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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).Other Information Published in: Annals of Operations Research License: https://creativecommons.org/licenses/by/4.0See article on publisher's website: http://dx.doi.org/10.1007/s10479-022-04954-3

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.

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.