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This study aims to identify different antecedents and reveal divergent moderating effects of horizontal collectivism, thereby unlocking the asymmetric mechanisms for employees’ brand citizenship behavior (BCB) and negative word-of-mouth (NWOM).

This study uses a survey data set and analyzes it with structural equation modeling along with common latent factor analysis designed to control for common method variance.

BCB is associated with pride at work but not perceived organizational support (POS), so POS drives BCB not directly but indirectly through the emotion of pride at work. In contrast, employees’ NWOM is associated with both POS and frustration, and POS drives NWOM directly and indirectly through the emotion of frustration. Horizontal collectivism has divergent moderating effects that strengthen the relationships of BCB with POS and pride at work and weaken the relationship between employees’ NWOM and frustration.

This study makes two major theoretical contributions to internal branding. First, as a response to the need for an investigation into drivers of employees’ brand-oriented behaviors, it will identify different psychological antecedents and mechanisms for BCB and employees’ NWOM. Second, capturing the potential of horizontal collectivism on employees’ brand-oriented behaviors, this study will reveal the potential divergent moderating effects of horizontal collectivism on BCB and employees’ NWOM. These two contributions will lead to a better understanding of the different mechanisms for employees’ BCB and NWOM.

After working through the case, the students should be able to understand Indian political economy and the brand building process of NaMo; identify the media mix strategies used to build the brand NaMo in India; evaluate possible future growth strategies for brand NaMo; and compare and contrast brand NaMo with business brands.

Narendra Modi popularly called as NaMo was the current Prime Minister of India. He belonged to Bhartiya Janata Party (BJP) which won India’s general elections in two consecutive terms 2014 and 2019. NaMo was recognised worldwide for his prudence in leading the country to greater heights of achievement. NaMo started his political journey as the worker of BJP at a tender age. His rise in political career was akin to flagship brand overtaking the parent brand. All the steps taken in the past to position himself as a cult brand, will it fortify to NaMo’s victory in 2024 general elections? Business firms may follow NaMo’s strategies. What can the business brands emulate from NaMo to market and position themselves? Can political success be transpired to business success?

This case is designed for use in a graduate-/postgraduate-level marketing course in segments on brand management, brand expansion and the marketing strategies of a market leader. The case can also be used in a brand management course to discuss brand management models (e.g. Keller’s brand resonance pyramid and brand value chain). This case has particular application for classes that focus on brand equity, STP for any brand (segmentation, targeting and positioning) and brand value chain. The case looks in detail at the Indian political market and brand building process of NaMo and examines competitive moves since its inception. This case can be used in brand management, media management courses. The dilemma can be explained as part of a marketing course for postgraduate and executive programmes.

Teaching notes are available for educators only. Please contact your library to gain login details or email support@emeraldinsight.com to request teaching notes.

CSS 8: Marketing.

Aster Retail (AR) is the retail pharmacy division of the Aster Dr Moopen's Healthcare (ADMH) Group. The group delivers healthcare services across the Middle East, India and the Far East, with a portfolio of hospitals, clinics, diagnostic centres and retail pharmacies. AR, under the leadership of Chief Executive Officer (CEO) Jobilal Vavachan, is well known for its people-centric approach, unique culture and innovative human resource (HR) practices. AR has won multiple awards for HR practices, service quality and business performance. In a recent corporate restructuring (2018), “Aster Primary Care” was carved out by combining the group's Clinics and Retail businesses. This case discusses the evolution of AR's HR journey and the challenges associated with integrating culturally diverse businesses without compromising the values of ADMH and its promise, “We'll Treat You Well.”

This study aims to examine the relationship between knowledge management and career development in an era characterized by high levels of youth unemployment and a demand for specialized skills. Despite the increasing transition to a knowledge-based economy, there is a significant gap between young people’s skills and career readiness, necessitating an in-depth analysis of the role of knowledge management at the individual, organizational and national levels.

The authors conducted a qualitative study using the theory-context-characteristics-methodology approach based on a systematic literature review. The authors created an ecological framework for reflecting on knowledge management and career development, arguing for a multidisciplinary approach that invites collaboration across sectors to generate innovative and reliable solutions.

This study presents a comprehensive review of the existing literature and trends, noting the need for more focus on the interplay between knowledge management and career development. It emphasizes the need for businesses to promote the acquisition, storage, diffusion and application of knowledge and its circulation and exchange to create international business human capital.

The findings may help multinational corporations develop managerial training programs and recruitment strategies, given the demand for advanced knowledge-based skills in the modern workspace. The study also discusses the influences of education, experience and job skills on business managers’ performance, guiding the future recruitment of talents.

To the best of the authors’ knowledge, this review is among the first to assess the triadic relationship between knowledge management, career development and the global unemployment crisis. The proposed multidisciplinary approach seeks to break down existing silos, thus fostering a more comprehensive understanding of how to address these ongoing global concerns.

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.