Abstract: China apparel industry, which is deeply embedded in
the global production network (GPN), faces the dual pressures of
social upgrading and economic upgrading. Based on the survey in
Ningbo apparel cluster, the paper shows the state of corporate social
responsibility (CSR) in China apparel industry is better than before.
And the investigation indicates that the firms who practice CSR
actively perform better both socially and economically than those who
inactively. The research demonstrates that CSR can be an initial
capital rather than cost, and “doing well by doing good" is also existed
in labor intensive industry.
Abstract: Simulation is a very helpful and valuable work tool in
manufacturing. It can be used in industrial field allowing the
system`s behavior to be learnt and tested. Simulation provides a low
cost, secure and fast analysis tool. It also provides benefits, which
can be reached with many different system configurations. Topics to
be discussed include: Applications, Modeling, Validating, Software
and benefits of simulation. This paper provides a comprehensive
literature review on research efforts in simulation.
Abstract: This paper presents an innovative approach within the area of Group Decision Support System (GDSS) by using tools based on intelligent agents. It introduces iGDSS, a software platform for decision support and collaboration and an application of this platform - eCollaborative Decisions - for academic environment, all these developed within a framework of a research project.
Abstract: Increasing concerns over climate change have limited
the liberal usage of available energy technology options. India faces
a formidable challenge to meet its energy needs and provide adequate
energy of desired quality in various forms to users in sustainable
manner at reasonable costs. In this paper, work carried out with an
objective to study the role of various energy technology options
under different scenarios namely base line scenario, high nuclear
scenario, high renewable scenario, low growth and high growth rate
scenario. The study has been carried out using Model for Energy
Supply Strategy Alternatives and their General Environmental
Impacts (MESSAGE) model which evaluates the alternative energy
supply strategies with user defined constraints on fuel availability,
environmental regulations etc. The projected electricity demand, at
the end of study period i.e. 2035 is 500490 MWYr. The model
predicted the share of the demand by Thermal: 428170 MWYr,
Hydro: 40320 MWYr, Nuclear: 14000 MWYr, Wind: 18000 MWYr
in the base line scenario. Coal remains the dominant fuel for
production of electricity during the study period. However, the
import dependency of coal increased during the study period. In
baseline scenario the cumulative carbon dioxide emissions upto 2035
are about 11,000 million tones of CO2. In the scenario of high nuclear
capacity the carbon dioxide emissions reduced by 10 % when nuclear
energy share increased to 9 % compared to 3 % in baseline scenario.
Similarly aggressive use of renewables reduces 4 % of carbon
dioxide emissions.
Abstract: This paper presents a set of guidelines for the design
of multi-user awareness systems. In a first step, general requirements
for team awareness systems are analyzed. In the second part of the
paper, the identified requirements are aggregated and transformed
into concrete design guidelines for the development of team
awareness systems.
Abstract: Educational institutions increasingly adopt the
students-as-customers concept to satisfy their students.
Understanding students- perspectives on the use of this business
concept in educational institutions is necessary for the institutions to
effectively align these perspectives with their management practice.
The study investigates whether students in technology and business
disciplines have significantly different attitudes toward using the
students-as-customers concept in educational institutions and
explores the impact of treating students as customers in technology
disciplines under students- perspectives. The results from
quantitative and qualitative data analyses show that technology
students, in contrast to business students, fairly disagree with
educational institutions to treat students as customers. Treating
students as customers in technology disciplines will have a negative
influence on teaching performance, instructor-student relationships
and educational institutions- aim, but a positive influence on service
quality in educational institutions. The paper discusses the findings
and concludes with implications and limitations of the study.
Abstract: Semantic Web Technologies enable machines to
interpret data published in a machine-interpretable form on the web.
At the present time, only human beings are able to understand the
product information published online. The emerging semantic Web
technologies have the potential to deeply influence the further
development of the Internet Economy. In this paper we propose a
scenario based research approach to predict the effects of these new
technologies on electronic markets and business models of traders
and intermediaries and customers. Over 300 million searches are
conducted everyday on the Internet by people trying to find what
they need. A majority of these searches are in the domain of
consumer ecommerce, where a web user is looking for something to
buy. This represents a huge cost in terms of people hours and an
enormous drain of resources. Agent enabled semantic search will
have a dramatic impact on the precision of these searches. It will
reduce and possibly eliminate information asymmetry where a better
informed buyer gets the best value. By impacting this key
determinant of market prices semantic web will foster the evolution
of different business and economic models. We submit that there is a
need for developing these futuristic models based on our current
understanding of e-commerce models and nascent semantic web
technologies. We believe these business models will encourage
mainstream web developers and businesses to join the “semantic web
revolution."
Abstract: Trust management and Reputation models are
becoming integral part of Internet based applications such as CSCW,
E-commerce and Grid Computing. Also the trust dimension is a
significant social structure and key to social relations within a
collaborative community. Collaborative Decision Making (CDM) is
a difficult task in the context of distributed environment (information
across different geographical locations) and multidisciplinary
decisions are involved such as Virtual Organization (VO). To aid
team decision making in VO, Decision Support System and social
network analysis approaches are integrated. In such situations social
learning helps an organization in terms of relationship, team
formation, partner selection etc. In this paper we focus on trust
learning. Trust learning is an important activity in terms of
information exchange, negotiation, collaboration and trust
assessment for cooperation among virtual team members. In this
paper we have proposed a reinforcement learning which enhances the
trust decision making capability of interacting agents during
collaboration in problem solving activity. Trust computational model
with learning that we present is adapted for best alternate selection of
new project in the organization. We verify our model in a multi-agent
simulation where the agents in the community learn to identify
trustworthy members, inconsistent behavior and conflicting behavior
of agents.