Service Operations Management Research Articles

A Management Model to Operate a Commercial Tennis Club in Zhanjiang, China

Background and Aim: After the Beijing Olympic Games in 2008, tennis was rapidly developing in China. In 2021. China's tennis population has risen to the second largest in the world but it has only 800 tennis clubs out of the total 115,584 tennis clubs in the world, and in Zhanjiang City, only 17 tennis clubs are operating for commercial purposes. The result of surveying the needs assessment among the customers showed that there was a wide gap between observed perceptions and expectations in many issues in service management operations, which means that the customers were not satisfied with the club's operations. The researchers were interested in studying these issues to find customers’ needs and suggestions to improve the tennis clubs' service and management in this area by applying service quality and marketing mix strategies. The objectives: (1) to develop a management model to operate commercial tennis clubs in Zhanjiang City, China, based on service quality and marketing mix concepts (2) to confirm the model’s satisfaction, appropriateness, and practicality of the developed management model. Materials and Methods: This research was a research and development research. The data collection was 1) Interviews with the managers or representatives of the 17 clubs, 2) a survey of the 398 clubs’ customers with a dual response questionnaire, and 3) The consensus with 19 experts by Delphi. 4) a focus group discussion with 11 stakeholders. The research tools were a structured form for interviews, a dual response questionnaire, a questionnaire for consensus with a Delphi, and a form for recording the focus group discussion. The data was analyzed in descriptive, content analysis, and PNI modified by the use of SPSS. Results: The clubs should 1) set the positioning of the clubs 2) set vision, goals, and missions to be the direction of a club’s management, 3) set the division of works to share and understand the scope of duty and responsibility of each section, 4) consider the staff motivation and welfare, service quality 5) create operation manuals, and job descriptions, collecting data to improve the service quality 6) applying marketing to create strategies such as; offering a variety of products, pricing at the same rate of other clubs, set the easy and convenient operation process for customers.7) Collect the problems and needs of customers, and the readiness of management resources, to formulate the management strategies with the application of service quality and marketing mix concepts which could respond to expectations them Conclusion: The clubs should consider management functions and marketing mixes to create the strategies to operate the clubs by the responses to the needs of customers survey.

Are we There yet? Thematic Analysis, NLP, and Machine Learning for Research

Thematic analysis is a well-established technique for qualitative analysis which is covered in traditional research methods training. The objective of thematic analysis is to elicit themes and significant topics from discursive data such as free style discussions and semi structured or unstructured interviews or comments. The approach is laborious and time consuming and requires a significant input from researchers for identifying and coding the themes although software tools such as NVivo, T-Lab and IRaMuTeQ can aid with results presentation. Recent developments in Machine Learning (ML) and Natural Language Processing (NLP) have boosted interest in text analytics and its applications to social science research. For example, automatic topic identification using ML NLP offers valuable insights in social media analytics. However, machine learning techniques conventionally rely on large data sets to enable the algorithm to elicit themes. More recent research efforts have turned to the performance of machine learning approaches with smaller data sets. This study aims to compare and contrast the effectiveness of Machine Learning NLP vs human generated themes using the text analytics tools NVivo, T-Lab, IRaMuTeQ, as well as the low-code ML tool KNIME for automatically eliciting themes from academic literature review in the contexts of service operations management research and semi-structured customer interviews. Results indicate that the ML NLP approach has the potential to automatically detect research themes even with small data sets, although the results vary across the different tools and are dependent on the capabilities of the built-in text analytic algorithms. In particular, T-Lab offered the best mapping of machine learning derived topics to researcher themes, and KNIME proved the most robust software, able to derive meaningful topics even with very small sample sizes. The implications for training research students are also significant as they suggest that the inclusion of ML NLP tools and algorithms in the training curriculum of social scientists may be beneficial.

Examining sharing economy operations: A process perspective

<scp>Examining sharing economy operations</scp>: A <scp>process perspective</scp>

Ancillary Services in Targeted Advertising: From Prediction to Prescription

Problem definition: Online retailers provide recommendations of ancillary services when a customer is making a purchase. Our goal is to predict the net present value (NPV) of these services, estimate the probability of a customer subscribing to each of them depending on what services are offered to them, and ultimately prescribe the optimal personalized service recommendation that maximizes the expected long-term revenue. Methodology/results: We propose a novel method called cluster-while-classify (CWC), which jointly groups observations into clusters (segments) and learns a distinct classification model within each of these segments to predict the sign-up propensity of services based on customer, product, and session-level features. This method is competitive with the industry state of the art and can be represented in a simple decision tree. This makes CWC interpretable and easily actionable. We then use double machine learning (DML) and causal forests to estimate the NPV for each service and, finally, propose an iterative optimization strategy—that is, scalable and efficient—to solve the personalized ancillary service recommendation problem. CWC achieves a competitive 74% out-of-sample accuracy over four possible outcomes and seven different combinations of services for the propensity predictions. This, alongside the rest of the personalized holistic optimization framework, can potentially result in an estimated 2.5%–3.5% uplift in the revenue based on our numerical study. Managerial implications: The proposed solution allows online retailers in general and Wayfair in particular to curate their service offerings and optimize and personalize their service recommendations for the stakeholders. This results in a simplified, streamlined process and a significant long-term revenue uplift.History: This paper has been accepted as part of the 2021 Manufacturing &amp; Service Operations Management Practice-Based Research Competition.Supplemental Material: The online appendix is available at https://doi.org/10.1287/msom.2020.0491 .

The economics of process transparency

We propose and analyze a novel framework to understand the role of noninstrumental information sharing in service operations management, that is, information shared by the firm not to affect consumers' actions, but to better manage their experience in the firm's process. The operations of the firm are organized as a process, consisting of a sequence of tasks, each of random duration. The firm shares real‐time information with the consumer about the progress of their flow unit in the firm's process via a process tracker. The consumer is delay‐sensitive and experiences gain–loss utility (loss aversion and diminishing sensitivity) over time due to changes in beliefs about anticipated delay, as he awaits completion of the process. We analyze when providing such real‐time progress information via process trackers help, or can possibly hurt a consumer. Our work draws upon the recent literature on belief‐based/news utility in Economics. We find that in the presence of loss aversion alone, not sharing progress information is beneficial. In the presence of loss aversion and diminishing sensitivity, if low delays are likely, then sharing information is beneficial; otherwise, not sharing information is preferred. Our findings inform a service firm's post‐sales transparency strategy.

TRANSFORMING SERVICE OPERATIONS WITH AI: A CASE FOR BUSINESS VALUE

Artificial Intelligence (AI) has rapidly become a critical technology for businesses seeking to improve efficiency and profitability. One area where AI is proving particularly impactful is in service operations management, where it is used to create AI-powered service operations (AIServiceOps) that deliver highvalue services to customers. AIServiceOps involve the use of AI to automate and optimize various business processes, such as customer service, sales, marketing, and supply chain management. The rapid development of Artificial Intelligence has prompted many changes in the field of Information Technology (IT) Service Operations. IT Service Operations are driven by AI, i.e., AIServiceOps. AI has empowered new vitality and addressed many challenges in IT Service Operations. However, there is a literature gap on the Business Value Impact of Artificial intelligence (AI) Powered IT Service Operations. It can help IT build optimized business resilience by creating value in complex and ever-changing environments as product organizations move faster than IT can handle. So, this research paper examines how AIServiceOps creates business value and sustainability, basically how AIServiceOps makes the IT staff liberation from a low-level, repetitive workout and traditional IT practices for a continuously optimized process. One of the research objectives is to compare Traditional IT Service Operations with AIServiceOPs. This paper provides the basis for how enterprises can evaluate AIServiceOps and consider it a digital transformation tool. The paper presents a case study of a company that implemented AI-powered service operations (AIServiceOps) and analyzes the resulting business outcomes. The study shows that AIServiceOps can significantly improve service delivery, reduce response times, and increase customer satisfaction. Furthermore, it demonstrates how AIServiceOps can deliver substantial cost savings, such as reducing labor costs and minimizing downtime.

Research and Governance of Power Quality of the Distribution Network Based on Large-Scale Distributed PV Access

The continuously increasing distributed photovoltaic installations have a great impact on power grid operation, which shows a trend of “local to global development and distribution network to the main network extension”. The situation puts forward higher requirements for power grid power balance, reactive power regulation, and power quality control. Based on the measured data of a regional power grid, this paper analyzes the new problems brought by the large-scale access of distributed photovoltaic to the power quality of distribution network from the aspects of voltage deviation, harmonics, three-phase imbalance, voltage fluctuation, low voltage ride through, and reactive power support in detail. Besides, it describes a series of management and control measures that the local government and grid companies can take to reduce the operation risk of the distribution network against these adverse effects. The purpose is to provide technical guidance for exploring the new mode of distributed photovoltaic grid connection service and regulation operation management in the whole county.

Proposed museum service operations framework for addressing overcrowding: A Taipei case study

AbstractAlthough the high growth of the tourism market contributes to a record number of visitors to landmark museums, they also encounter the overcrowding problem. This study reviewed the case of National Palace Museum (NPM) in Taipei to discuss the overcrowding problem and provide suggestions to alleviate this problem. This study adopted document analysis to summarize NPM's policies regarding the overcrowding problem that were identified by performing text mining of visitors' reviews for the NPM. And a content analysis involving semantic analysis and coding of negative reviews for NPM retrieved from TripAdvisor was performed for extracting complaint categories regarding the overcrowding problem. On the basis of the results, a service operation management framework in response to museum overcrowding is proposed. This study suggests that a museum can manage the overcrowding problem on the basis of visitor characteristics by adjusting visitor demand, optimizing the service design, diverting visitors' flow, elaborating the floor plan and exhibition design, or forming a decentralized museum hub to move must‐see exhibits for tourists to a stand‐alone pavilion or branch to disperse the crowd.

Managing the Personalized Order-Holding Problem in Online Retailing

Problem definition: A significant percentage of online consumers place consecutive orders within a short duration. To reduce the total order arrangement cost, an online retailer may consolidate consecutive orders from the same consumer. We investigate how long the retailer should hold the consumer’s orders before sending them to a third-party logistics provider (3PL) for processing. In this order-holding problem, we optimize the holding time to balance the total order arrangement cost and the potential delay in delivery. Methodology/results: We model the order-holding problem as a Markov decision process. We show that the optimal order-holding decisions follow a threshold-type policy that is straightforward to implement: Hold any pending orders if the holding time is within a threshold or send them to the 3PL otherwise. Whenever the consumer places a new order, the holding time is reset, and the threshold is updated based on a cumulative set of the past consecutive orders in the consumer’s shopping journey. Using a consumer’s sequential decision model, we personalize the threshold by finding its closed-form expression in the consumer’s order features. We determine the model’s coefficients and evaluate the threshold-type policy using the data of the 2020 MSOM Data Driven Research Challenge. Extensive numerical experiments suggest that the personalized threshold-type policy outperforms two commonly used benchmarks by having fewer order arrangements or shorter holding times. Furthermore, personalizing the order-holding decisions is significantly more valuable for “enterprise” customers. Managerial implications: Our research suggests a higher threshold for consumers who are more likely to place consecutive orders within a short duration. The consumers’ demographic information has a significant effect on the threshold. Specifically, the threshold is higher for “plus” consumers, female consumers, and consumers in the age group of 16–25 years. The threshold for tier 1 cities is lower than that for tier 2 to tier 4 cities but higher than that for tier 5 cities.History: This paper has been accepted as part of the 2020 MSOM Data Driven Challenge.Funding: This work was supported by the National Natural Science Foundation of China [Grants 71931009, 72201237, and 72231009], the Research Grants Council of Hong Kong [Grants 15501920 and 15501221], the Singapore Ministry of Education Academic Research Fund [Tier 1, Grant RG17/21; Tier 2, Grant MOE2019-T2-1-045], the Association of South-East Asian Nations Business Research Initiative Grant [Grant G17C20421], and the Neptune Orient Lines [Fellowship NOL21RP04].Supplemental Material: The online appendices are available at https://doi.org/10.1287/msom.2023.1201 .

Financial distress and service decisions: Empirical evidence from the US domestic airlines

AbstractUnderstanding the firm's service decisions under financial distress is of central importance to service quality regulators and operation management scholars. This study aims to add new evidence to the relationship between financial distress and airline services and reconcile the discrepancies between theory, practical implications, and empirical results. We examine if and how airlines in financial distress may change their service provisions to turn around, with a particular focus on the moderating role of operating unit costs and market share. In the study, four measures of service failure (the inverse of service quality) are constructed and evaluated: airline arrival delay minutes attributable to the carrier, cancelations attributable to the carrier, mishandled baggage, and involuntary denied boardings. In the US domestic airline business context, we estimate the mixed‐effects model using panel data from the Department of Transportation and Wharton Research Data Services. We find robust evidence that financial distress plays a vital role in airline service provisions, and the distress–service relationship is contingent upon the operating unit costs of airlines. The findings provide evidence to help explain the different and sometimes contradictory empirical findings. We depend on the aggressive strategy framework to understand the service effect of airline financial distress.

Introduction to the Virtual Special Issue: Strategy Science’s Contributions to Doctoral Reading Lists

Introduction to the Virtual Special Issue: <i>Strategy Science</i>’s Contributions to Doctoral Reading Lists

FAST: A Forecasting Model With Adaptive Sliding Window and Time Locality Integration for Dynamic Cloud Workloads

The workload predictor has attracted attention as a key component of the proactive service operation management framework. However, the request and resource workloads of cloud applications are highly dynamic. Existing approaches decompose the original workload into trend, seasonal, and random components, establish models accordingly, and then combine all outputs to generate results. Indeed, the random component usually has significant heteroscedasticity and noise, having little or even a negative effect on model accuracy improvement. In our model, trend and seasonal components are seen as macro workload changes, and the micro workload changes are obtained by an adaptive sliding window algorithm. Therefore, we propose an ensembling model named FAST for Forecasting workloads with Adaptive Sliding window and Time locality integration. Notably, we propose an adaptive sliding window algorithm that considers trend correlation, time correlation, and random fluctuations of workload for online regression to achieve higher accuracy with lower overhead; and for the error-based integration strategy, we propose a time locality concept for local-predictor behavior and develop a multi-class regression algorithm for model integration. Finally, we conduct experiments on Google cluster trace datasets which show FAST has better accuracy than all other state-of-the-art models for dynamic workloads.

Advancing the marketing‐operations interface in omnichannel retail

Advancing the marketing‐operations interface in omnichannel retail

Research on Rural Comprehensive Energy Service Operation Based on Data-driven of Electric Power Internet of Things

In order to promote the revolution of energy production and consumption, and build a clean, low-carbon, safe and efficient energy system, the research on rural comprehensive energy service operation based on the data drive of electric power Internet of Things was put forward. In this paper, the innovation of big data technology application concept drives the integrated energy service operation management mode, and improves the service quality and operation management efficiency. Focusing on the collaborative optimization of production and consumption, the construction and management of rural comprehensive energy services will make use of technological advantages to accurately locate customers’ needs, improve service efficiency, and benefit the concept of energy green ecological construction in rural comprehensive energy development planning to build a smart energy ecosystem.

Modi Mangoes: managing an uncertain future

Learning outcomes The case primarily focused on a real-life problem and shows that existing operations management tools can be used to solve a complex problem. Through this case, the participants will learn the application of the factor loading method and aggregate planning. Case overview/synopsis This case revolves around the Modi Agro Pvt. Ltd, a mango procurement and distribution business established in 1994 by Mr Dhanush Modi in Mumbai, India. Mr Mahendra Modi, son of the company owner, observed that the different seasons of cultivation and varied customer demands lead to changing workforce requirements during the procurement process. In addition, the production quality, variety, available resources, procurement location and cost play a significant role in establishing a long-term relationship with the customers. This case highlights the problem faced by Mahendra in determining an appropriate location among all available options for mango procurement and the optimal workforce for each month to meet the varying customers’ demands. Complexity academic level The case can be used as teaching material for participants of the course Service Operations Management, Operations Management, Decision Analysis and Quantitative Techniques Supplementary materials Teaching notes are available for educators only. Subject code CSS 9: Operations and logistics.

UPMRC: rewriting the success story of Lucknow Metro during Covid-19 crisis

Learning outcomes After working through the case and assignment questions, students will be able to understand the following aspects:▪ how good strategic planning can convert a crisis into an opportunity;▪ importance of service excellence and customer satisfaction through customer delight and customer feedback; and▪ utilisation of resources and excellent time management strategies. Case overview/synopsis This case discusses how vital teamwork and motivated leadership can convert a crisis such as Covid-19 into an opportunity. This case study talks about Uttar Pradesh Metro Rail Corporation (UPMRC), a metro rail corporation working to develop metro trains in the Indian state of Uttar Pradesh. The case discusses how challenging it was for the metro rail corporation to transform its processes in a short period and deal with the crisis on major fronts such as facilities maintenance, human resource management, ensuring safety and security of its staff and riders, motivation of staff, service quality and maintaining all operational aspects. The case discusses how UPMRC is a leader on all fronts and has excelled in its operational work. It talks about what challenges the lockdown and unlocking phase posed in front of the leadership and how teamwork, dedication to exemplary service quality and customer satisfaction gave the team the strength to make changes that improved their processes and helped them overcome the crisis.The case starts with a discussion of metro rail inception and incorporation of UPMRC and then how this newly formed metro has to face the challenges of pre-lockdown period where the team worked very hard for sanitisation and safety. The lockdown created a completely different set of challenges related to the facilities and the entire metro train systems, which was a difficult situation to deal because of restrictions and other challenges. However, the team dealt with situations with strength and strategic planning, leading to better managed processes and staff. The unlocking phase also gave many challenges that the team handled with a lot of care and efficiency. Complexity academic level This case is suitable for post-graduate-level courses on services marketing, service operations management, general management, crisis management and strategic management. Participants can use the case to develop an understanding of strategic planning and management.This case can also be used in the executive education program for managers to encourage them to think through challenges faced by metro rail corporations. Supplementary materials Teaching notes are available for educators only. Subject Code CSS 10: Public Sector Management.

Integration of rail freight with dry ports: A route for seaport regionalisation

Due to economic and geographical causes, the reformation of rail freight transport is now in progress in a number of countries. Railroads are ideal for moving huge quantities of non-bulk, specialised, and bulk cargo. Malaysia's rail network has developed in comparison to other modes. However, its administration and contributions are little researched and frequently unclear in the current literature. In this country, rail is mostly used for passenger transportation, and the amount of rail freight in maritime trade is negligible compared to road freight. This scenario affects the efficacy of inter-regional and intra-regional freight delivery, diminishes the competitiveness of seaports, creates a monopoly for road freight transport, and depletes road infrastructure for extensively facilitated road freight transit. This article focuses on investigating concerns about existing rail freight transport and its improvement methods, the influence of rail freight transport on seaport competitiveness, and the relationship between rail freight transport and seaport dwell time. An exploratory factor analysis (EFA) was conducted to achieve the goals of this work. Approximately forty (40) Malaysian rail operators, their clients, and multimodal transport operators participated in the online survey. The findings indicate that the primary challenges with the Malaysian rail system are performance, transit time reliability, and capacity use. Improving the rail infrastructure will have a good impact on the service dependability, freight distribution, and operation management of a seaport. By enforcing institutional integration, expanding infrastructure support systems, and successfully managing infrastructure maintenance, the rail system may be enhanced. By synchronising seaport operation, efficiency, management, and container rotation, rail freight operations can enhance seaport transit time. To maintain trade competitiveness and provide a balanced freight distribution network, the role of rail freight must be increased.

Applications of fluid models in service operations management

Applications of fluid models in service operations management

Introduction to the Manufacturing &amp; Service Operations Management Special Section on Responsible Research in Operations Management

AboutSectionsRequest Access ToolsAdd to favoritesDownload CitationsTrack CitationsPermissionsReprints ShareShare onFacebookTwitterLinked InEmail Go to Section HomeManufacturing & Service Operations ManagementVol. 24, No. 6 Introduction to the Manufacturing & Service Operations Management Special Section on Responsible Research in Operations ManagementSerguei Netessine , Christopher Tang , Michael Toffel Serguei Netessine , Christopher Tang , Michael Toffel Published Online:7 Oct 2022https://doi.org/10.1287/msom.2022.1155"Introduction to the Manufacturing & Service Operations Management Special Section on Responsible Research in Operations Management." Manufacturing & Service Operations Management, 24(6), pp. 2797–2798 Back to Top Next FiguresReferencesRelatedInformation Volume 24, Issue 6November-December 2022Pages 2797-3306, C2 Article Information Metrics Information Received:September 14, 2022Accepted:September 14, 2022Published Online:October 07, 2022 Copyright © 2022, INFORMSCite asSerguei Netessine, Christopher Tang, Michael Toffel (2022) Introduction to the Manufacturing & Service Operations Management Special Section on Responsible Research in Operations Management. Manufacturing & Service Operations Management 24(6):2797-2798. https://doi.org/10.1287/msom.2022.1155 PDF download

P6 - An Audit of the Early Pregnancy Assessment Service: A retrospective cohort Study

The Early pregnancy Assessment Service (EPAS) was implemented in 2008 at the Liverpool Hospital, NSW. EPAS opens daily assessing and providing care for women presenting with pain and/or vaginal bleeding in early pregnancy. Women with hyperemesis, medical issues such as diabetes, blood pressure, etc or requiring termination of pregnancy are not seen in this service. 1. Length of time women spend in the EPAS aid in the management of staffing and service operations 2. Provide consistent management for outcomes of the current pregnancy. 3. The optimal management for a miscarriage will reflect the woman’s preferred choice, mental health, gestation age, and best practice 4. All women will have a follow up plan after presenting to the EPAS. 1. Women seen in EPAS over a 1 year period (1/1/2018-31/12/2018) 2. Data was extracted from the electronic medical record and paper records of the clinic. 3. Data collected included demographic, background medical and pregnancy histories, gestational age, vaginal bleeding present at initial visit, outcome of current pregnancy, management undertaken, days spent in EPAS, representations to the Emergency Dept. 4. Data was analyzed using SPSSv 26. A total of 966 women were assessed; majority at 9 (7-10) weeks; were 32.2 (28.9-37.9) years of age with 40.7 of women born overseas, and 25.7% from culturally and linguistically diverse (CALD) population. Women presenting for one or more miscarriage accounted for 39% of EPAS presentation, of those women; 18.7% had previous cesarean section; 18.3% had pre-existing co-morbidity; Women presented mostly at 10(7-12) weeks with vaginal bleeding. Over all EPAS presentation; 63% were miscarriage, 30% were found to have a viable pregnancy, 4.3% had an ectopic diagnosed, 0.9% had a molar pregnancy diagnosed. 331 pregnant women has managed by conservative treatment (38.9%), 130 women by medical management (15.3%), and 176 women managed by surgical method (20.7%). 86% of either conservative/medical management (461) being successful, 11.8% of either method failed. The study examined the Early Pregnancy Assessment Service (EPAS) clinic in 2018. Total 966 patients have visited through EPAS. Treatment via EPAS is effective but requires significant time for assessment and follow up visits depending on the gestation presented and management option chosen. Women would find this information helpful when making an informed decision for their preferred care pathway. This would result in higher patient satisfaction. Women with recurrent miscarriage may need improved referral for follow-up and assessment.