국제 저널 논문

Abstract
A wafer bin map (WBM) represents the locational information of defective chips on the wafer. The spatial correlation of defects on the wafer provides crucial information for the root cause diagnosis of defects in wafer fabrication. The spatial correlation is classified as a defect pattern for efficient diagnostics. A defect pattern taxonomy should be defined in advance for coherent classification of defect patterns. Various taxonomies are used in previous studies, but they share common limitations in that the differentiation among defect patterns is unclear, the set of predefined defect patterns is insufficient, and they cannot accommodate newly-emerged defect patterns. A concept of spatial dimension-based defect pattern taxonomy and its development procedure are proposed. Defect patterns are defined by three spatial dimensions, namely, Shape, Size, and Location. The development procedure is applied to a major NAND flash memory semiconductor manufacturer for two years. Results show that spatial dimension-based taxonomy can improve the performance of the defect pattern classification system by alleviating common existing limitations. Moreover, meaningful defect patterns for diagnostics are retained through the engineers’ involvement in the development procedure.

Abstract
According to the increase in electricity consumption in residential buildings, advanced metering infrastructures (AMIs) have been widely installed in residential buildings. AMIs help households to monitor and reduce their electricity consumption. Although the usefulness of AMIs has been validated, there still exist several barriers to their widespread use. This study considers household’s information privacy concerns (IPCs) and perceived electricity usage habits (PEUHs) as barriers to AMI penetration. AMI data on electricity usage may reveal extensive information about households, such as indoor behaviors and appliance types. This information may cause households to be concerned about invasion of privacy, and such concerns may affect AMI usage intentions.In terms of PEUHs, electric power companies argue that households with undesirable electricity usage habits,such as large amount of electricity usage with unpredictable usage patterns, are prospective AMI customers. This study develops IPCs and PEUHs scales and conducts a path analysis based on the framework of technology acceptance model to validate the influence of IPCs and PEUHs on AMI usage intention. The identified effects of IPCs and PEUHs on AMI usage intention are expected to provide practical information for understanding how IPCs and PEUHs influence AMI usage intention from the perspective of households.

Abstract
The utilization of automatic test equipment (ATE) in a wafer test is crucial because it helps maintain optimal temperatures and simultaneously provides electrical power for multiple chips. Ensuring the proper functioning of ATE is essential to avoid productivity losses, such as extended wafer testing times and potential malfunctions. Although previous research has investigated the detection of ATE malfunctions using yields, probe cards, and electrical characteristic data, these approaches exhibit limitations in data characteristics and are not suitable for continuous monitoring. This study introduces a novel approach for detecting abnormal behavior within ATE by using event log data and an autoencoder. Autoencoders have demonstrated efficacy in identifying abnormal behavior, making them apt for detecting such behavior within ATE using event log data. To develop this approach, data analysts and wafer test engineers collaborated to establish a practical methodology that encapsulates the operational characteristics of ATE. This methodology comprises four distinct steps. The proposed approach was assessed using simulated event log data and was proven effective in detecting abnormal behavior within ATE. Furthermore, the approach was applied to real-world ATE data from a semiconductor company, identifying abnormal temperature control and monitoring behavior. This detection can lead to reduced wafer test times and contribute to environmental protection efforts.

Abstract
A wafer consists of several chips, and serial electrical tests are conducted for each chip to investigate whether the chip is defective. A bin indicates the test results for each chip with information on which tests the chip failed. A wafer bin map (WBM) shows the locations and bins of the defects on the wafer. WBMs showing spatial patterns of defects usually result from assignable causes in the wafer fabrication process; hence, they should be classified in advance. The existing defect-pattern taxonomies do not consider bins, although useful information can be obtained from them. We propose a taxonomy that consists of the shape, size, location, and bin dimensions. The bin dimension is developed using Bin2Vec method, which determines RGB (red-green-blue) code for each bin according to the spatial similarity between bins. Three levels of the bin dimension are defined by analyzing a large number of WBMs using Bin2Vec and clustering methods. Compared with the existing taxonomies, the proposed taxonomy has the advantage of identifying major bins of defect patterns, new defect patterns, and non-critical defect patterns. A high-quality training dataset was obtained using the proposed taxonomy; consequently, a defect pattern classification model with satisfactory classification performance could be obtained.

Abstract
As electricity consumption in residential buildings has increased, advanced metering infrastructures (AMIs) have been widely installed in residential buildings. AMIs measure and collect metering data of electricity (AMI data) consumed in each household. Companies, such as electricity suppliers and service and policy developers, have used AMI data to develop various services and policies. They rely on actual electricity consumption from AMI data to understand households’ needs. However, the actual consumption may be different from household’s perceived consumption. Household’s perceived consumption could be affected by their respective characteristics, such as demographic and psycho-social characteristics. Accordingly, this research examines a difference between household’s actual and perceived electricity consumption and determines household characteristics that affect the difference. For achieving this goal, this research uses AMI data (actual consumption) and survey data (perceived consumption) from 142 households in South Korea. Results confirm the existence of the difference and the influence of specific household characteristics on the difference between actual and perceived electricity consumptions. House and household sizes affect actual consumption, whereas behaviors that interact with electrical appliances affect perceived consumption. The findings would be useful for the companies in developing services and policies that satisfy household requirements.

Abstract
Currently, many manufacturing companies are obtaining a large amount of operational data from manufacturing lines due to advances in information technology. Thus, various data mining methods have been applied to analyze the data to optimize the manufacturing process. Most of the existing data mining-based optimization methods assume that the relationships between input and response variables do not change over time. However, because it often takes a long time to collect a large amount of operational data, the relationships may change during the data collection. In such a case, the operational data is regarded as time-series data and recent data should be regarded to be more important than old data. In this study, we employed a patient rule induction method (PRIM), which is one of the data mining methods applied for process optimization. In addition, we employed an exponentially weighted moving average (EWMA) statistic to assign a larger weight to the recent data. Based on the PRIM and EWMA, the proposed method attempts to obtain optimal intervals for input variables where current performance of the response is better. The proposed method is illustrated with a hypothetical example and validated through a real case study of a steel manufacturing process.

Abstract
A wafer consists of several chips, and a wafer map shows the locations of defective chips on the wafer. The locational pattern of defective chips on the wafer map provides crucial information for improving the semiconductor wafer fabrication process. Recently, automatic defect pattern classification using convolutional neural networks (CNN) has become popular because of its good classification performance. The good performance is guaranteed only when a large amount of well-balanced training and test data is available. However, such data are difficult to obtain in real practice because the training and test data are obtained by manual inspection. In this paper, we propose a systematic method to resolve the small and imbalanced wafer map data issues. Specifically, we first selected wafers showing clear defect patterns and then replicated them by randomly applying horizontal flip, vertical flip, and rotation. In the case study, we obtained real wafer map data from a semiconductor wafer company. By applying the proposed method, a large amount of well-balanced data was obtained. The CNN model for defect classification was fitted to the obtained data, and it showed good classification performance.

Abstract
The conventional approach for optimizing multiresponse is fitting multiple response surface models and then analyzing them to obtain optimal settings for the input variables. However, it is difficult to obtain reliable response surface models when dealing with large amounts of data. In this article, a new approach to multiresponse optimization based on a classification and regression tree method is presented. Desirability functions are employed to simultaneously optimize the multiple responses. The case study of steel manufacturing company with large amounts of data shows that the proposed method obtains an optimal region in which multiple responses are simultaneously optimized.

Abstract
In a multistage manufacturing process, identical machines are utilized at each process stage. According to such configuration, each product can go through a different process path (hereafter referred to as path) during production. In practice, however, machines at a process stage may have different operational performances, and thus the quality of the final product may vary depending on the path. This research proposes an approach to derive the golden paths (GPs), which are expected to produce products of higher quality than the user-defined quality level, when the machine performance degrades over time. To this end, the health indicator (HI) of a machine, which represents its performance at a certain time, is introduced to reflect the performance degradation. In deriving GPs, the proposed approach discovers a specific set of machine sequence patterns (MSPs), sequential combinations of machines in different process stages, satisfying several rules. Based on the discovered MSPs and the HI information, the proposed approach generates the GPs at a particular future point in time. The viability of the proposed approach is evaluated through simulated experiment. The derived GPs reflecting machine performance degradation over time are expected to contribute in increasing the production of superior quality products and reducing the re-production costs incurred by utilizing non-GPs.

Abstract
A multistage process consists of sequential consecutive stages. In this process, each stage has multiple responses and is affected by its preceding stage, while at the same time, affecting the following stage. This complex structure makes it difficult to optimize the multistage process. Recently, it became easy to obtain a large amount of operational data from the multistage process due to development of information technologies. The proposed method employs a data mining method called a classification and regression tree for analyzing the data and desirability functions for simultaneously optimizing the multiresponse. To consider the relationship between stages, a backward optimization procedure which treats the multiresponse of the preceding stage as the input variables is proposed. The proposed method is described using a steel manufacturing process example and is compared with existing multiresponse optimization methods. The case study shows that the proposed method works well and outperforms the existing methods.

Abstract
To solve multiple response optimization problems that often involve incommensurate and conflicting responses, a robust interactive desirability function approach is proposed. The proposed approach consists of a parameter initialization phase and calculation and decision-making phases. It considers a decision maker’s preference information regarding tradeoffs among responses and the uncertainties associated with predicted response surface models. The proposed method is the first to consider model uncertainty using an interactive desirability function approach. It allows a decision maker to adjust any of the preference parameters, including the shape, bound and target of a modified robust function with consideration of model uncertainty in a single and integrated framework. This property of the proposed method is illustrated using a tire-tread compound problem, and the robustness of the adjustments for the approach is also considered. Thus, the new method is shown to be highly effective in generating a compromise solution that is faithful to the decision maker’s preference structure and robust to uncertainties associated with model predictions.

Abstract
Background: A lifelogs-based wellness index (LWI) is a function for calculating wellness scores based on health behavior lifelogs (eg, daily walking steps and sleep times collected via a smartwatch). A wellness score intuitively shows the users of smart wellness services the overall condition of their health behaviors. LWI development includes estimation (ie, estimating coefficients in LWI with data). A panel data set comprising health behavior lifelogs allows LWI estimation to control for unobserved variables, thereby resulting in less bias. However, these data sets typically have missing data due to events that occur in daily life (eg, smart devices stop collecting data when batteries are depleted), which can introduce biases into LWI coefficients. Thus, the appropriate choice of method to handle missing data is important for reducing biases in LWI estimations with panel data. However, there is
a lack of research in this area.
Objective: This study aims to identify a suitable missing-data handling method for LWI estimation with panel data.
Methods: Listwise deletion, mean imputation, expectation maximization–based multiple imputation, predictive-mean matching–based multiple imputation, k-nearest neighbors–based imputation, and low-rank approximation–based imputation were comparatively evaluated by simulating an existing case of LWI development. A panel data set comprising health behavior lifelogs of 41 college students over 4 weeks was transformed into a reference data set without any missing data. Then, 200 simulated data sets were generated by randomly introducing missing data at proportions from 1% to 80%. The missing-data handling methods were each applied to transform the simulated data sets into complete data sets, and coefficients in a linear LWI were estimated for each complete data set. For each proportion for each method, a bias measure was calculated by comparing the estimated coefficient values with values estimated from the reference data set.
Results: Methods performed differently depending on the proportion of missing data. For 1% to 30% proportions, low-rank approximation–based imputation, predictive-mean matching–based multiple imputation, and expectation maximization–based multiple imputation were superior. For 31% to 60% proportions, low-rank approximation–based imputation and predictive-mean matching–based multiple imputation performed best. For over 60% proportions, only low-rank approximation–based imputation performed acceptably.
Conclusions: Low-rank approximation–based imputation was the best of the 6 data-handling methods regardless of the proportion of missing data. This superiority is generalizable to other panel data sets comprising health behavior lifelogs given their verified low-rank nature, for which low-rank approximation–based imputation is known to perform effectively. This result will guide missing-data handling in reducing coefficient biases in new development cases of linear LWIs with panel data.

Abstract
Real-time collection of household electricity consumption data has been facilitated by advanced metering infrastructure. In recent studies, collected data have been processed to provide information on household appliance usage. The noise caused by electrical appliances from neighboring households constitutes a major issue, which is related to discomfort and even mental diseases. The assessment of noise discomfort using electricity consumption data has not been dealt with in the literature up to this day. In this study, a method, which utilizes electricity consumption data for the assessment of noise discomfort levels caused by electrical appliances between neighboring households, is proposed. This method is based on the differences in the usage time of electrical appliances in a collective residential building. The proposed method includes the following four steps: data collection and preprocessing, residential units clustering, noise discomfort modeling, and evaluation of noise discomfort. This method is demonstrated through a case study of a campus apartment building. Variations in the noise discomfort assessment model and measures for alleviating noise discomfort are also discussed. The proposed method can guide the application of electricity consumption data to assessment and alleviation of noise discomfort from home appliances at an apartment building.

Abstract
A multistage manufacturing process (MMP) consists of several consecutive process stages, each of which has multiple machines performing the same functions in parallel. A manufacturing path (simply referred to as path) is defined as an ordered set indicating a record of machines assigned to a product at each process stage of an MMP. An MMP usually produces products through various paths. In practice, multiple machines in a process stage have different operational performances, which accumulate during production and affect the quality of products. This study proposes a heuristic approach to derive the golden paths that produce products whose quality exceeds the desired level. The proposed approach consists of the searching phase and the merging phase. The searching phase extracts two types of machine sequence patterns (MSPs) from a path dataset in an MMP. An MSP is a subset of the path that is defined as an ordered set of assigned machines from several process stages. The two extracted types of MSPs are: 1) superior MSP, which affects the production of superior-quality products, and 2) inferior MSP, which affects the production of inferior-quality products, called inferior MSP. The merging phase derives the golden paths by combining superior MSPs and excluding inferior MSPs. The proposed approach is verified by applying it to a hypothetical path dataset and the semiconductor tool fault isolation (SETFI) dataset. This verification shows that the proposed approach derives the golden paths that exceed the predefined product quality level. This outcome demonstrates the practical viability of the proposed approach in an MMP.

Abstract
Most manufacturing industries produce products through a series of sequential stages, known as a multistage process. In a multistage process, each stage affects the stage that follows and it often has multiple response variables. This paper suggests a new procedure for optimizing a multistage process with multiple response variables. It searches for an optimal setting of input variables directly from operational data according to a patient rule induction method to maximize a desirability function, to which multiple response variables are converted. The proposed method is explained by a step-by-step procedure using a steel manufacturing process example.

Abstract
This study compares the performances of the generalized confidence interval (GCI) and the modified sampling distribution (MSD) approaches in evaluating the capability of processes with one-sided tolerance under the presence of gauge measurement errors (GME). The performance of both approaches are measured through a series of simulation. In terms of coverage rates (CRs), GCI and MSD approaches appear to work satisfactorily in the presence of GME since the CRs of the lower confidence bound with considering GME were all close to the nominal value. Furthermore, some of the coverage rates with considering the GME of GCI approach were smaller than those of MSD approach. GCI approach has better ability to assess process capability in the presence of GME. Based on numerical results, both approaches can be recommended to practitioners who assess process performances for cases with one-sided tolerance when GME is actually inevitable.

Abstract
A multistage process consists of sequential stages where each stage is affected by its preceding stage, and it in turn affects the stage that follows. The process described in this paper also has several input and response variables whose relationships are complicated. These characteristics make it difficult to optimize all responses in the multistage process. We modify a data mining method called the patient rule induction method and combine it with desirability function methods to optimize the mean and variance of multiresponse in the multistage process. The proposed method is explained by a step-by-step procedure using a steel manufacturing process example.

Abstract
Online-to-offline (O2O) integration refers to the incorporation of separate online and offline service processes into a single service delivery. Advances in mobile devices and information and communication technology enable the O2Ointegration, which has been applied to many services. This study proposes a new service blueprint, called the O2O Service Blueprint (O2O SB), which is specialized in visualizing and analyzing the service processes of the O2O integration. A comprehensive literature review and text mining analysis are conducted on massive quantities of literature, articles, and application introductions to understand characteristics of the O2O integration and extract keywords relevant to the O2O integration. Comparisons of the O2O SB with the conventional service blueprint and Information Service Blueprint validate that our proposal can address the limitations of existing service blueprints. An evaluation through expert interviews confirms the completeness, utility, and versatility of the O2O SB. The proposed O2O SB presents a complete picture of the entire service process, whether online or offline. This SB helps users systematically understand the processes and formulate strategies for service improvement.

Abstract
Semiconductor wafers are fabricated through sequential process steps. Some process steps have a strong relationship with wafer yield, and these are called critical process steps. Because wafer yield is a key performance index in wafer fabrication, the critical process steps should be carefully selected and managed. This paper proposes a systematic and data-driven approach for identifying the critical process steps. The proposed method considers troublesome properties of the data from the process steps such as imbalanced data, missing values, and random sampling. As a case study, we analyzed hypothetical operational data and confirmed that the proposed method works well.

Abstract
In the semiconductor wafer fabrication process, wafers go through a series of sequential process steps. Typically, each process step has several machines, and the wafers are assigned to one whenever they enter the process step. When assigning wafers to machines, it is important to consider both the quality and productivity perspectives. Major semiconductor companies in Korea have recently implemented a wafer assignment system to improve wafer yield, a critical measure for semiconductor quality. This system, however, does not consider the productivity perspective. This paper presents a systematic method for assigning wafers to maximize the wafer yield while satisfying a predetermined target level of productivity. A simple hypothetical example is presented to illustrate the method.

Abstract
Purpose: The Fourth Industrial Revolution (4th IR) affects the mode of company management. In this paper, a revised social responsibility (SR) model is presented as an evaluation tool for corporate social responsibility (CSR) performance for sustainable organizational growth in the era of the 4th IR.

Design/methodology/approach: To develop an SR model that can be used well in the era of the 4th IR, the key references are “ISO 26000: Guidance on Social Responsibility” and “the Global Reporting Initiative (GRI) Guidelines.” For ISO 26000 and the GRI guidelines, see the homepages in the References section. On the basis of these guidelines, a new SR model for sustainable development in the 4th IR is developed in this paper.

Findings: For a new SR model in the 4th IR, the concepts of management quality, quality responsibility, creating shared value, social value and open data and open quality management (QM) are incorporated into the existing International Organization for Standardization (ISO) 26000 evaluation criteria.

Originality/value: The 4th IR is changing the concepts of both QM and SR. To the best of the authors’ knowledge, the new concept of SR is yet to be discussed extensively. In this paper, a new SR model is suggested to reflect the characteristics of the 4th IR.

Abstract
Purpose: The proliferation of customer-related data provides companies with numerous service opportunities to create customer value. This study develops a framework to use this data to provide services.

Design/methodology/approach: This study conducted four action research projects on the use of customer-related data for service design with industry and government. Based on these projects, a practical framework was designed, applied, and validated, and was further refined by analyzing relevant service cases and incorporating the service and operations management literature.

Findings: The proposed customer process management (CPM) framework suggests steps a service provider can take when providing information to its customers to improve their processes and create more value-in-use by using data related to their processes. The applicability of this framework is illustrated using real examples from the action research projects and relevant literature.

Originality/value: “Using data to advance service” is a critical and timely research topic in the service literature. This study develops an original, specific framework for a company’s use of customer-related data to advance its services and create customer value. Moreover, the four projects with industry and government are early CPM case studies with real data.

Abstract
Service design is a multidisciplinary area that helps innovate services by bringing new ideas to customers through a design-thinking approach. Services are affected by multiple factors, which should be considered in designing services. In this paper, we propose the multi-factor service design (MFSD) method, which helps consider the multi-factor nature of service in the service design process. The MFSD method has been developed through and used in five service design studies with industry and government. The method addresses the multi-factor nature of service for systematic service design by providing the following guidelines: (1) identify key factors that affect the customer value creation of the service in question (in short, value creation factors), (2) define the design space of the service based on the value creation factors, and (3) design services and represent them based on the factors. We provide real stories and examples from the five service design studies to illustrate the MFSD method and demonstrate its utility. This study will contribute to the design of modern complex services that are affected by varied factors.

Abstract
Mobile health (mHealth) services support the continuous health-related monitoring, feedback, and behavior modification of individuals and populations through the use of personal mobile communication devices. Poor service quality is a major reason why many users have discontinued using mHealth services. However, only a few studies have identified the critical quality components for continuance intention. The current study aims to identify the crucial quality dimensions for users’ continuance intention in an mHealth service called Onecare. This service provides various forms of support for the day-to-day health behavior monitoring of college students by utilizing daily behavior data. In this research, five major quality dimensions of mHealth services, namely, content quality, engagement, reliability, usability, and privacy, were derived from existing studies. The effect of each quality dimension on continuance intention was estimated by analyzing the survey responses of 191 Onecare service users. The quality dimension with the most considerable effect on continuance intention was determined to be engagement followed by content quality and reliability. By contrast, the effects of usability and privacy on continuance intention were insignificant. Furthermore, this study found that the optimal quality management strategy can change depending on the objective, i.e., to increase continuance intention or satisfaction. These results will help mHealth service managers allocate their limited resources to effectively and efficiently improve continuance intention. Future research is required to verify if the findings of this study are generalizable to any population because the sample used in this work was specific to Korean college students.

Abstract
Numerous companies in manufacturing industries have “servitized” their value propositions to address issues on product commoditization and sustainability. A key component of servitization is informatics, which transforms product and customer data into information for customers. In this study, informatics-based service is defined as a type of service wherein informatics is crucial to customer value creation. Despite the importance of this concept, studies on the design of informatics-based services in manufacturing industries are rare. This paper reports on two case studies on such designs. Informatics-based services have been designed for a major Korean automobile manufacturer and the Korea Transportation Safety Authority (TS) based on their large vehicle-related databases. The first case study with the automobile manufacturer aims to design vehicle operations and health management services for passenger vehicle drivers while the second study with TS focuses on the design of driving safety enhancement services for commercial vehicle (i.e., bus, taxi, and truck) drivers. Based on the case studies, this paper discusses various aspects of informatics-based service design in manufacturing industries. This study would assist researchers and practitioners in designing new informatics-based services and contribute to promoting and inspiring research on intelligent services in manufacturing industries under the current information economy.

Abstract
The South Korean government retains valuable health-related data of nearly all citizens. This data set includes insurance data, diagnosis history data, treatment history data, and medical examination data. This paper presents a case study with the National Health Insurance Service of South Korean government to develop service concepts that utilize such health-related data. A service concept indicates what to offer to customers and how to offer it. In the case study, 138 service ideas that utilize health-related data were generated based on literature and focus group interviews with 14 experts. The ideas were evaluated by 20 experts via focus group interviews. Eight new service concepts were designed based on the ideas, and then evaluated by 19 experts and 612 citizens. The service concepts are expected to improve the national healthcare system of South Korea. This paper also presents key dimensions of health-related data utilization services and issues in developing such services, which were identified based on the case study. The original contribution of this study is the development of health-related data utilization service concepts at a national scale. This work will serve as a foundational reference for future research aimed at developing such services.

Abstract
A product-service system (PSS) is an integrated bundle of products and services, which aims at creating customer utility and enhancing manufacturers’ competitiveness. Given that products and services are integrated over a specific cycle, PSS lifecycle has become a key notion in PSS research. However, most studies focus on PSS lifecycle from a provider perspective although the essence of PSS is to create customer utility. The current work proposes a customer-oriented model of PSS lifecycle as a solid basis for analyzing PSS from a customer perspective. A two-phase procedure was conducted to develop the model. In phase 1, customer activities in 118 PSS cases were analyzed to identify three specific types of PSS lifecycle (product-, use-, and result-oriented PSS). In phase 2, one general type of PSS lifecycle was analyzed by analyzing three specific types. In addition, their usefulness in PSS research was shown through an example on car-related PSS cases. The proposed model comprises one general and three specific types of PSS lifecycle. Four stages (“plan,” “decide,” “solve,” and “end”) were formed in the general type while seven stages were formed for product-oriented PSS, six for use-oriented PSS, and five for result-oriented PSS. The proposed model can be utilized to supplement the existing studies to consider the PSS mechanism from a customer perspective. A concrete notion of PSS lifecycle from a customer perspective may contribute to customer-oriented PSS innovations.

Abstract
Cities worldwide are attempting to transform themselves into smart cities. Recent cases and studies show that a key factor in this transformation is the use of urban big data from stakeholders and physical objects in cities. However, the knowledge and framework for data use for smart cities remain relatively unknown. This paper reports findings from an analysis of various use cases of big data in cities worldwide and the authors’ four projects with government organizations toward developing smart cities. Specifically, this paper classifies the urban data use cases into four reference models and identifies six challenges in transforming data into information for smart cities. Furthermore, building upon the relevant literature, this paper proposes five considerations for addressing the challenges in implementing the reference models in real-world applications. The reference models, challenges, and considerations collectively form a framework for data use for smart cities. This paper will contribute to urban planning and policy development in the modern data-rich economy.

Abstract
Smart wellness services collect various types of lifelogs such as walking steps and sleep duration via smart devices. However, most of the existing smart wellness services focus on displaying each individual lifelog to users. Therefore, they have limitations on supporting overall and easy understanding of various lifelogs. A lifelogs-based daily wellness score (LDWS) is a useful tool to resolve such limitations. LDWS combines the lifelogs into a score to represent an overall level of daily health behaviors, thus, supporting overall and easy health behavior monitoring of users. This research developed LDWS as part of developing a smart wellness service for college students (SWSCS) in collaboration with an IT company. Lifelogs of 41 college students were collected through a four-week pilot run of SWSCS and were subsequently fitted to a random effects model. Based on the model estimates, LDWS was determined by linearly aggregating seven behavior variables. Utility of the developed LDWS was validated through a second pilot run of SWSCS. This paper also discusses the potential use of LDWS for SWSCS and the factors to be considered for developing a lifelogs-based wellness score for a smart wellness service. This research would contribute to advancing smart wellness services with lifelogs.

Abstract
Experience-centric service (ExS) is a type of service through which customers experience emotionally appealing events and activities that result in distinctive memory. The literature argues that ExS design should be a research priority in this experience economy, yet little is known on how to articulate ExSs in their design. This paper proposes a tool called Experience Design Board for visualizing an ExS delivery process as a basis for its analysis and design. The tool is a matrix-shaped board where the key factors of experience creation in ExS (namely, servicescape, frontstage employees, other customers, backstage employees, and technology support systems) are represented in rows, and the customer experience phases are placed in columns. The tool is useful in analyzing and designing how the key factors of ExS create customer experience. The tool integrates several work streams within the evolving ExS literature into its structure and is generic enough to accommodate various ExSs in physical and digital experience contexts. By visualizing an ExS delivery process from beginning to end, the designer can obtain a systematic understanding of the essential attributes of ExS and can use it for an effective design. This tool would serve as a basis for service design in this experience economy.

Abstract
Various types and massive amounts of data are collected in the automotive industry. Such data proliferation facilitates and improves the design of services for vehicle operations management (VOM). A VOM service is a service that helps drivers drive safely, conveniently, and pleasurably with the use of VOM-related data. Despite the applicability of big data to VOM service design, few efforts have been made to establish a big data-based design process for VOM services. To fill the research gap, this study proposes an approach to analyzing and utilizing VOM-related data for designing VOM services. The proposed approach aids service designers in designing VOM services by using VOM-related data. A case study on the design of an eco-driving service, a popular VOM service, is presented to demonstrate the feasibility and effectiveness of the approach. The proposed approach could facilitate the design of VOM services and provide a foundation for data-driven service innovations.

Abstract
We propose a two-step procedure based on data analytics to help service providers to efficiently and effectively implement a health promotion program to prevent hypertension. First, we developed a prediction model to identify people who are at risk for developing hypertension. Then, to eliminate specific risk factors for each of these individuals, we proposed four methods to create an index that represents the importance of each intervention program, which is a subprogram of the health promotion program. This index can be used to recommend appropriate intervention programs for each individual. We used the national sample cohort databases of South Korea to offer a case study of the implementation of the proposed procedure. The constructed prediction model using logistic regression has adequate accuracy, and the proposed index that uses different methods has similar results to those of a doctor. This two-step procedure by automatic modeling based on data will be useful to save human resources and to provide informative and personalized results based on individual healthcare records.