Product-service systems (PSSs) deploy a selection of products and services in order to cope with diverse markets, so as to achieve a higher profit than would be possible by offering physical products alone. Modular design inherently contributes to the sustainability performance of PSS by material and resource reuse through the configuration of physical product and service modules. PSS configuration design is enacted through service configuration in line with product configuration; this entails two separate yet coordinated optimization problems, enabling customer satisfaction through service configuration and manufacturers' sales profits through product configuration, respectively. Traditional multiobjective optimization approaches assume that the conflicting goals between customers and manufacturers can be aggregated into one single objective function through cooperative protocols, such as a weighted sum; in practice, this scarcely holds true. Consistent with game-theory decision-making, it is necessary to leverage the concerns of customers and manufacturers within a coherent framework of equilibrium solutions. This paper proposes a bi-level coordinated optimization framework to support PSS configuration design. An upper-level optimization problem is formulated for service configuration to act as a leader in the achievement of customer satisfaction, and a lower-level optimization problem is formulated for product configuration to act as a follower in an effort to enhance sales profits. Coordination between the upper and lower levels coincides with the tradeoffs underlying the conflicting goals that exist between customers and manufacturers. A constrained genetic algorithm is developed to solve the bi-level optimization model, and a case study of transformer PSS configuration design is reported to illustrate the feasibility and potential of bi-level coordinated configuration.

Viral product design involves sophisticated interactions between product portfolio planning and viral marketing. However, social network effects are mainly considered in marketing-related activities, and there is still limited investigation of the interplay between product design and viral marketing. In the context of social networks, it is important to jointly leverage both viral product attributes and viral influence attributes for product adoption maximization and product line performance optimization. In order to deal with the joint optimization problem, this paper presents a systematic formulation of a bilevel decision-making strategy for viral product design based on the Stackelberg game theory. The product adoption maximization problem with viral influence and product attributes is modeled as the leader and the product portfolio optimization problem with product attributes is modeled as the follower. The interaction and coupling of these two optimization problems are addressed with a coordinate-wise optimization strategy, in which adoption maximization is tackled with an improved greedy algorithm and a hybrid Taguchi genetic algorithm. A case study of Kindle Fire HD tablets demonstrates the feasibility and potential of the bilevel decision-making strategy for viral product design, which is advantageous over the existing viral marketing methods that only consider viral influence attributes.

Unlike visual stimuli, little attention has been paid to auditory stimuli in terms of emotion prediction with physiological signals. This paper aimed to investigate whether auditory stimuli can be used as an effective elicitor as visual stimuli for emotion prediction using physiological channels. For this purpose, a well-controlled experiment was designed, in which standardized visual and auditory stimuli were systematically selected and presented to participants to induce various emotions spontaneously in a laboratory setting. Numerous physiological signals, including facial electromyogram, electroencephalography, skin conductivity and respiration data, were recorded when participants were exposed to the stimulus presentation. Two data mining methods, namely decision rules and k-nearest neighbor based on the rough set technique, were applied to construct emotion prediction models based on the features extracted from the physiological data. Experimental results demonstrated that auditory stimuli were as effective as visual stimuli in eliciting emotions in terms of systematic physiological reactivity. This was evidenced by the best prediction accuracy quantified by the F1 measure (visual: 76.2% vs. auditory: 76.1%) among six emotion categories (excited, happy, neutral, sad, fearful and disgusted). Furthermore, we also constructed culture-specific (Chinese vs. Indian) prediction models. The results showed that model prediction accuracy was not significantly different between culture-specific models. Finally, the implications of affective auditory stimuli in human–computer interaction, limitations of the study and suggestions for further research are discussed.

In order to incorporate both affective and cognitive factors in the decision-making process, a user experience (UX) evaluation function based on cumulative prospect theory is proposed for three different affective states and two different types of products (affect-rich versus affect-poor). In order to tackle multiple parameters involved in the UX evaluation function, a hierarchical Bayesian model is proposed with a technique called “Markov chain Monte Carlo.” It estimates parameters that represent different cognitive tendencies and affective influences for customers at the individual and group levels by generating posterior probability density functions of the parameters to incorporate inherent uncertainty. An experiment with four hypotheses was designed to test the proposed model. We found that: 1) anxious participants tend to be more risk-averse than those in joy and excitement; 2) joyful and excited participants tend to be more risk-seeking than those in anxiety in UX-related choice decision making; 3) all participants tend to be averse to unpleasant UX; and 4) participants tend to value by feeling for affect-rich products and value by calculation for affect-poor products. Furthermore, the models of five different types can predict choice decision making between product profiles with around 80% accuracy. In summary, the results explain affective-cognitive decision-making behavior in the complex domain of UX design and, thus, illustrate the potential and feasibility of the proposed method.

Product ecosystem design entails complex user experience (UX) that involves interactions among multiple users, products, and the ambience. This paper aims to capture causal relationships between UX and design elements and in turn to provide decision support to product ecosystem analysis. A fuzzy reasoning Petri net is developed to deal with the uncertainty, complexity, and dynamics associated with UX modeling. Reasoning of diverse constructs of UX is embedded in the fuzzy production rules that are derived from self-report UX data based on rough set mining. A fuzzy reasoning algorithm is implemented to perform parallel inference by multicriteria rules and to simulate most likely UX under different ambient factors. A case study of subway station UX design demonstrates the potential of product ecosystem FRPN formulation.

Elderly in-home assistance (EHA) has traditionally been tackled by human caregivers to equip the elderly with homecare assistance in their daily living. The emerging ambience intelligence (AmI) technology suggests itself to be of great potential for EHA applications, owing to its effectiveness in building a context-aware environment that is sensitive and responsive to the presence of humans. This paper presents a case-driven AmI (C-AmI) system, aiming to sense, predict, reason, and act in response to the elderly activities of daily living (ADLs) at home. The C-AmI system architecture is developed by synthesizing various sensors, activity recognition, case-based reasoning, along with EHA-customized knowledge, within a coherent framework. An EHA information model is formulated through the activity recognition, case comprehension, and assistive action layers. The rough set theory is applied to model ADLs based on the sensor platform embedded in a smart home. Assistive actions are fulfilled with reference to a priori case solutions and implemented within the AmI system through human–object–environment interactions. Initial findings indicate the potential of C-AmI for enhancing context awareness of EHA applications.

Evidence from prior research has demonstrated that exocentric views of the environment can facilitate the acquisition of survey knowledge in a virtual environment. The present study examined the effect of different exocentric views on judging the relative direction of objects. During the participants' vertical movement in a virtual room, participants learned the spatial layout in one of three conditions: two-perspective, attentive-elevation, and normal-elevation conditions, where the number of the exocentric perspectives from which the spatial layout was observed was different. After spatial learning, they made the judgment of the relative direction of objects. The analysis of spatial judgment showed that as the number of exocentric perspectives increased, the accuracy improved in the mental representation of spatial vertical information and spatial information in novel directions. Results indicated that the increased number of exocentric perspectives during the vertical movement facilitated the flexible acquisition of survey knowledge. Applications of this study included the design of effective navigation aids in virtual multilevel buildings.

Regardless of rapid adoption of digitalization in printing production processes, production management in most printing companies is still low efficient and operated in an old fashion. This paper applies the CIM philosophy to the print-manufacturing environment. A Computer Integrated Printing Manufacturing (CIPM) framework is proposed with integration of an Application Architecture, a System Integration Framework and a Job Definition Format (JDF) based Print Process Control Framework. The CIPM framework takes advantage of existing standards such as CIP4/JDF, XML, PDF and other advanced information technologies. These technologies make a CIPM system flexible, scalable and extensible.

This paper introduces an integrated modeling and analysis formalism based on colored Petri nets (CPNs) for supply chain configuration and evaluation. The structural changes of different configurations are explicitly expressed and formalized as CPNs. The formalism can deal with such issues as generic variety representation, constraint compliance and material flow control. The rationale of modeling supply chain configuration is to capture explicitly important information related to the supply chain network in conjunction with product, process and logistics decision making.

To support the collaborative development of custom products in current distributed manufacturing, this paper develops a Web-based configure-to-order platform. The proposed platform enables the integration of different Web-based services across companies and various lifecycle issues of product fulfillment into a collaborative Web of interactive commerce. As the kernel of the platform, a generic product family master model allows the collaboration of customization over the Internet while achieving a synergy of sales force automation, product design, manufacturing planning, and supply chain management within a coherent framework. Throughout the paper, a case of injection molded part development is used to detail the implementation of the proposal.