The proliferation of fake news across the internet has become a significant area of concern globally. The COVID-19 pandemic highlights that the propagation of fake news can jeopardize public health and heighten irrational behavior amongst consumers, like panic buying. However, the existing literature has not explored its impact on the supply chain. This study uses reactance and cognitive load theories to examine a model for fake news propagation causing supply chain disruption. Our research employed a computationally intensive big data-driven method across three studies to demonstrate misinformation's impact on supply chain disruption, identify the factors creating this impact, and validate an inferential analysis model to explain this phenomenon. Results highlight the relationship between unverified information sharing (UIS) and perceived threat, perceived scarcity, fear appeal, and information overload with panic buying. The paper dwells more profoundly on fake news disrupting the supply chain.

The flow of distorted information on social media platforms cannot always be handled. As a result, digital misinformation has become a significant social, political, and technological risk factor. Extant research on detecting misinformation in social networks has focused on using metadata or characteristics of influential actors (users) and their group dynamics in isolation, but less on the act (information content) itself and on developing an integrated approach. We unify them to produce a data science framework to detect valid instances of misinformation from social media such as Twitter. Here we develop novel and efficient algorithmic improvements to extract predictable components from users’ data. The model results demonstrate a significant increase in performance beyond typical incremental improvements. This research proposes a novel term weighting scheme, clique-based features, and a metadata-based feature. These contributions to the data science literature can be helpful for future studies in the social media context.

Artificial intelligence (AI) transits from merely adopted technology to fueling everyday decision-making systems from medication to navigation. With this combination of AI in decision-making systems (ADMS), the present study explores how text-based users' data from social media helps organize the users' perspectives of ADMS? To investigate our research questions, we used a framework consisting of three phases, exploratory, confirmatory, and validatory. We applied hierarchy clustering and topic modeling in the exploratory study, hypothesis building, and empirical analysis during the confirmatory study and support vector machine (SVM) in the validatory study. Our findings suggest that users are primarily concerned about the risk involved in using ADMS. Factors like accountability, self-efficacy, knowledge of ADMS individuals' attitudes towards ADMS impact the perception of ADMS among individuals. This study's theoretical and practical implications have great scope as ADMS is still in its elementary stage.

Experts claim that the world is increasingly polarized by emerging social media platforms. The political actors amplify the polarization through their agents' user-generated content. The extreme political ideologies sway the people sitting on the fence on these social media platforms. Using tweets on a recent policy change on identity in India, the present study seeks to perform a scientific analysis of the polarization of the debates within ordinary citizens' groups from a theoretical lens. We further highlight some of the crucial trends that triggered these polarized discussions in general. Through the lens of Echo chambers and Herd behavior, this study provides valuable insights surrounding the influencers and individuals involved in this discussion where the polarization of preferences is witnessed. Proposing a novel design of a root-level influencer, this study establishes them as polarization actors on a social media platform (Twitter). Through various engagement metrics, we also identify the role of targeted communication (hashtags) and similarity in the users' discussion across the political domain as potential behavioral explanations for opinion polarization on Twitter.

Monitoring buyer experience provides competitive advantages for suppliers as buyers explore the market before reaching a salesperson. Still, not many B2B suppliers monitor their buyers' expectations throughout their procurement journey, especially in MSMEs and SMEs. In addition, the inductive research on evaluating buyer experience in buyer-supplier relationships is minimal, leaving an unexplored research area. This study explores antecedents of buyer experience during the buyer-supplier relationship in MSMEs and SMEs. Further, we investigate the nature of the influence of extracted precursors on the buyer experience. Firstly, we obtain the possible antecedents from the literature on buyer-supplier experience and supplier selection criteria. We also establish hypotheses based on transaction cost theory, resource-based view (RBV), and information processing view. Secondly, we employ an investigation based on the social media analytics-based approach to uncover the antecedents of buyer experience and their nature of influence on MSMEs and SME suppliers. We found that buyer experience is influenced by sustainable orientation, management capabilities (such as crisis management and process innovation), and suppliers' technology capabilities (digital readiness, big data analytical capability).

The increasing user base of people using social media platforms to interact and report their individual opinions on a subject matter is generating a volume of text data that is nonlinear. This presents an enormous number of possibilities for experts to experiment, build a framework, and make insights related to user behavior. If a piece of information is factual, essential, and helpful, then the right set of words and social networks should be utilized to cascade the information to all. Through current research, we propose a novel deep learning framework to predict information popularity on Twitter, measured through the retweet feature of the tool and algorithmically created features. We perform this research with the hypothesis that retweeting behavior can be an outcome of a writer's practice of semantics and grasp of the language. When we read any sentence, the understanding of the sentence does not start from scratch, but instead builds upon the knowledge in a sequence of reading and interpretation of the phrases used in the text. This rule of semantics can be used to create word features. The extracted features can be utilized to train a deep learning model like long short-term memory that has firmly proven its importance in learning hidden trends in any data. The long short-term memory framework has the capability of storing previous learnings and using them when needed. As an outcome of the experimentation proposed we use the word expletives along with word-embedding features to successfully present a generalizable deep neural network framework to classify tweets with a high potential for being retweeted, and tweets with a low possibility of being retweeted.

With the advent of web 2.0, modern societies produce a vast amount of data, and merely keeping up with storage and transmission is difficult; analyzing it to extract useful information has become further challenging. All the historical research in healthcare data processing is more concentrated on formal clinical data. There lies a lot of valuable yet idle lying data in the non-clinical information as well. The proposed study combines the state of the art methods within distributed computing, text retrieval, clustering methods, and finally, using a classification method to a computationally efficient system that can clarify cancer patient trajectories based on non-clinical and freely available online forum posts. The motivation is that informed patients, caretakers, and relatives often lead to better overall treatment outcomes due to enhanced possibilities of proper disease management. The resulting software prototype is fully functional and built to serve as a test bench for various text information retrieval and visualization methods. Via the prototype, we demonstrate a computationally efficient clustering of posts into cancer-types and subsequent within-cluster classification into trajectory related classes. The system also provides an interactive graphical user interface allowing end-users to mine and oversee the valuable information.

Hosting conversational responses on the official websites of products and services companies is an essential marketing aspect. With Artificial Intelligence’s help to make conversational interactivity more intuitive to existing and potential customers visiting the websites, managers can notch up the return on marketing investments. This motivated us to study empirically and develop the MarkBot framework, a chatter robot on the management design principles. The framework uses an Artificial Intelligence application to respond to a website visitor’s browse through the product catalog. Neural network (NN) architectures are known to achieve remarkable performances in synthetic text predictions. We use a long short-term memory recurrent neural network (LSTM) to predict the user’s responses through a chatbot in the current work. The proposed framework reduces the lead time for the firms to adopt MarkBot. We empirically prove using user-generated content on social media platforms like Twitter in responses and queries to digital campaigns on the same product. With new businesses failing to venture into the space of hosting a chatbot owing to no historical data or existing firms yet to host a chatbot, the proposed MarkBot fuelled by user-generated content can have a substantial managerial implication. The management frameworks used to theorize the MarkBot also make it a theoretical contribution for future Information Systems scholars to conceptualize in the marketing field.

The importance of data-driven decisions and support is increasing day by day in every management area. The constant access to volume, variety, and veracity of data has made big data an integral part of management studies. New sub-management areas are emerging day by day with the support of big data to drive businesses. This study takes a systematic literature review approach to uncover the emerging management areas supported by big data in contemporary times. For this, we have analyzed the research papers published in the reputed management journals in the last ten years, fir using network analysis followed by natural language processing summarization techniques to find the emerging new management areas which are yet to get much attention. Furthermore, we ran the same exercise in each of these management areas to uncover these areas better. This research will act as a reference for future information systems (IS) scholars who want to perform analysis that is deep-dive in nature on each of these management areas, which in the coming times will get all the due attention to become dedicated research domains in the management area. We finally conclude the study by identifying the scope of future research in each of these management areas, which will be a true value addition for IS researchers.

Many B2B firms have widely accepted AI-based chatbots to provide human-like service interaction at different customer touchpoints in recent years. One of the objectives behind introducing this technology is to provide an enhanced, live channel Customer Experience (CX) all round the clock. Researchers have focused on delivering the CX by improvising the chatbot's internal algorithm, giving limited attention to CX theories from management literature, which leaves a gap. With the proposed paper, we have investigated the influencing factors of AI-based chatbots from the lens of CX theories for B2B firms. In this paper, a model for organizing CX has been proposed using the diffusion of innovation theory, trust commitment theory, information systems success model, and Hoffman & Novak's flow model for the computer-mediated environment and verified using the social media data. The methodology used for this study is the social media analytics-based content analysis method (sentiment analysis, hierarchical clustering, topic modeling) for data preparation, followed by lasso and ridge regression for model verification. The results suggest that CX in B2B enterprises using chatbots is influenced by these bots' overall system design, customers' ability to use technology, and customer trust towards brand and system.

Data-driven predictions have become an inseparable part of business decisions. Artificial Intelligence (AI) has started helping the product and support teams perform more accurate experiments in various business settings. This study proposes a framework for businesses based on inductive learnings related to success and barriers shared on social media platforms. Our goal is to analyse the signals emerging from these conversational opinions from the early adoption of AI, with a focus towards facilitators and barriers faced by teams. Factors like efficiency, innovation, business research, product novelty, manual intervention, adaptability, emotion, support, personal growth, experiential learning, fear of failure and fear of upgradation have been identified based on an exploratory study and then a confirmatory study. We present the learnings through a roadmap for practitioners. This study contributes to the IS literature by delineating AI as a determinant of success and introduces a lot of organizational factors into the model.

Researchers have been experimenting with various drivers of the diffusion rate like sentiment analysis which only considers the presence of certain words in a tweet. We theorize that the diffusion of particular content on Twitter can be driven by a sequence of nouns, adjectives, adverbs forming a sentence. We exhibit that the proposed approach is coherent with the intrinsic disposition of tweets to a common choice of words while constructing a sentence to express an opinion or sentiment. Through this paper, we propose a Custom Weighted Word Embedding (CWWE) to study the degree of diffusion of content (retweet on Twitter). Our framework first extracts the words, create a matrix of these words using the sequences in the tweet text. To this sequence matrix we further multiply custom weights basis the presence index in a sentence wherein higher weights are given if the impactful class of tokens/words like nouns, adjectives are used at the beginning of the sentence than at last. We then try to predict the possibility of diffusion of information using Long-Short Term Memory Deep Neural Network architecture, which in turn is further optimized on the accuracy and training execution time by a Convolutional Neural Network architecture. The results of the proposed CWWE are compared to a pre-trained glove word embedding. For experimentation, we created a corpus of size 230,000 tweets posted by more than 45,000 users in 6 months. Research experimentations reveal that using the proposed framework of Custom Weighted Word Embedding (CWWE) from the tweet there is a significant improvement in the overall accuracy of Deep Learning framework model in predicting information diffusion through tweets.

Artificial Intelligence has gradually materialized as an independent research field within information systems and business domains. The new forms of work evolving in the business require substantial experimentation, lead generations, and real-time recommendations. This has driven the extraordinary increase in the adoption of Artificial Intelligence technologies. Even with front runner organizations across the domain envisioning the advantages of early adoption of Artificial Intelligence technologies, some organizations scuffle the adoption owing to various barriers. This paper analyzes the characteristics that lead to and factors inhibiting the adoption of Artificial Intelligence at the organization-level. Through this paper, we report the results of Twitter conversations involving small and medium scale organizations about their level of adoption of Artificial Intelligence and barriers that they are facing. Through this analysis, we provide insights and agenda to help the executives of small and medium scale organizations to prepare for the adoption of Artificial Intelligence.

In a world of ever-increasing microblogs, the opinions, preferences, support, frustration, anger and other emotions of people regarding various events and individuals, surface in varied ways on social media. The purpose of this research is to find those hidden patterns in raw data, which can explain meaningful insights about its creation, the groups of people who created them and their sentiments which led to the generation of such data. Sentiment analysis has always been an effective methodology for discovering emotion and bias towards or against a situation, topic, thought or initiative and finding other meaningful insights from unstructured data. In this research, we attempted a type of document clustering wherein we attempted to classify the sentiments of the citizens of India as they micro-blogged their opinions, thoughts, views and ideas during the implication of the Citizenship Amendment Act (CAA) on the social networking site, Twitter. By analyzing the tweets of 13,000 twitter users during a specific timeline during which the discussion regarding the CAA was at its peak, we analyzed the sentiment of those twitter users by clustering their tweets (documents) into four sentiment groups with the help of Latent Dirichlet Allocation (LDA) which is an important tool for topic modelling in the domain of sentiment analysis. Using political ritual theory, the present paper examines the sentiments of people who tweeted during a protest in India. After the classification, our research also maps the online political behaviour of these 13,000 social media participants to the postulates of political ritual theory which is explained by previous research regarding the behaviour of physically co-existing political participants and also justifies this display of various sentiments regarding the CAA in the footsteps of political rituals.

Artificial Intelligence has been increasingly gaining acceptance across advanced functions in numerous fields and industries. This includes marketing, customer support, and leads generation in healthcare, transportation, education, and off late in e-commerce. Machine learning as a subset of artificial intelligence techniques provides various algorithms that enable machines to learn from historical data and make realtime predictions on numbers and texts. Most of the businesses nowadays are trying to increase their reach and making sure that they are available to cater to the customers when they need help. This also enables the companies to market and respond to the queries of potential customers on a realtime basis. Chatter robots or chatbot is one such application of machine learning which allows the business to provide round the clock support to customers and potential leads for marketing questions. Most of the business fail to venture in the domain of hosting chatbot on the website as they do not have enough conversational data with them to train the machine learning algorithm and wait for years to collect enough sample. With the proposed language model-driven chatbots, businesses starting fresh in the domain of the hosting this application can use the user-generated content on social media to fuel the backend framework for the chatbots and start hosting the application.

Artificial Intelligence (AI) has now evolved from a phase of being merely adopted as a new system to now fueling the decision-systems to generate specific data. Borrowing from the social sciences and emerging sub-body of mathematics research literature and theories around algorithmic knowledge and value realization leads to the formation of perceptions around the confidence of allowing AI to be at the heart of any domain’s decision-making. This amalgamation of AI in decision-making systems (ADMS). The current study has undertaken attempts to link personal attributes to perceptions around ADMS with the boundary constraints of these perceptions, namely the stretch to which these perceptions vary across media and domain contexts. A scenario-based survey instrument has been used to collect the data from two sets of ADMS stakeholders, vis-à-vis owners and end-users of ADMS. Analysis of the collected data from this sample (N = 558) reveals that these stakeholders are by and large anxious about the risks associated with ADMS. They have a mixed and general attitude towards the on-field usefulness and fairness of ADMS outcomes at the societal level. These generic frames of mind are driven mainly by the individual traits and involvement and accountability in the domain like a revenue-generating business, social (healthcare), and the oversight of ADMS. Theoretical, management practice, and societal impacts about these findings are also discussed along the current work’s final sections.