PurposeThe aim of this paper is to describe and apply a new three‐step approach to prioritizing service attributes in formulating quality‐improvement strategies. In particular, the paper seels to demonstrate the value of impact range‐performance analysis (IRPA) and impact‐asymmetry analysis (IAA) in prioritizing quality attributes for improvement.Design/methodology/approachThe proposed new analytical framework is developed and presented. Data from a survey on satisfaction with airport passenger services are then used to demonstrate the proposed approach. Improvement priorities are derived using a three‐step analytical framework.FindingsThis paper raises several conceptual issues concerning importance‐performance analysis (IPA). In particular, the study contends that direct and indirect measures of the “importance” of an attribute are not measuring the same construct.Practical implicationsManagers who use IPA to prioritize the improvement of service attributes might obtain misleading recommendations. In particular, managers should be aware that the impact of an attribute on overall customer satisfaction can vary significantly with different levels of performance of that attribute.Originality/valueThe study proposes a revised approach to IPA in which the traditional measure of “attribute‐importance” is replaced by a measure of the range of attribute‐impact on overall customer satisfaction (RIOCS). Moreover, a new analysis provides detailed information on asymmetric relationships between attribute‐level performance and overall customer satisfaction (OCS).

Purpose-The purpose of this paper is: to review the most commonly used approaches to the classification of quality attributes according to the Kano model; to identify the theoretical/practical strengths and weaknesses of these techniques; and to provide guidance for future research and managerial practice in this area. Design/methodology/approach-Based on an extensive review of the literature on the Kano model and the relevant marketing/management literature, five approaches (Kano's method; "penalty-reward contrast analysis"; "importance grid"; qualitative data methods; and "direct classification") are evaluated in terms of their validity and reliability for categorising attributes in the Kano model. Several illustrative examples provide empirical evidence for the theoretical arguments advanced in the study. Findings-The Kano questionnaire and the direct-classification method are the only approaches that are capable of classifying Kano attributes in the design stage of a product/service. Penalty-reward contrast analysis (PRCA) is useful for assessing the impact of product/service attributes on overall satisfaction with a product/service, but its applicability to the classification of Kano attributes is questionable. The importance grid (IG) is not recommended for use with the Kano model. The critical incident technique and the analysis of complaints/compliments are valid for the Kano model, but have questionable reliability. Originality/value-The study makes some important points about accurate semantic terminology in describing issues related to the Kano model. In particular, researchers should be aware that an attractive quality element (must-be quality element, respectively) might in fact be a dissatisfier (satisfier, respectively), due to significant conceptual differences between performance in terms of the Kano model (i.e. objective performance) and subjective performance perceptions.

Purpose Additive Manufacturing offers much potential for industry, but at the same time is likely to have a significant impact on supply chain theory and practice. To-date there has been limited empirical work exploring the effect of Additive Manufacturing, and this study aims to provide a detailed appraisal of how supply chain integration, supply chain performance and firm performance may be affected by the adoption of Additive Manufacturing. These are critical factors for supply chain management, but have received little quantified attention to date. Design/methodology/approach A theoretical model is developed from a detailed review of the literature, from which a quantitative analysis is performed. Using data collected from 124 automotive manufacturers in European Union and the partial least square- structural equation modeling method, the research examines the relationships among different dimensions of supply chain integration, supply chain performance and firm performance from the perspective of Additive Manufacturing adoption. Findings The findings indicate that Additive Manufacturing adoption positively influences supply chain performance and as a consequence, firm performance. In addition, supporting the resource-based view perspective, the results show a positive indirect effect of supply chain integration on the supply chain and firm performance improvements, enabled by the Additive Manufacturing adoption. This helps to explain some inconsistent findings in previous research regarding the impacts of supply chain integration on performance. Research limitations/implications The results of this study support the view that Additive Manufacturing can make a positive contribution to the supply chain, but this is not achieved solely by the technologies alone. Many of the traditional activities of supply chain management (i.e. integration) are still needed when using Additive Manufacturing, and research needs to understand whether Additive Manufacturing adoption will necessitate changes to the way these traditional activities are undertaken. Building on the findings of the current study, much more work is therefore needed to understand how operations within the supply chain may be changed, and how this may affect the integration and performance of the supply chain. Practical implications This study provides quantitative evidence to show that the adoption of Additive Manufacturing has the potential to affect both firm and supply chain performance. This is significant for those companies considering the adoption of Additive Manufacturing, and may serve as a valuable insight in the strategic decision-making process. For those already using Additive Manufacturing, this study serves to underline the potential for firm performance to be influenced, by focusing on improvements to their production strategies and policies. Originality/value This study provides an initial insight into some fundamental supply chain concepts within an Additive Manufacturing context, which have received very little research attention. It develops a novel conceptual model, and through a large-scale industry survey provides quantified evidence of the impact of Additive Manufacturing on the supply chain. To date, much of the supply chain research is exploratory and qualitative; the quantitative evidence presented in this work, therefore, makes an important and original contribution to both research and practice.

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