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While digital transformation holds immense promise, organizations often fail to realize its benefits. This study aims to address how policies for digital transformation benefits realization are translated into practice.

The authors apply a qualitative, comparative case study of two large, public-sector health care organizations in Sweden. Through document and interview data, the authors analyze the process of translation.

The study finds that practice variation is primarily caused by two types of decoupling: policy-practice and means-ends. Contrary to previous studies, coercion in policy compliance is not found to decrease practice variation.

The limitations primarily stem from the empirical selection of two large public health-care organizations in Sweden, affecting the study’s generalizability. Reducing practice variation is more effectively achieved through goal alignment than coercion, leading to implications for the design of governance and control.

Policymakers should, instead of focusing on control-related compliance, work to align organizational objectives and policies to decrease practice variation for successful benefits realization.

The study contributes to better benefits realization of digital transformation initiatives in health care. As such, the authors contribute to a better functioning and more transformative health care in times of increased demand and decreased supply of health-care services.

The study challenges conventional wisdom by identifying that coercion is less effective than goal alignment in reducing practice variation, thereby enhancing the understanding of policy implementation dynamics in health-care settings.

In recent years, additive manufacturing (AM) has started to be used for manufacturing real functional parts and assemblies for critical applications in aerospace, automotive, and machinery industries. Most complex or assembled parts require internal features (IF) such as holes, channels, slots, or guides for locational and mating requirements. Therefore, it is critical to understand and compare the structural and mechanical properties of additively manufactured and conventionally machined IFs.

In this study, mechanical and microstructural properties of Inconel 718 (Inc718) alloy internal features, manufactured either as-built with AM or machining of additively manufactured (AMed) part thereafter were investigated.

The results showed that the average ultimate tensile strength (UTS) of additively manufactured center internal feature (AM-IF) is almost analogous to the machined internal feature (M-IF). However, the yield strength of M-IF is greater than that of AM-IF due the greater surface roughness of the internal feature in AM-IF, which is deemed to surpass the effect of microstructure on the mechanical performance. The results of digital image correlation (DIC) analysis suggest that AM-IF and M-IF conditions have similar strain values under the same stress levels but the specimens with as built IF have a more locally ductile region around their IF, which is confirmed by hardness test results. But this does not change global elongation behavior. The microstructural evolution starting from as-built (AB) and heat-treated (HT) samples to specimens with IF are examined. The microstructure of HT specimens has bimodal grain structure with d phase while the AB specimens display a very fine dendritic microstructure with the presence of carbides. Although they both have close values, machined specimens have a higher frequency of finer grains based on SEM images.

It was shown that the concurrent creation of the IF during AM can provide a final part with a preserved ultimate tensile strength and elongation but a decreased yield strength. The variation in UTS of AM-IF increases due to the surface roughness near the internal feature as compared to smooth internal surfaces in M-IF. Hence, the outcomes of this study are believed to be valuable for the industry in terms of determining the appropriate production strategy of parts with IF using AM and postprocessing processes.

This study investigates the relationship between corporate social responsibility (CSR) and financial performance of the banking sector from the prospective of emerging countries.

This study obtained balance sheet and income statement data for 173 banks in 20 emerging countries from the Bankscope database from 2005–2018. The CSR-related data were taken from the Thomson Reuters ASSET4 database. Moreover, macroeconomic controls such as GDP per capita, inflation, and financial development are attained from the GFDD. The series of institutional quality indices (Political Stability, Rule of Law, Control of Corruption, Government Effectiveness, and Regulatory Quality) is obtained from the WGI. At the same time, national culture and bank regulation are attained from Hofstede Insights and Barth et al. (2013). We used the panel fixed-effects model in our baseline estimations, while 2SLS and GMM were applied to control for endogeneity.

The finding shows that CSR activities significantly improve bank performance, but the effect varies across the bank. Only environmentally friendly activities have shown a significant positive relationship with banking performance for CSR dimensions. However, the social and government dimensions did not significantly affect bank performance. Moreover, a sound institutional and regulatory environment and national norms play an important role in the nexus of CSR activities and bank performance.

This study provides empirical evidence that sheds light on CSR and bank performance in an emerging market context.

Public–private partnership (PPP), a project financing arrangement between private investors and the public sector, has revolutionized the approach to the funding and development of public infrastructure worldwide. However, the increasing cases of financial risks and poor financial risk management related to the model threaten the sustainability and financial success of PPP projects leading to huge financial investment losses. This study aims to review existing literature to establish the key measures to control the financial risks of sustainable PPP projects.

A PRISMA-compliant systematic literature review method was used in this study. Data were sourced from academic databases consisting of 56 impactful peer-reviewed journal articles.

The review outcomes demonstrate 41 critical factors (measures) in mitigating the financial risks of sustainable PPP projects. They include minimum revenue guarantee, strategic alliance with private investors, financial transparency and accountability and sound macroeconomic policies. The principal results of the study were categorized and conceptualized into a financial risk management maturity model for sustainable PPP projects. Lastly, the study reveals that further studies and project policies must focus more on addressing financial challenges relating to climate risks, and health and safety concerns such as COVID-19 outbreak that have negative impacts on PPP projects.

The results provide essential research gaps and directions for future studies on measures to mitigate the financial risks of sustainable PPP projects. However, this study used small but significant existing publications.

A checklist and a conceptual maturity model are provided in this study to help practitioners to learn and improve upon their practices to mitigate the financial risks of sustainable PPP projects.

This study contributes to managerial measures to reduce huge losses in financial investments of PPP projects and the attainment of sustainability in public infrastructure projects with a financial risk maturity model.

Given the lack of focus on a standardized measurement framework (e.g. benchmarking tool) to assess and quantify complexity within the supply chain, this study has developed a unified supply chain complexity (SCC) index and validated its utility by examining the relationship with firm performance. More importantly, it examines the role of firm owners' business knowledge, sales strategy and board management on the relationship between SCC and firm performance.

In this study, the unit of analysis is Indian manufacturing companies listed on the Bombay Stock Exchange (BSE). This research has merged panel data from two secondary data sources: Bloomberg and Prowess and empirically operationalized five key SCC drivers, namely, number of suppliers, the number of supplier countries, the number of products, the number of plants and the number of customers. The study employs panel data regression analyses to examine the proposed conceptual model and associated hypotheses. Moreover, the present study employs models that incorporate robust standard errors to account for heteroscedasticity.

The results show that complexity has a negative and significant effect on firm performance. Further, the study reveals that an owner's business knowledge and the firm's effective sales strategy and board management can significantly lessen the negative effect of SCC.

This study develops an SCC index and validates its utility. Also, it presents a novel idea to operationalize the measure for SCC characteristics using secondary databases like Prowess and Bloomberg.

Energy use occupies an important place among the service activities offered to tourists that guide the tourism industry. The realization of basic needs such as heating, cooling, ventilation, lighting and decontamination in these enterprises are among the important factors that directly affect energy use. In order to obtain the energy needed for the sustainability of services at a more affordable cost, renewable energy sources should be put into operation. In this direction, it makes it more advantageous for businesses in the tourism sector to invest by turning to renewable energy sources in order to maintain their activities more economically. In this context, the main purpose of the study carried out in this part of the book is to reveal the latest developments in the field of evaluation of renewable energy sources in tourism enterprises. Bibliometric analysis was carried out by using the Web of Science (WoS) database in the research and with the findings obtained, it was concluded that the field is new and up-to-date and needs to be studied more. When looking at the WoS categories of studies titled renewable energy in tourism enterprises; it was concluded that more energy fuel, green sustainable science technology, science themes were given weight. According to the network analysis, the most cited authors and countries' densities were determined and the intensive expressions in the network keywords in their studies titled renewable energy in tourism enterprises are renewable energy, renewable energy sources, sustainable tourism, sustainability, carbon dioxide (CO₂) emissions, green marketing, blue economy, and energy efficiency.

Using the technology acceptance model (TAM), this study investigates factors influencing the adoption of cryptocurrency in Bangladesh.

Data were collected from 346 members of the general public through a structured web survey using snowball sampling. Structural equation modeling was used to analyze the data and assess the reliability and validity of the measurement model.

The results show that knowledge of cryptocurrency, benefits of use (perceived usefulness), attitude and challenges all have a significant impact on the adoption of cryptocurrency.

This study was conducted in a single country, relied on self-reported data and used a cross-sectional design, which limits the ability to draw causal inferences. Future research could explore the factors that influence the adoption of cryptocurrency in different countries and regions and incorporate additional variables to provide a more comprehensive understanding of the drivers of intention to use cryptocurrency.

This study contributes to understanding the factors driving the adoption of and intention to use technology-based services, providing insights that can inform the design and implementation of future technology-based services.

Managers of megaprojects face social risk management challenges throughout the various design, construction, and operation stages, owing to the various conflicts of interest among stakeholders, public skepticism, and opposition. However, most existing studies have not focused on the dynamic analysis of integrating social risks in these stages. This study developed a dynamic analysis approach to explore the dynamics of critical social risk factors and related stakeholders of megaprojects and built the managerial maps for various stakeholders.

Based on the social analysis network (SNA), a dynamic network analysis approach for understanding the dynamics of social risk and related stakeholders has been developed by literature and case analysis. The approach comprises the following steps: (1) generating social risk–stakeholder networks in different stages; (2) analysis of the critical stakeholders and social risk factors; (3) dynamic analysis of social risk factors; and (4) developing social risk management maps for various stakeholders. To verify the feasibility and effectiveness of the approach, 40 megaprojects from China were analyzed.

According to the results, the local government is a critical stakeholder during all stages, inadequate information promotion (IIP) and imperfect communication and coordination mechanism (ICCM) are key social risk sources throughout the megaproject life cycle. Furthermore, the management maps for government organizations, project implementation groups, and external stakeholders were constructed.

This research has three contributions. First, a dynamic analysis approach of stakeholder-associated social risks in megaprojects is developed, which enriches the social risk management theory of megaprojects and provides inspiration for future research focus. Second, the social risk–stakeholder networks and critical social risks in different stages are confirmed to provide a more valid and accurate picture of social risk management in megaprojects. Third, the social risk managerial maps for different stakeholders built in this research will be beneficial for governments, project implementation groups, and external stakeholders to optimize management strategies.

This research investigates the threshold effects of the interplay between finance, development and carbon emissions across 97 countries, including 50 low-income and 47 high-income countries, during the period from 1991 to 2019.

Employing various econometric modeling techniques such as dynamic linear regression, dynamic panel threshold regression and in/out of sample splitting, this study analyzes the data obtained from the World Bank's world development indicators.

The results indicate that low-income countries require a minimum financial development threshold of 0.354 to effectively reduce carbon emissions. Conversely, high-income countries require a higher financial development threshold of 0.662 to mitigate finance-induced carbon emissions. These findings validate the presence of a finance-led Environmental Kuznet Curve (EKC). Furthermore, the study highlights those high-income countries exhibit greater environmental concern compared to their low-income counterparts. Additionally, a minimum GDP per capita of US$ 10,067 is necessary to facilitate economic development and subsequently reduce carbon emissions. Once GDP per capita surpasses this threshold, a rise in economic development by a certain percentage could lead to a 0.96% reduction in carbon emissions across all income levels.

This study provides a novel contribution by estimating practical financial and economic thresholds essential for reducing carbon emissions within countries at varying levels of development.

The current paper's goal is to examine the productivity of the closed banking sector evidenced from Ethiopia. In addition, the inclusion of intangibles on productivity was examined in the current paper.

First, the standard Malmquist Productivity Index (MPI) was employed for 13 commercial banks for both stages. Second, by excluding the state-owned commercial bank, the analysis employed a bootstrapped MPI for the robust and comprehensive conclusion. Furthermore, from 2010 to 2019, the fixed effect Ordinary Least Square (OLS) regression with balanced panel data was used.

The standard MPI in both stages shows that the productivity of Ethiopian commercial banks is declining. The technological shock was the main reason for the loss. The catch-up in both stages scored above unity, mainly due to the pure efficiency change. Besides, when combined with tangible resources, the inclusion of resource-based view (RBV) proxy variables reduces technological shock regress and ultimately improves productivity change. The bootstrapped MPI also reveals that technological shock is the primary source of the productivity decline. However, efficiency change also contributes to the productivity decline based on this estimation.

Future research could examine the more extensive productivity analysis by considering the primary sources of data collections for resource-based variables.

According to the study's results, banking regulatory authorities and bank management, including the shareholders, should continue to invest in cutting-edge technology to improve the productivity of the banking sector.

This is the first comprehensive study of productivity for Ethiopian commercial banks based on the standard MPI, bootstrapped MPI, and OLS by incorporating all resources into the analysis.