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The purpose of this paper is to provide an input-output life cycle assessment model to estimate the carbon footprint of US manufacturing sectors. To achieve this, the paper sets out the following objectives: develop a time series carbon footprint estimation model for US manufacturing sectors; analyze the annual and cumulative carbon footprint; analyze and identify the most carbon emitting and carbon intensive manufacturing industries in the last four decades; and analyze the supply chains of US manufacturing industries to help identify the most critical carbon emitting industries.

Initially, the economic input-output tables of US economy and carbon footprint multipliers were collected from EORA database (Lenzen et al., 2012). Then, economic input-output life cycle assessment models were developed to quantify the carbon footprint extents of the US manufacturing sectors between 1970 and 2011. The carbon footprint is assessed in metric tons of CO₂-equivalent, whereas the economic outputs were measured in million dollar economic activity.

The salient finding of this paper is that the carbon footprint stock has been increasing substantially over the last four decades. The steep growth in economic output unfortunately over-shadowed the potential benefits that were obtained from lower CO₂ intensities. Analysis of specific industry results indicate that the top five manufacturing sectors based on total carbon footprint share are “petroleum refineries,” “Animal (except poultry) slaughtering, rendering, and processing,” “Other basic organic chemical manufacturing,” “Motor vehicle parts manufacturing,” and “Iron and steel mills and ferroalloy manufacturing.”

This paper proposes a state-of-art time series input-output-based carbon footprint assessment for the US manufacturing industries considering direct (onsite) and indirect (supply chain) impacts. In addition, the paper provides carbon intensity and carbon stock variables that are assessed over time for each of the US manufacturing industries from a supply chain footprint perspective.

This study aims to conduct socio-technical analysis of the rapidly evolving Internet of Things (IoT) ecosystem and industry, including such factors as market growth and user experiences, policy and the impact of IoT on various areas.

By applying a multi-level socio-technical framework to IoT in South Korea, this study seeks an ecological understanding of how IoT will evolve and stabilize in a smart environment.

The study shows the values influencing potential users’ adoption of IoT by integrating cognitive motivations and user values as primary determining factors. Along with user modeling, the findings reveal the challenges of designing, deploying and sustaining the diverse components of IoT, and provides a snapshot of Korea’s current approach to meeting these challenges.

The study’s findings offer a contextualized socio-technical analysis of IoT, providing insight into its challenges and opportunities. This insight helps to conceptualize how IoT can be designed and situated within human-centered contexts.

The purpose of this paper is to investigate whether sell-side equity analysts use labor cost information when forming expectations of future earnings. The availability of disaggregated earnings components will benefit financial statement users to the extent that the additional information released by a firm is useful to infer differential persistence of disaggregated earnings components.

This paper employs ordinary least squares, logit, and two-stage Heckman (1979) regressions which test whether analysts incorporate labor cost information into their earnings forecasts after controlling for a managerial self-selection to disclose labor costs, and further test whether a firm’s decision to voluntarily disclose labor costs improves analyst forecast accuracy.

This research finds that analysts incorporate labor cost information into their earnings forecasts after controlling for other earnings components. More importantly, this research shows that voluntary disclosure of labor cost information is positively associated with analyst forecast accuracy. Additional tests show that the benefit of voluntary labor cost information is more pronounced for firms with high information uncertainty and for analysts with less firm-specific experience and analysts affiliated with small brokerage houses.

This paper contributes to the literatures on the effect of labor cost on investors’ behavior and on analyst-specific factors in explaining analyst ability to predict future earnings.

Global value changes continued to expand until the late 2000s. On the other hand, regional value chains have formed around major regional hubs due to the expansion of domestic demand in emerging economies, such as China, and strengthened trade protectionism since the global financial crisis. Such changes lead to the reorganisation of value chains, focusing on domestic markets (reshoring) or neighbouring countries (nearshoring). In particular, the importance of supply chain risk management has been highlighted following disruptions to the supply network due to the COVID-19 outbreak in December 2019. In this regard, major countries such as the USA and the EU are rapidly shifting to regional value chains for stable and sustainable production, rather than primarily aiming for production efficiency targeted at reducing costs. Industries in particular are more exposed to such supply chain risks under the existing structure and it now has become extremely important for businesses to take reaction to such risks. This is especially important for major industries in a country such as automobile or semiconductor manufacturing industries in South Korea. The aim of this study, therefore, is to establish the basis for the simultaneous growth of ports and linked industries by examining the existing structure of the global value chain for the automotive industry, which has a strong presence in South Korea’s domestic economy. In this regard, this research carries out a supply chain analysis focusing on the imports and exports of automotive parts. It also analyses the current structural risks and suggests risk management measures to secure a stable supply chain.

The purpose of the study was to explore heat-accumulative and thermal-conductive characteristics of copper-graphene composite film (Cu-G film) while applying it to a human-skin analogue.

In the preliminary experiment, the authors evaluated the thermal conductive characteristics of the Cu-G film in three covered conditions (no film, copper film, and Cu-G film conditions). For the first factorial experiment, the heat-accumulative properties over heated pig skin were compared at air temperatures of 10, 25 and 35°C. For the second factorial experiment, 105 trials were conducted on pig skin by combining air temperatures, trapped air volumes, and numbers of film layers.

The results from the preliminary experiment showed that the Cu-G film distributed the surface heat to the outside of the Cu-G film, which resulted in even distribution of heat inside and outside the Cu-G film, whereas the copper film accumulated heat inside the copper film. The human-skin analogue of pig skin, however, showed the opposite tendency from that of the plastic. The pig-skin temperatures beneath the Cu-G film were higher than those beneath the copper film, and those differences were remarkable at the air temperature of 10°C. The accumulative heat was affected by the trapped air volume, fit to the skin, and number of Cu-G film layers.

In conclusion, the Cu-G film more effectively accumulated heat on the human-skin analogue than copper film, and those effects were more marked in cold environments than in mild or hot environments.

The purpose of this paper is to develop online community attributes based on Preece's sociability and usability framework to assess how online community attributes hosted by retailers or national brand companies may generate positive outcomes for consumers. Also, this study seeks to examine the effects of attribute dimensions on consumer benefits to verify its predictability.

An online survey was administered in South Korea. A questionnaire was constructed which contained 26 items measuring online community attributes. These items were developed by the researchers to measure seven dimensions defining Preece's sociability and usability conceptual framework. The items were based on examples of determinants and measures of successful online communities. The Korean sample consisted of 135 male and 216 female adults in a large metropolitan area in South Korea.

Results partially supported Preece's sociability and usability framework. Results show three online community attribute dimensions purpose, people, and policy to be represented by a higher order factor sociability and two attribute dimensions dialogue and social interaction and navigation represented by usability. The structural model testing the relationship between sociability and social benefits and usability and functional benefits was confirmed and proved predictive validity for the online community attribute dimensions and the sociability/usability framework. On the other hand, no evidence of sociability influencing perceptions of functional benefits and usability influencing perceptions of social benefits could be found. Nonetheless, these results provide evidence that Preece's online community attribute framework measures “success” from a consumer benefits standpoint.

This study provides retailers and companies who host online communities with a practical tool with which they can assess their online communities as well as their online strategy.

The digitization of the life has brought complexities associated with addressing digital life after one’s death. This paper aims to investigate the two related issues of the privacy and property of postlife digital assets.

The understanding of digital assets has not been fully unpacked largely due to the current policy complexities in accessing and obtaining digital assets at death. This paper calls critical attention to the importance of respecting user rights in digital environments that currently favor service providers’ interests.

It is argued that there are ethical blind spots when protecting users’ rights, given no ontological difference between a person’s digital beings and physical existence. These derive from the restrictive corporate terms and ambiguous conditions drafted by digital service providers.

Fundamentally, the transition to the big data era, in which the collection, use and dissemination of digital activities became integral part of the ontology, poses new challenges to privacy and property rights after death.

The weapon system of The Navy is the small quantity producing system on multiple kinds. It is consisted of various equipment and the subordinate parts of those which can repair the damaged part. The operating procedure concerning warship's repair parts managed under these systems is as follows. Firstly, if demand of repair parts occurs from warship which is the operating unit of weapon, then the Fleet(the repair & supply support battalion) is in charge of dealing with these requests. If certain request from warship is beyond the battalion's capability, it is delivered directly to the Logistic Command. In short, the repair and supply support system of repair parts can be described as the multi-level support system. The various theoretical researches on inventory management of Navy's repair parts and simulation study that reflects reality in detail have been carried out simultaneously. However, the majority of existing research has been conducted on aircraft and tank's repairable items, in that, the studies is woefully deficient in the area concerning Navy's inventory management. For that reason, this paper firstly constructs the model of consumable items that is frequently damaged reflecting characteristics of navy's repair parts inventory management using ARENA simulation. After that, this paper is trying to propose methodology to analyze optimal inventory level of each supply unit through OptQuest, the optimization program of ARENA simulation.