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On average, a medium-sized satellite consist of almost 500 sensors where powering these sensors in space in such an unreachable environment is critical. Backing this, a compact energy harvester for powering up distant sensors is discussed here is the purpose of this paper. This is in line with the geostationary satellite-powered using the available electromagnetic energy on the satellite panels in space.

The designed rectenna makes use of a compact wideband receiving antenna operating at the targeted frequency band from 8 to 18 GHz. It also consists of a simple dual diode rectifier topology with a matching circuit, bandpass filter and a resistive load to convert the received radio frequency energy into usable direct current (DC) voltage.

The rectenna measurement is performed using three different configuration setups. This shows that a maximum DC voltage of 1.8 V and 5-10 mV is harvested from rectifier and rectenna (includes antenna and rectifier) when 20 dBm power is transmitted from the transmitting antenna operating at X and Ku band. This makes the rectenna feasible to power wireless sensors in a structural health monitoring system.

The measurements are performed by considering a real-time environment in space in terms of the distance between the transmitting and receiving antenna, which depends on the far-field of the transmitting antenna in a satellite.

This paper aims to theorize and investigate the use of effective color features in artificial intelligence (AI) influencers, an emerging marketing trend in the social media context.

By analyzing 6,132 pictures posted by ten AI influencers on Instagram, this paper examines the effect of warm colors in AI influencers’ social media posts on consumer responses, and how other color features may moderate the effect of warm color. In addition, two experimental studies reveal the underlying process driving the effect of warm color.

Warmer color generated more favorable consumer responses, with brightness significantly moderating the relationship between warm color and favorable consumer responses. Moreover, the results of the experiments establish that perceived warmth and emotional trust mediate the causal effect of warm colors on consumer responses.

There is still little understanding about consumer perceptions of AI influencers and their acceptance of AI influencers’ product recommendations. As such, this research offers theoretical understanding of the color features influencing the effectiveness of recommendations by AI influencers.

Brands have started deploying AI influencers as their brand ambassadors to make product recommendations, representing a new wave of advertising on social media. The findings will thus benefit marketers in developing effective product recommendations using AI influencers.

The present research provides a novel understanding of how visual features, such as color can influence the effectiveness of AI influencers.

The purpose of this study is to examine the mediator role of blockchain technology (BT) in developing the trust among the users of digital payment (DP).

The research is of conclusive in nature. Purposive convenience sampling method was used to collect the data through a structured questionnaire. Statistical software Smart PLS 4 and SPSS 26 is used for the analysis of 540 valid responses.

This study concludes that resulting model is capable to predict the mediating role of BT in framing the trust. It also identified that all the factors (perceived ease of use, perceived usefulness, social norms and facilitating conditions) examined were found to have significant influence on the adoption of DP system.

This study uses variables that influences human behaviour in DP adoption. In future the work can be extended to check the moderating effect of BT on trust towards DP system.

This study demonstrates a key driver of towards DP. In addition, it highlights the pivotal role of BT in developing trust in both current and future adoption of DPs.

This study aims to investigate the shopping behaviour of consumers, mainly in fashion apparels, and intends to understand consumer buying patterns in Indian context. The study was designed to determine the level of consumer's sense of belonging towards apparel shopping by applying the concept of self-congruence.

The study used variance-based partial least squares structural equational modelling (PLS-SEM) on a cross-sectional study conducted on 569 consumers. The study was conducted by using questionnaire to collect the responses from the central zone of India. The results support most of the projected hypotheses.

The study focused on the shopping behaviour of consumer such as self-congruence, impulse buying, hedonic values and consumer satisfaction. The results of the study highlight the association of constructs and analysed the mediation relation of hedonic and impulse buying constructs. The results revealed a positive association among the constructs and also found a partial mediation effect in their relation with constructs.

The findings are outcomes of an empirical study conducted in the fashion apparel industry of India based on the sample set of urban consumers. The study is restricted to the direct and indirect relationship of constructs. Further, research can examine by using moderating constructs like demographic factors (gender, age, income, etc.) and other shopping behaviours (like brand loyalty, brand love, brand attachment) for more clarity in results. Moreover, the study limited is with fashion apparel, whereas there are many categories in the fashion industry like accessories, perfumes, cosmetic products, footwear and also other products industry.

The study provided valuable inputs to the literature of marketing where self-congruence affects consumer shopping behaviour such as impulse buying, hedonic values and consumer satisfaction. The study proposes a practical approach that can help the marketing professionals and product developers to have a deep understanding about consumer shopping behaviour for facilitating consumer-oriented goods in the Indian fashion industry.

This is one of the first studies in the fashion industry to test the association of self-congruence with hedonic value and consumer satisfaction. This relation is not tested in context of fashion apparel. Additionally, this study also examined the mediating effect of hedonic value and impulse buying in relation with self-congruence and consumer satisfaction in the Indian context.

For the case of many content features, This paper aims to investigate which content features in video and text ads more contribute to accurately predicting the success of crowdfunding by comparing prediction models.

With 1,368 features extracted from 15,195 Kickstarter campaigns in the USA, the authors compare base models such as logistic regression (LR) with tree-based homogeneous ensembles such as eXtreme gradient boosting (XGBoost) and heterogeneous ensembles such as XGBoost + LR.

XGBoost shows higher prediction accuracy than LR (82% vs 69%), in contrast to the findings of a previous relevant study. Regarding important content features, humans (e.g. founders) are more important than visual objects (e.g. products). In both spoken and written language, words related to experience (e.g. eat) or perception (e.g. hear) are more important than cognitive (e.g. causation) words. In addition, a focus on the future is more important than a present or past time orientation. Speech aids (see and compare) to complement visual content are also effective and positive tone matters in speech.

This research makes theoretical contributions by finding more important visuals (human) and language features (experience, perception and future time). Also, in a multimodal context, complementary cues (e.g. speech aids) across different modalities help. Furthermore, the noncontent parts of speech such as positive “tone” or pace of speech are important.

Founders are encouraged to assess and revise the content of their video or text ads as well as their basic campaign features (e.g. goal, duration and reward) before they launch their campaigns. Next, overly complex ensembles may suffer from overfitting problems. In practice, model validation using unseen data is recommended.

Rather than reducing the number of content feature dimensions (Kaminski and Hopp, 2020), by enabling advanced prediction models to accommodate many contents features, prediction accuracy rises substantially.

This paper aims to identify the role of internet of things (IoT) in water supply chain management and helps to understand its future path from the junction of computer science and resource management.

The current research was studied through bibliometric review and content analysis, and various contributors and linkages were found. Also, the possible directions and implications of the field were analyzed.

The paper’s key findings include the role of modern computer science in water resource management through sensor technology, big data analytics, IoT, machine learning and cloud computing. This, in turn, helps in understanding future implications of IoT resource management.

A more extensive database can add up to more combinations of linkages and ideas about the future direction. The implications and understanding gained by the research can be used by governments and firms dealing with water management of smart cities. It can also help find ways for optimizing water resources using IoT and modern-day computer science.

This study is one of the very few investigations that highlighted IoT’s role in water supply management. Thus, this study helps to assess the scope and the trend of the case area.