Search results

The purpose of this paper is to propose and test a conceptual framework of attitudinal constructs that influence attitude toward the brand in movie product placements. Advertising literature is replete with studies on factors that influence attitude toward the brand (A_b). However, this topic remains under-explored for product placements.

Our framework showcases several theories to relate attitude and fit constructs to attitudes toward the product placement and attitude toward the brand. We use the structural equation model approach to estimate the conceptual framework.

Several attitudinal movie constructs (attitude toward the actor, the character and the movie) influence attitude toward the product placement, which in turn mediates the relationship between the former attitudinal constructs and attitude toward the brand. Interestingly, only the fit between the actor and placed brand impacted attitude toward the product placement, with no effects found for the fit between the character and the fit between the movie and brand and the attitude toward the product placement.

We focus on explicit attitudes; implicit attitudes need future research attention.

Findings affirm a key role for the actor featured in the placement in directly or indirectly shaping the attitude toward the brand.

This is the first study to apply the structural equation modeling approach to this research area.

Bioprinting is a promising technology, which has gained a recent attention, for application in all aspects of human life and has specific advantages in different areas of medicines, especially in ophthalmology. The three-dimensional (3D) printing tools have been widely used in different applications, from surgical planning procedures to 3D models for certain highly delicate organs (such as: eye and heart). The purpose of this paper is to review the dedicated research efforts that so far have been made to highlight applications of 3D printing in the field of ophthalmology.

In this paper, the state-of-the-art review has been summarized for bioprinters, biomaterials and methodologies adopted to cure eye diseases. This paper starts with fundamental discussions and gradually leads toward the summary and future trends by covering almost all the research insights. For better understanding of the readers, various tables and figures have also been incorporated.

The usages of bioprinted surgical models have shown to be helpful in shortening the time of operation and decreasing the risk of donor, and hence, it could boost certain surgical effects. This demonstrates the wide use of bioprinting to design more precise biological research models for research in broader range of applications such as in generating blood vessels and cardiac tissue. Although bioprinting has not created a significant impact in ophthalmology, in recent times, these technologies could be helpful in treating several ocular disorders in the near future.

This review work emphasizes the understanding of 3D printing technologies, in the light of which these can be applied in ophthalmology to achieve successful treatment of eye diseases.

There are two main objectives of this paper. The first is to analyze household consumption pattern of soyfood products. The second is to investigate effect of the United States Food and Drug Administration (FDA) allowed health claims on consumption of soyfoods.

The objectives were accomplished in two stages. In the first stage, the role of consumers' perceived attributes of soy‐based foods such as convenience of preparation and consumption, health benefits, and taste in consumers' decisions to consume soy‐based food products was investigated. In the second stage, the study analyzed whether the decision of the Food and Drug Administration to allow food manufacturers to use health claims had influenced consumers' willingness to participate in soy‐based food market or willingness to increase consumption, if they are currently consuming such foods. Lancaster's characteristics model was combined with Fishbein's multi‐attribute model to develop a soybean demand function that included perceived attributes of soyfood. Zero‐inflated negative binomial model (ZINB) was used as an empirical specification to address zero consumption of soyfood products. Data were collected using a convenience sample drawn from a Midwest college town in the United States. Two questionnaires (i.e. one with information about the FDA's decision and the other without it) were given to students taking introductory marketing courses. In total 400 questionnaires were distributed and 315 respondents returned completed questionnaires.

Attributes of soy‐based food products such as convenience and tastefulness had statistically significant effect on the consumption pattern. Consumers who perceived beneficial health attributes in soyfood products were more likely to participate in the soyfood market as well as increase consumption frequency. The results indicated that frequent users of soyfood products who were exposed to the decision of the FDA would be more inclined to increase their consumption of soy‐based foods as compared to those who were not exposed to such information. Yet the information about FDA's decision did not influence the behavioral intentions of infrequent consumers or non‐consumers.

Research evaluating the impact of government allowed health claims on food consumption pattern is scarce. This paper sets up a platform for carrying out the evaluation of such health claims by other food products.

Aluminium metal matrix composites are used in automotive and aerospace industries because of their high performance and weight reduction benefits. The current investigation aims to focus on the development of the stir cast aluminium-boron carbide composites with enhanced mechanical and tribological properties.

The aluminium-boron carbide composites are produced by stir casting process. Aluminium alloy A356 is chosen as the matrix material and three sets of composites are produced with different weight fractions of boron carbide particles. Higher particle size (63 µm) of boron carbide is chosen as the reinforcement material. Aluminium-boron carbide composites are tested for mechanical and tribological properties. The effect of process parameters like load, speed and percentage of reinforcement on the wear rate are studied using the pin-on-disc method. The interaction of the process parameters with the wear rate is analysed by DesignExpert software using RSM methodology and desirability analysis. The coded levels for parameters for independent variables used in the experimental design are arranged according to the central composite design. The worn surface of the pin is examined using a scanning electron microscope. The phases and reaction products of the composites are identified by X-ray diffraction (XRD) analysis.

Aluminium-boron carbide composites reveal better mechanical properties compared to monolithic aluminium alloys. Mechanical properties improved with the addition of strontium-based master alloy Al10Sr. The ultimate tensile strength, hardness and compressive strength increase with an increase in the reinforcement content. The wettability of the boron carbide particles in the matrix improved with the addition of potassium flurotitanate to the melt. Uniform dispersion of particles into the alloy during melting is facilitated by the addition of magnesium. Wear resistance is optimal at 8 per cent of boron carbide with a load 20 N and sliding speed of 348 RPM. The wear rate is optimized by the numerical optimization method using desirability analysis. The amount of wear is less in Al-B₄C composites when compared to unreinforced aluminium alloy. The wear rate increases with an increase in load and decreases with the sliding speed. The wear resistance increases with an increase in the weight fraction of the boron carbide particles.

The produced Al-B₄C composites can retain properties at high temperature. It is used in nuclear and automotive products owing its high specific strength and stiffness. The main applications are neutron absorbers, armour plates, high-performance bicycles, brake pads, sand blasting nozzles and pump seals.

Al/B₄C composites have good potential in the development of wear-resistant products.

The purpose of this paper is to investigate the prominence of mechanical excitations at the time of welding. In the past years, the process of welding technology has expanded its influence in manufacturing. The crucial drawback of conventional welding is prompted by internal stresses and distortions, which is the focal reason for weld defects. These weld defects can be diminished by the process called post-weld heat treatment (PWHT), which consumes more working hours and needs skilled workers. To replace these PWHT processes, mechanical vibrations are introduced during the process of welding to diminish these weld defects.

In the current research, the mechanical vibrations are transferred to weld-pool through vibro-motor and DC motor connected to the electrode. As per standards, the tensile test specimens were prepared for welding with different voltages of vibro-motor and DC motor respectively. The weld joints were tested for tensile strength and analyzed the microstructure at the fusion zone.

Melt-ability at fusion zone of 1018 mild steel was investigated by the single-stroke intense heat process of fusion welding. It is observed that the mechanical vibrations technique has a profound influence on the enhancement of the fusion zone characteristics and grain structure. The peak value of the tensile strength is observed at 100 s of vibration, 190 V of vibro-motor voltage and 18 V of electrode voltage. The tensile strength of the welded joints with vibrations is increased up to 22.64% when it is compared with conventional welding. The enhancement of the tensile strength of the weld bead was obtained because of the formation of fine grain structure. So, mechanical vibrations are identified as the most convenient method for improving the mild steel alloys weld quality.

A novel approach called mechanical vibrations during the process of welding is implemented for fusion zone refinement.

Knowledge management (KM) significantly affects supply chain management (SCM) and its performance in today's highly competitive corporate climate. It is crucial to consider this relationship to achieve optimal supply chain performance (SCP). This study aims to assess this impact by defining and examining the multi-dimensional relationships between KM Process Elements (KMPEs) and SCP Evaluation Criteria (SCPEC) within a comprehensive theoretical framework.

Integrating KMPEs and SCPEC becomes an uncertain decision-making problem due to data deficiency and the vagueness of decision-makers’ judgments. To address uncertainties, this study uses interval-valued neutrosophic (IVN) sets and proposes an IVN model consisting of SWARA, which is one of the effective multi-criteria decision-making (MCDM) approaches, and house of quality (HOQ) methods. IVN-SWARA is used to weight the SCPEC while IVN-HOQ establishes relationships and prioritizes the KMPEs and SCPEC.

The results show that reliability is the most significant SCP evaluation criterion. Among the KMPEs, capitalization, sharing, and transfer exhibit stronger associations with the SCPEC compared to the other elements. Capitalization as one of the KMPEs was found to be the most critical one, and efficiency is the criterion most affected by all elements of the KM process.

This study uses innovative methodologies to evaluate the adoption of KM processes on SCP under uncertain environments and involving multi-decision-makers. The proposed integrated model demonstrates flexibility and practicality in combining KM and SCM, leading to improved SCP. Notably, this study presents the development of IVN-SWARA and the use of the integrated IVN-SWARA - IVN-HOQ decision tool, which are novel contributions to the existing literature.

The poor leadership style is a key obstacle to the effective implementation of Industry 4.0 technologies. To successfully apply the Industry 4.0 technologies, which can enhance the sustainability of firms, senior management needs to be inspiring and transformational. On the other hand, numerous factors can hinder the Industry 4.0 transition and “Circular Supply Chain (CSC)” transformation. Therefore, the main purpose of this study is to evaluate the related barriers of CSCs in the era of Industry 4.0 transition.

The current study developed an innovative decision-making approach with the help of the “Combined Compromise Solution (CoCoSo)” method and “Criteria Importance Through Intercriteria Correlation (CRITIC)” method on the “q-Rung Orthopair Fuzzy Sets (q-ROFSs).” CRITIC in this combined method was used to predict the importance or weighting degrees of the CSCs barriers in the age of Industry 4.0 transition.

The results of this study found that the absence of knowledge about the Industry 4.0 technologies and circular approaches was the first barrier followed by the problems associated with data security in relationship management in circular flows, the deficiency of knowledge regarding the data management among stakeholders and the lack of awareness about the potential benefits of autonomous systems in labor-oriented “End-of-Life (EOL)” activities for CSCs in the era of Industry 4.0 transition.

A limitation may be that despite the generalizability of the proposed framework, the results may differ when it is implemented in different sectors. By emphasizing the obstacles to sustainable operations of supply chains (SCs) in the context of circular economy (CE) and Industry 4.0, researchers working in the same domain may be encouraged to find ways to remove such obstacles in different settings. As suggested in this study, the priority of various barriers helps researchers suggest effective strategies for the sustainable development of companies within the current dynamic business atmosphere.

The findings of this paper can aid industry practitioners in fixing their attention on the digitization or automation of their systems in the context of sustainability or resource circularity. Note that within the current context of CE, one of the crucial issues is how to conserve the existing resources; the answer to this question can save the environment.

The current paper proposed a new multi-criteria decision-making method using q-ROFSs to analyze, rank and evaluate the CSC barriers in the age of Industry 4.0 transition. To this end, a new decision-making approach with the help of CRITIC and CoCoSo methods on q-ROFSs called q-ROF-CRITIC-CoCoSo was introduced to evaluate the CSCs barriers in the era of Industry 4.0 transition.

The spread of broadband Internet has resulted in the increase of spyware and adware. This study highlights their damaging effects and proposes a model that captures defensive measures adopted by Internet users. Specifically, our model indicates that knowledge has a positive impact on self‐efficacy that, in turn, is presumed to trigger technical defensive measures. Moreover, concerns for privacy and previous experience with spyware and adware are likely to evoke both tactical and technical defense measures. Data collected by the PEW Internet and American Life research project are utilized to test the proposed model and the findings are discussed.

Due to stern management policies and increased community attentiveness, sustainable supply chain management (SSCM) performs a vast component in endeavor operation and production management. Sustainable circular supplier selection (SCSS) and evaluation presented the environmental and social concerns in the fields of circular economy and sustainable supplier selection. Choosing the optimal SCSS is vital for organizations to persuade SSCM, as specified in various researches. Based on the subjectivity of human behavior, the selection of ideal SCSS often involves uncertain information, and the Pythagorean fuzzy sets (PFSs) have a huge capability to tackle strong vagueness, uncertainty and inaccuracy in the multi-criteria decision-making (MCDM) procedure. Here, a framework is developed to assess and establish suitable suppliers in the SSCM and the circular economy.

This paper introduced an extended framework using the evaluation based on distance from average solution (EDAS) with PFSs and implemented it to solve the SCSS in the manufacturing sector. Firstly, the PFSs to handle the uncertain information of decision experts (DEs) is employed. Secondly, a novel divergence measure and parametric score function for calculating the criteria weights are proposed. Thirdly, an extended decision-making approach, known as PF-EDAS, is introduced.

The outcomes and comparative discussion show that the developed method is efficient and capable of facilitating the DEs to choose desirable SCSS. Therefore, the proposed framework can be used by organizations to assess and establish suitable suppliers in the SCSS process in the circular economy.

Selecting the optimal sustainable circular supplier (SCS) in the manufacturing sector is important for organizations to persuade SSCM, as specified in various research. However, corresponding to the subjectivity of human behavior, the selection of the best SCS often involves uncertain information, and the PFSs have a huge capability to tackle strong vagueness, uncertainty and inaccuracy in the MCDM procedure. Hence, manufacturing companies' administrators can implement the developed method to assess and establish suitable suppliers in the SCSS process in the circular economy.

The study aims at evaluating the most appropriate transport project which is one of the critical concerns of transport infrastructure scheduling. This process will be applied considering a set of criteria and discussed alternatives with sustainable perspectives.

In this paper, a complex proportional assessment (COPRAS) framework is discussed to handle the sustainable transport investment project (STIP) assessment problem within a single-valued neutrosophic set (SVNSs). To form the procedure more useful in handling with uncertain features, a SVNS is applied as a valuable procedure to handle uncertainty. First, a new discrimination measure for SVNSs is introduced and discussed some elegant properties to determine the significance degree or weight values of criteria with the sustainabality perspectives. Second, an integrated approach is introduced based on the discrimination measure and the COPRAS method on SVNSs and named as SVN-COPRAS.

A case study of an STIP evaluation problem is used to confirm the practicality and effectiveness of the SVN-COPRAS framework. Lastly, comparative discussion and sensitivity investigation are illustrated to prove the strength and solidity of the proposed framework.

The SVNSs enrich the essence of linguistic information when a decision expert (DE) vacillates among different linguistic values (LVs) to measure a sustainable transport project alternative problem. The utilization of SVNSs provides a more stable procedure to describe DEs' evaluations. So, an elegant methodology is developed to incorporate the DEs' awareness and experience for electing the desired STIPs. The introduced methodology has higher operability than the single-valued neutrosophic set technique for order preference by similarity to an ideal solution (SVN-TOPSIS) procedure during the larger numbers of attribute(s) or option(s). For the SVN-COPRAS methodology, there is no need to estimate the single-valued neutrosophic ideal solution (SVN-IS) and single-valued neutrosophic anti-ideal solution (SVNA-IS). The outcomes are calculated with handling the realistic data, which elucidates that the introduced model can tackle more intricate and realistic multi-criteria decision-making issues.