The emerging information revolution makes it necessary to manage vast amounts of unstructured data rapidly. As the world is increasingly populated by IoT devices and sensors that can sense their surroundings and communicate with each other, a digital environment has been created with vast volumes of volatile and diverse data. Traditional AI and machine learning techniques designed for deterministic situations are not suitable for such environments. With a large number of parameters required by each device in this digital environment, it is desirable that the AI is able to be adaptive and self-build (i.e. self-structure, self-configure, self-learn), rather than be structurally and parameter-wise pre-defined. This study explores the benefits of self-building AI and machine learning with unsupervised learning for empowering big data analytics for smart city environments. By using the growing self-organizing map, a new suite of self-building AI is proposed. The self-building AI overcomes the limitations of traditional AI and enables data processing in dynamic smart city environments. With cloud computing platforms, the self-building AI can integrate the data analytics applications that currently work in silos. The new paradigm of the self-building AI and its value are demonstrated using the IoT, video surveillance, and action recognition applications.

Purpose: Circular economy (CE) has evolved as a result of the growing environmental awareness, environmental legislation and the need for social responsibility. However, awareness levels of CE are not as high as expected and practices are further behind than they should be, with a significant lack of research around the subject in the literature. Therefore, the purpose of this paper is to examine the current state of awareness levels and the practices around CE in manufacturing firms. Moreover, the study aims to empirically validate one of the earlier proposed CE models.Design/methodology/approach: The study adopts a quantitative survey questionnaire based approach. More than 500 people from various manufacturing organisations were contacted directly over a 40-day long sampling process through the FAME database, personal contacts and LinkedIn. The survey resulted in 103 completed responses. Given the exploratory nature of the study, the data were mainly analysed using descriptive statistics. To validate the CE model, a correlation analysis was also conducted.Findings: The research findings show that with the growing emphasis on CE across the globe by governing bodies, firms are becoming more aware of CE practices. The analysis also shows some useful insights on the state of each of the pillars (economic benefits, environmental impact and resource scarcity) of CE. The findings also indicate that the environmental impact pillar of CE is at a more developed state than the other two pillars which are, mostly, in a research state.Research limitations/implications: The study provides manufacturing firms with a thorough understanding of the state of CE practices and importance of its successful implementation. The findings of the study advocates consideration of all three pillars of CE by managers as a guide to plan for an efficient strategy around CE implementation. Moreover, our study adds to existing efforts by the academic community to raise the awareness towards CE practice among all relevant stakeholders. The findings of this study are based on the responses from a limited 103 survey responses from manufacturing firms.Originality/value: This study adds to the very limited empirical literature on CE awareness and practices in manufacturing firms. This is also one of the first studies attempting to empirically validate an existing CE model.

Blockchain has enormous capabilities to transform the traditional business models in countless ways. Banks in India are building collaborative blockchain ecosystems to create an innovative business model and disrupt the traditional one to create further competitive advantages. The purpose of this study is to examine the relationship between blockchain capabilities (BCC), competitive advantages (CA) and organizational performance (OP). Further, to evaluate the mediating role of CA on the relationship between BCC and OP. In this context, scientific research model consisting of a hypothesis has been developed from the existing literature. The proposed model was tested using statistical data collected from Blockchain specialists, blockchain product marketing managers, experts of future and emergent technology and VP/AVP/Chief Manager/Branch head of banks/ financial Analyst/divisional Managers who are involved in planning and deployment of practical blockchain in banking/financial sector. Data was analyzed and tested through AMOS 22.0 and process macro using a sample of 289 responses. Our empirical result indicated that there is a significant positive relationship between BCC, CA and OP. Furthermore, relationship of BCC and OP partially mediated CA. This paper presents originality and contributes towards the body of knowledge on this subject to understand the relationship and mediation role of CA on the relationship between BCC and OP in Indian banking sector.

Individualized learning plans and corresponding training programs are maintained and organized in most organizations. Employees may be averse to training if they do not see how it contributes to their professional advancement. This is an example of conflict between management and employee interests in a business. The misalignment between management’s offerings and employees’ desires is a significant factor contributing to such a situation. Our research focused on how companies and individuals put training resources to use from a perspective of divergent goals. It provides insights into making employee training more effective. We investigate the relationship between organizational, individual, and training efficacy using the principal–agent theory and the concept of bounded rationality. We attempted to validate three a priori conditions relating to goal congruence, training motivation, and decision-making through in-depth interviews and focus group discussions. As per participant inputs, career aspirations drive employees’ training preferences. The significance of goal congruence in achieving corporate objectives is often neglected in the academic literature. Although goal congruence can be a useful tool in assisting organizations in achieving their stated objectives, enhanced communication and discussion between managers and employees are required in order to improve and align employee goals with the company’s, for the sake of the individual’s and organization’s development. Furthermore, firms should invest in technology-enabled learning that ensures better access to learning, in order to achieve the kind of productivity and profit margins that would benefit everyone involved. We have also proposed a training value transaction model that accommodates the diverse interests. The model depicts the role of goal congruence in enhanced value fulfilment of the principals as well as agents.

Purpose: Along with technology-based ecosystems, healthcare start-ups are expanding multi-fold. On the other hand, underlying uncertainties pose several challenges for these health-tech enterprises at macro-meso-micro level, influencing their business circumstances and profitability. The current study aims to comprehend the macro-, meso- and micro-level barriers that make it difficult for enterprises to set up healthcare technology start-ups. The study also observed the perceived effect of these challenges on firms' performance and cost structure over time. Using the theory of behaviour under uncertainty, the study revealed multiple systemic, sector-related, human and implementation barriers that hinder business growth and lead to uncertainties for health-tech start-ups.Design/methodology/approach: Using a grounded theory approach, the study collected the views of 51 health tech start-ups in the UK using an online participants pool. The data was collected using qualitative data techniques in the form of open-ended essays, and a content analysis using thematic coding process was conducted. The questions centered around the different institutional uncertainties or barriers while setting up or running a healthcare start-up.Findings: The study revealed several macro-, meso- and micro-level barriers these technology-based enterprises perceive in the healthcare industry. These are recognised as systemic barriers, such as lack of funding and procedural issues; sector-related barriers, such as market-related impediments; human barriers, including psychological barriers and resistance to new technology; and implementation barriers, such as operational and personnel issues.Research limitations/implications: The study used qualitative, open-ended essay techniques to collect the data. Future studies may use a mixed-methods approach to provide holistic insights. The study is conducted in a single developed country, the UK. Future work may expand these findings by comparing developed market challenges with those of emerging markets and by assessing the viewpoints of healthcare start-ups.Practical implications: This research will assist the healthcare sector and government understand health tech start-up hurdles and uncertainty. Policymakers must assist start-ups and encourage entrepreneurial innovation. Regulating and enabling policies will help. The paper examines start-ups' macro, meso and micro uncertainties. Policymakers promoting sector entrepreneurship must consider these barriers while designing policy guidelines.Originality/value: The study contributes to the existing literature on technology start-ups, particularly in the healthcare industry, and identifies significant barriers these start-ups face. The study synthesizes research on health-tech start-up uncertainty and bridges the gap between theory and practice by applying empirical findings.