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This study aims to help businesses cope with consumers' no-show behaviour from a multistage perspective. It specifically identifies no-show reasons at each stage of appointment services and proposes the corresponding coping strategies.

By focusing on an outpatient appointment service, we interviewed 921 no-show patients to extract no-show reasons, invited 18 hospital managers to propose coping strategies for these reasons using a Delphi method and evaluated the proposed strategies based on EDAS (Evaluation based on Distance from Average Solution).

The results reveal ten reasons for no-show behaviour (i.e. system service quality, overuse, did not know the appointment, self-judgment, forget, waiting time, lateness, uncontrollable problems, time conflict and service coordination), which have nine coping strategy themes (i.e. prepayment, system intelligence, target, subjective norm, system integration, ease of navigation, reminder, confirmation and cancellation). We classify the ten reasons and nine themes into scheduling, waiting and execution stages of an appointment service.

This study provides a package of coping strategies for no-show behaviour to deal with no-show reasons at each appointment service stage. It also extends the research in pre-service management through appointment services.

This study is carried out in one public and one private health-care centers based on different probabilities of patient’s no-show rate. The present study aims to determine the optimal queuing system capacity so that the expected total cost is minimized.

In this study an M/M/1/K queuing model is used for analytical properties of optimal queuing system capacity and appointment window so that total costs of these cases could be minimized. MATLAB software version R2014a is used to code the model.

In this paper, the optimal queuing system capacity is determined based on the changes in effective parameters, followed by a sensitivity analysis. Total cost in public center includes the costs of patient waiting time and rejection. However, the total cost in private center includes costs of physician idle time plus costs of public center. At the end, the results for public and private centers are compared to reach a final assessment.

Today, determining the optimal queuing system capacity is one of the most central concerns of outpatient clinics. The large capacity of the queuing system leads to an increase in the patient’s waiting-time cost, and on the other hand, a small queuing system will increase the cost of patient’s rejection. The approach suggested in this paper attempts to deal with this mentioned concern.

The purpose of this paper is to develop evident-based predictive no-show models considering patients’ each past appointment status, a time-dependent component, as an independent predictor to improve predictability.

A ten-year retrospective data set was extracted from a pediatric clinic. It consisted of 7,291 distinct patients who had at least two visits along with their appointment characteristics, patient demographics, and insurance information. Logistic regression was adopted to develop no-show models using two-thirds of the data for training and the remaining data for validation. The no-show threshold was then determined based on minimizing the misclassification of show/no-show assignments. There were a total of 26 predictive model developed based on the number of available past appointments. Simulation was employed to test the effective of each model on costs of patient wait time, physician idle time, and overtime.

The results demonstrated the misclassification rate and the area under the curve of the receiver operating characteristic gradually improved as more appointment history was included until around the 20th predictive model. The overbooking method with no-show predictive models suggested incorporating up to the 16th model and outperformed other overbooking methods by as much as 9.4 per cent in the cost per patient while allowing two additional patients in a clinic day.

The challenge now is to actually implement the no-show predictive model systematically to further demonstrate its robustness and simplicity in various scheduling systems.

This paper provides examples of how to build the no-show predictive models with time-dependent components to improve the overbooking policy. Accurately identifying scheduled patients’ show/no-show status allows clinics to proactively schedule patients to reduce the negative impact of patient no-shows.

High lateness and no-show percentages pose great challenges on the patient scheduling process. Usually this is addressed by optimizing the time between patients in the scheduling process and the percent of extra patients scheduled to account for absent patients. However, since the patient no-show and lateness is highly stochastic we might end up with many patients showing up on time which leads to crowded clinics and high waiting times. The clinic might end up as well with low utilization of the doctor time. The purpose of this paper is to study the effect of scheduled overload percentages and the patient interval on the waiting time, overtime, and the utilization.

Actual data collection and statistical modeling are used to model the distribution for common dentist procedures. Simulation and validation are used to model the treatment process. Then algorithm development is used to model and generate the patient arrival process. The simulation is run for various values of basic interval scheduled time between arrivals for the patients. Further, 3D graphical illustration for the objectives is prepared for the analysis.

This work initially reports on the statistical distribution for the common procedures in dentist clinics. This can be used for developing a scheduling system and for validating the scheduling algorithms developed. This work also suggest a model for generating patient arrivals in simulation. It was found that the overtime increases excessively when coupling both high basic interval and high overloading percentage. It was also found that: to obtain low overtime we must reduce the basic interval. Waiting time increases when reducing the basic scheduled appointment interval and increase the scheduled overload percentage. Also doctors’ utilization is increased when the basic interval is reduced.

This work was done at a local clinic and this might limit the value of the modeled procedure times.

This work presents a statistical model for the various procedures and a detailed technique to model the operations of the clinics and the patient arrival time which might assist researches and developers in developing their own model. This work presents a procedure for troubleshooting scheduling problems in outpatient clinics. For example, a clinic suffering from high patient waiting time is directly instructed to slightly increase their basic scheduled interval between patients or slightly reduce the overloading percentage.

This work is targeting an extremely important constituent of the health-care system which is the outpatient clinics. It is also targeting multiple objectives namely waiting times, utilization overtime, which in turn is related to the economics and doctor utilization.

This work presents a detailed modeling procedure for the outpatient clinics under high lateness and no-show and addresses the modeling procedure for the patient arrivals. This 3D graphical charting for the objectives includes a study of the multiple objectives that are of high concern to outpatient clinic scheduling interested parties in one paper.

The purpose of this paper is to evaluate the economic benefits of managing an outpatient appointments system with technological innovations.

This study uses a quantitative methodological procedures aiming to evaluate the cost-benefit relation and also the payback of the management and operation of an outpatient appointments system with technological innovations.

This study found a great benefit-cost relation of 30.6 showing the great economic value and social impact of managing an outpatient appointments regulation system with technological innovations.

This study presents contribution to the literature discussion about the economic evaluation of the benefits of managing and operating more effective outpatient appointments systems because of important technological innovations.

This paper presents and discusses the most important and commonly used strategies and technological innovations to deal with and to manage an outpatient appointment regulation system aiming to reduce the patient no-show rates.

The findings of this study show a great benefit-cost relation of about 30.6 which is being reverted to the society.

There not exist many similar studies in the pertinent literature, mostly with the Brazilian contexts.

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.

The purpose is to review literature on sports season ticket subscriptions to distil current knowledge and guide future research and practice.

A systematic literature review is conducted of research on sports season tickets, a long-established and innovative subscription category.

In-depth examination of 28 papers showed a focus on drivers of satisfaction, churn and renewal causes, and product utilisation rates. Subscription markets typically involve many “solely loyal” consumers, most purchasing one or two subscriptions in a category. From reduced barriers to entry and exit to “curated” subscriptions, subscription marketing is changing very quickly. Sports marketers build relationships with subscribers using behavioural data, tier benefits to distinguish between casual and subscribing customers, and create recall and scarcity around key aspects of subscription to combat churn and increase utilisation.

Scarce research on subscription marketing practices remains the primary limitation. Existing research suggests that strong connections between subscriber and organisation, heavy product utilisation and/or strong barriers to switching drive customer satisfaction and retention.

Rapid expansion of subscription products should reduce “excess loyalty”, meaning that subscription models' main benefit will be limited to reoccurring revenue. Exceptions occur when consumers are heavily connected to the product or have little provider choice, so allocate their category buying exclusively. New subscription products face myriad challenges. Guidance on effective subscription marketing from sports marketing research and practice is outlined.

By combining research on market structure, marketing empirical generalisations and subscription marketing, this paper guides future research and practice.

The objective of this paper is to respond to four research questions. The first two as how likely are college football season ticket holders to recommend (1) purchasing a similar season ticket package and (2) attending a home football game, to a friend or colleague. The third question examines if there is a difference between advocacy toward purchasing season tickets as compared to advocacy toward game attendance. Finally, we identify what factors impact advocacy for college football season ticket holders.

An online survey of 57,240 season ticket holders from 69 different National Collegiate Athletics Association (NCAA) Division I Football Bowl Subdivision programs was undertaken. The data were analyzed to build a model of the drivers of advocacy in season ticket holders from a conceptual base of advocacy, trust and loyalty.

The identified drivers include both institutionally influenced factors and factors related to season ticket holder behaviors/demographics. The season ticket holder is arguably the highest level of fan for any sports organization from an affinity perspective and clearly the most important from a business perspective. This research argues that the season ticket holder should not only be the focus of ticket sales efforts but also leveraged as marketing advocates with the objective of attracting additional fans.

The value of this research is the large sample of data from season ticket holders of NCAA Division 1 football clubs and the resulting learning it provides to researchers and practitioners.

The purpose of this study is to provide new insights into the factors that influence cancellation behaviour with respect to hotel bookings. The data are based on individual bookings drawn from a hotel reservation system database comprising nine hotels.

The determinants of cancellation probability are estimated using a probit model with cluster adjusted standard errors at the hotel level. Separate estimates are provided for rooms booked offline, through online travel agencies and through traditional travel agencies.

Evidence based on 233,000 bookings shows that the overall cancellation rate is 8 per cent. Cancellation rates are highest for online bookings (17 per cent), followed by offline bookings (12 per cent) and travel agency bookings (4 per cent). Probit estimations show that the probability of cancelling a booking is significantly higher for early bookings, large groups that book offline, offline bookings during high seasons, bookings not involving children and bookings made by guests from specific countries (e.g. China and Russia). Among the factors, booking lead time and country of residence play the largest role, particularly for online bookings.

The analysis is based on individual-level booking data from one hotel chain in Finland, and therefore cannot be generalised for the total population of hotels in the country under observation.

The main contribution of this paper is a thorough investigation of the factors that influence cancellation behaviour at both the theoretical and empirical levels. Detailed and unique data from a hotel reservation system allow for new empirical insights into this behaviour.

The purpose of this paper is to explore and critically review the existing literature on applications of Lean Methodology (LM) and Discrete-Event Simulation (DES) to improve resource utilization and patient experience in outpatient clinics. In doing, it is aimed to identify how to implement LM in outpatient clinics and discuss the advantages of integrating both lean and simulation tools towards achieving the desired outpatient clinics outcomes.

A theoretical background of LM and DES to define a proper implementation approach is developed. The search strategy of available literature on LM and DES used to improve outpatient clinic operations is discussed. Bibliometric analysis to identify patterns in the literature including trends, associated frameworks, DES software used, and objective and solutions implemented are presented. Next, an analysis of the identified work offering critical insights to improve the implementation of LM and DES in outpatient clinics is presented.

Critical analysis of the literature on LM and DES reveals three main obstacles hindering the successful implementation of LM and DES. To address the obstacles, a framework that integrates DES with LM has been recommended and proposed. The paper provides an example of such a framework and identifies the role of LM and DES towards improving the performance of their implementation in outpatient clinics.

This study provides a critical review and analysis of the existing implementation of LM and DES. The current roadblocks hindering LM and DES from achieving their expected potential has been identified. In addition, this study demonstrates how LM with DES combined to achieve the desired outpatient clinic objectives.