Understanding consumer perceptions and attitudes toward smart retail services

Purpose This study aims to identify the antecedent factors influencing consumer attitudes and patronage intentions toward an intelligent unmanned convenience store (IUCVS) in Taiwan. The IUCVS is a new smart service that offers customers a novel shopping experience, given that it avoids queues and physical contacts with cashiers. However, studies discussing IUCVS remain scant owing to its brief history. Design/methodology/approach This research develops a synergistic model combining original unified theory of acceptance and use of technology (UTAUT) constructs with perceived risk and value to test differences between unexperienced and experienced customers’ attitudes and patronage intentions toward IUCVSs. Data collected from 268 experienced and 156 unexperienced consumers were tested against the proposed research model using partial least squares (PLS) structural equation modeling and multi-group analysis (PLS-MGA). Findings In line with expectations, three UTAUT variables (i.e. performance, effort expectancy and social influence) and perceived value significantly and positively influence consumer attitudes toward IUCVSs. This research confirms the significant and negative direct effect of perceived risk on consumers’ patronage intentions toward IUCVSs. Furthermore, the PLS-MGA results unveil that a significant difference exist in the effects of perceived convenience value on attitudes toward IUCVS between consumers who had experience of using self-service machines and those who have not. Originality/value This research successfully fills the research gap by offering a synergistic model for evaluating consumers’ attitudes and patronage intentions toward a new smart service. Several important theoretical and practical implications are provided to help retail managers develop service strategies.

Human Sensory Neuron-like Cells and Glycated Collagen Matrix as a Model for the Screening of Analgesic Compounds

Increased collagen-derived advanced glycation end-products (AGEs) are consistently related to painful diseases, including osteoarthritis, diabetic neuropathy, and neurodegenerative disorders. We have recently developed a model combining a two-dimensional glycated extracellular matrix (ECM-GC) and primary dorsal root ganglion (DRG) that mimicked a pro-nociceptive microenvironment. However, culturing primary cells is still a challenge for large-scale screening studies. Here, we characterized a new model using ECM-GC as a stimulus for human sensory-like neurons differentiated from SH-SY5Y cell lines to screen for analgesic compounds. First, we confirmed that the differentiation process induces the expression of neuron markers (MAP2, RBFOX3 (NeuN), and TUBB3 (β-III tubulin), as well as sensory neuron markers critical for pain sensation (TRPV1, SCN9A (Nav1.7), SCN10A (Nav1.8), and SCN11A (Nav1.9). Next, we showed that ECM-GC increased c-Fos expression in human sensory-like neurons, which is suggestive of neuronal activation. In addition, ECM-GC upregulated the expression of critical genes involved in pain, including SCN9A and TACR1. Of interest, ECM-GC induced substance P release, a neuropeptide widely involved in neuroinflammation and pain. Finally, morphine, the prototype opiate, decreased ECM-GC-induced substance P release. Together, our results suggest that we established a functional model that can be useful as a platform for screening candidates for the management of painful conditions.

Hybrid simulation modelling of networks of heterogeneous care homes and the inter-facility spread of Covid-19 by sharing staff

Although system dynamics [SD] and agent-based modelling [ABM] have individually served as effective tools to understand the Covid-19 dynamics, combining these methods in a hybrid simulation model can help address Covid-19 questions and study systems and settings that are difficult to study with a single approach. To examine the spread and outbreak of Covid-19 across multiple care homes via bank/agency staff and evaluate the effectiveness of interventions targeting this group, we develop an integrated hybrid simulation model combining the advantages of SD and ABM. We also demonstrate how we use several approaches adapted from both SD and ABM practices to build confidence in this model in response to the lack of systematic approaches to validate hybrid models. Our modelling results show that the risk of infection for residents in care homes using bank/agency staff was significantly higher than those not using bank/agency staff (Relative risk [RR] 2.65, 95% CI 2.57–2.72). Bank/agency staff working across several care homes had a higher risk of infection compared with permanent staff working in a single care home (RR 1.55, 95%CI 1.52–1.58). The RR of infection for residents is negatively correlated to bank/agency staff’s adherence to weekly PCR testing. Within a network of heterogeneous care homes, using bank/agency staff had the most impact on care homes with lower intra-facility transmission risks, higher staff-to-resident ratio, and smaller size. Forming bubbles of care homes had no or limited impact on the spread of Covid-19. This modelling study has implications for policy makers considering developing effective interventions targeting staff working across care homes during the ongoing and future pandemics.

Electricity Substitution Potential Prediction Based on Tent-CSO-CG-SSA-Improved SVM—A Case Study of China

Nowadays, fossil energy continues to dominate China’s energy usage; its inefficient use and large crude emissions of coal and fuel oil in its end-consumption have brought about great pressure to reduce emissions. Electrical power substitution as a development strategy is an important step toward achieving sustainable development, the transformation of the end-use energy consumption structure, and double carbon goals. To better guide the broad promotion of electrical power substitution, and to offer theoretical support for its development, this paper quantifies the amount of electrical power substitution and the influencing factors that affect the potential of electrical energy substitution. This paper proposes a hybrid model, combining Tent chaos mapping (Tent), chicken swarm optimization (CSO), Cauchy–Gaussian mutation (CG), the sparrow search algorithm (SSA), and a support vector machine (SVM), as a Tent-CSO-CG-SSA-SVM model, which first uses the method of Tent chaos mapping to initialize the sparrow population in order to increase population diversity and improve the search ability of the algorithm. Then, the CSO is introduced to update the positions of sparrows, and the CG method is introduced to make the algorithm jump out of the local optimum, in order to improve the global search ability of the SSA. Finally, the final electrical power substitution potential prediction model is obtained by optimizing the SVM through a multi-algorithm combination approach. To verify the validity of the model, two regions in China were used as case studies for the prediction analysis of electrical energy substitution potential, and the prediction results were compared with multiple models. The results of the study show that Tent-CSO-CG-SSA-SVM offers a good improvement in prediction accuracy, and that Tent-CSO-CG-SSA-SVM is a promising method for the prediction of electrical power substitution potential.

Multifocality and opportunity structure: towards a mixed embeddedness model for transnational migrant entrepreneurship

This article addresses transnational migrant entrepreneurship, which refers to migrants involved in cross-border entrepreneurial activities. Previous models and concepts in migrant entrepreneurship studies have not fully succeeded in recognising the role played by differential groups and places in the pursuit of opportunities by transnational migrant entrepreneurs. This is due to a tendency to focus on the country of residence as well as on the inclination to view migrant entrepreneurs as members of a coherent ethnic or national group. To help fill this gap, we propose a new model combining the concept of multifocality, covering the simultaneous involvement of migrant entrepreneurs in both multiple places and multiple groups, with group modes of behaviour as an additional dimension influencing the opportunity structure. The case of Moroccan transnational entrepreneurs in Amsterdam shows that the role of multifocality in place, in combination with group modes of behaviour, is critical when it comes to pursuing entrepreneurial opportunities.

Predicting change-of-direction performance in elite young badminton players: A multiple regression analysis on acceleration- and deceleration-related qualities

The aim of this study was to examine the interrelationships between direct (sprint and change-of-direction [COD] velocities) and indirect measures (COD-deficit [CODD], deceleration deficit [DD], and sprint momentum) of speed-related performances in young badminton players. Thirty young male badminton players (age: 16.8 ± 1.4 years; body-mass: 61.5 ± 7.9 kg; height: 170 ± 5.8 cm) performed a 20-m sprint followed by 505 COD tests, on the same day. A Pearson product moment test was applied to determine the relationships among variables. A multiple regression analysis was used to verify whether the combination of CODD and DD increased the capacity to predict COD performance. Large and significant relationships were observed between COD and linear sprint velocity and sprint momentum ( r ranging from 0.62 to 0.84; P < 0.05). COD velocity revealed a moderate significant ( r = −0.38) and a small non-significant ( r = 0.29) relationship with CODD and DD, respectively. The multiple regression model combining CODD and DD explained 44% of the variance in COD performance. In summary, young badminton players who sprint faster are equally faster in COD manoeuvres but present higher levels of CODD and DD. Coaches should be aware that faster badminton players may exhibit greater magnitudes of CODD-DD, thus requiring specific interventions to optimize the transition between high deceleration and (re) acceleration phases.

Non-Invasive Preoperative Imaging Differential Diagnosis of Intracranial Hemangiopericytoma and Angiomatous Meningioma: A Novel Developed and Validated Multiparametric MRI-Based Clini-Radiomic Model

BackgroundAccurate preoperative differentiation of intracranial hemangiopericytoma and angiomatous meningioma can greatly assist operation plan making and prognosis prediction. In this study, a clini-radiomic model combining radiomic and clinical features was used to distinguish intracranial hemangiopericytoma and hemangioma meningioma preoperatively.MethodsA total of 147 patients with intracranial hemangiopericytoma and 73 patients with angiomatous meningioma from the Tiantan Hospital were retrospectively reviewed and randomly assigned to training and validation sets. Radiomic features were extracted from MR images, the elastic net and recursive feature elimination algorithms were applied to select radiomic features for constructing a fusion radiomic model. Subsequently, multivariable logistic regression analysis was used to construct a clinical model, then a clini-radiomic model incorporating the fusion radiomic model and clinical features was constructed for individual predictions. The calibration, discriminating capacity, and clinical usefulness were also evaluated.ResultsSix significant radiomic features were selected to construct a fusion radiomic model that achieved an area under the curve (AUC) value of 0.900 and 0.900 in the training and validation sets, respectively. A clini-radiomic model that incorporated the radiomic model and clinical features was constructed and showed good discrimination and calibration, with an AUC of 0.920 in the training set and 0.910 in the validation set. The analysis of the decision curve showed that the fusion radiomic model and clini-radiomic model were clinically useful.ConclusionsOur clini-radiomic model showed great performance and high sensitivity in the differential diagnosis of intracranial hemangiopericytoma and angiomatous meningioma, and could contribute to non-invasive development of individualized diagnosis and treatment for these patients.

Hybrid modeling method of dust suppression efficiency for dry fog dust suppression process

The original mechanism model of dust suppression efficiency for dry fog dust suppression could not guide the actual process. In order to accurately predict the dust suppression efficiency in the process, a new mechanism model was established by my analysis of the process. But there were some factors that I could not establish the model. So a hybrid model combining mechanism model and support vector machine (SVM) was proposed. Using the data of dry fog dust suppression oval process, the hybrid model was simulated. The simulation results show that the hybrid model can accurately predict the dry fog dust suppression.