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The purpose of this paper is to investigate the moderating effect of homophily between trust in social media influencers (SMIs) and credibility of the post in influencer marketing by incorporating the similarity attraction theory (SAT) and analysing the effect of trust in SMIs on online purchase intention and credibility of the post. This study also explored the mediating role of influencers’ credibility of the post between trust in SMIs and online purchase intention.

The data were collected from 417 respondents in Jaipur, India, using an online questionnaire via Google Forms. A convenience sampling technique was employed to collect the data. Partial least squares structural equation modelling (PLS-SEM) was used to test the proposed hypotheses with the help of SmartPLS version 4.

The results exhibit a positive and significant influence of trust in SMIs on credibility of the post and online purchase intention. Also, this study found a positive and significant relationship between credibility of the post and online purchase intention. Additionally, credibility of the post had a positive and significant mediation role in the relationship between trust in SMIs and online purchase intention. In addition, homophily positively moderated the relationship between trust in SMIs and credibility of the post.

The findings of this study can be used by marketing professionals working in the e-commerce industry to ensure their continued in success using the right influencers for their specific target audiences and help them create and implement more effective promotional strategies, increasing brand awareness, announcing sales, using them for creative content and so on.

Until now, there has been no study in the Indian context that has tested the moderation effect of homophily between the trust in SMIs and credibility of the post by incorporating the SAT, according to the researchers’ knowledge. Furthermore, this novel piece of research explored the relationship between trust in SMIs and online purchase intention with credibility of the post as a mediator.

The total defence (TD) concept constitutes a joint endeavour between the military forces and civil defence structures within a TD state. Logistics is essential for such joint collaboration to work; however, the mismatch between military and civil defence logistics structures poses challenges for such joint collaboration. The purpose of this paper is to identify logistics concept areas within the TD framework that allow for military and civil defence collaborations from a logistics operations perspective.

Pattern-matching analysis is used to compare patterns found in the investigated case with those prescribed from the literature and predicted to occur. The study seeks to identify logistics concepts within TD from the literature and from the events describing the Swedish response to the Covid-19 pandemic. Pattern matching thus allows for the reconciliation of logistics concepts from the literature to descriptions of how the response was handled, albeit under a TD framework.

Findings show quite distinct foci between the theoretical and observational realms in terms of logistics applications. While the theoretical realm identifies four main logistics concepts, the observational realm identifies five logistics conceptual themes. This goes on to show an incongruence between the military and civil parts of the TD.

This study provides basis for further research into the applications and management of logistics activity within TD and emergency response.

Logistics applications within TD have not, until now, received much attention in the literature. Given this knowledge gap, this study is of original value.

This research presents and evaluates a method for assessing the entrepreneurial mindset (EM) of students in higher education.

The research considers EM a multi-variable psychological construct, which can be broken down into several conceptual sub-categories. Using data from a master course in entrepreneurship, the authors show how these categories can be applied to analyze students’ written reflections to identify linguistic markers of EM.

The research reports three main findings: analyzing student reflections is an appropriate method to explore the state and development of students’ EM; the theoretically-derived EM categories can be nuanced and extended with insight from contextualized empirical insights; and student reflections reveal counter-EM categories that represent challenges in the educator’s endeavor to foster students’ EM.

The commitment of resources to researching EM requires the dedication of efforts to develop methods for assessing the state and development of students’ EM. The framework can be applied to enhance the theoretical rigor and methodological transparency of studies of EM in entrepreneurship education.

The framework can be of value to educators who currently struggle to assess if and how their educational design fosters EM attributes.

This inquiry contributes to the critical research discussion about how to operationalize EM in entrepreneurship education studies. The operationalization of a psychological concept such as EM is highly important because a research focus cannot be maintained on something that cannot be studied in a meaningful way.

Microfinance programs across the countries are designed on the self-help and peer pressure model, aim at microentrepreneurship development. Despite of significant studies on microfinance-supported microentrepreneurship (MSM), not a single literature examines it from the systems thinking. In addition to that, the extant literature did not look MSM from the behavioral perspectives. To address the above gaps, the present study aims to examine self-help group (SHG)-based microfinance programs from the systems approach using the Stimulus-Organism-Behavior-Consequence (SOBC) model.

Information gathered from 786 women SHG members from four states of India through a structured interview schedule. Confirmatory Factor Analysis (CFA) and Structural Equation Modeling (SEM) were conducted to process data. Additional statistical tests were performed to test the reliability and validity.

It was found that the “positive stimulus” (social intermediation, financial intermediation and business development services) positively impacted; and “negative stimulus” (intermediation accountability, and intermediation assumption) negatively impact, to “motive” (attitude, subjective norms, and perceived control) for micro-entrepreneurship in the SHG-based microfinance. Further, “motive” positively predicted “behavioral intention”; the “behavioral intention” positively determined “consequences” of micro-entrepreneurship. Intermediation as stimuli acted as “input”; the motive and behavioral intention acted as the “process”, and the consequence acted as the “output” in the SHG-based microentrepreneurship system.

To the best of the author's knowledge, this paper is the first one to examine the behavioral systems of microentrepreneurship programs through the Stimulus-Organism-Behavior-Consequence (SOBC) model.

The peer review history for this article is available at: https://publons.com/publon/10.1108/IJSE-12-2022-0801

COVID-19 has occurred in more than 150 countries and causes a huge impact on the health of many people. The main purpose of this work is, COVID-19 has occurred in more than 150 countries and causes a huge impact on the health of many people. The COVID-19 diagnosis is required to detect at the beginning stage and special attention should be given to them. The fastest way to detect the COVID-19 infected patients is detecting through radiology and radiography images. The few early studies describe the particular abnormalities of the infected patients in the chest radiograms. Even though some of the challenges occur in concluding the viral infection traces in X-ray images, the convolutional neural network (CNN) can determine the patterns of data between the normal and infected X-rays that increase the detection rate. Therefore, the researchers are focusing on developing a deep learning-based detection model.

The main intention of this proposal is to develop the enhanced lung segmentation and classification of diagnosing the COVID-19. The main processes of the proposed model are image pre-processing, lung segmentation and deep classification. Initially, the image enhancement is performed by contrast enhancement and filtering approaches. Once the image is pre-processed, the optimal lung segmentation is done by the adaptive fuzzy-based region growing (AFRG) technique, in which the constant function for fusion is optimized by the modified deer hunting optimization algorithm (M-DHOA). Further, a well-performing deep learning algorithm termed adaptive CNN (A-CNN) is adopted for performing the classification, in which the hidden neurons are tuned by the proposed DHOA to enhance the detection accuracy. The simulation results illustrate that the proposed model has more possibilities to increase the COVID-19 testing methods on the publicly available data sets.

From the experimental analysis, the accuracy of the proposed M-DHOA–CNN was 5.84%, 5.23%, 6.25% and 8.33% superior to recurrent neural network, neural networks, support vector machine and K-nearest neighbor, respectively. Thus, the segmentation and classification performance of the developed COVID-19 diagnosis by AFRG and A-CNN has outperformed the existing techniques.

This paper adopts the latest optimization algorithm called M-DHOA to improve the performance of lung segmentation and classification in COVID-19 diagnosis using adaptive K-means with region growing fusion and A-CNN. To the best of the authors’ knowledge, this is the first work that uses M-DHOA for improved segmentation and classification steps for increasing the convergence rate of diagnosis.

This study aims to investigate the daily performance of the proposed microgrid (MG) that comprises photovoltaic, wind turbines and is connected to the main grid. The load demand is a residential area that includes 20 houses.

The daily operational strategy of the proposed MG allows to vend and procure utterly between the main grid and MG. The smart metre of every consumer provides the supplier with the daily consumption pattern which is amended by demand side management (DSM). The daily operational cost (DOC) CO2 emission and other measures are utilized to evaluate the system performance. A grey wolf optimizer was employed to minimize DOC including the cost of procuring energy from the main grid, the emission cost and the revenue of sold energy to the main grid.

The obtained results of winter and summer days revealed that DSM significantly improved the system performance from the economic and environmental perspectives. With DSM, DOC on winter day was −26.93 ($/kWh) and on summer day, DOC was 10.59 ($/kWh). While without considering DSM, DOC on winter day was −25.42 ($/kWh) and on summer day DOC was 14.95 ($/kWh).

As opposed to previous research that predominantly addressed the long-term operation, the value of the proposed research is to investigate the short-term operation (24-hour) of MG that copes with vital contingencies associated with selling and procuring energy with the main grid considering the environmental cost. Outstandingly, the proposed research engaged the consumers by smart meters to apply demand-sideDSM, while the previous studies largely focused on supply side management.

This study establishes a method for predicting ground vibrations caused by railway tunnels in unsaturated soils with spatial variability.

First, an improved 2.5D finite-element-method-perfect-matching-layer (FEM-PML) model is proposed. The Galerkin method is used to derive the finite element expression in the ub-pl-pg format for unsaturated soil. Unlike the ub-v-w format, which has nine degrees of freedom per node, the ub-pl-pg format has only five degrees of freedom per node; this significantly enhances the calculation efficiency. The stretching function of the PML is adopted to handle the unlimited boundary domain. Additionally, the 2.5D FEM-PML model couples the tunnel, vehicle and track structures. Next, the spatial variability of the soil parameters is simulated by random fields using the Monte Carlo method. By incorporating random fields of soil parameters into the 2.5D FEM-PML model, the effect of soil spatial variability on ground vibrations is demonstrated using a case study.

The spatial variability of the soil parameters primarily affected the vibration acceleration amplitude but had a minor effect on its spatial distribution and attenuation over time. In addition, ground vibration acceleration was more affected by the spatial variability of the soil bulk modulus of compressibility than by that of saturation.

Using the 2.5D FEM-PML model in the ub-pl-pg format of unsaturated soil enhances the computational efficiency. On this basis, with the random fields established by Monte Carlo simulation, the model can calculate the reliability of soil dynamics, which was rarely considered by previous models.

This chapter provides an overview on the traditions and values of teaching students with traumatic brain injury (TBI). First, we discuss the prevalence, identification, and characteristics associated with TBI and how those characteristics affect learning, behavior, and daily life functioning. Next, we focus on instructional and behavioral interventions used in maintaining the traditions in classrooms for working with students with TBI. Findings from a review of the literature conclude that there are no specific academic curriculums designed specifically for teaching students with TBI; however, direct instruction and strategy instruction have been shown to be effective educational interventions. Current research on students with TBI is predominately being conducted in medical centers and clinics focusing on area of impairments (e.g., memory, attention, processing speed) rather than academic achievement and classroom interventions. Finally, we conclude with a list of accommodations and a discussion of recommendations for future work in teaching students with TBI.

What does special education mean for general education teachers of students with disabilities? In this chapter, we share our approach to advancing values in the classroom placement of special education students in the general education setting. We will take the reader on a journey through time with “Jessie,” a special education student, as we examine the historical exclusion of students with disabilities to their inclusion in general education schools, environments and finally, general education classrooms. In doing so, we will examine the evolution of the general education teacher's role and how the historical perspective impacts current practices. Then, we will elucidate the benefits of inclusion, not only for the special education student but for the nondisabled peers as well. We will recommend values that should be maintained and practices that should be examined. This chapter will conclude with a connection between the values and recommendations of best practices for inclusive instruction.