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This study aims to examine the financial reporting quality during the International Financial Reporting Standards (IFRS) enforcement period in the emerging markets of India and Indonesia by using Ohlson’s (1995) valuation model. The study further endeavors to compare the quality of the reporting environment and its impact on stock prices for both these emerging economies by using a price model during the IFRS conversion period.

This paper aspires to obtain insights about the value relevance of accounting information during the IFRS enforcement period for India and its Southeast Asian neighbor, Indonesia which is identical in terms of inclusive growth and development. In that context, 3,325 Indian (National Stock Exchange indexed) and 815 Indonesian (Indonesian stock exchange indexed) firm-year observations were examined by using Ohlson’s price valuation model for five years, representing the IFRS adherence period.

The findings of the paper insinuated that the value relevance of book values and earnings, both, have increased throughout the IFRS enforcement period for both economies. However, the investigation revealed that the incremental interpretive power of earnings is more substantial and evident during the IFRS adherence phase than book values which indicates investor’s inclination toward earnings management over book values.

The findings may assist the regulators, investors, firms and standard setters of both economies in examining the effectiveness of financial reporting curriculums as it brings forth informational improvement in the financial market. This study also outstretches the discussion on the subject in other emerging nations where the market is imperfect with insufficient information, poor enforcement and limited regulations. This investigation has few limitations such as limited data and period, only two emerging economies and two control variables, thus provide scope for future research.

This paper endeavors to investigate and compare the value relevance of accounting information during IFRS convergence period between India and Indonesia with an aim to assist in improved decision making for both, regulatory bodies and market participants in both the countries.

The key contribution of the study is to examine whether the accounting information is value relevant during the IFRS convergence period for the two fastest-growing economies in Asia, India and Indonesia and it is the first such empirical research to the best of the author’s knowledge. The study is an extended contribution to the modest research administered in developing nations.

The authors aim to examine the association between earnings and book values with stock prices in India during the IFRS convergence period because, in India, the literature is yet to investigate more about IFRS convergence and its impact on the financial reporting environment. Hence, the purpose of this study is to assess the influence of IFRS conversion on the value relevance of accounting information throughout the IFRS conversion period.

The current paper endeavors to investigate the earnings and book values affiliation with stock prices in India during the IFRS convergence period by employing a price valuation model (Ohlson’s Model). The study assembled a total of 3,440 firm-year observations from the National Stock Exchange in India over five years, which signifies the IFRS conversion period (2015–2019).

The research findings displayed that accounting information such as earnings, book value has value relevance throughout the IFRS enforcement period; however, the value relevance has been increasing for earnings and showing a descending association for book value. The significant explanatory power of earnings reveals that market participants give more weightage to earnings than book values. Overall, the findings of the study will facilitate improved decision making for both, capital market participants and regulators, by highlighting the key areas for improvement in the Indian capital market.

This study also extends a discussion on the subject in those economies where regulations are weak and the market is imperfect with asymmetrical information.

The research outcome provides for empirical shreds of evidence regarding the value relevance of accounting information during IFRS enforcement in India, where IFRS is a recent emergence.

This paper examines the value relevance of accounting information during IFRS convergence period in India which will felicitate improved decision making for both, market regulators and investors.

This research is the first factual documentation regarding value relevance of earnings and book value during the IFRS enforcement process in India with the most recent data and contributes to the limited study conducted in developing nations like India.

This study focuses on enhancing the accuracy and efficiency of employee performance prediction to enhance decision making and improve organisational productivity. By introducing advance machine learning (ML) techniques, this study aims to create a more reliable and data-driven approach to evaluate employee performance.

In this study, nine machine learning (ML) models were used for forecasting employee performance: Random Forest, AdaBoost, CatBoost, LGB Classifier, SVM, KNN, XGBoost, Decision Tree and one Hybrid model (SVM + XGBoost). Each ML model is trained on an HR data set covering various features such as employee demographics, job-related factors and past performance records, ensuring reliable performance predictions. Feature scaling techniques, namely, min-max scaling, Standard Scaler and PCA, have been used to enhance the effectiveness of employee performance prediction. The models are trained to classify data, predicting whether an employee’s performance meets expectations or needs improvement.

All proposed models used in the study can correctly categorize data with an average accuracy of 94%. Notably, the Random Forest model demonstrates the highest accuracy across all three scaling techniques, achieving optimise accuracy, respectively. The results presented have significant implications for HR procedures, providing businesses with the opportunity to make data-driven decisions, improve personnel management and foster a more effective and productive workforce.

The scope of the used data set limits the study, despite our models delivering high accuracy. Further research could extend to different data sets or more diverse organisational settings to validate the model’s effectiveness across various contexts.

The proposed ML models in the study provide essential tools for HR departments, enabling them to make more informed data driven decisions with regard to employee performance. This approach can enhance personnel management, improve workforce productivity and fostering a more effective organisational environment.

Although AI models have shown promising outcomes, it is crucial to recognise the constraints and difficulties involved in their use. To ensure the fair and responsible use of AI in employee performance prediction, ethical considerations, privacy problems and any biases in the data should be properly addressed. Future work will be required to improve and broaden the capabilities of AI models in predicting employee performance.

This study introduces an exclusive combination of ML models for accurately predicting employee performance. By employing these advanced techniques, the study offers novel insight into how organisations might transition from a conventional evaluation method to a more advanced and objective, data-backed approach.

Systematic literature review (SLR) papers have gained significant importance during the last years as many reputed journals have asked for literature review submissions from the authors. However, at the same time, authors are experiencing a high number of desk rejections because of a lack of quality and its contribution to the existing body of knowledge. Therefore, the purpose of this paper is to offer guidance to researchers who intend to communicate SLR papers in top-rated journals. We attempt to offer a guide to buddy researchers who plan to write SLR papers. This purpose is achieved by clearly stating how the traditional review method is different from SLR, when and how can each type of literature review method be used, writing effective motivation of a review paper and finally how to synthesize the available literature. We have also presented a few suggestions for writing an impactful SLR in the last. Overall, this chapter serves as a guide to various aspirants of SLR paper to understand the prerequisites of an SLR paper and offers deep insights to bring in more clarity before writing an SLR paper, thereby reducing the chances of desk rejection.

In the face of escalating urban populations, the quest for seamless mobility in cities becomes increasingly complex, even in regions where transit options are presumably accessible within the developing world. The imperative to confront urban mobility challenges and forge sustainable cities equipped with adept transportation and traffic management systems cannot be overstated. This study delves into the technological paradigms employed by developed nations and evaluates their pertinence in the current milieu for mitigating urban mobility challenges. Simultaneously, it scrutinizes the deployment of smart city technologies (SCTs) within developing nations, investigating potential technological strides that can be harnessed to achieve sustainable urban transportation. By dissecting the intricacies of SCTs in developing countries, the study aims to unearth viable technological advancements that can be judiciously implemented to foster sustainable urban mobility. It aspires to provide nuanced recommendations for the integration of latent SCTs, unlocking untapped potential to augment the sustainability of urban transportation in the developing world. The research also elucidates strategies geared towards fostering international collaborations which are instrumental in propelling the development of cities characterized by equity and inclusivity. The study underscores the significance of a global alliance in overcoming urban challenges, emphasizing the need for shared knowledge, resources and experiences to propel the evolution of cities towards a more sustainable and equitable future. This research serves as a comprehensive exploration of the intricate interplay between technology, urbanization and international cooperation, offering insights and recommendations pivotal to steering the trajectory of urban development in developing nations.

This study aims to examine the impact of online travel reviews (OTR) on tourists’ intention to travel based on the stimulus–organism–response (SOR) model. Further, it explored the mediating effects of tourist trust in OTR.

In this direction, this study proposes and empirically validates a conceptual model after collecting data from 299 Indian consumers. Proposed hypotheses were tested by applying the structural equation modelling technique. Bootstrapping method was used for mediation testing.

The findings revealed that various attributes of OTR exert differential impacts on travel intention. The study also confirmed the mediating role of tourist trust in OTR.

This study offers significant practical implications for travel marketers. To capitalize on OTR, travel marketers are recommended to develop an effective and efficient online reviews management system. This will improve the quality, valence, quantity and consistency of OTR, which in turn will enhance tourist trust in OTR, leading to improved travel intention.

No empirical evidence has been traced on how OTR enhances tourist trust in OTR and their travel intention. In support of this, the present study proposes and empirically validates an extensive model to comprehend the role of various drivers of OTR in improving tourist trust in OTR, leading to enhanced travel intention based on the SOR model.

The Metaverse and Internet of Things (IoT) have emerged like a tidal wave, and it is creating a transformative impact on society and industry. The metaverse and IoT changed the way companies were operating earlier and customers were living their lives. On the other hand, Metaverse enriches the customer experience by offering a matchless virtual experience using augmented reality and state-of-the-art technology. The metaverse and the IoT can be used in various sectors such as manufacturing, transportation, retailing, health care, banking, and automobiles to make cities smart. Metaverse and IoT provide real-time data, reduces operational cost and errors, improves efficiency, and helps industries to make intelligent decisions. Although the IoT and Metaverse offer significant benefits, it is not free from limitations. Ethical dilemmas, privacy issues, data breaches, and difficulty in extracting relevant data impose serious challenges that need to be addressed. There is an urgent and dire need to create a trade-off between the interest of the business and the privacy and security of customers. This chapter aims to discover the potential of Metaverse and IoT in various sectors (e.g., healthcare, transportation, and electronics). This study will bring significant insights to researchers and policymakers by exploring the likely benefits of IoT and metaverse in diverse sectors to develop smart cities. This chapter will also explain the challenges of metaverse and IoT, which can be addressed by integrating data analytics tools optimally and efficiently.