Search results

This chapter provides an empirical assessment of the effects of infrastructure provision on structural change and aggregate productivity using industrylevel data for a set of developed and developing countries over 1995–2010. A distinctive feature of the empirical strategy followed is that it allows the measurement of the resource reallocation directly attributable to infrastructure provision. To achieve this, a two-level top-down decomposition of aggregate productivity that combines and extends several strands of the literature is proposed. The empirical application reveals significant production losses attributable to misallocation of inputs across firms, especially among African countries. Also, the results show that infrastructure provision has stimulated aggregate total factor productivity growth through both within and between industry productivity gains.

Digitalization is one of the major factors that fosters economic growth across the world. However, the level of digitalization varies significantly between developed and developing countries, with the latter often lagging behind. To bridge this gap, it is crucial to pinpoint the drivers of digitalization, specifically from the macroeconomic and country-level governance dimensions. Therefore, this study aims to investigate the determinants of digitalization, particularly for countries in Asia and the Pacific region.

Our study utilizes unbalanced panel data from 46 Asian and Pacific countries for the period of 2001–2021. Initially, we analyzed the data using conventional econometric methods, such as pooled ordinary least squares (POLS), random-effects model (REM) and fixed-effects model (FEM). Moreover, we employed endogeneity-corrected techniques and alternative proxies to enhance the robustness and reliability of our findings.

Our findings reveal that economic development progress, government expenditure relative to country size and political stability are key drivers of digitalization. In contrast, corruption at the country level emerges as a significant impediment. Notably, our results remain robust to endogeneity-corrected techniques and alternative proxies of digitalization. Overall, these insights can inform policymakers, helping them to understand the macroeconomic and governance factors shaping digitalization and guide their decision-making toward effective policy interventions.

This study’s empirical findings add significant value to the existing literature by quantifying the impact of macroeconomic and governance factors on digitalization in selected countries. This offers valuable insights for policymakers, particularly in nations with lower levels of digitalization.

By drawing on the perceived organizational support (POS) theory and the extended job demands-resources model (JD-R model), the aim of this study is to investigate the influence of different levels of organizational support during change (i.e. organizational level, supervisory level, coworker level) on firm financial performance and to explore the role of employee work engagement.

Data were collected during the second outbreak of the COVID-19 pandemic in two waves, approximately 2 weeks apart. A total of 291 employees in the Greek telecommunication industry completed questionnaires examining the POS during change and consequently, their supervisors evaluated their work engagement. The research model was tested with the use of structural equation modeling.

The research findings note the importance of different levels of organizational support during change; they describe how each level influences employees' work engagement as well as they confirm that employee work engagement mediates the relationship between POS during change and firm financial performance. Theoretical and practical implications of these findings are discussed.

The results indicate that should firms manage to influence positively employees' work engagement by providing support at all levels during change, they may boost their financial performance.

The research findings provide new insights into how POS and work engagement may influence firm financial performance. The originality of this study lies in the finding that employees' work engagement mediates the relationship between POS during change and firm financial performance. Further, the study was carried out in the Greek telecommunication industry during the second outbreak of the COVID-19 pandemic.

The purpose of this paper is to propose a framework for evaluating the relationship between China and Peru, drawing on dependency theory, against the backdrop of China’s explicit policies towards foreign direct investment. It seeks to transcend traditional interpretations of this relationship in the literature that focuses on China as either hegemon or a South–South partner to Latin American countries to highlight a more nuanced relationship.

The paper adopts a case study approach, focusing on China in Peru. The authors examine three areas of traditional, strategic and emerging industries drawing from Chinese national policies, reviewing these against characteristics of dependency: control of production, heterogeneity of actors, transfer of knowledge and delinking.

The authors find that Chinese foreign direct investment (FDI) in Peru demonstrates mixed motives and collectively operates as an ambiguous player. Chinese firms appear to be willing to work with various actors, but this engagement does not translate into a decolonial development alternative in the absence of a Peruvian political will to delink and Chinese willingness to actively transfer control of production and knowledge.

This paper contributes to existing literature on China in Latin America by evaluating Chinese outward FDI in Peru against China’s strategic aims in terms of a re-evaluation of dependency theory.

This study aims to examine the implementation of International Financial Reporting Standards (IFRS) in Saudi Arabia. It investigates how the adoption of IFRS has affected four critical areas in the financial statements of publicly listed companies: profit and loss statement, balance sheet, cash flow statement and retained equity statement in Saudi Arabia. The paper also explores the essential factors/drivers that influence the adoption of IFRS and its implication in Saudi Arabia.

Data was obtained from Saudi Stock Exchange (Tadawul) listed companies from eleven industries in Saudi Arabia. This cross-sectional study analyses critical financial data across eleven distinctive industries. To identify the impact of adopting IFRS, the researchers use a paired t-test to evaluate seven key elements of financial statements underlying the critical areas: non-current asset, current asset, total assets, shareholders equity, non-current liability, current liability and total liability. The sample captures cross-sectional data from well-developed global industries in Saudi Arabia, pre- and post-implementation of IFRS. Thus, the analysis of the sample data gives a representative picture of the population of the Saudi Arabian industry.

The results reveal significant differences between GAAP and IFRS reporting standards in the measurement, recognition and classification of non-current assets and liabilities. The differences are expressed in the variance between the GAAP and IFRS. Specifically, the differences between GAAP and IFRS demonstrated by the t-value are significant and reliable (respectively, 5.3 and 4.1). Additionally, the t-value is validated by the p-value, which in both was significant.

The outcomes of this research will benefit accounting information users, practitioners, researchers and regulators. Since Saudi Arabia’s policymakers have mandated the full adoption of IFRS in financial reporting, the study contributes to the adoption of IFRS practices throughout the Saudi industry. Adopting full IFRS standards requires widespread IFRS expertise to cope with the transition.

This study advances research into the perennial issues associated with changes in reporting towards IFRS standards, especially in Saudi Arabia. The contribution to theory and practice enters new and fruitful areas.

This empirical study explores the intricate relationships between Industry 4.0 (I4), Lean practices and sustainable operational performance (SOP) within the dynamic context of the services sector. Rooted in the theoretical framework of Resource Orchestration Theory (ROT), the research investigates the nuanced interplay between these paradigms and their collective impact on firm performance.

The research methods included creation of a structural model, hypothesis formulation and advanced data analysis. Primary data were gathered through an online questionnaire distributed among service sector professionals. Analysis was completed using Partial Least Squares (PLS) Structural Equation Modeling (SEM) using the Smart-PLS software.

The results underscore the mediating role of Lean practices between I4 and SOP, emphasizing the imperative of harmonized integration to enhance overall firm performance. In alignment with ROT principles, the study illuminates the positive influence of Lean practices on sustainable operational outcomes.

The study contributes to the scholarly discourse on I4, Lean and Services, emphasizing the strategic necessity of integrating I4 capabilities with Lean practices. Practical insights guide practitioners in orchestrating a balanced adoption of I4 and Lean practices for SOP. This research offers actionable insights for industry leaders seeking to cultivate SOP within their organizational contexts.

This study contributes to the evolving understanding of the interplay between I4, Lean practices and SOP within the services sector, offering novel insights for both academia and industry practitioners.

The objective of this research work is to design a data-based solution for administering traffic organization in a smart city by using the machine learning algorithm.

A machine learning framework for managing traffic infrastructure and air pollution in urban centers relies on a predictive analytics model. The model makes use of transportation data to predict traffic patterns based on the information gathered from numerous sources within the city. It can be promoted for strategic planning determination. The data features volume and calendar variables, including hours of the day, week and month. These variables are leveraged to identify time series-based seasonal patterns in the data. To achieve accurate traffic volume forecasting, the long short-term memory (LSTM) method is recommended.

The study has produced a model that is appropriate for the transportation sector in the city and other innovative urban applications. The findings indicate that the implementation of smart transportation systems enhances transportation and has a positive impact on air quality. The study's results are explored and connected to practical applications in the areas of air pollution control and smart transportation.

The present paper has created the machine learning framework for the transportation sector of smart cities that achieves a reasonable level of accuracy. Additionally, the paper examines the effects of smart transportation on both the environment and supply chain.

This study is intended to analyze the impact of information and communication technology (ICT) infrastructure, technological innovation, renewable energy consumption and financial development on carbon dioxide emissions in emerging economies.

The present study adopts the autoregressive distributed lag (ARDL) cointegration technique for the annual data collection of Vietnam from 1990 to 2020.

The results of the study unveil that renewable energy consumption, the interaction between renewable energy consumption and ICT infrastructure and financial development have significant predictive power for carbon dioxide emissions. In the long term, renewable energy consumption, export and population growth reduce CO2 emissions, whereas the interaction between renewable energy consumption and ICT infrastructure and financial development increases CO2 emissions, while ICT infrastructure does not affect emissions. In the short run, changes in ICT infrastructure contribute to carbon dioxide emissions in Vietnam. In addition, changes in renewable energy consumption, financial development, the interaction between ICT infrastructure and renewable energy consumption and population growth have a significant effect on CO2 emissions. Notably, technological innovation has no impact on CO2 emissions in both the short and long run.

The current study provides new insights into the environmental effects of ICT infrastructure, technological innovation, renewable energy consumption and financial development. The interaction between renewable energy consumption and ICT infrastructure has a significant effect on carbon dioxide emissions. The paper suggests important implications for setting long-run policies to boost the effects of financial development, renewable energy consumption and ICT infrastructure on environmental quality in emerging countries like Vietnam in the coming time.

This study aims to adopt the microfoundation perspective to investigate undesirable knowledge rejection by individuals in organizations in the context of counterproductive knowledge behavior (CKB). The paper advances a conceptual framework of the conditions of knowledge rejection by individuals and their respective knowledge rejection behavior types.

This study reviews the limited literature on knowledge rejection and outline a set of antecedents leading to rejecting knowledge as well as a set of different types of knowledge rejection behaviors. This study reviews and synthesizes articles on knowledge rejection from a microfoundation perspective.

The proposed conceptual framework specifies four particular conditions for knowledge rejection and outlines four respective knowledge rejection behavior types resulting from these conditions. Recipients’ lack of capacity leads to ineptitude, lack of motivation leads to dismissal of knowledge, lack of alignment with the source leads to disruption and doubts about the validity of external knowledge lead to resistance. The authors treat these behaviors as variants of CKB, as they can hinder the productive use of knowledge resources in the organization.

Further investigation of both knowledge rejection causes and the resulting knowledge rejection behaviors will ensure a more thorough grasp of the relationships between them, both in terms of the inherent nature of these relationships and their dynamics that would likely be context-sensitive. Although this study focuses only on the individual level, future studies can conduct multi-level analyses of undesirable knowledge rejection, including team and organizational levels.

Practitioners can use the framework to identify, diagnose and manage knowledge rejection more meaningfully, accurately and purposefully in their organizations. This study offers valuable insights for managers facing undesirable knowledge rejection, and provides recommendations on how to address this behavior, improves the constructive use of knowledge resources and the effectiveness of knowledge processes in their organizations. Managers should be aware of undesirable knowledge rejection, its potential cost or concealed cost to their organizations and develop strategies to reduce or prevent it.

The paper contributes toward understanding the relatively neglected topic of knowledge rejection in the knowledge management field and offers a new way of conceptualizing the phenomenon. It proposes that there are two types of knowledge rejection – undesirable and desirable – and advances a more precise and up-to-date definition of undesirable knowledge rejection. Responding to calls for more research on CKBs, the study examines a hitherto unresearched behavior of knowledge rejection and provides a foundation for further study in this area.

Business models (BMs) are becoming increasingly crucial for value creation in the healthcare sector. The study explores the conceptualization and application of BM concepts within the healthcare sector and investigates their evolution in emerging economies (EEs) and developed economies (DEs). This study aims to uncover these two contexts' shared characteristics and unique variances through a comparative analysis.

The paper systematically investigates and consolidates the literature on healthcare by employing the antecedents, decisions and outcomes (ADO) framework and finally examines 71 shortlisted articles published between 2003 and 2022.

The recognition of the BM within healthcare is increasing, both in EEs and DEs. EEs prioritize value creation and capture through cost efficiency, while DEs focus on innovation. Key theories employed include a resource-based view, the network theory and the theory of innovation. Case studies are commonly used as a methodology. Further research is needed to explore the decisions and outcomes of BMs.

The study adopts stringent filtration and keyword criteria, potentially excluding relevant research. Future researchers are encouraged to broaden their selection criteria to encompass a more extensive range of relevant studies.

Beyond comparing and highlighting gaps in BMs between EEs and DEs, benchmarking DE's healthcare business models (HBMs) helps healthcare organizations in EEs align their practices, mitigate risks and establish efficient healthcare systems tailored to their specific contexts. The study adopts stringent filtration and keyword criteria, potentially excluding relevant research. Future researchers are encouraged to broaden their selection criteria to encompass a more extensive range of relevant studies.

The study analyzes HBMs using an SLR framework perspective and provides practical implications for academicians and practitioners to enhance their decision-making.