Chatbots are radically redefining the customer service landscape. With the advent of AI-enabled chatbots, like ChatGPT, organizations are adopting chatbots to provide better customer services; however, the user experience has been given less attention. Building on IS success model and cognitive absorption theory, we posit that system and user characteristics enhance cognitive absorption amongst users, such that the relationship varies between anthropomorphic (e.g., human-like) and non-anthropomorphic chatbots. We undertook a cross-sectional comparative study, which was analyzed using PLS-SEM and fsQCA. Where PLS-SEM provided limited inferential insights about the differences between anthropomorphic and non-anthropomorphic chatbots, the FsQCA analysis resulted in three configurations of attributes for non-anthropomorphic and two configurations for anthropomorphic chatbots, which lead to higher cognitive absorption. The findings extend the existing literature, suggesting that anthropomorphic and non-anthropomorphic chatbots impact cognitive absorption through separate system and user characteristics configurations.

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Governments worldwide invest heavily in digital initiatives to develop information societies with connected and actively engaged citizens, but problems like lacking sustained engagement and quality of participation still plague them. We undertook a systematised literature review on the Scopus and Web of Science (WoS) databases, covering dispersed literature surrounding Digital Citizen Empowerment (DCE) from the past two decades.Categorising the literature under four thematic categories or strategies of DCE: Digital Activism (DA), Multi-channel Service Delivery (MCSD), Participatory Budgeting (PB), and Deliberative Governance (DG) critical comparative analysis is done. A conceptual model of DCE, covering how theories from different inter-disciplinary areas of political, social, and information science influence the development of information societies and DCE is presented. Action points in our conceptual model are mapped to policy objectives targeting improved delivery of empowering policy goals by practitioners, and future research opportunities in the context of DCE are discussed.@en

Although information systems (IS) success has been given much attention in IS literature, failure has received lesser attention. This study empirically validates a model of digital service failure for consumers by integrating three dimensions from Tan's failure model and one dimension from DeLone and Mclean's Information Success model. The factors have been mapped to Expectation Disconfirmation Theory (EDT). Experiential survey approach has been used to collect primary data from information systems users who have experienced digital service failure. Structural equation modeling (SEM) has been used for model testing and validation to measure the impact of information, system, functional and service failure of digital service on end users. The findings suggests that information, functional, system and service failure has an impact on the consumers through the outcome, process, cost and user satisfaction of digital services.

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Purpose: Although blockchain is often discussed, its actual diffusion seems to be varying for different industries. The purpose of this paper is to explore the blockchain technology diffusion in different industries through a combination of academic literature and social media (Twitter). Design/methodology/approach: The insights derived from the academic literature and social media have been used to classify industries into five stages of the innovation-decision process, namely, knowledge, persuasion, decision, implementation and confirmation (Rogers, 1995). Findings: Blockchain is found to be diffused in almost all industries, but the level of diffusion varies. The analysis highlights that manufacturing industry is at the knowledge stage. Further public administration is at persuasion stage. Subsequently, transportation, communications, electric, gas and sanitary services and trading industry had reached to the decision stage. Then, services industries have reached to implementation stage while finance, insurance and real estate industries are the innovators of blockchain technologies and have reached the confirmation stage of innovation-decision process. Practical implications: Actual implementations of blockchain technology are still in its infancy stage for most of the industries. The findings suggest that specific industries are developing specific blockchain applications. Originality/value: To the best of the authors’ knowledge this is the first study which is using social media data for investigating the diffusion of blockchain in industries. The results show that the combination of Twitter and academic literature analysis gives better insights into diffusion than a single data source.

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The next step after smart cities is the creation of digital or smart nations. A digital nation requires a national transformation across diverse institutions including the urban and rural areas of a society. Besides diverse social innovation initiatives, sustainability is a key aspect, so that rather than greenfield projects, long-term solutions will involve brownfield, smart city projects. A digital transformation to a digital or smart nation requires a great deal of innovation in planning, process re-engineering and execution. Whereas research work and policy actions are traditionally focused on a city level, these efforts need to be widened to the national level. Existing city-scale theories and frameworks may be used and adapted to meet the larger-scale needs of the future smart nations. The new issues and research challenges which arise need to be addressed through interdisciplinary approaches. This special issue on the theme of digital nations attempts to address the need in the academic literature to provide a better understanding of digital nations.

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Although blockchain has attracted a great deal of attention from academia and industry there is a lack of studies on acceptance drivers. This study explores blockchain acceptance by mining the collective intelligence of users on Twitter. It maps blockchain user acceptance drivers to technology acceptance constructs. The analysis shows that users are attracted by security, privacy, transparency, trust and traceability aspects provided by blockchain. On Twitter more discussions on blockchain benefits than on drawbacks. Initial coin offering (ICO) is extensively discussed. The study provides guidelines for managers and concludes by presenting the limitations of the study along with future research directions.

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Elections are among the most critical events in a national calendar. During elections, candidates increasingly use social media platforms to engage voters. Using the 2016 US presidential election as a case study, we looked at the use of Twitter by political campaigns and examined how the drivers of voter behaviour were reflected in Twitter. Social media analytics have been used to derive insights related to theoretical frameworks within political science. Using social media analytics, we investigated whether the nature of social media discussions have an impact on voting behaviour during an election, through acculturation of ideologies and polarization of voter preferences. Our findings indicate that discussions on Twitter could have polarized users significantly. Reasons behind such polarization were explored using Newman and Sheth's model of voter's choice behaviour. Geographical analysis of tweets, users, and campaigns suggests acculturation of ideologies among voting groups. Finally, network analysis among voters indicates that polarization may have occurred due to differences between the respective online campaigns. This study thus provides important and highly relevant insights into voter behaviour for the future management and governance of successful political campaigns.

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Elections are the most critical events for any nation and paves the path for future growth and prosperity of the economy. Due to its high impact, a lot of discussions take place among all stakeholders in social media. In this study, we attempt to examine the discussions surrounding USA Election, 2016 in Twitter. Further we highlight some of the domains influencing the voter behaviour by applying the outcome of Twitter analytics to Newman and Sheth’s model of Voter Choice. Through the analysis of 784,153 tweets from 287,838 users over 18 weeks, we present interesting findings on what may have affected the polarization of USA elections.

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