Smart technology, which encompasses the notions of intelligence and sophistication, refers to technologies that have the ability to gather data from their immediate surroundings through embedded sensors and analyse this data remotely. This enables smart technology-based applications and products to possess capabilities such as monitoring, control, optimization, and autonomy (Porter & Hepplemann, 2014). Smart technologies are combinatorial in nature (Brynjolfsson & McAfee, 2014), as they integrate various emerging technologies such as the Internet of Things, cloud computing, and artificial intelligence applications, including voice recognition tools. The combination of these technologies primarily provides three critical capabilities: ubiquitous data, connectivity among objects, individuals, and organizations, and aggregation of information (Adner et al., 2019; Ross et al., 2018). These primary capabilities lead to other derived digital capabilities, including exceptional customer engagement and intelligence, personalization, customization, contextual interaction, and automation. The incorporation of such capabilities into products and applications is revolutionizing organizations and society by imbuing them with smartness and intelligence.
Smart Technologies are now being used everywhere to create smartness in society. Robust, inexpensive computer technologies; advanced algorithms; and vast amounts of data have made smart technologies and AI indispensable to organisations and personal users alike. On the one hand, the smart technologies are changing the way we conduct business by impacting every aspect of an organization, ranging from forecasting, production, and operations to the human resource management and strategy. On the other hand, technologies such as smart digital assistants, smart transportation, smart home solutions, and smart health monitors have created a unique and connected information ecosystem for an individual. This ecosystem is bourgeoning with the advent of new technologies and solutions.
However, the situation is not the same for everyone. For example, a report published by Gartner says only 48% of the CIOs are willing to adopt AI technologies in their organizations in some form or another. Some of the barriers are associated with the maturity of the organization, techno-centricity, fear of the unknown, staff turnover, change management, need for reskilling/training staff and others (Hughes et al., 2016). As a consequence, the uptake of smart technologies has been limited. At the individual level, for example in India, with more than a sixth of the world’s population, only 28% of the population has ever used a smartphone. Despite the need for creating smartness, the adoption of smart technologies is still sparse, and consumers are confronted with many challenges in making them part of their daily life. Thus, both scholarly community and practitioners are interested in understanding these challenges and possible strategies and solutions to overcome these challenges for adopting smart technologies at the individual as well as at the organizational level.
There exists a considerable body of knowledge about the adoption and diffusion of information technologies that have remained largely unchanged over time. Extending renowned scholar Rogers’ (1983) diffusion of innovation theory, Davis (1989) proposed the Technology Acceptance Model (TAM) to understand key factors influencing the adoption of a technological innovation. The fundamental constructs of TAM are “perceived ease of use” and “perceived usefulness”. These constructs determine the attitude and subsequently explain the user’s behavioural intention and actual use of the technological innovation. The extant literature noted that several research scholars (Sharma, 2015; Sharma et al., 2016) adopted the highly cited TAM to explain adoption of a number of technological innovations in various sectors. Venkatesh et al. (2003) reviewed a number of research models pertaining to the acceptance of technological innovations and proposed a unified theory of acceptance and use of technology (UTAUT) to explain technology adoption phenomenon in the context of organizational settings. Many scholars (Irani et al., 2009; Sharma et al., 2016; Yadav et al., 2016; Tamilmani et al., 2022) have adopted UTAUT model to explain the technology adoption and its use in the organizational context of several industries. Furthermore, Venkatesh et al. (2012) extended UTAUT to UTAUT2 with the inclusion of hedonic motivation, price value, and habit in the consumer context. Several scholars (Dwivedi et al., 2016; Alalwan et al., 2017; Shareef et al., 2017; Shareef et al., 2018; Sharma et al., 2018; Ramírez-Correa et al., 2019; Vimalkumar et al., 2021; Mishra et al., 2022) have contextualized UTAUT2 in explaining the adoption of recent technological innovations in various contexts. However, with the emergence of smart technologies such as smart virtual assistants and wearables, there is an urgent need to develop new knowledge about their diffusion and adoption. In response to this need, this special issue is based on a selection of articles presented at the IFIP WG8.6 Conference 2020 held at the Indian Institute of Management, Tiruchirappalli.
In this special issue, we have accepted seven articles on the basis of a rigorous and double-blind review process. The subsequent paragraphs briefly summarize each of the accepted papers with key contributions.
In recent years, the rise of social media and social networking technologies has led to an increased prominence of social commerce (s-commerce). Despite extensive prior research employing various theoretical perspectives to comprehend consumer behavior within the context of s-commerce, the results have been inconsistent. Dwivedi et al. (2023a) proposed an integrated research model that incorporates the perspectives of trust, social support, and social presence. By synthesizing previous empirical findings, the study explores the factors that influence behavioral intention and use behavior of s-commerce consumers. Using a meta-analytic structural equation modeling (MASEM) method, the study analyzed 189 findings reported in 68 social commerce studies to evaluate the structural model. The findings indicate that trust and informational support positively impact behavioral intention, whereas trust and emotional support positively affect use behavior. Additionally, the study reveals that behavioral intention plays a mediating role in the relationship between trust, informational support, and use behavior. Overall, the study’s results contribute to a deeper understanding of the drivers of social commerce behavior and have implications for both research and practice.
Artificial intelligence is finding applications in all functional areas of businesses (Duan et al., 2019; Dai & Singh, 2020, 2021; Dwivedi et al., 2023b; Chatterjee et al., 2023) presented the examination of the application of the meta-analysis based unified theory of acceptance and use of technology (meta-UTAUT) model to predict the behavioral intentions and use behavior of organizational users towards artificial intelligence (AI) integrated customer relationship management (CRM) systems. The study collected data from a sample of 315 organizational users in India, and the proposed hypotheses were tested based on their theoretical foundations, which were subsequently statistically validated. The findings of the study suggest that the quality and satisfaction of CRM systems significantly influence an organization’s employees’ attitudes and intentions toward using AI-integrated CRM systems. However, the compatibility of CRM systems has a limited impact on employees’ attitudes. The results align with the extended UTAUT model and provide valuable insights for organizations and decision-makers in designing AI-integrated CRM systems.
Effective curriculum design at universities requires a deep understanding of the students’ sentiments towards the learning process. The use of Learning Analytics (LA) has the potential to improve student learning experiences and support teacher inquiry. However, limited research exists on the actual use and adoption of LA to support teacher inquiries. Dennehy et al. (2023) conducted a four-year longitudinal study that integrates LA with the steps of teacher inquiries to capture the sentiments of postgraduate students at an Irish university. The study makes significant contributions to the teaching and learning literature. Firstly, it reports on the use of LA to support teacher inquiries over four one-year cycles of a Master of Science in Business Analytics programme between 2016 and 2020. Secondly, it provides evidence-based recommendations on how to optimize LA to support teacher inquiries, with specific attention given to the assimilation of LA into the curriculum design and delivery. Lastly, the study concludes with a research agenda to improve the adoption and integration of LA in the future. The study’s findings are relevant to curriculum designers, educators, and educational policymakers, who seek to enhance the learning experience and improve the quality of education. The integration of LA with the steps of teacher inquiry can lead to better-informed curriculum design decisions and ultimately enhance the student learning experience.
The COVID-19 pandemic has forced organizations and their employees to quickly adapt to working from home (WFH) and rely heavily on technology. Social interactions with colleagues, which are a fundamental feature of the traditional workplace and shape employees’ experience of work, must be reconsidered. However, there is currently limited understanding of how individuals maintain social interactions with colleagues via technology when working from home, which can lead to social isolation and other negative consequences. Lal et al. (2023) conducted a qualitative study utilizing diary-keeping to collect data from 29 individuals who started to work from home full-time due to the pandemic. The study explores how participants conduct social interactions using different technology platforms and how these interactions are embedded in their working lives. The findings highlight the difficulty in maintaining social interactions via technology, such as the absence of emotional intelligence and cues, as well as other factors, such as job uncertainty, increased workloads, and heavy usage of technology that affect their work lives. While some participants are apprehensive about returning to the traditional workplace, where social interactions may be perceived as a distraction, the study emphasizes that organizations need to be aware of the differences in perceptions and feelings towards increased use of digital media while working from home. Thus, managing these differences in a contextualized manner can increase the efficiency and effectiveness of working from home. This study contributes to the existing body of literature on remote working by highlighting employees’ experiences and practices around social interaction with colleagues.
In the present business environment, data-driven predictions play an important role in the decision-making process of organizations. The application of Artificial Intelligence (AI) has allowed product and support teams to conduct more precise experiments across different business domains. Kar and Kushwaha (2023) proposed a framework based on inductive learning, which examines the success and barriers associated with the early adoption of AI through social media platforms. The goal is to analyze the signals arising from conversational opinions, with a particular focus on the facilitators and barriers faced by teams. Through an exploratory and confirmatory study, factors such as 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. The findings are presented through a roadmap for practitioners, which delineates AI as a determinant of success and highlights various organizational factors critical to AI adoption’s success. The study contributes to the Information Systems (IS) literature by exploring AI as a key determinant of success and introducing several organizational factors into the model.
Venumuddala and Kamath (2023) conducted a study based on a workplace ethnography conducted between January and May 2020 in an AI research lab of a service-based IT organisation in India, which had shifted its operations from co-located to work-from-home (WFH) due to a recent pandemic. The qualitative data for this study is based on the field notes of the ethnographer, who worked as a full-time intern in a running AI project within this laboratory. In this paper, authors discussed socio-technical aspects and specific challenges of distributed team-working that are encountered due to the WFH norms faced by such emerging research units, which are rapidly diffusing across the IT industry in the offshoring context, particularly in India. Work system theory is used as a map to bring out key findings from our ethnographic observations. The findings highlight the importance of having workflows that are compatible with the specific work roles in such emerging work systems, particularly for the beginner roles in the AI space. Our study contributes to the Information Systems literature by depicting the challenges of distributed teams in a relatively novel setting that is emerging in offshoring contexts like the Indian IT sector. It also suggests implications for managers handling AI projects and tackling employee-focused Human Resource practices in such settings.
Enterprise Architecture (EA) initiatives are designed to guide the alignment of Information Technology (IT) with business through the use of functions, processes, tools, instruments, and principles. Despite being touted as a solution to ensure IT contributes effectively to business, many EA initiatives either do not succeed or fail altogether. Despite this, the literature on the reasons for these failures remains underdeveloped. Gong and Janssen (2023) conducted a study aiming to identify the factors that contribute to EA initiative failure. To achieve this, the authors identified 15 factors and asked eight EA experts to evaluate their influence based on a combination of Grey Systems Theory, Decision-Making and Trial Evaluation Laboratory (DEMATEL), and Interpretative Structural Modeling (ISM) methodologies. The findings indicate that the factors are interconnected and operate in complex causal chains. this study reveals the underlying factor and suggests that increasing high-level managers’ EA knowledge, as well as ensuring enterprise architects possess strong communication and leadership skills, can serve as a starting point to prevent EA failure. Only after these initial steps should organizations focus on organizing the EA function. This study contributes to the literature on EA failure by providing a comprehensive understanding of the factors that contribute to EA failure and the necessary steps to prevent it.
The Guest Editors would like to thank Professor H. Raghav Rao, who serves as the Editor-in-Chief of Information Systems Frontiers, for his invaluable suggestions and guidance throughout the process from the conception of the idea to the publication of this special issue. The journey of this special issue has been quite a long one, mainly due to the pandemic of Covid-19. Furthermore, the guest editor would like to express his sincere gratitude to all the authors and reviewers who have made significant contributions to this special issue.
