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Purpose: The main aim of the study is to provide a tool for non-financial information in decision-making. We analysed the non-financial data in the annual reports in order to show the usage of this information in financial decision processes.

Need for the Study: Main financial reports such as balance sheets and income statements can be analysed by statistical methods. However, an expanded financial reporting framework needs new analysing methods due to unstructured and big data. The study offers a solution to the analysis problem that comes with non-financial reporting, which is an essential communication tool in corporate reporting.

Methodology: Text mining analysis of annual reports is conducted using software named R. To simplify the problem, we try to predict the companies’ corporate governance qualifications using text mining. K Nearest Neighbor, Naive Bayes and Decision Tree machine learning algorithms were used.

Findings: Our analysis illustrates that K Nearest Neighbor has classified the highest number of correct classifications by 85%, compared to 50% for the random walk. The empirical evidence suggests that text mining can be used by all stakeholders as a financial analysis method.

Practical Implications: Combining financial statement analyses with financial reporting analyses will decrease the information asymmetry between the company and stakeholders. So stakeholders can make more accurate decisions. Analysis of non-financial data with text mining will provide a decisive competitive advantage, especially for investors to make the right decisions. This method will lead to allocating scarce resources more effectively. Another contribution of the study is that stakeholders can predict the corporate governance qualification of the company from the annual reports even if it does not include in the Corporate Governance Index (CGI).

Companies are increasingly leveraging digital technologies toward innovation strategies that deliver novel features to customers sequentially through successive new product generations (i.e., successive innovation). Extant literature examining successive innovation is both limited and fragmented across marketing and management literatures. Our goal is to synthesize literature on concepts related to successive innovation (such as versioning and upgrades) to identify the core dimensions of successive innovation and provide a cohesive framework to guide future research in this domain.

Given the equivocality in understanding the conceptual domain of successive innovation, we review and synthesize literature across three disciplinary domains: marketing, management, and information and decision sciences. Based on the emerging patterns from the literature review, we develop a conceptual framework of successive innovation with the aim of moving the discussion toward greater theoretical clarity.

Based on the literature review and synthesis, we identify three core-dimensions that define successive innovation and compare these across digital and physical product realms: coexistence, embeddedness, and adoption controllability.

Our proposed conceptual dimensions of successive innovation, and discussion of differences across physical and digital product domains, offer important directions for future research and a common vocabulary.

As physical and digital successive innovations can differ in coexistence, embeddedness, and adoption controllability, firms need to consider relevant barriers to adoption of successive product generations and select appropriate strategies to promote and communicate successive innovation. Our proposed successive innovation conceptual dimensions help managers comprehend the complexity of arranging such innovation in business and consumer segments.

Our contribution to the emerging literature on successive innovation is threefold. First, by conducting a comprehensive literature review, we integrate insights from different fields of inquiry (i.e., marketing, management, and information and decision sciences). Second, based on the synthesis of the literature, we offer a conceptual framework of successive innovation, which aims to move the discussion toward greater theoretical clarity. Third, based on our review and conceptual framework, we discuss a set of future research directions to guide academic research efforts.

Background: Artificial intelligence (AI) is a booming sector that has profoundly influenced every walk of life, and the education sector is no exception. In education, AI has helped to develop novel teaching and learning solutions that are currently being tested in various contexts. Businesses and governments across the globe have been pouring money into a wide array of implementations, and dozens of EdTech start-ups are being funded to capitalise on this technological force. The penetration of AI in classroom teaching is also a profound matter of discussion. These have garnered massive amounts of student big data and have a significant impact on the life of both students and educators alike.

Purpose: The prime focus of this chapter is to extensively review and analyse the vast literature available on the utilities of AI in health care, learning, and development. The specific objective of thematic exploration of the literature is to explicate the principal facets and recent advances in the development and employment of AI in the latter. This chapter also aims to explore how the EdTech and healthcare–education sectors would witness a paradigm shift with the advent and incorporation of AI.

Design/Methodology/Approach: To provide context and evidence, relevant publications were identified on ScienceDirect, PubMed, and Google Scholar using keywords like AI, education, learning, health care, and development. In addition, the latest articles were also thoroughly reviewed to underscore recent advances in the same field.

Results: The implementation of AI in the learning, development, and healthcare sector is rising steeply, with a projected expansion of about 50% by 2022. These algorithms and user interfaces economically facilitate efficient delivery of the latter.

Conclusions: The EdTech and healthcare sector has great potential for a spectrum of AI-based interventions, providing access to learning opportunities and personalised experiences. These interventions are often economic in the long run compared to conventional modalities. However, several ethical and regulatory concerns should be addressed before the complete adoption of AI in these sectors.

Originality/Value: The value in exploring this topic is to present a view on the potential of employing AI in health care, medical education, and learning and development. It also intends to open a discussion of its potential benefits and a remedy to its shortcomings.

The popular uprising (Aragalaya) combined a protest movement with a movement towards commons or a solidarity economy. The popular uprising from March to August 2022 was a reaction to the authoritarian heteropatriarchal Rajapaksa regime, which drained public revenues instigating an economic crisis. The Aragalaya was based on non-violence, independence from political parties, participatory democracy, collective leadership, politico-aesthetic strategies (art activism) and collective learning. While there were multiple contradictions, along with state repression, the Aragalaya expressed new forms of solidarity, strengthening struggles for democracy and citizenship.