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Owing to the significance of a healthy lifestyle, we investigate the antecedents of the healthy lifestyle of young consumers in Sri Lanka. 658 structured questionnaires were collected from young consumers in Sri Lanka as part of the survey procedure. The judgmental sampling method is used to choose the respondents. The analysis makes use of both descriptive and inferential statistics. The findings disclose a high degree of healthy lifestyle among young consumers in Sri Lanka. Further findings revealed that health consciousness, collective esteem, and neighborhood environment are the antecedents for a healthy lifestyle. As young consumers are more concerned about a healthy lifestyle, managers in certain industries such as food and beverages, hotels, and restaurants should adopt their products and services in line with a healthy lifestyle.

This study aims to examine the factors influencing consumers’ intentions to use QR code menus in the post-COVID-19 pandemic using the unified theory of acceptance and use of technology and rational choice theory as the theoretical foundations.

Using a survey method, 200 data are collected from consumers who had used QR code menus in the past. Partial least squares structural equation modelling is used to analyse the data.

Our findings show that performance expectancy, effort expectancy, social influence, habit and perceived privacy protection have a significant positive influence on intentions. However, facilitating conditions, hedonic motivation and perceived privacy risk do not have a significant influence on intentions.

This study further extends the work of previous studies by using the unified theory of acceptance and use of technology model, with additional two new predictors, namely perceived privacy protection and perceived privacy risk, to understand consumers’ intentions to use QR code menus.

The goal of this study was to determine the impact of access to credit facilities on financial performance among farmers of cooperative societies. The study also tested the predictive power of financial literacy.

The descriptive survey research design was used for the study while the sample size was 240 farmers of cooperative societies from South-East Nigeria. The farmers were categorised into those with access to credit facilities and those without access to credit facilities. A structured questionnaire was used to collect data for the study. Data were analysed using multiple analyses of variance (MANOVA) and multiple regression analysis.

Farmers with access to credit facilities reported higher financial performance such as return on investment, working capital, net profit, profit margin and sales. However, those without access to credit facilities reported lower mean scores on financial performance. Also, financial literacy, like financial knowledge, attitude and awareness, significantly predicts the impact of access to credit facilities on financial performance. It was also found that the duration of repayment of credit facilities, like medium and long term, contributes more to improving financial performance.

This study has shown that even though access to credit facilities impacts financial performance, financial literacy is an important consideration. Also, the duration of repayment is a crucial factor.

This study aims to explain the relationship between employer branding, social media, online reviews and intention to apply for a job vacancy (IAJV), which organizations should ponder upon while designing branding campaigns.

The sample belongs to 385 final-year management graduates and postgraduates enrolled in central universities in the state of Uttar Pradesh, India. The dual mediation model is tested by regression and PROCESS macro.

Out of five employer branding dimensions, three (corporate social responsibility, healthy work atmosphere and training and development) were found to be significant predictors of IAJV. On the other hand, the dimensions of compensation and benefits and work-life balance did not influence candidates’ intention to apply for a job. The findings indicate that social recruiting could act as an effective tool for leveraging an organization’s image as an employer and could communicate unique brand values to the target market. Moreover, review whether positive, negative or neutral attributes could help job seekers affirm and reaffirm employer branding attributes before applying for a job.

Studies in social media and employer branding areas lag far behind in practice, and the present research attempts to fill this research gap. A further contribution of this research work will be to assess the role of reviews for a meaningful analysis of potential employees’ intentions to apply in an organization.

Polymer-based thermal interface materials (TIMs) are having pump out problem and could be resolved for reliable application. Solid-based interface materials have been suggested and reported. The purpose of this paper is suggesting thin film-based TIM to sustain the light-emiting diode (LED) performance and electronic device miniaturization.

Consequently, ZnO thin film at various thicknesses was prepared by chemical vapour deposition (CVD) method and tested their thermal behaviour using thermal transient analysis as solid TIM for high-power LED.

Low value in total thermal resistance (R_th-tot) was observed for ZnO thin film boundary condition than bare Al boundary condition. The measured interface (ZnO thin film) resistance {(R_th-bhs) thermal resistance of the interface layer (thin film) placed between metal core printed circuit board (MCPCB) board and Al substrates} was nearly equal to Ag paste boundary condition and showed low values for ZnO film prepared at 30 min process time measured at 700 mA. The T_J value of LED mounted on ZnO thin film (prepared at 30 min.) coated Al substrates was measured to be 74.8°C. High value in junction temperature difference (ΔT_J) of about 4.7°C was noticed with 30 min processed ZnO thin film when compared with Al boundary condition. Low correlated colour temperature and high luminous flux values of tested LED were also observed with ZnO thin film boundary condition (processed at 30 min) compared with both Al substrate and Ag paste boundary condition.

Overall, 30 min CVD processed ZnO thin film would be an alternative for commercial TIM to achieve efficient thermal management. This will increase the life span of the LED as the proposed material decreases the T_J values.

Light emitting diode (LED) has been the best resource for commercial and industrial lighting applications. However, thermal management in high power LEDs is a major challenge in which the thermal resistance (R_th) and rise in junction temperature (T_J) are critical parameters. The purpose of this work is to evaluate the R_th and T_j of the LED attached with the modified heat transfer area of the heatsink to improve thermal management.

This paper deals with the design of metal substrate for heatsink applications where the surface area of the heatsink is modified. Numerical simulation on heat distribution proved the influence of the design aspects and surface area of heatsink.

T_J was low for outward step design when compared to flat heatsink design (ΔT ∼ 38°C) because of increase in surface area from 1,550 mm² (flat) to 3,076 mm² (outward step). On comparison with inward step geometry, the T_J value was low for outward step configuration (ΔT_J ∼ 6.6°C), which is because of efficient heat transfer mechanism with outward step design. The observed results showed that outward step design performs well for LED testing by reducing both R_th and T_J for different driving currents.

This work is authors’ own design and also has the originality for the targeted application. To the best of the authors’ knowledge, the proposed design has not been tried before in the electronic or LED applications.

This study investigates the impact of three parameters such as: number of LED chips, pitch and LED power on the junction temperature of LEDs using a best heat sink configuration selected according to a lower temperature. This study provides valuable insights into how to design LED arrays with lower junction temperatures.

To determine the best configuration of a heat sink, a numerical study was conducted in Comsol Multiphysics on 10 different configurations. The configuration with the lowest junction temperature was selected for further analysis. The number of LED chips, pitch and LED power were then varied to determine the optimal configuration for this heat sink. A general equation for the average LED temperature as a function of these three factors was derived using Minitab software.

Among 10 configurations of the rectangular heat sink, we deduce that the best configuration corresponds to the first design having 1 mm of width, 0.5 mm of height and 45 mm of length. The average temperature for this design is 50.5 C. For the power of LED equal to 50 W–200 W, the average temperature of this LED drops when the number of LED chips reduces and the pitch size decreases. Indeed, the best array-LED corresponds to 64 LED chips and a pitch size of 0.5 mm. In addition, a generalization equation for average temperature is determined as a function of the number of LED chips, pitch and power of LED which are key factors for reducing the Junction temperature.

The study is original in its focus on three factors that have not been studied together in previous research. A numerical simulation method is used to investigate the impact of the three factors, which is more accurate and reliable than experimental methods. The study considers a wide range of values for the three factors, which allows for a more comprehensive understanding of their impact. It derives a general equation for the average temperature of the LED, which can be used to design LED arrays with desired junction temperatures.

In a conventional sense, information technology has frequently been considered a source of Lean waste management. However, as the corporate world evolves, new models that provide a competitive edge by merging technical breakthroughs with the Lean paradigm must be developed. Enterprise resource planning (ERP), which is such technological advancement, is found to be highly influential for Lean implementation. However, there is a dearth of literature on the adaptability of ERP to minimise Lean waste in the construction industry. This paper, therefore, aims to investigate the possibility of applying ERP to minimise Lean waste in the construction industry.

The study used a qualitative approach, consisting of fifteen (15) expert interviews and code-based content analysis was used to analyse the empirical data.

The findings revealed the challenges faced when applying ERP with the Lean concept and the strategies that would help overcome the challenges. Most of the challenges could be overcome through training and awareness programmes and proper team management. The study also found that ERP could be applied with Lean to eliminate waste generation in the construction industry.

This paper contributes to the theory by providing an evaluation of the possibility of adopting ERP to eliminate Lean waste in the construction industry. The study will contribute to new knowledge related to strategies for proper use of ERP for Lean waste minimisation, which will be useful for future researchers in the area.

Web users rely heavily on online content make decisions without assessing the veracity of the content. The online content comprising text, image, video or audio may be tampered with to influence public opinion. Since the consumers of online information (misinformation) tend to trust the content when the image(s) supplement the text, image manipulation software is increasingly being used to forge the images. To address the crucial problem of image manipulation, this study focusses on developing a deep-learning-based image forgery detection framework.

The proposed deep-learning-based framework aims to detect images forged using copy-move and splicing techniques. The image transformation technique aids the identification of relevant features for the network to train effectively. After that, the pre-trained customized convolutional neural network is used to train on the public benchmark datasets, and the performance is evaluated on the test dataset using various parameters.

The comparative analysis of image transformation techniques and experiments conducted on benchmark datasets from a variety of socio-cultural domains establishes the effectiveness and viability of the proposed framework. These findings affirm the potential applicability of proposed framework in real-time image forgery detection.

This study bears implications for several important aspects of research on image forgery detection. First this research adds to recent discussion on feature extraction and learning for image forgery detection. While prior research on image forgery detection, hand-crafted the features, the proposed solution contributes to stream of literature that automatically learns the features and classify the images. Second, this research contributes to ongoing effort in curtailing the spread of misinformation using images. The extant literature on spread of misinformation has prominently focussed on textual data shared over social media platforms. The study addresses the call for greater emphasis on the development of robust image transformation techniques.

This study carries important practical implications for various domains such as forensic sciences, media and journalism where image data is increasingly being used to make inferences. The integration of image forgery detection tools can be helpful in determining the credibility of the article or post before it is shared over the Internet. The content shared over the Internet by the users has become an important component of news reporting. The framework proposed in this paper can be further extended and trained on more annotated real-world data so as to function as a tool for fact-checkers.

In the current scenario wherein most of the image forgery detection studies attempt to assess whether the image is real or forged in an offline mode, it is crucial to identify any trending or potential forged image as early as possible. By learning from historical data, the proposed framework can aid in early prediction of forged images to detect the newly emerging forged images even before they occur. In summary, the proposed framework has a potential to mitigate physical spreading and psychological impact of forged images on social media.

This study focusses on copy-move and splicing techniques while integrating transfer learning concepts to classify forged images with high accuracy. The synergistic use of hitherto little explored image transformation techniques and customized convolutional neural network helps design a robust image forgery detection framework. Experiments and findings establish that the proposed framework accurately classifies forged images, thus mitigating the negative socio-cultural spread of misinformation.

This paper aims to examine how interfirm transactional and relational assets drive firm performance (FP) in digitally integrated supply chains.

The authors combine the Transaction Cost Economics (TCE) and Relational Exchange Theory (RET) frameworks to hypothesize that FP will be a function of Asset Specificity (AS), Digital Technology Usage (DTU) and Collaborative Information Sharing (CIS). In addition, the authors hypothesize that Supply Chain Integration (SCI) will partially mediate the effect of DTU and fully mediate the impact of AS and CIS on FP. A cross-sectional survey of supply chain managers is used to test the hypotheses.

Findings indicate that specific investments in digitally integrated supply chains would increase FP. In addition, SCI fully mediates the relationships between AS and FP and CIS and FP, while SCI partially mediates the influence of DTU on FP.

Managers could strategically engage in the technologies that effectively fit within the firm’s supply chain strategies and seek to develop a pragmatic expertise that enables the effective use of technology in a comprehensive setting.

The study enriches the extant literature by incorporating TCE and RET as contradictory viewpoints on AS and investigating how transactional and relational assets affect FP in digitally integrated supply chains.