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Health systems are progressively stressed by health spending, which is partially explained by the increase in the cost of health technologies. Countries have defined processes to prioritize interventions to be covered. This study aims to compare for the first time health technology assessment (HTA) processes in Canada and Chile, to explain the factors driving these decisions.

This is a health policy analysis comparing HTA processes in Canada and Chile. An analysis of publicly available documents in Canada (for CADTH) and Chile (for the Ministry of Health (MoH)) was carried out. A recognized political science framework (the 3-I framework) was used to explain the similarities and differences in both countries. The comparison of processes was disaggregated into eligibility and evaluation processes.

CADTH has different programmes for different types of drugs (with two separate expert committees), whereas the MoH has a unified process. Although CADTH’s recommendations have a federal scope, the final coverage is a provincial decision. In Chile, the recommendation has a national scope. In both cases, past recommendations influence the scope of the evaluation. Pharmaceutical companies and patient associations are important interest groups in both countries. Whereas manufacturers and tumour groups are able to submit applications to CADTH, the Chilean MoH prioritizes applications submitted by patient associations.

Institutions, interests and ideas play important roles in driving HTA decisions in Canada and Chile, which is demonstrated in this novel analysis. This paper provides a unique comparison to highly relevant policy processes in HTA, which is often a research area dominated by effectiveness and cost-effectiveness studies.

The main goal of this research work is the optimization of the purchasing business process in the Moroccan public sector in terms of transparency and budgetary optimization. The authors have worked on the public university as an implementation field.

The design of the research work followed the design science research (DSR) methodology for information systems. DSR is a research paradigm wherein a designer answers questions relevant to human problems through the creation of innovative artifacts, thereby contributing new knowledge to the body of scientific evidence. The authors have adopted a techno-functional approach. The technical part consists of the development of an intelligent recommendation system that supports the choice of optimal information technology (IT) equipment for decision-makers. This intelligent recommendation system relies on a set of functional and business concepts, namely the Moroccan normative laws and Control Objectives for Information and Related Technology's (COBIT) guidelines in information system governance.

The modeling of business processes in public universities is established using business process model and notation (BPMN) in accordance with official regulations. The set of BPMN models constitute a powerful repository not only for business process execution but also for further optimization. Governance generally aims to reduce budgetary wastes, and the authors' recommendation system demonstrates a technical and methodological approach enabling this feature. Implementation of artificial intelligence techniques can bring great value in terms of transparency and fluidity in purchasing business process execution.

Business limitations: First, the proposed system was modeled to handle one type products, which are computer-related equipment. Hence, the authors intend to extend the model to other types of products in future works. Conversely, the system proposes optimal purchasing order and assumes that decision makers will rely on this optimal purchasing order to choose between offers. In fact, as a perspective, the authors plan to work on a complete automation of the workflow to also include vendor selection and offer validation. Technical limitations: Natural language processing (NLP) is a widely used sentiment analysis (SA) technique that enabled the authors to validate the proposed system. Even working on samples of datasets, the authors noticed NLP dependency on huge computing power. The authors intend to experiment with learning and knowledge-based SA and assess the' computing power consumption and accuracy of the analysis compared to NLP. Another technical limitation is related to the web scraping technique; in fact, the users' reviews are crucial for the authors' system. To guarantee timeliness and reliable reviews, the system has to look automatically in websites, which confront the authors with the limitations of the web scraping like the permanent changing of website structure and scraping restrictions.

The modeling of business processes in public universities is established using BPMN in accordance with official regulations. The set of BPMN models constitute a powerful repository not only for business process execution but also for further optimization. Governance generally aims to reduce budgetary wastes, and the authors' recommendation system demonstrates a technical and methodological approach enabling this feature.

The adopted techno-functional approach enabled the authors to bring information system governance from a highly abstract level to a practical implementation where the theoretical best practices and guidelines are transformed to a tangible application.

The purpose of this paper is to develop and validate an innovative Fuzzy Recognition Based‐Benefit Estimation Model (FRB‐BEM) to quantify the benefits obtained from a Mid‐Project Review (MPR) (e.g. the Gateway Review Process (GRP)). This is a quantitative assessment to evaluate the benefits obtained from conducting MPRs. With the wide adoption of MPR internationally, such measurements will better support critical decisions in capital projects and also assist to optimize project lifecycle performance.

This paper adopted Relative Membership Degree (RMD) based fuzzy sets as the fundamental theory to develop the FRB‐BEM utilizing linguistic information from MPR reports. It was then tested by analysis of an aviation IT project that underwent a Gateway review. A parametric study was also conducted to calibrate the model.

The FRB‐BEM developed and validated in this paper provided a viable approach to quantify the total benefits obtained from undertaking MPRs.

Refinement of the FRB‐BEM assumptions would benefit from testing against a wide project sample set.

Using the FRB‐BEM applications to better demonstrate the benefits of MPRs.

The paper demonstrates how FRB‐BEM has extended RMD based fuzzy sets theory into applications for MPRs and incorporated fuzzy level values based on linguistic interpretation of hard data.

With the continuous increase of users, resources and tags, social tagging systems gradually present the characteristics of “big data” such as large number, fast growth, complexity and unreliable quality, which greatly increases the complexity of recommendation. The contradiction between the efficiency and effectiveness of recommendation service in social tagging is increasingly becoming prominent. The purpose of this study is to incorporate topic optimization into collaborative filtering to enhance both the effectiveness and the efficiency of personalized recommendations for social tagging.

Combining the idea of optimization before service, this paper presents an approach that incorporates topic optimization into collaborative recommendations for social tagging. In the proposed approach, the recommendation process is divided into two phases of offline topic optimization and online recommendation service to achieve high-quality and efficient personalized recommendation services. In the offline phase, the tags' topic model is constructed and then used to optimize the latent preference of users and the latent affiliation of resources on topics.

Experimental evaluation shows that the proposed approach improves both precision and recall of recommendations, as well as enhances the efficiency of online recommendations compared with the three baseline approaches. The proposed topic optimization–incorporated collaborative recommendation approach can achieve the improvement of both effectiveness and efficiency for the recommendation in social tagging.

With the support of the proposed approach, personalized recommendation in social tagging with high quality and efficiency can be achieved.

This article proposes an architecture of an intelligent and autonomous recommendation system to be applied to any virtual learning environment, with the objective of efficiently recommending digital resources. The paper presents the architectural details of the intelligent and autonomous dimensions of the recommendation system. The paper describes a hybrid recommendation model that orchestrates and manages the available information and the specific recommendation needs, in order to determine the recommendation algorithms to be used. The hybrid model allows the integration of the approaches based on collaborative filter, content or knowledge. In the architecture, information is extracted from four sources: the context, the students, the course and the digital resources, identifying variables, such as individual learning styles, socioeconomic information, connection characteristics, location, etc. Tests were carried out for the creation of an academic course, in order to analyse the intelligent and autonomous capabilities of the architecture.

Social recommendation has been recognized as a kind of e-commerce with large potential, but how social recommendations influence consumer decisions is still unclear. This paper aims to investigate how recommendations from different social ties influence consumers’ purchase intentions through both behavior and brain activity.

Utilizing behavioral (N = 70) and electroencephalogram (EEG) (N = 49) experiments, this study explored participants’ behavior and brain responses after being recommended by different social ties. The data were analyzed using statistical inference and event-related potential (ERP) analysis.

Behavioral results show that social tie strength positively impacts purchase intention, which can be fitted by a logarithmic model. Moreover, recommender-to-customer similarity and product affect mediate the effect of tie strength on purchase intention serially. EEG findings show that recommendations from weak tie strength elicit larger N100, N200 and P300 amplitudes than those from strong tie strength. These results imply that weak tie strength may motivate individuals to recruit more mental resources in social recommendation, including unconscious processing of consumer attention and conscious processing of cognitive conflict and negative emotion.

This study considers the effects of continuous social ties on purchase intention and models them mathematically, exploring the intrinsic mechanisms by which strong and weak ties influence purchase intentions through recommender-to-customer similarity and product affect, contributing to the applications of the stimulus-organism-response (SOR) model in the field of social recommendation. Furthermore, our study adopting EEG techniques bridges the gap of relying solely on self-report by providing an avenue to obtain relatively objective findings about the consumers’ early-occurred (unconscious) attentional responses and late-occurred (conscious) cognitive and emotional responses in purchase decisions.

The purpose of this paper is to present a framework for social business process management (BPM) in which social tagging is used to capture process knowledge emerging during the design and enactment of the processes. Process knowledge concerns both the type of activities chosen to fulfil a certain goal and the skills and experience of users in executing specific tasks. This knowledge is exploited by recommendation tools to support the design and enactment of current and future process instances.

The literature about traditional BPM is analysed to highlight the limitations of traditional BPM regarding management of ad hoc and semi-structured processes. Having identified this gap, an innovative BPM framework based on social tagging is proposed to address these limitations. This model is exemplified in a real case scenario and evaluated through the implementation of a prototype and a case study in real world non-profit organisation.

An overview of the social BPM framework is presented, introducing the concepts of role and task recommendation, which are supported by social tagging. The prototype shows the buildability of the social BPM framework as an extension of a Wiki platform. The case study demonstrates that the social BPM framework improves user collaborativeness in designing and executing process instances.

The applicability of the framework is targeted to ad hoc and possibly semi-structured business processes and it does not extend to highly procedural and codified processes. A single case study limits the generalisability of the evaluation results.

The social BPM framework is the first to introduce task and role recommendation supported by social tagging to overcome the limitations of traditional BPM models.

The purpose of this paper is to improve the customer satisfaction by offering online personalized recommendation system.

By employing an innovative associative classification method, this paper is able to predict a customer’s pleasure during the online while-recommending process. Consumers can make an active decision to recommended products. Based on customer’s characteristics, a product will be recommended to the potential buyer if the model predicts that he/she will click to view the product. That is, he/she is satisfied with the recommended product. Finally, the feasibility of the proposed recommendation system is validated through a Taobao shop.

The results of the experimental study clearly show that the online personalized recommendation system maximizes the customer’s satisfaction during the online while-recommending process based on an innovative associative classification method on the basis of consumer initiative decision.

Conventionally, customers are considered as passive recipients of the recommendation system. However, customers are tired of the recommendation system, and they can do nothing sometimes. This paper designs a new recommendation system on the basis of consumer initiative decision. The proposed recommendation system maximizes the customer’s satisfaction during the online while-recommending process.

How analysts make recommendations to the budget director and governor depends partly on the nature of the state budget office (SBO). This paper contrasts the development of a budget recommendation in an office with a strong policy orientation with recommendations fashioned in an office with a strong control orientation. One important difference is that control oriented analysts focus almost exclusively on the technical and legal facets of budget problems, whereas their policy oriented counterparts spend considerable time on the social, legal, and political (SLP) facets. The SLP framework enables the policyoriented analysts to apply economic rationality to evaluate requests and make recommendations that are consonant with the governor’s policy agenda.

This study aims to identify the developer’s objectives, current state-of-the-art techniques, challenges and performance evaluation metrics, and presents outlines of a knowledge-based application programming interfaces (API) recommendation system for the developers. Moreover, the current study intends to classify current state-of-the-art techniques supporting automated API recommendations.

In this study, the authors have performed a systematic literature review of studies, which have been published between the years 2004–2021 to achieve the targeted research objective. Subsequently, the authors performed the analysis of 35 primary studies.

The outcomes of this study are: (1) devising a thematic taxonomy based on the identified developers’ challenges, where mashup-oriented APIs and time-consuming process are frequently encountered challenges by the developers; (2) categorizing current state-of-the-art API recommendation techniques (i.e. clustering techniques, data preprocessing techniques, similarity measurements techniques and ranking techniques); (3) designing a taxonomy based on the identified objectives, where accuracy is the most targeted objective in API recommendation context; (4) identifying a list of evaluation metrics employed to assess the performance of the proposed techniques; (5) performing a SWOT analysis on the selected studies; (6) based on the developer’s challenges, objectives and SWOT analysis, presenting outlines of a recommendation system for the developers and (7) delineating several future research dimensions in API recommendations context.

This study provides complete guidance to the new researcher in the context of API recommendations. Also, the researcher can target these objectives (accuracy, response time, method recommendation, compatibility, user requirement-based API, automatic service recommendation and API location) in the future. Moreover, the developers can overcome the identified challenges (including mashup-oriented API, Time-consuming process, learn how to use the API, integrated problem, API method usage location and limited usage of code) in the future by proposing a framework or recommendation system. Furthermore, the classification of current state-of-the-art API recommendation techniques also helps the researchers who wish to work in the future in the context of API recommendation.

This study not only facilitates the researcher but also facilitates the practitioners in several ways. The current study guides the developer in minimizing the development time in terms of selecting relevant APIs rather than following traditional manual selection. Moreover, this study facilitates integrating APIs in a project. Thus, the recommendation system saves the time for developers, and increases their productivity.

API recommendation remains an active area of research in web and mobile-based applications development. The authors believe that this study acts as a useful tool for the interested researchers and practitioners as it will contribute to the body of knowledge in API recommendations context.