• Journal of the Korean Society of Systems Engineering
• /
• v.19 no.2
• /
• pp.59-73
• /
• 2023

Due to the steady growth of the online shopping market and contact-free consumption, the volume of parcels in South Korea continues to increase. However, there is a lack of manpower for delivery workers to handle the growing parcel volume, leading to frequent accidents related to delivery work. As a result, the government and local authorities strive to enhance last-mile logistics efficiency. As one of these measures, unmanned parcel storage lockers are installed and utilized to handle last-mile deliveries. However, the existing parcel storage involves the inconvenience of couriers having to put each parcel in each locker, and this is somewhat insufficient to relieve the workload of delivery workers. In this study, we propose parcel storage devices that use active loading technology to minimize the workload of delivery workers, extract operation risk factors to apply this system to actual sites, and establish risk reduction methods based on the ALARP concept. Through this study, we have laid the groundwork for improving the safety of the system by identifying and proposing mitigation measures for the risk factors associated with the proposed parcel storage devices utilizing active loading technology. When applied in practical settings in the future, this foundation will contribute to the development of a more efficient and secure system. By applying the ALARP concept, a systems engineering technique used in this research, to the development and maintenance of storage devices leveraging active loading technology, it is thought to make the development process more systematic and structured. Furthermore, through the risk management of the proposed system, it is anticipated that a systematic approach to quality management can be employed to minimize defects and provide a stable system. This is expected to be more useful than the existing unmanned parcel storage devices.

• Information Systems Review
• /
• v.23 no.4
• /
• pp.165-182
• /
• 2021

In nowadays, Covid-19 has transformed patterns of consumers' behavior into a non-face-to-face mode. As the patterns of consumption have been digitalized, it has become a daily routine for consumers who perform so-called shadow work, which involves unpaid jobs that they have to do by themselves. In mobile grocery service context, consumers' shadow work could lead to shopping avoidance as well as switching toward other shopping channels. Thus, this study is to examine how consumers' perception of shadow work affect mobile shopping avoidance and switching intention toward other shopping channels. This study collected 283 survey data from online respondents who have experience on subscription services for ordering groceries in online. We also tested our research model by using partial least squares. Based on our results, this study has found that the perception of shadow work had a positive effect on mobile shopping avoidance as well as switching intention. We expect that our findings could contribute to relevant research on shadow work and suggest practical implications for digital platforms dealing with subscription business models

• Journal of Korean Society of Forest Science
• /
• v.113 no.2
• /
• pp.153-163
• /
• 2024

Since the onset of the COVID-19 pandemic, there has been a significant shift in the lifestyle patterns of the populace across various domains. Concerns surrounding COVID-19 have emerged as pivotal catalysts of change in recreational habits with people giving a particular preference for environments with low population density and increased openness. This trend has resulted in an uptick in excursions to natural reserves, coastlines, and parks. However, during the peak of infectious outbreaks, widespread adherence to social distancing measures has precipitated a steep decline in tourist footfall across natural recreation forests, exacerbating financial deficits to a considerable extent. Thus, this research sought to compare and analyze the operational efficacy and productivity of national, public, and private natural recreation forests pre- and post-COVID-19 pandemic by utilizing non-parametric methodologies, such as data envelopment analysis and the Malmquist productivity index analysis. The objective was to identify the factors contributing to the decreases in efficiency and productivity and ultimately offer nuanced recommendations tailored to respective administrative bodies. This study's distinctive focus on the analysis of management efficiency and productivity in natural recreation forests nationwide offers significant academic and practical relevance.

• Journal of Practical Engineering Education
• /
• v.16 no.2
• /
• pp.129-141
• /
• 2024

In recent times, there has been a shift towards student-centered higher education policies, leading to a growing interest among universities to improve students' learning outcomes. To aid in this endeavor, this study aims to provide guidance for University K to enhance their learning outcome management by comparing and analyzing their learning outcome indicators with those of other domestic and foreign universities. The study examined detailed measurement questions from major learning outcome measurement tools such as AHELO, NSSE, and CLA+. Upon comparison and analysis of University K's major learning outcome indicators with those of other universities, it was found that most of the indicators overlapped. However, some indicators such as student support/facilities for learning, instructor quality, and communication were absent from University K. Therefore, it is crucial to decide whether to add these indicators to the existing learning outcomes or to confirm them through other surveys. Moreover, even for the same indicator, some indicators with different measurement need to consider changing the measurement.

• Knowledge Management Research
• /
• v.24 no.4
• /
• pp.153-176
• /
• 2023

Generative AI, especially conversational AI like ChatGPT, has recently gained traction as a technological alternative for automating customer service. However, there is still a lack of research on whether current generative AI technologies can effectively replace traditional human managers in customer service automation, and whether they are advantageous in some situations and disadvantageous in others, depending on the conditions and environment. To answer the question, "Can generative AI replace human managers in customer service activities?", this study conducted experiments and surveys on customer online reviews of a food delivery platform. We applied the perspective of the elaboration likelihood model to generate hypotheses about whether there is a difference between positive and negative online reviews, and analyzed whether the hypotheses were supported. The analysis results indicate that for positive reviews, generative AI can effectively replace human managers. However, for negative reviews, complete replacement is challenging, and human managerial intervention is considered more desirable. The results of this study can provide valuable practical insights for organizations looking to automate customer service using generative AI.

• Knowledge Management Research
• /
• v.24 no.4
• /
• pp.177-193
• /
• 2023

This study explores the role of organizational culture, job characteristics, and work values and orientation in shaping the competitiveness of a multinational company (MNC) based in Korea. The purpose of the study was to examine the impact of these variables on the competitiveness attributes of the organizational culture profile through MR-QAP analysis. Data were collected from 161 employees in 15 different teams at a Korean automotive company headquartered in Seoul. The results of the study revealed the impact of network characteristics associated with competitive organizational culture on competitiveness. 'found to have a negative effect on competitiveness. Among the organizational culture profiles, social responsibility, supportiveness, innovation, and performance orientation have a significant positive effect on competitive organizational culture, while emphasis on rewards and stability have no significant effect. These findings provide practical implications for understanding the complex dynamics of organizational culture and promoting strategic approaches to enhance organizational competitiveness.

• Information Systems Review
• /
• v.25 no.3
• /
• pp.121-138
• /
• 2023

The advancement in mobile technology brought disruptive innovation in media industry. The introduction of mobile devices broke spatial and temporal restrictions in media consumption. This study investigates the impact of mobile channel adoption on video viewing behavior, using real-world dataset obtained from a particular on-demand service provider in South Korea. We find that the adoption of a mobile channel significantly increases the total viewing time of video-on-demand via TV and the number of contents viewed. Our results suggest that the mobile channels act as a complement channel to conventional TV channels. We provide theoretical and practical insights on consumer usage in the emerging over-the-top market.

• Journal of the Korea Institute of Building Construction
• /
• v.24 no.3
• /
• pp.355-362
• /
• 2024

The sustained upkeep of apartment buildings necessitates ongoing maintenance and timely repairs, particularly given their complex nature due to extensive areas, common facilities, and multiple residential and service structures. Additionally, the need for cost-effective maintenance is paramount for ensuring safety, preserving value, and maintaining economic efficiency. However, the multitude of external variables influencing apartment complex maintenance, coupled with the challenges in data collection, have resulted in limited research in this domain. To address this gap, the current study aims to develop a framework for predicting maintenance costs utilizing deep learning techniques, grounded in real-world apartment complex maintenance cost data. This study intends to provide a practical and valuable contribution to the field of apartment complex management, empowering stakeholders with enhanced predictive capabilities for optimizing maintenance strategies and resource allocation.

• Journal of Korean Society for Quality Management
• /
• v.52 no.1
• /
• pp.1-21
• /
• 2024

Purpose: This study seeks to explore the impact of COVID-19-induced boredom, a prevalent form of pandemic-related stress, on travel motivation and post-pandemic travel intentions. Additionally, it examines the interplay among travel motivation, travel constraints, and the willingness to pay more for travel experiences in the post-pandemic context. Methods: A PLS-SEM analysis was conducted to analyze the data. Data collection took place through an online survey in February and March 2021, with a total of 575 respondents participating. Participants provided responses regarding their current levels of boredom due to COVID-19, five different travel motivations, seven travel constraints, and their post-pandemic travel intentions. Additionally, participants were asked about their willingness to pay more for travel. Results: This study highlights the significant role of COVID-19-induced boredom in predicting post-pandemic travel intentions and the willingness to pay more for travel. Contrary to previous perceptions, boredom emerges as a driving factor, enhancing travel intentions during the pandemic. Additionally, relaxation becomes the primary motivation for travel during COVID-19, and structural constraints exert a noticeable impact on travel intentions, challenging previous assumptions. Stress levels directly influence the willingness to pay more during travel experiences, expanding the understanding of additional payment behavior in the context of travel. Conclusion: This study offers practical insights for tourism stakeholders. Recognizing and addressing boredom in marketing strategies, implementing aggressive additional payment options, and focusing on relaxation-oriented travel products are recommended to cater to post-pandemic traveler preferences and revive the tourism industry effectively.

• Journal of Korean Society for Quality Management
• /
• v.52 no.1
• /
• pp.43-56
• /
• 2024

Purpose: The injection molding process, crucial for plastic shaping, encounters difficulties in sustaining product quality when replacing injection machines. Variations in machine types and outputs between different production lines or factories increase the risk of quality deterioration. In response, the study aims to develop a system that optimally adjusts conditions during the replacement of injection machines linked to molds. Methods: Utilizing a dataset of 12 injection process variables and 52 corresponding sensor variables, a predictive model is crafted using Decision Tree, Random Forest, and XGBoost. Model evaluation is conducted using an 80% training data and a 20% test data split. The dependent variable, classified into five characteristics based on temperature and pressure, guides the prediction model. Bayesian optimization, integrated into the selected model, determines optimal values for process variables during the replacement of injection machines. The iterative convergence of sensor prediction values to the optimum range is visually confirmed, aligning them with the target range. Experimental results validate the proposed approach. Results: Post-experiment analysis indicates the superiority of the XGBoost model across all five characteristics, achieving a combined high performance of 0.81 and a Mean Absolute Error (MAE) of 0.77. The study introduces a method for optimizing initial conditions in the injection process during machine replacement, utilizing Bayesian optimization. This streamlined approach reduces both time and costs, thereby enhancing process efficiency. Conclusion: This research contributes practical insights to the optimization literature, offering valuable guidance for industries seeking streamlined and cost-effective methods for machine replacement in injection molding.