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In the coming decades, the aging of European population will continue at a rapid pace. The National Transfer Accounts (NTA) methodology breaks down the income and consumption by age to analyze the impact of population aging on economic sustainability and economy in general. This chapter uses fully comparable results of NTA for 25 European Union countries in 2010 to indicate the potential increase in dependency in the future given the current institutional setting. Next to the conventionally defined demographic dependency ratio, we add (1) the NTA dependency ratio using the age patterns of production and consumption in the market and (2) the National Time Transfer Accounts dependency ratio using age patterns of production and consumption originating from unpaid work that is mostly provided within the households. The authors show that imbalances will originate from the impact of population aging on market part of the economy. Further, some imbalances will also be coming from unpaid work but of much lesser extent.

It has proved useful in studies of the personal social services, and in other areas of social policy, to make a distinction between final and intermediate outputs. Final outputs measure changes in individual client well‐being compared with changes in well‐being in the absence of a caring intervention. In other words, final outputs measure the degree of success of a service or a care unit in meeting its client‐level policy objectives, where due consideration is paid to client states had care not been available. In contrast, intermediate outputs are operationally defined in terms of the care services themselves rather than the effects of these services on clients.

With the rapid expansion of social networking sites, researchers and practitioners are challenged to understand drivers of customer loyalty in fan pages. The purpose of this paper is to identify the main drivers of Facebook fan page loyalty in order to promote the creation of affective links and long-term relationships with users.

The impact of trust, fan page content dependency, attitude and consumer beliefs on loyalty to fan pages was tested through structural equation modelling techniques. The sample consisted of 691 Spanish Facebook users.

Data analysis shows that attitude appears as a key variable in increasing loyalty to fan pages. The empirical study also found a significant positive influence of perceived usefulness, attitude, trust and dependency on loyalty in fan pages, and an indirect influence of perceived ease of use mediated by perceived usefulness and attitude.

This research enables managers to know what aspects to highlight in their communication strategies to increase fan page use and positive word-of-mouth. Research findings show managers that Facebook fan page content should provide valuable information, be fun and foster user interactions in order to improve user attitude and loyalty. Practical recommendations to reinforce consumer trust on Facebook fan pages are also provided.

There are still too few studies that analyse the effects of trust and fan page dependency on fan page users’ loyalty. This work aims to combine the influence of dependency on fan pages, trust, attitude and Technology Acceptance Model beliefs in order to construct an improved model for fan page loyalty formation.

The last reformation in Iran’s population policy was announced inside “General Population Policy” (GPP) in 2013. One of the main objectives of the GPP is controlling population aging. The aim was to designing and evaluating different scenarios for achieving this objective.

For this purpose, a system dynamics model was built from cohort age groups. The model simulated Iran’s population structure from 2000 to 2050. The system dynamics model was validated in 2000 till 2011 period (R² = 94%). Data were extracted from the United Nations population division repository and represent a reducing trend in the fertility rate of Iran. This situation was named the “base” scenario. The simulation results for this scenario showed that Iran will face aging such that between 2000 and 2050 the median age will increase from 25 to 43 years. Based on these results, the base scenario could not achieve the GPP objective. So three alternative scenarios were designated: stabilization, increasing and hyper increasing.

The median age and the aging index are descending only in the hyper increasing scenario which means controlling aging. Therefore, the hyper increasing scenario is the only way to realize the GPP’s objective. To realize the hyper increasing scenario, it is essential to consider the total dependency ratio which shows the level of pressure on the workforce. Reducing this pressure increases the propensity to have more children (fertility index) and this is essential for maintaining high fertility rate.

The value of the research rests on a precise simulation model to forecast the population structure and aging. The research will serve as a guide for Iranian policymaker and support strong recommendations to bring the GPP along with supporting policies such as childbearing and child care assistance.

The Logistics Performance Index (LPI), published by the World Bank, is a key measure of national-level logistics performance. It comprises six indicators: customs, infrastructure, international shipments, service quality, timeliness, and tracking and tracing. The objective of this study is to explore temporal dependencies among the six LPI indicators while operationalizing the World Bank’s LPI framework in terms of mapping the input indicators (customs, infrastructure, and service quality) to the outcome indicators (international shipments representing cost, timeliness, and tracking and tracing representing reliability).

A Bayesian Belief Network (BBN)-based methodology was adopted to effectively map temporal dependencies among variables in a probabilistic network setting. Using forward and backward propagation features of BBN inferencing, critical variables were also identified. A BBN model was developed using the World Bank’s LPI datasets for 2010, 2012, 2014, 2016, 2018, and 2023, covering the six LPI indicators for 118 countries.

The prediction accuracy of the model is 88.1%. Strong dependencies are found across the six LPI indicators over time. The forward propagation analysis of the model reveals that “logistics competence and quality” is the most critical input indicator that can influence all three outcome indicators over time. The backward propagation analysis indicates that “customs” is the most critical indicator for improving the performance on the “international shipments” indicator, whereas “logistics competence and quality” can significantly improve the performance on the “timeliness” and “tracking and tracing” indicators. The sensitivity analysis of the model reveals that “logistics competence and quality” and “infrastructure” are the key indicators that can influence the results across the three outcome indicators. These findings provide useful insights to researchers regarding the importance of exploring the temporal modeling of dependencies among the LPI indicators. Moreover, policymakers can use these findings to help their countries target specific input indicators to improve country-level logistics performance.

This paper contributes to the literature on logistics management by exploring the temporal dependencies among the six LPI indicators for 118 countries over the last 14 years. Moreover, this paper proposes and operationalizes a data-driven BBN modeling approach in this unique context.

The purpose of this paper is to discuss accounting development in Tunisia, which is a developing North African country little known in the international accounting literature.

Methodologically, this paper is based on an exploratory approach. It uses the descriptive tradition of research by collecting and analyzing numerical and narrative data to identify and describe environmental factors that favor or hamper accounting development in Tunisia.

This paper indicates that Tunisian companies have been applying the Enterprise Accounting System (EAS) since 1996. This system, while keeping with the logic of a chart of accounts, represents a first attempt to harmonize with international accounting standards. Accounting harmonization in Tunisia is meant to support the strategy, launched in the early 1990s, to integrate the country into the globalization process. Accordingly, the EAS has helped to achieve macroeconomic benefits (public interests). However, it does not lead to the desired level of financial transparency (private interests), especially that of large companies. Currently, Tunisian Accounting Standards neither reflect the rapid evolution of business activity nor changes in international accounting standards. This unachieved harmonization has led some listed companies to comply with some International Financial Reporting Standards which are not included in the EAS.

The unachieved harmonization in Tunisia is mainly related to the political system, taxation factors, the legal system, the weak state of corporate governance and governmental control over standardization.

This paper provides insights into the problems of developing countries that harmonize with international standards to achieve public interests. These countries may encounter many difficulties in bringing their accounting standards up to date. These difficulties seem to be associated with environmental specificities. Accordingly, international standardization bodies and developing country regulators should take into account environmental factors which are determinant for the harmonization decision to succeed.

This paper contributes to the existing literature on accounting development in developing countries. It implies that recent accounting development, as it is designed in Tunisia, is better suited to the needs of small businesses. Large companies would be compelled to complement local generally accepted accounting principles by standards they choose, voluntarily, among international standards.

Selecting the most meaningful performance indicators, i.e. key performance indicators (KPIs), represents one of the major challenges that companies have to face for developing an effective performance measurement system (PMS). Selecting KPIs can be interpreted as a multiple criteria decision‐making (MCDM) problem, involving a number of factors and related interdependencies. The purpose of this paper is to propose a model, based on the analytic network process (ANP), for driving managers in the selection of KIPs. The model draws upon the consideration that KPIs can be evaluated and selected on the basis of a set of criteria, theoretically founded, and the feedback dependencies between the criteria and performance indicators as well as among indicators.

Based on a review of management literature regarding the information quality required by performance measures, the paper identifies a set of criteria for selecting KIPs. The criteria form the building blocks of the proposed ANP model. The feasibility of the model is proved through its application to a real case.

The paper proposes and illustrates the practical application of an ANP‐based model for selecting KPIs. The use of the ANP makes it possible to extracts weights for setting the priorities among indicators, by taking account of mutual dependencies among indicators and criteria. This enhances the quality of the selection process.

Often managers choose KPIs without an accurate approach. The paper offers a novel model for driving managers towards the choice of KPIs through a rigorous approach, based on the ANP method. The model draws on a solid theoretical foundation and has been proven in practice.

The objective of this article is to carry out the driving power and dependency analysis of green human resource management (GHRM) indicators of the automotive service sector to identify the most significant ones.

The GHRM indicators were identified through exhaustive literature search and validated through the semi-structured interview with 15 domain experts. The ‘Total Interpretive Structural Modelling (TISM)’ approach was applied for exploring the contextual relationship between the indicators and simultaneously developing their structural hierarchy. The MICMAC analysis was used for categorising the indicators based on their ability to influence the other ones.

In the present study, indicators namely ‘Green organisational culture and adoption of green strategy (C5)’ and ‘Green training and development (C1)’ were found to be the significant ones, whereas ‘Green employee relations and union-management (C10)’ was found to be highly dependent on the rest of the indicators.

The proposed model has been developed in the Indian context and is limited to the automotive sector. However, the same model may apply to other domains of different economies by carrying out slight modifications to the same. Also, the inputs taken from the experts of the case sector could be biased. For the HR professionals, the present study helps to identify the key indicators which need to be considered for enlightening the environmental performance of the service organisation.

This research adds a significant assessment to the current knowledge base by assessing the contextual relationship between the indicators of GHRM as none of the past studies focused on the same by using the TISM method in the Indian service sectors context.

The purpose of the paper is to clarify the distinction between vulnerability and energy dependence of a country and to present simple indicators that are used to analyse energy vulnerability.

This paper introduces the different facets of the energy vulnerability concept and discusses the options available to manage risks arising from energy vulnerability.

The concept of energy vulnerability takes various forms and cannot be reduced to appreciating the degree of dependency of a country concerning energy supplies.

The contribution of the paper lies in offering a coherent set of indicators for analysing the energy vulnerability issue and in identifying a potential research area of developing a composite indicator of vulnerability similar to that of the human development indicator.