Search results

The aim of this study was the development and validation (D&V) of an assessment tool to measure administrators' attitudes towards the ‘Apprenticeship Embedded Degree Program (AEDP)’ in higher education institutions (HEIs).

A rigorous empirical method was followed encompassing four D&V phases: literature review, theoretical or face validation, validation with possible respondents or semantic validation and statistical validation. A pilot study was conducted among 150 randomly selected administrators from 50 different HEIs in India. The collected data were analyzed for statistical validation using exploratory factor analysis followed by confirmatory factor analysis.

The final version of the 21-item three-dimensional scale was found effective having significant degrees of reliability and validity. Exploratory factor analysis endorsed the factor extractions and data adequacy. The average variance extracted (AVE) for the three constructs (0.59, 0.70 and 0.66, respectively) were higher than the threshold value of 0.5, authorizing the convergent validity. The Cronbach alpha values (0.79, 0.81 and 0.77) were higher than the threshold value of 0.70, endorsing factors as reliable. Confirmatory factor analysis ascertained the multi-dimensionality of the scale and model fit having passable convergent validity. Discriminant validity (DV) was determined using the Fornell-Larcker criterion.

The newly developed “Administrators’ Attitude towards AEDP Scale” will serve as a valid psychometric tool for future research accosting AEDP implementation. It could be administered as an electronic tool as well, subject to potential adjustments reducing the identified ceiling effects and floor effects.

The scale is a unique addition to the allied literature based on an original empirical survey finding conducted in India.

This study aims to identify specific factors that facilitate engagement and stay intention among Generation Z employees in the Indian banking, financial services and insurance (BFSI) context. Furthermore, using the frequency distribution of the identified factors, this study has ranked them in order of their association with stay intention.

Data were collected from 22 Gen Z employees working in the Indian private BFSI sector using unstructured interviews. Inductive content analysis was applied to identify the factors improving engagement and stay intention. Moreover, quantitative content analysis was applied to calculate the frequency distribution of the identified factors.

The study identified six prominent factors, namely, transformational leadership, employee investment practices, egalitarian practices, work-life balance, job crafting and sustainability, which significantly enhance employee engagement and stay intention among Gen Z employees. Moreover, based on the results of quantitative content analysis, it was found that transformational leadership exhibited the highest frequency in association with employee engagement and stay intention. Following this were employee involvement, egalitarian practices, work-life balance, job crafting and sustainability.

In the coming days, Generation Z will contribute to almost one-third of India’s workforce, of which the BFSI sector will be the major employer. However, the issue with this generation is their retention. Hence, the study identifies factors ensuring engagement and stay intention.

Owing to the paucity of research on stay intention as a variable of interest, this study tries to capture the perceptions of Gen Z towards factors inducing their engagement and stay intention. This study assesses intention to stay (ITS) as compared to intention to leave (ITL) as it is a proactive indicator of turnover. Lastly, this study uses a qualitative approach to identify factors influencing stay intention and engagement based on interactions with employees, which, to the best of the authors’ knowledge, no prior study has attempted.

This study aims to reveal how mobile app stickiness is formed and how the stickiness formation process differs for apps of different social levels.

This study proposed and validated a stickiness formation model following the cognitive–affective–conative framework. Data were collected from surveys of 1,240 mobile app users and analyzed using structural equation modeling. Multigroup analysis was applied to contrast the stickiness formation process among apps of different social levels.

This study revealed a causal link between cognitive, affective and conative factors. It found partial mediation effects of trust in the association between perceptions and satisfaction, and the full mediation role of satisfaction and personal investment (PI) in the effects of subjective norm (SN) on stickiness. The multigroup analysis results suggested that social media affordances benefit stickiness through increased PI and strengthened effects of SN on PI. However, it damages stickiness through increased perceived privacy risk (PPR), decreased trust and strengthened effects of PPR on trust.

This study contributes to both stickiness scholars and practitioners, as it builds a model to understand the stickiness formation process and reveals the effects of the “go social” strategy. The novelty of this study is that it examined social influences, considered privacy issues and revealed two mediation mechanisms. The findings can guide the improvement of mobile app stickiness and the application of the “go social” strategy.

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.