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This study explores the determinants of the intention to adopt blockchain technology for sustainable supply chain management in Indian micro, small, and medium enterprises. Different from existing studies that advocate the use of socio-technical theory for blockchain technologies, we develop a new theoretical framework, called "SOS," based on a review of the existing literature. This is an adaptation of the technology–organization–environment framework that examines the measures and scales from socio-technical, organizational, and sustainability contexts. We use ADANCO 2.3.2 for variance-based structural equation modeling. The results show that two of the nine hypotheses are negatively significant, while the rest are positive. In our context, social sustainability and computer self-efficacy are strongly negatively significant for the adoption intention of blockchain technology in our context. Software quality and environmental sustainability are strongly positively significant. Meanwhile, collaboration, economic sustainability, and relative advantage mediated by experience are positively significant. Our study contributes to the literature by offering a new theoretical framework, fresh insights from the Indian industry, and several recommendations to practitioners. [ABSTRACT FROM AUTHOR]

Driven by the need to provide continuous, timely, and efficient customer service, firms are constantly experimenting with emerging technological solutions. In recent times firms have shown an increased interest in designing and implementing artificial intelligence (AI)-based interactional technologies, such as conversational AI agents and chatbots, that obviate the need for having human service agents for the provision of customer service. However, the business impact of conversational AI is contingent on customers using and adequately engaging with these tools. This engagement depends, in turn, on conversational AI's similarity, or likeness to the human beings it is intended to replace. Businesses therefore need to understand what human-like characteristics and competencies should be embedded in customer-facing conversational AI agents to facilitate smooth user interaction. This focus on "human-likeness" for facilitating user engagement in the case of conversational AI agents is in sharp contrast to most prior information systems (IS) user engagement research, which is predicated on the "instrumental value" of information technology (IT). Grounding our work in the individual human competency and media naturalness literatures, we theorize the key role of human-like interactional competencies in conversational AI agents—specifically, cognitive, relational, and emotional competencies—in facilitating user engagement. We also hypothesize the mediating role of user trust in these relationships. Following a sequential mixed methods approach, we use a quantitative two-wave, survey-based study to test our model. We then examine the results in light of findings from qualitative follow-up interviews with a sampled set of conversational AI users. Together, the results offer a nuanced understanding of desirable human-like competencies in conversational AI agents and the salient role of user trust in fostering user engagement with them. We also discuss the implications of our study for research and practice. [ABSTRACT FROM AUTHOR]

This study aims at finding out the important determinants of prevalence rate of COVID-19 in the Asian continent using spatial analysis. The impact of climatic, socioeconomic, demographic, and health status variables on the prevalence rate of COVID-19 is seen through various spatial models such as Spatial lag, Spatial error, Geographically Weighted regression model, and Multiscale Geographically Weighted regression model. The performance of the models is compared under different comparison criteria. It is found that among all, Multiscale Geographically Weighted regression model outperformed other competitive models. Findings also indicate that cardiovascular health, prevalence of smoking habit, human development index, and net migration rate played significant role in defining the prevalence rate of COVID-19 in Asia. [ABSTRACT FROM AUTHOR]

Given the high employee turnover rates among information technology (IT) professionals, firms are on the lookout for ways to retain them. With this end in view, to keep their professionals committed and satisfied, many IT firms are making proactive efforts to induce a favourable organisational environment by encouraging employees towards extra‐role organizational citizenship behaviors (OCBs). However, there is scant evidence as to whether orchestrating proactively induced OCB results in positive work attitudes. Our study contributes by theorising the mechanisms through which OCB fosters positive work attitudes among IT professionals, which is expected to lower their turnover rates. Grounding our research in self‐perception and self‐determination theories, we hypothesize the relationships between IT professionals' OCB and their affective attitudes towards their organisation and job, as being mediated by their cognitive evaluations of the 'meaning of their IT work'. We test the theorised model with data collected through a large‐scale two‐wave survey design from a multinational IT‐services company. The results offer a nuanced understanding of the relationship between OCB and positive work attitudes for IT professionals, which have significant implications for research and practice. [ABSTRACT FROM AUTHOR]

A two warehouse inventory model for deteriorating items is considered with exponential demand rate and permissible delay in payment. Shortage is not allowed and deterioration rate is constant. In the model, one warehouse is rented and the other is owned. The rented warehouse is provided with better facility for the stock than the owned warehouse, but is charged more. The objective of this model is to find the best replenishment policies for minimizing the total appropriate inventory cost. A numerical illustration and sensitivity analysis is provided.

An amplified reflection and exploitation-based distributed denial of service (DDoS) attack allows an attacker to launch a volumetric attack on the target server or network. These attacks exploit network protocols to generate amplified service responses through spoofed requests. Spoofing the source addresses allows attackers to redirect all of the service responses to the victim's device, overwhelming it and rendering it unresponsive to legitimate users. Mitigating amplified reflection and exploitation attacks requires robust defense mechanisms that are capable of promptly identifying and countering the attack traffic while maintaining the availability and integrity of the targeted systems. This paper presents a collaborative prediction approach based on machine learning to mitigate amplified reflection and exploitation attacks. The proposed approach introduces a novel feature selection technique called closeness index of features (CIF) calculation, which filters out less important features and ranks them to identify reduced feature sets. Further, by combining different machine learning classifiers, a voting-based collaborative prediction approach is employed to predict network traffic accurately. To evaluate the proposed technique's effectiveness, experiments were conducted on CICDDoS2019 datasets. The results showed impressive performance, achieving an average accuracy, precision, recall and F1 score of 99.99%, 99.65%, 99.28% and 99.46%, respectively. Furthermore, evaluations were conducted by using AUC-ROC curve analysis and the Matthews correlation coefficient (MCC) statistical rate to analyze the approach's effectiveness on class imbalance datasets. The findings demonstrated that the proposed approach outperforms recent approaches in terms of performance. Overall, the proposed approach presents a robust machine learning-based solution to defend against amplified reflection and exploitation attacks, showcasing significant improvements in prediction accuracy and effectiveness compared to existing approaches. [ABSTRACT FROM AUTHOR]

The ever-increasing number of multi-vector cyberattacks has become a concern for all levels of organizations. Attackers are infecting Internet-enabled devices and exploiting them to carry out attacks. These devices are unwittingly becoming part of carrying out cyberattacks. Many studies have proposed machine learning–based promising solutions to stamp out cyberattacks preemptively. We review the machine learning techniques and highlight some promising solutions in recent studies. This study provides the advantage of experimenting with the developed solutions on modern datasets. This survey aims to provide an insightful organization of current developments in cybersecurity datasets and give suggestions for further research. We identified the most frightful cyberattacks and suitable datasets having records related to the attack. This paper discusses modern datasets such as CICIDS2017, CSE-CIC-IDS-2018, CIC-DDoS2019, UNSW-NB15, UNSW-TonIOT, UNSW-BotIoT, DoHBrw2020, and ISCX-URL-2016, which include records of recent sophisticated cyberattacks. This paper will focus on these modern datasets, retrieve detailed knowledge, and experiment with the most commonly used machine learning algorithms. We identify datasets as a significant centric topic that can be addressed with innovative machine learning approaches and solutions to defend against cyberattacks. [ABSTRACT FROM AUTHOR]

Background: Globalization has resulted in social, economic, political, commercial, and technological integration. A social problem needs a global collaborative view to find a solution. Wide-ranging partnerships are essential to achieve developmental goals, with public and private partners pooling their resources and competencies. The private sector contributes by engaging in corporate social responsibility (CSR) initiatives. These initiatives can significantly impact by leveraging emerging technologies such as Artificial Intelligence (AI). While many support AI, some believe that AI is a threat to humanity. With mixed attitudes towards AI, its adoption in CSR is somewhat limited. This research leverages the Technology-Organization-Environment (TOE) framework to explore factors influencing AI adoption intention from an organizational perspective. Method: The factors were identified from a thorough literature review and mapped with Carroll's CSR framework. The theorized model was tested via a sample response of 124 Indian firms. Results: The findings of this research share insight into the influence of the nine technological, organizational, and environmental factors and dives deeper through the post-hoc analysis of the variations due to the size of the firm, public or private orientation, and industry sector. Conclusions: Along with the contributions to literature and theory, this research study has several significant contributions to firms, AI products, service companies, AI strategists, and application developers. [ABSTRACT FROM AUTHOR]