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Research suggests that service‐dominant designs are superior to goods‐dominant; but why? The purpose of this paper is to answer three questions. First, in what way is a service system based on service‐dominant logic (SDL) superior to one based on goods‐dominant logic (GDL)? Second, which characteristics of the service system facilitate the co‐creation of value‐in‐context as perceived by the customer? Third, how do customers describe the contents of these characteristics?

In an experiment, the authors compared two different service systems designed with different mindsets. The experiment was carried out with a group of habitual bus travellers to plan a specific journey using two online service systems by two different organizations; one exhibited a goods‐dominant mindset, and the other a service‐dominant mindset. The subjects' opinions of the two systems were gathered, and sentiment analysis was performed on the opinions to uncover the rationale behind the operational superiority of an SDL‐based system in value co‐creation.

The sentiment analysis identified three key differentiators: intangibles; operant resources; and information symmetry as the reason why an SDL‐based service system is superior to a GDL‐based system. The study also identified specific attributes linked to the key differentiators that could be built into a service system design and verified during a review of that design.

The paper contributes to research by: showing why an SDL based service system is superior to a GDL based one; proposing guidelines for service system design based on SDL to arrive at a favourable customer experience; and to managers by showing that customers have much to contribute in service development and realisation.

Few empirical studies have been conducted to explore the mechanisms and drivers of service exchange and value co‐creation. In particular, no study has compared a service system design informed by service‐dominant logic (SDL) with a service system design informed by goods‐dominant logic (GDL). The purpose of this paper is to address this knowledge gap. The research question is: does a service‐dominant system design result in a more favourable customer experience than a goods‐dominant service system?

An experiment was carried out on a group of habitual bus travellers. The subjects were asked to plan a specific journey using two online journey planning systems. Two hypotheses were tested: first, an SDL informed service system will evoke a better overall customer experience than a GDL informed service system. Second, this better customer experience arises out of seven service system design characteristics. Both objective and subjective data were gathered to compare the customers' experiences of using the two service systems.

The results show that a service‐dominant service system outperforms a goods‐dominant service system in terms of both objective and subjective criteria. Moreover, the experiment elucidates the subjects' perceived importance of the characteristics of a service‐dominant service system. Analysis of the subjects' perception of the two service systems reveals that certain characteristics set the service‐dominant service system more distinctly apart from the goods‐dominant one.

The paper contributes by extending the empirical foundation for service‐dominant logic, providing new knowledge on value co‐creation and design characteristics of service systems, and identifying the most important service system characteristics perceived by the customer.

Nitrogen oxides (NO_x) have been considered as primarily responsible for many serious environmental problems. Removing NO is the key task to remove NO_x hazards. To clarify, NO removal process for pitch-based spherical-activated carbons (PSACs), an online prediction and optimization technique in real-time based on support vector machine algorithm in regression (support vector regression [SVR]) is discussed. The purpose of this paper is to develop a predictor and optimizer system on selective catalytic reduction of NO (SCRN) using experimental data and data-driven SVR intelligence methods.

Predictor and optimizer using developed SVR have been proposed. To modify the training efficiency of SVR, the authors especially customize batch normalization and k-fold cross-validation techniques according to the unique characteristics of PSACs model.

The results present that SVR provides a property regression model since it can linkage linear and non-linear process and property relationships in few experimental data sets. Also, the integrated normalization and k-fold cross-validation show a satisfying improvement and results for SVR optimization. The predicted results of predictor and optimizer in single and double factor systems are in excellent agreement with the experimental data.

SCRN-PO for predicting and optimization SCRN problems is developed by data-driven methods. The outperformed SCRN-PO system is used to predict multiple-factors property parameters and obtain optimum technological parameters in real-time. Also, experiment duration is greatly shortened.

The purpose of this paper was to investigate whether small holes in an impermeable patch at the wrist improve perceived comfort during exercise.

Nine male participants participated in this study. During the experiment, participants cycled 60 W in a hot room (35°C, 30 percent relative humidity) while an impermeable 20 cm² patch was located on the ventral side of one wrist and at the same time a patch of identical shape with 5 mm diameter holes (17.7 percent uncovered) on the other wrist. The participants could not see the patches. Participants were forced to choose which patch they perceived as more comfortable. Chest and arm skin temperature, thermal comfort, thermal sensation and wetness perception were assessed.

Participants preferred 5 mm holes over no holes (p=0.017). Chest skin temperature (p=0.018) but not arm skin temperature correlates with this preference. Thermal comfort, thermal sensation and wetness perception did not differ significantly between patches. It is concluded that patches with 5 mm holes are preferred over impermeable patches during work in the heat in particular when the torso skin is warm.

The wrist is a preferred location for smart wearables. Generally, wrist bands are made of air-impermeable materials leading to sensation of wetness and discomfort. This study has shown that manufacturers should consider to make small holes in their wrist bands to optimize wearing comfort.

Intelligent manufacturing has attracted extensive attention from national strategy, academic research and enterprises' practices. The purpose of this study is to investigate the influence of intelligent manufacturing on performance in manufacturing firms. Moreover, how intelligent manufacturing technology affects enterprise performance, this study provided a practice that can be replicated by other businesses.

This study uses text mining to collect the intelligence level of Chinese listed companies. It uses quantitative analysis to test the proposed model based on samples of 2,091 manufacturers.

Intelligent manufacturing has positive effect on short-term performance and long-term performance. Intelligent manufacturing can empower firms with ambidextrous capabilities, including exploit capability and explore capability. Exploit capability has positive effects on short-term performance and long-term performance. Explore capability has negative effects on short-term performance, but has positive effects on long-term performance.

On the theoretical side, it enriches the research framework between intelligent manufacturing and enterprise performance. This study explains the preconditions and results of ambidextrous capabilities. Moreover, based on the practice-based view (PBV), this study proposes that technologies can be used as strategies, filling a gap in the existing research on strategic management. On the practical side, how to quantify the intelligent manufacturing level of enterprises provides a certain reference. Also, this study provides an easy to imitate practice that can serve as a model for under-performing enterprises.

Generally, the user requires customized information reflecting his/her current needs and interests that are stored in his/her profile. There are many sources which may provide beneficial information to enrich the user’s interests such as his/her social network for recommendation purposes. The proposed approach rests basically on predicting the reliability of the users’ profiles which may contain conflictual interests. The paper aims to discuss this issue.

This approach handles conflicts by detecting the reliability of neighbors’ profiles of a user. The authors consider that these profiles are dependent on one another as they may contain interests that are enriched from non-reliable profiles. The dependency relationship is determined between profiles, each of which contains interests that are structured based on k-means algorithm. This structure takes into consideration not only the evolutionary aspect of interests but also their semantic relationships.

The proposed approach was validated in a social-learning context as evaluations were conducted on learners who are members of Moodle e-learning system and Delicious social network. The quality of the created interest structure is assessed. Then, the result of the profile reliability is evaluated. The obtained results are satisfactory. These results could promote recommendation systems as the selection of interests that are considered of enrichment depends on the reliability of the profiles where they are stored.

Some specific limitations are recorded. As the quality of the created interest structure would evolve in order to improve the profile reliability result. In addition, as Delicious is used as a main data source for the learner’s interest enrichment, it was necessary to obtain interests from other sources, such as e-recruitement systems.

This research is among the pioneer papers to combine the semantic as well as the hierarchical structure of interests and conflict resolution based on a profile reliability approach.

This study aims to satisfy the thermal management of gallium nitride (GaN) high-electron mobility transistor (HEMT) devices, microchannel-cooling is designed and optimized in this work.

A numerical simulation is performed to analyze the thermal and flow characteristics of microchannels in combination with computational fluid dynamics (CFD) and multi-objective evolutionary algorithm (MOEA) is used to optimize the microchannels parameters. The design variables include width and number of microchannels, and the optimization objectives are to minimize total thermal resistance and pressure drop under constant volumetric flow rate.

In optimization process, a decrease in pressure drop contributes to increase of thermal resistance leading to high junction temperature and vice versa. And the Pareto-optimal front, which is a trade-off curve between optimization objectives, is obtained by MOEA method. Finally, K-means clustering algorithm is carried out on Pareto-optimal front, and three representative points are proposed to verify the accuracy of the model.

Each design variable on the effect of two objectives and distribution of temperature is researched. The relationship between minimum thermal resistance and pressure drop is provided which can give some fundamental direction for microchannels design in GaN HEMT devices cooling.

Intervention of artificial intelligence (AI) has brought up the issue of future job prospects in terms of the employability of the professionals and their readiness to harness the benefits of the AI. The purpose of this study is to recognize the implications of AI on employability by analyzing the issues in the health-care sector that if not addressed, can dampen the possibilities offered by AI intervention and its pervasiveness (Cornell University, INSEAD, and WIPO, 2019).

To get an insight on these concerns, an approach of total interpretive structural modelling, cross impact matrix multiplication applied to classification and path analysis have been used to understand the role of the critical factors influencing employability in the health-care sector.

This study primarily explores the driving-dependence power of the critical factors of the employability and displays hierarchical relationships. It also discusses measures which, if adopted, can enhance employability in the health-care sector with the intervention of AI.

Employability also has an impact on the productivity of the health-care service delivery which may provide a holistic opportunity to the management in health-care organizations to forecast the allocation and training of human resources and technological resources.

The paper attempts to analyze AI intervention and other driving factors (operational changes, customized training intervention, openness to learning, attitude toward technology, job-related skills and AI knowledge) to analyze their impact on employability with the changing needs. It establishes the hierarchical relationship among the critical factors influencing employability in the health-care sector because of the intervention of AI.

COVID-19 disrupted the lives of consumers across the globe, and the retail sector has been one of the hardest hits. The impact of COVID-19 on consumers' retail choice behaviour and retailers' responses has been studied in detail through multiple lenses. Now that the effect of COVID-19 is abating, there is a need to consolidate the learnings during the lifecycle of COVID-19 and set the agenda for research post-COVID-19.

Scopus database was searched to cull out academic papers published between March 2020 and June 6, 2022, using keywords; shopping behaviour, retailing, consumer behaviour, and retail channel choice along with COVID-19 (171 journals, 357 articles). Bibliometric analysis followed by selective content analysis was conducted.

COVID-19 was a black swan event that impacted consumers' psychology, leading to reversible and irreversible changes in retail consumer behaviour worldwide. Research on changes in consumer behaviour and consumption patterns has been mapped to the different stages of the COVID-19 lifecycle. Relevant research questions and potential theoretical lenses have been proposed for further studies.

This paper collates, classifies and organizes the extant research in retail from the onset of the COVID-19 pandemic. It identifies three retail consumption themes: short-term, long-term reversible and long-term irreversible changes. Research agenda related to the retailer and consumer behaviour is identified; for each of the three categories, facilitating the extraction of pertinent research questions for post-COVID-19 studies.