PurposeThis article evaluates how consumer expectations evolve over time and if three antecedents (negative experiences, alternative attractiveness and level of visitation) explain possible changes in expectations.Design/methodology/approachA conceptual model is structured with six hypotheses that are tested through articulated studies. First, a study with a longitudinal approach is developed and applied to a sample of students. Data collection is carried out over three periods and a latent growth model (LGM) is applied. Further ahead, another essay is developed to reexamine the moderating role of corporate image and level of visitation on the effect of negative experiences on expectations. For this, the role-playing approach is applied.FindingsStudy 1 reveals that patterns of expectations change from one service meeting to another, and these mutations are influenced by negative experiences and alternative attractiveness. Three pieces of evidence are highlighted. First, negative experiences produce contradictory and simultaneous movements in consumer expectations. Negative experiences reduce desired expectations and, at the same time, increase adequate expectations. These effects change in magnitude because of the corporate image. This confirms the moderating role of the corporate image in the relationship between negative experiences and expectations. This does not happen with the level of visitation, in which the moderating function is not sustained. The findings about moderating effects are confirmed by Study 2. Second, as customers have alternative companies, the minimum level of expectation rises. Alternative attractiveness positively impacts only adequate expectations. Third, the results do not support the relationship between the level of visitation and expectations. This reveals that more frequent customers do not necessarily have higher expectations.Originality/valueThis paper is the first to provide empirical results about the moderating effects of corporate image and level of visitation on the relationship between negative experiences and expectations.
{"title":"How to offer good service? A study on behavior of consumer expectations over time in educational service","authors":"Evadio Pereira filho, M. Añez, Kleber Cavalcanti Nobrega, Leandro Trigueiro Fernandes","doi":"10.1108/ijqrm-04-2021-0116","DOIUrl":"https://doi.org/10.1108/ijqrm-04-2021-0116","url":null,"abstract":"PurposeThis article evaluates how consumer expectations evolve over time and if three antecedents (negative experiences, alternative attractiveness and level of visitation) explain possible changes in expectations.Design/methodology/approachA conceptual model is structured with six hypotheses that are tested through articulated studies. First, a study with a longitudinal approach is developed and applied to a sample of students. Data collection is carried out over three periods and a latent growth model (LGM) is applied. Further ahead, another essay is developed to reexamine the moderating role of corporate image and level of visitation on the effect of negative experiences on expectations. For this, the role-playing approach is applied.FindingsStudy 1 reveals that patterns of expectations change from one service meeting to another, and these mutations are influenced by negative experiences and alternative attractiveness. Three pieces of evidence are highlighted. First, negative experiences produce contradictory and simultaneous movements in consumer expectations. Negative experiences reduce desired expectations and, at the same time, increase adequate expectations. These effects change in magnitude because of the corporate image. This confirms the moderating role of the corporate image in the relationship between negative experiences and expectations. This does not happen with the level of visitation, in which the moderating function is not sustained. The findings about moderating effects are confirmed by Study 2. Second, as customers have alternative companies, the minimum level of expectation rises. Alternative attractiveness positively impacts only adequate expectations. Third, the results do not support the relationship between the level of visitation and expectations. This reveals that more frequent customers do not necessarily have higher expectations.Originality/valueThis paper is the first to provide empirical results about the moderating effects of corporate image and level of visitation on the relationship between negative experiences and expectations.","PeriodicalId":506639,"journal":{"name":"International Journal of Quality & Reliability Management","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-02-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140447203","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
PurposeThe objective of this study is to systematically identify, categorize and assess the driving factors and interdependencies associated with various types of healthcare waste. The study specifically focuses on waste that has been managed or is recommended for treatment through the application of Lean Six Sigma (LSS) methodologies.Design/methodology/approachTo accomplish the study’s objectives, interpretive structural modeling (ISM) was utilized. This analytical tool aided in quantifying the driving power and dependencies of each form of healthcare waste, referred to as “enablers,” as well as their related variables. As a result, these enablers were classified into four distinct categories: autonomous, dependent, linkage and drivers or independents.FindingsIn the healthcare sector, the “high cost” (HC) emerges as an autonomous variable, operating with substantial independence. Conversely, variables such as skill wastage, poor service quality and low patient satisfaction are identified as dependent variables. These are distinguished by their low driving power and high dependency. On the flip side, variables related to transportation, production, processing and defect waste manifest strong driving forces and minimal dependencies, categorizing them as independent factors. Notably, inventory waste (IW) is highlighted as a salient issue within the healthcare domain, given its propensity to engender additional forms of waste.Research limitations/implicationsEmploying the ISM model, along with comprehensive case study analyses, provides a detailed framework for examining the complex hierarchies of waste existing within the healthcare sector. This methodological approach equips healthcare leaders with the tools to accurately pinpoint and eliminate unnecessary expenditures, thereby optimizing operational efficiency and enhancing patient satisfaction. Of particular significance, the study calls attention to the key role of IW, which often acts as a trigger for other forms of waste in the sector, thus identifying a crucial area requiring focused intervention and improvement.Originality/valueThis research reveals new insights into how waste variables are structured in healthcare, offering a useful guide for managers looking to make their waste-reduction strategies more efficient. These insights are highly relevant not just for healthcare providers but also for the administrators and researchers who are helping to shape the industry. Using the classification and ranking model developed in this study, healthcare organizations can more easily spot and address common types of waste. In addition, the model serves as a useful tool for practitioners, helping them gain a deeper, more detailed understanding of how different factors are connected in efforts to reduce waste.
{"title":"Determination of driving power and dependency of wastes in the healthcare sector: a lean and ISM-Based approach","authors":"Manjeet Kharub, Himanshu Gupta, Sudhir Rana, Olivia McDermott","doi":"10.1108/ijqrm-11-2021-0380","DOIUrl":"https://doi.org/10.1108/ijqrm-11-2021-0380","url":null,"abstract":"PurposeThe objective of this study is to systematically identify, categorize and assess the driving factors and interdependencies associated with various types of healthcare waste. The study specifically focuses on waste that has been managed or is recommended for treatment through the application of Lean Six Sigma (LSS) methodologies.Design/methodology/approachTo accomplish the study’s objectives, interpretive structural modeling (ISM) was utilized. This analytical tool aided in quantifying the driving power and dependencies of each form of healthcare waste, referred to as “enablers,” as well as their related variables. As a result, these enablers were classified into four distinct categories: autonomous, dependent, linkage and drivers or independents.FindingsIn the healthcare sector, the “high cost” (HC) emerges as an autonomous variable, operating with substantial independence. Conversely, variables such as skill wastage, poor service quality and low patient satisfaction are identified as dependent variables. These are distinguished by their low driving power and high dependency. On the flip side, variables related to transportation, production, processing and defect waste manifest strong driving forces and minimal dependencies, categorizing them as independent factors. Notably, inventory waste (IW) is highlighted as a salient issue within the healthcare domain, given its propensity to engender additional forms of waste.Research limitations/implicationsEmploying the ISM model, along with comprehensive case study analyses, provides a detailed framework for examining the complex hierarchies of waste existing within the healthcare sector. This methodological approach equips healthcare leaders with the tools to accurately pinpoint and eliminate unnecessary expenditures, thereby optimizing operational efficiency and enhancing patient satisfaction. Of particular significance, the study calls attention to the key role of IW, which often acts as a trigger for other forms of waste in the sector, thus identifying a crucial area requiring focused intervention and improvement.Originality/valueThis research reveals new insights into how waste variables are structured in healthcare, offering a useful guide for managers looking to make their waste-reduction strategies more efficient. These insights are highly relevant not just for healthcare providers but also for the administrators and researchers who are helping to shape the industry. Using the classification and ranking model developed in this study, healthcare organizations can more easily spot and address common types of waste. In addition, the model serves as a useful tool for practitioners, helping them gain a deeper, more detailed understanding of how different factors are connected in efforts to reduce waste.","PeriodicalId":506639,"journal":{"name":"International Journal of Quality & Reliability Management","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-02-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139958391","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-01-19DOI: 10.1108/ijqrm-04-2023-0138
P. Samaranayake, Michael William McLean, S. Weerabahu
PurposeThe application of lean and quality improvement methods is very common in process improvement projects at organisational levels. The purpose of this research is to assess the adoption of Lean Six Sigma™ approaches for addressing a complex process-related issue in the coal industry.Design/methodology/approachThe sticky coal problem was investigated from the perspective of process-related issues. Issues were addressed using a blended Lean value stream of supply chain interfaces and waste minimisation through the Six Sigma™ DMAIC problem-solving approach, taking into consideration cross-organisational processes.FindingsIt was found that the tendency to “solve the problem” at the receiving location without communication to the upstream was, and is still, a common practice that led to the main problem of downstream issues. The application of DMAIC Six Sigma™ helped to address the broader problem. The overall operations were improved significantly, showing the reduction of sticky coal/wagon hang-up in the downstream coal handling terminal.Research limitations/implicationsThe Lean Six Sigma approaches were adopted using DMAIC across cross-organisational supply chain processes. However, blending Lean and Six Sigma methods needs to be empirically tested across other sectors.Practical implicationsThe proposed methodology, using a framework of Lean Six Sigma approaches, could be used to guide practitioners in addressing similar complex and recurring issues in the manufacturing sector.Originality/valueThis research introduces a novel approach to process analysis, selection and contextualised improvement using a combination of Lean Six Sigma™ tools, techniques and methodologies sustained within a supply chain with certified ISO 9001 quality management systems.
目的在组织层面的流程改进项目中，精益和质量改进方法的应用非常普遍。本研究旨在评估采用精益六西格玛™方法解决煤炭行业复杂流程相关问题的情况。研究结果发现，在接收地点 "解决问题 "而不与上游沟通的倾向过去是、现在仍然是导致下游问题的主要问题的常见做法。DMAIC Six Sigma™ 的应用有助于解决更广泛的问题。研究的局限性/影响在跨组织供应链流程中采用了 DMAIC™精益六西格玛方法。然而，将精益和六西格玛方法结合起来，还需要在其他部门进行实证测试。原创性/价值本研究引入了一种新方法，即在供应链中结合使用精益六西格玛™工具、技术和方法，进行流程分析、选择和情景化改进，并持续采用已通过认证的 ISO 9001 质量管理体系。
{"title":"Application of lean and quality improvement methods for improving operational performance in coal supply chains: a case study","authors":"P. Samaranayake, Michael William McLean, S. Weerabahu","doi":"10.1108/ijqrm-04-2023-0138","DOIUrl":"https://doi.org/10.1108/ijqrm-04-2023-0138","url":null,"abstract":"PurposeThe application of lean and quality improvement methods is very common in process improvement projects at organisational levels. The purpose of this research is to assess the adoption of Lean Six Sigma™ approaches for addressing a complex process-related issue in the coal industry.Design/methodology/approachThe sticky coal problem was investigated from the perspective of process-related issues. Issues were addressed using a blended Lean value stream of supply chain interfaces and waste minimisation through the Six Sigma™ DMAIC problem-solving approach, taking into consideration cross-organisational processes.FindingsIt was found that the tendency to “solve the problem” at the receiving location without communication to the upstream was, and is still, a common practice that led to the main problem of downstream issues. The application of DMAIC Six Sigma™ helped to address the broader problem. The overall operations were improved significantly, showing the reduction of sticky coal/wagon hang-up in the downstream coal handling terminal.Research limitations/implicationsThe Lean Six Sigma approaches were adopted using DMAIC across cross-organisational supply chain processes. However, blending Lean and Six Sigma methods needs to be empirically tested across other sectors.Practical implicationsThe proposed methodology, using a framework of Lean Six Sigma approaches, could be used to guide practitioners in addressing similar complex and recurring issues in the manufacturing sector.Originality/valueThis research introduces a novel approach to process analysis, selection and contextualised improvement using a combination of Lean Six Sigma™ tools, techniques and methodologies sustained within a supply chain with certified ISO 9001 quality management systems.","PeriodicalId":506639,"journal":{"name":"International Journal of Quality & Reliability Management","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-01-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139524587","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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