The importance of the services sector can not be overstated; it employs 82.1 percent of the U. S. workforce and 69 percent of graduates from an example technological university. Yet, university research and education have not followed suit. Clearly, services research and education deserve our critical attention and support since services - and services innovation - serve as an indispensable engine for global economic growth. The theme of this paper is that we can and should build services research and education on what has occurred in manufacturing research (especially in regard to customization and intellectual property) and education; indeed, services and manufactured goods become indistinguishable as they are jointly co-produced in real-time. Fortunately, inasmuch as manufacturing concepts, methodologies and technologies have been developed and refined over a long period of time (i.e., since the 1800s), the complementary set of concepts, methodologies and technologies for services are more obvious. However, while new technologies (e.g., the Internet) and globalization trends have served to enable, if not facilitate, services innovation, the same technologies (e.g., the Internet) and 21st Century realities (e.g., terrorism) are making services innovation a far more complex problem and, in fact, may be undermining previous innovations in both services and manufacturing. Finally, there is a need to define a "knowledge-adjusted" GDP metric that can more adequately measure the growing knowledge economy, one driven by intangible ideas and services innovation.

Urban infrastructures are the focus of terrorist acts because, quite simply, they produce the most visible impact, if not casualties. While terrorist acts are the most insidious and onerous of all disruptions, it is obvious that there are many similarities to the way one should deal with these willful acts and those caused by natural and accidental incidents that have also resulted in adverse and severe consequences. However, there is one major and critical difference between terrorist acts and the other types of disruptions: the terrorist acts are willful - and therefore also adaptive, if not coordinated. One must counter these acts with the same, if not more sophisticated, willful, adaptive and informed approach. Real-time, information-based decision making - which Tien (2003) has called the decision informatics paradigm - is the approach advanced herein to help make the right decisions at the various stages of a disruption. It is focused on decisions and based on multiple data sources, data fusion and analysis methods, timely information, stochastic decision models and a systems engineering outlook; moreover, it is multidisciplinary, evolutionary and systemic in practice. The approach provides a consistent way to address real-time emergency issues, including those concerned with the preparation for a major disruption, the prediction of such a disruption, the prevention or mitigation of the disruption, the detection of the disruption, the response to the disruption, and the recovery steps that are necessary to adequately, if not fully, recuperate from the disruption. The efforts of the U. S. Department of Homeland Security and its academically-based Homeland Security Centers of Excellence are considered within the proposed types, stages and decisions framework.

The services sector employs a large and growing proportion of workers in the industrialized nations, and it is increasingly dependent on information and communication technologies. While the interdependences, similarities and complementarities of manufacturing and services are significant, there are considerable differences between goods and services, including the shift in focus from mass production to mass customization (whereby a service is produced and delivered in response to a customer's stated or imputed needs). In general, services can be considered to be knowledge-intensive agents or components which work together as providers and consumers to create or co-produce value. Like manufacturing systems, an efficient service system must be an integrated system of systems, leading to greater connectivity and interdependence. Integration must occur over the physical, temporal, organizational and functional dimensions, and must include methods concerned with the component, the management, and the system. Moreover, an effective service system must also be an adaptable system, leading to greater value and responsiveness. Adaptation must occur over the dimensions of monitoring, feedback, cybernetics and learning, and must include methods concerned with space, time, and system. In sum, service systems are indeed complex, especially due to the uncertainties associated with the human-centered aspects of such systems. Moreover, the system complexities can only be dealt with methods that enhance system integration and adaptation. The paper concludes with several insights, including a plea to shift the current misplaced focus on developing a science or discipline for services to further developing a systems engineering approach to services, an approach based on the integration and adaptation of a host of sciences or disciplines (e.g., physics, mathematics, statistics, psychology, sociology, etc.). In fact, what is required is a services-related transdisciplinary - beyond a single disciplinary - ontology or taxonomy as a basis for disciplinary integration and adaptation.

Innovation in the services area - especially in the electronic services (e-services) domain - can be systematically developed by first considering the strategic drivers and foci, then the tactical principles and enablers, and finally the operational decision attributes, all of which constitute a process or calculus of services innovation. More specifically, there are four customer drivers (i.e., collaboration, customization, integration and adaptation), three business foci (i.e., creation-focused, solution-focused and competition-focused), six business principles (i.e., reconstruct market boundaries, focus on the big picture not numbers, reach beyond existing demand, get strategic sequence right, overcome organizational hurdles and build execution into strategy), eight technical enablers (i.e., software algorithms, automation, telecommunication, collaboration, standardization, customization, organization, and globalization), and six attributes of decision informatics (i.e., decision-driven, information-based, real-time, continuously-adaptive, customer-centric and computationally-intensive). It should be noted that the four customer drivers are all directed at empowering the individual - that is, at recognizing that the individual can, respectively, contribute in a collaborative situation, receive customized or personalized attention, access an integrated system or process, and obtain adaptive real-time or just-in-time input. The developed process or calculus serves to identify the potential white spaces or blue oceans for innovation. In addition to expanding on current innovations in services and related experiences, white spaces are identified for possible future innovations; they include those that can mitigate the unforeseen consequences or abuses of earlier innovations, safeguard our rights to privacy, protect us from the always-on, interconnected world, provide us with an authoritative search engine, and generate a GDP metric that can adequately measure the growing knowledge economy, one driven by intangible ideas and services innovation.

Infectious disease outbreaks occurred many times in the past and are more likely to happen in the future. In this paper the problem of allocating and scheduling limited multiple, identical or non-identical, resources employed in parallel, when there are several infected areas, is considered. A heuristic algorithm, based on Shih's (1974) and Pappis and Rachaniotis' (2010) algorithms, is proposed as the solution methodology. A numerical example implementing the proposed methodology in the context of a specific disease outbreak, namely influenza, is presented. The proposed methodology could be of significant value to those drafting contingency plans and healthcare policy agendas.

We examine a capacitated system exposed to random stepwise capacity disruptions with exponentially distributed interarrival times and uniformly distributed magnitudes. We explore two countermeasure policies for a risk-neutral decision maker who seeks to maximize the long-run average reward. A one-phase policy considers implementation of countermeasures throughout the entirety of a disruption cycle. The results of this analysis form a basis for a two-phase model which implements countermeasures during only a fraction of a disruption cycle. We present an extensive numerical analysis as well as a sensitivity study on the fluctuations of some system parameter values.

Rapid advances in machine learning combined with wide availability of online social media have created considerable research activity in predicting what might be the news of tomorrow based on an analysis of the past. In this work, we present a deep learning forecasting framework which is capable to predict tomorrow's news topics on Twitter and news feeds based on yesterday's content and topic-interaction features. The proposed framework starts by generating topics from words using word embeddings and K-means clustering. Then temporal topic-networks are constructed where two topics are linked if the same user has worked on both topics. Structural and dynamic metrics calculated from networks along with content features and past activity, are used as input of a long short-term memory (LSTM) model, which predicts the number of mentions of a specific topic on the subsequent day. Utilizing dependencies among topics, our experiments on two Twitter datasets and the HuffPost news dataset demonstrate that selecting a topic's historical local neighbors in the topic-network as extra features greatly improves the prediction accuracy and outperforms existing baselines.

Emergency events are unexpected and dangerous situations which the authorities must manage and respond to as quickly as possible. The main objectives of emergency management are to provide human safety and security, and Social Big Data (SBD) offers an important information source, created directly from eyewitness reports, to assist with these issues. However, the manual extraction of hidden meaning from SBD is both time-consuming and labor-intensive, which are major drawbacks for a process that needs accurate information to be produced in real-time. The solution is an automatic approach to knowledge discovery, and we propose a semantic description technique based on the use of triple store indexing for named entity recognition and relation extraction. Our technique can discover hidden SBD information more effectively than traditional approaches, and can be used for intelligent emergency management.

Grain security is one of the most important issues worldwide. Many developing countries, including China, have adopted the Agriculture Support Price (ASP) program to stimulate farmers' enthusiasm for growing grain, to ensure self-sufficiency in grain and the stable development of the grain market. To propose decision support for the government in designing a more reasonable support price in the ASP program, we formulate an agent-based model to simulate the operation of the wheat market in the harvest period. To formulate the formation process of the market price influenced by farmers' expected sale price, processors' expected purchase price, and the ASP, the time series and regression methods are adopted. Based on the proposed market price model, to quantitatively analyze the grain transaction process and the ASP program's impacts on market agents, we develop an agent-based simulation model to describe the adaptive evolution and interaction among market agents. Furthermore, we validate and implement the simulation model with public wheat market data. Finally, insights and suggestions about the decision of the ASP program are provided.

Artificial Intelligence is revolutionising our communication practices and the ways in which we interact with each other. This revolution does not only impact how we communicate, but it affects the nature of the partners with whom we communicate. Online discussion platforms now allow humans to communicate with artificial agents in the form of socialbots. Such agents have the potential to moderate online discussions and even manipulate and alter public opinions. In this paper, we propose to study this phenomenon using a constructed large-scale agent platform. At the heart of the platform lies an artificial agent that can moderate online discussions using argumentative messages. We investigate the influence of the agent on the evolution of an online debate involving human participants. The agent will dynamically react to their messages by moderating, supporting, or attacking their stances. We conducted two experiments to evaluate the platform while looking at the effects of the conversational agent. The first experiment is a large-scale discussion with 1076 citizens from Afghanistan discussing urban policy-making in the city of Kabul. The goal of the experiment was to increase the citizen involvement in implementing Sustainable Development Goals. The second experiment is a small-scale debate between a group of 16 students about globalisation and taxation in Myanmar. In the first experiment, we found that the agent improved the responsiveness of the participants and increased the number of identified ideas and issues. In the second experiment, we found that the agent polarised the debate by reinforcing the initial stances of the participant.

As an important element of the airport ecosystem, airport billboards are playing a crucial role in publicizing the city image and facilitating humanistic airport construction. At the same time, airport billboards have great commercial value and is a popular channel for enterprises to prompt their products and to build their brand image. Currently, most airports in China adopt a simple fixed pricing mechanism for airport billboards. Specifically, for any type of billboard, the advertising price is mainly determined by considering historical prices and the total passenger flow of the entire airport during a whole year. However, this seemingly crude pricing mechanism only considers macro-level data of passenger flow and fails in reflecting the real value of billboards in different locations effectively, since the value of a particular billboard depends not only on its media form, but also on the number of passengers flowing through and whether these passengers are the target customers of the advertising content. Based on big data on airport layout, flight information, and passenger attributes, this paper proposes a time- and location-based value assessment model for airport billboards. Using sample data collected from the Beijing Capital International Airport, the assessment model is adopted to evaluate the value of two real billboards in Terminal T3. Application of this model can reflect the difference in the value of airport billboards located in different spots during various periods. Furthermore, this model provides a solid foundation for airport executives to develop differentiated/dynamic pricing and flexible advertisement scheduling strategies, thereby improving the overall efficiency.

Sales promotion is getting more and more prosperous in Chinese cross-border e-commerce platforms where the demand is uncertain. However, most existing literature on promotion strategies is focusing on deterministic demand. In this paper, we propose a game-theoretical model under multiplicative stochastic demand to investigate the pricing, inventory quantity and sales promotion strategies for a supply chain which is consisted of one cross-border distributor and one capital-constrained retailer under a consignment contract. We obtain the equilibrium outcomes under stochastic demand, and find that the optimal price and promotion investment depend on demand uncertainty under endogenous inventory decisions. With exogenous unlimited inventory, the retailer prefers owing promotion right when the elasticity of price and promotion is small enough and its capital is sufficient, while the distributor always prefers to control sales promotion. With endogenous inventory quantity, the sensitivity of demand to price is influence by the demand uncertainty. The retailer prefers to decide the promotion when the price-elasticity is small, while the distributor prefers to decide the promotion under large promotion-elasticity. And the intensity of optimal sales promotion made by retailers may be stronger than that when the distributor owns the promotion right, which depends on the elasticity of price and promotion. More importantly, it is always better for consumers when the distributor reserves the promotion right as a lower optimal retailing price is offered.

Since 2019 humanity has been subjected to the perturbations of pandemic, economic disruption, war, civil unrest and changes in whole-Earth dynamics associated with a human-induced Anthropocene. Each perturbation is like a wave-front breaking on the shore of our historical ways of thinking and acting, increasingly unfit for our human circumstances. This challenge to humanity is not new. In 1970 the French term 'problematique' was coined to refer to a set of 49 interrelated global problems; the classic description of wicked and tame problems was published soon after, yet little progress has been made towards answering the question: what purposeful action will aid human flourishing, create and sustain a viable space for humanity, in our ongoing co-evolution with the Anthropocene-Biosphere? A case for innovation in our ways of knowing and doing is made based on arguments that our social world is constrained by: (i) explanations we accept that are no longer relevant to our circumstances; (ii) outdated historical institutions (in the institutional economics sense) that contribute as social technologies to a broader human created and ungoverned technosphere; (iii) inadequate theory-informed practices, or praxis, and (iv) governance-systems no longer adequate for purpose. Practitioners of knowledge science and systems science are urged to act reflexively to critically evaluate the traditions-of-understanding out of which they think and act.

Many firms have already been actively or passively involved in sustainable supply chain management with the objective of improving the triple bottom line (TBL). But whether the limited funds should be allocated to both community responsibility activities (e.g., corporate philanthropy) and environmental protection activities (e.g., recycling) is a confusing question. This paper provides deep insights into the combination strategy of two corporate social responsibility (CSR) types in a two-tier sustainable supply chain by modeling analysis. The decision models in eight scenarios with different combinations of CSR types are proposed and applied for the determination of equilibrium scenarios. The paper's findings highlight: under certain conditions, (1) the supply chain with two types of CSR is the equilibrium scenario and improves the TBL; (2) counter-intuitively, balancing short- and long-term benefits, firms are more willing to cooperate with partners with relatively low consumers sensitivity of CSR activities; (3) it is wise for the manufacturer to allocate more funding to environmental responsibility than to community responsibility. In addition, considering both short- and long-term benefits, comparing with the manufacturer, the retailer has a stronger incentive to improve recycling efficiency.

As the main channel for people to obtain information and express their opinions, online media generate a huge amount of unstructured news documents every day and make it difficult for people to perceive major societal events and grasp the evolution of events. Previous studies on storyline generation are generally based on document clustering without considering event arguments and relations between events. Event-centric knowledge graph has been used to facilitate the construction of news documents to form structured event representation. Although some studies have attempted to construct timelines based on event-centric knowledge graphs, it is difficult for timelines to depict the complex structures of event evolution. In this paper, we try to represent news documents as an event-centric knowledge graph, and compress the whole knowledge graph into salient complex events in temporal order to generate storylines named narrative graph. We first collect news documents from news platforms, construct an event ontology, and build an event-centric knowledge graph with temporal relations. Graph neural network is used to detect events, while BERT fine-tuning is leveraged to identify temporal relations between events. Then, a novel generation framework of narrative graph with constraints of coherence and coverage is proposed. In addition, a case study is implemented to demonstrate how to utilize narrative graph to analyze real-world event. The experiment results show that our approach significantly outperforms the baseline approaches.