Search results

Nowadays, in many organizations, products are not delivered instantly. So, the customers should wait to receive their needed products, which will form a queueing-inventory model. Waiting a long time in the queue to receive products may cause dissatisfaction and churn of loyal customers, which can be a significant loss for organizations. Although many studies have been done on queueing-inventory models, more practical models in this area are needed, such as considering customer prioritization. Moreover, in many models, minimizing the total cost for the organization has been overlooked.

This paper will compare several machine learning (ML) algorithms to prioritize customers. Moreover, benefiting from the best ML algorithm, customers will be categorized into different classes based on their value and importance. Finally, a mathematical model will be developed to determine the allocation policy of on-hand products to each group of customers through multi-channel service retailing to minimize the organization’s total costs and increase the loyal customers' satisfaction level.

To investigate the application of the proposed method, a real-life case study on vaccine distribution at Imam Khomeini Hospital in Tehran has been addressed to ensure model validation. The proposed model’s accuracy was assessed as excellent based on the results generated by the ML algorithms, problem modeling and case study.

Prioritizing customers based on their value with the help of ML algorithms and optimizing the waiting queues to reduce customers' waiting time based on a mathematical model could lead to an increase in satisfaction levels among loyal customers and prevent their churn. This study’s uniqueness lies in its focus on determining the policy in which customers receive products based on their value in the queue, which is a relatively rare topic of research in queueing management systems. Additionally, the results obtained from the study provide strong validation for the model’s functionality.

Disruptions at ports may destroy the planned ship schedules profoundly, which is an imperative operation problem that shipping companies need to overcome. This paper attempts to help shipping companies cope with port disruptions through recovery scheduling.

This paper studies the ship coping strategies for the port disruptions caused by severe weather. A novel mixed-integer nonlinear programming model is proposed to solve the ship schedule recovery problem (SSRP). A distributionally robust mean conditional value-at-risk (CVaR) optimization model was constructed to handle the SSRP with port disruption uncertainties, for which we derive tractable counterparts under the polyhedral ambiguity sets.

The results show that the size of ambiguity set, confidence level and risk-aversion parameter can significantly affect the optimal values, decision-makers should choose a reasonable parameter combination. Besides, sailing speed adjustment and handling rate adjustment are effective strategies in SSRP but may not be sufficient to recover the schedule; therefore, port skipping and swapping are necessary when multiple or longer disruptions occur at ports.

Since the port disruption is difficult to forecast, we attempt to take the uncertainties into account to achieve more meaningful results. To the best of our knowledge, there is barely a research study focusing on the uncertain port disruptions in the SSRP. Moreover, this is the first paper that applies distributionally robust optimization (DRO) to deal with uncertain port disruptions through the equivalent counterpart of DRO with polyhedral ambiguity set, in which a robust mean-CVaR optimization formulation is adopted as the objective function for a trade-off between the expected total costs and the risk.

This study empirically examines the moderating role of geopolitical risk on the tourism–economic growth nexus by applying a recent geopolitical risk indicator developed by Caldara and Iacoviello (2022) in a cross-country panel data growth model context for a sample of 24 countries.

A Dummy Variable Least Squares panel data model, nonparametric covariance matrix estimator and SYS-GMM estimation techniques are employed for the analysis. The authors capture the GPR moderating effect by disaggregating the cross-country sample according to low versus high country GPR score and through a GPR interaction coefficient. Several controls are included in the models such as gross fixed capital formation and—consistent with Barro (1990)—government consumption. Trade openness is used to account for the export-led growth effect. In line with neoclassical growth theory (e.g. Barro, 1991), the authors also include the real interest rate, to account for policy makers' commitment to macroeconomic stability, financial depth, as a proxy for financial development, population growth and the level of secondary school education. The authors also control for unobserved country-specific and time-invariant effects.

The research finds that the interaction term of geopolitical risk significantly contributes to the predictive ability of the regression and provides empirical evidence that confirms that only in low geopolitical risk countries international tourism positively and significantly contributes to economic growth. Important theoretical and policy implications flow from these findings.

The study not only contributes to advancing academic knowledge on the tourism–growth nexus, it also has impact beyond academia. Many countries have in the past pursued and many continue to pursue, tourism specialization and/or tourism-led growth strategies based on the theoretically well-established and empirically validated positive link between inbound tourism and economic growth. The findings alert policy makers in such countries to the significant moderating role that geopolitical risk plays in affecting the above-mentioned relationship and to the importance of prioritizing geopolitical stability as a policy precursor for the successful implementation of such strategies.

This paper focusses on the aftermath of disruptions and the importance of the two largest canals (Suez and Panama), commenting on how during the pandemic the canal fees were lowered. Considering the ongoing efforts to decarbonize shipping, some of the ongoing disruptions will help reach these objectives faster.

Following a literature review of route choice in shipping, and a presentation of significant disruptions in recent years, the author deploys a simplified fuel consumption model and conduct case study analyses to compare different routes environmentally and economically.

The results explain why at times of low fuel prices as in 2020, canals provided discounts to entice ship operators to keep transiting these, instead of opting for longer routes. Considering the ongoing repercussions of the pandemic in supply chains, as well as the potential introduction of market-based measures in shipping, the value of transiting canals will be much higher in the coming years.

The main limitation in this work is that the author used the publicly available information on canal tolls, for the different ship types examined.

The envisioned model is simple, and it can be readily used for any ship and route (port to port) combination available, if ship data are available to researchers.

It is possible that canal tolls will increase, to account for the additional environmental benefits brought to ship operators.

The methodology is simple and transferable, and the author proposes several interesting research questions for follow-up work.

The purpose of this study is to investigate the effect of the COVID-19 pandemic on key performance metrics of accommodation properties by elaborating on the roles of business models (i.e. franchised, chain-managed and independent hotels, and the sharing economy) and state-level restrictions in the US.

The pandemic is considered a variable interference against the average daily rate, occupancy and revenue per available room, which permits the examination of the before and after effects of the pandemic. The panel data model is used to examine the effect of the recent pandemic on the accommodation sector in the USA.

The results showed that chain-managed hotels were the most adversely impacted by the COVID-19 pandemic, while independent hotels were the least adversely impacted. Interestingly, and consistent with emerging consumer needs suggested by spatial distance theory, the pandemic does not have significant negative effects on Airbnb. The adverse impact of the pandemic on hotels was exacerbated in more restrictive states, while Airbnb remained immune to regulatory differences.

This study addresses the dearth of research on the types, roles and efficacy of business models in the accommodation industry and makes important theoretical contributions to the study of business model resilience in the accommodation industry, leveraging the resource-based theory of the firm and spatial distance theory.

The findings of this study make a significant contribution to the extant literature on the resilience of business models in the accommodation industry and have important implications for hotels, Airbnb owners, accommodation brands and destination and health policymakers. They demonstrate that a lower level of corporate control and greater flexibility in brand and operational standards allow for a more effective response to business disruptions such as a global pandemic.

Indian railways (IR) is one of the largest railway networks in the world. As a part of its strategic development initiative, demand forecasting can be one of the indispensable activities, as it may provide basic inputs for planning and control of various activities such as coach production, planning new trains, coach augmentation and quota redistribution. The purpose of this study is to suggest an approach to demand forecasting for IR management.

A case study is carried out, wherein several models i.e. automated autoregressive integrated moving average (auto-ARIMA), trigonometric regressors (TBATS), Holt–Winters additive model, Holt–Winters multiplicative model, simple exponential smoothing and simple moving average methods have been tested. As per requirements of IR management, the adopted research methodology is predominantly discursive, and the passenger reservation patterns over a five-year period covering a most representative train service for the past five years have been employed. The relative error matrix and the Akaike information criterion have been used to compare the performance of various models. The Diebold–Mariano test was conducted to examine the accuracy of models.

The coach production strategy has been proposed on the most suitable auto-ARIMA model. Around 6,000 railway coaches per year have been produced in the past 3 years by IR. As per the coach production plan for the year 2023–2024, a tentative 6551 coaches of various types have been planned for production. The insights gained from this paper may facilitate need-based coach manufacturing and optimum utilization of the inventory.

This study contributes to the literature on rail ticket demand forecasting and adds value to the process of rolling stock management. The proposed model can be a comprehensive decision-making tool to plan for new train services and assess the rolling stock production requirement on any railway system. The analysis may help in making demand predictions for the busy season, and the management can make important decisions about the pricing of services.

This study evaluates “the potential role of cultural heritage resources in tourism and community development at Musina Municipality, Limpopo, South Africa.”

Data on the local communities were collected by questionnaire surveys, focus group discussions and field observations.

The study revealed a variety of cultural and heritage resources; however, with current fewer implications of tourism welfare on the livelihoods statuses of the communities. Thus, there was a need for a potentially sound tourism strategy in cultural heritage resources to empower the local communities in the study area.

Musina Municipality has some of the most challenging impoverishment attributes within the province, defined by evident poor livelihoods. However, it remarkably possesses rich natural biodiversity and tourism destination areas. In line with the probe on the role of cultural heritage resources in tourism and community development, the study uncovers the values of attributing factors associated with the current nature of social heritage resources and their impacts on tourism and community developments. Issues of heritage and cultural resources on tourism and community development have become of main interest within the tourism industry.

The primacy of commerce in livelihood security cannot be overstated. However, in a rural context defined by involuntary socio-ecological displacement, commerce can assume a sociologically distinct character, with far-reaching implications. Based on first-hand encounters with victims of the devastating 2014 flood in Tokwe-Mukorsi, Zimbabwe, this paper analyses how the processes of “recreating” village markets in the resettlement site of Chingwizi impacted the victims’ experiences of resource provisioning and livelihood security.

Qualitative data were collected through 10 in-depth interviews, 10 key informant interviews and two focus group discussions, five years into the flood victims’ resettlement in Chingwizi. The data analysis focused on the dynamics around the recreation of village markets, and the consequences of this on the household economic standing of the resettled flood victims.

The paper reveals how the formation of village markets in Chingwizi was influenced not primarily by the ethno-commercial and ethno-economic impulses reminiscent of life in their ancestral home but mostly by new, disruptive dynamics and challenges unique to the resettlement site. The paper elucidates the constellation of factors that, together, exacerbated the flood victims’ overall socio-economic dislocation and disadvantage.

The study provides a systematic understanding of the dynamics of ethno-commerce, particularly on the evolution of village market activities and livelihoods, among Zimbabwe’s Chingwizi community over a period of five years into their resettlement. It brings to the fore, the often ignored, but significant nuances that 'village market' formation and livelihoods recreation takes in a resettlement context.

The peer review history for this article is available at: https://publons.com/publon/10.1108/IJSE-09-2023-0682

The purpose of this study is to develop a new framework to measure waqif preference of waqf-based qardhul hassan financing in Malaysia.

Using a maqāṣid approach, this study’s data were drawn from 286 valid usable questionnaires to examine the effects of consumer, family, ummah and humanity factors on the preference.

The study found that the said factors sourced from Attia’s maqāṣid al-Shariah were instrumental in determining waqif preference to donate in waqf-based qardhul hassan financing.

Like others, this study’s findings are limited in terms of their generalisations and applications. The theory, context and variables used should be expanded in future works.

The results obtained are useful as a yardstick to enable the offered waqf-based qardhul hassan financing for improved mutual well-being among different classes of the wealth of societal groups in Malaysia. Furthermore, the results provide valuable insights into the direction for practitioners mainly managers involved in introducing waqf-based qardhul hassan financing as a new Islamic social financial instrument for poor and needy folks, at best.

This study is novel in terms of the proposed conceptual framework, where the waqif perspective comes into play.

We aimed to analyze the impact of the transition to the hybrid model of teamwork and team dynamics from the perspective of the five key challenges, i.e. communication, coordination, connection, creativity and culture.

To achieve the stated aim, we conducted a literature review and then an exploratory qualitative study. We split the research into phases: December 2021 to January 2022 and July to August 2022. In the first phase, we conducted computer-assisted online interviews (CAWIs) with all members of the remote team and an in-depth interview with the manager. After the transition from remote to hybrid work in February 2022, we returned to the team to conduct in-depth interviews with team leaders and the manager.

We identified key findings, i.e. managerial implications of differences across the 5 Cs (communication, coordination, connection, creativity and culture) noted in the functioning of the analyzed team as the team shifted from fully remote work to the hybrid work model.

We concluded that if people do not spend time together and are not impregnated with the unique culture and values of a given organization, they will not feel a connection to its distinctive ethos and may choose to leave. In the longer-term, the last challenge may be the biggest single opportunity for employees post-pandemic and concurrently the single biggest challenge that organizational leadership will need to address, given that sustainable market success depends on talent.

The results showed that team communication, teamwork coordination, social and emotional connections among team members, nurturing of creativity, as well as of the organizational culture were of high importance to the team in the hybrid work model. Thus, we confirmed the findings of other authors. The study contributes to our understanding of the impact of the hybrid work model on teamwork and team dynamics and provides some guidance on how organizations can mitigate these, in particular through the team manager.