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Present bias (PB) is a cognitive bias that stimulates the individual decision-maker to favour the present reward even over the higher reward in the future to avoid the uncertainty attached to the reward in an uncertain future. The article attempts to examine the prevalence of PB amongst Indians and the effect of such bias on savings and borrowings.

Secondary data on 47,132 respondents from the Financial Inclusion Insights, 2017 database was used in the study. The theory of self-control, which is captured by the widely accepted hyperbolic discounting model, was used to explore the presence of PB. Suitable statistical techniques and the binary probit regression model were employed to attain the objectives of the study.

The prevalence of PB was found amongst 8.2% of the sample respondents. The outcome of the study endorses the view of previous researchers that present-biased people tend to save less and borrow more.

Although the exploration of the role of various cognitive biases on financial behaviour is gaining momentum in recent times, there is a dearth of studies exploring the prevalence of PB and its implication towards financial behaviour, especially in the context of the emerging economy of India. The study makes an original contribution in this regard by using a very rich dataset of 47,132 individuals in the Indian context for the first time.

In 2015, the Supreme Court of India directed the Government of India to confer the citizenship right to the Chakma refugees, who settled in North-Eastern States in India. Arunachal Pradesh, the former North Eastern Frontier Agency, holds a large number of Chakma refugees who had migrated to India from the erstwhile East Pakistan during the late 1960s. The present benevolent approach of the Government of India towards this ethno-refugee community is having domestic as well as external implication in the backdrop of rampant deportation of refugees from its neighbouring state, Bangladesh. Mere citizenship right may result in the administrative integration of the Chakmas but could not resolve their crises as alien versus indigenous debate intensifies the refugee crises today. Over the decades, political alienation of the Chakma refugees extended their sense of deprivation and marginalization. A separate perspective is required to assess the Chakmas’ claim that they are after all not alien to India since their ancestral land Chittagong Hill Tracts were under Indian territory and they have had a deep allegiance to this territory because of India's accommodative pluralistic outlook and multi-ethnic characters. Permanent means of livelihood, legal rights over land holding and bridging social capital would help ethnic integration, not merely ‘limited’ citizenship right. This study from ethno-political perspective would assess the crises of the Chakma refugees in Arunachal Pradesh in India.

Migration appears to be a determinant in health-care utilisation, particularly among the elderly in India. Ageing and migration are essential socio-demographic phenomena in the 21st century for developing and developed countries to establish better public health-care policies. This study aims to focus on the status and determinants of health-care utilisation among elderly migrants who have migrated after attaining the age of 45 and above.

This study used the data from the first wave of the longitudinal ageing study in India (LASI) in 2017–2018. Two outcome variables were used to examine the health-care utilisation, including in-patient and out-patient care. Binary logistic regression was used to explore the predictors of healthcare utilisation in terms of in-patient and out-patient care among the elderly migrant population.

A total of 82.9% of elderly migrants had visited out-patient care when they were sick, whereas 15.3% have used in-patient care. Enabling factors, such as wealth quintile and health insurance, and need factor, such as chronic disease and self-rated health, were more significant factors influencing the health-care utilisation.

This study contributes to our understanding of older migrants’ health-care utilisation. Focussing on this study’s outcome, policymakers and decision makers may consider improving older migrants’ access to health-care by raising their income level, offering local health insurance and health awareness programs.

This paper proposes a framework to assist in managing predictive maintenance by detecting progressive surface wear on spur gears through the analysis of digital images of gear teeth using computer vision (CV) techniques.

An experimental setup was constructed to capture images of gear teeth using endoscopic cameras. The images were selected, pre-processed, stored in a database and used in the experimental study of the proposed framework. Three CV techniques were explored within the framework for detecting wear in spur gears: (1) edge detection; (2) gray level co-occurrence matrix (GLCM) combined with machine learning (ML) algorithms and (3) deep learning with convolutional neural networks (CNN).

The results showed 85% accuracy using the edge detection algorithm. Among the ML algorithms, accuracy was above 60% for the support vector machine (SVM) and above 70% for K-nearest neighbors (KNN). Principal component analysis (PCA) indicated that as the distance between the principal components increased, it characterized the formation and progression of surface wear on the gear teeth. With the CNN, an accuracy of 99.999981% was achieved in the training loss rate, with a classification accuracy rate (CAR) of 91.6666%, an F1 score of 90.9090% and a recall of 83.3334% during the testing phase.

This framework is applicable to a variety of gear systems and industrial contexts requiring predictive maintenance, making it a highly scalable solution for industry professionals.

This paper proposes a novel framework that considers various CV techniques to detect and assess the level of wear on spur gear surfaces. Moreover, the results provide guidelines for selecting the most appropriate method for detecting wear in gear systems.

This case facilitates the learning of marketing concepts like segmentation, targetting and positioning, marketing mix, branding strategies and digital marketing strategies.

The case is written based on the facts available in the public domain and hence it follows secondary data research design. The secondary sources include company websites, industry reports, newspaper articles, social media sites and other online articles and reports. The case is classroom tested with MBA students in digital marketing course and PGDM students in brand management course.

Cycle Pure agarbathi, the leading brand of NR Group, became the coveted brand among the households of India. This success amidst high competition can be attributed to the concerted effort on product development coupled with mindful branding. To keep abreast of time and competition the company opted to go digital with an e-portal. Cycle Pure had a digital presence much earlier through social media, but the e-portal www.cycle.in, was a novel attempt. All the fragrance products of the brand were available for consumers through www.cycle.in. Moreover, the product assortment consisted of a collection of top-quality products and auxiliaries linked to multiple categories such as invocation necessities, personal care, air care and lifestyle. Furthermore, using in-house fragrance research lab, the company experimented with local aromas through numerous variants and also extended to related products such as sambrani (benzene) and dhoops. With consistent product augmentations along with access to innovative sectors such as air fresheners, the company expected to grow at a rate of 15%–16% annually. However, the company targeted to grab one-third share in the total market within the next five years.

This case can be used in Marketing Management, Brand Management, Digital marketing and Strategic Marketing courses at the Master’s level. It is suitable for MBA and executive MBA students.

By addressing the dearth of literature on the subject of cybersecurity risks and artificial intelligence (AI), this study aims to close a research gap by concentrating on the ever-changing environment of online social networks (OSNs) and technology. The main goals are to classify cyberattacks into categories like malware, phishing/spam and network intrusion detection; to identify efficient algorithms for preventing cyber threats; to review relevant literature from 2019 to 2020; and to use machine learning algorithms to detect suspicious behavior related to malware. The study offers a novel framework that suggests particular machine learning algorithms for every kind of cyber threat, hence improving cybersecurity knowledge and reaction capacities. This makes the research useful for examining the impact of cybersecurity on smart cities.

Thirty papers have been examined on AI and machine learning algorithms, including K-nearest-neighbor (KNN), convolutional neural networks (CNN) and Random Forest (RF), that were published in 2019 and 2020. Using analytical software (NVivo), a qualitative approach is used to retrieve pertinent data from the chosen research. The researchers divide cyberattacks into three groups: network intrusion detection, phishing/spam and malware.

The study’s conclusions center on how AI and machine learning algorithms linked to cybersecurity are reviewed in the literature, how cyberattacks are classified and how an inventive framework for identifying and reducing risks is proposed. This makes the research useful for researching the implications of cybersecurity for smart cities.

The practical implications of this research are noteworthy, particularly in the realms of technology, AI, machine learning and innovation. The utilization of the NVivo technique enhances decision-making in uncertain situations, making the study’s results more reliable. The findings showcase the applicability of tools in analyzing malicious cyberattacks to address issues related to social media attacks, emphasizing their practical utility. The study’s relevance is further highlighted by a real-world example, where a Kuwaiti public sector fell victim to a malware attack, underlining the importance of cybersecurity measures aligned with the New Kuwait 2035 strategic development plan. The innovative framework presented in the research guides the selection of algorithms for detecting specific malicious attacks, offering practical insights for securing information technology (IT) infrastructure in Kuwait.

The rapid digitization in Kuwait, accelerated by the COVID-19 pandemic, underscores the pivotal role of technology in government services. Ma’murov et al. (2023) emphasize the significance of digitization, particularly in accessing and verifying COVID-19 information. The call for a dedicated digital library for preserving pandemic-related material aligns with the evolving digital landscape. Cybersecurity emerges as a critical concern in Kuwait and the Gulf Cooperation Council (GCC), necessitating transnational cooperation (Nasser Alshabib and Tiago Martins, 2022). In the local context, the inefficiency of information security systems and low awareness among government employees pose cybersecurity challenges (Abdulkareem et al., 2014). Social media’s role during the pandemic highlights its significance, yet the need for cybersecurity in this domain remains underexplored (Ma’murov et al., 2023; Safi et al., 2023).

The unique aspect of the paper is its in-depth investigation of the relationship between cybersecurity and AI in OSNs. It uses a special application of machine learning methods, including CNN, RF and KNN, to identify suspicious behavior patterns linked to malware. The detailed analysis of 30 research papers released between 2019 and 2020, which informs the choice of suitable algorithms for diverse cyber threats, further emphasizes the study’s uniqueness. The novel framework that has been suggested categorizes assaults and suggests certain machine learning techniques for identification, offering a useful instrument to improve comprehension and reactions to a variety of cybersecurity issues.