On-demand home service application (HSA) is a technological advancement that has brought various day-to-day services to our doorstep with just a few clicks. By using consumer-perceived values (utilitarian, hedonic, and social), trust transfer theory, and commitment-trust theory, the present study aims to investigate the factors influencing sharing intention and repurchase intention of consumers towards using HSAs on their mobile phones. This study involves collecting data from 357 respondents in India and analyzing the same using the SmartPLS 3 software. The results indicate that trust in HSA is influenced by utilitarian value and social value, but not by hedonic value. Trust in HSA results in repurchase intention, commitment, and sharing intention. Interestingly, trust in the user community affects sharing intention but not repurchase intention and commitment. The study integrates the consumer-perceived value, trust transfer theory, and commitment-trust theory to build an integrative framework that explains the consumers' sharing and repurchase intentions towards HSAs.

This study investigates and compares the continuance intention of full-time business school students and faculty in India and Italy who moved from traditional pedagogy style to the digital classroom due to the COVID-19 pandemic. The study integrates the Expectation Confirmation Model (ECM) and Task-Technology Fit (TTF) to examine their continuance intention. Survey data was collected from 396 business school students and 130 faculty members from India and Italy and analysed using SmartPLS 3 software. The study found that perceived usefulness, satisfaction, and task-technology fit significantly impact the continuance intentions of students and faculty. Multigroup analysis of students indicates that Italian students are more driven by task-technology fit as compared to Indian students in their continuance intention; in comparison, Indian students rely more on gaining experience and knowhow on technology. Finally, the multigroup study of faculty suggests that Italian educators have a comparatively stronger orientation towards the fit between digital classroom technology and a portfolio of related tasks. In comparison, their Indian counterparts rely more on the perceived usefulness of technology. The strength of relationship between task-technology fit and continuance intention is comparatively lower for faculty as compared to students in both countries. Finally, implications for theory and practice are discussed.

Abstract One of the most essential characteristics that is taken into consideration while dealing with wireless sensor networks (WSNs) is to optimize energy consumption during the transmission of data packets. Routing algorithms must provide optimal solutions to reduce the amount of energy consumed during the process. In wireless sensor networks, the end devices layer comprises of clustering of sensor nodes using ant lion optimization (ALO) which is followed by fog layer consisting of k‐means clustering technique. When nature inspired optimization solution is implemented, the accuracy majorly depends upon complexity and number of parameters. The algorithm proposed majorly focuses on the bottom layer and not on the fog layer. The results of the proposed algorithm were proved to significantly better than the conventional algorithms used for the very same purpose. The proposed algorithm focuses on ALO only on the bottom layer and not on the fog layer. To evaluate the performance of the proposed algorithm, the results are compared with the findings of several traditional algorithms, such as energy‐efficient cross‐layer‐sensing clustering method, Distributed and Morphological Operation‐based Data Collection Algorithm, and trust‐based secure routing. Comparative results showed that the proposed algorithm presented significant improvement.

Summary Through the enhancement of numerous social media sites, the rumor spread more rapidly among society and influences people in a very negative way. Nowadays, more attention is given by researchers to mitigating the threats produced by rumors. Bibliometric analysis is a prevalent and rigorous technique for discovering and investigating large volumes of systematic data. It assists us to identify the important aspects of that particular field. Motivated by earlier research we used this approach for our present study. The present study shows bibliometric analysis through VOSviewer software of 2935 records related to rumor dissemination by collecting the data from the web of science from 1989 to 2021. The bibliometric results have shown publication trends, main journals, most cited articles, most productive country, prominent authors, and institutions. Further net map analysis illustrates the growth of rumor detection in past, present, and future as well. Bibliometric and network analysis results from this research will significantly facilitate understanding the progress and trends in rumor detection.

Summary Sometimes, unverified information is disseminated as if it is true information on social media sites. Most of the times, it goes viral and affects the belief of people and their emotions. Rumors and fake news are the most popular form of false and unconfirmed information. Such news must be identified quickly for preventing its negative impact on society. In the last decade, operational procedures for rumors and false news detection came into existence. This paper provides a holistic view of different web waves from web 1.0 to web 5.0 and their usages. Further, taxonomy describes various malicious information contents at different stages. It discusses features used for classification, publicly available datasets, the rumor detection methods proposed during web 1.0, 2.0, 3.0, 4.0, 5.0 periods, and comprehensive analysis related to various methods and techniques. Numerous research gaps and future directions are illustrated to make online information more trustworthy for knowledge sharing and decision‐making purposes.

The present study performs the bibliometric analysis of the social commerce (s-commerce) literature, highlights the major research themes, and suggests future research directions. The HistCite software has been used for bibliometric analysis on a sample of 660 s-commerce papers obtained from the ISI Web of Science database. This study analyses these papers to present the details about the influential journals, authors, and universities regarding s-commerce research. Following research themes have been identified based on the content analysis as well as citation mapping of the top-cited 53 s-commerce papers: 1) S-commerce – Purchase Intention, 2) S-commerce – Sharing Intention, 3) Social Media – Marketing and Consumer Engagement, 4) S-commerce – User Preferences and Concerns. Subsequently, a multi-dimensional conceptual model has been developed to highlight the coupling and flow between s-commerce growth drivers, practice indicators, and performance metrics. Finally, future research directions have been recommended.

Summary Cloud computing is becoming a profitable technology because of it offers cost‐effective IT solutions globally. A well‐designed task scheduling algorithm ensures the optimal utilization of clouds resources and reducing execution time dynamically. This research article deals with the task scheduling of inter‐dependent subtasks on unrelated parallel computing machines in a cloud computing environment. This article considers two variants of the problem‐based on two different objective function values. The first variant considers the minimization of the total completion time objective function while the second variant considers the minimization of the makespan objective function. Heuristic and meta‐heuristic (HEART) based algorithms are proposed to solve the task scheduling problems. These algorithms utilize the property of list scheduling algorithm of unrelated parallel machine scheduling problem. A mixed integer linear programming (MILP) formulation has been provided for the two variants of the problem. The optimal solution is obtained by solving MILP formulation using A Mathematical Programming Language (AMPL) software. Extensive numerical experiments have been performed to evaluate the performance of proposed algorithms. The solutions obtained by the proposed algorithms are found to out‐perform the existing algorithms. The proposed algorithms can be used by cloud computing service providers (CCSPs) for enhancing their resources utilization to reduce their operating cost.