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The purpose of this paper is to present an ontology‐based approach of context‐sensitive computing for the optimization of flexible manufacturing systems (FMS).

A context‐sensitive computing approach is presented, integrated on top of FMS control platform. The approach addresses how to extract manufacturing contexts at source, how to process contextual entities by developing an ontology‐based context model and how to utilize this approach for real time decision making to optimize the key performance indicators (KPIs). A framework for such an optimization support system is proposed. A practical FMS use case within SOA‐based control architecture is considered as an illustrative example and the implementation of the core functionalities to the use case is reported.

Continuous improvement of the factory can be enhanced utilizing context‐sensitive support applications, which provides an intelligent interface for knowledge acquisition and elicitation. This can be used for improved data analysis and diagnostics, real time feedback control and support for optimization.

The performance of context‐sensitive computing increases with the extraction, modeling and reasoning of as much contexts as possible. However, more computational resources and processing times are associated to this. Hence, the trade‐off should be in between the extent of context processing and the required outcome of the support applications.

This paper includes the practical implications of context‐sensitive applications development in manufacturing, especially in the dynamic operating environment of FMS.

Reported results provide a modular approach of context‐sensitive computing and a practical use case implementation to achieve context awareness in FMS. The results are seen extendable to other manufacturing domains.

This survey aims to study and analyze current techniques and methods for context‐aware web service systems, to discuss future trends and propose further steps on making web services systems context‐aware.

The paper analyzes and compares existing context‐aware web service‐based systems based on techniques they support, such as context information modeling, context sensing, distribution, security and privacy, and adaptation techniques. Existing systems are also examined in terms of application domains, system type, mobility support, multi‐organization support and level of web services implementation.

Supporting context‐aware web service‐based systems is increasing. It is hard to find a truly context‐aware web service‐based system that is interoperable and secure, and operates on multi‐organizational environments. Various issues, such as distributed context management, context‐aware service modeling and engineering, context reasoning and quality of context, security and privacy issues have not been well addressed.

The number of systems analyzed is limited. Furthermore, the survey is based on published papers. Therefore, up‐to‐date information and development might not be taken into account.

Existing surveys do not focus on context‐awareness techniques for web services. This paper helps to understand the state of the art in context‐aware techniques for web services that can be employed in the future of services which is built around, amongst others, mobile devices, web services, and pervasive environments.

This study aims to understand their emergence and types of business models in the Internet of Things (IoT) ecosystems.

The paper builds upon a systematic literature review of IoT ecosystems and business models to construct a conceptual framework on IoT business models, and uses qualitative research methods to analyze seven industry cases.

The study identifies four types of IoT business models: value chain efficiency, industry collaboration, horizontal market and platform. Moreover, it discusses three evolutionary paths of new business model emergence: opening up the ecosystem for industry collaboration, replicating the solution in multiple services and return to closed ecosystem as technology matures.

Identifying business models in rapidly evolving fields such as the IoT based on a small number of case studies may result in biased findings compared to large-scale surveys and globally distributed samples. However, it provides more thorough interpretations.

The study provides a framework for analyzing the types and emergence of IoT business models, and forwards the concept of “value design” as an ecosystem business model.

This paper identifies four archetypical IoT business models based on a novel framework that is independent of any specific industry, and argues that IoT business models follow an evolutionary path from closed to open, and reversely to closed ecosystems, and the value created in the networks of organizations and things will be shareable value rather than exchange value.

In the last decade, web services have become a major technology to implement loosely coupled business processes and perform application integration. Through the use of context, a new generation of web services, namely context‐aware web services (CASs), is currently emerging as an important technology for building innovative context‐aware applications. Unfortunately, CASs are still difficult to build. Issues like lack of context provisioning management approach and lack of generic approach for formalizing the development process need to be solved in the first place for easy and effective development of CASs. The purpose of this paper is to investigate the techniques on developing CASs.

The paper focuses on introducing a model‐driven platform, called ContextServ, and showcasing how to use this platform to rapidly develop a context‐aware web application, Smart Adelaide Guide. ContextServ adopts a model‐driven development (MDD) approach where a Unified Modeling Language (UML)‐based modeling language – ContextUML – is used to model web services and its context‐awareness features.

The paper presents novel techniques for efficient and effective development of CASs using a MDD approach. The ContextServ platform is the only one that provides a comprehensive software toolset that supports graphical modeling and automatic model transformation of CASs.

The proposed approach has been validated in practice by developing various CASs. The experimental study demonstrates the efficiency and effectiveness of the approach.

The paper presents a novel platform called ContextServ, which offers a set of visual editing and automation tools for easy and fast generating and deploying CASs.

This study aims to evaluate the impact of perceived cause- related marketing (perceived-CRM) on the repurchase intention (CRIN). Besides, brand image (BIMA) and customer satisfaction (CSAT) connect this relationship as mediating variables. Especially, the role of perceived corporate social responsibility (perceived-CSR) contributed to this nexus between perceived-CRM and BIMA, perceived-CRM and CSAT in emerging economies.

The paper follows a quantitative approach. Based on a comprehensive literature review on perceived-CSR, perceived-CRM , BIMA, CSAT and repurchase intention, the authors evaluate the impact of those constructs on repurchase intention in an emerging market. The study sample was composed of 395 responses covering customers of consumer goods. The study uses the Smart PLS-SEM version 3.3.2 to analyze the data.

The findings revealed significant contributions to the extant CRM literature in some ways. This study's outcomes contribute to extending the existing literature on CRM and CSR. Specifically, the extension focuses on the mediating and moderating effects of BIMA, CSAT and perceived-CSR, respectively, in the relationship between perceived-CRM and CRIN. Moreover, the novelty of this study lies in providing a new approach to the influence of perceived-CRM on CRIN, with the mediating of BIMA, CSAT and moderating effects perceived-CSR integrated into a conceptual model.

From a management perspective, the contribution of this study plays a very important role in strategic planning to enhance competitive advantage and improve business performance on a sustainable basis. This sustainability is founded on an insight into how changes in contextual factors affect the perception and consumer behavior of millennials in fast-moving consumer goods (FMCG) market, especially in a context of Covid-19 global crisis. It is important to emphasize that genuineness and transparency in all activities and communications are a prerequisite in today's sensitive context. The application of acquired insight into practice will help businesses operating in the consumer sector improve brand reputation and CSAT. As a result, this leads to enhanced competitive advantage of the business in the market, improved market performance and ultimately to an improvement in the overall performance of the enterprise.

This is the first study that explores the moderating role of perceived CSR on the nexus between perceived-CRM with brand image (BIMA) and CSAT to the best of our knowledge. Besides, the study also discovers the mediating role of BIMA and CSAT between perceived-CRM and repurchase-intention in an emerging economy. Findings in this study provided additional evidence to the increasingly important roles of perceived-CRM and perceived-CSR in creating win-win relationships with customers, aiming to solve specific social causes jointly. Further, the perceived-CRM and perceived-CSR mechanisms help businesses enhance their intangible assets and competitive advantages through enhanced BIMA and stronger CRIN. In the current context, the business environment is changing rapidly due to many factors that lead to increased competition at a global level. Therefore, improving competitive advantage is a mandatory condition for businesses to survive and develop sustainably.

This article argues the value of integrating pragmatism in applying behavioural science to complex challenges. We describe a behaviour change-led knowledge co-production process in the specific context of climate change in Australia. This process was led by an interdisciplinary research team who struggled with the limitations of the prevailing deterministic behaviour change paradigms, such as the “test, learn, adapt” model, which often focuses narrowly on individual behaviours and fails to integrate multiple interpretations from diverse stakeholders into their knowledge co-production process.

This article uses collaborative reflection as a method of inquiry. We document the team’s experience of a recent challenge-led, programatic research initiative that applied behaviour change strategies to reduce climate vulnerabilities. We demonstrate the necessity of critical reflection and abductive reasoning in the face of the complexities inherent in knowledge co-production addressing complex problems. It underscores the importance of accommodating diverse perspectives and contextual nuances over a one-size-fits-all method.

The article shares lessons learnt about integrating collaborative and critical reflection throughout a project cycle and demonstrates the capacity of abductive reasoning to ease the challenges arising from the tension between behaviour change paradigms and knowledge co-production principles. This approach allows for a more adaptable and context-sensitive application, acknowledging the multiplicity of understandings and the dynamic nature of behavioural change in relation to climate adaptation.

This reflection contributes original insights into the fusion of pragmatism with behaviour change strategies, proposing a novel framework that prioritises flexibility, context-specificity and the recognition of various stakeholder perspectives in the co-production of knowledge.

In view of the intensive spread of Coronavirus disease 2019 (COVID-19) and in order to reduce the rate of spread of this disease; the objective of this article is to propose an approach to detect in real time suspect person of Coronavirus disease 2019 (COVID-19).

The ubiquitous computing offers a new opportunity to reshape the form of conventional solutions for personalized services according to the contextual situations of each environment. The health system is seen as a key part of ubiquitous computing, which means that health services are available anytime, anywhere to monitor patients based on their context. This paper aims to design and validate a contextual model for ubiquitous health systems designed to detect in real time suspect person of COVID-19, to reduce the propagation of this infectious disease and to take the necessary instructions.

This paper presents the performance results of the COVID-19 detection approach. Thus, the reduction of the COVID-19 propagation rate thanks to the real-time intervention of the system.

Following the COVID-19 pandemic spread, the authors tried to find a solution to detect the disease in real time. In this paper, a real-time COVID-19 detection system based on the ontological description supported by Semantic Web Rule Language (SWRL) rules was developed. The proposed ontology contains all relevant concepts related to COVID-19, including personal information, location, symptoms, risk factors, laboratory test results and treatment planning. The SWRL rules are constructed from medical recommendations.

In this article, the authors draw-upon an extended unified theory of acceptance and use of technology (UTAUT) and propose a research model involving performance expectancy (PE), effort expectancy (EE), facilitating conditions (FC) and competitive pressure (CP) as potential salient factors explaining the adoption of digitalization in European SMEs. The authors also postulate that there may be cross-cultural differences, thereby leading us to include the country as a moderator in the model.

The authors validate this model with a cross-cultural sample involving 188 owner-managers from the Czech Republic and Slovakia and through the partial least square structural equation modeling (PLS-SEM) techniques as well as multi-group analysis.

The results using the study’s global dataset indicate that PE, FC and CP significantly affect owner-managers intentions toward digitalization in SMEs. The authors’ application of the multi-group analysis also suggests that although the two countries differ in digitalization adoption intention, the differences are statistically insignificant. In the conclusion, the authors highlight several implications these findings have for theory and practice.

The authors recommend that the providers of emerging digital technologies should improve on the performance features of those technologies and ensure they are relevant to the SMEs. By doing so, the adoption of digitalization will grow, because owner-managers of SMEs will have the confidence that adopting such technologies will improve their operations. Second, SMEs are required to provide adequate organizational and technical infrastructure to support digitalization adoption.

Aside from being among the few attempts to extend the explanatory power of UTAUT with PE, EE, FC and CP in investigating digitalization adoption in SMEs context, this study also validates its model with rigorous methodological approach as well as three datasets (global, Czech Republic and Slovakia) thereby strengthening the validity of the results.

This section describes sustainable development (SD) in relation to infrastructure projects and explains how to evaluate SD. SD is assessed as context-dependent, considering the project’s economic, social, and ecological context. Sustainable road infrastructure projects (SRIP) should encapsulate the complete life cycle from idea to development, functionality, and maintenance. SD should be considered as part of the evaluation process prior to project execution, but it can also serve other functions. Sustainability evaluation must start with project appraisal or evaluation and the earliest stages of project decision-making. Sustainable infrastructure projects (SIPs) are evaluated using a variety of techniques and models, such as cost-benefit analysis (CBA), multi-criteria techniques, ecological and societal impact assessments, ranking techniques, models, and evaluation guidelines. Established SD structures and modelling techniques for infrastructure projects are presented from an SD perspective, with the primary objective of investigating how they operate and determining whether existing models provide an effective method for applying the SD idea into infrastructure development. CBA is a widely used strategy for evaluating alternatives to maximize sociocultural well-being. It is based on the likelihood of costing customer advantages and negative impacts and has been discussed in scholarly articles. The multi-criteria decision analysis (MCDA) approach is an acceptable methodology for addressing complex matters involving high risk, conflicting objectives, different types of information and data, different concerns and points of view, and the representation of complex and evolving biological, ecological, and financial frameworks. It combines many methodologies and offers various advantages over more conventional ways of decision-making and plan development. It should be used to increase community participation and empower partner organizations and should apply several criteria at the same time, including those that are difficult to adjust and quantify. The key difficulty with this strategy is the usage of weightings, which has been sharply criticized by several researchers. Life-cycle assessment (LCA) is an adaptive tool used to assess the ecological effects of a particular action, task, or procedure. It is applied globally to decision-making in numerous fields, including transportation, energy, and water, and has become a typical tool for determining the ecological performance of infrastructure projects. However, it has a few flaws and could benefit from improvements to assess SD with greater precision. It is a fragmented mechanism for assessing the three components of SD, but its incorporation into other evaluation approaches is desirable. The evaluation of societal implications has been conducted using a variety of methods and techniques, but there is currently no standard method for assessing the communal and appropriation consequences of infrastructure initiatives. Social life-cycle assessments (SLCAs) are advancing, but consensus remains a challenge. The Evaluation Partnership and the Centre for European Policy Studies identified several obstacles and challenges to implementing an outstanding societal assessment, such as the term ‘societal impacts’ being potentially overbroad and not adequately defined, and the lack of a suitable method for quantitatively evaluating sociological effects. Additionally, a large section of societal assessments is biased and frequently inconsequential. The chapter discussed the theoretical and methodological stances on sustainable road infrastructure, using current SID concepts and evaluation techniques thoroughly.