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The purpose of this study is to analyse user-generated content and firm-generated content on perceived quality and brand trust, and eventually how it impacted brand loyalty with pandemic fear as the moderator.

This study employed an online survey questionnaire method in the Facebook online shopping groups to collect the data. The filter question technique was adopted to verify the respondent's validity. A total of 434 samples was collected using purposive sampling. The data were then analysed using SmartPLS 3.0.

The analysis showed that firm-generated content appeared to have a stronger positive relationship on perceived quality and brand trust than on user-generated content. Brand trust and perceived quality are found to influence brand loyalty positively. However, pandemic fears only moderate the relationship between firm-generated content and brand trust and perceived quality. This study revealed that the main components in social media communication do not influence perceived quality and brand trust to the same extent.

This study contributes to the knowledge of social media communication during the pandemic period that has not been studied empirically in the Malaysian context. The main components in social media communication were delineated and the impact of pandemic fears on how they would possibly affect the established relationships in the literature were examined. This study enables the researchers and practitioners to take a closer look at how the pandemic crisis could provide a shift on what has been understood so far.

The primary objective of this article is to examine current procurement, inventory control and management practices in modern healthcare, with a particular focus on the procurement and management of surgical supplies in a prominent public, highly specialized healthcare sector.

This study was conducted in three phases. In Phase 1, the study team interacted with various hospital management stakeholders, including the surgical hospital store, examined the current procurement process and identified challenges. Phase 2 focused on selecting items for a detailed study and collected the qualitative and quantitative details of the store department of the healthcare sector chosen. A detailed study analyzed revenue, output/demand, inventory levels, etc. In Phase 3, a decision-making framework is proposed, and inventory control systems are redesigned and demonstrated for the selected items.

It was observed that the demand for many surgical items had increased significantly over the years due to an increase in disposable/disposable items, while inventories fluctuated widely. Maximum inventory levels varied between 50 and 75%. Storage and availability were important issues for the hospital. It is assumed the hospital adopts the proposed inventory control system. In this case, the benefits can be a saving of 62% of the maximum inventory, 20% of the average stock in the system and optimal use of storage space, improving the performance and productivity of the hospital.

This study can help the healthcare sector administration to develop better systems for the procurement and delivery of common surgical items and efficient resource allocation. It can help provide adequate training to store staff. This study can help improve management/procurement policies, ordering and delivery systems, better service levels, and inventory control of items in the hospital business context. This study can serve as a pilot study to further investigate the overall hospital operations.

This study can help the healthcare sector administration develop better systems for procuring and delivering common surgical items and efficient resource allocation. It can help provide adequate training to store staff. This study can help improve management/procurement policies, ordering and delivery systems, better service levels and inventory control of items in the hospital business context. This study can serve as a pilot study to further investigate the overall hospital operations.

This study is an early attempt to develop a decision framework and inventory control system from the perspective of healthcare inventory management. The gaps identified in real hospital scenarios are investigated, and theoretically based-inventory management strategies are applied and proposed.

The purpose of this paper is to assess the application of graph coloring and domination to solve the airline-scheduling problem. Graph coloring and domination in graphs have plenty of applications in computer, communication, biological, social, air traffic flow network and airline scheduling.

The process of merging the concept of graph node coloring and domination is called the dominator coloring or the χ_d coloring of a graph, which is defined as a proper coloring of nodes in which each node of the graph dominates all nodes of at least one-color class.

The smallest number of colors used in dominator coloring of a graph is called the dominator coloring number of the graph. The dominator coloring of line graph, central graph, middle graph and total graph of some generalized Petersen graph P_(n ,1) is obtained and the relation between them is established.

The dominator coloring number of certain graph is obtained and the association between the dominator coloring number and domination number of it is established in this paper.

Agriculture is the backbone of a country, contributing more than half of the sector of economy throughout the world. The need for precision agriculture is essential in evaluating the conditions of the crops with the aim of determining the proper selection of pesticides. The conventional method of pest detection fails to be stable and provides limited accuracy in the prediction. This paper aims to propose an automatic pest detection module for the accurate detection of pests using the hybrid optimization controlled deep learning model.

The paper proposes an advanced pest detection strategy based on deep learning strategy through wireless sensor network (WSN) in the agricultural fields. Initially, the WSN consisting of number of nodes and a sink are clustered as number of clusters. Each cluster comprises a cluster head (CH) and a number of nodes, where the CH involves in the transfer of data to the sink node of the WSN and the CH is selected using the fractional ant bee colony optimization (FABC) algorithm. The routing process is executed using the protruder optimization algorithm that helps in the transfer of image data to the sink node through the optimal CH. The sink node acts as the data aggregator and the collection of image data thus obtained acts as the input database to be processed to find the type of pest in the agricultural field. The image data is pre-processed to remove the artifacts present in the image and the pre-processed image is then subjected to feature extraction process, through which the significant local directional pattern, local binary pattern, local optimal-oriented pattern (LOOP) and local ternary pattern (LTP) features are extracted. The extracted features are then fed to the deep-convolutional neural network (CNN) in such a way to detect the type of pests in the agricultural field. The weights of the deep-CNN are tuned optimally using the proposed MFGHO optimization algorithm that is developed with the combined characteristics of navigating search agents and the swarming search agents.

The analysis using insect identification from habitus image Database based on the performance metrics, such as accuracy, specificity and sensitivity, reveals the effectiveness of the proposed MFGHO-based deep-CNN in detecting the pests in crops. The analysis proves that the proposed classifier using the FABC+protruder optimization-based data aggregation strategy obtains an accuracy of 94.3482%, sensitivity of 93.3247% and the specificity of 94.5263%, which is high as compared to the existing methods.

The proposed MFGHO optimization-based deep-CNN is used for the detection of pest in the crop fields to ensure the better selection of proper cost-effective pesticides for the crop fields in such a way to increase the production. The proposed MFGHO algorithm is developed with the integrated characteristic features of navigating search agents and the swarming search agents in such a way to facilitate the optimal tuning of the hyperparameters in the deep-CNN classifier for the detection of pests in the crop fields.

This paper aims to guide about prioritisation and ranking of the solutions and overcoming barriers to facilitate the adoption of Lean Six Sigma (LSS) by using a hybrid framework.

It identifies LSS barriers and solutions to facilitate LSS adoption through literature review and by involving subject experts. The study makes use of fuzzy set theory and proposes a fuzzy analytical hierarchy process (AHP)-modified TOPSIS (technique for order preference by similarity to ideal solution) framework. It uses sensitivity analysis to establish framework robustness.

The key findings of this techno-managerial study are identification and prioritisation of 27 LSS barriers and 22 solutions to overcome adoption challenges, proposition and usage of fuzzy AHP-modified TOPSIS framework, guidance regarding where to focus for facilitating LSS adoption and ensuring robustness using sensitivity analysis, which establishes insignificant deviation in rankings when criteria weights are altered.

Some biasness and subjectivity may exist during pairwise comparisons as human judgements are involved.

Handling a hybrid solution like LSS is never easy. It is expected that the study will help industry professionals to plan their LSS adoption attempts effectively. Guidance regarding LSS barriers will assist in observing necessary precautions to avoid failures. It will open up new research fronts for researchers also.

Literature is full of studies regarding LSS barriers and its rankings. It is very rare to witness a study like ours, which discusses the barriers and links with solutions and its prioritisation. Proposed hybrid framework for a hybrid techno-managerial approach such as LSS is unique and acts as the roadmap for smooth implementation.

The purpose of this paper is to investigate effect of material properties in 3D knitted fabrics on thermo-physiological comfort.

In the present study six different spacer fabrics were developed. Among these six fabrics, it was classified into two groups for convenient analysis of results, the first group has been developed using polyester/polypropylene blend with three different proportion and second group with polyester/polypropylene/lycra blend having another three different composition. As a spacer yarn, three different types of 88 dtex polyester monofilament yarn and polyester multifilament yarns (167 dtex and 14.5 tex) were used and 14.5 tex polypropylene and 44 dtex lycra multifilament yarns were also used for the face and back side of the spacer fabrics (Table I). These fabrics were developed in Syntax Pvt Ltd Czech Republic.

The main influence on the water vapour permeability of warp knitted spacer fabrics is the kind of raw material, i.e. fibre wetting and wicking. Also there is no correlation between air permeability and water vapour permeability. It is found that both air permeability and thermal conductivity are closely related to the fabric density. It is also found that the fabric characteristics of spacer fabric show a very significant effect on the air permeability, thermal conductivity and mechanical properties of spacer fabric. Therefore, selection of spacer fabric for winter clothing according to its fabric characteristics.

The main objective of the present study is to produce spacer knitted 3D fabrics suitable for defined climatic conditions to be used as clothing or in sports goods.

New 3D knitted spacer fabrics can be produced with improved comfort properties.

The purpose of this paper is to provide an initial empirical insight into the mediating role of quality performance on the association between six specific organisational factors (top management support, employee involvement, employee empowerment, reward and recognition, training and customer focus) and competitive advantage.

A survey questionnaire was used to collect data from 179 garment organisations in a developing country, Bangladesh.

The results show that quality performance mediates the association between three specific organisational factors (top management support, employee involvement and reward and recognition) and competitive advantage. In addition, a fourth organisational factor, employee empowerment, exhibited a direct association with competitive advantage.

The findings provide managers of developing economies with an important insight into the critical role of quality in achieving competitive advantage. In line with the resources-based view, the findings suggest that managers should deploy their limited resources to focus on providing top management support for, encourage employee involvement in and provide adequate rewards and recognition in respect to quality initiatives.

This paper aims to evaluate the effects of climate variables on the mean yield and yield variability of major pulse crops in the Telangana state of India.

Authors have estimated the Just and Pope (1978, 1979) production function using panel data at the district level of four major pulses in nine former districts of Telangana for 36 years during 1980–2015. A three-stage feasible generalized least squares estimation procedure has been followed. The mean yield and yield variance functions have been estimated individually for each of these study crops, namely, Bengal gram, green gram, red gram and horse gram.

Results have shown that changes in climatic factors such as rainfall and temperature have significant influences on the mean yield levels and yield variance of pulses. The maximum temperature is observed to have a significant adverse impact on the mean yield of a majority of pulses, and it is also a risk-enhancing factor for a majority of pulses except horse gram. However, the minimum temperature is positively related to the mean yields of the study crops except for Bengal gram, and it is having a risk-reducing impact for a majority of study crops. Rainfall is observed to have a negative impact on the mean yields of all pulses, but it is a risk-enhancing factor for only one crop, i.e. Bengal gram. Thus, rising temperatures and excess rainfall are not favorable to the productivity of pulses in study districts.

The present study is based on the secondary data at the district level and is considering only one state. Season-wise primary data, including farm-specific characteristics, could have been better. The projected climate change and its impact on the mean yields and yield variance of pulses need to be considered in a future study.

According to the best of our knowledge, this is the first study to empirically evaluate the impact of climatic variables on the mean yields and yield variability of major pulses in Telangana using a panel data for major pulses and nine districts of 36 years time-series during 1980–2015. The study has given useful policy recommendations.

Health-care delivery organizations (hospitals) constitute a complex adaptive system; hence, a contingency perspective is imperative to guide the design of customized approaches to quality management in different health-care settings. Accordingly, this paper aims to propose a contingency framework to advance the understanding of the relationship between situational factors and effectiveness of quality approaches in health-care organizations (HCOs), such as hospitals in India.

Related literature was reviewed to identify existing research and theories related to quality and quality approaches, situational factors of the HCOs (hospitals) and some existing logical evidence on public and private hospitals in India. Then a contingencies framework for quality and quality approaches was conceptualized.

This paper proposes contingent determinants arise out of conceptualization of the HCOs (hospitals) from different system perspective such as rational system, natural system, open system and integrative system; uncertainty because of physicians’ behaviour, nurses’ approach and a dual line of authority; and the task environment such as patients, competition and economic pressure. These determinants represent situational constructs to the quality enhancement of any attempt at quality approaches. While these determinants have an influence on the quality and quality approaches of the HCOs (hospital), it is imperative to build any quality improvement strategy to work effectively, i.e., quality approach is dependent on determinants of the contingencies of the hospital’s environment, be it external or internal. Propositions for future research are also incorporated.

This paper proposes a conceptual model as well as research propositions that need to be validated and confirmed empirically. It advances the research and theory related to quality and quality approaches in a health-care setting. It can enable policymakers, hospital managers to analyze and gauge the appropriateness of quality approaches in a given context before implementing them and could help to improve the introverted quality approaches and quality dimensions currently followed in HCOs (hospitals).

Contingency framework is a new approach for research on the effectiveness of quality approaches in hospitals. The fundamental idea behind this framework is that effectiveness of quality approaches can be understood best by examining its contingent determinants. Thus, it has the capacity to contribute to the efforts of government and policymakers to make the quality of care affordable to all in India. Essentially, we examine the contexts and variables that determine the effectiveness of quality approaches.

In this article, a novel hybrid composite patch consisting of unidirectional carbon fiber and glass fiber is considered for repair of the aircraft structure. The purpose of this paper is to assess the performance of hybrid composite patch repair of cracked structure and propose an optimized solution to a designer for selection of the appropriate level of a parameter to ensure effective repair solution.

Elastic properties of the hybrid composites are estimated by micromechanical modeling. Performance of hybrid composite patch repair is evaluated by numerical analysis of stress intensity factor (SIF), shear stress, and peel stress. Design of experiment is used to determine responses for a different combination of design parameters. The second-order mathematical model is suggested for SIF and peel stress. Adequacy of the model is checked by ANOVA and used as a fitness function. Multiobjective optimization is carried out with a genetic algorithm to arrive at the optimal solution.

The hybrid composite patch has maintained equilibrium between the SIF reduction and rise of the peel stress. The repair efficiency and repair durability can be ensured by selection of an optimum value of volume fraction of glass fiber, applied stress, and adhesive thickness.

The composite patch with varying stiffness is realized by hybridization with different volume fraction of fibers. Analysis and identification of optimum parameter to reduce the SIF and peel stress for hybrid composite patch repair are presented in this article.