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Purpose: Supply chain management (SCM) focuses smart logistics and quality service. Diverse elements such as design, procurement, production and sale policies are the keys to SCM efficiency. Due to worsening environmental pollution in recent years, many businesses, government agencies and consumers have become more aware of greenhouse gases (GHG) emissions. In response, the government has established new environmental regulations to control various GHG, such as CO2 and sulfur dioxide. Therefore, to reduce pollution and its adverse effects, the authors have promoted environmental concerns by developing environmental friendly policies. The purpose of this paper is to develop a multi-objective decision making model that integrates both forward and reverse logistics to determine how best to incorporate recycling and reduce manufacturing costs. Design/methodology/approach: In this study, the authors developed a multi-objective decision-making model that integrates both forward and reverse logistics to determine how best to incorporate recycling and reduce manufacturing costs. They used the normalized normal constraint method as proposed by Messac et al. (2003) to generate a series of uniform lines on a Pareto Frontier chart. Findings: Based on the results of this study, the authors can determine the trade-off between costs and emissions and design the most environmental-friendly and economical strategy for production. Research limitations/implications: This study is limited to a case study on paper manufacturing. Practical implications: The authors considered the full truckload discount policy in which buyers can reduce their purchase costs by increasing the number of full truckload product orders; this will reduce transportation costs and also minimize overall carbon emissions. Social implications: This study encourages industries to focus on environmental friend policies and social responsibilities. Originality/value: The authors investigated the impacts of the paper making industry on economy and environment. An increase in demand will negatively impact the environment by causing CO2 emissions to increase from higher production and the felling of more trees to provide raw materials for manufacturers (paper mills). [ABSTRACT FROM AUTHOR]

Purpose -- The purpose of this paper is to analyze and access the extent that emotion contagion (EC) affects the passenger aggressive behavior, and then reveal key factors triggering the passenger mass violence event (MVE) under an irregular flight situation. Design/methodology/approach -- This paper presents the group behaviors simulation framework based on multi-agent. The EC model is put forward to explore the EC effect among passengers, integrating the influence of intervention measures, power difference and intimacy. Moreover, the authors propose the aggressive behavior decision-making model tailed to the passenger MVE by combining the improved social force model and adaptive neuro-fuzzy inference system. Findings -- The experiment results show that the method proposed in this paper can simulate the realistic evolution process of passenger MVE .The EC effect among passengers is the catalyst for the quick spreading of MVE. The intervention measures, such as providing food and endorsing service, can effectively prevent from the MVE situation deterioration. Originality/value -- The research of this paper has important practical significance for the evolution analysis of passenger MVE and provides the basis for airport safety management. [ABSTRACT FROM AUTHOR]

Purpose: The purpose of this paper is to examine how signals of uncertainty in the media affect retail investor decisions and initial public offering (IPO) underpricing through theoretical and empirical methods. Design/methodology/approach: The authors construct a theoretical model of the influence of media signals on IPO pricing, which describes the micro process in which uncertain signals in media influence retail investors' decisions and IPO underpricing. Besides, the authors take 516 small and medium-size enterprises (SMEs) listed in A-share from July 2009 to December 2012 as samples for empirical tests and establish an in-depth learning model for text analysis with Java programming to measure Chinese media tone. Finally, the results of the model analysis are verified by empirical results. Findings: The results show that authoritative media with high credibility can reduce the uncertainty of information sources attract more investors' attention and improve the valuation and demand of retail investors. The higher the media credibility is the higher the IPO underpricing rate is. The uncertain tone of the media will increase the decision-making cost of investors reduce the valuation expectation and demand of the secondary market and lead to a lower IPO underpricing rate. Originality/value: The authors study the influence of the uncertainty of media source and media content on the degree of IPO underpricing of SMEs. This is a useful supplement to the Chinese media tone research system that is still in the exploration stage. The research has reference value for government regulation and investor decision-making. [ABSTRACT FROM AUTHOR]

Purpose: The current study aims to empirically analyze the influence of different information sources, together with the persuasiveness of recommender systems (RSs) on the consumer's purchase intention (PI). It also expands the research on RSs from the point of view of consumer behavior and psychology, considering perceived usefulness and relevance. In addition, it analyzes how different types of personalized recommendations, along with non-personalized ones, influence PI. Design/methodology/approach: The proposed model has been validated using partial least squares structural equation modeling (PLS-SEM), based on the data collected from 597 online shoppers. Findings: This study proves that both information search and RSs influence PI, being complementary rather than mutually exclusive. Recommender systems' findings indicate that the PI is primarily influenced by the perceived relevance of RSs, the information provided by manufacturers and reviews. Moreover, only the influence of the perceived usefulness of personalized recommendations strongly affects PI. Conversely, non-personalized recommendations do not affect PI. Practical implications: Developers should focus on increasing the perceived usefulness and relevance of RSs. Thus, they could adopt the hybridization of RSs with the aggregation of both personal shopping behavior and social network contacts. It should integrate information signals from multiple sources to include sentiment extracted from reviews or links to the manufacturer's page. Furthermore, the recommendation of discounted products must be only for products preferred by customers, because only these influence the PI. Originality/value: This research provides a structural model that examines together, for the first time, the influence on the PI of the main RSs and sources of information. [ABSTRACT FROM AUTHOR]

Purpose: Collaborative robotics and autonomous driving are fairly new disciplines, still with a long way to go to achieve goals, set by the research community, manufacturers and users. For technologies like collaborative robotics and autonomous driving, which focus on closing the gap between humans and machines, the physical, psychological and emotional needs of human individuals becoming increasingly important in order to ensure effective and safe human–machine interaction. The authors' goal was to conceptualize ways to combine experience from both fields and transfer artificial intelligence knowledge from one to another. By identifying transferable meta-knowledge, the authors will increase quality of artificial intelligence applications and raise safety and contextual awareness for users and environment in both fields. Design/methodology/approach: First, the authors presented autonomous driving and collaborative robotics and autonomous driving and collaborative robotics' connection to artificial intelligence. The authors continued with advantages and challenges of both fields and identified potential topics for transferrable practices. Topics were divided into three time slots according to expected research timeline. Findings: The identified research opportunities seem manageable in the presented timeline. The authors' expectation was that autonomous driving and collaborative robotics will start moving closer in the following years and even merging in some areas like driverless and humanless transport and logistics. Originality/value: The authors' findings confirm the latest trends in autonomous driving and collaborative robotics and expand them into new research and collaboration opportunities for the next few years. The authors' research proposal focuses on those that should have the most positive impact to safety, complement, optimize and evolve human capabilities and increase productivity in line with social expectations. Transferring meta-knowledge between fields will increase progress and, in some cases, cut some shortcuts in achieving the aforementioned goals. [ABSTRACT FROM AUTHOR]

Purpose: The research objective is to increase the computational efficiency of the automated teller machine (ATM) cash demand forecasting problem. It proposes a practical decision-making process that uses aggregated time series of a bank's ATM network. The purpose is to decrease ATM numbers that will be forecasted by individual models, by finding the machines' cluster where the forecasting results of the aggregated series are appropriate to use. Design/methodology/approach: A comparative statistical forecasting approach is proposed in order to reduce the calculation complexity of an ATM network by using the NN5 competition data set. Integrated autoregressive moving average (ARIMA) and its seasonal version SARIMA are fitted to each time series. Then, averaged time series are introduced to simplify the forecasting process carried out for each ATM. The ATMs that are forecastable with the averaged series are identified by calculating the forecasting accuracy change in each machine. Findings: The proposed approach is evaluated by different error metrics and is compared to the literature findings. The results show that the ATMs that have tolerable accuracy loss may be considered as a cluster and can be forecasted with a single model based on the aggregated series. Research limitations/implications: The research is based on the public data set. Financial institutions do not prefer to share their ATM transactions data, therefore accessible data are limited. Practical implications: The proposed practical approach will be beneficial for financial institutions to use, that hold an excessive number of ATMs because it reduces the computational time and resources allocated for the forecasting process. Originality/value: This study offers an effective simplified methodology to the challenging cash demand forecasting process by introducing an aggregated time series approach. [ABSTRACT FROM AUTHOR]