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Asthma is a chronic inflammatory airways disease characterized by acute exacerbations interspaced by symptom‐free periods. Its management imposes a substantial burden on healthcare services, as well as personal suffering and significant financial tolls. The aim of this paper is to demonstrate links between routinely used computerized databases and to establish an automatic mechanism for monitoring asthma patients.

The study population was all adult subscribers to a major health maintenance organization (HMO) in Southern Israel (230,000 adults, age 20‐65 years). Relevant data for this retrospective analysis (2000 to 2004) were extracted from several computerized databases routinely used in the service: pharmacy; administrative; and each person's personal computerized medical file in the primary care clinic.

Based on data from 72 regional primary care clinics, during the study period, 11,054 adults were treated simultaneously by β₂ agonists and steroids – assumed to be asthmatics. In contrast, asthma diagnosis was recorded in only 4,061 personal files. The intersection between two databases yielded 2,569 persons recorded in both. These findings attest to the feasibility of developing computerized automatic surveillance systems for monitoring asthma patients with certain algorithms to assure service quality.

Data extracted from the various databases were unequal quality, a factor that imposed data management difficulties.

Similar surveillance systems can be developed relatively easily by using comparable algorithms for monitoring different chronic diseases or introducing management indices to secure quality of services.

The paper focuses on developing an automatic asthma monitoring model, using information from routinely used computerized HMO DBs.

This article reviews the quantitative marketing literature on artificial intelligence (AI) through an economics lens. We apply the framework in Prediction Machines: The Simple Economics of Artificial Intelligence to systematically categorize 96 research papers on AI in marketing academia into five levels of impact, which are prediction, decision, tool, strategy, and society. For each paper, we further identify each individual component of a task, the research question, the AI model used, and the broad decision type. Overall, we find there are fewer marketing papers focusing on strategy and society, and accordingly, we discuss future research opportunities in those areas.

The optimal control of the steady‐state temperature distribution in radiating panels using control heat sources is considered. The problem has important applications in the thermal control of space structures. A mathematical model leads to an elliptic nonlinear optimal control problem. A numerical optimal control method, based on finite element (FE) discretization and sequential quadratic programming (SQP), is employed. Results are presented for some specific examples.

The increasing importance and proliferation of text data provide a unique opportunity and novel lens to study human communication across a myriad of business and marketing applications. For example, consumers compare and review products online, individuals interact with their voice assistants to search, shop, and express their needs, investors seek to extract signals from firms' press releases to improve their investment decisions, and firms analyze sales call transcripts to increase customer satisfaction and conversions. However, extracting meaningful information from unstructured text data is a nontrivial task. In this chapter, we review established natural language processing (NLP) methods for traditional tasks (e.g., LDA for topic modeling and lexicons for sentiment analysis and writing style extraction) and provide an outlook into the future of NLP in marketing, covering recent embedding-based approaches, pretrained language models, and transfer learning for novel tasks such as automated text generation and multi-modal representation learning. These emerging approaches allow the field to improve its ability to perform certain tasks that we have been using for more than a decade (e.g., text classification). But more importantly, they unlock entirely new types of tasks that bring about novel research opportunities (e.g., text summarization, and generative question answering). We conclude with a roadmap and research agenda for promising NLP applications in marketing and provide supplementary code examples to help interested scholars to explore opportunities related to NLP in marketing.

Citizens are substantial stakeholders in every e-government system, thus their willingness to use and ability to access the system are critical. Unequal access and information and communication technology usage, which is known as digital divide, however has been identified as one of the major obstacles to the implementation of e-government system. As digital divide inhibits citizen’s acceptance to e-government, it should be overcome despite the lack of deep theoretical understanding on this issue. This research aimed to investigate the digital divide and its direct impact on e-government system success of local governments in Indonesia as well as indirect impact through the mediation role of trust. In order to get a comprehensive understanding of digital divide, this study introduced a new type of digital divide, the innovativeness divide.

The research problems were approached by applying two-stage sequential mixed method research approach comprising of both qualitative and quantitative studies. In the first phase, an initial research model was proposed based on a literature review. Semi-structured interview with 12 users of e-government systems was then conducted to explore and enhance this initial research model. Data collected in this phase were analyzed with a two-stage content analysis approach and the initial model was then amended based on the findings. As a result, a comprehensive research model with 16 hypotheses was proposed for examination in the second phase.

In the second phase, quantitative method was applied. A questionnaire was developed based on findings in the first phase. A pilot study was conducted to refine the questionnaire, which was then distributed in a national survey resulting in 237 useable responses. Data collected in this phase were analyzed using Partial Least Square based Structural Equation Modeling.

The results of quantitative analysis confirmed 13 hypotheses. All direct influences of the variables of digital divide on e-government system success were supported. The mediating effects of trust in e-government in the relationship between capability divide and e-government system success as well as in the relationship between innovativeness divide and e-government system success were supported, but was rejected in the relationship between access divide and e-government system success. Furthermore, the results supported the moderating effects of demographic variables of age, residential place, and education.

This research has both theoretical and practical contributions. The study contributes to the developments of literature on digital divide and e-government by providing a more comprehensive framework, and also to the implementation of e-government by local governments and the improvement of e-government Readiness Index of Indonesia.

The purpose of this chapter was to deconstruct the underlying contradictions of crowdfunding practices and to show how crowdfunding practitioners develop a schizophrenic use of these contradictions.

The main contradictions of crowdfunding practices are introduced with theoretical references. Then short cases are used to illustrate how crowdfunding practitioners try to cope with these contradictions.

The crowd addresses many contradictions, first because it is a syncretic concept, second because online crowds are still to be proven crowds. In any case, crowdfunding practitioners do their best to take the advantage of these contradictions, and run the risk of falling between two stools.

An attempt to provide an analysis of crowdfunding as a social, and not only economic, phenomenon, to suggest avenues for further critical research on crowdfunding.

This paper proposes a new perspective on the enormous and unresolved challenge to existing practices of publication and documentation posed by the outputs of digital research projects in the humanities, where much good work is being lost due to resource or technical challenges.

The paper documents and analyses both the existing literature on promoting sustainability for the outputs of digital humanities projects and the innovative approach of a single large-scale project.

The findings of the research presented show that sustainability planning for large-scale research projects needs to consider data and technology but also community, communications and process knowledge simultaneously. In addition, it should focus not only on a project as a collection of tangible and intangible assets, but also on the potential user base for these assets and what these users consider valuable about them.

The conclusions of the paper have been formulated in the context of one specific project. As such, it may amplify the specificities of this project in its results.

An approach to project sustainability following the recommendations outlined in this paper would include a number of uncommon features, such as a longer development horizon, wider perspective on project results, and an audit of tacit and explicit knowledge.

These results can ultimately preserve public investment in projects.

This paper supplements more reductive models for project sustainability with a more holistic approach that others may learn from in mapping and sustaining user value for their projects for the medium to long terms.