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Though there is consensus that mindfulness induces mindful consumption (MC), empirical testing is needed to uncover the mechanism underlying temperance behaviour in the MC model proposed by Sheth et al. (2011). The role of mindful advertising in influencing MC needs deeper investigation. The purpose of this research paper is to bridge the gap.

The relationship between mindfulness and temperance in consumption was investigated using an online simulation. Mindful advertising by Patagonia, with a message to buy less yet demand organic, fair-trade and recycled products, was introduced as a moderator in experimental group one. The second group was exposed to an aspirational advertisement of Tommy Hilfiger, symbolic of consumption-driving communication.

Not buying any brands was the uppermost preference by the participants followed by Patagonia, which used a mindful advertisement. Tommy Hilfiger was a distant third despite using an aspirational advertisement. A predictive relationship between mindfulness and temperance in consumption remained elusive.

Consumer purchase decisions favouring mindfully advertised Patagonia make a strong business case for nurturing a mindful mindset and promoting mindful behaviour. The customer-centric sustainability strategy of caring for the people and the planet beforehand should take precedence over corporate social responsibility which is usually an afterthought.

Measuring mindfulness and MC, two constructs combined in one experimental design, using a simulation built around real-life marketing communication distinguishes this research paper.

Nature’s evolution has shaped intelligent behaviors in creatures like insects and birds, inspiring the field of Swarm Intelligence. Researchers have developed bio-inspired algorithms to address complex optimization problems efficiently. These algorithms strike a balance between computational efficiency and solution optimality, attracting significant attention across domains.

Bio-inspired optimization techniques for feature engineering and its applications are systematically reviewed with chief objective of assessing statistical influence and significance of “Bio-inspired optimization”-based computational models by referring to vast research literature published between year 2015 and 2022.

The Scopus and Web of Science databases were explored for review with focus on parameters such as country-wise publications, keyword occurrences and citations per year. Springer and IEEE emerge as the most creative publishers, with indicative prominent and superior journals, namely, PLoS ONE, Neural Computing and Applications, Lecture Notes in Computer Science and IEEE Transactions. The “National Natural Science Foundation” of China and the “Ministry of Electronics and Information Technology” of India lead in funding projects in this area. China, India and Germany stand out as leaders in publications related to bio-inspired algorithms for feature engineering research.

The review findings integrate various bio-inspired algorithm selection techniques over a diverse spectrum of optimization techniques. Anti colony optimization contributes to decentralized and cooperative search strategies, bee colony optimization (BCO) improves collaborative decision-making, particle swarm optimization leads to exploration-exploitation balance and bio-inspired algorithms offer a range of nature-inspired heuristics.

The purpose of this paper is to give an overview of artificial intelligence (AI) and other AI-enabled technologies and to describe how COVID-19 affects various industries such as health care, manufacturing, retail, food services, education, media and entertainment, banking and insurance, travel and tourism. Furthermore, the authors discuss the tactics in which information technology is used to implement business strategies to transform businesses and to incentivise the implementation of these technologies in current or future emergency situations.

The review provides the rapidly growing literature on the use of smart technology during the current COVID-19 pandemic.

The 127 empirical articles the authors have identified suggest that 39 forms of smart technologies have been used, ranging from artificial intelligence to computer vision technology. Eight different industries have been identified that are using these technologies, primarily food services and manufacturing. Further, the authors list 40 generalised types of activities that are involved including providing health services, data analysis and communication. To prevent the spread of illness, robots with artificial intelligence are being used to examine patients and give drugs to them. The online execution of teaching practices and simulators have replaced the classroom mode of teaching due to the epidemic. The AI-based Blue-dot algorithm aids in the detection of early warning indications. The AI model detects a patient in respiratory distress based on face detection, face recognition, facial action unit detection, expression recognition, posture, extremity movement analysis, visitation frequency detection, sound pressure detection and light level detection. The above and various other applications are listed throughout the paper.

Research is largely delimited to the area of COVID-19-related studies. Also, bias of selective assessment may be present. In Indian context, advanced technology is yet to be harnessed to its full extent. Also, educational system is yet to be upgraded to add these technologies potential benefits on wider basis.

First, leveraging of insights across various industry sectors to battle the global threat, and smart technology is one of the key takeaways in this field. Second, an integrated framework is recommended for policy making in this area. Lastly, the authors recommend that an internet-based repository should be developed, keeping all the ideas, databases, best practices, dashboard and real-time statistical data.

As the COVID-19 is a relatively recent phenomenon, such a comprehensive review does not exist in the extant literature to the best of the authors’ knowledge. The review is rapidly emerging literature on smart technology use during the current COVID-19 pandemic.

The purpose of this paper is to analyse the effect of enhanced knowledge and technology innovations, which were resulted from training, on the simultaneous production of rice and soybean in Java, Indonesia.

A model of product possibility frontier with two outputs produced using the same resources was employed. Based on the model, supply responses of soybean and rice were derived. Aggregate data consisting of 12 regions during the ten years of 2000–2009 were compiled from relevant agricultural institutions at the provincial level.

Improvement in farmers’ capacity has been able to increase production of soybean and rice simultaneously. Farmers’ capacity increased after completion of the school. Knowledge and skill gained from the school have been applied to both rice and soybean farming. Other economic factors also affected the supply response of both commodities.

Available data covered periods 2000–2009. However, the outcomes are still relevant to the current situation because food crops are the basic necessity. This study used secondary aggregate data, which might be less accurate than primary data. However, secondary data have the advantage concerning coverage and time span.

The Government, in collaboration with non-government organisations and the private sectors, should continue to enhance farmers’ capacity to increase the production of food crops to feed people in Indonesia, and over the world in general.

An analysis of joint production using a concept of product transformation curve can measure the impact of training on multi products.