PurposeThis paper aims to develop a quality evaluation model for artificial intelligence (AI)-based products/services that is applicable to startups utilizing AI technology. Although AI-based service has risen dramatically and replaced many service offerings, in reality, startups are rarely to develop and evaluate AI services. The features of AI service are fundamentally different from the properties of existing services and have a great influence on the customer's service selection.Design/methodology/approachThis paper reviews startups' development process, existing quality evaluation models and characteristics of services utilizing AI technology, and develops a quality evaluation model for AI-based services. A detailed analysis of a survey (application of the model) on customer satisfaction for AI speakers is provided.FindingsThis paper provides seven key features and 24 evaluation items for evaluating AI-based services.Originality/valueThis paper contributes to the growing need for methodologies that reflect the new era of AI-based products/services in quality evaluation research.

Purpose: This paper reviews the papers on statistical quality control issues which are published in Journal of the Korean Society for Quality Management (KSQM) since 1965. The literature review is purposed to survey a variety of statistical quality control issues. Methods: By grouping all of statistical quality control issues into 3 categories:; quality inspections, control charts, and process capability analysis. Results: Grouping all of papers on statistical quality control published in journal of the KSQM for 50 years into 3 categories, we provide a chronological roadmap for individual categories, and summarize the contents and contributions of surveyed papers. Conclusion: The review paper is expected to provide future direction to improve statistical quality control theories and applications in manufacturing and service industries.

Purpose: To obtain the area for improvement, the Importance-Performance analysis(IPA) uses relatively simple questions, that is, satisfaction and importance at attribute level. However, no attempt has been made to consider the gap between own company's performance and those of competitors in IPA, in the field of quality management. This study is aimed to suggest a new prioritizing method for improvement and to test for validity of the proposed technique.Methods: This study used data collected from Song and Lim(2015), which is satisfaction of employees, customers and competitors as well as importance data for 7 quality attributes of K animal hospital. A correlation comparison with other priority methods such as Bacon( 2003)'s model and Matzler and Hinterhuber(1998)'s QI index is conducted. Results:The priority results by the proposed method shows better in correlation coefficient with customer perceived priority for improvement than other methods. Conclusion:From the result of the current study, it can be concluded that the result of the proposed method is valid, while it is relatively easy to understand and analyze, and therefore no additional survey is necessary for improvement priority.