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Previous research has shown that consumers respond differently to decisions made by humans versus algorithms. Many tasks, however, are not performed by humans anymore but entirely by algorithms. In fact, consumers increasingly encounter algorithm-controlled products, such as robotic vacuum cleaners or smart refrigerators, which are steered by different types of algorithms. Building on insights from computer science and consumer research on algorithm perception, this research investigates how consumers respond to different types of algorithms within these products. This research compares high-adaptivity algorithms, which can learn and adapt, versus low-adaptivity algorithms, which are entirely pre-programmed, and explore their impact on consumers' product preferences. Six empirical studies show that, in general, consumers prefer products with high-adaptivity algorithms. However, this preference depends on the desired level of product outcome range—the number of solutions a product is expected to provide within a task or across tasks. The findings also demonstrate that perceived algorithm creativity and predictability drive the observed effects. This research highlights the distinctive role of algorithm types in the perception of consumer goods and reveals the consequences of unveiling the mind of the machine to consumers. [ABSTRACT FROM AUTHOR]

This paper aims to quantify the innovation ecosystem model for allowing the development of smart products at the country level. In this regard, the research used an empirical approach to scale and validate the six dimensions of an innovation ecosystem model among the small and medium-sized enterprises of 21 European countries. The quantitative methods of panel data analysis and Pearson correlation tests between variables of the innovation ecosystem and smart products were considered to examine six research hypotheses. Three dimensions of the innovation ecosystem model, i.e., configuration, change, and capability, have enough effects to accelerate high levels of smart products in the small and medium-sized enterprises of European countries, supporting the external and internal economic partnerships of institutions and companies, cultural changes in functional status, and knowledge-based capabilities of technological skills in each ecosystem. In addition, hierarchical clustering analysis for the classification of the countries showed that some countries, e.g., the United Kingdom, Netherlands, Sweden, Switzerland, Germany, Denmark, France, and Norway, could support their powerful smart products for small and medium-sized enterprises at the national level due to their high mean innovation ecosystem values. Overall, the research can describe the managerial implications regarding the knowledge-based capabilities of the technological skills in each ecosystem to be utilized by managers and stakeholders in small and medium-sized enterprises. [ABSTRACT FROM AUTHOR]

Considering circularity aspects during the engineering of smart products implies a significantly increased complexity of engineering processes. Especially technical reconfiguration of smart products, offered as services in availability-oriented business models, enables the integration of circular economy aspects in sustainable products and lifecycles, through realizing several aspects of the 9R strategies. This paper introduces an analysis of interdependencies between 9R strategies potential reconfiguration options, technical characteristics of smart products, different maturity levels of smart product and abilities for circularity. Partial engineering models managed in different product lifecycle management systems provide a technical basis for systematization and evaluation of circular abilities of reconfigurable smart products in different lifecycle phases. The approach aims to improve circularity-related decision making in systems engineering processes in the early development phases and during the reconfiguration of smart products during utilization phase. An industrial use case considering a microelectronic-centered smart product used in e-mobility solutions validates the approach. [ABSTRACT FROM AUTHOR]

The paper proves the possibility of providing magnetic properties in the process of saturation of textile fibers with a nanomixture of divalent and trivalent iron oxides. The mechanisms of interaction of nanostructures with natural and synthetic fibers are analyzed. The peculiarities of the introduction of magnetic components into flax, cotton, polyamide, and polyester fibers are determined. The relative number of particles with nanoscale in the total was statistically proved. To evaluate the macro effects of saturation, the relative amount of the accumulated mixture of divalent and trivalent iron oxides was determined, taking into account the previous fiber weight. Two basic constants characterizing the properties of textile fibers to magnetic interaction are proposed. It is proved that flax fibers exhibit the highest magnetic efficiency. The average size of iron oxide nanoparticles is 80–90 nanometers, which is confirmed by studies using electron microscopy. The final magnetization of the obtained textile materials provides an attractive magnetization of 20–30 mPascal. The magnetic field of magnetic textile fibers acts at a height of up to 6–7 mm. The directions of the use of fibers with magnetic properties are shown as a prospect for the development of textile smart products.

The paper compares the effectiveness of selected machine learning methods as modelling tools supporting the selection of a packaging type in new product development process. The main goal of the developed model is to reduce the risk of failure in compatibility tests which are preformed to ensure safety, durability, and efficacy of the finished product for the entire period of its shelf life and consumer use. This kind of testing is mandatory inter alia for all aerosol packaging as any mechanical alterations of the packaging can cause the pressurized product to unseal and stop working properly. Moreover, aerosol products are classified as dangerous goods and any leaking of the product or propellent can be a serious hazard to the storage place, environment, and final consumer. Thus, basic compatibility observations of metal aerosol packaging (i.e. general corrosion, pitting corrosion, coating blistering or detinning) and different compatibility factors (e.g. formula ingredients, water contamination, pH, package material and coatings) were discussed. Artificial intelligence methods applied in the design process can reduce the lengthy testing time as well as developing costs and help benefit from the knowledge and experience of technologists stored in historical data in databases. [ABSTRACT FROM AUTHOR]

Considering circularity aspects during the engineering of smart products implies a significantly increased complexity of engineering processes. Especially technical reconfiguration of smart products, offered as services in availability-oriented business models, enables the integration of circular economy aspects in sustainable products and lifecycles, through realizing several aspects of the 9R strategies. This paper introduces an analysis of interdependencies between 9R strategies potential reconfiguration options, technical characteristics of smart products, different maturity levels of smart product and abilities for circularity. Partial engineering models managed in different product lifecycle management systems provide a technical basis for systematization and evaluation of circular abilities of reconfigurable smart products in different lifecycle phases. The approach aims to improve circularity-related decision making in systems engineering processes in the early development phases and during the reconfiguration of smart products during utilization phase. An industrial use case considering a microelectronic-centered smart product used in e-mobility solutions validates the approach.

In Smart Manufacturing, the recent opportunities provided by the Information and Communication Technologies have paved the way to a seamless connection of the manufactured product throughout its entire lifecycle, leading to the diffusion of the concepts of Smart Product and Digital Thread, which leverage digital technologies to assure a continuous flow of data encompassing the design phase of a product, manufacturing, operation, maintenance and also its eventual disposal or recycling. This compelling need to obtain a unified view of information associated with Smart Products has stimulated the so-called Internet of Services (IoS) paradigm, allowing for the sharing of products data and the execution of functions among various participants in intertwined supply chains. In these contexts, service-oriented architectures are being more and more employed to meet the complex and ever-evolving data analysis requirements, particularly when implementing Digital Thread solutions for Smart Products, where several issues must be considered, ranging from the heterogeneity of (Big) data to data sovereignty and data access policies, as information may cross the borders of multiple actors participating in intertwined supply chains. This survey discusses about the technological solutions and challenges to implement Digital Threads for Smart Products in Smart Manufacturing contexts, providing insights on opportunities for future research directions. In addition, the survey proposes a comprehensive multi-tier service-oriented architectural model to jointly tackle (Big) data heterogeneity, data sovereignty and data access policies issues, as they are only partially addressed by the research efforts examined in the literature review.

In response to the challenge of accurately assessing age-friendly design requirements during the improvement phase of smart products, a novel evaluation model is proposed. This model is grounded in the technology acceptance model and considers the interplay between user weights and requirements. The aim is to establish precise product indicators and development strategies, thereby maximizing user satisfaction and market competitiveness. Initially, design evaluation indicators are derived from the technology acceptance model. Subsequently, the TextRank algorithm and grounded theory are employed to determine the initial weights of these indicators. The DEMATEL method is utilized to analyze the weight of influence relationships between indicators, while the Kano model determines importance coefficients for adjusting weights. Through normalization, final weights are assigned, and the TOPSIS method implements sorting and significance analysis of age-friendly design requirements. Five age-friendly smartphone samples serve as case studies to evaluate and analyze the five age-friendly design requirement indicators. Method comparison and advantage/disadvantage sorting analyses are conducted to validate the effectiveness and feasibility of the proposed approach.

The Internet of Things (IoT) is increasingly transforming the way we work, live, and travel. IoT devices collect, store, analyze, and act upon a continuous stream of data as a by-product of everyday use. However, IoT devices need unrestricted data access to fully function. As such, they invade users' virtual and physical space and raise far-reaching privacy challenges that are unlike those examined in other contexts. As advanced IoT devices, connected cars offer a unique setting to review and extend established theory and evidence on privacy and data sharing. Employing a sequential mixed methods design, we conducted an interview study (n=120), a survey study (n=333), and a field experiment (n=324) among car drivers to develop and validate a contextualized model of individuals' data sharing decisions. Our findings from the three studies highlight the interplay between virtual and physical risks in shaping drivers' privacy concerns and data sharing decisions-with information privacy and data security emerging as discrete yet closely interrelated concepts. Our findings also highlight the importance of psychological ownership, conceptualized as drivers' feelings of possession toward their driving data, as an important addition to established privacy calculus models of data sharing. This novel perspective explains why individuals are reluctant to share even low-sensitivity data that do not raise privacy concerns. The psychological ownership perspective has implications for designing incentives for data-enabled services in ways that augment drivers' self-efficacy and psychological ownership and thereby encourage them to share driving data. These insights help reconcile a fundamental tension among IoT users-how to avail the benefits of dataenabled IoT devices while reducing the psychological costs associated with the sharing of personal data. [ABSTRACT FROM AUTHOR]

Manufacturing firms that engage in digital transformation develop increasingly smarter versions of their tangible products to reinvigorate growth in shrinking markets. However, they often struggle with translating their investments in digitalization capabilities into actual returns in the form of sales growth. The associated technological advantages often remain unexploited, and digital product innovations frequently fail. Building on the resource‐based view of the firm and the demand‐side perspective, we theorize that there is a need for complementary capabilities that integrate heterogeneous customer demands, thus, allowing firms to capture more value from smart products. We empirically investigate the mediating role of smart customization capability on the relationship between digitalization capabilities and sales growth. Moreover, we argue that this relationship is further strengthened by integrating information and data across sales and service channels (i.e., channel integration). We test and find support for our hypotheses based on a dataset comprising survey and archival data of 136 smart product manufacturers in Austria, Germany, Switzerland, and the United States. In doing so, we enhance the theoretical understanding of resource and capability configurations needed for digital transformation in general and smart product success in particular. We further update the traditional concept of mass customization by showing how customization with smart products helps manufacturing firms provide personalized solutions at scale. [ABSTRACT FROM AUTHOR]

This paper introduces joint product design and non-linear pricing in the context of sharing markets. Product ecosystems enable user sensing, setting the stage for the control of post-purchase consumption patterns. By varying the degree to which products can be reused and transferred among peers, a company can engineer their shareability, which together with a capacity of aftermarket control, allows for flexible non-linear pricing that involves charging for the initial purchase and for subsequent collaborative transfers separately. Using a dynamic model with heterogeneous consumers and asymmetric information, we analyze a firm's economic strategy, including ecosystem design and flexible pricing, for long-term profitability. We show that an optimal product design balances durability-driven demand and price effects. Furthermore, for any given product design a profit-maximizing non-linear pricing schedule features retail price and sharing tariff in a robustly quadratic relationship, independent of the specifics of the consumer distribution. Various extensions, relating to the interaction of the firm's policy with a sharing market and the possibility of time-varying sales distributions, are also considered. [ABSTRACT FROM AUTHOR]

This paper investigates the different relationships consumers build with anthropomorphised devices and how these relationships affect actual and intended future usage. An exploratory, three-week empirical study of 39 informants using voice controls on their smartphone uncovered a diversity of relationships that the informants built with such devices. We complement anthropomorphism theory by drawing on extended-self theorising to identify three primary roles that emerge from consumers' interactions with these devices. Our findings theorise the distinct ways in which consumers perceive the object agency of anthropomorphised smart devices and how these perceptions impact the consumers' engagement and future use intentions. [ABSTRACT FROM AUTHOR]

Due to the transformation of traditional domain specific products to smart, connected and interdisciplinary products that are increasingly developed within value creation networks, an exchange of innovation-relevant knowledge in early engineering phases is required, covering interdependencies of several technical systems. Knowledge-based collaboration within a value creation network could enable an acceleration during the innovation development of smart products. Discipline- and stakeholder-specific knowledge, e.g. semiconductor research and production knowledge along the product lifecycle must be prepared for the usage within innovation development. This paper introduces a conceptual approach of a knowledge base to support the collaboration during innovation processes within value creation networks for smart products. The main focus is the sharing of relevant knowledge from different stakeholders within the innovation value creation network. Through knowledge networking, decentrally collected initial knowledge is centralized, made explorable and realized through a three-stage tagging architecture similar to an ontology. The collaboration of different stakeholders is enabled through mutual knowledge interests and automated suggestions independent from their respective IT ecosystems. The presented approach is instantiated and validated by considering the function to charge mobile phones wirelessly while car driving. Prerequisites for the presented approach are discussed. [ABSTRACT FROM AUTHOR]

Prior to the rapid growth of Artificial Intelligence ("AI") in the consumer market, smart products received great attention from marketers and consumers. Given the recent increase in attention to AI technologies, this research explores consumers' preferences and intentions when products are framed as "smart" versus "AI-powered." While previous literature has explored AI products and smart products individually, little is known about consumers' preferences between the two products simultaneously. Three empirical experiments demonstrate that consumers show preference for products labeled as "smart" over those labeled as "AI-powered." This preference is mediated by enhanced learning anxiety related to AI. The findings provide insights for marketers applying message framing, suggesting that labeling products as "smart" may evoke more favorable consumer behavior compared to the "AI-powered" label. Moreover, this research significantly contributes to the existing literature on perceptions and intentions related to AI and smart products by concurrently exploring consumer preferences regarding both smart products and AI-powered products. • This research sheds light on consumer behavior regarding products framed as "smart" versus "AI-powered.". • Across three empirical studies, consumers prefer products labeled as "smart" over their "AI-powered" counterparts. • The preference for smart products was shown to be mediated by enhanced learning anxiety to AI. • For marketers applying message framing, "smart" may evoke more favorable consumer behavior compared to "AI-powered.". [ABSTRACT FROM AUTHOR]

While the move towards Industry 4.0 has motivated a re-evaluation of how a manufacturing organization should operate in light of the availability of a new generation of digital production equipment, the new emphasis is on human worker inclusion to provide decision making activities or physical actions (at decision nodes) within an otherwise automated process flow; termed by some authors as Industry 5.0 and seen as related to the earlier Japanese Society 5.0 concept (seeking to address wider social and environmental problems with the latest developments in digital system, artificial Intelligence and automation solutions). As motivated by the EU the Industry 5.0 paradigm can be seen as a movement to address infrastructural resilience, employee and environmental concerns in industrial settings. This is coupled with a greater awareness of environmental issues, especially those related to Carbon output at production and throughout manufactured products lifecycle. This paper proposes the concept of dynamic Life Cycle Assessment (LCA), enabled by the functionality possible with intelligent products. A particular focus of this paper is that of human in the loop assisted decision making for end-of-life disassembly of products and the role intelligent products can perform in achieving sustainable reuse of components and materials. It is concluded by this research that intelligent products must provide auditable data to support the achievement of net zero carbon and circular economy goals. The role of the human in moving towards net zero production, through the increased understanding and arbitration powers over information and decisions, is paramount; this opportunity is further enabled through the use of intelligent products. [ABSTRACT FROM AUTHOR]

The uptake of IoTs in households worldwide is lower than predicted and comparatively slower in developing countries than in developed countries due to inadequate digital infrastructure. This study aims to understand why the Internet of Things in households (specifically smart household appliances) have been resisted and have not been adopted to predicted levels by comparing Germany as a developed country to South Africa as a developing country. The specific focus was on innovation and consumer characteristics, perceived risk of adoption, and personality considerations, using a smart refrigerator. Through regression analysis of data generated from a quantitative survey of 1665 consumers from Germany and South Africa, it was found that both resistance and adoption are influenced by how intrusive the smart appliance is, how the consumer perceives its usefulness and whether it is considered novel or new. The perceived price of the smart appliance also played a role in adoption. Only South Africans’ perception of their capability to use a technologically innovative product influences their resistance to the smart appliance. Adoption is not the inverse of resistance when considering the constructs in their totality. Consumer innovativeness, influenced by uncertainty avoidance, played a significant role among the German and South African participants both resisting and adopting smart products. However, the cultural dimension of uncertainty avoidance also influenced consumer innovativeness. On the other hand, South Africans’ spirit of innovation influenced their resistance to and adoption of smart appliances more strongly than their German counterparts.

Sharing markets create mutual insurance for consumers who are unsure about their future needs for goods, thus rendering products more valuable both before and after the purchase. By embedding intelligence in their products, enabling them to sense, monitor, and authorize transfers between users, firms can economically participate in the collaborative consumption of their goods after they have been sold. Building on a dynamic model with overlapping generations of heterogeneous agents, we determine a firm’s jointly optimal product price and sharing tariff. The active use of product intelligence as a gatekeeper for collaborative consumption can narrow the gap between the retail and the equilibrium price in the sharing market. Because of its tendency to decrease the demand for ownership, the use of smart products with a positive sharing tariff does not always maximize the firm’s overall expected profits. A positive sharing tariff tends to be profitable with relatively high unit production cost and impatient consumers. [ABSTRACT FROM PUBLISHER]

The fourth industrial revolution is shaping a new industrial landscape. A variety of technologies related to software, information and communication technologies embody a ubiquitous digital and connectivity era. These technologies enable the creation of new products with the integration of connectivity, data collection and processing capacities which require combining engineering disciplines. Increasing product multidisciplinarity compels companies to adapt their product development practices. The scientific literature offers a variety of concepts and techniques to support multidisciplinary product development. This paper seeks to organize the landscape of concepts and techniques available for multidisciplinary product development. An extensive literature review was conducted, and 236 concepts and techniques were identified. Multidisciplinary products of interest deal with both software and hardware development and can be encountered through the denominations of cyber-physical systems, mechatronics and smart products and systems. An in-depth analysis led to the classification and mapping, for each product denomination, of the concepts and techniques available to support their development. The classification relies on a four-level model paired with a decision tree to thoroughly sort the variety of concepts and techniques into the approach, process, method, and tool levels. The mapping between the sorted concepts and techniques enabled the generation of graphical representations called cartographies. These cartographies serve to support companies' transformation towards the fourth industrial revolution from the product development perspective by giving a general overview of the related literature, and guiding them in the identification of the most suitable approaches, processes, methods and tools. [ABSTRACT FROM AUTHOR]

The development of smart products and systems includes a dramatically growing share of non-mechanical domains like microelectronic and software. This determines fundamentally new collaboration challenges and needs for system and product developers and their industrial organizations. Besides the rising number of relevant engineering disciplines (e.g. product-related internet-based service engineering and data platform engineering), the collaboration across several disciplines needs to become more systematic and has to be based on consistent system models. Since traditional value chains are transforming into new agile and distributed value creation networks, engineering collaboration approaches and solutions also have to stronger emphasize model-based, cross-company engineering collaboration. Also, the number of engineering stakeholders involved is increasing, while at the same time stakeholders are taking on new roles and completely new players are emerging, the collaboration share also has to grow continuously. This paper introduces a generic methodical approach for modeling collaborative innovation processes in value creation networks for smart products and supporting them with information technology by using an integrated, cross-company knowledge base. The support of engineering collaboration with a generic common reference innovation process along the value creation network with a seamless transformation into the engineering phase, are part of the focused approach goals. This generic approach has been instantiated according to the specific needs of early innovation phases in the field of automotive microelectronic integration considering the whole value creation network. [ABSTRACT FROM AUTHOR]