging among contemporary shoppers. This study explores the unique market of “smart solo shoppers”. Two empirical studies are conducted, which aim to identify the major dimensions of multi-channel shopping activities that are engaged by the shoppers (Study 1, n = 64) and to differentiate the shoppers' valuation of time invested in the distinct dimensions under different cultural influences (Study 2, n = 519).

A survey questionnaire is used for data collection, and data are analyzed using structural equation modeling.

Results reveal that the shopping activities converge into four principal dimensions: offline shopping, online shopping, post-shopping delivery and product return activities. Shoppers who perceive offline shopping activities as a time burden and online shopping and delivery activities as venues of value creation are more strongly self-identified as smart solo shoppers. Furthermore, smart solo shoppers who are under a strong influence of individualistic culture are found to spend time on multi-channel shopping to make the right purchase the first time, whereas shoppers perceiving being in a weak individualistic culture prefer to engage online channels while being prepared to return the unwanted purchases.

This study highlights an emerging research field in the nexus of solo consumption and smart shopping. Emphasizing the utility-driven and ego-expressive nature of smart solo shoppers, the authors provide an initial profile of these shoppers based on their time-valuation patterns and the contextual impacts of individualistic culture.

This study aims to propose the use of time series autoregressive integrated moving average (ARIMA) models to predict gas path performance in aero engines. The gas path is a critical component of an aero engine and its performance is essential for safe and efficient operation of the engine.

The study analyzes a data set of gas path performance parameters obtained from a fleet of aero engines. The data is preprocessed and then fitted to ARIMA models to predict the future values of the gas path performance parameters. The performance of the ARIMA models is evaluated using various statistical metrics such as mean absolute error, mean squared error and root mean squared error. The results show that the ARIMA models can accurately predict the gas path performance parameters in aero engines.

The proposed methodology can be used for real-time monitoring and controlling the gas path performance parameters in aero engines, which can improve the safety and efficiency of the engines. Both the Box-Ljung test and the residual analysis were used to demonstrate that the models for both time series were adequate.

To determine whether or not the two series were stationary, the Augmented Dickey–Fuller unit root test was used in this study. The first-order ARIMA models were selected based on the observed autocorrelation function and partial autocorrelation function.

Further, the authors find that the trend of predicted values and original values are similar and the error between them is small.

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.

The purpose of this study is to introduce an enterprise's productivity management named “Production efficiency improvement - PEFF”. This study shows the way of developing the management system to keep their shop floor “flexible to change” and “continuously controlling and improving” from the different levels and in various factories.

This study refers to Toyota's PEFF management system in the context of productivity enhancement through detailed management processes including yearly management, monthly management, daily management and its application in a case study from another sector as a model case of PEFF expansion. The methodology of this study is to introduce a method for production efficiency analysis, measure and select standard time through PEFF calculation.

Toyota's PEFF management can be extremely effective at developing management's ability to conduct day-to-day shop-floor management, know-how sharing and how Toyota applies PEFF to develop the world-standard on manpower efficiency for their factories. Besides, this study shows the applicable of PEFF improvement has successfully conducted in other manufacturers in a flexible way to achieve the improvement targets.

The results of this study will aid the managers in production lines to find the method of calculating and evaluating production efficiency through Toyota's management techniques such as PEFF, YIP, WVACT and standard time. However, the approach for this paper was from a synchronized system as Toyota is limited to generalized to small and medium-sized enterprises.

This paper is introducing the original Toyota's management technique to sustainable enhance their manpower performance and efficiency and answer the question of why TPS still exists in the age of digital management. PEFF management serves as an example of a value management process to help manufacturers to set guidelines to improve their productivity.