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Artificial intelligence (AI) applications’ usage in Chief Officers’ (CXOs’) decision-making is a topic of current research interest. A fundamental dilemma is carefully planning an effective combination of a CXO’s professional experiences and AI applications’ decision-making responsibility. However, the existing literature fails to specify the value of co-creation of AI applications and the human experience in managerial decision-making. To address this gap in the literature, the authors’ examine how an ideal cognitive-technology fit can be created between human experiences and AI-based solutions at CXO-level decision-making using the theoretical lens of the Service-Dominant Logic.

The authors’ employed a grounded theory approach and conducted a focus group discussion with seven participants to shed light on the factors that may balance AI applications’ usage and CXOs’ experience in making business decisions. This was followed by 21 in-depth interviews with employees from knowledge-intensive professional service firms to validate the findings further of a new phenomenon. Further, given the newness of the phenomenon, this approach allowed researchers a retrospective and real-time understanding of interviewees’ experiences of the phenomenon under consideration.

The advantages and constraints of both CXOs’ experiences and AI applications deserve due consideration for successfully implementing technology in knowledge-intensive professional service organizations.

This study may appeal to researchers and practitioners interested in the future of decision-making, as the authors’ study findings advocate for balancing CXO’s expertise and the use of AI in decision-making.

Based on the preliminary findings, the authors developed a theoretical framework to understand the factors that govern AI implementation in an organization and how a competitive strategy may emerge from value co-created by AI applications and CXOs’ experience, particularly in knowledge-intensive professional service firms.

This study aims to investigate India’s poor performance in the overall implementation of e-Governance despite significant improvements in the quality and scope of e-Government services in the country.

The study proposes a conceptual model based on four hypotheses that are tested using the structural equation modeling technique. Based on the results obtained, the conceptual model is suitably modified.

The major finding of the study is that “English proficiency” and “digital divide” sequentially mediate the relationship between “service quality” and the “willingness to adopt e-Governance” in India.

The conceptual model emerging from the study adds to the literature on e-Governance service quality and adoption. However, a longitudinal or experimental study may provide more insights into the context.

Primarily, this study would interest agencies involved in developing and delivering e-Governance services. Besides, the findings may be useful for the “big data companies” that are interested in gaining direct access to the last-mile users of developing countries.

The study intends to provide directions to policymakers on improving access to e-Governance services for the people who are deprived of the benefits of such services.

This is the first study to incorporate both English proficiency and digital divide in a single conceptual model in the literature related to the relationship between service quality and the willingness to adopt e-Governance.

From a technological determinist perspective, machine learning (ML) may significantly contribute towards sustainable development. The purpose of this study is to synthesize prior literature on the role of ML in promoting sustainability and to encourage future inquiries.

This study conducts a systematic review of 110 papers that demonstrate the utilization of ML in the context of sustainable development.

ML techniques may play a vital role in enabling sustainable development by leveraging data to uncover patterns and facilitate the prediction of various variables, thereby aiding in decision-making processes. Through the synthesis of findings from prior research, it is evident that ML may help in achieving many of the United Nations’ sustainable development goals.

This study represents one of the initial investigations that conducted a comprehensive examination of the literature concerning ML’s contribution to sustainability. The analysis revealed that the research domain is still in its early stages, indicating a need for further exploration.

Citizens often perceive surveillance by government authorities as oppressive and, hence, demonstrate reluctance in value co-creation from such services. This study aims to investigate the challenges and benefits of citizen empowerment through technology-driven surveillance or “smart surveillance.”

Guided by Dynamic Capability theory, the authors conduct in-depth interviews with officers in-charge of surveillance in smart cities. Given the contemporary advancements, this approach allows a retrospective and real-time understanding of interviewees’ experiences with smart surveillance.

The authors develop five propositions for citizen empowerment through smart surveillance to summarize the findings of this study.

This study advances the relevance of Dynamic Capability in public administration.

Smart city authorities and policymakers may leverage the insights provided in this study to design appropriate policies for smart surveillance.

The authors find that factors such as digital technology and infrastructure, information management, skill divide and perceived return on investment may influence citizen empowerment through smart surveillance.