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Product intervention power is introduced under the markets in financial instruments regulation (MiFIR) and packaged retail and insurance-based investment products (PRIIPs) Regulation for all EU Member States and gives National Competent Authorities (NCAs), European Securities and Markets Authority (ESMA), and European Banking Authority (EBA) powers to monitor financial products (and services) under their supervision and to “temporarily” prohibit or restrict the marketing, distribution, or sale of certain financial instruments, or to intervene in relation to certain financial activities or practice. This extends the supervisory measures defined in MiFID II to any PRIIPs (including insurance-based investment products “IBI products”) that would not otherwise fall under the scope of MiFID II. Product intervention power is given to the NCAs, and in order to use power, it requires to take the specifics of the individual case into account and a series of conditions, criteria, and factors to fulfill. Moreover, ESMA and the EBA have a type of control function and ability to override national regulators on product. The aim of product intervention powers is to ensure strengthening of investor protection, but given the potential significant impact of this power, calls into question of possibility to delay innovation and slow down product developments on the capital market.

This paper provided an overview of supervisory measures on product intervention, that is, scope of the product intervention power, criteria, factors, and risks which have to be taken into consideration when using this regulator’s tool.

This paper examines the impact of the COVID-19 pandemic on low-carbon consumption of dairy products through informational interventions. The empirical findings seek to enlighten developing countries' efforts in coping with climate change and potential dietary transitions.

A randomized controlled trial was designed to examine the effects of purpose-differentiated information interventions on individual dairy consumption. The experiment recruited and randomly assigned 1,002 college students into four groups to receive (or not) environmental or/and health information interventions.

The empirical analysis finds that health and combined information interventions have a positive impact on dairy consumption, while environmental information interventions' effect on dairy consumption is insignificant. In the context of the pandemic, health information interventions positively affected participants' perceptions and preferences for dairy products by delivering knowledge about their role in boosting immunity. However, environmental information interventions failed to do the same things as their insignificant effects on both perception and preference.

Macro-external shocks, such as public health events, may offset the impact of universal information interventions promoting pro-environmental behaviors. For a smooth dietary transition to achieve long-term environmental sustainability, diverse stakeholders must be included in more individualized interventions to guide daily consumption, especially in developing countries with large populations.

Treatment integrity (TI; also known as fidelity of implementation, treatment fidelity, and procedural reliability) refers to the degree to which an intervention is implemented as intended. TI data provides evidence of the internal validity of a study; without TI data, one cannot attribute observed effects to an intervention or distinguish whether interventions that fail do so because of problems with the intervention, its delivery, or both. Unfortunately, the field of intervention research has seen limited progress in the assessment and reporting of TI over time. This chapter describes the development of models of TI across fields, options for measuring TI, and important issues yet to be resolved.

This paper aims to contribute further to QFD applications in product development system of organisations by drawing reflections from managing an action research program on QFD applications in Brazilian organisations in the last ten years. The reflection follows two interwoven lines, theory and practice, as it is assumed that effective practice requires good understanding of methodological basis and methodological basis is continuously refined by good actual practices, so the construction and accumulation of QFD knowledge may be obtained. Thus, this article attempts to highlight the methodological characteristics of QFD in a structured manner by bringing a three‐dimensional analytical framework (phenomenon of interest, “manifest” methodological characteristics, and “underlying” methodological characteristics) and to formulate a guide for application of QFD method in intervention processes, with a potential for context diagnosis, circumscription of problem situation, and operational features.

Decision support systems are becoming an indispensable tool for managing complex supply chains. The purpose of this paper is to develop a multi-agent-based supply chain management system that incorporates big data analytics that can exert autonomous corrective control actions. The effects of the system on supply chain agility are explored.

For the development of the architecture of the system, a sequential approach is adopted. First three fundamental dimensions of supply chain agility are identified – responsiveness, flexibility and speed. Then the organisational design of the system is developed. The roles for each of the agents within the framework are defined and the interactions among these agents are modelled.

Applications of the model are discussed, to show how the proposed model can potentially provide enhanced levels in each of the dimensions of supply chain agility.

The study shows how the multi-agent systems can assist to overcome the trade-off between supply chain agility and complexity of global supply chains. It also opens up a new research agenda for incorporation of big data and semantic web applications for the design of supply chain information systems.

The proposed information system provides integrated capabilities for production, supply chain event and disruption risk management under a collaborative basis.

A novel aspect in the design of multi-agent systems is introduced for inter-organisational processes, which incorporates semantic web information and a big data ontology in the agent society.

The purpose of this paper is to provide a structured methodology for performing build‐in reliability (BIR) investigation during a new product development cycle.

The methodology in this paper represents an extension of the Quality Functional Deployment/House of Quality (QFD/HoQ) concepts to reliability studies. It is able to translate the reliability requisites of customers into functional requirements for the product in a structured manner based on a Failure Mode And Effect Analysis (FMEA). Besides, it then allows it to build a completely new operative tool, named House of Reliability (HoR), that enhances standard analyses, introducing the most significant correlations among failure modes. Using the results from HoR, a cost‐worth analysis can be easily performed, making it possible to analyse and to evaluate the economical consequences of a failure.

The paper finds that the application of the proposed approach allows users to identify and control the design requisites affecting reliability. The methodology enhances the reliability analysis introducing and managing the correlations among failure modes, splitting the severity into a detailed series of basic severity aspects, performing also cost/worth assessments.

It is shown that the methodology enables users to finely analyse failure modes by splitting severity according to the product typology and the importance of each Severity criterion according to laws or international standards. Moreover the methodology is able to consider the “domino effects” and so to estimate the impact of the correlation between the causes of failure. Finally a cost/worth analysis evaluates the economical consequences of a failure with respect to the incurred costs to improve the final reliability level of the product.

The paper proposes a completely new approach, robust, structured and useful in practice, for reliability analysis. The methodology, within an integrated approach, overcomes some of the largely known limits of standard FMECA: it takes into account multiple criteria, differently weighted, it analyses the product considering not only the direct consequence of a failure, but also the reaction chain originated by a starting failure.

This paper aims to investigate sustainability-led innovation, focusing on the interplay between product and process innovation for sustainability goals and the underlying supplier–customer relationships. Thus, the paper delves into sustainability-led innovation and how it affects supplier–customer relationships, and vice versa, thus providing a twofold perspective.

The textile industry is the empirical context of this study, which is exploratory research based on in-depth, semi-structured interviews with entrepreneurs, managers and experts in the textile industry.

In the textile industry, sustainability-led product innovation concerns mainly product durability and performance, product recyclability and the use of waste for new product development. Process innovation deals with circular economy, traceability and water and chemical use minimization. The paper also shows how sustainability-led innovation is implemented in more technical terms and regarding supplier–customer relationships.

The paper adopts an original perspective on how processes take place in the relationships between suppliers and customers, where there is no dominance of one actor, but innovation emerges from interdependence and interaction. Such perspective allows to provide an in-depth analysis of the supplier–customer relationships and underlying dynamics that affect sustainability-led innovation; moreover, the authors study how such innovation impacts supplier–customer relationships and the underlying relational dynamics. The value of the paper also stands in delivering a real representation of the innovation processes grounded in the textile industry.

The purpose of this paper is to analyze what the concept of agility means for communication evaluation and measurement and to challenge assumptions of goal-oriented and organization-centric approaches to evaluation and measurement.

This paper is a development debate based on a literature review, regarding agility, evaluation theory, communication evaluation approaches and what agility means for communication measurement.

Agility teaches that what works is more important than what was agreed upon in advance, so it is with more emphasis on needs rather than objectives. Regarding evaluation, the findings show that in today’s communication evaluation theory, evaluation is equated with summative evaluation of smart designed and fixed objectives. In agility, evaluation is always formative, to foster development and improvement within an ongoing activity. Consequently smart objectives are no longer valid as fixed benchmarks and ex ante and ex post evaluations do not exist; instead evaluation is an on-going and forward looking activity during action. Regarding measurement, the basic focus in agility on user needs implies that qualitative methods are more obvious than quantitative. The classic Weberian idea of “Verstehen” is helpful to understand how to focus on needs rather than objectives. This paper finally explores the merits of action research and sense-making methodology as applicable measurements in which “Verstehen” is the basis.

Agility is a very radical concept. The practical and theoretical implications of agile evaluation and measurement mean a total change for practice as well as for communication measurement and evaluation theory building.

The value of this paper is that it is the first to include agility into communication evaluation and measurement and that it, consequently, moves beyond organization-centric concepts of evaluation and measurement by bringing the often overlooked user needs into the game.

The purpose of this paper is to explore and propose how product-in-use data can be used in, and improve the performance of, the demand planning process for automotive aftermarket services.

A literature review and a single case study investigate the underlying reasons for the demand for spare parts by conducting in-depth interviews, observing actual demand-generating activities, and studying the demand planning process.

This study identifies the relevant product-in-use data and divides them into five main categories. The authors have analysed how product-in-use data are best utilised in planning spare parts with different attributes, e.g. different life cycle phases and demand frequencies. Furthermore, the authors identify eight potentially relevant areas of application of product-in-use data in the demand planning process, and elaborate on their performance effects.

This study details the understanding of what impact context has on the potential performance effects of using product-in-use data in aftermarket demand planning. Propositions generate several strands for future research.

This study shows the potential impact of using product-in-use data, using eight different types of interventions for spare parts, in the aftermarket demand planning.

The literature focusses on single applications of product-in-use data, but would benefit from considering the context of application. This study presents interventions and explores how these enable improved demand planning by analysing usage and effects.