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In the existing era, the Internet of Things (IoT) can be considered entirely as a system of embedding intelligence. The transverse character of IoT systems and various components associated with the arrangement of IoT systems have confronted impediments in the form of security and trust. There is a requirement to efficiently secure the IoT environment. The present study recommends a framework for impediments to secure and trustworthy IoT environments.

The present study identifies thirteen potential impediments to secure and trustworthy IoT environment. Further, a framework is developed employing Total Interpretive Structural Model (TISM) and Cross-Impact Matrix Multiplication Applied to Classification (MICMAC) approach. The integrated approach is employed as TISM organizes inter-relations among the identified impediments, while MICMAC analysis organizes interpretations related to the driving and dependence power of the impediments.

The results from the study represents that security of IoT from arbitrary attacks is the impediment that has attained the highest driving power. The impediments such as “security of IoT from arbitrary attacks”, “profiling” and “trust and prominence structure” are identified at the top level in the analysis.

The previous studies highlight the facilitating contribution of IoT on various devices but neglect the impediments that can contribute towards a safe and trustworthy IoT environment. Also, the present study has its limitations as it depends upon the experts’ recommendations and suggestions.

The existing framework could be beneficial in constructing policies and suggestions to efficiently cater the impediments to a secure and trustworthy IoT environment.

This review paper delves into the comprehensive understanding of Ashwagandha, spanning its botanic occurrence, conventional applications, extraction techniques and pivotal role in addressing various disorders.

Introduction Ashwagandha, also known as Withania somnifera, is a remarkable botanical resource with a rich history of use in traditional medicine.

In botany, Withania somnifera thrives in diverse ecosystems, particularly in tropical and subtropical regions. Its extensive distribution across regions, the Canary Islands, South Africa, the Middle East, Sri Lanka, India and China underscores its adaptability and resilience. The traditional uses of Ashwagandha in Ayurvedic and indigenous medicine systems have persisted for over 3,000 years. With over 6,000 plant species utilized historically, India, often regarded as the “botanical garden of the world,” has firmly established Ashwagandha as a cornerstone in traditional healing practices.

Extraction methods play a pivotal role in harnessing the therapeutic potential of Ashwagandha. Ultrasonic-assisted extraction and high-performance liquid chromatography are among the techniques employed to obtain the key bioactive compounds. Ashwagandha’s significance in modern medicine is underscored by its potential to address a spectrum of health issues. The multifaceted bioactivity of Ashwagandha is attributed to its antioxidant, anti-inflammatory, heart conditions, metabolic disorders, renal ailments, hepatic diseases and adaptogenic properties, making it a subject of increasing interest in contemporary medical research. This review synthesizes the assorted perspectives of Ashwagandha, from its botanical roots and conventional employments to its advanced extraction strategies and its intention to basic well-being challenges, advertising important bits of knowledge for analysts, specialists and healthcare experts alike.

The purpose of this paper is to review the nature and extent of foodborne diseases in India due to chemical and microbial agents.

The scientific investigations/reports on outbreak of foodborne diseases in India for the past 29 (1980‐2009) years due to adulteration, chemical, and microbiological contamination have been reviewed. Reported scientific information on foodborne pathogens detected and quantified in Indian foods has also been reviewed.

A total of 37 outbreaks involving 3,485 persons who have been affected due to food poisoning have been reported in India. Although the common forms of foodborne diseases are those due to bacterial contamination of foods, however, higher numbers of deaths have been observed due to chemical contaminants in foods.

A national foodborne disease surveillance system needs to be developed in India in order to enable effective detection, control and prevention of foodborne disease outbreaks.

Green manufacturing (GM) has emerged as a vital strategy to minimize environmental impacts and maximize resource efficiency in industrial production. The main aim of this work is to identify and validate essential criteria for GM and prioritize drivers of successful GM implementation frameworks for the manufacturing industry based on Best–Worst Methodology (BWM).

This work explores the essential factors required to achieve long-term success in GM, followed by their comparison using the BWM to determine the most and least important indicators. The study conducted purposive sampling to gather data from 15 experts representing diverse industries in the manufacturing sector. The research methodology consists of three main steps: criteria identification through literature review, criteria validation using the content validity ratio (CVR) method and the BWM application to rank the indicators.

The main success factors identified included top management commitment, organizational culture, employee training, cost saving, investment in innovation and technology, environmental regulation, zero-emission and waste management. The results obtained through BWM indicated top management commitment, investment in innovation and technology and organizational culture as the most critical factors for successful GM implementation. Other factors, such as zero-emission, waste management and cost savings, were also significant but ranked lower in significance. In conclusion, this study highlights the importance of top management commitment to successfully adopting GM initiatives.

This research provides insights into the key success factors, through which decision-makers are assisted in prioritizing efforts and implementing sustainable and eco-friendly practices in manufacturing processes. However, further research is recommended to address existing gaps and foster a deeper understanding of crucial success factors for successful GM implementation.

This research aimed to find whether information about a product can give rise to negative perceptions even in inert situations (nocebo effects), and to understand how price levels impact such judgments.

In all experiments, participants were exposed to negative product information in the form of potential side-effects. In an initial study, a higher non-discounted versus a discounted price frame was presented for a health drink after customers were exposed to negative aspects. Then, in experiment 1, price (high vs low) and exposure to information (no information vs negative information) was manipulated for skin creams where participants physically evaluated the cream. In experiment 2, price was manipulated at three levels (low, high, discounted) orthogonally with product information (no negative information vs with negative information) to get a more nuanced understanding.

In the initial study, after exposure to negative information, the non-discounted group had more positive ratings for the drink. Study 1 showed that reading about negative information resulted in a nocebo effect on perception of dryness (side-effect). Moreover, when no information was presented, perception of dryness by low and high price groups were similar but in the face of negative information, perception of dryness by low-price group was more pronounced compared to a high-price group. Study 2 conceptually replicated the effect and also confirmed that not only discounts (commonly linked with product quality), but absolute price levels also show a similar effect.

Nocebo effects have been rarely documented in consumer research. This research showed how simply reading generically about potential side effects gives rise to nocebo effects. In addition, even though marketers might find it tempting to lower prices when there is negative information about certain product categories, such an action could backfire.

To the best of our knowledge, the link between observable nocebo effects and its link with pricing actions is a novel research thread. We were able to show a nocebo effect on product perception after reading about negative information and also find that a higher price can mitigate the nocebo effect to some extent.

Artificial intelligence (AI) offers many benefits to improve predictive marketing practice. It raises ethical concerns regarding customer prioritization, market share concentration and consumer manipulation. This paper explores these ethical concerns from a contemporary perspective, drawing on the experiences and perspectives of AI and predictive marketing professionals. This study aims to contribute to the field by providing a modern perspective on the ethical concerns of AI usage in predictive marketing, drawing on the experiences and perspectives of professionals in the area.

The study conducted semistructured interviews for 6 weeks with 14 participants experienced in AI-enabled systems for marketing, using purposive and snowball sampling techniques. Thematic analysis was used to explore themes emerging from the data.

Results reveal that using AI in marketing could lead to unintended consequences, such as perpetuating existing biases, violating customer privacy, limiting competition and manipulating consumer behavior.

The authors identify seven unique themes and benchmark them with Ashok’s model to provide a structured lens for interpreting the results. The framework presented by this research is unique and can be used to support ethical research spanning social, technological and economic aspects within the predictive marketing domain.

La Inteligencia Artificial (IA) ofrece muchos beneficios para mejorar la práctica del marketing predictivo. Sin embargo, plantea preocupaciones éticas relacionadas con la priorización de clientes, la concentración de cuota de mercado y la manipulación del consumidor. Este artículo explora estas preocupaciones éticas desde una perspectiva contemporánea, basándose en las experiencias y perspectivas de profesionales en IA y marketing predictivo. El estudio tiene como objetivo contribuir a la literatura de este ámbito al proporcionar una perspectiva moderna sobre las preocupaciones éticas del uso de la IA en el marketing predictivo, basándose en las experiencias y perspectivas de profesionales en el área.

Para realizar el estudio se realizaron entrevistas semiestructuradas durante seis semanas con 14 participantes con experiencia en sistemas habilitados para IA en marketing, utilizando técnicas de muestreo intencional y de bola de nieve. Se utilizó un análisis temático para explorar los temas que surgieron de los datos.

Los resultados revelan que el uso de la IA en marketing podría tener consecuencias no deseadas, como perpetuar sesgos existentes, violar la privacidad del cliente, limitar la competencia y manipular el comportamiento del consumidor.

El estudio identifica siete temas y los comparan con el modelo de Ashok para proporcionar una perspectiva estructurada para interpretar los resultados. El marco presentado por esta investigación es único y puede utilizarse para respaldar investigaciones éticas que abarquen aspectos sociales, tecnológicos y económicos dentro del ámbito del marketing predictivo.

人工智能（AI）为改进预测营销实践带来了诸多益处。然而, 这也引发了与客户优先级、市场份额集中和消费者操纵等伦理问题相关的观点。本文从当代角度深入探讨了这些伦理观点, 充分借鉴了人工智能和预测营销领域专业人士的经验和观点。旨在通过现代视角提供关于在预测营销中应用人工智能时所涉及的伦理观点, 为该领域做出有益贡献。

本研究采用了目的性和雪球抽样技术, 与14位在人工智能营销系统领域具有丰富经验的参与者进行为期六周的半结构化访谈。研究采用主题分析方法, 旨在深入挖掘数据中显现的主要主题。

研究结果表明, 在营销领域使用人工智能可能引发一系列意外后果, 包括但不限于加强现有偏见、侵犯客户隐私、限制竞争以及操纵消费者行为。

本研究通过明确定义七个独特的主题, 并采用阿肖克模型进行基准比较, 为读者提供了一个结构化的视角, 以解释研究结果。所提出的框架具有独特之处, 可有效支持在跨足社会、技术和经济领域的预测营销中展开的伦理研究。